METHODS, KITS, AND DEVICES FOR IDENTIFYING BIOMARKERS OF TREATMENT RESPONSE AND USE THEREOF TO PREDICT TREATMENT

EFFICACY

FIELD OF THE INVENTION

The invention features methods, kits, and devices for identifying biomarkers of patient sensitivity to medical treatments, e.g., sensitivity to chemotherapeutic agents, and predicting treatment efficacy using the biomarkers.

BACKGROUND OF THE INVENTION

DNA microarrays have been used to measure gene expression in tumor samples from patients and to facilitate diagnosis. Gene expression can reveal the presence of cancer in a patient, its type, stage, and origin, and whether genetic mutations are involved. Gene expression may even have a role in predicting the efficacy of chemotherapy. Over recent decades, the National Cancer Institute (NCI) has tested compounds, including chemotherapy agents, for their effect in limiting the growth of 60 human cancer cell lines. The NCI has also measured gene expression in these 60 cancer cell lines using DNA microarrays. Various studies have explored the relationship between gene expression and compound effect using the NCI datasets. Critical time is often lost due to a trial and error approach to finding an effective chemotherapy for patients with cancer. In addition, cancer cells often develop resistance to a previously effective therapy. In such situations, patient outcome could be greatly improved by early detection of such resistance.

There remains a need for proven methods and devices that predict the sensitivity or resistance of cancer patients to a medical treatment.

SUMMARY OF THE INVENTION

The invention features methods, kits, and devices for determining the sensitivity or resistance of a patient, e.g., a cancer patient, to a treatment, e.g., treatment with a

compound, such as a chemotherapeutic agent, or radiation. In particular, the methods, kits, and devices can be used to determine the sensitivity or resistance of a cancer patient to any medical treatment, including, e.g., treatment with a compound, drug, or radiation. The methods, kits, and devices of the invention have been used to accurately determine treatment efficacy in cancer patients (e.g., patients with lung, lymphoma, and brain cancer) and can be used to determine treatment efficacy in patients diagnosed with any cancer.

Methods, kits, and devices for detecting the level of expression of biomarkers (e.g., genes and microRNAs) that indicate sensitivity or resistance to radiation therapy or the chemotherapy agents Vincristine, Cisplatin, Azaguanine, Etoposide, Adriamycin, Aclarubicin, Mitoxantrone, Mitomycin, Paclitaxel, Gemcitabine, Taxotere, Dexamethasone, Ara-C, Methylprednisolone, Methotrexate, Bleomycin, Methyl-GAG, Carboplatin, 5-FU (5-Fluorouracil), Rituximab, , PXDlOl, (a histone deacetylase (HDAC) inhibitor), 5-Aza-2'-deoxycytidine (Decitabine), Melphalan, IL4-PE38 fusion protein, IL13-PE38QQR fusion protein (cintredekin besudotox), Valproic acid (VPA), All-trans retinoic acid (ATRA), Cytoxan, Topotecan (Hycamtin), Suberoylanilide hydroxamic acid (SAHA, vorinostat, Zolinza), Depsipeptide (FR901229), Bortezomib, Leukeran, Fludarabine, Vinblastine, Busulfan, Dacarbazine, Oxaliplatin, Hydroxyurea, Tegafur, Daunorubicin, Bleomycin, Estramustine, Chlorambucil, Mechlorethamine, Streptozocin, Carmustine, Lomustine, Mercaptopurine, Teniposide, Dactinomycin, Tretinoin, Sunitinib, SPC2996, Ifosfamide, Tamoxifen, Floxuridine, Irinotecan, and Satraplatin are also provided. The methods, kits, and devices can be used to predict the sensitivity or resistance of a subject (e.g., a cancer patient) diagnosed with a disease condition, e.g., cancer (e.g., cancers of the breast, prostate, lung and bronchus, colon and rectum, urinary bladder, skin, kidney, pancreas, oral cavity and pharynx, ovary, thyroid, parathyroid, stomach, brain, esophagus, liver and intrahepatic bile duct, cervix larynx, heart, testis, small and large intestine, anus, anal canal and anorectum, vulva, gallbladder, pleura, bones and joints, hypopharynx, eye and orbit, nose, nasal cavity and middle ear, nasopharynx, ureter, peritoneum, omentum and mesentery, or gastrointestines, as well as any form of cancer including, e.g., chronic myeloid leukemia, acute lymphocytic leukemia, non-Hodgkin's lymphoma, melanoma,

carcinoma, basal cell carcinoma, malignant mesothelioma, neuroblastoma, multiple myeloma, leukemia, retinoblastoma, acute myeloid leukemia, chronic lymphocytic leukemia, Hodgkin's lymphoma, carcinoid tumors, acute tumor, or soft tissue sarcoma) to a treatment, e.g., treatment with a compound or drug, e.g., a chemotherapeutic agent, or radiation.

In a first aspect, the invention features a method of determining sensitivity of a cancer in a patient to a treatment for cancer by measuring the level of expression of at least one gene in a cell (e.g., a cancer cell) of the patient, in which the gene is selected from the group consisting of ACTB, ACTN4, ADA, ADAM9, ADAMTS 1 , ADD 1 , AFlQ, AIFl, AKAPl , AKAP13, AKRlCl, AKTl , ALDH2, ALDOC, ALG5, ALMSl, ALOXl 5B, AMIG02, AMPD2, AMPD3, ANAPC5, ANP32A, ANP32B, ANXAl, AP1G2, APOBEC3B, APRT, ARHE, ARHGAPl 5, ARHGAP25, ARHGDIB, ARHGEF6, ARL7, ASAHl, ASPH, ATF3, ATIC, ATP2A2, ATP2A3, ATP5D, ATP5G2, ATP6V1B2, BC008967, BCATl, BCHE, BCLl IB, BDNF, BHLHB2, BIN2, BLMH, BMIl, BNIP3, BRDT, BRRNl, BTN3A3, Cl lorf2, C14orfl39, C15orf25, C18orflO, Clorf24, Clorf29, Clorf38, ClQRl, C22orfl 8, C6or02, CACNAlG, CACNB3, CALMl, CALML4, CALU, CAP350, CASP2, CASP6, CASP7, CAST, CBLB, CCNA2, CCNBlIPl , CCND3, CCR7, CCR9, CDlA, CDlC, CDlD, CDlE, CD2, CD28, CD3D, CD3E, CD3G, CD3Z, CD44, CD47, CD59, CD6, CD63, CD8A, CD8B1, CD99, CDClO, CDC 14B, CDHI l, CDH2, CDKL5, CDKN2A, CDW52, CECRl, CENPB, CENTBl, CENTG2, CEPl, CG018, CHRNA3, CHSl, CIAPINl , CKAP4, CKIP-I , CNP, COL4A1, COL5A2, COL6A1, COROlC, CRABPl , CRK, CRYl , CSDA, CTBPl, CTSC, CTSL, CUGBP2, CUTC, CXCLl , CXCR4, CXorf9, CYFIP2, CYLD, CYR61, DATFl, DAZAPl, DBNl, DBT, DCTNl , DDXl 8, DDX5, DGKA, DIAPHl, DKCl , DKFZP434J154, DKFZP564C186, DKFZP564G2022, DKFZp564J157, DKFZP564K0822, DNAJClO, DNAJC7, DNAPTP6, DOCKlO, DOCK2, DPAGTl , DPEP2, DPYSL3, DSIPI, DUSPl, DXS9879E, EEF 1 B2, EFNB2, EHD2, EIF5A, ELK3, ENO2, EPASl, EPB41L4B, ERCC2, ERG, ERP70, EVERl , EVI2A, EVL, EXTl, EZH2, F2R, FABP5, FADl 04, FAM46A, FAU, FCGR2A, FCGR2C, FER1L3, FHLl , FHODl , FKBPlA, FKBP9, FLJ1O35O, FLJ10539, FLJ 10774, FLJ 12270, FLJ 13373, FLJ20859, FLJ21 159, FLJ22457, FLJ35036,

FU46603, FLNC, FLOTl, FMNLl , FNBPl, FOLHl , FOXF2, FSCNl, FTL, FYB, FYN, G0S2, G6PD, GALIG, GALNT6, GATA2, GATA3, GFPTl, GIMAP5, GIT2, GJAl , GLRB, GLTSCR2, GLUL, GMDS, GNAQ, GNB2, GNB5, G0T2, GPR65, GPRASPl, GPSM3, GRP58, GSTM2, GTF3A, GTSEl, GZMA, GZMB, HlFO, HlFX, H2AFX, H3F3A, HA-I, HEXB, HIC, HIST1H4C, HKl, HLA-A, HLA-B, HLA-DRA, HMGAl, HMGN2, HMMR, HNRPAl, HNRPD, HNRPM, HOXA9, HRMTlLl, HSA9761, HSPA5, HSU79274, HTATSFl, ICAMl, ICAM2, IER3, IFIl 6, IFI44, IFITM2, IFITM3, IFRG28, IGFBP2, IGSF4, IL13RA2, IL21R, IL2RG, IL4R, IL6, IL6R, IL6ST, IL8, IMPDH2, INPP5D, INSIGl , IQGAPl, IQGAP2, IRS2, ITGA5, ITM2A, JARID2, JUNB, K-ALPHA-I, KHDRBSl, KIAA0355, KIAA0802, KIAA0877, KIAA0922, KIAA1078, KIAAl 128, KIAA1393, KIFCl, LAIRl, LAMBl, LAMB3, LAT, LBR, LCK, LCPl, LCP2, LEFl, LEPREl, LGALSl, LGALS9, LHFPL2, LNK, LOC54103, LOC55831, LOC81558, LOC94105, LONP, LOX, LOXL2, LPHN2, LPXN, LRMP, LRP12, LRRC5, LRRN3, LSTl, LTB, LUM, LY9, LY96, MAGEB2, MAL, MAPlB, MAP1LC3B, MAP4K1, MAPKl, MARCKS, MAZ, MCAM, MCLl, MCM5, MCM7, MDH2, MDNl, MEF2C, MFNG, MGC17330, MGC21654, MGC2744, MGC4083, MGC8721, MGC8902, MGLL, MLPH, MPHOSPH6, MPPl, MPZLl, MRP63, MRPS2, MTlE, MTlK, MUFl, MVP, MYB, MYL9, MYOlB, NAPlLl, NAPl L2, NARF, NASP, NCOR2, NDN, NDUFABl, NDUFS6, NFKBIA, NID2, NIPA2, NME4, NME7, NNMT, NOL5A, N0L8, N0M02, NOTCHl, NPCl, NQOl, NRl D2, NUDC, NUP210, NUP88, NVL, NXFl, OBFCl, OCRL, OGT, OXAlL, P2RX5, P4HA1, PACAP, PAF53, PAFAH1B3, PALM2- AKAP2, PAX6, PCBP2, PCCB, PFDN5, PFNl, PFN2, PGAMl, PHEMX, PHLDAl, PIM2, PITPNCl , PLAC8, PLAGLl, PLAUR, PLCBl, PLEK2, PLEKHCl, PL0D2, PLSCRl, PNAS-4, PNMA2, P0LR2F, PPAP2B, PRFl, PRGl, PRIMl, PRKCH, PRKCQ, PRKD2, PRNP, PRP19, PRPF8, PRSS23, PSCDBP, PSMB9, PSMC3, PSME2, PTGER4, PTGES2, PTOVl, PTP4A3, PTPN7, PTPNSl , PTRF, PURA, PWPl, PYGL, QKI, RAB3GAP, RAB7L1, RAB9P40, RAC2, RAFTLIN, RAG2, RAPlB, RASGRP2, RBPMS, RCNl, RFC3, RFC5, RGC32, RGS3, RHOH, RIMS3, RI0K3, RIPK2, RISl , RNASE6, RNF 144, RPLlO, RPLlOA, RPLl 2, RPLl 3 A, RPLl 7, RPL18, RPL36A, RPLPO, RPLP2, RPS15, RPS19, RPS2, RPS4X, RPS4Y1, RRAS,

RRAS2, RRBPl , RRM2, RUNXl , RUNX3, S100A4, SART3, SATBl , SCAPl , SCARBl , SCN3A, SEC31L2, SEC61G, SELL, SELPLG, SEMA4G, SEPTlO, SEPT6, SERPINAl , SERPINBl , SERPINB6, SFRS5, SFRS6, SFRS7, SH2D1A, SH3GL3, SH3TC1, SHDl , SHMT2, SIATl , SKBl, SKP2, SLA, SLC1A4, SLC20A1, SLC25A15, SLC25A5, SLC39A14, SLC39A6, SLC43A3, SLC4A2, SLC7A1 1 , SLC7A6, SMAD3, SMOX, SNRPA, SNRPB, SOD2, SOX4, SP140, SPANXC, SPIl, SRF, SRM, SSA2, SSBP2, SSRPl, SSSCAl, STAG3, STATl, STAT4, STAT5A, STCl, STC2, STOML2, T3JAM, TACCl, TACC3, TAF5, TALI, TAPl, TARP, TBCA, TCF 12, TCF4, TFDP2, TFPI, TIMM 17 A, TIMPl , TJPl , TK2, TM4SF1, TM4SF2, TM4SF8, TM6SF1, TMEM2, TMEM22, TMSBlO, TMSNB, TNFAIP3, TNFAIP8, TNFRSFlOB, TNFRSFlA, TNFRSF7, TNIK, TNPOl, TOBl , TOMM20, TOX, TPKl, TPM2, TRA@, TRAl, TRAM2, TRB@, TRD@, TRIM, TRIM 14, TRIM22, TRIM28, TRIPl 3, TRPV2, TUBGCP3, TUSC3, TXN, TXNDC5, UBASH3A, UBE2A, UBE2L6, UBE2S, UCHLl, UCK2, UCP2, UFDlL, UGDH, ULK2, UMPS, UNG, USP34, USP4, VASP, VAVl, VLDLR, VWF, WASPIP, WBSCR20A, WBSCR20C, WHSCl , WNT5A, ZAP70, ZFP36L1 , ZNF32, ZNF335, ZNF593, ZNFNlAl , and ZYX; in which change in the level of expression of the gene indicates the cell is sensitive or resistant to the treatment.

In an embodiment, the method further includes determining a patient's resistance or sensitivity to radiation therapy or the chemotherapy agents Vincristine, Cisplatin, Adriamycin, Etoposide, Azaguanine, Aclarubicin, Mitoxantrone, Paclitaxel, Mitomycin, Gemcitabine, Taxotere, Dexamethasone, Methylprednisolone, Ara-C, Methotrexate, Bleomycin, Methyl-GAG, Rituximab, PXDlOl (a histone deacetylase (HDAC) inhibitor), 5-Aza-2'-deoxycytidine (Decitabine), Melphalan, IL4-PE38 fusion protein, IL13-PE38QQR fusion protein (cintredekin besudotox), Valproic acid (VPA), All-trans retinoic acid (ATRA), Cytoxan, Topotecan (Hycamtin), Suberoylanilide hydroxamic acid (SAHA, vorinostat, Zolinza), Depsipeptide (FR901229), Bortezomib, Leukeran, Fludarabine, Vinblastine, Busulfan, Dacarbazine, Oxaliplatin, Hydroxyurea, Tegafur, Daunorubicin, Bleomycin, Estramustine, Chlorambucil, Mechlorethamine, Streptozocin, Carmustine, Lomustine, Mercaptopurine, Teniposide, Dactinomycin, Tretinoin, Sunitinib, SPC2996, Ifosfamide, Tamoxifen, Floxuridine, Irinotecan, and

Satraplatin by measuring the level of expression of one or more of the genes known to change (e.g., to increase or decrease) in a patient sensitive to treatment with these agents (e.g., a patient is determined to be sensitive, or likely to be sensitive, to the indicated treatment if the level of expression of one or more of the gene(s) increases or decreases relative to the level of expression of the gene(s) in a control sample (e.g., a cell or tissue) in which increased or decreased expression of the gene(s) indicates sensitivity to the treatment, and vice versa). Alternatively, a patient's resistance or sensitivity to radiation therapy or any of the chemotherapy agents listed above can be determined by measuring the level of expression of at least one microRNA in a cell (e.g., a cancer cell) known to change (e.g., the level of expression is increased or decreased) in a patient sensitive to a treatment with these agents, in which the microRNA is selected from the group consisting of ath-MIR180aNo2, HcdlO2 left, Hcdl 1 1 left, Hcdl 15 left, Hcdl20 left, Hcdl 42 right, Hcdl 45 left, Hcdl48_HPR225 left, Hcdl 81 left, Hcdl 81 right, Hcd210_HPR205 right, Hcd213_HPR182 left, Hcd230 left, Hcd243 right, Hcd246 right, Hcd248 right, Hcd249 right, Hcd250 left, Hcd255 left, Hcd257 left, Hcd257 right, Hcd263 left, Hcd266 left, Hcd270 right, Hcd279 left, Hcd279 right, , Hcd28_HPR391eft, Hcd28_HPR39right, Hcd282PO right, Hcd289 left, Hcd294 left, Hcd318 right, Hcd323 left, Hcd330 right, Hcd338 left, Hcd340 left, Hcd35O right, Hcd355_HPR190 left, Hcd361 right, Hcd366 left, Hcd373 right, Hcd383 left, Hcd383 right, Hcd384 left, Hcd397 left, Hcd404 left, Hcd412 left, Hcd413 right, Hcd415 right, Hcd417 right, Hcd421 right, Hcd425 left, Hcd43 8right, Hcd434 right, Hcd438 left, Hcd440_HPR257 right, Hcd444 right, Hcd447 right, Hcd448 left, Hcd498 right, Hcd503 left, Hcd51 1 right, Hcd512 left, Hcd514 right, Hcd517 left, Hcd517 right, Hcd530 right, Hcd536_HPR104 right, Hcd542 left, Hcd544 left, Hcd547 left, Hcd559 right, Hcd562 right, Hcd569 right, Hcd570 right, Hcd578 right, Hcd581 right, Hcd586 left, Hcd586 right, Hcd587 right, Hcd605 left, Hcd605 left, Hcd605 right, Hcd608 right, Hcd627 left, Hcd631 left, Hcd631 right, Hcd634 left, Hcd642 right, Hcd649 right, Hcd654 left, Hcd658 right, Hcd669 right, Hcd674 left, Hcd678 right, Hcd683 left, Hcd684 right, Hcd689 right, Hcd690 right, Hcd691 right, Hcd693 right, Hcd697 right, Hcd704 left, Hcd704 left, Hcd712 right, Hcd716 right, Hcd731 left, Hcd738 left, Hcd739 right, Hcd739 right, Hcd749 right, Hcd753 left, Hcd754 left, Hcd755 left,

Hcd760 left, Hcd763 right, Hcd768 left ,Hcd768 right, Hcd770 left, Hcd773 left, Hcd777 left, Hcd778 right, Hcd781 left, Hcd781 right, Hcd782 left, Hcd783 left, Hcd788 left, Hcd794 right, Hcd796 left, Hcd799 left, Hcd807 right, Hcd812 left, Hcd817 left, Hcd817 right, Hcd829 right, Hcd852 right, Hcd861 right, Hcd863PO right, Hcd866 right, Hcd869 left, Hcd873 left, Hcd886 right, Hcd889 right, Hcd891 right, Hcd892 left, Hcd913 right, Hcd923 left, Hcd923 right, Hcd938 left, Hcd938 right, Hcd939 right, Hcd946 left, Hcd948 right, Hcd960 left, Hcd965 left, Hcd970 left, Hcd975 left, Hcd976 right, Hcd99 right, HPRlOO right, HPR129 left, HPR154 left, HPRl 59 left, HPRl 63 left, HPRl 69 right, HPRl 72 right, HPRl 81 left, HPRl 87 left, HPR 199 right, HPR206 left, HPR213 right, HPR214 right, HPR220 left , HPR220 right, HPR227 right, HPR232 right, HPR233 right, HPR244 right, HPR262 left, HPR264 right, HPR266 right, HPR271 right, HPR76 right, hsa_mir_490_Hcd20 right, HSHELAOl, HSTRNL, HUMTRAB, HUMTRF, HUMTRN, HUMTRS, HUMTRVlA, let-7f-2-prec2, mir-OOlb-1-precl , mir-OOlb-2-prec, mir-007-l-prec, mir-007-2- precNo2, mir-OlOa-precNol, mir-015b-precNo2, mir-016a-chrl3, mir-016b-chr3, mir- 017-precNol , mir-017-precNo2, mir-018-prec, mir-019a-prec, mir-019b-l-prec, mir- 019b-2-prec, mir-020-prec, mir-022-prec, mir-023a-prec, mir-023b-prec, mir-024-2- prec, mir-025-prec, mir-027b-prec, mir-029c-prec, mir-032-precNo2, mir-033b-prec, mir-033-prec, mir-034-precNol, mir-034-precNo2, mir-092-prec-l 3=092- 1NO2, mir- 092-prec-X=092-2, mir-093-prec-7.1=093-l, mir-095-prec-4 ,mir-096-prec-7Nol ,mir- 096-prec-7No2, mir-098-prec-X, mir-099b-prec-19Nol, mir-lOO-l/2-prec, mir-lOONol, mir-lOl-prec-9, mir-102-prec-l, mir-103-2-prec, mir-103-prec-5=103-l , mir-106aNol, mir-106-prec-X,mir-107Nol , mir-107-prec-lO, mir-122a-prec, mir-123-precNol, mir- 123-precNo2, mir-124a-l-precl, mir-124a-2-prec, mir-124a-3-prec, mir-125b-l, mir- 125b-2-precNo2, mir-127-prec, mir-128b-precNol, mir-128b-precNo2, mir-133a-l , mir-135-2-prec, mir-136-precNo2, mir-138-l-prec, mir-140No2, mir-142-prec, mir- 143-prec, mir-144-precNo2, mir-145-prec, mir-146bNol , mir-146-prec, mir-147-prec, mir-148aNol, mir-148-prec, mir-149-prec, mir-150-prec, mir-153-l-precl, mir-154- preclNol, mir-155-prec, mir-15aNol , mir-16-lNol, mir-16-2Nol , mir-181a-precNol , mir- 18Ib-INoI, mir- 181 b-2No 1 , mir- 181 b-precNo 1 , mir- 181 b-precNo2, mir- 181 c- precNol , mir-181dNol , mir-188-prec, mir-18bNo2, mir-191 -prec, mir-192No2, mir-

193bNo2, mir-194-2Nol, mir-195-prec, mir-196-2-precNo2, mir- 197-prec, mir-198- prec, mir-199a-l-prec, mir-199a-2-prec, mir-199b-precNol, mir-200a-prec, mir- 20ObNoI , mir-200bNo2, mir-202*, mir-202-prec, mir-204-precNo2, mir-205-prec, mir- 208-prec, mir-20bNol , mir-212-precNol, mir-212-precNo2, mir-213-precNol , mir- 214-prec, mir-215-precNo2, mir-216-precNol , mir-219-2Nol , mir-219-prec, mir-223- prec, mir-29b-lNol, mir-29b-2=102prec7.1=7.2, mir-321Nol , mir-321No2, mir- 324NoI, mir-324No2, mir-328Nol, mir-342Nol , mir-361Nol, mir-367Nol , mir- 370NoI, mir-371Nol, miR-373*Nol, mir-375, mir-376aNol, mir-379Nol, mir-380-5p, mir-382, mir-384, mir-409-3p, mir-423Nol, mir-424No2, mir-429Nol , mir-429No2, mir-4323p, mir-4325p, mir-449Nol, mir-450-1, mir-450-2Nol , mir-483Nol, mir-484, mir-487Nol, mir-495Nol, mir-499No2, mir-501No2, mir-503Nol, mir-509Nol , mir- 514-lNo2, mir-515-15p, mir-515-23p, mir-516-33p, mir-516-43p, mir-518e/526c, mir- 519a- 1/52, mir-519a-2No2, mir-519b, mir-519c/52, mir-520c/52, mir-526a-2Nol, mir- 526a-2No2, MPRl 03 right, MPRl 21 left, MPRl 21 left, MPRl 30 left, MPRl 30 right, MPR133 right, MPR141 left, MPR151 left, MPR156 left, MPR162 left, MPR174 left, MPRl 74 right, MPRl 85 right, MPRl 97 right, MPR203 left, MPR207 right, MPR215 left, MPR216 left, MPR224 left, MPR224 right, MPR228 left, MPR234 right, MPR237 left, MPR243 left, MPR244 right, MPR249 left, MPR254 right, MPR74 left, MPR88 right, and MPR95 left.

In an embodiment, the method includes determining the expression of two of the listed genes or microRNAs, more preferably three, four, five, six, seven, eight, nine, or ten of the listed genes, and most preferably twenty, thirty, forty, fifty, sixty, seventy, eighty, ninety, or one hundred or more of the listed genes. In another embodiment, the change in the level of gene or microRNA expression (e.g., an increase or decrease) is determined relative to the level of gene or microRNA expression in a cell or tissue known to be sensitive to the treatment, such that a similar level of gene or microRNA expression exhibited by a cell or tissue of the patient indicates the patient is sensitive to the treatment. In another embodiment, the change in the level of gene or microRNA expression (e.g., an increase or decrease) is determined relative to the level of gene or microRNA expression in a cell or tissue known to be resistant to the treatment, such that a similar level of gene or microRNA expression exhibited by a cell or tissue of the

patient indicates the patient is resistant to the treatment.

In a second aspect, the invention features a method of determining sensitivity of a cancer in a patient to a treatment for cancer by measuring the level of expression of at least one microRNA in a cell (e.g., a cancer cell) of the patient, in which the microRNA is selected from the group set forth in the first aspect of the invention. In an embodiment, the method further includes determining a patient's resistance or sensitivity to radiation therapy or any of the chemotherapy agents set forth in the first aspect of the invention by measuring the level of expression of one or more of the microRNAs known to change (e.g., to increase or decrease) in a patient sensitive to treatment with these agents (e.g., a patient is determined to be sensitive, or likely to be sensitive, to the indicated treatment if the level of expression of one or more of the microRNA(s) increases or decreases relative to the level of expression of the microRNA(s) in a control sample (e.g., a cell or tissue) in which increased or decreased expression of the microRNA(s) indicates sensitivity to the treatment, and vice versa). In an embodiment, the method includes determining the expression of two of the listed genes or microRNAs, more preferably three, four, five, six, seven, eight, nine, or ten of the listed genes, and most preferably twenty, thirty, forty, fifty, sixty, seventy, eighty, ninety, or one hundred or more of the listed genes. In another embodiment, the change in the level of microRNA expression (e.g., an increase or decrease) is determined relative to the level of microRNA expression in a cell or tissue known to be sensitive to the treatment, such that a similar level of microRNA expression exhibited by a cell or tissue of the patient indicates the patient is sensitive to the treatment. In another embodiment, the change in the level of microRNA expression (e.g., an increase or decrease) is determined relative to the level of microRNA expression in a cell or tissue known to be resistant to the treatment, such that a similar level of microRNA expression exhibited by a cell or tissue of the patient indicates the patient is resistant to the treatment.

In another embodiment, the invention features a method for determining the development of resistance by a patient (e.g., resistance of a cell, such as a cancer cell, in the patient) to a treatment to which the patient was previously sensitive. The method includes measuring the level of expression of one or more of the microRNAs set forth

in the first aspect of the invention, such that the level of expression of a micro RN A which is decreased in a cell or tissue known to be sensitive to the treatment indicates that the patient is resistant to or has a propensity to become resistant to the treatment. Alternatively, a decrease in the expression level of a microRNA which is increased in a cell or tissue known to be sensitive to the treatment indicates that the patient is resistant to or has a propensity to become resistant to the treatment.

In a third aspect, the invention features a kit that includes a single-stranded nucleic acid molecule (e.g., one or a plurality thereof; e.g., a deoxyribonucleic acid molecule or a ribonucleic acid molecule) that is substantially complementary to (e.g., that has at least 80%, 90%, 95% 97%, 99%, or 100% identical to the complement of) or that is substantially identical to (e.g., that has at least 80%, 90%, 95% 97%, 99%, or 100% identity to) at least 5 consecutive nucleotides (more preferably at least 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, or more consecutive nucleotides; the nucleic acid can also be 5-20, 25, 5-50, 50-100, or over 100 consecutive nucleotides long) of at least one of the genes (e.g., at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, or more of the genes) set forth in the first aspect of the invention, such that the single-stranded nucleic acid molecule is sufficient for measuring the level of expression of the gene(s) by allowing specific hybridization between the single-stranded nucleic acid molecule and a nucleic acid molecule encoded by the gene, or a complement thereof. Alternatively, the kit includes one or more single-stranded nucleic acid molecules that are substantially complementary to or substantially identical to at least 5 consecutive nucleotides of at least one of the microRNAs set forth in the first aspect of the invention, such that the single-stranded nucleic acid molecule is sufficient for measuring the level of expression of the microRNA(s) by allowing specific hybridization between the single-stranded nucleic acid molecule and the microRNA, or a complement thereof. The kit further includes instructions for applying nucleic acid molecules collected from a sample from a cancer patient (e.g., from a cell of the patient), determining the level of expression of the gene(s) or microRNA(s) hybridized to the single-stranded nucleic acid, and determining the patient's sensitivity to a treatment for cancer when use of the kit indicates that the level of expression of the gene(s) or microRNA(s) changes (e.g.,

increases or decreases relative to a control sample (e.g., tissue or cell) known to be sensitive or resistant to the treatment, as is discussed above in connection with the first aspect of the invention). In an embodiment, the instructions further indicate that a change in the level of expression of the gene(s) or microRNA(s) relative to the expression of the gene(s) or microRNA(s) in a control sample (e.g., a cell or tissue known to be sensitive or resistant to the treatment) indicates a change in sensitivity of the patient to the treatment (e.g., a decrease in the level of expression of a gene or microRNA known to be expressed in cells sensitive to the treatment indicates that the patient is becoming resistant to the treatment or is likely to become resistant to the treatment, and vice versa).

In another embodiment, the kit can be utilized to determine a patient's resistance or sensitivity to radiation therapy or the chemotherapy agents Vincristine, Cisplatin, Adriamycin, Etoposide, Azaguanine, Aclarubicin, Mitoxantrone, Paclitaxel, Mitomycin, Gemcitabine, Taxotere, Dexamethasone, Methylprednisolone, Ara-C, Methotrexate, Bleomycin, Methyl-GAG, Rituximab, PXDlOl (a histone deacetylase (HDAC) inhibitor), 5-Aza-2'-deoxycytidine (Decitabine), Melphalan, IL4-PE38 fusion protein, IL13-PE38QQR fusion protein (cintredekin besudotox), Valproic acid (VPA), All-trans retinoic acid (ATRA), Cytoxan, Topotecan (Hycamtin), Suberoylanilide hydroxamic acid (SAHA, vorinostat, Zolinza), Depsipeptide (FR901229), Bortezomib, Leukeran, Fludarabine, Vinblastine, Busulfan, Dacarbazine, Oxaliplatin, Hydroxyurea, Tegafur, Daunorubicin, Bleomycin, Estramustine, Chlorambucil, Mechlorethamine, Streptozocin, Carmustine, Lomustine, Mercaptopurine, Teniposide, Dactinomycin, Tretinoin, Sunitinib, SPC2996, Ifosfamide, Tamoxifen, Floxuridine, Irinotecan, and Satraplatin by measuring the level of expression of one or more of the genes or microRNAs set forth in the first aspect of the invention and known to change (e.g., to increase or decrease) in a patient sensitive to treatment with these agents (e.g., a patient is determined to be sensitive, or likely to be sensitive, to the indicated treatment if the level of expression of one or more of the gene(s) or micro RN A(s) increases or decreases relative to the level of expression of the gene(s) or microRNA(s) in a control sample (e.g., a cell or tissue) in which increased or decreased expression of the gene(s) or microRNA(s) indicates sensitivity to the treatment, and vice versa).

In another embodiment, the nucleic acid molecules are characterized by their ability to specifically identity nucleic acid molecules complementary to the genes or microRNAs in a sample collected from a cancer patient.

In a fourth aspect, the invention features a kit that includes a single-stranded nucleic acid molecule (e.g., one or a plurality thereof; e.g., a deoxyribonucleic acid molecule or a ribonucleic acid molecule) that is substantially complementary to (e.g., that has at least 80%, 90%, 95% 97%, 99%, or 100% identical to the complement of) or that is substantially identical to (e.g., that has at least 80%, 90%, 95% 97%, 99%, or 100% identity to) at least 5 consecutive nucleotides (more preferably at least 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, or more consecutive nucleotides; the nucleic acid can also be 5-20, 25, 5-50, 50-100, or over 100 consecutive nucleotides long) of at least one of the microRNAs (e.g., at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, or more of the microRNAs) set forth in the first aspect of the invention, such that the single-stranded nucleic acid molecule is sufficient for measuring the level of expression of the microRNA(s) by allowing specific hybridization between the single-stranded nucleic acid molecule and a microRNA, or a complement thereof. The kit further includes instructions for applying nucleic acid molecules collected from a sample from a cancer patient (e.g., from a cell of the patient), determining the level of expression of the micro RNA(s) hybridized to the single-stranded nucleic acid, and determining the patient's sensitivity to a treatment for cancer when use of the kit indicates that the level of expression of microRNA(s) changes (e.g., increases or decreases relative to a control sample (e.g., tissue or cell) known to be sensitive or resistant to the treatment, as is discussed above in connection with the first aspect of the invention). In an embodiment, the instructions further indicate that a change in the level of expression of microRNA(s) relative to the expression of microRNA(s) in a control sample (e.g., a cell or tissue known to be sensitive or resistant to the treatment) indicates a change in sensitivity of the patient to the treatment (e.g., a decrease in the level of expression of a microRNA known to be expressed in cells sensitive to the treatment indicates that the patient is becoming resistant to the treatment or is likely to become resistant to the treatment, and vice versa).

In another embodiment, the kit can be utilized to determine a patient's resistance or sensitivity to radiation therapy or the chemotherapy agents Vincristine, Cisplatin, Adriamycin, Etoposide, Azaguanine, Aclarubicin, Mitoxantrone, Paclitaxel, Mitomycin, Gemcitabine, Taxotere, Dexamethasone, Methylprednisolone, Ara-C, Methotrexate, Bleomycin, Methyl-GAG, Rituximab, PXDlOl (a histone deacetylase (HDAC) inhibitor), 5-Aza-2'-deoxycytidine (Decitabine), Melphalan, IL4-PE38 fusion protein, IL13-PE38QQR fusion protein (cintredekin besudotox), Valproic acid (VPA), All-trans retinoic acid (ATRA), Cytoxan, Topotecan (Hycamtin), Suberoylanilide hydroxamic acid (SAHA ₅ vorinostat, Zolinza), Depsipeptide (FR901229), Bortezomib, Leukeran, Fludarabine, Vinblastine, Busulfan, Dacarbazine, Oxaliplatin, Hydroxyurea, Tegafur, Daunorubicin, Bleomycin, Estramustine, Chlorambucil, Mechlorethamine, Streptozocin, Carmustine, Lomustine, Mercaptopurine, Teniposide, Dactinomycin, Tretinoin, Sunitinib, SPC2996, Ifosfamide, Tamoxifen, Floxuridine, Irinotecan, and Satraplatin by measuring the level of expression of one or more of the microRNAs set forth in the first aspect of the invention and known to change (e.g., to increase or decrease) in a patient sensitive to treatment with these agents (e.g., a patient is determined to be sensitive, or likely to be sensitive, to the indicated treatment if the level of expression of one or more of the microRNA(s) increases or decreases relative to the level of expression of the microRNA(s) in a control sample (e.g., a cell or tissue) in which increased or decreased expression of the or micro RN A(s) indicates sensitivity to the treatment, and vice versa).

In another embodiment, the nucleic acid molecules are characterized by their ability to specifically identify nucleic acid molecules complementary to the microRNAs in a sample collected from a cancer patient.

In a fifth aspect, the invention features a method of identifying biomarkers (e.g., genes and microRNAs) indicative of sensitivity of a cancer patient to a treatment for cancer by obtaining pluralities of measurements of the expression level of a gene or microRNA (e.g., by detection of the expression of a gene or microRNA using a single probe or by using multiple probes directed to a single gene or microRNA) in different cell types and measurements of the growth of those cell types in the presence of a treatment for cancer relative to the growth of the cell types in the absence of the

treatment for cancer; correlating each plurality of measurements of the expression level of the gene or microRNA in cells with the growth of the cells to obtain a correlation coefficient; selecting the median correlation coefficient calculated for the gene or microRNA; and identifying the gene or microRNA as a biomarker for use in determining the sensitivity of a cancer patient to said treatment for cancer if said median correlation coefficient exceeds 0.3 (preferably the gene or microRNA is identified as a biomarker for a patient's sensitivity to a treatment if the correlation coefficient exceeds 0.4, 0.5, 0.6, 0.7, 0.8. 0.9, 0.95, or 0.99 or more). In an embodiment, the method is performed in the presence of a second treatment.

In a sixth aspect, the invention features a method of determining sensitivity of a patient (e.g., a cancer patient) to a treatment for cancer by obtaining a measurement of the level of expression of a gene or microRNA in a sample (e.g., a cell or tissue) from the patient; applying a model predictive of sensitivity to a treatment for cancer to the measurement, in which the model is developed using an algorithm selected from the group consisting of linear sums, nearest neighbor, nearest centroid, linear discriminant analysis, support vector machines, and neural networks; and determining whether or not the patient will be responsive to the treatment for cancer. In an embodiment, the measurement is obtained by measuring the level of expression of any of the genes or microRNAs set forth in the first aspect of the invention in a cell known to be sensitive or resistant to the treatment. In another embodiment, the method is performed in the presence of a second treatment. In another embodiment, the model combines the outcomes of linear sums, linear discriminant analysis, support vector machines, neural networks, k-nearest neighbors, and nearest centroids, or the model is cross-validated using a random sample of multiple measurements. In another embodiment, treatment, e.g., a compound, has previously failed to show efficacy in a patient. In several embodiments, the linear sum is compared to a sum of a reference population with known sensitivity; the sum of a reference population is the median of the sums derived from the population members' biomarker gene expression. In another embodiment, the model is derived from the components of a data set obtained by independent component analysis or is derived from the components of a data set obtained by principal component analysis. In another embodiment, the invention features a kit, apparatus,

and software used to implement the method of the sixth aspect of the invention.

In several embodiments of all aspects of the invention, the level of expression of the gene(s) is determined by measuring the level of mRNA transcribed from the gene(s), by detecting the level of a protein product of the gene(s), or by detecting the level of the biological activity of a protein product of the gene(s). In further embodiments of all aspects of the invention, an increase or decrease in the expression level of the gene(s) or microRNA(s), relative to the expression level of the gene(s) or microRNA(s) in a cell or tissue sensitive to the treatment, indicates increased sensitivity of the cancer patient to the treatment. Alternatively, an increase or decrease in the expression level of the gene(s) or microRNA(s), relative to the expression level of the gene(s) or microRNA(s) in a cell or tissue resistant to the treatment, indicates increased resistance of the cancer patient to the treatment. In another embodiment of all aspects of the invention, the cell is a cancer cell. In another embodiment of all aspects of the invention, the expression level of the gene(s) is measured using a quantitative reverse transcription-polymerase chain reaction (qRT-PCR). In an embodiment of all aspects of the invention, the level of expression of two of the listed genes or microRNAs is measured, more preferably the level of expression of three, four, five, six, seven, eight, nine, or ten of the listed genes or microRNAs is measured, and most preferably twenty, thirty, forty, fifty, sixty, seventy, eighty, ninety, or one hundred or more of the listed genes or microRNAs is measured. In another embodiment of all aspects of the invention, the expression level of the gene(s) or microRNA(s) is determined using the kit of the third or fourth aspects of the invention.

In another embodiment of all aspects of the invention, the treatment is radiation therapy or a compound, such as a chemotherapy agent selected from the group consisting of Vincristine, Cisplatin, Adriamycin, Etoposide, Azaguanine, Aclarubicin, Mitoxantrone, Paclitaxel, Mitomycin, Gemcitabine, Taxotere, Dexamethasone, Methylprednisolone, Ara-C, Methotrexate, Bleomycin, Methyl-GAG, Rituximab, PXDl Ol (a histone deacetylase (HDAC) inhibitor), 5-Aza-2'-deoxycytidine (Decitabine), Melphalan, IL4-PE38 fusion protein, IL13-PE38QQR fusion protein (cintredekin besudotox), Valproic acid (VPA), All-trans retinoic acid (ATRA), Cytoxan, Topotecan (Hycamtin), Suberoylanilide hydroxamic acid (SAHA, vorinostat,

Zolinza), Depsipeptide (FR901229), Bortezomib, Leukeran, Fludarabine, Vinblastine, Busulfan, Dacarbazine, Oxaliplatin, Hydroxyurea, Tegafur, Daunorubicin, Bleomycin, Estramustine, Chlorambucil, Mechlorethamine, Streptozocin, Carmustine, Lomustine, Mercaptopurine, Teniposide, Dactinomycin, Tretinoin, Sunitinib, SPC2996, Ifosfamide, Tamoxifen, Floxuridine, Irinotecan, and Satraplatin. In another embodiment of all aspects of the invention, the treatment has previously failed to show effect in a subject (e.g., a subject selected from a subpopulation determined to be sensitive to the treatment, a subject selected from a subpopulation predicted to die without treatment, a subject selected from a subpopulation predicted to have disease symptoms without treatment, a subject selected from a subpopulation predicted to be cured without treatment.

In another embodiment of all aspects of the invention, the treatment is, e.g., administration of a compound, a protein, an antibody, an oligonucleotide, a chemotherapeutic agent, or radiation to a patient. In an emobodiment of all aspects of the invention, the treatment is, e.g., a chemotherapeutic agent, such as, e.g., Vincristine, Cisplatin, Azaguanine, Etoposide, Adriamycin, Aclarubicin, Mitoxantrone, Mitomycin, Paclitaxel, Gemcitabine, Taxotere, Dexamethasone, Ara-C, Methylprednisolone, Methotrexate, Bleomycin, Methyl-GAG, Carboplatin, 5-FU (5-Fluorouracil), a histone deacetylase (HDAC) inhibitor such as PXDlOl, 5-Aza-2'-deoxycytidine (Decitabine), alpha emitters such as astatine-211 , bismuth-212, bismuth-213, lead-212, radium-223, actinium-225, and thorium-227, beta emitters such as tritium, strontium-90, cesium- 137, carbon-1 1, nitrogen-13, oxygen-15, fluorine-18, iron-52, cobalt-55, cobalt-60, copper-61, copper-62, copper-64, zinc-62, zinc-63, arsenic-70, arsenic-71 , arsenic-74, bromine-76, bromine-79, rubidium-82, yttrium-86, zirconium-89, indium- 1 10, iodine- 120, iodine- 124, iodine- 129, iodine-131, iodine- 125, xenon- 122, technetium-94m, technetium-94, technetium-99m, and technetium-99, gamma emitters such as cobalt-60, cesium-137, and technetium-99m, Alemtuzumab, Daclizumab, Rituximab (e.g., MABTHERA™), Trastuzumab (e.g., HERCEPTIN™), Gemtuzumab, Ibritumomab, Edrecolomab, Tositumomab, CeaVac, Epratuzumab, Mitumomab, Bevacizumab, Cetuximab, Edrecolomab, Lintuzumab, MDX-210, IGN-101 , MDX-010, MAb, AME, ABX-EGF, EMD 72 000, Apolizumab, Labetuzumab, ior-tl , MDX-220, MRA, H-1 1

scFv, Oregovomab, huJ591 MAb, BZL, Visilizumab, TriGem, TriAb, R3, MT-201, G- 250, unconjugated, ACA- 125, Onyvax-105, CDP-860, BrevaRex MAb, AR54, IMC- ICl 1 , GHoMAb-H, ING-I ₅ Anti-LCG MAbs, MT-103, KSB-303, Therex, KW-2871 , Anti-HMI.24, Anti-PTHrP, 2C4 antibody, SGN-30, TRAIL-RI MAb, CAT, Prostate cancer antibody, H22xKi-4, ABX-MAl , Imuteran, Monopharm-C, Acivicin, Aclarubicin, Acodazole Hydrochloride, Acronine, Adozelesin, Adriamycin, Aldesleukin, Altretamine, Ambomycin, A. metantrone Acetate, Aminoglutethimide, Amsacrine, Anastrozole, Anthramycin, Asparaginase, Asperlin, Azacitidine, Azetepa, Azotomycin, Batimastat, Benzodepa, Bicalutamide, Bisantrene Hydrochloride, Bisnafide Dimesylate, Bizelesin, Bleomycin Sulfate, Brequinar Sodium, Bropirimine, Busulfan, Cactinomycin, Calusterone, Camptothecin, Caracemide, Carbetimer, Carboplatin, Carmustine, Carubicin Hydrochloride, Carzelesin, Cedefingol, Chlorambucil, Cirolemycin, Cisplatin, Cladribine, Combretestatin A-4, Crisnatol Mesylate, Cyclophosphamide, Cytarabine, Dacarbazine, DACA (N- [2- (Dimethyl- amino) ethyl] acridine-4-carboxamide), Dactinomycin, Daunorubicin Hydrochloride, Daunomycin, Decitabine, Dexormaplatin, Dezaguanine, Dezaguanine Mesylate, Diaziquone, Docetaxel, Dolasatins, Doxorubicin, Doxorubicin Hydrochloride, Droloxifene, Droloxifene Citrate, Dromostanolone Propionate, Duazomycin, Edatrexate, Eflornithine Hydrochloride, Ellipticine, Elsamitrucin, Enloplatin, Enpromate, Epipropidine, Epirubicin Hydrochloride, Erbulozole, Esorubicin Hydrochloride, Estramustine, Estramustine Phosphate Sodium, Etanidazole, Ethiodized Oil 1 131, Etoposide, Etoposide Phosphate, Etoprine, Fadrozole Hydrochloride, Fazarabine, Fenretinide, Floxuridine, Fludarabine Phosphate, Fluorouracil, 5-FdUMP, Flurocitabine, Fosquidone, Fostriecin Sodium, Gemcitabine, Gemcitabine Hydrochloride, Gold Au 198, Homocamptothecin, Hydroxyurea, Idarubicin Hydrochloride, Ifosfamide, Ilmofosine, Interferon Alfa-2a, Interferon Alfa-2b, Interferon Alfa-nl, Interferon Alfa-n3, Interferon Beta-I a, Interferon Gamma-I b, Iproplatin, Irinotecan Hydrochloride, Lanreotide Acetate, Letrozole, Leuprolide Acetate, Liarozole Hydrochloride, Lometrexol Sodium, Lomustine, Losoxantrone Hydrochloride, Masoprocol, Maytansine, Mechlorethamine Hydrochloride, Megestrol Acetate, Melengestrol Acetate, Melphalan, Menogaril, Mercaptopurine, Methotrexate,

Methotrexate Sodium, Metoprine, Meturedepa, Mitindomide, Mitocarcin, Mitocromin, Mitogillin, Mitomalcin, Mitomycin, Mitosper, Mitotane, Mitoxantrone Hydrochloride, Mycophenolic Acid, Nocodazole, Nogalamycin,Ormaplatin, Oxisuran, Paclitaxel, Pegaspargase, Peliomycin, Pentamustine, PeploycinSulfate, Perfosfamide, Pipobroman, Piposulfan, Piroxantrone Hydrochloride, Plicamycin, Plomestane, Porfimer Sodium, Porfiromycin, Prednimustine, Procarbazine Hydrochloride, Puromycin, Puromycin Hydrochloride, Pyrazofurin, Rhizoxin, Rhizoxin D, Riboprine, Rogletimide, Safingol, Safingol Hydrochloride, Semustine, Simtrazene, Sparfosate Sodium, Sparsomycin, Spirogermanium Hydrochloride, Spiromustine, Spiroplatin, Streptonigrin, Streptozocin, Strontium Chloride Sr 89, Sulofenur, Talisomycin, Taxane, Taxoid, Tecogalan Sodium, Tegafur, Teloxantrone Hydrochloride, Temoporfin, Teniposide, Teroxirone, Testolactone, Thiamiprine, Thioguanine, Thiotepa, Thymitaq, Tiazofurin, Tirapazamine, Tomudex, TOP53, Topotecan Hydrochloride, Toremifene Citrate, Trestolone Acetate, Triciribine Phosphate, Trimetrexate, Trimetrexate Glucuronate, Triptorelin, Tubulozole Hydrochloride, Uracil Mustard, Uredepa, Vapreotide, Verteporfin, Vinblastine, Vinblastine Sulfate, Vincristine, Vincristine Sulfate, Vindesine, Vindesine Sulfate, Vinepidine Sulfate, Vinglycinate Sulfate, Vinleurosine Sulfate, Vinorelbine Tartrate, Vinrosidine Sulfate, Vinzolidine Sulfate, Vorozole, Zeniplatin, Zinostatin, Zorubicin Hydrochloride, 2-Chlorodeoxyadenosine, 2' Deoxyformycin, 9-aminocamptothecin, raltitrexed, N-propargyl-5,8-dideazafolic acid, 2chloro-2'-arabino-fluoro-2'-deoxyadenosine, 2-chloro-2'-deoxyadenosine, anisomycin, trichostatin A, hPRL-G129R, CEP-751, linomide, sulfur mustard, nitrogen mustard (mechlor ethamine), cyclophosphamide, melphalan, chlorambucil, ifosfamide, busulfan, N-methyl-Nnitrosourea (MNU), N, N'-Bis (2-chloroethyl)-N-nitrosourea (BCNU), N- (2-chloroethyl)-N' cyclohexyl-N-nitrosourea (CCNU), N- (2-chloroethyl)-N'- (trans-4- methylcyclohexyl-N-nitrosourea (MeCCNU), N- (2-chloroethyl)-N- (diethyl) ethylphosphonate-N-nitrosourea (fotemustine), streptozotocin, diacarbazine (DTIC), mitozolomide, temozolomide, thiotepa, mitomycin C, AZQ, adozelesin, Cisplatin, Carboplatin, Ormaplatin, Oxaliplatin,C 1-973, DWA 21 14R, JM216, JM335, Bis (platinum), tomudex, azacitidine, cytarabine, gemcitabine, 6-Mercaptopurine, 6- Thioguanine, Hypoxanthine, teniposide 9-amino camptothecin, Topotecan, CPT-1 1 ,

Doxorubicin, Daunomycin, Epirubicin, darubicin, mitoxantrone, losoxantrone, Dactinomycin (Actinomycin D), amsacrine, pyrazoloacridine, all-trans retinol, 14- hydroxy-retro-retinol, all-trans retinoic acid, N- (4- Hydroxyphenyl) retinamide, 13-cis retinoic acid, 3-Methyl TTNEB, 9-cis retinoic acid, fludarabine (2-F-ara-AMP), 2- chlorodeoxyadenosine (2-Cda), 20-pi-l,25 dihydroxyvitamin D3, 5-ethynyluracil, abiraterone, aclarubicin, acylfulvene, adecypenol, adozelesin, aldesleukin, ALL-TK antagonists, altretamine, ambamustine, amidox, amifostine, aminolevulinic acid, amrubicin, amsacrine, anagrelide, anastrozole, andrographolide, angiogenesis inhibitors, antagonist D, antagonist G, antarelix, anti-dorsalizing morphogenetic protein- 1, antiandrogen, prostatic carcinoma, antiestrogen, antineoplaston, antisense oligonucleotides, aphidicolin glycinate, apoptosis gene modulators, apoptosis regulators, apurinic acid, ara-CDP-DL-PTBA, argininedeaminase, asulacrine, atamestane, atrimustine, axinastatin 1, axinastatin 2, axinastatin 3, azasetron, azatoxin, azatyrosine, baccatin III derivatives, balanol, batimastat, BCR/ ABL antagonists, benzochlorins, benzoylstaurosporine, beta lactam derivatives, beta-alethine, betaclamycin B, betulinic acid, bFGF inhibitor, bicalutamide, bisantrene, bisaziridinylspermine, bisnafide, bistratene A, bizelesin, breflate, bleomycin A2, bleomycin B2, bropirimine, budotitane, buthionine sulfoximine, calcipotriol, calphostin C, camptothecin derivatives (e.g., 10-hydroxy-camptothecin), canarypox IL-2, capecitabine, carboxamide-amino-triazole, carboxyamidotriazole, CaRest M3, CARN 700, cartilage derived inhibitor, carzelesin, casein kinase inhibitors (ICOS), castanospermine, cecropin B, cetrorelix, chlorins, chloroquinoxaline sulfonamide, cicaprost, cis-porphyrin, cladribine, clomifene analogues, clotrimazole, collismycin A, collismycin B, combretastatin A4, combretastatin analogue, conagenin, crambescidin 816 , crisnatol, cryptophycin 8, cryptophycin A derivatives, curacin A, cyclopentanthraquinones, cycloplatam, cypemycin, cytarabine ocfosfate, cytolytic factor, cytostatin, dacliximab, decitabine, dehydrodidemnin B, 2'deoxycoformycin (DCF), deslorelin, dexifosfamide, dexrazoxane, dexverapamil, diaziquone, didemnin B, didox, diethylnorspermine, dihydro-5-azacytidine, 9-dihydrotaxol, dioxamycin, diphenyl spiromustine, discodermolide, docosanol, dolasetron, doxifluridine, droloxifene, dronabinol, duocarmycin SA, ebselen, ecomustine, edelfosine,

edrecolomab, eflornithine, elemene, emitefur, epirubicin, epothilones (A, R = H, B, R = Me), epithilones, epristeride, estramustine analogue, estrogen agonists, estrogen antagonists, etanidazole, etoposide, etoposide 4'-phosphate (etopofos), exemestane, fadrozole, fazarabine, fenretinide, filgrastim, finasteride, flavopiridol, flezelastine, fluasterone, fludarabine, fluorodaunorunicin hydrochloride, forfenimex, formestane, fostriecin, fotemustine, gadolinium texaphyrin, gallium nitrate, galocitabine, ganirelix, gelatinase inhibitors, gemcitabine, glutathione inhibitors, hepsulfam, heregulin, hexamethylene bisacetamide, homoharringtonine (HHT), hypericin, ibandronic acid, idarubicin, idoxifene, idramantone, ilmofosine, ilomastat, imidazoacridones, imiquimod, immunostimulant peptides, insulin-like growth factor- 1 receptor inhibitor, interferon agonists, interferons, interleukins, iobenguane, iododoxorubicin, 4- ipomeanol, irinotecan, iroplact, irsogladine, isobengazole, isohomohalicondrin B, itasetron, jasplakinolide, kahalalide F, lamellarin-N triacetate, lanreotide, leinamycin, lenograstim, lentinan sulfate, leptolstatin, letrozole, leukemia inhibiting factor, leukocyte alpha interferon, leuprolide, estrogen, and progesterone combinations, leuprorelin, levamisole, liarozole, linear polyamine analogue, lipophilic disaccharide peptide, lipophilic platinum compounds, lissoclinamide 7, lobaplatin, lombricine, lometrexol, lonidamine, losoxantrone, lovastatin, loxoribine, lurtotecan, lutetium texaphyrin, lysofylline, lytic peptides, maytansine, mannostatin A, marimastat, masoprocol, maspin, matrilysin inhibitors, matrix metalloproteinase inhibitors, menogaril, merbarone, meterelin, methioninase, metoclopramide, MIF inhibitor, ifepristone, miltefosine, mirimostim, mismatched double stranded RNA, mithracin, mitoguazone, mitolactol, mitomycin analogues, mitonafide, mitotoxin fibroblast growth factor-saporin, mitoxantrone, mofarotene, molgramostim, monoclonal antibody, human chorionic gonadotrophin, monophosphoryl lipid A and myobacterium cell wall skeleton combinations, mopidamol, multiple drug resistance gene inhibitor, multiple tumor suppressor 1 -based therapy, mustard anticancer agent, mycaperoxide B, mycobacterial cell wall extract, myriaporone, N-acetyldinaline, N-substituted benzamides, nafarelin, nagrestip, naloxone and pentazocine combinations, napavin, naphterpin, nartograstim, nedaplatin, nemorubicin, neridronic acid, neutral endopeptidase, nilutamide, nisamycin, nitric oxide modulators, nitroxide antioxidant, nitrullyn, 06-benzylguanine, octreotide,

okicenone, oligonucleotides, onapristone, ondansetron, ondansetron, oracin, oral cytokine inducer, ormaplatin, osaterone, oxaliplatin, oxaunomycin, paclitaxel analogues, paclitaxel derivatives, palauamine, palmitoylrhizoxin, pamidronic acid, panaxytriol, panomifene, parabactin, pazelliptine, pegaspargase, peldesine, pentosan polysulfate sodium, pentostatin, pentrozole, perflubron, perfosfamide, perillyl alcohol, phenazinomycin, phenylacetate, phosphatase inhibitors, picibanil, pilocarpine hydrochloride, pirarubicin, piritrexim, placetin A, placetin B, plasminogen activator inhibitor, platinum complex, platinum compounds, platinum-triamine complex, podophyllotoxin, porfimer sodium, porfiromycin, propyl bis-acridone, prostaglandin 32, proteasome inhibitors, protein A-based immune modulator, protein kinase C inhibitor, protein kinase C inhibitors, microalgal, protein tyrosine phosphatase inhibitors, purine nucleoside phosphorylase inhibitors, purpurins, pyrazoloacridine, pyridoxylated hemoglobin polyoxyethylene conjugate, raf antagonists, raltitrexed, ramosetron, ras farnesyl protein transferase inhibitors, ras inhibitors, ras-GAP inhibitor, retelliptine demethylated, rhenium Re 186 etidronate, rhizoxin, ribozymes, RII retinamide, rogletimide, rohitukine, romurtide, roquinimex, rubiginone B 1 , ruboxyl, safingol, saintopin, SarCNU, sarcophytol A, sargramostim, Sdi 1 mimetics, semustine, senescence derived inhibitor 1 , sense oligonucleotides, signal transduction inhibitors, signal transduction modulators, single chain antigen binding protein, sizofiran, sobuzoxane, sodium borocaptate, sodium phenylacetate, solverol, somatomedin binding protein, sonermin, sparfosic acid, spicamycin D, spiromustine, splenopentin, spongistatin 1, squalamine, stem cell inhibitor, stem-cell division inhibitors, stipiamide, stromelysin inhibitors, sulfinosine, superactive vasoactive intestinal peptide antagonist, suradista, suramin, swainsonine, synthetic glycosaminoglycans, tallimustine, tamoxifen methiodide, tauromustine, tazarotene, tecogalan sodium, tegafur, tellurapyrylium, telomerase inhibitors, temoporfin, temozolomide, teniposide, tetrachlorodecaoxide, tetrazomine, thaliblastine, thalidomide, thiocoraline, thrombopoietin, thrombopoietin mimetic, thymalfasin, thymopoietin receptor agonist, thymotrinan, thyroid stimulating hormone, tin ethyl etiopurpurin, tirapazamine, titanocene dichloride, topotecan, topsentin, toremifene, totipotent stem cell factor, translation inhibitors, tretinoin, triacetyluridine, triciribine, trimetrexate, triptorelin, tropisetron, turosteride, tyrosine

kinase inhibitors, tyrphostins, UBC inhibitors, ubenimex, urogenital sinus-derived growth inhibitory factor, urokinase receptor antagonists, vapreotide, variolin B, vector system, erythrocyte gene therapy, velaresol, veramine, verdins, verteporfin, vinorelbine, vinxaltine, vitaxin, vorozole, zanoterone, zeniplatin, zilascorb, or zinostatin stimalamer. In another embodiment of all aspects of the invention, a second treatment is utilized to determine gene expression in a sample from the patient.

In another embodiment of all aspects of the invention, the gene is selected from the group consisting of ABLl, ACTB, ACTNl, ACTN4, ACTR2, ADA, ADAM9, ADAMTSl , ADDl, AD0RA2A, AFlQ, AIFl, AKAPl, AKAP13, AKRlBl , AKRlCl, AKTl , ALDH2, ALDH3 Al, ALDOC, ALG5, ALMSl, ALOX15B, AMIG02, AMPD2, AMPD3, ANAPC5, ANP32A, ANP32B, ANPEP, ANXAl, ANXA2, AP1G2, APOBEC3B, APRT, ARHE, ARHGAP15, ARHGAP25, ARHGDIB, ARHGEF6, ARL7, ASAHl, ASPH, ATF3, ATIC, ATOXl , ATPl B3, ATP2A2, ATP2A3, ATP5D, ATP5G2, ATP6V1B2, B2M, BASPl , BAX, BC008967, BCATl, BCHE, BCLl IB, BDNF, BHLHB2, BIN2, BLM, BLMH, BLVRA, BMIl, BNIP3, BRDT, BRRNl, BTN3A2, BTN3A3, Cl Iorf2, C14orfl39, C15orf25, C18orflO, Clorf24, Clorf29, Clorf38, ClQRl, C22orfl8, C5orω3, C6orf32, CACNAlG, CACNB3, CALDl, CALMl , CALML4, CALU, CAP35O, CAPG, CAPN2, CAPN3, CASP2, CASP6, CASP7, CAST, CBFB, CBLB, CBRl, CBX3, CCL2, CCL21, CCNA2, CCNBlIPl , CCND3, CCR7, CCR9, CCT5, CD151, CDlA, CDlB, CDlC, CDlD, CDlE, CD2, CD28, CD37, CD3D, CD3E, CD3G, CD3Z, CD44, CD47, CD53, CD59, CD6, CD63, CD81, CD8A, CD8B1, CD99, CDClO, CDC14B, CDHl 1, CDH2, CDKL5, CDKN2A, CDW52, CECRl, CENPB, CENTBl, CENTG2, CEPl, CGOl 8, CHRNA3, CHSl , CIAPINl , CKAP4, CKIP-I, CNN3, CNP, COLlAl, COL4A1, COL4A2, COL5A2, COL6A1 , COL6A2, COPA, COPEB, COROlA, COROlC, C0X7B, CPSFl, CRABPl , CREB3L1 , CRIP2, CRK, CRYl, CSDA, CSPG2, CSRPl , CST3, CTBPl, CTGF, CTNNAl, CTSB, CTSC, CTSD, CTSL, CUGBP2, CUTC, CXCLl , CXCR4, CXorf9, CYFIP2, CYLD, CYR61, DATFl, DAZAPl, DBNl , DBT, DCTNl, DDOST, DDX18, DDX5, DGKA, DIAPHl, DIPA, DKCl, DKFZP434J154, DKFZP564C186, DKFZP564G2022, DKFZp564J157, DKFZP564K0822, DNAJClO, DNAPTP6, DOCKlO, D0CK2, DPAGTl, DPEP2, DPYSL3, DSIPI, DUSPl , DUSP3,

DXS9879E, DYRK2, E2F4, ECEl, ECMl , EEFlAl , EEF1B2, EEFlG, EFNB2, EHD2, EIF2S2, EIF3S2, EIF4B, EIF4G3, EIF5A, ELA2B, ELK3, EMP3, ENO2, EPASl , EPB41L4B, ERCC2, ERG, ERP70, EVERl, EVI2A, EVL, EXTl , EZH2, F2R, FABP5, FAD 104, FAM46A, FARSLA, FAT, FAU, FBL, FCGR2A, FCGR2C, FER1L3, FGFRl, FHLl , FHODl , FKBPlA, FKBP9, FLII, FLJ10350, FLJ10539, FLJ10774, FLJ12270, FLJ13373, FLJ20859, FLJ21159, FLJ22457, FLJ35036, FLJ46603, FLNC, FLOTl , FMNLl, FNl, FNBPl, FOLHl, FOXF2, FSCNl, FSTLl, FTHl, FTL, FYB, FYN, G0S2, G6PD, GALIG, GALNT6, GAPD, GAS7, GATA2, GATA3, GFPTl, GIMAP5, GIT2, GJAl, GLRB, GLTSCR2, GLUL, GMDS, GMFG, GNAl 5, GNAI2, GNAQ, GNB2, GNB5, GOT2, GPNMB, GPR65, GPRASPl, GPSM3, GRP58, GSTM2, GTF3A, GTSEl, GYPC, GZMA, GZMB, HlFO, HlFX, H2AFX, H3F3A, HA-I, HCLSl, HEMl, HEXB, HIC, HIST1H4C, HKl, HLA-A, HLA-B, HLA-DRA, HMGAl, HMGB2, HMGN2, HMMR, HNRPAl, HNRPD, HNRPM, HOXA9, HPRTl, HRMTlLl, HSA9761, HSPA5, HSU79274, HTATSFl, HU6800, ICAMl, ICAM2, IER3, IFI16, IFI44, IFITM2, IFITM3, IFRG28, IGFBP2, IGFBP3, IGSF4, IL13RA2, IL21R, IL2RG, IL4R, IL6, IL6R, IL6ST, IL8, IMPDH2, INPP5D, INSIGl , IQGAPl, IQGAP2, IRS2, ITGA3, ITGA5, ITGB2, ITK, ITM2A, JAKl, JARID2, JUNB, K-ALPHA-I , KHDRBSl , KIAA0220, KIAA0355, KIAA0802, KIAA0877, KIAA0922, KIAA1078, KIAAl 128, KIAA1393, KIFCl, KPNBl, LAIRl, LAMBl , LAMB3, LAMRl, LAPTM5, LAT, LBR, LCK, LCPl, LCP2, LDHB, LEFl, LEPREl, LGALSl, LGALS9, LHFPL2, LMNBl, LNK, LOC54103, LOC55831, LOC81558, LOC94105, LONP, LOX, LOXL2, LPHN2, LPXN, LRMP, LRP12, LRRC5, LRRN3, LSTl, LTB, LUM, LY9, LY96, M6PRBP1, MAD2L1BP, MAGEB2, MAL, MANlAl , MAPlB, MAP1LC3B, MAP4K1 , MAPKl, MAPREl, MARCKS, MAZ, MCAM, MCLl, MCM5, MCM7, MDH2, MDK, MDNl , MEF2C, MFNG, MGC17330, MGC21654, MGC2744, MGC4083, MGC8721, MGC8902, MGLL, MIA, MICA, MLPH, MME, MMP2, MPHOSPH6, MPPl, MPZLl, MRP63, MRPL12, MRPS2, MSN, MTlE, MTlK, MUFl, MVP, MYB, MYC, MYL6, MYL9, MYOlB, NAPlLl , NAPl L2, NARF, NARS, NASP, NBLl , NCL, NCOR2, NDN, NDUFABl, NDUFS6, NFIL3, NFKBIA, NID2, NIPA2, NK4, NME4, NME7, NNMT, NOL5A, NOL8, NOMO2, NOTCHl, NPCl, NQOl , NRl D2, NUCB2, NUDC, NUP210,

NUP88, NVL, NXFl , OBFCl , OCRL, OGT, OK/SW-cl.56, OPTN, OXAlL, P2RX5, P4HA1 , PACAP, PAF53, PAFAHl B3, PALM2-AKAP2, PAX6, PBEFl , PCBP2, PCCB, PEAl 5, PFDN5, PFNl , PFN2, PGAMl , PGKl , PHEMX, PHLDAl , PIM2, PITPNCl, PKM2, PLAC8, PLAGLl, PLAU, PLAUR, PLCBl , PLEK2, PLEKHCl , PLOD2, PLSCRl, PNAS-4, PNMA2, P0LR2F, PON2, PPAP2B, PPIA, PPIF, PPPlRl 1, PPP2CB, PRFl, PRGl, PRIMl, PRKCA, PRKCBl, PRKCH, PRKCQ, PRKD2, PRNP, PRP 19, PRPF8, PRPSl, PRSSI l, PRSS23, PSCDBP, PSMB9, PSMC3, PSMC5, PSME2, PTGER4, PTGES2, PTMA, PTOVl , PTP4A3, PTPN7, PTPNSl , PTPRC, PTPRCAP, PTRF, PTS, PURA, PWPl, PYGL, QKI, RAB31, RAB3GAP, RAB7, RAB7L1, RAB9P40, RAC2, RAFTLIN, RAG2, RALY, RAPlB, RASGRP2, RBMX, RBPMS, RCNl, REA, RFC3, RFC5, RGC32, RGS3, RHOC, RHOH, RIMS3, RI0K3, RIPK2, RISl , RNASE6, RNF 144, RNPSl, RPLlO, RPLlOA, RPLl 1, RPL12, RPLl 3, RPLl 3A, RPLl 7, RPLl 8, RPLl 8A, RPL24, RPL3, RPL32, RPL36A, RPL39, RPL7, RPL9, RPLPO, RPLP2, RPSlO, RPSl 1, RPS 15, RPS 15 A, RPS19, RPS2, RPS23, RPS24, RPS25, RPS27, RPS28, RPS4X, RPS4Y1, RPS6, RPS7, RPS9, RRAS, RRAS2, RRBPl, RRM2, RUNXl, RUNX3, S100A13, S100A4, SART3, SATBl, SCAPl , SCARBl, SCARB2, SCN3A, SCTR, SEC31L2, SEC61G, SELL, SELPLG, SEMA4G, SEPT6, SEPTlO, SEPWl, SERPINAl, SERPINBl , SERPINB6, SFRS3, SFRS5, SFRS6, SFRS7, SH2D1A, SH3GL3, SH3TC1, SHDl, SHFMl, SHMT2, SIATl , SKBl, SKP2, SLA, SLC 1A4, SLC20A1, SLC25A15, SLC25A5, SLC39A14, SLC39A6, SLC43A3, SLC4A2, SLC7A11, SLC7A6, SMA3, SMAD3, SMARCD3, SMOX, SMS, SNDl, SNRPA, SNRPB, SNRPB2, SNRPE, SNRPF, SOD2, SOX4, SP 140, SPANXC, SPARC, SPIl, SRF, SRM, SRRMl, SSA2, SSBP2, SSRPl, SSSCAl , STAG3, STATl, STAT4, STAT5A, STCl, STC2, STMNl, STOML2, SUIl, T3JAM, TACCl, TACC3, TAF5, TAGLN, TALI, TAPl , TARP, TBCA, TCF 12, TCF4, TCF7, TFDP2, TFPI, TFRC, TGFBl, TIMM 17A, TIMPl, TJPl, TK2, TM4SF1, TM4SF2, TM4SF8, TM6SF1 , TMEM2, TMEM22, TMSBlO, TMSNB, TNFAIP3, TNFAIP8, TNFRSFlOB, TNFRSFlA, TNFRSF7, TNIK, TNPOl , TOBl, TOMM20, TOP2A, TOX, TPKl, TPM2, TRA@, TRAl, TRAM2, TRB@, TRD@, TRIM, TRIM14, TRIM22, TRIM28, TRIP13, TRPV2, TUBA3, TUBGCP3, TUFM, TUSC3, TXN, TXNDC5, UBASH3A, UBB, UBC, UBE2A, UBE2L6, UBE2S,

UCHLl, UCK2, UCP2, UFDlL, UGCG, UGDH, UGT2B17, ULK2, UMPS, UNG, UROD, USP34, USP4, USP7, VASP, VAVl , VIM, VLDLR, VWF, WARS, WASPIP, WBSCR20A, WBSCR20C, WHSCl , WNT5A, XPOl, ZAP128, ZAP70, ZFP36L1, ZNF32, ZNF335, ZNF593, ZNFNlAl, or ZYX.

The nucleic acid sequence of each listed genes is publicly available through the GenBank or RefSeq database (http://www.ncbi.nlm.nih.gov/sites/gquery). The gene sequences are also included as part of the HG-Ul 33A GeneChip from Affymetrix, Inc.

"Resistant" or "resistance" as used herein means that a cell, a tumor, a patient (e.g., a human), or a living organism is able to withstand treatment, e.g., with a compound, such as a chemotherapeutic agent, or radiation treatment, in that the treatment inhibits the growth of a cell, e.g., a cancer cell, in vitro or in a tumor, patient, or living organism by less than 10%, 20%, 30%, 40%, 50%, 60%, or 70% relative to the growth of a similar cell not exposed to the treatment. Resistance to treatment can be determined by a cell-based assay that measures the growth of treated cells as a function of the cells' absorbance of an incident light beam as used to perform the NCI60 assays described herein. In this example, greater absorbance indicates greater cell growth, and thus, resistance to the treatment. A reduction in growth indicates more resistance to a treatment. By "chemoresistant" or "chemoresistance" is meant resistance to a compound.

"Sensitive" or "sensitivity" as used herein means that a cell, a tumor, a patient (e.g., a human), or a living organism is responsive to treatment, e.g., with a compound, such as a chemotherapeutic agent, or radiation treatment, in that the treatment inhibits the growth of a cell, e.g., a cancer cell, in vitro or in a tumor, patient, or living organism by 70%, 80%, 90%, 95%, 99%, or 100%. Sensitivity to treatment may be determined by a cell-based assay that measures the growth of treated cells as a function of the cells' absorbance of an incident light beam as used to perform the NCI60 assays described herein. In this example, lesser absorbance indicates reduced cell growth, and thus, sensitivity to the treatment. A greater reduction in growth indicates more sensitivity to the treatment. By "chemosensitive" or "chemosensitivity" is meant sensitivity to a compound.

"Complement" of a nucleic acid sequence or a "complementary" nucleic acid

sequence as used herein refers to an oligonucleotide which is in "antiparallel association" when it is aligned with the nucleic acid sequence such that the 5' end of one sequence is paired with the 3' end of the other. Nucleotides and other bases can have complements and may be present in complementary nucleic acids. Bases not commonly found in natural nucleic acids that can be included in the nucleic acids of the present invention include, for example, inosine and 7-deazaguanine. "Complementarity" may not be perfect; stable duplexes of complementary nucleic acids can contain mismatched base pairs or unmatched bases. Skilled artisans can determine duplex stability empirically considering a number of variables including, for example, the length of the oligonucleotide, percent concentration of cytosine and guanine bases in the oligonucleotide, ionic strength, and incidence of mismatched base pairs. Typically, complementarity is determined by comparing contiguous nucleic acid sequences.

When complementary nucleic acid sequences form a stable duplex, they are said to be "hybridized" or to "hybridize" to each other or it is said that "hybridization" has occurred. Nucleic acids are referred to as being "complementary" if they contain nucleotides or nucleotide homologues that can form hydrogen bonds according to Watson-Crick base-pairing rules (e.g., G with C, A with T, or A with U) or other hydrogen bonding motifs such as, for example, diaminopurine with T, 5-methyl C with G, 2-thiothymidine with A, inosine with C, and pseudoisocytosine with G. Anti-sense RNA can be complementary to other oligonucleotides, e.g., mRNA.

"Biomarker" as used herein indicates a transcription product (e.g., RNA, such as an RNA primary transcript, mRNA, tRNA, rRNA, microRNA (miRNA), or complementary RNA or DNA (e.g., cDNA) strands thereof) or a translation product (e.g., a polypeptide or metabolite thereof) of a biomarker gene, as defined herein, whose level of expression indicates the sensitivity or resistance of a cell (e.g., a cancer cell), tissue, organism, or patient (e.g., a human) to a treatment (e.g., chemotherapy, radiation therapy, or surgery).

"Compound" as used herein means a chemical or biological substance, e.g., a drug, a protein, an antibody, or an oligonucleotide, which can be used to treat a disease or which has biological activity in vivo or in vitro. Compounds may or may not be approved by the U.S. Food and Drug Administration (FDA). Preferred compounds

include, e.g., chemotherapy agents that can inhibit cancer growth. Preferred chemotherapy agents include, e.g., Vincristine, Cisplatin, Azaguanine, Etoposide, Adriamycin, Aclarubicin, Mitoxantrone, Mitomycin, Paclitaxel, Gemcitabine, Taxotere, Dexamethasone, Ara-C, Methylprednisolone, Methotrexate, Bleomycin, Methyl-GAG, Carboplatin, 5-FU (5-Fluorouracil), Rituximab (e.g., MABTHERA™), , histone deacetylase (HDAC) inhibitors, and 5-Aza-2'-deoxycytidine (Decitabine). Exemplary radioactive chemotherapeutic agents include compounds containing alpha emitters such as astatine-211 , bismuth-212, bismuth-213, lead-212, radium-223, actinium-225, and thorium-227, beta emitters such as tritium, strontium-90, cesium-137, carbon-11 , nitrogen-13, oxygen-15, fluorine-18, iron-52, cobalt-55, cobalt-60, copper-61, copper- 62, copper-64, zinc-62, zinc-63, arsenic-70, arsenic-71 , arsenic-74, bromine-76, bromine-79, rubidium-82, yttrium-86, zirconium-89, indium- 1 10, iodine- 120, iodine- 124, iodine-129, iodine-131, iodine-125, xenon-122, technetium-94m, technetium-94, technetium-99m, and technetium-99, and gamma emitters such as cobalt-60, cesium- 137, and technetium-99m. Exemplary chemotherapeutic agents also include antibodies such as Alemtuzumab, Daclizumab, Rituximab (e.g., MABTHERA™), Trastuzumab (e.g., HERCEPTIN™), Gemtuzumab, Ibritumomab, Edrecolomab, Tositumomab, CeaVac, Epratuzumab, Mitumomab, Bevacizumab, Cetuximab, Edrecolomab, Lintuzumab, MDX-210, IGN-101, MDX-010, MAb, AME, ABX-EGF, EMD 72 000, Apolizumab, Labetuzumab, ior-tl, MDX-220, MRA, H-11 scFv, Oregovomab, huJ591 MAb, BZL, Visilizumab, TriGem, TriAb, R3, MT-201 , G-250, ACA-125, Onyvax-105, CDP-860, BrevaRex MAb, AR54, IMC-ICl 1, GlioMAb-H, ING-I, Anti-LCG MAbs, MT-103, KSB-303, Therex, KW-2871, Anti-HMI.24, Anti-PTHrP, 2C4 antibody, SGN-30, TRAIL-RI MAb, CAT, Prostate cancer antibody, H22xKi-4, ABX-MAl, Imuteran, and Monopharm-C. Exemplary chemotherapeutic agents also include Acivicin; Aclarubicin; Acodazole Hydrochloride; Acronine; Adozelesin; Adriamycin; Aldesleukin; Altretamine; Ambomycin; A. metantrone Acetate; Aminoglutethimide; Amsacrine; Anastrozole; Anthramycin; Asparaginase; Asperlin; Azacitidine; Azetepa; Azotomycin; Batimastat; Benzodepa; Bicalutamide; Bisantrene Hydrochloride; Bisnafide Dimesylate; Bizelesin; Bleomycin Sulfate; Brequinar Sodium; Bropirimine; Busulfan; Cactinomycin; Calusterone; Camptothecin; Caracemide; Carbetimer;

Carboplatin; Carmustine; Carubicin Hydrochloride; Carzelesin; Cedefingol; Chlorambucil; Cirolemycin; Cisplatin; Cladribine; Combretestatin A-4; Crisnatol Mesylate; Cyclophosphamide; Cytarabine; Dacarbazine; DACA (N- [2- (Dimethyl- amino) ethyl] acridine-4-carboxamide); Dactinomycin; Daunorubicin Hydrochloride; Daunomycin; Decitabine; Dexormaplatin; Dezaguanine; Dezaguanine Mesylate; Diaziquone; Docetaxel; Dolasatins; Doxorubicin; Doxorubicin Hydrochloride; Droloxifene; Droloxifene Citrate; Dromostanolone Propionate; Duazomycin; Edatrexate; Eflornithine Hydrochloride; Ellipticine; Elsamitrucin; Enloplatin; Enpromate; Epipropidine; Epirubicin Hydrochloride; Erbulozole; Esorubicin Hydrochloride; Estramustine; Estramustine Phosphate Sodium; Etanidazole; Ethiodized Oil I 131 ; Etoposide; Etoposide Phosphate; Etoprine; Fadrozole Hydrochloride; Fazarabine; Fenretinide; Floxuridine; Fludarabine Phosphate; Fluorouracil; 5-FdUMP; Flurocitabine; Fosquidone; Fostriecin Sodium; Gemcitabine; Gemcitabine Hydrochloride; Gold Au 198; Homocamptothecin; Hydroxyurea; Idarubicin Hydrochloride; Ifosfamide; Ilmofosine; Interferon Alfa-2a; Interferon Alfa-2b; Interferon Alfa-nl; Interferon Alfa-n3; Interferon Beta-I a; Interferon Gamma-I b; Iproplatin; Irinotecan Hydrochloride; Lanreotide Acetate; Letrozole; Leuprolide Acetate; Liarozole Hydrochloride; Lometrexol Sodium; Lomustine; Losoxantrone Hydrochloride; Masoprocol; Maytansine; Mechlorethamine Hydrochloride; Megestrol Acetate; Melengestrol Acetate; Melphalan; Menogaril; Mercaptopurine; Methotrexate; Methotrexate Sodium; Metoprine; Meturedepa; Mitindomide; Mitocarcin; Mitocromin; Mitogillin; Mitomalcin; Mitomycin; Mitosper; Mitotane; Mitoxantrone Hydrochloride; Mycophenolic Acid; Nocodazole; Nogalamycin;Ormaplatin; Oxisuran; Paclitaxel; Pegaspargase; Peliomycin; Pentamustine; PeploycinSulfate; Perfosfamide; Pipobroman; Piposulfan; Piroxantrone Hydrochloride; Plicamycin; Plomestane; Porfimer Sodium; Porfiromycin; Prednimustine; Procarbazine Hydrochloride; Puromycin; Puromycin Hydrochloride; Pyrazofurin; Rhizoxin; Rhizoxin D; Riboprine; Rogletimide; Safingol; Safingol Hydrochloride; Semustine; Simtrazene; Sparfosate Sodium; Sparsomycin; Spirogermanium Hydrochloride; Spiromustine; Spiroplatin; Streptonigrin; Streptozocin; Strontium Chloride Sr 89; Sulofenur; Talisomycin; Taxane; Taxoid; Tecogalan Sodium; Tegafur; Teloxantrone Hydrochloride; Temoporfin; Teniposide; Teroxirone;

Testolactone; Thiamiprine; Thioguanine; Thiotepa; Thymitaq; Tiazofurin; Tirapazamine; Tomudex; TOP53; Topotecan Hydrochloride; Toremifene Citrate; Trestolone Acetate; Triciribine Phosphate; Trimetrexate; Trimetrexate Glucuronate; Triptorelin; Tubulozole Hydrochloride; Uracil Mustard; Uredepa; Vapreotide; Verteporfin; Vinblastine; Vinblastine Sulfate; Vincristine; Vincristine Sulfate; Vindesine; Vindesine Sulfate; Vinepidine Sulfate; Vinglycinate Sulfate; Vinleurosine Sulfate; Vinorelbine Tartrate; Vinrosidine Sulfate; Vinzolidine Sulfate; Vorozole; Zeniplatin; Zinostatin; Zorubicin Hydrochloride; 2-Chlorodeoxyadenosine; 2' Deoxyformycin; 9-aminocamptothecin; raltitrexed; N-propargyl-5,8-dideazafolic acid; 2chloro-2'-arabino-fluoro-2'-deoxyadenosine; 2-chloro-2'-deoxyadenosine; anisomycin; trichostatin A; hPRL-G129R; CEP-751 ; linomide; sulfur mustard; nitrogen mustard (mechlor ethamine); cyclophosphamide; melphalan; chlorambucil; ifosfamide; busulfan; N-methyl-Nnitrosourea (MNU); N, N'-Bis (2-chloroethyl)-N-nitrosourea (BCNU); N- (2-chloroethyl)-N' cyclohexyl-N-nitrosourea (CCNU); N- (2-chloroethyl)- N'- (trans-4-methylcyclohexyl-N-nitrosourea (MeCCNU); N- (2-chloroethyl)-N'- (diethyl) ethylphosphonate-N-nitrosourea (fotemustine); streptozotocin; diacarbazine (DTIC); mitozolomide; temozolomide; thiotepa; mitomycin C; AZQ; adozelesin; Cisplatin; Carboplatin; Ormaplatin; Oxaliplatin;C 1-973; DWA 21 14R; JM216; JM335; Bis (platinum); tomudex; azacitidine; cytarabine; gemcitabine; 6-Mercaptopurine; 6- Thioguanine; Hypoxanthine; teniposide 9-amino camptothecin; Topotecan; CPT-1 1 ; Doxorubicin; Daunomycin; Epirubicin; darubicin; mitoxantrone; losoxantrone; Dactinomycin (Actinomycin D); amsacrine; pyrazoloacridine; all-trans retinol; 14- hydroxy-retro-retinol; all-trans retinoic acid; N- (4-Hydroxyphenyl) retinamide; 13-cis retinoic acid; 3-Methyl TTNEB; 9-cis retinoic acid; fludarabine (2-F-ara-AMP); and 2- chlorodeoxyadenosine (2-Cda).

Other chemotherapeutic agents include, but are not limited to, 20-pi-l ,25 dihydroxyvitamin D3; 5-ethynyluracil; abiraterone; aclarubicin; acylfulvene; adecypenol; adozelesin; aldesleukin; ALL-TK antagonists; altretamine; ambamustine; amidox; amifostine; aminolevulinic acid; amrubicin; amsacrine; anagrelide; anastrozole; andrographolide; angiogenesis inhibitors; antagonist D; antagonist G; antarelix; anti-dorsalizing morphogenetic protein-1 ; antiandrogen,; antiestrogen;

antineoplaston; antisense oligonucleotides; aphidicolin glycinate; apoptosis gene modulators; apoptosis regulators; apurinic acid; ara-CDP-DL-PTBA; argininedeaminase; asulacrine; atamestane; atrimustine; axinastatin 1 ; axinastatin 2; axinastatin 3; azasetron; azatoxin; azatyrosine; baccatin III derivatives; balanol; batimastat; BCR/ABL antagonists; benzochlorins; benzoylstaurosporine; beta lactam derivatives; beta-alethine; betaclamycin B; betulinic acid; bFGF inhibitor; bicalutamide; bisantrene; bisaziridinylspermine; bisnafide; bistratene A; bizelesin; breflate; bleomycin A2; bleomycin B2; bropirimine; budotitane; buthionine sulfoximine; calcipotriol; calphostin C; camptothecin derivatives (e.g., 10-hydroxy-camptothecin); canarypox IL- 2; capecitabine; carboxamide-amino-triazole; carboxyamidotriazole; CaRest M3; CARN 700; cartilage derived inhibitor; carzelesin; casein kinase inhibitors (ICOS); castanospermine; cecropin B; cetrorelix; chlorins; chloroquinoxaline sulfonamide; cicaprost; cis-porphyrin; cladribine; clomifene analogues; clotrimazole; collismycin A; collismycin B; combretastatin A4; combretastatin analogue; conagenin; crambescidin 816; crisnatol; cryptophycin 8; cryptophycin A derivatives; curacin A; cyclopentanthraquinones; cycloplatam; cypemycin; cytarabine ocfosfate; cytolytic factor; cytostatin; dacliximab; decitabine; dehydrodidemnin B; 2'deoxycoformycin (DCF); deslorelin; dexifosfamide; dexrazoxane; dexverapamil; diaziquone; didemnin B; didox; diethylnorspermine; dihydro-5-azacytidine; 9-dihydrotaxol; dioxamycin; diphenyl spiromustine; discodermolide; docosanol; dolasetron; doxifluridine; droloxifene; dronabinol; duocarmycin SA; ebselen; ecomustine; edelfosine; edrecolomab; eflornithine; elemene; emitefur; epirubicin; epothilones (A, R = H; B, R = Me); epithilones; epristeride; estramustine analogue; estrogen agonists; estrogen antagonists; etanidazole; etoposide; etoposide 4'-phosphate (etopofos); exemestane; fadrozole; fazarabine; fenretinide; filgrastim; finasteride; flavopiridol; flezelastine; fluasterone; fludarabine; fluorodaunorunicin hydrochloride; forfenimex; formestane; fostriecin; fotemustine; gadolinium texaphyrin; gallium nitrate; galocitabine; ganirelix; gelatinase inhibitors; gemcitabine; glutathione inhibitors; hepsulfam; heregulin; hexamethylene bisacetamide; homoharringtonine (HHT); hypericin; ibandronic acid; idarubicin; idoxifene; idramantone; ilmofosine; ilomastat; imidazoacridones; imiquimod; immunostimulant peptides; insulin-like growth factor- 1 receptor inhibitor;

interferon agonists; interferons; interleukins; iobenguane; iododoxorubicin; 4- ipomeanol; irinotecan; iroplact; irsogladine; isobengazole; isohomohalicondrin B; itasetron; jasplakinolide; kahalalide F; lamellarin-N triacetate; lanreotide; leinamycin; lenograstim; lentinan sulfate; leptolstatin; letrozole; leukemia inhibiting factor; leukocyte alpha interferon; leuprolide, estrogen, and progesterone combinations; leuprorelin; levamisole; liarozole; linear polyamine analogue; lipophilic disaccharide peptide; lipophilic platinum compounds; lissoclinamide 7; lobaplatin; lombricine; lometrexol; lonidamine; losoxantrone; lovastatin; loxoribine; lurtotecan; lutetium texaphyrin; lysofylline; lytic peptides; maytansine; mannostatin A; marimastat; masoprocol; maspin; matrilysin inhibitors; matrix metalloproteinase inhibitors; menogaril; merbarone; meterelin; methioninase; metoclopramide; MIF inhibitor; ifepristone; miltefosine; mirimostim; mismatched double stranded RNA; mithracin; mitoguazone; mitolactol; mitomycin analogues; mitonafide; mitotoxin fibroblast growth factor-saporin; mitoxantrone; mofarotene; molgramostim; monoclonal antibody, human chorionic gonadotrophin; monophosphoryl lipid A and myobacterium cell wall skeleton combinations; mopidamol; multiple drug resistance gene inhibitor; multiple tumor suppressor 1 -based therapy; mustard anticancer agent; mycaperoxide B; mycobacterial cell wall extract; myriaporone; N-acetyldinaline; N-substituted benzamides; nafarelin; nagrestip; naloxone and pentazocine combinations; napavin; naphterpin; nartograstim; nedaplatin; nemorubicin; neridronic acid; neutral endopeptidase; nilutamide; nisamycin; nitric oxide modulators; nitroxide antioxidant; nitrullyn; 06-benzylguanine; octreotide; okicenone; oligonucleotides; onapristone; ondansetron; ondansetron; oracin; oral cytokine inducer; ormaplatin; osaterone; oxaliplatin; oxaunomycin; paclitaxel analogues; paclitaxel derivatives; palauamine; palmitoylrhizoxin; pamidronic acid; panaxytriol; panomifene; parabactin; pazelliptine; pegaspargase; peldesine; pentosan polysulfate sodium; pentostatin; pentrozole; perflubron; perfosfamide; perillyl alcohol; phenazinomycin; phenylacetate; phosphatase inhibitors; picibanil; pilocarpine hydrochloride; pirarubicin; piritrexim; placetin A; placetin B; plasminogen activator inhibitor; platinum complex; platinum compounds; platinum-triamine complex; podophyllotoxin; porfimer sodium; porfiromycin; propyl bis-acridone; prostaglandin J2; proteasome inhibitors; protein A-based immune modulator; protein kinase C inhibitor;

protein kinase C inhibitors, microalgal; protein tyrosine phosphatase inhibitors; purine nucleoside phosphorylase inhibitors; purpurins; pyrazoloacridine; pyridoxylated hemoglobin polyoxyethylene conjugate; raf antagonists; raltitrexed; ramosetron; ras farnesyl protein transferase inhibitors; ras inhibitors; ras-GAP inhibitor; retelliptine demethylated; rhenium Re 186 etidronate; rhizoxin; ribozymes; RII retinamide; rogletimide; rohitukine; romurtide; roquinimex; rubiginone B 1; ruboxyl; safϊngol; saintopin; SarCNU; sarcophytol A; sargramostim; Sdi 1 mimetics; semustine; senescence derived inhibitor 1 ; sense oligonucleotides; signal transduction inhibitors; signal transduction modulators; single chain antigen binding protein; sizofiran; sobuzoxane; sodium borocaptate; sodium phenylacetate; solverol; somatomedin binding protein; sonermin; sparfosic acid; spicamycin D; spiromustine; splenopentin; spongistatin 1 ; squalamine; stem cell inhibitor; stem-cell division inhibitors; stipiamide; stromelysin inhibitors; sulfinosine; superactive vasoactive intestinal peptide antagonist; suradista; suramin; swainsonine; synthetic glycosaminoglycans; tallimustine; tamoxifen methiodide; tauromustine; tazarotene; tecogalan sodium; tegafur; tellurapyrylium; telomerase inhibitors; temoporfin; temozolomide; teniposide; tetrachlorodecaoxide; tetrazomine; thaliblastine; thalidomide; thiocoraline; thrombopoietin; thrombopoietin mimetic; thymalfasin; thymopoietin receptor agonist; thymotrinan; thyroid stimulating hormone; tin ethyl etiopurpurin; tirapazamine; titanocene dichloride; topotecan; topsentin; toremifene; totipotent stem cell factor; translation inhibitors; tretinoin; triacetyluridine; triciribine; trimetrexate; triptorelin; tropisetron; turosteride; tyrosine kinase inhibitors; tyrphostins; UBC inhibitors; ubenimex; urogenital sinus-derived growth inhibitory factor; urokinase receptor antagonists; vapreotide; variolin B; vector system, erythrocyte gene therapy; velaresol; veramine; verdins; verteporfin; vinorelbine; vinxaltine; vitaxin; vorozole; zanoterone; zeniplatin; zilascorb; and zinostatin stimalamer.

To "inhibit growth" as used herein means causing a reduction in cell growth in vivo or in vitro by, e.g., 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, or 99% or more, as evident by a reduction in the size or number of cells exposed to a treatment (e.g., exposure to a compound), relative to the size or number of cells in the absence of the treatment. Growth inhibition can be the result of a treatment that induces

apoptosis in a cell, induces necrosis in a cell, slows cell cycle progression, disrupts cellular metabolism, induces cell lysis, or induces some other mechanism that reduces the size or number of cells.

"Biomarker gene" as used herein means a gene in a cell (e.g., a cancer cell) the expression of which, as measured by, e.g., detecting the level of one or more biomarkers produced from the gene, correlates to sensitivity or resistance of the cell, tissue, organism, or patient (e.g., a human) to a treatment (e.g., chemotherapy, radiation therapy, or surgery).

"Microarray" as used herein means a device employed by any method that quantifies one or more subject oligonucleotides, e.g., DNA or RNA, or analogues thereof, at a time. One exemplary class of microarrays consists of DNA probes attached to a glass or quartz surface. Many microarrays, e.g., those made by Affymetrix, use several probes for determining the expression of a single gene. The DNA microarray can contain oligonucleotide probes that may be, e.g., full-length cDNAs complementary to an RNA or cDNA fragments that hybridize to part of an RNA. Exemplary RNAs include mRNA, miRNA, and miRNA precursors. Exemplary microarrays also include a "nucleic acid microarray" having a substrate-bound plurality of nucleic acids, hybridization to each of the plurality of bound nucleic acids being separately detectable. The substrate can be solid or porous, planar or non-planar, unitary or distributed. Exemplary nucleic acid microarrays include all of the devices so called in Schena (ed.), DNA Microarrays: A Practical Approach (Practical Approach Series), Oxford University Press (1999); Nature Genet. 21(l)(suppl.):l-60 (1999); and Schena (ed.), Microarray Biochip: Tools and Technology, Eaton Publishing Company/BioTechniques Books Division (2000). Additionally, exemplary nucleic acid microarrays can include a substrate-bound plurality of nucleic acids in which the plurality of nucleic acids is disposed on a plurality of beads, rather than on a unitary planar substrate, as is described, inter alia, in Brenner et al., Proc. Natl. Acad. Sci. USA 97(4): 1665- 1670 (2000). Examples of nucleic acid microarrays may be found in U.S. Patent Nos. 6,391 ,623, 6,383,754, 6,383,749, 6,380,377, 6,379,897, 6,376,191, 6,372,431, 6,351,712 6,344,316, 6,316,193, 6,312,906, 6,309,828, 6,309,824, 6,306,643, 6,300,063, 6,287,850, 6,284,497, 6,284,465, 6,280,954, 6,262,216, 6,251,601,

6,245,518, 6,263,287, 6,251,601 , 6,238,866, 6,228,575, 6,214,587, 6,203,989, 6,171 ,797, 6,103,474, 6,083,726, 6,054,274, 6,040,138, 6,083,726, 6,004,755, 6,001 ,309, 5,958,342, 5,952,180, 5,936,731, 5,843,655, 5,814,454, 5,837,196, 5,436,327, 5,412,087, and 5,405,783, herein incorporated by reference.

Exemplary microarrays can also include "peptide microarrays" or "protein microarrays" having a substrate-bound plurality of polypeptides, the binding of a oligonucleotide, a peptide, or a protein to the plurality of bound polypeptides being separately detectable. Alternatively, the peptide microarray, can have a plurality of binders, including, but not limited to, monoclonal antibodies, polyclonal antibodies, phage display binders, yeast 2 hybrid binders, aptamers, that can specifically detect the binding of specific oligonucleotides, peptides, or proteins. Examples of peptide arrays may be found in International Patent Publication Nos. WO 02/31463, WO 02/25288, WO 01/94946, WO 01/88162, WO 01/68671, WO 01/57259, WO 00/61806, WO 00/54046, WO 00/47774, WO 99/40434, WO 99/39210, and WO 97/42507, and in U.S. Patent Nos. 6,268,210, 5,766,960, and 5,143,854, herein incorporated by reference.

"Gene expression" as used herein means the level of expression of a biomarker gene (e.g,. the level of a transcription product, such as an mRNA, tRNA, or microRNA, or its complement (e.g., a cDNA complement of the transcription product), or a translation product, such as a polypeptide or metabolite thereof) in a cell, tissue, organism, or patient (e.g., a human). Gene expression can be measured by detecting the presence, quantity, or activity of a DNA, RNA, or polypeptide, or modifications thereof (e.g., splicing, phosphorylation, and acetylation) associated with a given gene.

"NCI60" as used herein means a panel of 60 cancer cell lines from lung, colon, breast, ovarian, leukemia, renal, melanoma, prostate, and brain cancers including the following cancer cell lines: NSCLC_NCIH23, NSCLC_NCIH522, NSCLC_A549ATCC, NSCLC_EKVX, NSCLC_NCIH226, NSCLC_NCIH332M, NSCLC_H460, NSCLC_HOP62, NSCLC_HOP92, COLON HT29, COLON_HCC- 2998, COLON_HCT1 16, COLON_SW620, COLON_COLO205, COLON_HCT15, COLON KM 12, BREAST MCF7, BREAST_MCF7ADRr, BREAST MDAMB231 , BREAST HS578T, BREAST MDAMB435, BREAST_MDN, BREAST BT549, BREAST_T47D, OVAR_OVCAR3, OVAR_OVCAR4, 0VAR 0VCAR5,

OVAR_OVCAR8, OVARJGROV 1 , OVAR_SKOV3, LEUK_CCRFCEM, LEUK_K562, LEUK_MOLT4, LEUK_HL60, LEUK RPMI8266, LEUK SR, RENAL_UO31, RENAL_SN12C, RENAL_A498, RENAL_CAKI1 , RENAL RXF393, RENAL_7860, RENAL ACHN, RENAL_TK10, MELAN LOXIMVI, MELAN_MALME3M, MELAN_SKMEL2, MELAN_SKMEL5, MELAN_SKMEL28, MELAN_M14, MELANJJ ACC62, MELAN JJ ACC257, PR0STATE_PC3, PROSTATE_DU145, CNS_SNB19, CNS_SNB75, CNS_U251 , CNS SF268, CNS_SF295, and CNS_SF539.

"Treatment" or "medical treatment" means administering to a patient (e.g., a human) or living organism or exposing to a cell or tumor a compound (e.g., a drug, a protein, an antibody, an oligonucleotide, a chemotherapeutic agent, and a radioactive agent) or some other form of medical intervention used to treat or prevent cancer or the symptoms of cancer (e.g., cryotherapy and radiation therapy). Radiation therapy includes the administration to a patient of radiation generated from sources such as particle accelerators and related medical devices that emit X-radiation, gamma radiation, or electron (Beta radiation) beams. A treatment may further include surgery, e.g., to remove a tumor from a patient or living organism.

Other features and advantages of the invention will be apparent from the following description, drawings, and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

Figure 1 depicts an illustration of the method of identifying biomarkers and predicting patient sensitivity to a medical treatment. The method has an in vitro component where the growth inhibition of a compound or medical treatment is measured on cell lines (6 of the 60 cell lines tested are shown). The gene expression is measured on the same cell lines without compound treatment. Those genes that have a correlation above a certain cutoff (e.g., a preffered cutoff of 0.3, in which a correlation coefficient equal to or greater than the cutoff of 0.3 is deemed statistcally significant by, e.g., cross-validation) to the growth inhibition are termed marker genes and the expression of those genes in vivo, e.g., may predict the sensitivity or resistance of a patient's cancer to a compound or other medical treatment. The in vivo component is

applied to a patient to determine whether or not the treatment will be effective in treating disease in the patient. Here, the gene expression in cells of a sample of the suspected disease tissue (e.g., a tumor) in the patient is measured before or after treatment. The activity of the marker genes in the sample is compared to a reference population of patients known to be sensitive or resistant to the treatment. The expression of marker genes in the cells of the patient known to be expressed in the cells of reference patients sensitive to the treatment indicates that the patient to be treated is sensitive to the treatment and vice versa. Based on this comparison the patient is predicted to be sensitive or resistant to treatment with the compound.

Figure 2 depicts the treatment sensitivity predictions for a 5-year-old American boy with a brain tumor. The subject had surgery to remove the tumor and, based on the analysis of gene expression in cells from a sample of the tumor, the subject was predicted to be chemosensitive to ten chemotherapy drugs. The subject received Vincristine and Cisplatin and survived.

Figure 3 depicts the treatment sensitivity predictions for a 7-month-old American girl with a brain tumor. The subject had surgery to remove the tumor, and based on the analysis of gene expression in cells from a sample of the tumor, the subject was predicted to be chemoresistant to twelve chemotheraphy drugs. The subject received Vincristine and Cisplatin, but passed away 9 months later.

Figure 4 depicts the survival rate of 60 brain cancer patients divided into a group predicted to be chemosensitive to Cisplatin and a group predicted to be chemoresistant to Cisplatin. All patients received Cisplatin after surgery.

Figure 5 depicts the survival rate of 56 lymphoma patients divided into a group predicted to be chemosensitive to Vincristine and Adriamycin and a group predicted to be chemoresistant. All patients received Vincristine and Adriamycin.

Figure 6 depicts the survial rate of 19 lung cancer patients divided into a group predicted to be chemosensitive to Cisplatin and a group predicted to be chemoresistant. All patients received Cisplatin.

Figure 7 depicts the survival rate of 14 diffuse large-B-cell lymphoma (DLBCL) patients divided into a group predicted to be chemosensitive to the drug combination R- CHOP and a group predicted to be chemoresistant. All patients were treated with R-

CHOP.

Figure 8 depicts the predictions of sensitivity or resistance to treatment of a patient diagnosed with DLBCL. Various drug combinations and radiation therapy are considered. The drug combinations (indicated by abbreviations) are those commonly used to treat DLBCL.

Figure 9 depicts the survival rate of 60 brain cancer patients divided into a group predicted to be sensitive to radiation treatment and a group predicted to be resistant. All patients were treated with radiation.

Figure 10 depicts the survival rate of 60 brain cancer patients divided into a group predicted to be sensitive to radiation treatment and a group predicted to be resistant. All patients were treated with radiation. Gene biomarkers used in predicting radiation sensitivity or resistance were obtained using the correlation of the median gene expression measurement to cancer cell growth as opposed to the median of the correlations as employed in Figure 9.

Figure 1 1 depicts the predicted sensitivity of cancer patients to sunitinib. The cancer patients are grouped according to cancer type or origin and cancer types with predicted high sensitivity are labeled.

DETAILED DESCRIPTION

The invention features methods for identifying biomarkers of treatment sensitivity, e.g., chemosensitivity to compounds, or resistance, devices that include the biomarkers, kits that include the devices, and methods for predicting treatment efficacy in a patient (e.g., a human diagnosed with cancer). The kits of the invention include microarrays having oligonucleotide probes that are biomarkers of sensitivity or resistance to treatment (e.g., treatment with a chemotherapeutic agent) that hybridize to nucleic acids derived from or obtained from a subject and instructions for using the device to predict the sensitivity or resistance of the subject to the treatment. The invention also features methods of using the microarrays to determine whether a subject, e.g., a cancer patient, will be sensitive or resistant to treatment with, e.g., a chemotherapy agent. Also featured are methods of identifying biomarkers of sensitivity or resistance to a medical treatment based on the correlation of gene or microRNA

expression to treatment efficacy, e.g., the growth inhibition of cancer cells. Gene or microRNA biomarkers that identify subjects as sensitive or resistant to a treatment can also be identified within patient populations already thought to be sensitive or resistant to that treatment. Thus, the methods, devices, and kits of the invention can be used to identify patient subpopulations that are responsive to a treatment thought to be ineffective for treating disease (e.g., cancer) in the general population. More generally, cancer patient sensitivity to a compound or other medical treatment can be predicted using biomarker expression regardless of prior knowledge about patient responsiveness to treatment. The method according to the present invention can be implemented using software that is run on an apparatus (e.g., a computer) for measuring biomarker expression in connection with a microarray. The microarray (e.g., a DNA microarray), included in a kit for processing a tumor sample from a patient, and the apparatus for reading the microarray and turning the result into a chemosensitivity profile for the patient may be used to implement the methods of the invention.

Microarrays Containing Oligonucleotide Probes

The microarrays of the invention include one or more oligonucleotide probes that have nucleotide sequences that are substantially identical to or substantially complementary to, e.g., at least 5, 8, 12, 20, 30, 40, 60, 80, 100, 150, or 200 consecutive nucleotides (or nucleotide analogues) of the biomarker genes or biomarker gene products (e.g., transcription or translation gene products, such as microRNAs) listed below. The oligonucleotide probes may be, e.g., 5-20, 25, 5-50, 50-100, or over 100 nucleotides long. The oligonucleotide probes may be deoxyribonucleic acids (DNA) or ribonucleic acids (RNA). Consecutive nucleotides within the oligonucleotide probes (e.g., 5-20, 25, 5-50, 50-100, or over 100 consecutive nucleotides), which are used as biomarkers of chemosensitivity, may also appear as consecutive nucleotides in one or more of the genes described herein beginning at or near, e.g., the first, tenth, twentieth, thirtieth, fortieth, fiftieth, sixtieth, seventieth, eightieth, ninetieth, hundredth, hundred- fiftieth, two-hundredth, five-hundredth, or one-thousandth nucleotide of the genes or microRNAs listed in Tables 1-136 below. Column List_2006 of Tables 1-21 indicates the preferred biomarker genes for the compound lists. Column List_Preferred of Tables

1-21 indicates the most preferred biomarker genes. Column List_2005 of Tables 1-21 indicates additional biomarkers employed in Examples 1-8. Column Correlation of Tables 1-21 indicates the correlation coefficient of the biomarker gene expression to cancer cell growth inhibition. Tables 80-136 indicate microRNA biomarkers that can be used to determine a patient's (e.g., a human's) sensitivity to a treatment. The following combinations of biomarkers have been used to detect a patient's sensitivity to the indicated treatment:

a) One or more of the gene sequences SFRS3, CCT5, RPL39, SLC25A5, UBE2S, EEFlAl, RPLP2, RPL24, RPS23, RPL39, RPL18, NCL, RPL9, RPLlOA, RPSlO, EIF3S2, SHFMl, RPS28, REA, RPL36A, GAPD, HNRPAl, RPSI l, HNRPAl , LDHB, RPL3, RPLl 1, MRPLl 2, RPL 18 A, COX7B, and RPS7, preferably gene sequences UBB, RPS4X, S100A4, NDUFS6, B2M, C14orfl39, MANlAl, SLC25A5, RPLlO, RPLl 2, EIF5A, RPL36A, SUIl, BLMH, CTBPl , TBCA, MDH2, and DXS9879E, and most preferably gene sequences RPS4X, S100A4, NDUFS6, C14orfl39, SLC25A5, RPLlO, RPL12, EIF5A, RPL36A, BLMH, CTBPl, TBCA, MDH2, and DXS9879E, whose expression indicates chemosensitivity to Vincristine.

b) One or more of the gene sequences B2M, ARHGDIB, FTL, NCL, MSN, SNRPF, XPOl, LDHB, SNRPF, GAPD, PTPN7, ARHGDIB, RPS27, IFI 16, C5orfl3, and HCLSl, preferably gene sequences ClQRl, HCLSl, CD53, SLA, PTPN7, PTPRCAP, ZNFNlAl, CENTBl, PTPRC, IFI 16, ARHGEF6, SEC31L2, CD3Z, GZMB, CD3D, MAP4K1 , GPR65, PRFl, ARHGAPl 5, TM6SF1, and TCF4, and most preferably gene sequences ClQRl, SLA, PTPN7, ZNFNlAl, CENTBl, IFI 16, ARHGEF6, SEC31L2, CD3Z, GZMB, CD3D, MAP4K1, GPR65, PRFl , ARHGAP 15, TM6SF1 , and TCF4, whose expression indicates chemosensitivity to Cisplatin.

c) One or more of the gene sequences PRPS 1 , DDOST, B2M, SPARC, LGALS 1 , CBFB, SNRPB2, MCAM, MCAM, EIF2S2, HPRTl, SRM, FKBPlA, GYPC, UROD, MSN, HNRPAl, SNDl , COPA, MAPREl, EIF3S2, ATP1B3, EMP3, ECMl, ATOXl , NARS, PGKl, OK/SW-cl.56, FNl, EEFlAl, GNAI2, PRPSl, RPL7, PSMB9,

GPNMB, PPPlRl 1 , MIA, RAB7, VIM, and SMS, preferably gene sequences MSN, SPARC, VIM, SRM, SCARBl , SIATl, CUGBP2, GAS7, ICAMl , WASPIP, ITM2A, PALM2-AKAP2, ANPEP, PTPNSl, MPPl, LNK, FCGR2A, EMP3, RUNX3, EVI2A, BTN3A3, LCP2, BCHE, LY96, LCPl, IFI16, MCAM, MEF2C, SLC1A4, BTN3A2, FYN, FNl, Clorf38, CHSl, CAPN3, FCGR2C, TNIK, AMPD2, SEPT6, RAFTLIN, SLC43A3, RAC2, LPXN, CKIP-I, FLJ10539, FLJ35036, DOCKlO, TRPV2, IFRG28, LEFl, and ADAMTSl, and most preferably gene sequences SRM, SCARBl, SIATl, CUGBP2, ICAMl, WASPIP, ITM2A, PALM2-AKAP2, PTPNSl, MPPl, LNK, FCGR2A, RUNX3, EVI2A, BTN3A3, LCP2, BCHE, LY96, LCPl, IFIl 6, MCAM, MEF2C, SLC 1A4, FYN, ClorOδ, CHSl, FCGR2C, TNIK, AMPD2, SEPT6, RAFTLIN, SLC43A3, RAC2, LPXN, CKIP-I, FLJ10539, FLJ35036, DOCKlO, TRPV2, IFRG28, LEFl , and ADAMTSl, whose expression indicates chemosensitivity to Azaguanine.

d) One or more of the gene sequences B2M, MYC, CD99, RPS24, PPIF, PBEF 1 , and ANP32B, preferably gene sequences CD99, INSIGl , LAPTM5, PRGl, MUFl, HCLSl , CD53, SLA, SSBP2, GNB5, MFNG, GMFG, PSMB9, EVI2A, PTPN7, PTGER4, CXorf9, PTPRCAP, ZNFNlAl, CENTBl , PTPRC, NAPlLl, HLA-DRA, IFI 16, COROlA, ARHGEF6, PSCDBP, SELPLG, LAT, SEC31L2, CD3Z, SH2D1A, GZMB, SCN3A, ITK, RAFTLIN, D0CK2, CD3D, RAC2, ZAP70, GPR65, PRFl, ARHGAPl 5, NOTCHl, and UBASH3A, and most preferably gene sequences CD99, INSIGl, PRGl, MUFl , SLA, SSBP2, GNB5, MFNG, PSMB9, EVI2A, PTPN7, PTGER4, CXorf9, ZNFNlAl , CENTBl, NAPlLl , HLA-DRA, IFI 16, ARHGEF6, PSCDBP, SELPLG, LAT, SEC31L2, CD3Z, SH2D1A, GZMB, SCN3A, RAFTLIN, D0CK2, CD3D, RAC2, ZAP70, GPR65, PRFl , ARHGAP 15, NOTCHl, and UBASH3A, whose expression indicates chemosensitivity to Etoposide.

e) One or more of the gene sequences KIAA0220, B2M, TOP2A, CD99, SNRPE, RPS27, HNRPAl, CBX3, ANP32B, HNRPAl, DDX5, PPIA, SNRPF, and USP7, preferably gene sequences CD99, LAPTM5, ALDOC, HCLSl , CD53, SLA, SSBP2, IL2RG, GMFG, CXorf9, RHOH, PTPRCAP, ZNFNlAl, CENTBl, TCF7, CDlC,

MAP4K1 , CDlB, CD3G, PTPRC, CCR9, COROlA, CXCR4, ARHGEF6, HEMl , SELPLG, LAT, SEC31L2, CD3Z, SH2D1A, CDlA, LAIRl, ITK, TRB@, CD3D, WBSCR20C, ZAP70, IFI44, GPR65, AIFl, ARHGAPl 5, NARF, and PACAP, and most preferably gene sequences CD99, ALDOC, SLA, SSBP2, IL2RG, CXorf9, RHOH, ZNFNlAl , CENTBl, CDlC, MAP4K1, CD3G, CCR9, CXCR4, ARHGEF6, SELPLG, LAT, SEC31L2, CD3Z, SH2D1A, CDlA, LAIRl, TRB@, CD3D, WBSCR20C, ZAP70, IFI44, GPR65, AIFl , ARHGAP 15, NARF, and PACAP, whose expression indicates chemosensitivity to Adriamycin.

f) One or more of the gene sequences RPLP2, LAMRl , RPS25, EIF5A, TUFM, HNRPAl, RPS9, MYB, LAMRl, ANP32B, HNRPAl, HNRPAl, EIF4B, HMGB2, RPS 15 A, and RPS7, preferably gene sequences RPLl 2, RPL32, RPLP2, MYB, ZNFNlAl, SCAPl, STAT4, SP140, AMPD3, TNFAIP8, DDX18, TAF5, FBL, RPS2, PTPRC, D0CK2, GPR65, H0XA9, FLJ 12270, and HNRPD, and most preferably gene sequences RPL 12, RPLP2, MYB, ZNFNlAl, SCAPl, STAT4,

SP 140, AMPD3, TNFAIP8, DDX 18, TAF5, RPS2, DOCK2, GPR65, HOXA9,

FLJ 12270, and HNRPD, whose expression indicates chemosensitivity to Aclarubicin.

g) One or more of the gene sequences ARHGEF6, B2M, TOP2A, TOP2A, ELA2B, PTMA, LMNBl, TNFRSFlA, NAPlLl, B2M, HNRPAl, RPL9, C5orfl3, NCOR2, ANP32B, OK/SW-cl.56, TUBA3, HMGN2, PRPSl, DDX5, PRGl, PPIA, G6PD, PSMB9, SNRPF, and MAPlB, preferably gene sequences PGAMl, DPYSL3, INSIGl , GJAl, BNIP3, PRGl, G6PD, BASPl, PLOD2, LOXL2, SSBP2, Clorf29, TOX, STCl, TNFRSFlA, NCOR2, NAPlLl, LOC94105, COL6A2, ARHGEF6, GAT A3, TFPI, LAT, CD3Z, AFlQ, MAPlB, PTPRC, PRKCA, TRIM22, CD3D, BCATl, IFI44, CCL2, RAB31 , CUTC, NAP1L2, NME7, FLJ21 159, and COL5A2, and most preferably gene sequences PGAMl , DPYSL3, INSIGl , GJAl, BNIP3, PRGl , G6PD, PLOD2, LOXL2, SSBP2, Clorf29, TOX, STCl , TNFRSFlA, NC0R2, NAPlLl, LOC94105, ARHGEF6, GATA3, TFPI, LAT, CD3Z, AFlQ, MAPlB, TRIM22, CD3D, BCATl , IFI44, CUTC, NAP1L2, NME7, FLJ21 159, and COL5A2, whose expression indicates chemosensitivity to Mitoxantrone.

h) One or more of the gene sequences GAPD, GAPD, GAPD, TOP2A, SUIl , TOP2A, FTL, HNRPC, TNFRSFlA, SHCl, CCT7, P4HB, CTSL, DDX5, G6PD, and SNRPF, preferably gene sequences STCl , GPR65, DOCKlO, COL5A2, FAM46A, and LOC54103, and most preferably gene sequences STCl, GPR65, DOCKlO, COL5A2, FAM46A, and LOC54103, whose expression indicates chemosensitivity to Mitomycin.

i) One or more of the gene sequences RPS23, SFRS3, KIAAOl 14, RPL39, SFRS3, LOC51035, RPS6, EXOSC2, RPL35, IFRD2, SMN2, EEFlAl, RPS3, RPS18, and RPS7, preferably gene sequences RPLlO, RPS4X, NUDC, RALY, DKCl , DKFZP564C186, PRP 19, RAB9P40, HSA9761, GMDS, CEPl, IL13RA2, MAGEB2, HMGN2, ALMSl, GPR65, FLJ10774, NOL8, DAZAPl, SLC25A15, PAF53, DXS9879E, PITPNCl, SPANXC, and KIAA1393, and most preferably RPLlO, RPS4X, NUDC, DKCl, DKFZP564C186, PRP 19, RAB9P40, HSA9761, GMDS, CEPl , ILl 3RA2, MAGEB2, HMGN2, ALMSl, GPR65, FLJ 10774, NOL8, DAZAPl, SLC25A15, PAF53, DXS9879E, PITPNCl , SPANXC, and KIAA1393, whose expression indicates chemosensitivity to Paclitaxel.

j) One or more of the gene sequences CSDA, LAMRl , and TUBA3, preferably gene sequences PFNl , PGAMl , K-ALPHA-I, CSDA, UCHLl, PWPl, PALM2- AKAP2, TNFRSFlA, ATP5G2, AFlQ, NME4, and FHODl, and most preferably gene sequences PFNl, PGAMl, K-ALPHA-I, CSDA, UCHLl, PWPl, PALM2-AKAP2, TNFRSFlA, ATP5G2, AFlQ, NME4, and FHODl, whose expression indicates chemosensitivity to Gemcitabine.

k) One or more of the gene sequences RPS23, SFRS3, KIAAOl 14, SFRS3, RPS6, DDX39, and RPS7, preferably gene sequences ANP32B, GTF3A, RRM2, TRIM 14, SKP2, TRIPl 3, RFC3, CASP7, TXN, MCM5, PTGES2, OBFCl , EPB41L4B, and CALML4, and most preferably gene sequences ANP32B, GTF3A, RRM2, TRIM 14, SKP2, TRIPl 3, RFC3, CASP7, TXN, MCM5, PTGES2, OBFCl, EPB41L4B, and CALML4, whose expression indicates chemosensitivity to Taxotere.

1) One or more of the gene sequences IL2RG, H 1 FX, RDBP, ZAP70, CXCR4, TM4SF2, ARHGDIB, CDA, CD3E, STMNl, GNA15, AXL, CCND3, SATBl, EIF5A, LCK, NKX2-5, LAPTM5, IQGAP2, FLII, EIF3S5, TRB, CD3D, HOXB2, GATA3, HMGB2, PSMB9, ATP5G2, COROlA, ARHGDIB, DRAPl, PTPRCAP, RHOH, and ATP2A3, preferably gene sequences IFITM2, UBE2L6, LAPTM5, USP4, ITM2A, ITGB2, ANPEP, CD53, IL2RG, CD37, GPRASPl, PTPN7, CXorf9, RHOH, GIT2, AD0RA2A, ZNFNlAl, GNA15, CEPl, TNFRSF7, MAP4K1, CCR7, CD3G, PTPRC, ATP2A3, UCP2, COROlA, GATA3, CDKN2A, HEMl, TARP, LAIRl, SH2D1A, FLII, SEPT6, HA-I, CREB3L1, ERCC2, CD3D, LSTl, AIFl, ADA, DATFl , ARHGAPl 5, PLAC8, CECRl, LOC81558, and EHD2, and most preferably gene sequences IFITM2, UBE2L6, USP4, ITM2A, IL2RG, GPRASPl, PTPN7, CXorf9, RHOH, GIT2, ZNFNlAl, CEPl, TNFRSF7, MAP4K1, CCR7, CD3G, ATP2A3, UCP2, GATA3, CDKN2A, TARP, LAIRl, SH2D1A, SEPT6, HA-I, ERCC2, CD3D, LSTl, AIFl, ADA, DATFl, ARHGAP15, PLAC8, CECRl , LOC81558, and EHD2, whose expression indicates chemosensitivity to Dexamethasone.

m) One or more of the gene sequences TM4SF2, ARHGDIB, ADA, H2 AFZ, NAPlLl , CCND3, FABP5, LAMRl, REA, MCM5, SNRPF, and USP7, preferably gene sequences ITM2A, RHOH, PRIMl , CENTBl, GNA15, NAPlLl, ATP5G2, GATA3, PRKCQ, SH2D1A, SEPT6, PTPRC, NME4, RPL13, CD3D, CDlE, ADA, and FHODl, and most preferably gene sequences ITM2A, RHOH, PRIMl, CENTBl , NAPlLl , ATP5G2, GATA3, PRKCQ, SH2D1A, SEPT6, NME4, CD3D, CDlE, ADA, and FHODl , whose expression indicates chemosensitivity to Ara-C.

n) One or more of the gene sequences LGALS9, CD7, IL2RG, PTPN7, ARHGEF6, CENTBl, SEPT6, SLA, LCPl, IFITMl, ZAP70, CXCR4, TM4SF2, ZNF91 , ARHGDIB, TFDP2, ADA, CD99, CD3E, CDlC, STMNl , CD53, CD7, GNAl 5, CCND3, MAZ, SATBl, ZNF22, AES, AIFl, MYB, LCK, C5orfl3, NKX2-5, ZNFNlAl, STAT5A, CHI3L2, LAPTM5, MAP4K1, DDXl 1, GPSM3, TRB, CD3D, CD3G, PRKCBl, CDlE, HCLSl , GATA3, TCF7, RHOG, CDW52, HMGB2, DGKA,

ITGB2, PSMB9, IDH2, AES, MCM5, NUCB2, COROlA, ARHGDIB, PTPRCAP, CD47, RHOH, LGALS9, and ATP2A3, preferably gene sequences CD99, SRRMl , ARHGDIB, LAPTM5, VWF, ITM2A, ITGB2, LGALS9, INPP5D, SATBl, CD53, TFDP2, SLA, IL2RG, MFNG, CD37, GMFG, SELL, CDW52, LRMP, ICAM2, RIMS3, PTPN7, ARHGAP25, LCK, CXorf9, RHOH, PTPRCAP, GIT2, ZNFNlAl, CENTBl, LCP2, SPIl, GNAl 5, GZMA, CEPl, BLM, CD8A, SCAPl, CD2, CDlC, TNFRSF7, VAVl, MAP4K1 , CCR7, C6or02, AL0X15B, BRDT, CD3G, PTPRC, LTB, ATP2A3, NVL, RASGRP2, LCPl, COROlA, CXCR4, PRKD2, G AT A3, TRA@, PRKCBl, HEMl, KIAA0922, TARP, SEC31L2, PRKCQ, SH2D1A, CHRNA3, CDlA, LSTl, LAIRl , CACNAlG, TRB@, SEPT6, HA-I, D0CK2, CD3D, TRD@, T3JAM, FNBPl, CD6, AIFl, FOLHl, CDlE, LY9, UGT2B17, ADA, CDKL5, TRIM, EVL, DATFl , RGC32, PRKCH, ARHGAPl 5, NOTCHl, BIN2, SEMA4G, DPEP2, CECRl , BCLl IB, STAG3, GALNT6, UBASH3A, PHEMX, FLJ13373, LEFl, IL21R, MGC17330, AKAP13, ZNF335, and GIMAP5, and most preferably gene sequences CD99, ARHGDIB, VWF, ITM2A, LGALS9, INPP5D, SATBl, TFDP2, SLA, IL2RG, MFNG, SELL, CDW52, LRMP, ICAM2, RIMS3, PTPN7, ARHGAP25, LCK, CXorf9, RHOH, GIT2, ZNFNlAl, CENTBl, LCP2, SPIl, GZMA, CEPl , CD8A, SCAPl , CD2, CDlC, TNFRSF7, VAVl, MAP4K1, CCR7, C6orf32, ALOXl 5B, BRDT, CD3G, LTB, ATP2A3, NVL, RASGRP2, LCPl, CXCR4, PRKD2, GATA3, TRA@, KIAA0922, TARP, SEC31L2, PRKCQ, SH2D1A, CHRNA3, CDlA, LSTl, LAIRl, CACNAlG, TRB@, SEPT6, HA-I, D0CK2, CD3D, TRD@, T3JAM, FNBPl , CD6, AIFl, FOLHl , CDlE, LY9, ADA, CDKL5, TRIM, EVL, DATFl , RGC32, PRKCH, ARHGAPl 5, NOTCHl , BIN2, SEMA4G, DPEP2, CECRl, BCLl IB, STAG3, GALNT6, UBASH3A, PHEMX, FLJ13373, LEFl, IL21R, MGCl 7330, AKAPl 3, ZNF335, and GIMAP5, whose expression indicates chemosensitivity to Methylprednisolone.

o) One or more of the gene sequences RPLP2, RPL4, HMGAl, RPL27, IMPDH2, LAMRl , PTMA, ATP5B, NPMl , NCL, RPS25, RPL9, TRAPl, RPL21, LAMRl , REA, HNRPAl , LDHB, RPS2, NMEl , PAICS, EEF1B2, RPS15A, RPL19, RPL6, ATP5G2, SNRPF, SNRPG, and RPS7, preferably gene sequences PRPF8, RPL 18,

RNPSl, RPL32, EEFlG, GOT2, RPL13A, PTMA, RPS15, RPLP2, CSDA, KHDRBSl, SNRPA, IMPDH2, RPS 19, NUP88, ATP5D, PCBP2, ZNF593, HSU79274, PRIMl, PFDN5, OXAlL, H3F3A, ATIC, RPL13, CIAPINl, FBL, RPS2, PCCB, RBMX, SHMT2, RPLPO, HNRPAl , STOML2, RPS9, SKBl , GLTSCR2, CCNBlIPl, MRPS2, FLJ20859, and FLJ 12270, and most preferably gene sequences PRPF8, RPLl 8, GOT2, RPLl 3 A, RPS 15, RPLP2, CSDA, KHDRBSl, SNRPA, IMPDH2, RPS 19, NUP88, ATP5D, PCBP2, ZNF593, HSU79274, PRIMl, PFDN5, OXAlL, H3F3A, ATIC, CIAPINl, RPS2, PCCB, SHMT2, RPLPO, HNRPAl, STOML2, SKBl, GLTSCR2, CCNBlIPl, MRPS2, FLJ20859, and FLJ 12270, whose expression indicates chemosensitivity to Methotrexate.

p) One or more of the gene sequences ACTB, COL5A1, MTlE, CSDA, COL4A2, MMP2, COLlAl, TNFRSFlA, CFHLl , TGFBI, FSCNl, NNMT, PLAUR, CSPG2, NFIL3, C5orfl3, NCOR2, TUBB4, MYLK, TUB A3, PLAU, COL4A2, COL6A2, COL6A3, IFITM2, PSMB9, CSDA, and COLlAl, preferably gene sequences MSN, PFNl, HKl, ACTR2, MCLl, ZYX, RAPlB, GNB2, EPASl, PGAMl, CKAP4, DUSPl, MYL9, K-ALPHA-I , LGALSl, CSDA, AKRlBl, IFITM2, ITGA5, VIM, DPYSL3, JUNB, ITGA3, NFKBIA, LAMBl, FHLl, INSIGl, TIMPl, GJAl , PSME2, PRGl, EXTl, DKFZP434J154, OPTN, M6PRBP1, MVP, VASP, ARL7, NNMT, TAPl, COLlAl , BASPl, PLOD2, ATF3, PALM2-AKAP2, IL8, ANPEP, LOXL2, TGFBl, IL4R, DGKA, STC2, SEC61G, NFIL3, RGS3, NK4, F2R, TPM2, PSMB9, LOX, STCl , CSPG2, PTGER4, IL6, SMAD3, PLAU, WNT5A, BDNF, TNFRSFlA, FLNC, DKFZP564K0822, FLOTl, PTRF, HLA-B, COL6A2, MGC4083, TNFRSFlOB, PLAGLl, PNM A2, TFPI, LAT, GZMB, CYR61, PLAUR, FSCNl, ERP70, AFlQ, UBC, FGFRl, HIC, BAX, COL4A2, COL6A1, IFITM3, MAPlB, FLJ46603, RAFTLIN, RRAS, FTL, KIAA0877, MTlE, CDClO, DOCK2, TRIM22, RISl, BCATl, PRFl , DBNl , MTlK, TMSBlO, RAB31 , FLJ10350, Clorf24, NME7, TMEM22, TPKl, COL5A2, ELK3, CYLD, ADAMTSl, EHD2, and ACTB, and most preferably gene sequences PFNl, HKl, MCLl, ZYX, RAPlB, GNB2, EPASl , PGAMl , CKAP4, DUSPl , MYL9, K-ALPHA- 1 , LGALS 1 , CSDA, IFITM2, ITGA5, DPYSL3, JUNB, NFKBIA, LAMBl, FHLl, INSIGl, TIMPl, GJAl, PSME2, PRGl,

EXTl, DKFZP434J154, MVP, VASP, ARL7, NNMT, TAPl , PLOD2, ATF3, PALM2- AKAP2, IL8, LOXL2, IL4R, DGKA, STC2, SEC61G, RGS3, F2R, TPM2, PSMB9, LOX, STCl, PTGER4, IL6, SMAD3, WNT5A, BDNF, TNFRSFlA, FLNC, DKFZP564K0822, FLOTl , PTRF, HLA-B, MGC4083, TNFRSFlOB, PLAGLl , PNMA2, TFPI, LAT, GZMB, CYR61 , PLAUR, FSCNl , ERP70, AFlQ, HIC, COL6A1, IFITM3, MAPlB, FLJ46603, RAFTLIN, RRAS, FTL, KIAA0877, MTlE, CDClO, DOCK2, TRIM22, RISl, BCATl, PRFl, DBNl , MTlK, TMSBlO, FLJ10350, Clorf24, NME7, TMEM22, TPKl, COL5A2, ELK3, CYLD, ADAMTSl, EHD2, and ACTB, whose expression indicates chemosensitivity to Bleomycin.

q) One or more of the gene sequences NOS2A, MUCl , TFF3, GPlBB, IGLLl, BATF, MYB, PTPRS, NEFL, AIP, CEL, DGKA, RUNXl, ACTRlA, and CLCNKA, preferably gene sequences PTMA, SSRPl, NUDC, CTSC, AP1G2, PSME2, LBR, EFNB2, SERPINAl, SSSCAl, EZH2, MYB, PRIMl, H2AFX, HMGAl, HMMR, TK2, WHSCl, DIAPHl, LAMB3, DPAGTl , UCK2, SERPINBl, MDNl , BRRNl, G0S2, RAC2, MGC21654, GTSEl, TACC3, PLEK2, PLAC8, HNRPD, and PNAS-4, and most preferably gene sequences SSRPl, NUDC, CTSC, AP1G2, PSME2, LBR, EFNB2, SERPINAl, SSSCAl , EZH2, MYB, PRIMl, H2AFX, HMGAl , HMMR, TK2, WHSCl, DIAPHl, LAMB3, DPAGTl , UCK2, SERPINBl , MDNl , BRRNl, G0S2, RAC2, MGC21654, GTSEl, TACC3, PLEK2, PLAC8, HNRPD, and PNAS-4, whose expression indicates chemosensitivity to Methyl-GAG.

r) One or more of the gene sequences MSN, ITGA5, VIM, TNFAIP3, CSPG2, WNT5A, FOXF2, LOC94105, IFIl 6, LRRN3, FGFRl, DOCKlO, LEPREl, COL5A2, and ADAMTSl, and most preferably gene sequences ITGA5, TNFAIP3, WNT5A, FOXF2, LOC94105, IFI16, LRRN3, DOCKlO, LEPREl, COL5A2, and ADAMTSl, whose expression indicates chemosensitivity to carboplatin.

s) One or more of the gene sequences RPLl 8, RPLlOA, RNPSl , ANAPC5, EEF1B2, RPL13A, RPS15, AKAP1 , NDUFAB1, APRT, ZNF593, MRP63, IL6R, RPLl 3, SART3, RPS6, UCK2, RPL3, RPLl 7, RPS2, PCCB, TOMM20, SHMT2,

RPLPO, GTF3A, STOML2, DKFZp564J157, MRPS2, ALG5, and CALML4, and most preferably gene sequences RPL18, RPLlOA, ANAPC5, EEF1B2, RPL13A, RPS15, AKAPl, NDUFABl , APRT, ZNF593, MRP63, IL6R, SART3, UCK2, RPL17, RPS2, PCCB, TOMM20, SHMT2, RPLPO, GTF3A, STOML2, DKFZp564J157, MRPS2, ALG5, and CALML4, whose expression indicates chemosensitivity to 5-FU(5- Fluorouracil).

t) One or more of the gene sequences ITK, KIFC 1 , VLDLR, RUNX 1 , PAFAH 1B3, HlFX, RNF 144, TMSNB, CRYl, MAZ, SLA, SRF, UMPS, CD3Z, PRKCQ, HNRPM, ZAP70, ADDl, RFC5, TM4SF2, PFN2, BMIl, TUBGCP3, ATP6V1B2, RALY, PSMC5, CDlD, ADA, CD99, CD2, CNP, ERG, MYL6, CD3E, CDlA, CDlB, STMNl, PSMC3, RPS4Y1, AKTl, TALI, GNA15, UBE2A, TCF12, UBE2S, CCND3, PAX6, MDK, CAPG, RAG2, ACTNl, GSTM2, SATBl, NASP, IGFBP2, CDH2, CRABPl, DBNl, CTNNAl, AKRlCl, CACNB3, FARSLA, CASP2, CASP2, E2F4, LCP2, CASP6, MYB, SFRS6, GLRB, NDN, CPSFl, GNAQ, TUSC3, GNAQ, JARID2, OCRL, FHLl , EZH2, SMOX, SLC4A2, UFDlL, SEPWl, ZNF32, HTATSFl, SHDl, PTOVl, NXFl , FYB, TRIM28, BC008967, TRB@, TFRC, HlFO, CD3D, CD3G, CENPB, ALDH2, ANXAl, H2AFX, CDlE, DDX5, ABLl, CCNA2, EN02, SNRPB, GATA3, RRM2, GLUL, TCF7, FGFRl, SOX4, MAL, NUCB2, SMA3, FAT, UNG, ARHGDIB, RUNXl, MPHOSPH6, DCTNl, SH3GL3, VIM, PLEKHCl, CD47, POLR2F, RHOH, ADDl, and ATP2A3, preferably gene sequences ITK, KIFCl, VLDLR, RUNXl , PAF AHl B3, HlFX, RNF 144, TMSNB, CRYl, MAZ, SLA, SRF, UMPS, CD3Z, PRKCQ, HNRPM, ZAP70, ADDl, RFC5, TM4SF2, PFN2, BMIl, TUBGCP3, ATP6V1B2, RALY, PSMC5, CDlD, ADA, CD99, CD2, CNP, ERG, MYL6, CD3E, CDlA, CDlB, STMNl , PSMC3, RPS4Y1, AKTl , TALI, GNA15, UBE2A, TCF12, UBE2S, CCND3, PAX6, MDK, CAPG, RAG2, ACTNl, GSTM2, SATBl , NASP, IGFBP2, CDH2, CRABPl , DBNl, CTNNAl, AKRlCl, CACNB3, FARSLA, CASP2, CASP2, E2F4, LCP2, CASP6, MYB, SFRS6, GLRB, NDN, CPSFl , GNAQ, TUSC3, GNAQ, JARID2, OCRL, FHLl , EZH2, SMOX, SLC4A2, UFDlL, SEPWl, ZNF32, HTATSFl, SHDl , PTOVl, NXFl, FYB, TRIM28, BC008967, TRB@, TFRC, HlFO, CD3D, CD3G, CENPB, ALDH2, ANXAl, H2AFX,

CDlE, DDX5, ABLl, CCNA2, ENO2, SNRPB, GATA3, RRM2, GLUL, TCF7, FGFRl , SOX4, MAL, NUCB2, SMA3, FAT, UNG, ARHGDIB, RUNXl , MPHOSPH6, DCTNl, SH3GL3, VIM, PLEKHCl , CD47, POLR2F, RHOH, ADDl, and ATP2A3, and most preferably gene sequences KIFCl, VLDLR, RUNXl, PAFAH 1 B3, HlFX, RNF 144, TMSNB, CRYl, MAZ, SLA, SRF, UMPS, CD3Z, PRKCQ, HNRPM, ZAP70, ADDl , RFC5, TM4SF2, PFN2, BMIl , TUBGCP3, ATP6V1B2, CDlD, ADA, CD99, CD2, CNP, ERG, CD3E, CDlA, PSMC3, RPS4Y1 , AKTl, TALI, UBE2A, TCF12, UBE2S, CCND3, PAX6, RAG2, GSTM2, SATBl, NASP, IGFBP2, CDH2, CRABPl, DBNl, AKRlCl, CACNB3, CASP2, CASP2, LCP2, CASP6, MYB, SFRS6, GLRB, NDN, GNAQ, TUSC3, GNAQ, JARID2, OCRL, FHLl, EZH2, SMOX, SLC4A2, UFDlL, ZNF32, HTATSFl, SHDl, PTOVl, NXFl, FYB, TRIM28, BC008967, TRB@, HlFO, CD3D, CD3G, CENPB, ALDH2, ANXAl , H2AFX, CDlE, DDX5, CCNA2, ENO2, SNRPB, GATA3, RRM2, GLUL, S0X4, MAL, UNG, ARHGDIB, RUNXl, MPHOSPH6, DCTNl, SH3GL3, PLEKHCl , CD47, P0LR2F, RHOH, and ADDl, whose expression indicates chemosensitivity to Rituximab (e.g., MABTHERA™).

u) One or more of the gene sequences CCL21, ANXA2, SCARB2, MAD2L1BP, CAST, PTS, NBLl, ANXA2, CD151, TRAM2, HLA-A, CRIP2, UGCG, PRSSl 1 , MME, CBRl, LGALSl , DUSP3, PFN2, MICA, FTHl, RHOC, ZAP 128, P0N2, COL5A2, CST3, MCAM, IGFBP3, MMP2, GALIG, CTSD, ALDH3A1, CSRPl , S100A4, CALDl, CTGF, CAPG, HLA-A, ACTNl, TAGLN, FSTLl, SCTR, BLVRA, COPEB, DIPA, SMARCD3, FNl , CTSL, CD63, DUSPl, CKAP4, MVP, PEA15, SlOOAl 3, and ECEl, preferably gene sequences TRAl, ACTN4, WARS, CALMl, CD63, CD81 , FKBPlA, CALU, IQGAPl , CTSB, MGC8721 , STATl, TACCl, TM4SF8, CD59, CKAP4, DUSPl , RCNl , MGC8902, LGALSl , BHLHB2, RRBPl, PKM2, PRNP, PPP2CB, CNN3, ANXA2, IER3, JAKl, MARCKS, LUM, FERl L3, SLC20A1, EIF4G3, HEXB, EXTl , TJPl , CTSL, SLC39A6, RI0K3, CRK, NNMT, COLlAl , TRAM2, ADAM9, DNAJC7, PLSCRl, PRSS23, PLOD2, NPC1, TOBl , GFPTl , IL8, DYRK2, PYGL, LOXL2, KIAA0355, UGDH, NFIL3, PURA, ULK2, CENTG2, NID2, CAP350, CXCLl, BTN3A3, IL6, WNT5A, F0XF2, LPHN2, CDHl 1 ,

P4HA1, GRP58, ACTNl , CAPN2, DSIPI, MAP1LC3B, GALIG, IGSF4, IRS2, ATP2A2, OGT, TNFRSFlOB, KIAAl 128, TM4SF1, RBPMS, RIPK2, CBLB, NRl D2, BTN3A2, SLC7A1 1 , MPZLl , IGFBP3, SS A2, FNl ₅ NQOl , ASPH, ASAHl, MGLL, SERPINB6, HSPA5, ZFP36L1, COL4A2, COL4A1, CD44, SLC39A14, NIPA2, FKBP9, IL6ST, DKFZP564G2022, PPAP2B, MAPlB, MAPKl, MYOlB, CAST, RRAS2, QKI, LHFPL2, 38970, ARHE, KIAAl 078, FTL, KIAA0877, PLCBl, KIAA0802, KPNBl, RAB3GAP, SERPINBl, TIMM 17A, SOD2, HLA-A, NOMO2, LOC55831, PHLDAl, TMEM2, MLPH, FAD104, LRRC5, RAB7L1, FLJ35036, DOCKlO, LRP12, TXNDC5, CDC14B, HRMTlLl, COROlC, DNAJClO, TNPOl, LONP, AMIGO2, DNAPTP6, and ADAMTSl, and most preferably gene sequences TRAl, ACTN4, CALMl, CD63, FKBPlA, CALU, IQGAPl, MGC8721, STATl , TACCl , TM4SF8, CD59, CKAP4, DUSPl , RCNl, MGC8902, LGALSl, BHLHB2, RRBPl , PRNP, IER3, MARCKS, LUM, FER1L3, SLC20A1, HEXB, EXTl, TJPl, CTSL, SLC39A6, RIOK3, CRK, NNMT, TRAM2, ADAM9, DNAJC7, PLSCRl, PRSS23, PL0D2, NPCl , TOBl, GFPTl, IL8, PYGL, L0XL2, KIAA0355, UGDH, PURA, ULK2, CENTG2, NID2, CAP350, CXCLl, BTN3A3, IL6, WNT5A, F0XF2, LPHN2, CDHI l, P4HA1, GRP58, DSIPI, MAP1LC3B, GALIG, IGSF4, IRS2, ATP2A2, OGT, TNFRSFlOB, KIAAl 128, TM4SF1, RBPMS, RIPK2, CBLB, NRl D2, SLC7A1 1, MPZLl, SSA2, NQOl, ASPH, ASAHl, MGLL, SERPINB6, HSPA5, ZFP36L1, C0L4A1, CD44, SLC39A14, NIPA2, FKBP9, IL6ST, DKFZP564G2022, PPAP2B, MAPlB, MAPKl , MYOlB, CAST, RRAS2, QKI, LHFPL2, 38970, ARHE, KIAAl 078, FTL, KIAA0877, PLCBl, KIAA0802, RAB3GAP, SERPINBl, TIMM17A, S0D2, HLA-A, N0M02, LOC55831, PHLDAl, TMEM2, MLPH, FAD104, LRRC5, RAB7L1 , FLJ35036, DOCKlO, LRP12, TXNDC5, CDC14B, HRMTlLl , COROlC, DNAJClO, TNPOl , LONP, AMIG02, DNAPTP6, and ADAMTS 1 , whose expression indicates sensitivity to radiation therapy.

v) One or more of the gene sequences FAU, NOL5 A, ANP32A, ARHGDIB, LBR, FABP5, ITM2A, SFRS5, IQGAP2, SLC7A6, SLA, IL2RG, MFNG, GPSM3, PIM2, EVERl , LRMP, ICAM2, RIMS3, FMNLl , MYB, PTPN7, LCK, CXorf9, RHOH, ZNFNlAl , CENTBl , LCP2, DBT, CEPl , IL6R, VAVl , MAP4K1, CD28, PTP4A3,

CD3G, LTB, USP34, NVL, CD8B1, SFRS6, LCPl ₅ CXCR4, PSCDBP, SELPLG, CD3Z, PRKCQ, CDlA, GATA2, P2RX5, LAIRl , Clorf38, SH2D1A, TRB@, SEPT6, HA-I , DOCK2, WBSCR20C, CD3D, RNASE6, SFRS7, WBSCR20A, NUP210, CD6, HNRPAl , AIFl, CYFIP2, GLTSCR2, Cl lorf2, ARHGAPl 5, BIN2, SH3TC1, STAG3, TM6SF1, C15orf25, FLJ22457, PACAP, and MGC2744, whose expression indicates sensitivity to an HDAC inhibitor.

w) One or more of the gene sequences CD99, SNRPA, CUGBP2, STAT5A, SLA, IL2RG, GTSEl, MYB, PTPN7, CXorf9, RHOH, ZNFNlAl, CENTBl , LCP2, HIST1H4C, CCR7, APOBEC3B, MCM7, LCPl, SELPLG, CD3Z, PRKCQ, GZMB, SCN3A, LAIRl, SH2D1A, SEPT6, CG018, CD3D, C18orflO, PRFl , AIFl, MCM5, LPXN, C22orfl8, ARHGAPl 5, and LEFl, whose expression indicates sensitivity to 5- Aza-2'-deoxycytidine (Decitabine).

Probes that may be employed on microarrays of the invention include oligonucleotide probes having sequences complementary to any of the biomarker gene or microRNA sequences described above. Additionally, probes employed on microarrays of the invention may also include proteins, peptides, or antibodies that selectively bind any of the oligonucleotide probe sequences or their complementary sequences. Exemplary probes are listed in Tables 22-44, wherein for each treatment listed, the biomarkers indicative of treatment sensitivity, the correlation of biomarker expression to growth inhibition, and the sequence of an exemplary probe (Tables 22-44) to detect biomarker (Tables 1-21) expression are shown.

Identification of Biomarker Genes

The gene expression measurements of the NCI60 cancer cell lines were obtained from the National Cancer Institute and the Massachusetts Institute of Technology (MIT). Each dataset was normalized so that sample expression measured by different chips could be compared. The preferred method of normalization is the logit transformation, which is performed for each gene y on each chip:

logit(y) = log [(y-background) I {saturation -y)],

where background is calculated as the minimum intensity measured on the chip minus 0.1% of the signal intensity range: min-0.001 *(max-min), and saturation is calculated as the maximum intensity measured on the chip plus 0.1% of the signal intensity range: max+0.001 *(max-min). The resulting logit transformed data is then z-transformed to mean zero and standard deviation 1.

Next, gene expression is correlated to cancer cell growth inhibition. Growth inhibition data (GI50) of the NCI60 cell lines in the presence of any one of thousands of tested compounds was obtained from the NCI. The correlation between the logit- transformed expression level of each gene in each cell line and the logarithm of GI50 (the concentration of a given compound that results in a 50% inhibition of growth) can be calculated, e.g., using the Pearson correlation coefficient or the Spearman Rank- Order correlation coefficient. Instead of using GI50s, any other measure of patient sensitivity to a given compound may be correlated to the patient's gene expression. Since a plurality of measurements may be available for a single gene, the most accurate determination of correlation coefficient was found to be the median of the correlation coefficients calculated for all probes measuring expression of the same gene.

The median correlation coefficient of gene expression measured on a probe to growth inhibition or patient sensitivity is calculated for all genes, and genes that have a median correlation above 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95, or 0.99 are retained as biomarker genes. Preferably, the correlation coefficient of biomarker genes will exceed 0.3. This is repeated for all the compounds to be tested. The result is a list of marker genes that correlates to sensitivity for each compound tested.

Predicting Patient Sensitivity or Resistance to Medical Treatment

For a given compound, the biomarker whose expression has been shown to correlate to chemosensitivity can be used to classify a patient, e.g., a cancer patient, as sensitive to a medical treatment, e.g., administration of a chemotherapeutic agent or radiation. Using a tumor sample or a blood sample (e.g., in case of leukemia or lymphoma) from a patient, expression of the biomarker in the cells of the patient in the

presence of the treatment agent is determined (using, for example, an RNA extraction kit, a DNA microarray and a DNA microarray scanner). The biomarker expression measurements are then logit transformed as described above. The sum of the expression measurements of the biomarkers is then compared to the median of the sums derived from a training set population of patients having the same tumor. If the sum of biomarker expression in the patient is closest to the median of the sums of expression in the surviving members of the training set, the patient is predicted to be sensitive to the compound or other medical treatment. If the sum of expression in the patient is closest to the median of the sums of expression in the non-surviving members of the training set, the patient is predicted to be resistant to the compound.

Machine learning techniques such as Neural Networks, Support Vector Machines, K Nearest Neighbor, and Nearest Centroids may also be employed to develop models that discriminate patients sensitive to treatment from those resistant to treatment using biomarker expression as model variables which assign each patient a classification as resistant or sensitive. Machine learning techniques used to classify patients using various measurements are described in U.S. Patent No. 5,822,715; U.S. Patent Application Publication Nos. 2003/0073083, 2005/0227266, 2005/0208512, 2005/0123945, 2003/0129629, and 2002/0006613; and in Vapnik V N. Statistical Learning Theory, John Wiley & Sons, New York, 1998; Hastie et al., 2001, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, N. Y.; Agresti, 1996, An Introduction to Categorical Data Analysis, John Wiley & Sons, New York; and V. Tresp et al., "Neural Network Modeling of Physiological Processes", in Hanson S. J. et al. (Eds.), Computational Learning Theory and Natural Learning Systems 2, MIT Press, 1994, hereby incorporated by reference.

Other variables can be used to determine relative biomarker expression between a patient (e.g., a cancer patient) and a normal subject (e.g., a control subject), including but not limited to, measurement of biomarker DNA copy number and the identification of biomarker genetic mutations.

A more compact microarray can be designed using only the oligonucleotide probes having measurements yielding the median correlation coefficients with cancer cell growth inhibition. Thus, in this embodiment, only one probe needs to be used to

measure expression of each biomarker.Biomarkers include polypeptides and metabolites thereof. A skilled artisan can use employ assays that measure changes in polypeptide biomarker expression (e.g., Western blot, immunofluorescent staining, and flow cytometry) to determine a patient's sensitivity to a treatment (e.g., chemotherapy, radiation therapy, or surgery).

Identifying a Subpopulation of Patients Sensitive to a Treatment for Cancer

The invention can also be used to identify a subpopulation of patients, e.g., cancer patients, that are sensitive to a compound or other medical treatment previously thought to be ineffective for the treatment of cancer. To this end, genes or microRNAs whose expression correlates to sensitivity to a compound or other treatment can be identified so that patients sensitive to a compound or other treatment may be identified. To identify such biomarkers, gene or microRNA expression within cell lines can be correlated to the growth of those cell lines in the presence of the same compound or other treatment. Preferably, genes or microRNAs whose expression correlates to cell growth with a correlation coefficient exceeding 0.3 may be considered possible biomarkers.

Alternatively, genes or microRNAs can be identified as biomarkers according to their ability to discriminate patients known to be sensitive to a treatment from those known to be resistant. The significance of the differences in gene or microRNA expression between the sensitive and resistant patients may be measured using, e.g., t- tests. Alternatively, naive Bayesian classifiers may be used to identify gene biomarkers that discriminate sensitive and resistant patient subpopulations given the gene expressions of the sensitive and resistant subpopulations within a treated patient population.

The patient subpopulations considered can be further divided into patients predicted to survive without treatment, patients predicted to die without treatment, and patients predicted to have symptoms without treatment. The above methodology may be similarly applied to any of these further defined patient subpopulations to identify biomarkers able to predict a subject's sensitivity to compounds or other treatments for the treatment of cancer.

Patients with elevated expression of biomarkers correlated to sensitivity to a compound or other medical treatment would be predicted to be sensitive to that compound or other medical treatment.

The invention is particularly useful for recovering compounds or other treatments that failed in clinical trials by identifying sensitive patient subpopulations using the gene or microRNA expression methodology disclosed herein to identify biomarkers that can be used to predict clinical outcome.

Kit, Apparatus, and Software for Clinical Use

This invention can also be used to predict patients who are resistant or sensitive to a particular treatment by using a kit that includes a kit for RNA extraction from tumors (e.g., Trizol from Invitrogen Inc.), a kit for RNA amplification (e.g., MessageAmp from Ambion Inc.), a microarray for measuring biomarker expression (e.g., HG-U 133 A GeneChip from Affymetrix Inc.), a microarray hybridization station and scanner (e.g., GeneChip System 3000Dx from Affymetrix Inc.), and software for analyzing the expression of marker genes as described in herein (e.g., implemented in R from R-Project or S-Plus from Insightful Corp.).

Methodology of the In Vitro Cancer Growth Inhibition Screen

The human tumor cell lines of the cancer screening panel are grown in RPMI 1640 medium containing 5% fetal bovine serum and 2 mM L-glutamine. Cells are inoculated into 96 well microtiter plates in 100 μL at plating densities ranging from 5,000 to 40,000 cells/well depending on the doubling time of individual cell lines. After cell inoculation, the microtiter plates are incubated at 37°C, 5% CO ₂ , 95% air, and 100% relative humidity for 24 hrs prior to addition of experimental compounds.

After 24 hrs, two plates of each cell line are fixed in situ with TCA, to represent a measurement of the cell population for each cell line at the time of compound addition (Tz). Experimental compounds are solubilized in dimethyl sulfoxide at 400-fold the desired final maximum test concentration and stored frozen prior to use. At the time of compound addition, an aliquot of frozen concentrate is thawed and diluted to twice the desired final maximum test concentration with complete medium containing 50 μg/mL

Gentamicin. Additional four, 10-fold or ¹ A log serial dilutions are made to provide a total of five compound concentrations plus control. Aliquots of 100 μL of these different compound dilutions are added to the appropriate microtiter wells already containing 100 μL of medium, resulting in the required final compound concentrations. Following compound addition, the plates are incubated for an additional 48 hrs at 37°C, 5% CO ₂ , 95% air, and 100% relative humidity. For adherent cells, the assay is terminated by the addition of cold TCA. Cells are fixed in situ by the gentle addition of 50 μL of cold 50% (w/v) TCA (final concentration, 10% TCA) and incubated for 60 min at 4°C. The supernatant is discarded, and the plates are washed five times with tap water and air-dried. Sulforhodamine B (SRB) solution (100 μL) at 0.4% (w/v) in 1% acetic acid is added to each well, and plates are incubated for 10 min at room temperature. After staining, unbound dye is removed by washing five times with 1% acetic acid and the plates are air-dried. Bound stain is subsequently solubilized with 10 mM trizma base, and the absorbance is read on an automated plate reader at a wavelength of 515 nm. For suspension cells, the methodology is the same except that the assay is terminated by fixing settled cells at the bottom of the wells by gently adding 50 μL of 80% TCA (final concentration, 16 % TCA). Using the seven absorbance measurements [time zero, (Tz), control growth, (C), and test growth in the presence of compound at the five concentration levels (Ti)], the percentage growth is calculated at each of the compound concentrations levels. Percentage growth inhibition is calculated as:

[(Ti-Tz)/(C-Tz)] x 100 for concentrations for which Ti>/=Tz [(Ti-Tz)/Tz] x 100 for concentrations for which Ti<Tz

Three dose response parameters are calculated for each experimental agent. Growth inhibition of 50% (GI50) is calculated from [(Ti-Tz)/(C-Tz)] x 100 = 50, which is the compound concentration resulting in a 50% reduction in the net protein increase (as measured by SRB staining) in control cells during the compound incubation. The compound concentration resulting in total growth inhibition (TGI) is calculated from Ti = Tz. The LC50 (concentration of compound resulting in a 50% reduction in the

measured protein at the end of the compound treatment as compared to that at the beginning) indicating a net loss of cells following treatment is calculated from [(Ti- Tz)/Tz] x 100 = -50. Values are calculated for each of these three parameters if the level of activity is reached; however, if the effect is not reached or is exceeded, the value for that parameter is expressed as greater or less than the maximum or minimum concentration tested.

RNA Extraction and Gene Expression Measurement

Cell/tissue samples are snap frozen in liquid nitrogen until processing. RNA is extracted using e.g., Trizol Reagent (Invitrogen) following manufacturers instructions. RNA is amplified using e.g., MessageAmp kit (Ambion) following manufacturers instructions. Amplified RNA is quantified using e.g., HG-U 133 A GeneChip (Affymetrix) and compatible apparatus e.g., GCS3000Dx (Affymetrix), using manufacturers instructions.

The resulting gene expression measurements are further processed as described in this document. The procedures described can be implemented using R software available from R-Project and supplemented with packages available from Bioconductor.

For many drugs 10-30 biomarkers are sufficient to give an adequate response, thus, given the relatively small number of biomarkers required, procedures, such as quantitative reverse transcriptase polymerase chain reaction (qRT-PCR), can be performed to measure, with greater precision, the amount of biomarker genes expressed in a sample. This will provide an alternative to or a complement to microarrays so that a single companion test, typically more quantitative than microarrays alone, employing biomarkers of the invention can be used to predict sensitivity to a new drug. qRT-PCR can be performed alone or in combination with a microarray described herein. Procedures for performing qRT-PCR are described in, e.g., U.S. Patent No. 7,101,663 and U.S. Patent Application Nos. 2006/0177837 and 2006/0088856. The methods of the invention are readily applicable to newly discovered drugs as well as drugs described herein.

The following examples are provided so that those of ordinary skill in the art can see how to use the methods and kits of the invention. The examples are not

intended to limit the scope of what the inventor regards as their invention.

EXAMPLES

Example 1 : Identification of gene biomarkers for chemosensitivity to common chemotherapy drugs.

DNA chip measurements of the 60 cancer cell lines of the NCI60 data set were downloaded from the Broad Institute (Cambridge, Massachusetts) and logit normalized. Growth inhibition data of thousands of compounds against the same cell lines were downloaded from the National Cancer Institute. Compounds where the difference concentration to achieve 50% in growth inhibition (GI50) was less than 1 log were deemed uninformative and rejected. Each gene's expression in each cell line was correlated to its growth (-log(GI50)) in those cell lines in the presence of a given compound. The median Pearson correlation coefficient was used when multiple expression measurements were available for a given gene, and genes having a median correlation coefficient greater than 0.3 were identified as biomarkers for a given compound.

Example 2: Prediction of treatment sensitivity for brain cancer patients.

DNA chip measurements of gene expression in tumors from 60 brain cancer patients were downloaded from the Broad Institute. All data files were logit normalized. For each of the common chemotherapy drugs Cisplatin, Vincristine, Adriamycine, Etoposide, Aclarubicine, Mitoxantrone and Azaguanine, the gene expression for the marker genes was summed. The sum was normalized by dividing by the standard deviation of all patients and compared to the median of the sums of patients who survived and the median of the sums of patients who died:

Normal i zed S um(compound) = sum(marker genes for compound)/sd(sums of all patients)

Sensitivity(compound) = [NormalizedSum(compound)- median(NormalizedSumdeadpatients(compound))] ²

[NormalizedSum(compound) - median(TslormalizedSumsurvivingpatients(compound))] ²

Figures 2 and 3 show the resulting treatment sensitivity predictions for two of the 60 patients. All patients received Cisplatin and the prediction of survival amongst the 60 patients based on their Cisplatin chemosensitivity yielded the Kaplan-Meier survival curve shown in Figure 4. The expression of the 16 Cisplatin biomarker genes was first reduced to 5 components (dimensions) using Independent Component Analysis (fastICA). Five different classification methods were trained on the five components from the 60 patients: K Nearest Neighbor with K=I, K Nearest Neighbor with K=3, Nearest Centroid, Support Vector Machine, and Neural Network. Chemosensitivity or sensitivity to radiation treatment was predicted by combining the classifications of the five methods wherein each classification method was assigned a single vote: unanimous chemosensitive/treatment sensitive prediction resulted in a prediction of chemosensitive/treatment sensitive. All other predictions resulted in a prediction of chemoresistant/treatment resistant. The performance of the combined classifier was validated using leave-one-out cross validation and the survival of the two predicted groups shown in Figure 4. The survival rate of the patients predicted to be chemosensitive was higher than the patients predicted to be chemoresistant.

Example 3: Prediction of chemosensitivity for lymphoma (DLBCL) patients.

DNA chip measurements of gene expression in the tumors from 56 DLBCL (diffuse large B-cell lymphoma) patients were downloaded from the Broad Institute. All data files were logit normalized. All patients received Vincristine and Adriamycine and the prediction of survival amongst the 56 patients based on their Vincristine and Adriamycine chemosensitivity yielded the Kaplan-Meier survival curve shown in Figure 5. The expression of the 33 Vincristine genes and 16 Adriamycine genes was first reduced to 3 components (dimensions) using Independent Component Analysis (fastICA). Five different classification methods were trained on the independent components from the 56 patients: K Nearest Neighbor with K=I , K Nearest Neighbor with K=3, Nearest Centroid, Support Vector Machine, and Neural Network.

Chemosensitivity was predicted by combining the classifications of the five methods wherein each classification method was assigned a single vote: unanimous chemosensitive prediction resulted in a prediction of chemosensitive. All other predictions resulted in a prediction of chemoresistant. The performance of the combined classifier was validated using leave-one-out cross validation and the survival of the two predicted groups is shown in Figure 5. The survival rate of the patients predicted to be chemosensitive was higher than the patients predicted to be chemoresistant.

Example 4: Prediction of chemosensitivity for lung cancer patients.

DNA chip measurements of gene expression in the tumors from 86 lung cancer (adenocarcinoma) patients was downloaded from the University of Michigan, Ann Arbor. Of the 86 patients, 19 had Stage III of the disease and received adjuvant chemotherapy. Raw data was logit normalized. Instead of the combined classifier described for the brain cancer and lymphoma examples above, the sum of biomarker gene expression was calculated for each patient and used to discriminate chemosensitive and chemoresistant patients. For each patient, the gene expression of the 16 marker genes for Cisplatin sensitivity (all Stage III patients received Cisplatin after surgery) was summed. If the sum was closer to the median of the sums of the surviving patients, the patient was predicted to be sensitive to Cisplatin. If the sum was closest to the median of the sums of the non-surviving patients, the patient was predicted to be resistant to Cisplatin. The survival rates of the two predicted groups are shown in Figure 6. The survival rate of the patients predicted to be chemosensitive was higher than the patients predicted to be chemoresistant.

Example 5: Prediction of Rituximab sensitivity for lymphoma (DLBCL) patients.

The method is not limited to cytotoxic chemicals. It is also applicable to predicting the efficacy of protein therapeutics, such as monoclonal antibodies, approved for treating cancer. For example, the monoclonal antibody Rituximab (e.g., MABTHERA™ and RITUXAN™) was examined. Data for cytotoxicity of Rituximab in cell lines in vitro were obtained from published reports (Ghetie et al., Blood

97(5): 1392-1398, 2001). This cytotoxicity in each cell line was correlated to the expression of genes in these cell lines (downloaded from the NCBI Gene Expression Omnibus database using accession numbers GSE2350, GSE 1880, GDS 181). The identified marker genes were used to predict the sensitivity of DLBCL to Rituximab in a small set of 14 patients treated with Rituximab and CHOP (R-CHOP) (downloaded from NCBI Gene Expression Omnibus under accession number GSE4475). Conversion between different chip types was performed using matching tables available through Affymetrix.

The survival of patients predicted to be sensitive to be R-CHOP is compared to the survival of patients predicted to be resistant to R-CHOP in Figure 7. The survival rate of the patients predicted to be chemosensitive was higher than the patients predicted to be chemoresistant.

To predict the sensitivity toward combination therapies, such as those used to treat Diffuse Large B-cell Lymphoma (DLBCL), patient sensitivity to a particular combination therapy is predicted by combining the marker genes for the individual compounds used in the combination. An example of this is shown in Figure 8, where the predicted sensitivities of one patient towards a number of combination therapies used against DLBCL (identified by their acronyms) are shown: R-CHOP contains Rituximab (e.g., MABTHERA™), Vincristine, Doxorubicin (Adriamycin), Cyclophosphamide, and Prednisolone; R-ICE contains Rituximab, Ifosfamide, Carboplatin, and Etoposide; R-MIME contains Rituximab, Mitoguazone, Ifosfamide, Methotrexate, and Etoposide; CHOEP contains Cyclophosphamide, Doxorubicin, Etoposide, Vincristine and Prednisone; DHAP contains Dexamethasone, Cytarabine (Ara C), and Cisplatin; ESHAP contains Etoposide, Methylprednisolone (Solumedrol), Cytarabine (Ara-C) and Cisplatin; and HOAP-Bleo contains Doxorubicin, Vincristine, Ara C, Prednisone, and Bleomycin.

Example 6: Prediction of radiosensitivity for brain tumor (medulloblastoma) patients.

The method of identifying biomarkers can also be applied to other forms of treatment such as radiation therapy. For example, sensitivity to radiation therapy was predicted for brain tumor patients. Radiation therapy in the form of craniospinal irradiation yielding 2,400-3,600 centiGray (cGy) with a tumor dose of 5,300-7,200 cGy was administered to the brain tumor patients using a medical device that emits beams of radiation. Sensitivity of the 60 cancer cell lines used in the NCI60 dataset to radiation treatment was obtained from published reports. This sensitivity was correlated to the expression of genes in the cell lines as described above to identify marker genes. DNA microarray measurements of gene expression in brain tumors obtained from patients subsequently treated with radiation therapy were obtained from the Broad Institute. The identified gene biomarkers were used to classify the patients as sensitive or resistant to radiation therapy. The survival of the patients in the two predicted categories is shown in Figure 9. The survival rate of the patients predicted to be sensitive to radiation therapy was higher than the patients predicted to be resistant to radiation therapy.

Example 7: Drug rescue.

Every member of a population may not be equally responsive to a particular treatment. For example, new compounds often fail in late clinical trials because of lack of efficacy in the population tested. While such compounds may not be effective in the overall population, there may be subpopulations sensitive to those failed compounds due to various reasons, including inherent differences in gene expression. The method as described herein can be used to rescue failed compounds by identifying a patient subpopulation sensitive to a compound using their gene expression as an indicator. Subsequent clinical trials restricted to a sensitive patient subpopulation may demonstrate efficacy of a previously failed compound within that particular patient subpopulation, advancing the compound towards approval for use in that subpopulation.

To this end, in vitro measurements of the inhibitory effects of a compound on various cancer cell lines are compared to the gene expression of cells. The growth of the cancer cell samples can be correlated to gene expression measurements as described

above. This will identify marker genes that can be used to predict patient sensitivity to the failed compound. Once biomarkers are identified, the expression of biomarker genes in cells obtained from patients can be measured according to the procedure detailed above. The patients are predicted to be responsive or non-responsive to compound treatment according to their gene biomarker expression profile. Clinical effect must then be demonstrated in the group of patients that are predicted to be sensitive to the failed compound.

The method may be further refined if patients responsive to the compound treatment are further subdivided into those predicted to survive without the compound and those predicted to die or suffer a relapse without the compound. Clinical efficacy in the subpopulation that is predicted to die or suffer relapse can be further demonstrated. Briefly, the gene expression at the time of diagnosis of patients who later die from their disease is compared to gene expression at the time of diagnosis of patients who are still alive after a period of time (e.g., 5 years). Genes differentially expressed between the two groups are identified as prospective biomarkers and a model is built using those gene biomarkers to predict treatment efficacy.

Examples of compounds that have failed in clinical trials include Gefinitib (e.g., Iressa, AstraZeneca) in refractory, advanced non-small-cell lung cancer (NSCLC), Bevacizumab (e.g., Avastin, Genentech) in first-line treatment for advanced pancreatic cancer, Bevacizumab (e.g., Avastin, Genentech) in relapsed metastatic breast cancer patients, and Erlotinib (e.g., Tarceva, Genentech) in metastatic non-small cell lung cancer (NSCLC). The method of the invention may be applied to these compounds, among others, so that sensitive patient subpopulations responsive to those compounds may be identified.

Example 8: Median of the correlations versus correlation of the median.

The median of the correlations of the individual probe measurements to cancer cell growth as employed by the invention was compared to the correlation of the median probe measurements: this will determine at which step of the method a median calculation should be performed. In the former, several correlations are calculated for each gene since multiple probes measure a given gene's expression, but only the median

of the correlation coefficients is finally retained to identify biomarkers. In the latter, only one correlation is calculated for each gene because only the median gene expression measurement is considered for each gene. Figure 10 shows the results of using the correlation of the median expression measurements to identify biomarker genes of radiation sensitivity predicting the survival of 60 brain cancer patients. The difference in survival between the group predicted to be radiation sensitive and the group predicted to be radiation resistant in Figure 10 is much smaller than the difference depicted in Figure 9 which employed a median correlation coefficient suggesting that the invention's median of the correlations employed in Figure 9 outperforms the correlation of the median depicted in Figure 10.

If we look at individual marker genes like OMD, the median of the correlation to measured radiosensitivity of cell lines in vitro is 0.32. The correlation of the median, however, is 0.39. Adjusting the cutoff from 0.3 to 0.4 to compensate for the difference does not improve on Figure 10, however.

We have also compared median correlation to weighted voting as proposed by Staunton et al., PNAS 98(19): 10787- 10792, 2001). Weighted voting produced a poor result similar to that of Figure 10, with a P-value of 0.1 1.

Example 9: Other methods of identifying biomarkers.

The examples shown above all rely on the availability of measurements of inhibition by a compound or treatment of the growth of cell lines in vitro. Such measurements may not always be available or practical. In that case an alternative method of identifying biomarkers can be employed. If the target(s) of the compound is/are known, it is possible to build a model based on the gene expression of the known target(s). One example is the drug sunitinib (SUl 1248), for which eight targets are known. Sunitinib inhibits at least eight receptor protein-tyrosine kinases including vascular endothelial growth factor receptors 1-3 (VEGFRl -VEGFR3), platelet-derived growth factor receptors (PDGFRA and PDGFRB), stem cell factor receptor (Kit), Flt-3, and colony-stimulating factor- 1 receptor (CSF-IR). U.S. Patent Application Publication 2006/0040292 mentions prediction of response measuring just two targets, PDGFRA and KIT. Using the sum of the gene expression of four targets it is possible

to predict with more reliability the response to sunitinib. As an example, the predicted sunitinib sensitivity of cell lines HT29, Ul 18, 786, and H226 is 0.24, 2.3, 0.14 and 0.60, respectively, based on the sum of the four targets PDGFRB, KDR, KIT and FLT3. This correlates well with the measured response in mouse xenografts of these cells (correlation coefficient 0.86) as well as with the measured anti-angiogenetic effect measured in mouse xenografts (Potapova et al. Contribution of individual targets to the antitumor efficacy of the multitargeted receptor tyrosine kinase inhibitor SUl 1248 (MoI. Cancer Ther. 5(5): 1280-9, 2006). This is better than a model based only on two targets PDGFRA and KIT (correlaton coefficient 0.56).

This four-gene predictor of sunitinib response can be applied to a large number of tumor samples from patients with different tumors from which gene expression analysis has been performed in order to get an idea of the range of sensitivities within each cancer type as well as which cancer types are most susceptible to treatment with sunitinib. Figure 11 shows just a small fraction of the cancer samples available from www.intgen.org/expo.html. The comparison is based on normalizing the samples in such a way (e.g., logit normalization) that different cancer types become comparable. Sunitinib is currently approved by the FDA for renal cancer and gastrointestinal cancer. Both kidney and colon show a good response in this plot.

Any other drug response response predictor based on gene expression can be tested in the same manner as shown in Figure 1 1.

The approach of identifying biomarkers based on known targets can also be applied to RNA antagonists such as SPC2996 targeted against Bcl-2. A response predictor can be built based on measuring the gene expression of Bcl-2 in samples from cancer patients. The same approach can be used for the targets of all mRN A antagonists or inhibitors.

Example 10: Identifying candidate drugs for a known target.

The methods of the invention described herein can also be used for identifying candidate drugs to a known target. Basically, the method of identifying biomarkers is run backwards in order to identify candidate drugs. If one starts with a known target, the expression of its corresponding gene is determined in the NCI 60 cell lines and

correlated to the measured growth inhibition of all the thousands of drugs tested in the NCI 60 cell lines. This provides a list, ranked by correlation coefficient, of candidate drugs for the target. It is even possible to test new drugs and compare their correlation coefficient to the target gene expression to the correlation coefficients of the already tested drugs.

Example 11: Using microRNAs as biomarkers of drug response.

In recent years it has become clear that microRNAs (miRNA) play an important role in regulating the translation of mRNAs. As such, microRNAs may contain important information relevant for the prediction of drug sensitivity. This information may be complementary to the information contained in mRNA expression. Shown below is the correlation between predicted and measured chemosensitivity of the NCI 60 cell lines. The prediction is based either on mRNA measurements with DNA microarrays as described herein or predictions based on measurements of microRNA concentration (ArrayExpress accession number E-MEXP- 1029) using a microRNA specific microarray (ArrayExpress accession number A-MEXP-620). Whenever more than one probe is used to determine the concentration of a given microRNA, the median correlation procedure is used for calculating correlation between microRNA concentration and -log(GI50).

miRNA mRNA Combined cisplatin 0.16 0.02 0.21

PXDlOl 0.44 0.31 0.50 vincristine 0.06 0.11 0.26 etoposide 0.32 0.41 0.44 adriamycine 0.24 0.22 0.28

As the above table shows, the correlation (determined using leave-one-out cross- validation) is highest when using a combination (linear sum) of microRNA and mRNA predictions. These results suggest that a more accurate drug response predictor can be built using a combination of microRNA and mRNA. It is possible to measure both in

the same experiment, as long as one takes into consideration that microRNAs in general do not have a polyA tail as mRNA does. Only slight modifications to the amplification and labeling methods used for mRNA may be needed to incorporate microRNAs into the analysis. Commercial kits for microRNA extraction, amplification, and labeling are available from suppliers (e.g., Ambion Inc.).

Tables 22A-76A list the microRNA probes that are useful for detection of sensitivity to individual drugs, as determined by their median correlation to -log(GI50) for the indicated drug.

Other Embodiments

All publications and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each independent publication or patent application was specifically and individually indicated to be incorporated by reference. While the invention has been described in connection with specific embodiments thereof, it will be understood that it is capable of further modifications and this application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure that come within known or customary practice within the art to which the invention pertains and may be applied to the essential features hereinbefore set forth.

Legend:

List_2006: biomarkers identified in 2006 using the new U133A chip measurements

List_2005: biomarkers listed in 2005 patent filing

HU6800: biomarkers obtained with old HU6800 chip measurements

List_Prior: matching biomakrers in prior art

List_Preferr : Prederred list of biomarkers

Correlation: The correlation of the biomarker to sensitivity to the compound

Table 1. Vincristine biomarkers

List 2006 List_2005 List Prior List Preferr Correlation

[1,1 UBB UBB ^~ 0.39

[2,] RPS4X RPS4X 0.34

[3,] S100A4 S100A4 0.32

[4,] NDUFS6 NDUFS6 0.31

[5,] B2M B2M 0.35

[6,] C14orfl39 C14orfl39 0.3

[7,] MANlAl MANlAl 0.33

[8,] SLC25A5 SLC25A5 SLC25A5 0.32

[9,] RPLlO RPLlO 0.38

[10,] RPL12 RPL12 0.31

[12,] RPL36A RPL36A RPL36A 0.3

[13,] SUIl SUIl 0.33

[14,] BLMH BLMH 0.32

[15,] CTBPl CTBPl 0.32

[16,] TBCA TBCA 0.3

[17,] MDH2 MDH2 0.34

[18,] DXS9879E DXS9879E 0.35

[19,] SFRS3

[20,] CCT5

[21,] RPL39

[22,] UBE2S

[23,] EEFlAl

[24,] COX7B

[25,] RPLP2

[26,] RPL24

[27,] RPS23

[28,] RPL18

[29,] NCL

[30,] RPL9

[31,] RPLlOA

[32,] RPSlO

[33,] EIF3S2

[34,] SHFMl

[35,] RPS28

[36,] REA

[37,] GAPD

[38,] HNRPAl

[39,] RPSIl

[40,] LDHB

[41,] RPL3

[42,] RPLIl

[43,] MRPL12

[44,] RPL18A

[45,] RPS7

Fable 2. Cisplatin biomarkers

List 2006 List_2005 List Prior List Preferr Correlation

[1,] ClQRl ClQRl 0.3

[2,] HCLSl HCLSl HCLSl 0.33

[3,] CD53 CD53 0.35

[4,] SLA SLA 0.37

[5,] PTPN7 PTPN7 PTPN7 0.31

[6,] PTPRCAP PTPRCAP 0.32

[7,] ZNFNlAl ZNFNlAl 0.33

[8,] CENTBl CENTBl 0.37

[9,] PTPRC PTPRC 0.36

[10,] IFI16 IFI16 IFI16 0.31

[11,] ARHGEF6 ARHGEF6 0.35

[12,] SEC31L2 SEC31L2 0.32

[13,] CD3Z CD3Z 0.32

[14,] GZMB GZMB 0.3

[15,] CD3D CD3D 0.34

[16,] MAP4Kl MAP4K1 0.32

[17,] GPR65 GPR65 0.39

[18,] PRFl PRFl 0.31

[19,] ARHGAP15 ARHGAP15 0.35

[20,] TM6SF1 TM6SF1 0.41

[21,] TCF4 TCF4 0.4

[22,] GAPD

[23,] ARHGDIB

[24,] RPS27

[25,] C5orfl3

[26,] LDHB

[27,] SNRPF

[28,] B2M

[29,] FTL

[30,] NCL

[31,] MSN

[32,] XPOl

Table 3. Azaguanine biomarkers

List_2006 List_2005 List_Prior List_Preferr Correlation

[1,] MSN MSN MSN 0.36

[2,] SPARC SPARC SPARC 0.48

[3,] VIM VIM VIM 0.47

[4,] SRM SRM SRM 0.32

[5,] SCARBl SCARBl 0.4

[6, ] SIATl SIATl 0.31

[7, ] CUGBP2 CUGBP2 0.37

[8, ] GAS7 GAS7 0.34

[9, ] ICAMl ICAMl 0.43

[10, ] WASPIP WASPIP 0.44

[12, ] PALM2-AKAP2 PALM2-AKAP2 0.31

[13, ] ANPEP ANPEP 0.33

[14, ] PTPNSl PTPNSl 0.39

[15, ] MPPl MPPl 0.32

[16, ] LNK LNK 0.43

[17, ] FCGR2A FCGR2A 0.3

[18, ] EMP3 EMP3 EMP3 0.33

[19, ] RUNX3 RUNX3 0.43

[20, ] EVI2A EVI2A 0.4

[21, ] BTN3A3 BTN3A3 0.4

[22, ] LCP2 LCP2 0.34

[23, ] BCHE BCHE 0.35

[24, ] LY96 LY96 0.47

[25, ] LCPl LCPl 0.42

[26, ] IFI16 IFI16 0.33

[27, ] MCAM MCAM MCAM 0.37

[28, ] MEF2C MEF2C 0.41

[29, ] SLC1A4 SLC1A4 0.49

[30, ] BTN3A2 BTN3A2 0.43

[31, ] FYN FYN 0.31

[32, ] FNl FNl FNl 0.33

[33, ] Clorf38 Clorf38 0.37

[34,] CHSl CHSl 0.33

[35,] CAPN3 CAPN3 0.5

[36,] FCGR2C FCGR2C 0.34

[37,] TNIK TNIK 0.35

[38,] AMPD2 AMPD2 0.3

[39,] SEPT6 SEPT6 0.41

[40,] RAFTLIN RAFTLIN 0.39

[41,] SLC43A3 SLC43A3 0.52

[42,] RAC2 RAC2 0.33

[43,] LPXN LPXN 0.54

[44,] CKIP-I CKIP-I 0.33

[45,] FLJ10539 FLJ10539 0.33

[46,] FLJ35036 FLJ35036 0.36

[47,] DOCKlO DOCKlO 0.3

[48,] TRPV2 TRPV2 0.31

[49,] IFRG28 IFRG28 0.3

[50,] LEFl LEFl 0.31

[51,] ADAMTSl ADAMTSl 0.36

[52,] PRPSl

[53,] DDOST

[54,] B2M

[55,] LGALSl

[56,] CBFB

[57,] SNRPB2

[58,] EIF2S2

[59,] HPRTl

[60,] FKBPlA

[61,] GYPC

[62,] UROD

[63,] HNRPAl

[64,] SNDl

[65,] COPA

[66,] MAPREl

[67,] EIF3S2

[68,] ATP1B3

[69,] ECMl

[70,] ATOXl

[71,] NARS

[72,] PGKl

[73,] OK/SW-cl.56

[74,] EEFlAl

[75,] GNAI 2

[76,] RPL7

[77,] PSMB9

[78,] GPNMB

[79,] PPPlRIl

[80,] MIA

[81,] RAB 7

[82,] SMS

Table 4. Etoposide biomarkers

List_2006 List_2005 List_Prior List_Preferr Correlation

[1,] CD99 CD99 CD99 0.3

[2,] INSIGl INSIGl 0.35

[3,] LAPTM5 LAPTM5 0.32

[4,] PRGl PRGl 0.34

[5,] MUFl MUFl 0.35

[6,] HCLSl HCLSl 0.33

[7,] CD53 CD53 0.32

[8,] SLA SLA 0.37

[9,] SSBP2 SSBP2 0.37

[10,] GNB5 GNB5 0.35

[11,1 MFNG MFNG 0.33

[12,] GMFG GMFG 0.32

[13,] PSMB9 PSMB9 0.31

[14,] EVI2A EVI2A 0.41

[15,] PTPN7 PTPN7 0.3

[16,] PTGER4 PTGER4 0.3

[17,] CXorf9 CXorf9 0.3

[18,] PTPRCAP PTPRCAP 0.3

[19,] ZNFNlAl ZNFNlAl 0.35

[20,] CENTBl CENTBl 0.3

[21,] PTPRC PTPRC 0.31

[22,] NAPlLl NAPlLl 0.31

[23,] HLA-DRA HLA-DRA 0.34

[24,] IFI16 IFI16 0.38

[25,] COROlA COROlA 0.3

[26,] ARHGEF6 ARHGEF6 0.33

[27,] PSCDBP PSCDBP 0.4

[28,] SELPLG SELPLG 0.35

[29,] LAT LAT 0.3

[30,] SEC31L2 SEC31L2 0.42

[31,] CD3Z CD3Z 0.36

[32,] SH2D1A SH2D1A 0.33

[33,] GZMB GZMB 0.34

[34,] SCN3A SCN3A 0.3

[35,] ITK ITK 0.35

[36,] RAFTLIN RAFTLIN 0.39

[37,] DOCK2 DOCK2 0.33

[38,] CD3D CD3D 0.31

[39,] RAC2 RAC2 0.34

[40,] ZAP70 ZAP70 0.35

[41,] GPR65 GPR65 0.35

[42,] PRFl PRFl 0.32

[43,] ARHGAP15 ARHGAPl 5 0.32

[44,] NOTCHl NOTCHl 0.31

[45,] UBASH3A UBASH3A 0.32

[46,] B2M

[47,] MYC

[48,] RPS24

[49,] PPIF

[50,] PBEFl

[51,] ANP32B

Table 5. Adriamycin biomarkers

List 2006 List 2005 List Prior List Preferr Correlation

[1,] CD99 CD99 CD99 0.41

[2,] LAPTM5 LAPTM5 0.39

[3,] ALDOC ALDOC 0.31

[4,] HCLSl HCLSl 0.32

[5,] CD53 CD53 0.31

[6,] SLA SLA 0.35

[7,] SSBP2 SSBP2 0.34

[8,] IL2RG IL2RG 0.38

[9,] GMFG GMFG 0.32

[10,] CXorf9 CXorf9 0.32

[11,] RHOH RHOH 0.31

[12,] PTPRCAP PTPRCAP 0.32

[13,] ZNFNlAl ZNFNlAl 0.43

[14,] CENTBl CENTBl 0.36

[15,] TCF7 TCF7 0.32

[16,] CDlC CDlC 0.3

[17 _< π_,] MAP4K1 MAP4K1 0.35

[18,] CDlB CDlB 0.39

[19,] CD3G CD3G 0.31

[20,] PTPRC PTPRC 0.38

[21,] CCR9 CCR9 0.34

[22,] COROlA COROlA 0.38

[23,] CXCR4 CXCR4 0.3

[24,] ARHGEF6 ARHGEF6 0.31

[25,] HEMl HEMl 0.32

[26,] SELPLG SELPLG 0.31

[27,] LAT LAT 0.31

[28,] SEC31L2 SEC31L2 0.33

[29,] CD3Z CD3Z 0.37

[30,] SH2D1A SH2D1A 0.37

[31,] CDlA CDlA 0.4

[32,] LAIRl LAIRl 0.39

[33,] ITK ITK 0.3

[34,] TRB@ TRB@ 0.34

[35,] CD3D CD3D 0.33

[36,] WBSCR20C WBSCR20C 0.34

[37,] ZAP70 ZAP70 0.33

[38,] IFI44 IFI44 0.32

[39,] GPR65 GPR65 0.31

[40,] AIFl AIFl 0.3

[41,] ARHGAP15 ARHGAP15 0.37

[42,] NARF NARF 0.3

[43,] PACAP PACAP 0.32

[44,] KIAA0220

[45,] B2M

[46,] TOP2A

[47,] SNRPE

[48,] RPS27

[49, ] HNRPAl

[50,] CBX3

[51,] ANP32B

[52,] DDX5

[53,] PPIA

[54,] SNRPF

[55,] USP7

Table 6. Aclarubicin biomarkers

List 2006 List 2005 List Prior List Preferr Correlation

[1,] RPL12 RPL12 0.3

[2,] RPL32 RPL32 0.37

[3,] RPLP2 RPLP2 RPLP2 0.37

[4,] MYB MYB MYB 0.31

ZNFNlAl ZNFNlAl 0.34

[6,] SCAPl SCAPl 0.33

[7,] STAT4 STAT4 0.31

[8,] SP140 SP140 0.4

[9,] AMPD3 AMPD3 0.3

10,] TNFAIP8 TNFAIP8 0.4

11,] DDX18 DDXl8 0.31

[12,] TAF5 TAF5 0.3

[13,] FBL FBL 0. 41

[14,] RPS2 RPS2 0. 34

[15,] PTPRC PTPRC 0. 37

[16,] DOCK2 DOCK2 0. 32

[17,] GPR65 GPR65 0. 35

[18,] HOXA9 HOXA9 0. 33

[19,] FLJ12270 FLJ12270 0. 31

[20,] HNRPD HNRPD 0. 4

[21,] LAMRl

[22,] RPS25

[23,] EIF5A

[24,] TUFM

[25,] HNRPAl

[26,] RPS9

[27,] ANP32B

[28,] EIF4B

[29,] HMGB2

[30,] RPS15A

[31,] RPS7

Table 7. Mitoxantrone biomarkers

List 2006 List 2005 List Prior List Preferr Correlation

[1,] PGAMl PGAMl 0.32

[2,] DPYSL3 DPYSL3 0.36

[3,] INSIGl INSIGl 0.32

[4,] GJAl GJAl 0.31

[5,] BNIP3 BNIP3 0.31

[6,] PRGl PRGl PRGl 0.39

[7,] G6PD G6PD G6PD 0.34

[8,] BASPl BASPl 0.31

[9,] PLOD2 PLOD2 0.34

[10,] LOXL2 LOXL2 0.31

[11,] SSBP2 SSBP2 0.36

[12,] Clorf29 Clorf29 0.35

[13,] TOX TOX 0.35

[14,] STCl STCl 0.39

[15,] TNFRSFlA TNFRSFlA TNFRSFlA 0.34

[16,] NCOR2 NCOR2 NCOR2 0.3

[17,] NAPlLl NAPlLl NAPlLl 0.32

[18,] LOC94105 LOC94105 0.34

[19,] COL6A2 COL6A2 0.3

[20,] ARHGEF6 ARHGEF6 ARHGEF6 0.34

[21,] GATA3 GATA3 0.35

[22,] TFPI TFPI 0.31

[23,] LAT LAT 0.31

[24,] CD3Z CD3Z 0.37

[25,] AFlQ AFlQ 0.33

[26,] MAPlB MAPlB MAPlB 0.34

[27,] PTPRC PTPRC 0.31

[28,] PRKCA PRKCA 0.35

[29,] TRIM22 TRIM22 0.3

[30,] CD3D CD3D 0.31

[31,] BCATl BCATl 0.32

[32,] IFI44 IFI44 0.33

[33,] CCL2 CCL2 0.37

[34,] RAB31 RAB 31 0.31

[35,] CUTC CUTC 0.33

[36,] NAP1L2 NAP1L2 0.33

[3,] NUDC NUDC 0.3

[4,] RALY RALY 0.31

[5,] DKCl DKCl 0.3

[6,] DKFZP564C186 DKFZP564C186 0.32

[7,] PRP19 PRP19 0.31

[8,] RAB9P40 RAB9P40 0.33

[9,] HSA9761 HSA9761 0.37

[10,] GMDS GMDS 0.3

[H,] CEPl CEPl 0.3

[12,] IL13RA2 IL13RA2 0.34

[13,] MAGEB2 MAGEB2 0.41

[14, ] HMGN2 HMGN2 0.35

[15,] ALMSl ALMSl 0.3

[16,] GPR65 GPR65 0.31

[17,] FLJ10774 FLJ10774 0.31

[18,] NOL8 NOL8 0.31

[19,] DAZAPl DAZAPl 0.32

[20,] SLC25A15 SLC25A15 0.31

[21,] PAF53 PAF53 0.36

[22,] DXS9879E DXS9879E 0.31

[23,] PITPNCl PITPNCl 0.33

[24,] SPANXC SPANXC 0.3

[25,] KIAA1393 KIAA1393 0.33

Table 10. Gemcitabine (Gemzar) biomarkers

List_2006 HU6800 List_Prior List_Preferr Correlation

[1,] PFNl PFNl 0.37 [2,] PGAMl PGAMl 0.35 [3,] K-ALPHA-I K-ALPHA-I 0.34 [4,] CSDA CSDA 0.31 [5,] UCHLl UCHLl 0.36 [6,] PWPl PWPl 0.37 [7,] PALM2-AKAP2 PALM2-AKAP2 0.31 [8,] TNFRSFlA TNFRSFlA 0.31

[12,] FHODl FHODl 0.32

Table 11. Taxotere (docetaxel) biomarkers

List 2006 List 2005 List Prior List Preferr Correlation

[1,] ANP32B ANP32B 0, 45

[2,] GTF3A GTF3A 0, 31

[3,] RRM2 RRM2 0.31

[4,] TRIM14 TRIM14 0.31

[5,] SKP2 SKP2 0.33

[6,] TRIP13 TRIP13 0.36

[7,] RFC3 RFC3 0.45

[8,] CASP7 CASP7 0.32

[9,] TXN TXN 0.36

[10,] MCM5 MCM5 0.34

[H,] PTGES2 PTGES2 0.39

[12,] OBFCl OBFCl 0.37

[13,] EPB41L4B EPB41L4B 0.32

[14,] CALML4 CALML4 0.31

Table 12. Dexamethasone biomarkers

List_2006 HU6800 List_Prior List_Preferr Correlation

[1,] IFITM2 IFITM2 0.38 [2,] UBE2L6 UBE2L6 0.32 [3,] LAPTM5 LAPTM5 LAPTM5 0.36 [4,] USP4 USP4 0.33 [5,] ITM2A ITM2A 0.38 [6,] ITGB2 ITGB2 0.42 [7,] ANPEP ANPEP 0.31 [8,] CD53 CD53 0.34 [9,] IL2RG IL2RG IL2RG 0.36 [10,] CD37 CD37 0.34

[ H / ] GPRASPl GPRASPl 0.36 [ 12 , ] PTPN7 PTPN7 0.31 [ 13 , ] CXorf9 CXorf9 0.36 [ 14 , ] RHOH RHOH RHOH 0.33 [ 15 , ] GIT2 GIT2 0.31 [ 16 , ] ADORA2A ADORA2A 0.31 [ 17 , ] ZNFNlAl ZNFNlAl 0.35

[18,] GNAl5 GNAl5 GNAl5 0.33 [19,] CEPl CEPl 0.31 [20,] TNFRSF7 TNFRSF7 0.46 [21,] MAP4K1 MAP4K1 0.3 [22,] CCR7 CCR7 0.33 [23,] CD3G CD3G 0.35 [24,] PTPRC PTPRC 0.41 [25,] ATP2A3 ATP2A3 ATP2A3 0.4 [26,] UCP2 UCP2 0.3 [27,] COROlA COROlA COROlA 0.39 [28,] GATA3 GATA3 GATA3 0.37 [29,] CDKN2A CDKN2A 0.32 [30,] HEMl HEMl 0.3 [31,] TARP TARP 0.3 [32,] LAIRl LAIRl 0.34 [33,] SH2D1A SH2D1A 0.34 [34,] FLII FLII FLII 0.33 [35,] SEPT6 SEPT6 0.34 [36, ] HA-I HA-I 0.34 [37,] CREB3L1 CREB3L1 0.31 [38,] ERCC2 ERCC2 0.65 [39,] CD3D CD3D CD3D 0.32 [40,] LSTl LSTl 0.39 [41,] AIFl AIFl 0.35 [42,] ADA ADA 0.33 [43,] DATFl DATFl 0.41 [44,] ARHGAPl5 ARHGAP15 0.3 [45,] PLAC8 PLAC8 0.31 [46,] CECRl CECRl 0.31 [47,] LOC81558 LOC81558 0.33 [48,] EHD2 EHD2 0.37

Table 13. Ara-C (Cytarabine hydrochloride) biomarkers

List_2006 HU6800 List Prior List Preferr Correlation

[1,] ITM2A ITM2A 0.32

[2,] RHOH RHOH 0.31

[3,] PRIMl PRIMl 0.3

[4,] CENTBl CENTBl 0.31

[5,] GNAl5 GNAl5 0.32

[6,] NAPlLl NAPlLl NAPlLl 0.31

[7,] ATP5G2 ATP5G2 0.31

[8,] GATA3 GATA3 0.33

[9,] PRKCQ PRKCQ 0.32

[10,] SH2D1A SH2D1A 0.3

[11,] SEPT6 SEPT6 0.42

[12,] PTPRC PTPRC 0.35

[13,] NME4 NME4 0.33

[14,] RPL13 RPL13 0.3

[15,] CD3D CD3D 0.31

[16,] CDlE CDlE 0.32

[17,] ADA ADA ADA 0.34

[18,] FHODl FHODl 0.31

Table 14. Methylprednisolone biomarkers

List_2006 HU6800 List_Prior List_Preferr Correlation [1,] CD99 CD99 CD99 0.31

[2,] SRRMl SRRMl 0.31

[3,] ARHGDIB ARHGDIB ARHGDIB 0.31

[4,] LAPTM5 LAPTM5 LAPTM5 0.37

[5,] VWF VWF 0.45

[6,] ITM2A ITM2A 0.35

[7,] ITGB2 ITGB2 ITGB2 0.43

[8,] LGALS9 LGALS9 LGALS9 0.43

[9,] INPP5D INPP5D 0.34

[10,] SATBl SATBl SATBl 0.32

[H,] CD53 CD53 CD53 0.33

[12,] TFDP2 TFDP2 TFDP2 0.4

[13,] SLA SLA SLA 0.31

[14,] IL2RG IL2RG IL2RG 0.3

[15,] MFNG MFNG 0.3

[16,] CD37 CD37 0.37

[17,] GMFG GMFG 0.4

[18,] SELL SELL 0.33

[19,] CDW52 CDW52 CDW52 0.33

[20,] LRMP LRMP 0.32

[21,] ICAM2 ICAM2 0.38

[22,] RIMS3 RIMS3 0.36

[23,] PTPN7 PTPN7 PTPN7 0.39

[24,] ARHGAP25 ARHGAP25 0.37

[25,] LCK LCK LCK 0.3

[26,] CXorf9 CXorf9 0.3

[27,] RHOH RHOH RHOH 0.51

[28,] PTPRCAP PTPRCAP PTPRCAP 0.5

[29,] GIT2 GIT2 0.33

[30,] ZNFNlAl ZNFNlAl ZNFNlAl 0.53

[31, ] CENTBl CENTBl CENTBl 0.36

[32,] LCP2 LCP2 0.34

[33,] SPIl SPIl 0.3

[34,] GNAl 5 GNAl 5 GNAl5 0.39

[35,] GZMA GZMA 0.31

[36,] CEPl CEPl 0.37

[37,] BLM BLM 0.33

[38,] CD8A CD8A 0.38

[39,] SCAPl SCAPl 0.32

[40,] CD2 CD2 0.48

[41,] CDlC CDlC CDlC 0.37

[42,] TNFRSF7 TNFRSF7 0.31

[43,] VAVl VAVl 0.41

[44,] MAP4K1 MAP4K1 MAP4K1 0.36

[45,] CCR7 CCR7 0.37

[46,] C6orf32 C6orf32 0.38

[47,] ALOX15B AL0X15B 0.43

[48,] BRDT BRDT 0.33

[49,] CD3G CD3G CD3G 0.51

[50,] PTPRC PTPRC 0.37

[51,] LTB LTB 0.32

[52,] ATP2A3 ATP2A3 ATP2A3 0.3

[53,] NVL NVL 0.31

[54, ] RASGRP2 RASGRP2 0.35

[55,] LCPl LCPl LCPl 0.34

[56,] COROlA COROlA COROlA 0.41

[57,] CXCR4 CXCR4 CXCR4 0.3

[58,] PRKD2 PRKD2 0.33

[59,] GATA3 GATA3 GATA3 0.39

[60,] TRA@ TRAg 0.4

[61,] PRKCBl PRKCBl PRKCBl 0.35

[62,] HEMl HEMl 0.32

[63,] KIAA0922 KIAA0922 0.36

[64,] TARP TARP 0.49

[65,] SEC31L2 SEC31L2 0.32

[66,] PRKCQ PRKCQ 0.37

[67,] SH2D1A SH2D1A 0.33

[68,] CHRNA3 CHRNA3 0.5

[69,] CDlA CDlA 0.44

[70,] LSTl LSTl 0.36

[71,] LAIRl LAIRl 0.47

[72,] CACNAlG CACNAlG 0.33

[73,] TRB@ TRB@ TRB@ 0.31

[74,] SEPT6 SEPT6 SEPT6 0.33

[75,] HA-I HA-I 0.42

[76,] DOCK2 DOCK2 0.32

[77,] CD3D CD3D CD3D 0.41

[78,] TRD@ TRD@ 0.38

[79,] T3JAM T3JAM 0.37

[80,] FNBPl FNBPl 0.37

[81,] CD6 CD6 0.4

[82,] AIFl AIFl AIFl 0.31

[83,] FOLHl FOLHl 0.45

[84,] CDlE CDlE CDlE 0.58

[85,] LY9 LY9 0.39

[86,] UGT2B17 UGT2B17 0.47

[87,] ADA ADA ADA 0.39

[88,] CDKL5 CDKL5 0.44

[89,] TRIM TRIM 0.38

[90,] EVL EVL 0.39

[91,] DATFl DATFl 0.31

[92,] RGC32 RGC32 0.51

[93,] PRKCH PRKCH 0.3

[94,] ARHGAPl 5 ARHGAPl5 0.34

[95,] NOTCHl NOTCHl 0.36

[96,] BIN2 BIN2 0.31

[97,] SEMA4G SEMA4G 0.35

[98,] DPEP2 DPEP2 0.33

[99,] CECRl CECRl 0.36

[100,] BCLIlB BCLIlB 0.33

[101,] STAG3 STAG3 0.41

[102,] GALNT6 GALNT6 0.32

[103,] UBASH3A UBASH3A 0.3

[104,] PHEMX PHEMX 0.38

[105,] FLJ13373 FLJ13373 0.34

[106,] LEFl LEFl 0.49

[107,] IL21R IL21R 0.42

[108,] MGC17330 MGC17330 0.33

[109,] AKAP13 AKAP13 0.53

[110,] ZNF335 ZNF335 0.3

[in,] GIMAP5 GIMAP5 0.34

Table 15. Methotrexate biomarkers

List 2006 HU6800 List_Prior List Preferr Correlation

[1,] PRPF8 PRPF8 0.34

[2,] RPL18 RPL18 0.34

[3,] RNPSl RNPSl 0.36

[4,] RPL32 RPL32 0.39

[5,] EEFlG EEFlG 0.34

[6,] GOT2 GOT2 0.31

[7,] RPL13A RPL13A 0.31

[ 8 , ] PTMA PTMA PTMA 0.41

[9,] RPS15 RPS15 0.39

[10,] RPLP2 RPLP2 RPLP2 0.32

[11,] CSDA CSDA 0.39

[12,] KHDRBSl KHDRBSl 0.32

[13,] SNRPA SNRPA 0.31

[14,] IMPDH2 IMPDH2 IMPDH2 0.39

[15,] RPS19 RPS19 0.47

[16,] NUP88 NUP88 0.36

[17,] ATP5D ATP5D 0.33

[18,] PCBP2 PCBP2 0.32

[19,] ZNF593 ZNF593 0.4

[20,] HSU79274 HSU79274 0.32

[21,] PRIMl PRIMl 0.3

[22,] PFDN5 PFDN5 0.33

[23,] OXAlL OXAlL 0.37

[24,] H3F3A H3F3A 0.42

[25,] ATIC ATIC 0.31

[26,] RPL13 RPL13 0.36

[27,] CIAPINl CIAPINl 0.34

[28,] FBL FBL 0.33

[29,] RPS2 RPS2 RPS2 0.32

[30,] PCCB PCCB 0.36

[31,] RBMX RBMX 0.33

[32,] SHMT2 SHMT2 0.34

[33,] RPLPO RPLPO 0.35

[34,] HNRPAl HNRPAl HNRPAl 0.35

[35,] STOML2 STOML2 0.32

[36,] RPS9 RPS9 0.36

[37,] SKBl SKBl 0.33

[38,] GLTSCR2 GLTSCR2 0.37

[39,] CCNBlIPl CCNBlIPl 0.3

[40,] MRPS2 MRPS2 0.33

[41,] FLJ20859 FLJ20859 0.34

[42,] FLJ12270 FLJ12270 0.3

Table 16. Bleomycin biomarkers

List 2006 HU6800 List Prior List Preferr Correlation

[1,] MSN MSN 0. .3

[2,] PFNl PFNl 0. .45

[3,] HKl HKl 0. .33

[4,] ACTR2 ACTR2 0. .31

[5,] MCLl MCLl 0. .31

[6,] ZYX ZYX 0. .32

[7,] RAPlB RAPlB 0. .34

[8,] GNB2 GNB2 0. .32

[9,] EPASl EPASl 0. ,31

[10,] PGAMl PGAMl 0. .42

[H,] CKAP4 CKAP4 0.31

[12,] DUSPl DUSPl 0.4

[13,] MYL9 MYL9 0.4

[14,] K-ALPHA-I K-ALPHA-I 0.37

[15,] LGALSl LGALSl 0.38

[16,] CSDA CSDA CSDA 0.3

[17,] AKRlBl AKRlBl 0.32

[18,] IFITM2 IFITM2 IFITM2 0.36

[19,] ITGA5 ITGA5 0.43

[20,] VIM VIM 0.39

[21,] DPYSL3 DPYSL3 0.44

[22,] JUNB JUNB 0.32

[23,] ITGA3 ITGA3 0.38

[24,] NFKBIA NFKBIA 0.32

[25,] LAMBl LAMBl 0.37

[26,] FHLl FHLl 0.31

[27,] INSIGl INSIGl 0.31

[28,] TIMPl TIMPl 0.48

[29,] GJAl GJAl 0.54

[30,] PSME2 PSME2 0.34

[31,] PRGl PRGl 0.46

[32,] EXTl EXTl 0.35

[33,] DKFZP434J154 DKFZP434J154 0.31

[34,] OPTN OPTN 0.31

[35,] M6PRBP1 M6PRBP1 0.52

[36,] MVP MVP 0.34

[37,] VASP VASP 0.31

[38,] ARL7 ARL7 0.39

[39,] NNMT NNMT NNMT 0.34

[40,] TAPl TAPl 0.3

[41,] COLlAl COLlAl COLlAl 0.33

[42,] BASPl BASPl 0.35

[43,] PLOD2 PLOD2 0.37

[44,] ATF3 ATF3 0.42

[45,] PALM2-AKAP2 PALM2-AKAP2 0.33

[46,] IL8 IL8 0.34

[47,] ANPEP ANPEP 0.35

[48,] LOXL2 LOXL2 0.32

[49,] TGFBl TGFBl 0.31

[50,] IL4R IL4R 0.31

[51,] DGKA DGKA 0.32

[52,] STC2 STC2 0.31

[53,] SEC61G SEC61G 0.41

[54,] NFIL3 NFIL3 NFIL3 0.47

[55,] RGS3 RGS3 0.37

[56,] NK4 NK4 0.34

[57,] F2R F2R 0.34

[58,] TPM2 TPM2 0.35

[59,] PSMB9 PSMB9 PSMB9 0.34

[60,] LOX LOX 0.37

[61,] STCl STCl 0.35

[62,] CSPG2 CSPG2 CSPG2 0.35

[63,] PTGER4 PTGER4 0.31

[64,] IL6 IL6 0.34

[65,] SMAD3 SMAD3 0.38

[66,] PLAU PLAU PLAU 0.35

[67,] WNT5A WNT5A 0.44

[68,] BDNF BDNF 0.34

[69,] TNFRSFlA TNFRSFlA TNFRSFlA 0.46

[70,] FLNC FLNC 0.34

[71,] DKFZP564K0822 DKFZP564K0822 0.34

[72,] FLOTl FLOTl 0.38

[73,] PTRF PTRF 0.39

[74,] HLA-B HLA-B 0.36

[75,] COL6A2 COL6A2 COL6A2 0.32

[76,] MGC4083 MGC4083 0.32

[77,] TNFRSFlOB TNFRSFlOB 0.34

[78,] PLAGLl PLAGLl 0.31

[79,] PNMA2 PNMA2 0.38

[80,] TFPI TFPI 0.38

[81,] LAT LAT 0.46

[82,] GZMB GZMB 0.51

[83,] CYR61 CYR61 0.37

[84,] PLAUR PLAUR PLAUR 0.35

[85,] FSCNl FSCNl FSCNl 0.32

[86, ] ERP70 ERP70 0.32

[87,] AFlQ AFlQ 0.3

[88,] UBC UBC 0.37

[89,] FGFRl FGFRl 0.33

[90,] HIC HIC 0.33

[91,] BAX BAX 0.35

[92,] COL4A2 COL4A2 COL4A2 0.32

[93,] COL6A1 COL6A1 0.32

[94,] IFITM3 IFITM3 0.3

[95,] MAPlB MAPlB 0.38

[96,] FLJ46603 FLJ46603 0.37

[97,] RAFTLIN RAFTLIN 0.34

[98,] RRAS RRAS 0.31

[99,] FTL FTL 0.3

[100, ] KIAA0877 KIAA0877 0.31

[101, ] MTlE MTlE MTlE 0.31

[102, ] CDClO CDClO 0.51

[103, ] DOCK2 DOCK2 0.32

[104, ] TRIM22 TRIM22 0.36

[105, ] RISl RISl 0.37

[106, ] BCATl BCATl 0.42

[107, ] PRFl PRFl 0.34

[108, ] DBNl DBNl 0.36

[109, ] MTlK MTlK 0.3

[110, ] TMSBlO TMSBlO 0.42

[in, ] RAB31 RAB31 0.45

[112, ] FLJ10350 FLJ10350 0.4

[113, ] Clorf24 Clorf24 0.34

[114, ] NME7 NME7 0.46

[115, ] TMEM22 TMEM22 0.3

[116, ] TPKl TPKl 0.37

[117, ] COL5A2 COL5A2 0.34

[118, ] ELK3 ELK3 0.38

[119, ] CYLD CYLD 0.4

[120, ] ADAMTSl ADAMTSl 0.31

[121, ] EHD2 EHD2 0.41

[122, ] ACTB ACTB ACTB 0.33

Table 17. Methyl-GAG (Methyl glyoxal bis (amidinohydrazonel dihydrochloride)

M List 2006 HU6800 List Prior ] ist Preferr Correlation

[1,] PTMA PTMA 0.32

[2,] SSRPl SSRPl 0.37

[3,] NUDC NUDC 0.35

[4,] CTSC CTSC 0.35

[5,] AP1G2 AP1G2 0.33

[6,] PSME2 PSME2 0.3

[7,] LBR LBR 0.38

[8,] EFNB2 EFNB2 0.31

[9,] SERPINAl SERPINAl 0.34

[10,] SSSCAl SSSCAl 0.32

[H, ] EZH2 EZH2 0.36

[12,] MYB MYB MYB 0.33

[13,] PRIMl PRIMl 0.39

[14,] H2AFX H2AFX 0.33

[15,] HMGAl HMGAl 0.35

[16,] HMMR HMMR 0.33

[17,] TK2 TK2 0.42

[18,] WHSCl WHSCl 0.35

[19,] DIAPHl DIAPHl 0.34

[20,] LAMB3 LAMB3 0.31

[21,] DPAGTl DPAGTl 0.42

[22,] UCK2 UCK2 0.31

[23,] SERPINBl SERPINBl 0.31

[24,] MDNl MDNl 0.35

[25,] BRRNl BRRNl 0.33

[26,] G0S2 G0S2 0.43

[27,] RAC2 RAC2 0.35

[28,] MGC21654 MGC21654 0.36

[29,] GTSEl GTSEl 0.35

[30,] TACC3 TACC3 0.31

[31,] PLEK2 PLEK2 0.32

[32,] PLAC8 PLAC8 0.31

[33,] HNRPD HNRPD 0.35

[34,] PNAS-4 PNAS-4 0.3 rable 18. Carboplatin biomarker

List 2006 HU6800 L Liisstt Prior List Preferr Correlation

[1,] MSN MSN 0.31

ITGA5 ITGA5 0.43

[3,] VIM VIM 0.34

[4,] TNFAIP3 TNFAIP3 0.4

[5, ] CSPG2 CSPG2 0.35

[6,] WNT5A WNT5A 0.34

[7,] FOXF2 FOXF2 0.36

[8,] LOC94105 LOC94105 0.32

[9,] IFI16 IFI16 0.38

[10,] LRRN3 LRRN3 0.33

[11,] FGFRl FGFRl 0.37

[12,] DOCKlO DOCKlO 0.4

[13, ] LEPREl LEPREl 0.32

[14,] COL5A2 COL5A2 0.3

[15,] ADAMTSl ADAMTSl 0.34

Table 19. 5-FU (5-Fluorouracil) biomarkers

List 2006 HU6800 List Prior List Preferr Correlation

[1,] RPL18 RPL18 0.39

[2,] RPLlOA RPLlOA 0.36

[3,] RNPSl RNPSl 0.3

[4,] ANAPC5 ANAPC5 0.5

[5,] EEF1B2 EEF1B2 0.4

[6,] RPL13A RPL13A 0.38

[7,] RPS15 RPS15 0.34

[8,] AKAPl AKAPl 0.37

[9,] NDUFABl NDUFABl 0.3

[10,] APRT APRT 0.32

[11,] ZNF593 ZNF593 0.37

[12,] MRP63 MRP63 0.31

[13,] IL6R IL6R 0.31

[14,] RPL13 RPL13 0.31

[15,] SART3 SART3 0.35

[16,] RPS6 RPS6 0.49

[17, ] UCK2 UCK2 0.38

[18,] RPL3 RPL3 0.32

[19,] RPL17 RPL17 0.34

[20,] RPS2 RPS2 0.32

[21,] PCCB PCCB 0.31

[22, ] TOMM20 TOMM20 0.39

[23,] SHMT2 SHMT2 0.36

[24,] RPLPO RPLPO 0.3

[25,] GTF3A GTF3A 0.5

[26,] STOML2 STOML2 0.4

[27,] DKFZp564J157 DKFZp564J157 0.38

[28,] MRPS2 MRPS2 0.34

[29,] ALG5 ALG5 0.37

[30,] CALML4 CALML4 0.3 rable 20. Rituximab (e.g., Mabthera) biomarkers

List 2006 List Prior List Preferr Correlation

[1, ] ITK ITK 0.36

[2, ] KIFCl KIFCl 0.36

[3, ] VLDLR VLDLR 0.39

[4, ] RUNXl RUNXl 0.32

[5, ] PAFAH1B3 PAFAH1B3 0.32

[6, ] HlFX HlFX 0.43

[7, ] RNFl44 RNF144 0.38

[8, ] TMSNB TMSNB 0.47

[9, ] CRYl CRYl 0.37

[10, ] MAZ MAZ 0.33

[12, ] SRF SRF 0.37

[13, ] UMPS UMPS 0.41

[14, ] CD3Z CD3Z 0.33

[15, ] PRKCQ PRKCQ 0.31 ^'

[16, ] HNRPM HNRPM 0.45

[17, ] ZAP70 ZAP70 0.38

[18, ] ADDl ADDl 0.31

[19,] RFC5 RFC5 0.35 [20,] TM4SF2 TM4SF2 0.33 [21,] PFN2 PFN2 0.3 [22,] BMIl BMIl 0.31 [23,] TUBGCP3 TUBGCP3 0.33 [24,] δTP6V1B2 ATP6V1B2 0.42 [25,] RALY RALY 0.31 [26,] PSMC5 PSMC5 0.36 [27,] CDlD CDlD 0.32 [28,] ADA ADA 0.34 [29,] CD99 CD99 0.33 [30,] CD2 CD2 0.43 [31,] CNP CNP 0.48 [32,] ERG ERG 0.47 [33,] MYL6 MYL6 0.41 [34,] CD3E CD3E 0.36 [35,] CDlA CDlA 0.46 [36,] CDlB CDlB 0.47 [37,] STMNl STMNl 0.32 [38,] PSMC3 PSMC3 0.38 [39,] RPS4Y1 RPS4Y1 0.36 [40,] AKTl AKTl 0.38 [41,] TALI TALI 0.37 [42,] GNAl5 GNAl5 0.37 [43,] UBE2A UBE2A 0.35 [44,] TCF12 TCF12 0.35 [45,] UBE2S UBE2S 0.52 [46,] CCND3 CCND3 0.38 [47,] PAX6 PAX6 0.35 [48,] MDK MDK 0.3 [49,] CAPG CAPG 0.36 [50,] RAG2 RAG2 0.39 [51,] ACTNl ACTNl 0.37 [52,] GSTM2 GSTM2 0.47 [53,] SATBl SATBl 0.36 [54,] NASP NASP 0.3 [55,] IGFBP2 IGFBP2 0.46 [56,] CDH2 CDH2 0.49 [57, ] CRABPl CRABPl 0.36 [58,] DBNl DBNl 0.49 [59,] CTNNAl CTNNAl 0.53 [60,] AKRlCl AKRlCl 0.32 [61,] CACNB3 CACNB3 0.37 [62,] FARSLA FARSLA 0.35 [63,] CASP2 CASP2 0.42 [64,] CASP2 CASP2 0.31 [65,] E2F4 E2F4 0.36 [66,] LCP2 LCP2 0.35 [67,] CASP6 CASP6 0.32 [68,] MYB MYB 0.3 [69,] SFRS6 SFRS6 0.44 [70,] GLRB GLRB 0.34 [71,] NDN NDN 0.39 [72,] CPSFl CPSFl 0.33 [73,] GNAQ GNAQ 0.44 [74,] TUSC3 TUSC3 0.41

[75,] GNAQ GNAQ 0.54

[76,] JARI D2 JARI D2 0.44

[77,] OCRL OCRL 0.5

[78,] FHLl FHLl 0.36

[79,] EZH2 EZH2 0.4

[80,] SMOX SMOX 0.35

[81,] SLC4A2 SLC4A2 0.35

[82,] UFDlL UFDlL 0.3

[83,] SEPWl SEPWl 0.31

[84,] ZNF32 ZNF32 0.35

[85,] HTATSFl HTATSFl 0.35

[86,] SHDl SHDl 0.43

[87,] PTOVl PTOVl 0.42

[88,] NXFl NXFl 0.46

[89,] FYB FYB 0.47

[90,] TRIM28 TRIM28 0.38

[91,] BC008967 BC008967 0.4

[92,] TRB@ TRB@ 0.3

[93,] TFRC TFRC 0.31

[94,] HlFO HlFO 0.36

[95,] CD3D CD3D 0.32

[96,] CD3G CD3G 0.4

[97,] CENPB CENPB 0.36

[98,] ALDH2 ALDH2 0.33

[99,] ANXAl ANXAl 0.35

[100,] H2AFX H2AFX 0.51

[101,] CDlE CDlE 0.33

[102,] DDX5 DDX5 0.39

[103,] ABLl ABLl 0.3

[104,] CCNA2 CCNA2 0.3

[105,] ENO2 ENO2 0.35

[106,] SNRPB SNRPB 0.38

[107,] GATA3 GATA3 0.36

[108,] RRM2 RRM2 0.48

[109,] GLUL GLUL 0.4

[HO,] TCF7 TCF7 0.39

[in,] FGFRl FGFRl 0.33

[112,] SOX4 SOX4 0.3

[113,] MAL MAL 0.3

[114, ] NUCB2 NUCB2 0.38

[115,] SMA3 SMA3 0.31

[116,] FAT FAT 0.52

[117,] UNG UNG 0.31

[118,] ARHGDIB ARHGDIB 0.36

[119,] RUNXl RUNXl 0.38

[120,] MPHOSPH6 MPHOSPH6 0.5

[121,] DCTNl DCTNl 0.34

[122,] SH3GL3 SH3GL3 0.38

[123,] VIM VIM 0.41

[124,] PLEKHCl PLEKHCl 0.3

[125,] CD47 CD47 0.32

[126,] POLR2F POLR2F 0.37

[127,] RHOH RHOH 0.43

[128,] ADDl ADDl 0.46

[129, ] ATP2A3 ATP2A3 0.38

Table 21. Radiation sensitivity biomarkers

List 2006 HU6800 List Prior List Preferr

Correlation

[1,] TRAl TRAl 0.36

[2,] ACTN4 ACTN4 0.36

[3,] WARS WARS 0.39

[4,] CALMl CALMl 0.32

[5,] CD63 CD63 CD63 0.32

[6,] CD81 CD81 0.43

[7,] FKBPlA FKBPlA 0.38

[8,] CALU CALU 0.47

[9,] IQGAPl IQGAPl 0.37

[10,] CTSB CTSB 0.33

[11,] MGC8721 MGC8721 0.35

[12,] STATl STATl 0.37

[13,] TACCl TACCl 0.41

[14,] TM4SF8 TM4SF8 0.33

[15,] CD59 CD59 0.31

[16,] CKAP4 CKAP4 CKAP4 0.45

[17,] DUSPl DUSPl DUSPl 0.38

[18,] RCNl RCNl 0.31

[19,] MGC8902 MGC8902 0.35

[20,] LGALSl LGALSl LGALSl 0.33

[21,] BHLHB2 BHLHB2 0.3

[22,] RRBPl RRBPl 0.31

[23,] PKM2 PKM2 0.33

[24,] PRNP PRNP 0.42

[25,] PPP2CB PPP2CB 0.31

[26,] CNN3 CNN3 0.36

[27,] ANXA2 ANXA2 ANXA2 0.32

[28,] IER3 IER3 0.34

[29,] JAKl JAKl 0.33

[30,] MARCKS MARCKS 0.43

[31,] LUM LUM 0.48

[32,] FER1L3 FER1L3 0.47

[33,] SLC20A1 SLC20A1 0.41

[34,] EIF4G3 EIF4G3 0.36

[35,] HEXB HEXB 0.46

[36,] EXTl EXTl 0.47

[37,] TJPl TJPl 0.32

[38,] CTSL CTSL CTSL 0.38

[39,] SLC39A6 SLC39A6 0.36

[40,] RIOK3 RIOK3 0.38

[41,] CRK CRK 0.37

[42,] NNMT NNMT 0.37

[43,] COLlAl COLlAl 0.35

[44,] TRAM2 TRAM2 TRAM2 0.35

[45,] ADAM9 ADAM9 0.52

[46,] DNAJC7 DNAJC7 0.38

[47,] PLSCRl PLSCRl 0.35

[48,] PRSS23 PRSS23 0.3

[49,] PLOD2 PLOD2 0.36

[50,] NPCl NPCl 0.39

[51,] TOBl TOBl 0.37

[52,] GFPTl GFPTl 0.47

[53,] IL8 IL8 0.36

[54,] DYRK2 DYRK2 0.3

[55,] PYGL PYGL 0.46

[56,] LOXL2 LOXL2 0.49

[57,] KIAA0355 KIAA0355 0.36

[58,] UGDH UGDH 0.49

[59,] NFIL3 NFIL3 0.53

[60,] PURA PURA 0.32

[61,] ULK2 ULK2 0.37

[62,] CENTG2 CENTG2 0.35

[63,] NID2 NID2 0.42

[64, ] CAP350 CAP350 0.31

[65,] CXCLl CXCLl 0.36

[66,] BTN3A3 BTN3A3 0.35

[67,] IL6 IL6 0.32

[68, ] WNT5A WNT5A 0.3

[69,] FOXF2 FOXF2 0.44

[70,] LPHN2 LPHN2 0.34

[71,] CDHIl CDHIl 0.39

[72,] P4HAl P4HA1 0.33

[73,] GRP58 GRP58 0.44

[74,] ACTNl ACTNl ACTNl 0.41

[75,] CAPN2 CAPN2 0.54

[76,] DSIPI DSIPI 0.44

[77, ] MAP1LC3B MAP1LC3B 0.5

[78,] GALIG GALIG GALIG 0.36

[79,] IGSF4 IGSF4 0.4

[80,] IRS2 IRS2 0.35

[81,] ATP2A2 ATP2A2 0.35

[82,] OGT OGT 0.3

[83,] TNFRSFlOB TNFRSFlOB 0.31

[84,] KIAA1128 KIAA1128 0.35

[85,] TM4SF1 TM4SF1 0.35

[86,] RBPMS RBPMS 0.43

[87,] RIPK2 RIPK2 0.42

[88,] CBLB CBLB 0.46

[89,] NR1D2 NR1D2 0.47

[90,] BTN3A2 BTN3A2 0.38

[91,] SLC7A11 SLC7A11 0.4

[92,] MPZLl MPZLl 0.3

[93,] IGFBP3 IGFBP3 IGFBP3 0.31

[94,] SSA2 SSA2 0.36

[95,] FNl FNl FNl 0.32

[96,] NQOl NQOl 0.4

[97,] ASPH ASPH 0.36

[98,] ASAHl ASAHl 0.33

[99,] MGLL MGLL 0.35

[100, ] SERPINB6 SERPINB6 0.51

[101, ] HSPA5 HSPA5 0.33

[102, ] ZFP36L1 ZFP36L1 0.39

[103, ] COL4A2 COL4A2 0.3

[104, ] COL4A1 COL4A1 0.3

[105, ] CD44 CD44 0.35

[106, ] SLC39A14 SLC39A14 0.38

[107, ] NIPA2 NIPA2 0.36

[108, ] FKBP9 FKBP9 0.48

[109, ] IL6ST IL6ST 0.4

[110,] DKFZP564G2022 DKFZP564G2022 0.39

[in,] PPAP2B PPAP2B 0.33

[112,] MAPlB MAPlB 0.3

[113,] MAPKl MAPKl 0.3

[114,] MYOlB MYOlB 0.38

[115,] CAST CAST CAST 0.31

[116,] RRAS2 RRAS2 0.52

[117,] QKI QKI 0.31

[118,] LHFPL2 LHFPL2 0.36

[119,] SEPTlO SEPTlO 0.38

[120,] ARHE ARHE 0.5

[121,] KIAA1078 KIAA1078 0.34

[122,] FTL FTL 0.38

[123,] KIAA0877 KIAA0877 0.41

[124,] PLCBl PLCBl 0.3

[125,] KIAA0802 KIAA0802 0.32

[126,] KPNBl KPNBl 0.37

[127,] RAB3GAP RAB3GAP 0.43

[128,] SERPINBl SERPINBl 0.46

[129,] TIMM17A TIMM17A 0.38

[130,] SOD2 SOD2 0.35

[131,] HLA-A HLA-A HLA-A 0.33

[132,] NOMO2 NOMO2 0.43

[133,] LOC55831 LOC55831 0.32

[134,] PHLDAl PHLDAl 0.32

[135,] TMEM2 TMEM2 0.47

[136, ] MLPH MLPH 0.35

[137,] FADl04 FADl04 0.34

[138,] LRRC5 LRRC5 0.42

[139,] RAB7L1 RAB7L1 0.41

[140,] FLJ35036 FLJ35036 0.36

[141,] DOCKlO DOCKlO 0.41

[142,] LRP12 LRP12 0.36

[143,] TXNDC5 TXNDC5 0.4

[144,] CDC14B CDC14B 0.39

[145,] HRMTlLl HRMTlLl 0.38

[146,] COROlC COROlC 0.38

[147,] DNAJClO DNAJClO 0.31

[148,] TNPOl TNPOl 0.33

[149,] LONP LONP 0.32

[150,] AMIGO2 AMIGO2 0.38

[151,] DNAPTP6 DNAPTP6 0.31

[152,] ADAMTSl ADAMTSl 0.37

[153,] CCL21

[154,] SCARB2

[155,] MAD2L1BP

[156,] PTS

[157,] NBLl

[158,] CD151

[159,] CRIP2

[160,] UGCG

[161,] PRSSIl

[162,] MME

[163,] CBRl

[164,] DUSP3

[165,] PFN2

[166,] MICA

[167,] FTHl

[168, ] RHOC

[169,] ZAP128

[170,] PON2

[171,] COL5A2

[172,] CST3

[173,] MCAM

[174,] MMP2

[175,] CTSD

[176, ALDH3A1

[177, CSRPl

[178, S100A4

[179, CALDl

[180, CTGF

[181, CAPG

[182, TAGLN

[183, FSTLl

[184, SCTR

[185, BLVRA

[186, COPEB

[187, DIPA

[188, SMARCD3

[189, MVP

[190, PEAl 5

[191, ] S100A13 [192,] ECEl

Table 22. Vincristine biomarkers.

SEQIDNO Gene Correlation Medianprobe

1 SLC25A5 0 .32 TCCTGTACTTGTCCTCAGCTTGGGC

2 RPLlO 0 .38 GCCCCACTGGACAACACTGATTCCT

3 RPL12 0 .31 TGCCTGCTCCTGTACTTGTCCTCAG

4 RPS4X 0 .39 AAATGTTTCCTTGTGCCTGCTCCTG

5 EIF5A 0 .31 TCCTGTACTTGTCCTCAGCTTGGGC

6 BLMH 0 .32 AAGCCTATACGTTTCTGTGGAGTAA

7 TBCA 0 .3 ACTTGTCCTCAGCTTGGGCTTCTTC

8 MDH2 0 .34 TCCTGTACTTGTCCTCAGCTTGGGC

9 S100A4 0.32 TGGACCCCACTGGCTGAGAATCTGG

10 C14orfl39 0.3 TTGGACATCTCTAGTGTAGCTGCCA

Table 23 . Cisplatin bic

SEQIDNO Gene Corre Medianprobe

11 ClQRl 0.3 CACCCAGCTGGTCCTGTGGATGGGA

12 SLA 0.37 TGCCTGCTCCTGTACTTGTCCTCAG

13 PTPN7 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC

14 ZNFNlAl 0.33 CACCCAGCTGGTCCTGTGGATGGGA

15 CENTBl 0.37 TTGGACATCTCTAGTGTAGCTGCCA

16 IFI16 0.31 TCCTCCATCACCTGAAACACTGGAC

17 ARHGEF6 0.35 TGCCTGCTCCTGTACTTGTCCTCAG

18 SEC31L2 0.32 AAGCCTATACGTTTCTGTGGAGTAA

19 CD3Z 0.32 TTGGACATCTCTAGTGTAGCTGCCA

20 GZMB 0.3 TCCTCCATCACCTGAAACACTGGAC

21 CD3D 0.34 TCCTCCATCACCTGAAACACTGGAC

22 MAP4K1 0.32 CACCCAGCTGGTCCTGTGGATGGGA

23 GPR65 0.39 CACCCAGCTGGTCCTGTGGATGGGA

24 PRFl 0.31 TCCTTGTGCCTGCTCCTGTACTTGT

25 ARHGAPl 5 0.35 CACCCAGCTGGTCCTGTGGATGGGA

26 TM6SF1 0.41 TGCCTGCTCCTGTACTTGTCCTCAG

27 TCF4 0.4 AAATGTTTCCTTGTGCCTGCTCCTG

Table 24 . Etoposide biomarker

SEQIDNO Gene Correlation Medianprobe

28 CD99 0.3 AAGCCTATACGTTTCTGTGGAGTAA

29 INSIGl 0.35 TCCTTGTGCCTGCTCCTGTACTTGT

30 PRGl 0.34 GCCCCACTGGACAACACTGATTCCT

31 MUFl 0.35 AAGCCTATACGTTTCTGTGGAGTAA

32 SLA 0.37 CACCCAGCTGGTCCTGTGGATGGGA

33 SSBP2 0.37 TGGACCCCACTGGCTGAGAATCTGG

34 GNB5 0.35 TCCTTGTGCCTGCTCCTGTACTTGT

35 MFNG 0.33 GCCCCACTGGACAACACTGATTCCT

36 PSMB9 0.31 AAGCCTATACGTTTCTGTGGAGTAA

37 EVI2A 0.41 TCCTCCATCACCTGAAACACTGGAC

38 PTPN7 0.3 AAGCCTATACGTTTCTGTGGAGTAA

39 PTGER4 0.3 TGCCTGCTCCTGTACTTGTCCTCAG

40 CXorf9 0.3 GCCCCACTGGACAACACTGATTCCT

41 ZNFNlAl 0.35 ACTTGTCCTCAGCTTGGGCTTCTTC

42 CENTBl 0.3 TGGACCCCACTGGCTGAGAATCTGG

43 NAPlLl 0.31 TCCTCCATCACCTGAAACACTGGAC

44 HLA-DRA 0.34 TGCCTGCTCCTGTACTTGTCCTCAG

45 IFI16 0.38 CACCCAGCTGGTCCTGTGGATGGGA

46 ARHGEF6 0.33 TGGACCCCACTGGCTGAGAATCTGG

47 PSCDBP 0.4 AAGCCTATACGTTTCTGTGGAGTAA

48 SELPLG 0.35 TTGGACATCTCTAGTGTAGCTGCCA

49 SEC31L2 0.42 AAATGTTTCCTTGTGCCTGCTCCTG

50 CD3Z 0.36 TGCCTGCTCCTGTACTTGTCCTCAG

51 SH2D1A 0.33 CACCCAGCTGGTCCTGTGGATGGGA

52 GZMB 0.34 TGGACCCCACTGGCTGAGAATCTGG

53 SCN3A 0.3 GCCCCACTGGACAACACTGATTCCT

54 RAFTLIN 0.39 TCCTCCATCACCTGAAACACTGGAC

55 DOCK2 0.33 TGCCTGCTCCTGTACTTGTCCTCAG

56 CD3D 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC

57 ZAP70 0.35 TCCTCCATCACCTGAAACACTGGAC

58 GPR65 0.35 TGGACCCCACTGGCTGAGAATCTGG

59 PRFl 0.32 TGGACCCCACTGGCTGAGAATCTGG

60 ARHGAP15 0.32 ACTTGTCCTCAGCTTGGGCTTCTTC

61 NOTCHl 0.31 TGCCTGCTCCTGTACTTGTCCTCAG

62 UBASH3A 0.32 ACTTGTCCTCAGCTTGGGCTTCTTC

Table 25 . Azaguanme biomarkers .

SEQIDNO Gene Correlation Medianprobe

63 SRM 0.32 TGCCTGCTCCTGTACTTGTCCTCAG

64 SCARBl 0.4 TTGGACATCTCTAGTGTAGCTGCCA

65 SIATl 0.31 AAATGTTTCCTTGTGCCTGCTCCTG

66 CUGBP2 0.37 TGGACCCCACTGGCTGAGAATCTGG

67 WASPIP 0.44 TCCTGTACTTGTCCTCAGCTTGGGC

68 ITM2A 0.31 AAGCCTATACGTTTCTGTGGAGTAA

69 PALM2-AKAP2 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC

70 LNK 0.43 TTGGACATCTCTAGTGTAGCTGCCA

71 FCGR2A 0.3 TGCCTGCTCCTGTACTTGTCCTCAG

72 RUNX3 0.43 TCCTGTACTTGTCCTCAGCTTGGGC

73 EVI2A 0.4 AAATGTTTCCTTGTGCCTGCTCCTG

74 BTN3A3 0.4 ACTTGTCCTCAGCTTGGGCTTCTTC 75 LCP2 0.34 TCCTTGTGCCTGCTCCTGTACTTGT 76 BCHE 0.35 TCCTCCATCACCTGAAACACTGGAC 77 LY96 0.47 TGCCTGCTCCTGTACTTGTCCTCAG 78 LCPl 0.42 ACTTGTCCTCAGCTTGGGCTTCTTC 79 IFI16 0.33 CACCCAGCTGGTCCTGTGGATGGGA 80 MCAM 0.37 TTGGACATCTCTAGTGTAGCTGCCA 81 MEF2C 0.41 CACCCAGCTGGTCCTGTGGATGGGA 82 FYN 0.31 TCCTGTACTTGTCCTCAGCTTGGGC 83 Clorf38 0.37 AAGCCTATACGTTTCTGTGGAGTAA 84 FCGR2C 0.34 TGCCTGCTCCTGTACTTGTCCTCAG 85 TNIK 0.35 AAGCCTATACGTTTCTGTGGAGTAA 86 AMPD2 0.3 TCCTGTACTTGTCCTCAGCTTGGGC 87 SEPT6 0.41 AAATGTTTCCTTGTGCCTGCTCCTG 88 RAFTLIN 0.39 TCCTTGTGCCTGCTCCTGTACTTGT 89 SLC43A3 0.52 CACCCAGCTGGTCCTGTGGATGGGA 90 LPXN 0.54 AAGCCTATACGTTTCTGTGGAGTAA 91 CKIP-I 0.33 TCCTGTACTTGTCCTCAGCTTGGGC 92 FLJ10539 0.33 TCCTTGTGCCTGCTCCTGTACTTGT 93 FLJ35036 0.36 AAGCCTATACGTTTCTGTGGAGTAA 94 DOCKlO 0.3 GCCCCACTGGACAACACTGATTCCT 95 TRPV2 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC 96 IFRG28 0.3 TCCTTGTGCCTGCTCCTGTACTTGT 97 LEFl 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC 98 ADAMTSl 0.36 TGGACCCCACTGGCTGAGAATCTGG

Table 26, Carboplatin biomarkers. SEQIDNO Gene Correlation Medianprobe 99 ITGA5 0.43 A AAAAATTGGTTTTTTCCCCTTTGTGCCTGCTCCTG

100 TNFAIP3 0.4 TTGGCCCCTTGGCCTTCCCCTTGTACTTGTCCTCAG

101 WNT5A 0.34 TTCCCCTTCCCCAATTCCAACCCTGAAACACTGGAC

102 FOXF2 0.36 TTGGCCCCTTGGCCTTCCCCTTGTACTTGTCCTCAG

103 LOC94105 0.32 AAATGTTTCCTTGTGCCTGCTCCTG

104 IFI16 0.38 TCCTCCATCACCTGAAACACTGGAC

105 LRRN3 0.33 TTGGACATCTCTAGTGTAGCTGCCA

106 DOCKlO 0.4 TCCTGTACTTGTCCTCAGCTTGGGC

107 LEPREl 0.32 GCCCCACTGGACAACACTGATTCCT

108 ADAMTSl 0.34 TGGACCCCACTGGCTGAGAATCTGG

Table 27 Adriamycin biomarkers. SEQIDNO Gene Correlation Medianprobe

109 CD99 0.41 A AAAGGCCCCTTAATTAACCGGTTTCTGTGGAGTAA

110 ALDOC 0.31 TTCCCCTTTTGGTTGGCCCCTTGCTCCTGTACTTGT

111 SLA 0.35 TTGGCCCCTTGGCCTTCCCCTTGTACTTGTCCTCAG

112 SSBP2 0.34 TTCCCCTTCCCCAATTCCAACCCTGAAACACTGGAC

113 IL2RG 0.38 TTCCCCTTTTGGTTGGCCCCTTGCTCCTGTACTTGT

114 CXorf9 0.32 TTGGGGAACCCCCCCCAACCTTGGCTGAGAATCTGG

115 RHOH 0.31 AACCTTTTGGTTCCCCTTCCAAGCTTGGGCTTCTTC

116 ZNFNlAl 0.43 TTTTGGGGAACCAATTCCTTCCTAGTGTAGCTGCCA

117 CENTBl 0.36 AAAAGGCCCCTTAATTAACCGGTTTCTGTGGAGTAA

118 MAP4K1 0.35 TTCCCCTTCCCCAATTCCAACCCTGAAACACTGGAC

119 CD3G 0.31 AAAAAATTGGTTTTTTCCCCTTTGTGCCTGCTCCTG

120 CCR9 0.34 CCAACCCCCCAAGGCCTTGGGGTCCTGTGGATGGGA

121 CXCR4 0.3 TTCCCCTTTTGGTTGGCCCCTTGCTCCTGTACTTGT

122 ARHGEF6 0.31 TTCCCCTTCCCCAATTCCAACCCTGAAACACTGGAC

123 SELPLG 0.31 TTGGGGAACCCCCCCCAACCTTGGCTGAGAATCTGG

124 SEC31L2 0.33 TGCCTGCTCCTGTACTTGTCCTCAG 125 CD3Z 0.37 ACTTGTCCTCAGCTTGGGCTTCTTC 126 SH2D1A 0.37 TTGGACATCTCTAGTGTAGCTGCCA 127 CDlA 0.4 AAGCCTATACGTTTCTGTGGAGTAA 128 LAIRl 0.39 AAGCCTATACGTTTCTGTGGAGTAA 129 TRBg 0.34 TCCTCCATCACCTGAAACACTGGAC 130 CD3D 0.33 TCCTTGTGCCTGCTCCTGTACTTGT 131 WBSCR20C 0.34 ACTTGTCCTCAGCTTGGGCTTCTTC 132 ZAP70 0.33 TCCTGTACTTGTCCTCAGCTTGGGC 133 IFI44 0.32 TGCCTGCTCCTGTACTTGTCCTCAG 134 GPR65 0.31 AAGCCTATACGTTTCTGTGGAGTAA 135 AIFl 0.3 CACCCAGCTGGTCCTGTGGATGGGA 136 ARHGAPl 5 0.37 TCCTGTACTTGTCCTCAGCTTGGGC 137 NARF 0.3 TCCTCCATCACCTGAAACACTGGAC 138 PACAP 0.32 CACCCAGCTGGTCCTGTGGATGGGA

Table 28. Aclarubicin biomarkers. SEQIDNO Gene Correlation Medianprobe

139 RPL12 0.3 AAATGTTTCCTTGTGCCTGCTCCTG

140 RPLP2 0.37 TTGGACATCTCTAGTGTAGCTGCCA

141 MYB 0.31 TCCTTGTGCCTGCTCCTGTACTTGT

142 ZNFNlAl 0.34 AAATGTTTCCTTGTGCCTGCTCCTG

143 SCAPl 0.33 TGCCTGCTCCTGTACTTGTCCTCAG

144 STAT4 0.31 AAATGTTTCCTTGTGCCTGCTCCTG

145 SP140 0.4 AAGCCTATACGTTTCTGTGGAGTAA

146 AMPD3 0.3 TGCCTGCTCCTGTACTTGTCCTCAG

147 TNFAIP8 0.4 AAGCCTATACGTTTCTGTGGAGTAA

148 DDXl8 0.31 TCCTTGTGCCTGCTCCTGTACTTGT

149 TAF5 0.3 TCCTTGTGCCTGCTCCTGTACTTGT

150 RPS2 0.34 CACCCAGCTGGTCCTGTGGATGGGA

151 DOCK2 0.32 AAGCCTATACGTTTCTGTGGAGTAA

152 GPR65 0.35 AAGCCTATACGTTTCTGTGGAGTAA

153 HOXA9 0.33 TCCTTGTGCCTGCTCCTGTACTTGT

154 FLJ12270 0.31 AAATGTTTCCTTGTGCCTGCTCCTG

155 HNRPD 0.4 ACTTGTCCTCAGCTTGGGCTTCTTC

Table 29. Mitoxantrone biomarkers. SEQIDNO Gene C Coorrrrelation Medianprobe

156 PGAMl 0.32 TGCCTGCTCCTGTACTTGTCCTCAG

157 DPYSL3 0.36 AAATGTTTCCTTGTGCCTGCTCCTG

158 INSIGl 0.32 TCCTTGTGCCTGCTCCTGTACTTGT

159 GJAl 0.31 TTGGACATCTCTAGTGTAGCTGCCA

160 BNIP3 0.31 TTGGACATCTCTAGTGTAGCTGCCA

161 PRGl 0.39 GCCCCACTGGACAACACTGATTCCT

162 G6PD 0.34 TGCCTGCTCCTGTACTTGTCCTCAG

163 PLOD2 0.34 GCCCCACTGGACAACACTGATTCCT

164 LOXL2 0.31 TCCTTGTGCCTGCTCCTGTACTTGT

165 SSBP2 0.36 TCCTCCATCACCTGAAACACTGGAC

166 Clorf29 0.35 TCCTTGTGCCTGCTCCTGTACTTGT

167 TOX 0.35 TCCTTGTGCCTGCTCCTGTACTTGT

168 STCl 0.39 TCCTGTACTTGTCCTCAGCTTGGGC

169 TNFRSFlA 0.34 AAATGTTTCCTTGTGCCTGCTCCTG

170 NCOR2 0.3 TCCTCCATCACCTGAAACACTGGAC

171 NAPlLl 0.32 TCCTTGTGCCTGCTCCTGTACTTGT

172 LOC94105 0.34 AAGCCTATACGTTTCTGTGGAGTAA

173 ARHGEF6 0.34 TCCTCCATCACCTGAAACACTGGAC

174 GATA3 0.35 TCCTTGTGCCTGCTCCTGTACTTGT

175 TFPI 0.31 TCCTGTACTTGTCCTCAGCTTGGGC

176 CD3Z 0.37 AAGCCTATACGTTTCTGTGGAGTAA

177 AFlQ 0.33 GCCCCACTGGACAACACTGATTCCT

178 MAPlB 0.34 TGCCTGCTCCTGTACTTGTCCTCAG

179 CD3D 0.31 TCCTTGTGCCTGCTCCTGTACTTGT

180 BCATl 0.32 TCCTGTACTTGTCCTCAGCTTGGGC

181 IFI44 0.33 TGGACCCCACTGGCTGAGAATCTGG

182 CUTC 0.33 AAATGTTTCCTTGTGCCTGCTCCTG

183 NAP1L2 0.33 AAGCCTATACGTTTCTGTGGAGTAA

184 NME7 0.35 AAATGTTTCCTTGTGCCTGCTCCTG

185 FLJ21159 0.33 TCCTGTACTTGTCCTCAGCTTGGGC

Table 30. Mitomycin biomarkers.

SEQIDNO Gene Correlation Medianprobe

186 STCl 0. .34 TGCCTGCTCCTGTACTTGTCCTCAG

187 GPR65 0. .32 GCCCCACTGGACAACACTGATTCCT

188 DOCKlO 0. .35 ACTTGTCCTCAGCTTGGGCTTCTTC

189 FAM46A 0. .36 TCCTTGTGCCTGCTCCTGTACTTGT

190 LOC54103 0. .39 ACTTGTCCTCAGCTTGGGCTTCTTC

Table 31 . Paclitaxel (Taxo biomarkers .

SEQIDNO Gene C Coorrrrelation Medianprobe

191 RPLlO 0.31 TCCTCCATCACCTGAAACACTGGAC

192 RPS4X 0.31 TCCTCCATCACCTGAAACACTGGAC

193 DKCl 0.3 TCCTTGTGCCTGCTCCTGTACTTGT

194 DKFZP564C186 0.32 ACTTGTCCTCAGCTTGGGCTTCTTC

195 PRP19 0.31 TGCCTGCTCCTGTACTTGTCCTCAG

196 RAB9P40 0.33 GCCCCACTGGACAACACTGATTCCT

197 HSA9761 0.37 AAATGTTTCCTTGTGCCTGCTCCTG

198 GMDS 0.3 AAATGTTTCCTTGTGCCTGCTCCTG

199 CEPl 0.3 AAATGTTTCCTTGTGCCTGCTCCTG

200 IL13RA2 0.34 AAATGTTTCCTTGTGCCTGCTCCTG

201 MAGEB2 0.41 ACTTGTCCTCAGCTTGGGCTTCTTC

202 HMGN2 0.35 CACCCAGCTGGTCCTGTGGATGGGA

203 ALMSl 0.3 TCCTCCATCACCTGAAACACTGGAC

204 GPR65 0.31 TGCCTGCTCCTGTACTTGTCCTCAG

205 FLJ10774 0.31 TGGACCCCACTGGCTGAGAATCTGG

206 NOL8 0.31 TGCCTGCTCCTGTACTTGTCCTCAG

207 DAZAPl 0.32 TGCCTGCTCCTGTACTTGTCCTCAG

208 SLC25A15 0.31 TTGGACATCTCTAGTGTAGCTGCCA

209 PAF53 0.36 TCCTCCATCACCTGAAACACTGGAC

210 PITPNCl 0.33 TCCTCCATCACCTGAAACACTGGAC

211 SPANXC 0.3 TGGACCCCACTGGCTGAGAATCTGG

212 KIAA1393 0.33 CACCCAGCTGGTCCTGTGGATGGGA

Table 32. Gemcitabine (Gemzar) biomarkers.

SEQIDNO Gene Correlation Medianprobe

213 UBE2L6 0.38 CACCCAGCTGGTCCTGTGGATGGGA

214 TAPl 0.33 CACCCAGCTGGTCCTGTGGATGGGA

215 F2R 0.3 TCCTGTACTTGTCCTCAGCTTGGGC

216 PSMB9 0.31 TGCCTGCTCCTGTACTTGTCCTCAG

217 IL7R 0.31 AAGCCTATACGTTTCTGTGGAGTAA

218 TNFAIP8 0.33 AAGCCTATACGTTTCTGTGGAGTAA

219 HLA-C 0.33 TGGACCCCACTGGCTGAGAATCTGG

220 IFI44 0.31 TGGACCCCACTGGCTGAGAATCTGG

Table 33 . Taxotere (docetaxel I biomarkers.

SEQIDNO Gene Correlation Medianprobe

221 ANP32B 0.45 GCCCCACTGGACAACACTGATTCCT

222 GTF3A 0.31 TTGGACATCTCTAGTGTAGCTGCCA

223 TRIM14 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC

224 SKP2 0.33 GCCCCACTGGACAACACTGATTCCT

225 TRIP13 0.36 TCCTGTACTTGTCCTCAGCTTGGGC

226 RFC3 0.45 GCCCCACTGGACAACACTGATTCCT

227 CASP7 0.32 TGCCTGCTCCTGTACTTGTCCTCAG

228 TXN 0.36 AAGCCTATACGTTTCTGTGGAGTAA

229 MCM5 0.34 AAATGTTTCCTTGTGCCTGCTCCTG

230 PTGES2 0.39 AAATGTTTCCTTGTGCCTGCTCCTG

231 OBFCl 0.37 TGGACCCCACTGGCTGAGAATCTGG

232 EPB41L4B 0.32 GCCCCACTGGACAACACTGATTCCT

233 CALML4 0.31 TCCTCCATCACCTGAAACACTGGAC

Table 34 . Dexamethasone bioma

SEQIDNO Gene Correlation

234 IFITM2 0.38 ATATATGGACCTAGCTTGAGGCAAT

235 UBE2L6 0.32 AAGCCTATACGTTTCTGTGGAGTAA

236 ITM2A 0.38 CACCCAGCTGGTCCTGTGGATGGGA

237 IL2RG 0.36 TCCTCCATCACCTGAAACACTGGAC

238 GPRASPl 0.36 TCCTGTACTTGTCCTCAGCTTGGGC

239 PTPN7 0.31 TCCTTGTGCCTGCTCCTGTACTTGT

240 CXorf9 0.36 GCCCCACTGGACAACACTGATTCCT

241 RHOH 0.33 TGCCTGCTCCTGTACTTGTCCTCAG

242 GIT2 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC

243 ZNFNlAl 0.35 TCCTTGTGCCTGCTCCTGTACTTGT

244 CEPl 0.31 CACCCAGCTGGTCCTGTGGATGGGA

245 MAP4K1 0.3 AAGCCTATACGTTTCTGTGGAGTAA

246 CCR7 0.33 AAATGTTTCCTTGTGCCTGCTCCTG

247 CD3G 0.35 CACCCAGCTGGTCCTGTGGATGGGA

248 UCP2 0.3 AAGCCTATACGTTTCTGTGGAGTAA

249 GATA3 0.37 TGGACCCCACTGGCTGAGAATCTGG

250 CDKN2A 0.32 TCCTGTACTTGTCCTCAGCTTGGGC

251 TARP 0.3 GCCCCACTGGACAACACTGATTCCT

252 LAIRl 0.34 TTGGACATCTCTAGTGTAGCTGCCA

253 SH2D1A 0.34 TCCTTGTGCCTGCTCCTGTACTTGT

254 SEPT6 0.34 TGCCTGCTCCTGTACTTGTCCTCAG

255 HA-I 0.34 TCCTTGTGCCTGCTCCTGTACTTGT

256 CD3D 0.32 TCCTCCATCACCTGAAACACTGGAC

257 LSTl 0.39 CACCCAGCTGGTCCTGTGGATGGGA

258 AIFl 0.35 AAGCCTATACGTTTCTGTGGAGTAA

259 ADA 0.33 TGCCTGCTCCTGTACTTGTCCTCAG

260 DATFl 0.41 CACCCAGCTGGTCCTGTGGATGGGA

261 ARHGAP15 0.3 TCCTGTACTTGTCCTCAGCTTGGGC

262 PLAC8 0.31 CACCCAGCTGGTCCTGTGGATGGGA

263 CECRl 0.31 GCCCCACTGGACAACACTGATTCCT

264 LOC81558 0.33 TGGACCCCACTGGCTGAGAATCTGG

265 EHD2 0.37. ACTTGTCCTCAGCTTGGGCTTCTTC

Table 35. Ara-C (Cytarabine hydrochloride) biomarkers. SEQIDNO Gene Correlation Medianprobe 266 ITM2A 0.32 TGGACCCCACTGGCTGAGAATCTGG

267 RHOH 0.31 AAATGTTTCCTTGTGCCTGCTCCTG 268 PRIMl 0.3 TCCTCCATCACCTGAAACACTGGAC 269 CENTBl 0.31 TCCTTGTGCCTGCTCCTGTACTTGT 270 NAPlLl 0.31 GCCCCACTGGACAACACTGATTCCT 271 ATP5G2 0.31 TCCTCCATCACCTGAAACACTGGAC 272 GATA3 0.33 AAATGTTTCCTTGTGCCTGCTCCTG 273 PRKCQ 0.32 AAGCCTATACGTTTCTGTGGAGTAA 274 SH2D1A 0.3 GCCCCACTGGACAACACTGATTCCT 275 SEPT6 0.42 ACTTGTCCTCAGCTTGGGCTTCTTC 276 NME4 0.33 ACTTGTCCTCAGCTTGGGCTTCTTC 277 CD3D 0.31 AAGCCTATACGTTTCTGTGGAGTAA 278 CDlE 0.32 TGGACCCCACTGGCTGAGAATCTGG 279 ADA 0.34 GCCCCACTGGACAACACTGATTCCT 280 FHODl 0.31 CACCCAGCTGGTCCTGTGGATGGGA

Table 36 Methylprednisolone biomarkers . SEQIDNO Gene Correlation Medianprobe

281 CD99 0.31 GCCCCACTGGACAACACTGATTCCT

282 ARHGDIB 0.31 TGCCTGCTCCTGTACTTGTCCTCAG

283 ITM2A 0.35 GCCCCACTGGACAACACTGATTCCT

284 LGALS9 0.43 TCCTCCATCACCTGAAACACTGGAC

285 INPP5D 0.34 TGGACCCCACTGGCTGAGAATCTGG

286 SATBl 0.32 TCCTTGTGCCTGCTCCTGTACTTGT

287 TFDP2 0.4 AAATGTTTCCTTGTGCCTGCTCCTG

288 SLA 0.31 TGGACCCCACTGGCTGAGAATCTGG

289 IL2RG 0.3 TGCCTGCTCCTGTACTTGTCCTCAG

290 MFNG 0.3 TGCCTGCTCCTGTACTTGTCCTCAG

291 SELL 0.33 AAATGTTTCCTTGTGCCTGCTCCTG

292 CDW52 0.33 TCCTCCATCACCTGAAACACTGGAC

293 LRMP 0.32 TCCTGTACTTGTCCTCAGCTTGGGC

294 ICAM2 0.38 CACCCAGCTGGTCCTGTGGATGGGA

295 RIMS3 0.36 TGCCTGCTCCTGTACTTGTCCTCAG

296 PTPN7 0.39 TGGACCCCACTGGCTGAGAATCTGG

297 ARHGAP25 0.37 TCCTGTACTTGTCCTCAGCTTGGGC

298 LCK 0.3 TCCTCCATCACCTGAAACACTGGAC

299 CXorf9 0.3 TTGGACATCTCTAGTGTAGCTGCCA

300 RHOH 0.51 AAGCCTATACGTTTCTGTGGAGTAA

301 GIT2 0.33 ACTTGTCCTCAGCTTGGGCTTCTTC

302 ZNFNlAl 0.53 TCCTTGTGCCTGCTCCTGTACTTGT

303 CENTBl 0.36 TCCTCCATCACCTGAAACACTGGAC

304 LCP2 0.34 TCCTGTACTTGTCCTCAGCTTGGGC

305 SPIl 0.3 TCCTGTACTTGTCCTCAGCTTGGGC

306 GZMA 0.31 AAGCCTATACGTTTCTGTGGAGTAA

307 CEPl 0.37 AAGCCTATACGTTTCTGTGGAGTAA

308 CD8A 0.38 TGGACCCCACTGGCTGAGAATCTGG

309 SCAPl 0.32 TCCTCCATCACCTGAAACACTGGAC

310 CD2 0.48 GCCCCACTGGACAACACTGATTCCT

311 VAVl 0.41 ACTTGTCCTCAGCTTGGGCTTCTTC

312 MAP4K1 0.36 TCCTGTACTTGTCCTCAGCTTGGGC

313 CCR7 0.37 ACTTGTCCTCAGCTTGGGCTTCTTC

314 C6orf32 0.38 TCCTTGTGCCTGCTCCTGTACTTGT

315 ALOX15B 0.43 TGCCTGCTCCTGTACTTGTCCTCAG

316 BRDT 0.33 AAGCCTATACGTTTCTGTGGAGTAA

317 CD3G 0.51 AAGCCTATACGTTTCTGTGGAGTAA

318 LTB 0.32 ACTTGTCCTCAGCTTGGGCTTCTTC

319 NVL 0.31 TTGGACATCTCTAGTGTAGCTGCCA

320 RASGRP2 0.35 TGCCTGCTCCTGTACTTGTCCTCAG

321 LCPl 0.34 AAATGTTTCCTTGTGCCTGCTCCTG

322 CXCR4 0.3 AAGCCTATACGTTTCTGTGGAGTAA

323 PRKD2 0.33 CACCCAGCTGGTCCTGTGGATGGGA

324 GATA3 0.39 TCCTGTACTTGTCCTCAGCTTGGGC

325 KIAA0922 0.36 GCCCCACTGGACAACACTGATTCCT

326 TARP 0.49 TCCTCCATCACCTGAAACACTGGAC

327 SEC31L2 0.32 ACTTGTCCTCAGCTTGGGCTTCTTC

328 PRKCQ 0.37 TTGGACATCTCTAGTGTAGCTGCCA

329 SH2D1A 0.33 AAGCCTATACGTTTCTGTGGAGTAA

330 CHRNA3 0.5 AAGCCTATACGTTTCTGTGGAGTAA

331 CDlA 0.44 AAGCCTATACGTTTCTGTGGAGTAA

332 LSTl 0.36 CACCCAGCTGGTCCTGTGGATGGGA

333 LAIRl 0.47 CACCCAGCTGGTCCTGTGGATGGGA

334 CACNAlG 0.33 GCCCCACTGGACAACACTGATTCCT

335 TRB@ 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC

336 SEPT6 0.33 TCCTTGTGCCTGCTCCTGTACTTGT

337 HA-I 0.42 CACCCAGCTGGTCCTGTGGATGGGA

338 DOCK2 0.32 TCCTGTACTTGTCCTCAGCTTGGGC

339 CD3D 0.41 TCCTGTACTTGTCCTCAGCTTGGGC

340 TRD@ 0.38 TGCCTGCTCCTGTACTTGTCCTCAG

341 T3JAM 0.37 TGCCTGCTCCTGTACTTGTCCTCAG

342 FNBPl 0.37 TCCTGTACTTGTCCTCAGCTTGGGC

343 CD6 0.4 CACCCAGCTGGTCCTGTGGATGGGA

344 AIFl 0.31 TGCCTGCTCCTGTACTTGTCCTCAG

345 FOLHl 0.45 TCCTGTACTTGTCCTCAGCTTGGGC

346 CDlE 0.58 CACCCAGCTGGTCCTGTGGATGGGA

347 LY9 0.39 TCCTTGTGCCTGCTCCTGTACTTGT

348 ADA 0.39 AAATGTTTCCTTGTGCCTGCTCCTG

349 CDKL5 0.44 GCCCCACTGGACAACACTGATTCCT

350 TRIM 0.38 AAGCCTATACGTTTCTGTGGAGTAA

351 DATFl 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC

352 RGC32 0.51 TCCTTGTGCCTGCTCCTGTACTTGT

353 ARHGAPl 5 0.34 CACCCAGCTGGTCCTGTGGATGGGA

354 NOTCHl 0.36 TCCTTGTGCCTGCTCCTGTACTTGT

355 BIN2 0.31 AAATGTTTCCTTGTGCCTGCTCCTG

356 SEMA4G 0.35 AAGCCTATACGTTTCTGTGGAGTAA

357 DPEP2 0.33 CACCCAGCTGGTCCTGTGGATGGGA

358 CECRl 0.36 TCCTGTACTTGTCCTCAGCTTGGGC

359 BCLIlB 0.33 TGCCTGCTCCTGTACTTGTCCTCAG

360 STAG3 0.41 TTGGACATCTCTAGTGTAGCTGCCA

361 GALNT6 0.32 TGCCTGCTCCTGTACTTGTCCTCAG

362 UBASH3A 0.3 AAATGTTTCCTTGTGCCTGCTCCTG

363 PHEMX 0.38 TCCTCCATCACCTGAAACACTGGAC

364 FLJ13373 0.34 TCCTTGTGCCTGCTCCTGTACTTGT

365 LEFl 0.49 TCCTCCATCACCTGAAACACTGGAC

366 IL21R 0.42 TTGGACATCTCTAGTGTAGCTGCCA

367 MGC17330 0.33 TCCTTGTGCCTGCTCCTGTACTTGT

368 AKAP13 0.53 TCCTTGTGCCTGCTCCTGTACTTGT

369 GIMAP5 0.34 AAATGTTTCCTTGTGCCTGCTCCTG

Table 37. Methotrexate biomarkers.

SEQIDNO Gene Correlation Medianprobe

370 PRPF8 0.34 TCCTCCATCACCTGAAACACTGGAC

371 RPL18 0.34 AAGCCTATACGTTTCTGTGGAGTAA

372 GOT2 0.31 CACCCAGCTGGTCCTGTGGATGGGA

373 RPL13A 0.31 TCCTGTACTTGTCCTCAGCTTGGGC 374 RPS15 0.39 CACCCAGCTGGTCCTGTGGATGGGA 375 RPLP2 0.32 GCCCCACTGGACAACACTGATTCCT 376 CSDA 0.39 GCCCCACTGGACAACACTGATTCCT 377 KHDRBSl 0.32 TCCTCCATCACCTGAAACACTGGAC 378 SNRPA 0.31 TCCTGTACTTGTCCTCAGCTTGGGC 379 IMPDH2 0.39 AAATGTTTCCTTGTGCCTGCTCCTG 380 RPS19 0.47 AAATGTTTCCTTGTGCCTGCTCCTG 381 NUP88 0.36 CACCCAGCTGGTCCTGTGGATGGGA 382 ATP5D 0.33 TGCCTGCTCCTGTACTTGTCCTCAG 383 PCBP2 0.32 AAATGTTTCCTTGTGCCTGCTCCTG 384 ZNF593 0.4 AAATGTTTCCTTGTGCCTGCTCCTG 385 HSU79274 0.32 TGGACCCCACTGGCTGAGAATCTGG 386 PRIMl 0.3 CACCCAGCTGGTCCTGTGGATGGGA 387 PFDN5 0.33 TCCTCCATCACCTGAAACACTGGAC 388 OXAlL 0.37 CACCCAGCTGGTCCTGTGGATGGGA 389 ATIC 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC 390 CIAPINl 0.34 ACTTGTCCTCAGCTTGGGCTTCTTC 391 RPS2 0.32 CACCCAGCTGGTCCTGTGGATGGGA 392 PCCB 0.36 GCCCCACTGGACAACACTGATTCCT 393 SHMT2 0.34 ACTTGTCCTCAGCTTGGGCTTCTTC 394 RPLPO 0.35 AAGCCTATACGTTTCTGTGGAGTAA 395 HNRPAl 0.35 TGGACCCCACTGGCTGAGAATCTGG 396 STOML2 0.32 TGCCTGCTCCTGTACTTGTCCTCAG 397 SKBl 0.33 ACTTGTCCTCAGCTTGGGCTTCTTC 398 GLTSCR2 0.37 AAGCCTATACGTTTCTGTGGAGTAA 399 CCNBlIPl 0.3 TCCTTGTGCCTGCTCCTGTACTTGT 400 MRPS2 0.33 TTGGACATCTCTAGTGTAGCTGCCA 401 FLJ20859 0.34 TGCCTGCTCCTGTACTTGTCCTCAG 402 FLJ12270 0.3 ACTTGTCCTCAGCTTGGGCTTCTTC

Table 38 . Bleomycin biomarkers . SEQIDNO Gene Correlation Medianprobe

403 PFNl 0.45 GCCCCACTGGACAACACTGATTCCT

404 HKl 0.33 TTGGACATCTCTAGTGTAGCTGCCA

405 MCLl 0.31 TGGACCCCACTGGCTGAGAATCTGG

406 ZYX 0.32 TGGACCCCACTGGCTGAGAATCTGG

407 RAPlB 0.34 ACTTGTCCTCAGCTTGGGCTTCTTC

408 GNB2 0.32 CACCCAGCTGGTCCTGTGGATGGGA

409 EPASl 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC

410 PGAMl 0.42 TGCCTGCTCCTGTACTTGTCCTCAG

411 CKAP4 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC

412 DUSPl 0.4 AAATGTTTCCTTGTGCCTGCTCCTG

413 MYL9 0.4 TTGGACATCTCTAGTGTAGCTGCCA

414 K-ALPHA-I 0.37 TTGGACATCTCTAGTGTAGCTGCCA

415 CSDA 0.3 TCCTTGTGCCTGCTCCTGTACTTGT

416 IFITM2 0.36 TTGGACATCTCTAGTGTAGCTGCCA

417 ITGA5 0.43 GCCCCACTGGACAACACTGATTCCT

418 DPYSL3 0.44 TGGACCCCACTGGCTGAGAATCTGG

419 JUNB 0.32 TCCTGTACTTGTCCTCAGCTTGGGC

420 NFKBIA 0.32 TCCTCCATCACCTGAAACACTGGAC

421 LAMBl 0.37 AAATGTTTCCTTGTGCCTGCTCCTG

422 FHLl 0.31 TGGACCCCACTGGCTGAGAATCTGG

423 INSIGl 0.31 TGGACCCCACTGGCTGAGAATCTGG

424 TIMPl 0.48 TGGACCCCACTGGCTGAGAATCTGG

425 GJAl 0.54 AAGCCTATACGTTTCTGTGGAGTAA

426 PRGl 0.46 TCCTTGTGCCTGCTCCTGTACTTGT

427 EXTl 0.35 TCCTTGTGCCTGCTCCTGTACTTGT

428 DKFZP434J154 0.31 GCCCCACTGGACAACACTGATTCCT

429 MVP 0.34 CACCCAGCTGGTCCTGTGGATGGGA

430 VASP 0.31 TCCTCCATCACCTGAAACACTGGAC

431 ARL7 0.39 TGGACCCCACTGGCTGAGAATCTGG

432 NNMT 0.34 TCCTGTACTTGTCCTCAGCTTGGGC

433 TAPl 0.3 TCCTGTACTTGTCCTCAGCTTGGGC

434 PLOD2 0.37 GCCCCACTGGACAACACTGATTCCT

435 ATF3 0.42 CACCCAGCTGGTCCTGTGGATGGGA

436 PALM2-AKAP2 0.33 TGGACCCCACTGGCTGAGAATCTGG

437 IL8 0.34 GCCCCACTGGACAACACTGATTCCT

438 LOXL2 0.32 GCCCCACTGGACAACACTGATTCCT

439 IL4R 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC

440 DGKA 0.32 GCCCCACTGGACAACACTGATTCCT

441 SEC61G 0.41 CACCCAGCTGGTCCTGTGGATGGGA

442 RGS3 0.37 TGGACCCCACTGGCTGAGAATCTGG

443 F2R 0.34 CACCCAGCTGGTCCTGTGGATGGGA

444 TPM2 0.35 CACCCAGCTGGTCCTGTGGATGGGA

445 PSMB9 0.34 CACCCAGCTGGTCCTGTGGATGGGA

446 LOX 0.37 TCCTGTACTTGTCCTCAGCTTGGGC

447 STCl 0.35 TCCTCCATCACCTGAAACACTGGAC

448 PTGER4 0.31 CACCCAGCTGGTCCTGTGGATGGGA

449 SMAD3 0.38 TTGGACATCTCTAGTGTAGCTGCCA

450 WNT5A 0.44 TGGACCCCACTGGCTGAGAATCTGG

451 BDNF 0.34 TCCTCCATCACCTGAAACACTGGAC

452 TNFRSFlA 0.46 TCCTCCATCACCTGAAACACTGGAC

453 FLNC 0.34 ACTTGTCCTCAGCTTGGGCTTCTTC

454 DKFZP564K0822 0.34 TTGGACATCTCTAGTGTAGCTGCCA

455 FLOTl 0.38 TTGGACATCTCTAGTGTAGCTGCCA

456 PTRF 0.39 TGGACCCCACTGGCTGAGAATCTGG

457 HLA-B 0.36 TTGGACATCTCTAGTGTAGCTGCCA

458 MGC4083 0.32 GCCCCACTGGACAACACTGATTCCT

459 TNFRSFlOB 0.34 TGCCTGCTCCTGTACTTGTCCTCAG

460 PLAGLl 0.31 TGCCTGCTCCTGTACTTGTCCTCAG

461 PNMA2 0.38 GCCCCACTGGACAACACTGATTCCT

462 TFPI 0.38 TCCTGTACTTGTCCTCAGCTTGGGC

463 GZMB 0.51 TCCTCCATCACCTGAAACACTGGAC

464 PLAUR 0.35 AAGCCTATACGTTTCTGTGGAGTAA

465 FSCNl 0.32 ACTTGTCCTCAGCTTGGGCTTCTTC

466 ERP70 0.32 ACTTGTCCTCAGCTTGGGCTTCTTC

467 AFlQ 0.3 TTGGACATCTCTAGTGTAGCTGCCA

468 HIC 0.33 TGCCTGCTCCTGTACTTGTCCTCAG

469 COL6A1 0.32 AAGCCTATACGTTTCTGTGGAGTAA

470 IFITM3 0.3 GCCCCACTGGACAACACTGATTCCT

471 MAPlB 0.38 CACCCAGCTGGTCCTGTGGATGGGA

472 FLJ46603 0.37 TCCTCCATCACCTGAAACACTGGAC

473 RAFTLIN 0.34 TGGACCCCACTGGCTGAGAATCTGG

474 RRAS 0.31 TCCTGTACTTGTCCTCAGCTTGGGC

475 FTL 0.3 CACCCAGCTGGTCCTGTGGATGGGA

476 KIAA0877 0.31 CACCCAGCTGGTCCTGTGGATGGGA

477 MTlE 0.31 TCCTTGTGCCTGCTCCTGTACTTGT

478 CDClO 0.51 AAATGTTTCCTTGTGCCTGCTCCTG

479 DOCK2 0.32 AAGCCTATACGTTTCTGTGGAGTAA

480 RISl 0.37 ACTTGTCCTCAGCTTGGGCTTCTTC

481 BCATl 0.42 TTGGACATCTCTAGTGTAGCTGCCA

482 PRFl 0.34 TCCTCCATCACCTGAAACACTGGAC

483 DBNl 0.36 GCCCCACTGGACAACACTGATTCCT

484 MTlK 0.3 TGCCTGCTCCTGTACTTGTCCTCAG

485 TMSBlO 0.42 GCCCCACTGGACAACACTGATTCCT

486 FLJ10350 0.4 AAATGTTTCCTTGTGCCTGCTCCTG

487 Clorf24 0.34 TGCCTGCTCCTGTACTTGTCCTCAG

488 NME7 0.46 TCCTGTACTTGTCCTCAGCTTGGGC

489 TMEM22 0.3 TGCCTGCTCCTGTACTTGTCCTCAG

490 TPKl 0.37 TCCTCCATCACCTGAAACACTGGAC

491 ELK3 0.38 TGCCTGCTCCTGTACTTGTCCTCAG

492 CYLD 0.4 TCCTTGTGCCTGCTCCTGTACTTGT

493 ADAMTSl 0.31 AAGCCTATACGTTTCTGTGGAGTAA

494 EHD2 0.41 TCCTCCATCACCTGAAACACTGGAC

495 ACTB 0.33 TCCTTGTGCCTGCTCCTGTACTTGT

Table 39. Methyl-GAG (methyl glyoxal bis amidinohydrazone dihydrochloride) biomarkers.

SEQIDNO Gene Correlation Medianprobe

496 SSRPl 0.37 TGCCTGCTCCTGTACTTGTCCTCAG

497 CTSC 0.35 CACCCAGCTGGTCCTGTGGATGGGA

498 LBR 0.38 ACTTGTCCTCAGCTTGGGCTTCTTC

499 EFNB2 0.31 AAATGTTTCCTTGTGCCTGCTCCTG

500 SERPINAl 0.34 TCCTTGTGCCTGCTCCTGTACTTGT

501 SSSCAl 0.32 TCCTGTACTTGTCCTCAGCTTGGGC

502 EZH2 0.36 TTGGACATCTCTAGTGTAGCTGCCA

503 MYB 0.33 GCCCCACTGGACAACACTGATTCCT

504 PRIMl 0.39 TCCTCCATCACCTGAAACACTGGAC

505 H2AFX 0.33 TCCTTGTGCCTGCTCCTGTACTTGT

506 HMGAl 0.35 TTGGACATCTCTAGTGTAGCTGCCA

507 HMMR 0.33 TCCTTGTGCCTGCTCCTGTACTTGT

508 TK2 0.42 CACCCAGCTGGTCCTGTGGATGGGA

509 WHSCl 0.35 AAATGTTTCCTTGTGCCTGCTCCTG

510 DIAPHl 0.34 GCCCCACTGGACAACACTGATTCCT

511 LAMB3 0.31 GCCCCACTGGACAACACTGATTCCT

512 DPAGTl 0.42 TGCCTGCTCCTGTACTTGTCCTCAG

513 UCK2 0.31 GCCCCACTGGACAACACTGATTCCT

514 SERPINBl 0.31 TCCTTGTGCCTGCTCCTGTACTTGT

515 MDNl 0.35 TGCCTGCTCCTGTACTTGTCCTCAG

516 G0S2 0.43 CACCCAGCTGGTCCTGTGGATGGGA

517 MGC21654 0.36 TGGACCCCACTGGCTGAGAATCTGG

518 GTSEl 0.35 ACTTGTCCTCAGCTTGGGCTTCTTC

519 TACC3 0.31 TCCTCCATCACCTGAAACACTGGAC

520 PLAC8 0.31 CACCCAGCTGGTCCTGTGGATGGGA

521 HNRPD 0.35 TTGGACATCTCTAGTGTAGCTGCCA

522 PNAS-4 0.3 TTGGACATCTCTAGTGTAGCTGCCA

Table 40 . HDAC inhibitors bic

SEQIDNO Gene Correlation

523 FAU 0.33 TTGGACATCTCTAGTGTAGCTGCCA

524 NOL5A 0.33 TGGACCCCACTGGCTGAGAATCTGG

525 ANP32A 0.32 CACCCAGCTGGTCCTGTGGATGGGA

526 ARHGDIB 0.3 ACTTGTCCTCAGCTTGGGCTTCTTC

527 LBR 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC

528 FABP5 0.33 TCCTCCATCACCTGAAACACTGGAC

529 ITM2A 0.32 TTGGACATCTCTAGTGTAGCTGCCA

530 SFRS5 0.34 TCCTCCATCACCTGAAACACTGGAC

531 IQGAP2 0.4 CACCCAGCTGGTCCTGTGGATGGGA

532 SLC7A6 0.35 AAGCCTATACGTTTCTGTGGAGTAA

533 SLA 0.31 TGCCTGCTCCTGTACTTGTCCTCAG

534 IL2RG 0.31 TCCTCCATCACCTGAAACACTGGAC

535 MFNG 0.39 TCCTGTACTTGTCCTCAGCTTGGGC

536 GPSM3 0.32 TTGGACATCTCTAGTGTAGCTGCCA

537 PIM2 0.3 TTGGACATCTCTAGTGTAGCTGCCA

538 EVERl 0.35 GCCCCACTGGACAACACTGATTCCT

539 LRMP 0.35 TGCCTGCTCCTGTACTTGTCCTCAG

540 ICAM2 0.44 TCCTGTACTTGTCCTCAGCTTGGGC

541 RIMS3 0.43 TGGACCCCACTGGCTGAGAATCTGG

542 FMNLl 0.35 TTGGACATCTCTAGTGTAGCTGCCA

543 MYB 0.37 TGCCTGCTCCTGTACTTGTCCTCAG

544 PTPN7 0.36 TCCTTGTGCCTGCTCCTGTACTTGT

545 LCK 0.48 CACCCAGCTGGTCCTGTGGATGGGA

546 CXorf9 0.3 ACTTGTCCTCAGCTTGGGCTTCTTC

547 RHOH 0.31 TCCTTGTGCCTGCTCCTGTACTTGT

548 ZNFNlAl 0.33 AAATGTTTCCTTGTGCCTGCTCCTG

549 CENTBl 0.45 CACCCAGCTGGTCCTGTGGATGGGA

550 LCP2 0.31 TGCCTGCTCCTGTACTTGTCCTCAG

551 DBT 0.32 TCCTGTACTTGTCCTCAGCTTGGGC

552 CEPl 0.31 TTGGACATCTCTAGTGTAGCTGCCA

553 IL6R 0.31 TGGACCCCACTGGCTGAGAATCTGG

554 VAVl 0.32 TCCTTGTGCCTGCTCCTGTACTTGT

555 MAP4K1 0.3 AAGCCTATACGTTTCTGTGGAGTAA

556 CD28 0.36 TCCTTGTGCCTGCTCCTGTACTTGT

557 PTP4A3 0.3 TTGGACATCTCTAGTGTAGCTGCCA

558 CD3G 0.33 CACCCAGCTGGTCCTGTGGATGGGA

559 LTB 0.4 TCCTGTACTTGTCCTCAGCTTGGGC

560 USP34 0.44 GCCCCACTGGACAACACTGATTCCT

561 NVL 0.41 TCCTTGTGCCTGCTCCTGTACTTGT

562 CD8B1 0.33 ACTTGTCCTCAGCTTGGGCTTCTTC

563 SFRS6 0.31 GCCCCACTGGACAACACTGATTCCT

564 LCPl 0.34 TCCTGTACTTGTCCTCAGCTTGGGC

565 CXCR4 0.36 TGCCTGCTCCTGTACTTGTCCTCAG

566 PSCDBP 0.33 TGGACCCCACTGGCTGAGAATCTGG

567 SELPLG 0.33 TTGGACATCTCTAGTGTAGCTGCCA

568 CD3Z 0.3 TCCTTGTGCCTGCTCCTGTACTTGT

569 PRKCQ 0.33 TTGGACATCTCTAGTGTAGCTGCCA

570 CDlA 0.34 GCCCCACTGGACAACACTGATTCCT

571 GATA2 0.31 TTGGACATCTCTAGTGTAGCTGCCA

572 P2RX5 0.32 TGCCTGCTCCTGTACTTGTCCTCAG

573 LAIRl 0.35 TGGACCCCACTGGCTGAGAATCTGG

574 Clorf38 0.4 GCCCCACTGGACAACACTGATTCCT

575 SH2D1A 0.44 TCCTTGTGCCTGCTCCTGTACTTGT

576 TRB@ 0.33 CACCCAGCTGGTCCTGTGGATGGGA

577 SEPT6 0.34 GCCCCACTGGACAACACTGATTCCT

578 HA-I 0.32 AAGCCTATACGTTTCTGTGGAGTAA

579 DOCK2 0.3 TCCTTGTGCCTGCTCCTGTACTTGT

580 WBSCR20C 0.31 TGCCTGCTCCTGTACTTGTCCTCAG

581 CD3D 0.3 ACTTGTCCTCAGCTTGGGCTTCTTC

582 RNASE6 0.31 GCCCCACTGGACAACACTGATTCCT

583 SFRS7 0.32 AAATGTTTCCTTGTGCCTGCTCCTG

584 WBSCR20A 0.3 AAGCCTATACGTTTCTGTGGAGTAA

585 NUP210 0.31 TTGGACATCTCTAGTGTAGCTGCCA

586 CD6 0.34 TCCTTGTGCCTGCTCCTGTACTTGT

587 HNRPAl 0.3 GCCCCACTGGACAACACTGATTCCT 588 AIFl 0.34 AAGCCTATACGTTTCTGTGGAGTAA 589 CYFIP2 0.38 TGGACCCCACTGGCTGAGAATCTGG 590 GLTSCR2 0.38 TCCTTGTGCCTGCTCCTGTACTTGT 591 Cllorf2 0.31 AAGCCTATACGTTTCTGTGGAGTAA 592 ARHGAP15 0.33 TGGACCCCACTGGCTGAGAATCTGG 593 BIN2 0.35 TTGGACATCTCTAGTGTAGCTGCCA 594 SH3TC1 0.35 ACTTGTCCTCAGCTTGGGCTTCTTC 595 STAG3 0.32 AAATGTTTCCTTGTGCCTGCTCCTG 596 TM6SF1 0.34 ACTTGTCCTCAGCTTGGGCTTCTTC 597 C15orf25 0.33 TCCTCCATCACCTGAAACACTGGAC 598 FLJ22457 0.36 AAATGTTTCCTTGTGCCTGCTCCTG 599 PACAP 0.34 TGCCTGCTCCTGTACTTGTCCTCAG 600 MGC2744 0.31 GCCCCACTGGACAACACTGATTCCT

Table 41. 5-Fluorouracil biomarkers. SEQIDNO Gene Correlation Medianprobe

601 RPL18 0.38 AAATGTTTCCTTGTGCCTGCTCCTG

602 RPLlOA 0.39 TGGACCCCACTGGCTGAGAATCTGG

603 ANAPC5 0.37 ACTTGTCCTCAGCTTGGGCTTCTTC

604 EEF1B2 0.3 TCCTGTACTTGTCCTCAGCTTGGGC

605 RPL13A 0.5 TGCCTGCTCCTGTACTTGTCCTCAG

606 RPS15 0.4 ACTTGTCCTCAGCTTGGGCTTCTTC

607 NDUFABl 0.38 GCCCCACTGGACAACACTGATTCCT

608 APRT 0.32 AAATGTTTCCTTGTGCCTGCTCCTG

609 ZNF593 0.34 TCCTCCATCACCTGAAACACTGGAC

610 MRP63 0.32 AAATGTTTCCTTGTGCCTGCTCCTG

611 IL6R 0.41 TGGACCCCACTGGCTGAGAATCTGG

612 SART3 0.37 TCCTCCATCACCTGAAACACTGGAC

613 UCK2 0.32 GCCCCACTGGACAACACTGATTCCT

614 RPL17 0.31 AAGCCTATACGTTTCTGTGGAGTAA

615 RPS2 0.35 CACCCAGCTGGTCCTGTGGATGGGA

616 PCCB 0.38 TCCTTGTGCCTGCTCCTGTACTTGT

617 TOMM20 0.32 TGGACCCCACTGGCTGAGAATCTGG

618 SHMT2 0.32 TTGGACATCTCTAGTGTAGCTGCCA

619 RPLPO 0.31 TCCTTGTGCCTGCTCCTGTACTTGT

620 GTF3A 0.32 CACCCAGCTGGTCCTGTGGATGGGA

621 STOML2 0.33 TGGACCCCACTGGCTGAGAATCTGG

622 DKFZp564J157 0.4 AAATGTTTCCTTGTGCCTGCTCCTG

623 MRPS2 0.32 TCCTGTACTTGTCCTCAGCTTGGGC

624 ALG5 0.3 TTGGACATCTCTAGTGTAGCTGCCA

625 CALML4 0.33 CACCCAGCTGGTCCTGTGGATGGGA

Table 42 Radiation sensitivity biomarkers. SEQIDNO Gene Correlation Medianprobe

626 TRAl 0.36 TGGACCCCACTGGCTGAGAATCTGG

627 ACTN4 0.36 ACTTGTCCTCAGCTTGGGCTTCTTC

628 CALMl 0.32 TCCTCCATCACCTGAAACACTGGAC

629 CD63 0.32 TCCTGTACTTGTCCTCAGCTTGGGC

630 FKBPlA 0.38 TGGACCCCACTGGCTGAGAATCTGG

631 CALU 0.47 ACTTGTCCTCAGCTTGGGCTTCTTC

632 IQGAPl 0.37 TTGGACATCTCTAGTGTAGCTGCCA

633 MGC8721 0.35 AAATGTTTCCTTGTGCCTGCTCCTG

634 STATl 0.37 TGGACCCCACTGGCTGAGAATCTGG

635 TACCl 0.41 ACTTGTCCTCAGCTTGGGCTTCTTC

636 TM4SF8 0.33 AAGCCTATACGTTTCTGTGGAGTAA

637 CD59 0.31 TCCTCCATCACCTGAAACACTGGAC

638 CKAP4 0.45 TCCTTGTGCCTGCTCCTGTACTTGT

639 DUSPl 0.38 TCCTGTACTTGTCCTCAGCTTGGGC

640 RCNl 0.31 TGCCTGCTCCTGTACTTGTCCTCAG

641 MGC8902 0.35 TGCCTGCTCCTGTACTTGTCCTCAG

642 RRBPl 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC

643 PRNP 0.42 TTGGACATCTCTAGTGTAGCTGCCA

644 IER3 0.34 GCCCCACTGGACAACACTGATTCCT

645 MARCKS 0.43 GCCCCACTGGACAACACTGATTCCT

646 FER1L3 0.47 TGCCTGCTCCTGTACTTGTCCTCAG

647 SLC20A1 0.41 ACTTGTCCTCAGCTTGGGCTTCTTC

648 HEXB 0.46 AAATGTTTCCTTGTGCCTGCTCCTG

649 EXTl 0.47 CACCCAGCTGGTCCTGTGGATGGGA

650 TJPl 0.32 AAATGTTTCCTTGTGCCTGCTCCTG

651 CTSL 0.38 TCCTGTACTTGTCCTCAGCTTGGGC

652 SLC39A6 0.36 TCCTGTACTTGTCCTCAGCTTGGGC

653 RIOK3 0.38 TCCTCCATCACCTGAAACACTGGAC

654 CRK 0.37 TGCCTGCTCCTGTACTTGTCCTCAG

655 NNMT 0.37 TGCCTGCTCCTGTACTTGTCCTCAG

656 TRAM2 0.35 TTGGACATCTCTAGTGTAGCTGCCA

657 ADAM9 0.52 TCCTGTACTTGTCCTCAGCTTGGGC

658 PLSCRl 0.35 TGGACCCCACTGGCTGAGAATCTGG

659 PRSS23 0.3 TGCCTGCTCCTGTACTTGTCCTCAG

660 PLOD2 0.36 TGCCTGCTCCTGTACTTGTCCTCAG

661 NPCl 0.39 TGCCTGCTCCTGTACTTGTCCTCAG

662 TOBl 0.37 CACCCAGCTGGTCCTGTGGATGGGA

663 GFPTl 0.47 CACCCAGCTGGTCCTGTGGATGGGA

664 IL8 0.36 AAATGTTTCCTTGTGCCTGCTCCTG

665 PYGL 0.46 TCCTCCATCACCTGAAACACTGGAC

666 LOXL2 0.49 TTGGACATCTCTAGTGTAGCTGCCA

667 KIAA0355 0.36 TCCTTGTGCCTGCTCCTGTACTTGT

668 UGDH 0.49 TTGGACATCTCTAGTGTAGCTGCCA

669 PURA 0.32 TGCCTGCTCCTGTACTTGTCCTCAG

670 ULK2 0.37 AAGCCTATACGTTTCTGTGGAGTAA

671 CENTG2 0.35 GCCCCACTGGACAACACTGATTCCT

672 CAP350 0.31 GCCCCACTGGACAACACTGATTCCT

673 CXCLl 0.36 TCCTGTACTTGTCCTCAGCTTGGGC

674 BTN3A3 0.35 AAGCCTATACGTTTCTGTGGAGTAA

675 WNT5A 0.3 AAGCCTATACGTTTCTGTGGAGTAA

676 FOXF2 0.44 AAATGTTTCCTTGTGCCTGCTCCTG

677 LPHN2 0.34 GCCCCACTGGACAACACTGATTCCT

678 CDHIl 0.39 TGGACCCCACTGGCTGAGAATCTGG

679 P4HAl 0.33 TCCTCCATCACCTGAAACACTGGAC

680 GRP58 0.44 CACCCAGCTGGTCCTGTGGATGGGA

681 DSIPI 0.44 TGGACCCCACTGGCTGAGAATCTGG

682 MAP1LC3B 0.5 AAGCCTATACGTTTCTGTGGAGTAA

683 GALIG 0.36 AAATGTTTCCTTGTGCCTGCTCCTG

684 IGSF4 0.4 TCCTCCATCACCTGAAACACTGGAC

685 IRS2 0.35 TGGACCCCACTGGCTGAGAATCTGG

686 ATP2A2 0.35 CACCCAGCTGGTCCTGTGGATGGGA

687 OGT 0.3 TCCTGTACTTGTCCTCAGCTTGGGC

688 TNFRSFlOB 0.31 AAGCCTATACGTTTCTGTGGAGTAA

689 KIAA1128 0.35 CACCCAGCTGGTCCTGTGGATGGGA

690 TM4SF1 0.35 CACCCAGCTGGTCCTGTGGATGGGA

691 RIPK2 0.42 TGCCTGCTCCTGTACTTGTCCTCAG

692 NR1D2 0.47 TTGGACATCTCTAGTGTAGCTGCCA

693 SSA2 0.36 TTGGACATCTCTAGTGTAGCTGCCA

694 NQOl 0.4 AAGCCTATACGTTTCTGTGGAGTAA

695 ASPH 0.36 TGCCTGCTCCTGTACTTGTCCTCAG

696 ASAHl 0.33 ACTTGTCCTCAGCTTGGGCTTCTTC

697 MGLL 0.35 TGGACCCCACTGGCTGAGAATCTGG

698 SERPINB6 0.51 AAGCCTATACGTTTCTGTGGAGTAA

699 HSPA5 0.33 TCCTTGTGCCTGCTCCTGTACTTGT

700 ZFP36L1 0.39 TCCTTGTGCCTGCTCCTGTACTTGT

701 COL4A1 0.3 ACTTGTCCTCAGCTTGGGCTTCTTC

702 NIPA2 0.36 ACTTGTCCTCAGCTTGGGCTTCTTC

703 FKBP9 0.48 AAATGTTTCCTTGTGCCTGCTCCTG

704 IL6ST 0.4 GCCCCACTGGACAACACTGATTCCT

705 DKFZP564G2022 0.39 TTGGACATCTCTAGTGTAGCTGCCA

706 PPAP2B 0.33 TGGACCCCACTGGCTGAGAATCTGG

707 MAPlB 0.3 CACCCAGCTGGTCCTGTGGATGGGA

708 MAPKl 0.3 TGGACCCCACTGGCTGAGAATCTGG

709 MYOlB 0.38 ACTTGTCCTCAGCTTGGGCTTCTTC

710 CAST 0.31 TGCCTGCTCCTGTACTTGTCCTCAG

711 RRAS2 0.52 AAATGTTTCCTTGTGCCTGCTCCTG

712 QKI 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC

713 LHFPL2 0.36 TCCTTGTGCCTGCTCCTGTACTTGT

714 SEPTlO 0.38 GCCCCACTGGACAACACTGATTCCT

715 ARHE 0.5 AAGCCTATACGTTTCTGTGGAGTAA

716 KIAA1078 0.34 AAGCCTATACGTTTCTGTGGAGTAA

717 FTL 0.38 TCCTGTACTTGTCCTCAGCTTGGGC

718 KIAA0877 0.41 AAATGTTTCCTTGTGCCTGCTCCTG

719 PLCBl 0.3 AAGCCTATACGTTTCTGTGGAGTAA

720 KIAA0802 0.32 TGCCTGCTCCTGTACTTGTCCTCAG

721 RAB3GAP 0.43 TGCCTGCTCCTGTACTTGTCCTCAG

722 SERPINBl 0.46 TGCCTGCTCCTGTACTTGTCCTCAG

723 TIMM17A 0.38 AAATGTTTCCTTGTGCCTGCTCCTG

724 SOD2 0.35 TTGGACATCTCTAGTGTAGCTGCCA

725 HLA-A 0.33 TTGGACATCTCTAGTGTAGCTGCCA

726 NOMO2 0.43 CACCCAGCTGGTCCTGTGGATGGGA

727 LOC55831 0.32 TCCTGTACTTGTCCTCAGCTTGGGC

728 PHLDAl 0.32 CACCCAGCTGGTCCTGTGGATGGGA

729 TMEM2 0.47 TGGACCCCACTGGCTGAGAATCTGG

730 MLPH 0.35 ACTTGTCCTCAGCTTGGGCTTCTTC

731 FAD104 0.34 ACTTGTCCTCAGCTTGGGCTTCTTC

732 LRRC5 0.42 CACCCAGCTGGTCCTGTGGATGGGA

733 RAB7L1 0.41 TTGGACATCTCTAGTGTAGCTGCCA

734 FLJ35036 0.36 TCCTGTACTTGTCCTCAGCTTGGGC

735 DOCKlO 0.41 TCCTCCATCACCTGAAACACTGGAC

736 LRP12 0.36 AAGCCTATACGTTTCTGTGGAGTAA

737 TXNDC5 0.4 ACTTGTCCTCAGCTTGGGCTTCTTC

738 CDC14B 0.39 TGCCTGCTCCTGTACTTGTCCTCAG

739 HRMTlLl 0.38 CACCCAGCTGGTCCTGTGGATGGGA

740 DNAJClO 0.31 TTGGACATCTCTAGTGTAGCTGCCA

741 TNPOl 0.33 GCCCCACTGGACAACACTGATTCCT

742 LONP 0.32 AAATGTTTCCTTGTGCCTGCTCCTG

743 AMIGO2 0.38 AAGCCTATACGTTTCTGTGGAGTAA

744 DNAPTP6 0.31 TGCCTGCTCCTGTACTTGTCCTCAG

745 ADAMTSl 0.37 TTGGACATCTCTAGTGTAGCTGCCA

Table 43. Rituximab (e.g., Mabthera) biomarkers.

SEQIDNO Gene Correlation Medianprobe

746 PSMB2 0.89 TCCTCCATCACCTGAAACACTGGAC

747 BATl 0.88 AAGCCTATACGTTTCTGTGGAGTAA

748 ASCC3L1 0.89 TCCTTGTGCCTGCTCCTGTACTTGT

749 SET 0.94 AAATGTTTCCTTGTGCCTGCTCCTG

750 YWHAZ 0.83 TCCTTGTGCCTGCTCCTGTACTTGT

751 GLUL 0.8 TGGACCCCACTGGCTGAGAATCTGG

752 LDHA 0.8 TCCTTGTGCCTGCTCCTGTACTTGT

753 HMGBl 0.84 AAATGTTTCCTTGTGCCTGCTCCTG

754 SFRS2 0.87 AAATGTTTCCTTGTGCCTGCTCCTG

755 DPYSL2 0.82 TCCTGTACTTGTCCTCAGCTTGGGC

756 MGC8721 0.82 CACCCAGCTGGTCCTGTGGATGGGA

757 NOL5A 0.86 TGCCTGCTCCTGTACTTGTCCTCAG

758 SFRSlO 0.88 AAATGTTTCCTTGTGCCTGCTCCTG

759 SF3B1 0.82 TCCTGTACTTGTCCTCAGCTTGGGC

760 K-ALPHA-I 0.86 TGCCTGCTCCTGTACTTGTCCTCAG

761 TXNRDl 0.86 TGGACCCCACTGGCTGAGAATCTGG

762 ARHGDIB 0.83 CACCCAGCTGGTCCTGTGGATGGGA

763 ZFP36L2 0.92 TTGGACATCTCTAGTGTAGCTGCCA

764 DHXl5 0.81 TGGACCCCACTGGCTGAGAATCTGG

765 SOX4 0.85 CACCCAGCTGGTCCTGTGGATGGGA

766 GRSFl 0.81 TGGACCCCACTGGCTGAGAATCTGG

767 MCM3 0.85 GCCCCACTGGACAACACTGATTCCT

768 IFITMl 0.82 TCCTCCATCACCTGAAACACTGGAC

769 RPA2 0.86 TCCTCCATCACCTGAAACACTGGAC

770 LBR 0.87 ACTTGTCCTCAGCTTGGGCTTCTTC

771 CKSlB 0.85 AAGCCTATACGTTTCTGTGGAGTAA

772 NASP 0.82 TGGACCCCACTGGCTGAGAATCTGG

773 HNRPDL 0.81 TCCTCCATCACCTGAAACACTGGAC

774 CUGBP2 0.81 TGCCTGCTCCTGTACTTGTCCTCAG lib PTBPl 0.87 TCCTTGTGCCTGCTCCTGTACTTGT

116 ARL7 0.83 TTGGACATCTCTAGTGTAGCTGCCA

111 CTCF 0.83 ACTTGTCCTCAGCTTGGGCTTCTTC

778 HMGCR 0.86 TCCTTGTGCCTGCTCCTGTACTTGT

779 ITM2A 0.88 AAATGTTTCCTTGTGCCTGCTCCTG

780 SFRS3 0.93 TCCTTGTGCCTGCTCCTGTACTTGT

781 SRPK2 0.82 TCCTTGTGCCTGCTCCTGTACTTGT

782 JARID2 0.92 CACCCAGCTGGTCCTGTGGATGGGA

783 M96 0.84 TCCTGTACTTGTCCTCAGCTTGGGC

784 MAD2L1 0.87 TCCTCCATCACCTGAAACACTGGAC

785 SATBl 0.81 ACTTGTCCTCAGCTTGGGCTTCTTC

786 TMPO 0.9 ACTTGTCCTCAGCTTGGGCTTCTTC

787 SIVA 0.84 ACTTGTCCTCAGCTTGGGCTTCTTC

788 SEMA4D 0.9 TCCTCCATCACCTGAAACACTGGAC

789 TFDP2 0.87 TCCTTGTGCCTGCTCCTGTACTTGT

790 SKP2 0.86 AAGCCTATACGTTTCTGTGGAGTAA

791 SH3YL1 0.88 GCCCCACTGGACAACACTGATTCCT

792 RFC4 0.87 TCCTCCATCACCTGAAACACTGGAC

793 PCBP2 0.83 AAGCCTATACGTTTCTGTGGAGTAA

794 IL2RG 0.84 GCCCCACTGGACAACACTGATTCCT

795 CDC45L 0.89 TCCTGTACTTGTCCTCAGCTTGGGC

796 GTSEl 0.83 TTGGACATCTCTAGTGTAGCTGCCA

797 KIFIl 0.85 AAGCCTATACGTTTCTGTGGAGTAA

798 FENl 0.88 TTGGACATCTCTAGTGTAGCTGCCA

799 MYB 0.9 TGGACCCCACTGGCTGAGAATCTGG

800 LCK 0.87 TCCTCCATCACCTGAAACACTGGAC

801 CENPA 0.84 GCCCCACTGGACAACACTGATTCCT

802 CCNE2 0.84 GCCCCACTGGACAACACTGATTCCT

803 H2AFX 0.88 TTGGACATCTCTAGTGTAGCTGCCA

804 SNRPG 0.84 TCCTCCATCACCTGAAACACTGGAC

805 CD3G 0.94 TCCTTGTGCCTGCTCCTGTACTTGT

806 STK6 0.9 ACTTGTCCTCAGCTTGGGCTTCTTC

807 PTP4A2 0.81 TGCCTGCTCCTGTACTTGTCCTCAG

808 FDFTl 0.91 AAATGTTTCCTTGTGCCTGCTCCTG

809 HSPA8 0.84 AAATGTTTCCTTGTGCCTGCTCCTG

810 HNRPR 0.94 TCCTTGTGCCTGCTCCTGTACTTGT

811 MCM7 0.92 AAATGTTTCCTTGTGCCTGCTCCTG

812 SFRS6 0.85 TGGACCCCACTGGCTGAGAATCTGG

813 PAK2 0.8 CACCCAGCTGGTCCTGTGGATGGGA

814 LCPl 0.85 TCCTGTACTTGTCCTCAGCTTGGGC

815 STAT3 0.81 ACTTGTCCTCAGCTTGGGCTTCTTC

816 OK/SW-cl.56 0.8 TCCTTGTGCCTGCTCCTGTACTTGT

817 WHSCl 0.81 TGGACCCCACTGGCTGAGAATCTGG

818 DIAPHl 0.88 AAGCCTATACGTTTCTGTGGAGTAA

819 KIF2C 0.88 TCCTGTACTTGTCCTCAGCTTGGGC

820 HDGFRP3 0.89 CACCCAGCTGGTCCTGTGGATGGGA

821 PNMA2 0.93 TTGGACATCTCTAGTGTAGCTGCCA

822 GATA3 0.93 TCCTGTACTTGTCCTCAGCTTGGGC

823 BUBl 0.88 AAATGTTTCCTTGTGCCTGCTCCTG

824 TPX2 0.8 CACCCAGCTGGTCCTGTGGATGGGA

825 SH2D1A 0.86 TCCTTGTGCCTGCTCCTGTACTTGT

826 TNFAIP8 0.9 TCCTCCATCACCTGAAACACTGGAC

827 CSElL 0.83 AAATGTTTCCTTGTGCCTGCTCCTG

828 MCAM 0.8 TCCTGTACTTGTCCTCAGCTTGGGC

829 AFlQ 0.83 GCCCCACTGGACAACACTGATTCCT

830 CD47 0.86 CACCCAGCTGGTCCTGTGGATGGGA

831 SFRSl 0.85 AAGCCTATACGTTTCTGTGGAGTAA

832 FYB 0.92 TCCTGTACTTGTCCTCAGCTTGGGC

833 TRB@ 0.84 ACTTGTCCTCAGCTTGGGCTTCTTC

834 CXCR4 0.94 GCCCCACTGGACAACACTGATTCCT

835 H3F3B 0.84 TCCTCCATCACCTGAAACACTGGAC

836 MKI67 0.83 ACTTGTCCTCAGCTTGGGCTTCTTC

837 MAC30 0.82 TCCTTGTGCCTGCTCCTGTACTTGT

838 ARID5B 0.88 AAGCCTATACGTTTCTGTGGAGTAA

839 LOC339287 0.81 AAGCCTATACGTTTCTGTGGAGTAA

840 CD3D 0.82 TCCTTGTGCCTGCTCCTGTACTTGT

841 ZAP70 0.87 AAGCCTATACGTTTCTGTGGAGTAA

842 LAPTM4B 0.83 TCCTCCATCACCTGAAACACTGGAC

843 SFRS7 0.87 TCCTTGTGCCTGCTCCTGTACTTGT

844 HNRPAl 0.9 AAGCCTATACGTTTCTGTGGAGTAA

845 HSPCA 0.88 AAGCCTATACGTTTCTGTGGAGTAA

846 AIFl 0.82 TCCTTGTGCCTGCTCCTGTACTTGT

847 GTF3A 0.87 AAGCCTATACGTTTCTGTGGAGTAA

848 MCM5 0.91 TTGGACATCTCTAGTGTAGCTGCCA

849 GTL3 0.85 AAGCCTATACGTTTCTGTGGAGTAA

850 ZNF22 0.89 TGCCTGCTCCTGTACTTGTCCTCAG

851 FLJ22794 0.83 GCCCCACTGGACAACACTGATTCCT

852 LZTFLl 0.89 ACTTGTCCTCAGCTTGGGCTTCTTC

853 e(y)2 0.87 TCCTCCATCACCTGAAACACTGGAC

854 FLJ20152 0.92 TCCTCCATCACCTGAAACACTGGAC

855 C10orf3 0.86 ACTTGTCCTCAGCTTGGGCTTCTTC

856 NRNl 0.86 AAATGTTTCCTTGTGCCTGCTCCTG

857 FLJ10858 0.81 GCCCCACTGGACAACACTGATTCCT 858 BCLIlB 0.89 GCCCCACTGGACAACACTGATTCCT 859 ASPM 0.91 AAGCCTATACGTTTCTGTGGAGTAA 860 LEFl 0.9 TTGGACATCTCTAGTGTAGCTGCCA 861 LOC146909 0.83 ACTTGTCCTCAGCTTGGGCTTCTTC

Table 44 5-Aza-2 ' -deoxycytidine (decitabine) biomarkers . SEQIDNO Gene Correlation Medianprobe

862 CD99 0.31 T TTTGGGGAACCAATTCCTTCCTAGTGTAGCTGCCA

863 SNRPA 0.32 TTCCCCTTGGTTAACCTTTTGGTCCTCAGCTTGGGC

864 CUGBP2 0.32 TTCCCCTTGGTTAACCTTTTGGTCCTCAGCTTGGGC

865 STAT5A 0.32 GGCCCCCCCCAACCTTGGGGAACAACACTGATTCCT

866 SLA 0.38 TTTTGGGGAACCAATTCCTTCCTAGTGTAGCTGCCA

867 IL2RG 0.33 TTGGGGAACCCCCCCCAACCTTGGCTGAGAATCTGG

868 GTSEl 0.32 AACCTTTTGGTTCCCCTTCCAAGCTTGGGCTTCTTC

869 MYB 0.36 TTGGGGAACCCCCCCCAACCTTGGCTGAGAATCTGG

870 PTPN7 0.33 TTCCCCTTGGTTAACCTTTTGGTCCTCAGCTTGGGC

871 CXorf9 0.42 TTCCCCTTGGTTAACCTTTTGGTCCTCAGCTTGGGC

872 RHOH 0.38 AAAAAATTGGTTTTTTCCCCTTTGTGCCTGCTCCTG

873 ZNFNlAl 0.33 AAAAGGCCCCTTAATTAACCGGTTTCTGTGGAGTAA

874 CENTBl 0.35 CCAACCCCCCAAGGCCTTGGGGTCCTGTGGATGGGA

875 LCP2 0.3 AAAAAATTGGTTTTTTCCCCTTTGTGCCTGCTCCTG

876 HIST1H4C 0.33 TTGGGGAACCCCCCCCAACCTTGGCTGAGAATCTGG

877 CCR7 0.37 TTGGCCCCTTGGCCTTCCCCTTGTACTTGTCCTCAG

878 APOBEC3B 0.31 TTCCCCTTTTGGTTGGCCCCTTGCTCCTGTACTTGT

879 MCM7 0.31 TTGGGGAACCCCCCCCAACCTTGGCTGAGAATCTGG

880 LCPl 0.31 AAAAGGCCCCTTAATTAACCGGTTTCTGTGGAGTAA

881 SELPLG 0.4 TTGGGGAACCCCCCCCAACCTTGGCTGAGAATCTGG

882 CD3Z 0.35 TTCCCCTTGGTTAACCTTTTGGTCCTCAGCTTGGGC

883 PRKCQ 0.39 TTGGCCCCTTGGCCTTCCCCTTGTACTTGTCCTCAG

884 GZMB 0.32 GGCCCCCCCCAACCTTGGGGAACAACACTGATTCCT

885 SCN3A 0.4 AAAAGGCCCCTTAATTAACCGGTTTCTGTGGAGTAA

886 LAIRl 0.35 TTGGCCCCTTGGCCTTCCCCTTGTACTTGTCCTCAG

887 SH2D1A 0.35 GGCCCCCCCCAACCTTGGGGAACAACACTGATTCCT

SEPT6 0.35 AACCTTTTGGTTCCCCTTCCAAGCTTGGGCTTCTTC

889 CG018 0.32 AACCTTTTGGTTCCCCTTCCAAGCTTGGGCTTCTTC 890 CD3D 0.31 TTGGGGAACCCCCCCCAACCTTGGCTGAGAATCTGG 891 ClδorflO 0.33 TTCCCCTTTTGGTTGGCCCCTTGCTCCTGTACTTGT 892 PRFl 0.31 TTCCCCTTCCCCAATTCCAACCCTGAAACACTGGAC 893 AIFl 0.31 TTTTGGGGAACCAATTCCTTCCTAGTGTAGCTGCCA 894 MCM5 0.31 AACCTTTTGGTTCCCCTTCCAAGCTTGGGCTTCTTC 895 LPXN 0.35 TTCCCCTTCCCCAATTCCAACCCTGAAACACTGGAC 896 C22orfl8 0.33 AAAAAATTGGTTTTTTCCCCTTTGTGCCTGCTCCTG 897 ARHGAP15 0.31 AAAAAATTGGTTTTTTCCCCTTTGTGCCTGCTCCTG 898 LEFl 0.43 GGCCCCCCCCAACCTTGGGGAACAACACTGATTCCT

Table 45 . I Iddaarruubbicin biomarkers.

SEQIDNO Gene Correlation Medianprobe

899 SLC9A3P 0.31 TGGACCCCACTGGCTGAGAATCTGG

900 RPS19 0.32 TGGACCCCACTGGCTGAGAATCTGG

901 ITM2A 0.34 TCCTGTACTTGTCCTCAGCTTGGGC

902 SSBP2 0.31 AAGCCTATACGTTTCTGTGGAGTAA

903 CXorf9 0.31 TCCTGTACTTGTCCTCAGCTTGGGC

904 RHOH 0.32 TCCTCCATCACCTGAAACACTGGAC

905 ZNFNlAl 0.36 AAATGTTTCCTTGTGCCTGCTCCTG

906 FXYD2 0.35 CACCCAGCTGGTCCTGTGGATGGGA

907 CCR9 0.39 TGGACCCCACTGGCTGAGAATCTGG 908 NAPlLl 0.3 TTGGACATCTCTAGTGTAGCTGCCA 909 CXCR4 0.31 AAATGTTTCCTTGTGCCTGCTCCTG 910 SH2D1A 0.3 TCCTGTACTTGTCCTCAGCTTGGGC 911 CDlA 0.3 AAGCCTATACGTTTCTGTGGAGTAA 912 TRB@ 0.32 AAATGTTTCCTTGTGCCTGCTCCTG 913 SEPT6 0.32 GCCCCACTGGACAACACTGATTCCT 914 RPS2 0.33 TGCCTGCTCCTGTACTTGTCCTCAG 915 DOCK2 0.32 TGCCTGCTCCTGTACTTGTCCTCAG 916 CD3D 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC 917 CD6 0.3 GCCCCACTGGACAACACTGATTCCT 918 ZAP70 0.34 ACTTGTCCTCAGCTTGGGCTTCTTC 919 AIFl 0.3 TGCCTGCTCCTGTACTTGTCCTCAG 920 CDlE 0.32 TGCCTGCTCCTGTACTTGTCCTCAG 921 CYFIP2 0.3 TTGGACATCTCTAGTGTAGCTGCCA 922 ADA 0.41 TCCTGTACTTGTCCTCAGCTTGGGC 923 TRIM 0.31 TCCTTGTGCCTGCTCCTGTACTTGT 924 GLTSCR2 0.32 TGCCTGCTCCTGTACTTGTCCTCAG 925 FLJ10858 0.35 GCCCCACTGGACAACACTGATTCCT 926 BCLIlB 0.34 TCCTGTACTTGTCCTCAGCTTGGGC 927 GIMAP6 0.36 TGCCTGCTCCTGTACTTGTCCTCAG 928 STAG3 0.34 TTGGACATCTCTAGTGTAGCTGCCA 929 UBASH3A 0.39 ACTTGTCCTCAGCTTGGGCTTCTTC

Table 46. Melphalan biomarkers . SEQIDNO Gene Correlation Medianprobe

930 CD99 0.31 TGGACCCCACTGGCTGAGAATCTGG

931 HLA-DPBl 0.32 TGCCTGCTCCTGTACTTGTCCTCAG

932 ARHGDIB 0.35 TGCCTGCTCCTGTACTTGTCCTCAG

933 IFITMl 0.33 CACCCAGCTGGTCCTGTGGATGGGA

934 UBE2L6 0.32 TCCTTGTGCCTGCTCCTGTACTTGT

935 ITM2A 0.37 TCCTTGTGCCTGCTCCTGTACTTGT

936 SERPINAl 0.31 AAATGTTTCCTTGTGCCTGCTCCTG

937 STAT5A 0.38 AAATGTTTCCTTGTGCCTGCTCCTG

938 INPP5D 0.37 TCCTTGTGCCTGCTCCTGTACTTGT

939 DGKA 0.3 TGCCTGCTCCTGTACTTGTCCTCAG

940 SATBl 0.34 TGCCTGCTCCTGTACTTGTCCTCAG

941 SEMA4D 0.37 AAATGTTTCCTTGTGCCTGCTCCTG

942 TFDP2 0.31 CACCCAGCTGGTCCTGTGGATGGGA

943 SLA 0.49 TCCTCCATCACCTGAAACACTGGAC

944 IL2RG 0.42 CACCCAGCTGGTCCTGTGGATGGGA

945 CD48 0.33 TCCTTGTGCCTGCTCCTGTACTTGT

946 MFNG 0.48 ACTTGTCCTCAGCTTGGGCTTCTTC

947 ALOX5AP 0.3 CACCCAGCTGGTCCTGTGGATGGGA

948 GPSM3 0.31 AAGCCTATACGTTTCTGTGGAGTAA

949 PSMB9 0.34 GCCCCACTGGACAACACTGATTCCT

950 KIAA0711 0.37 TGGACCCCACTGGCTGAGAATCTGG

951 SELL 0.32 AAATGTTTCCTTGTGCCTGCTCCTG

952 ADA 0.31 TGCCTGCTCCTGTACTTGTCCTCAG

953 EDGl 0.49 TTGGACATCTCTAGTGTAGCTGCCA

954 RIMS3 0.3 CACCCAGCTGGTCCTGTGGATGGGA

955 FMNLl 0.33 AAGCCTATACGTTTCTGTGGAGTAA

956 MYB 0.3 GCCCCACTGGACAACACTGATTCCT

957 PTPN7 0.34 AAATGTTTCCTTGTGCCTGCTCCTG

958 LCK 0.31 AAATGTTTCCTTGTGCCTGCTCCTG

959 CXorf9 0.55 CACCCAGCTGGTCCTGTGGATGGGA

960 RHOH 0.35 TGGACCCCACTGGCTGAGAATCTGG

961 ZNFNlAl 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC

962 CENTBl 0.32 TGCCTGCTCCTGTACTTGTCCTCAG

963 LCP2 0.32 TCCTTGTGCCTGCTCCTGTACTTGT

964 FXYD2 0.55 CACCCAGCTGGTCCTGTGGATGGGA

965 CDlD 0.44 AAGCCTATACGTTTCTGTGGAGTAA

966 BATF 0.32 TGCCTGCTCCTGTACTTGTCCTCAG

967 STAT4 0.33 TCCTCCATCACCTGAAACACTGGAC

968 VAVl 0.31 TCCTCCATCACCTGAAACACTGGAC

969 MAP4K1 0.39 CACCCAGCTGGTCCTGTGGATGGGA

970 CCR7 0.44 TCCTGTACTTGTCCTCAGCTTGGGC

971 PDE4C 0.32 TCCTGTACTTGTCCTCAGCTTGGGC

972 CD3G 0.32 AAGCCTATACGTTTCTGTGGAGTAA

973 CCR9 0.36 TTGGACATCTCTAGTGTAGCTGCCA

974 SPIlO 0.34 TCCTGTACTTGTCCTCAGCTTGGGC

975 LCPl 0.35 AAATGTTTCCTTGTGCCTGCTCCTG

976 IFI16 0.32 GCCCCACTGGACAACACTGATTCCT

977 CXCR4 0.36 ACTTGTCCTCAGCTTGGGCTTCTTC

978 ARHGEF6 0.47 AAGCCTATACGTTTCTGTGGAGTAA

979 GATA3 0.55 TTGGACATCTCTAGTGTAGCTGCCA

980 SELPLG 0.47 TTGGACATCTCTAGTGTAGCTGCCA

981 SEC31L2 0.36 TGGACCCCACTGGCTGAGAATCTGG

982 CD3Z 0.5 TTGGACATCTCTAGTGTAGCTGCCA

983 PRKCQ 0.56 GCCCCACTGGACAACACTGATTCCT

984 SH2D1A 0.33 TCCTCCATCACCTGAAACACTGGAC

985 GZMB 0.39 TGCCTGCTCCTGTACTTGTCCTCAG

986 CDlA 0.55 TGCCTGCTCCTGTACTTGTCCTCAG

987 SCN3A 0.64 CACCCAGCTGGTCCTGTGGATGGGA

988 LAIRl 0.32 CACCCAGCTGGTCCTGTGGATGGGA

989 FYB 0.49 TTGGACATCTCTAGTGTAGCTGCCA

990 TRB@ 0.37 TTGGACATCTCTAGTGTAGCTGCCA

991 SEPT6 0.32 GCCCCACTGGACAACACTGATTCCT

992 HA-I 0.48 GCCCCACTGGACAACACTGATTCCT

993 DOCK2 0.33 TTGGACATCTCTAGTGTAGCTGCCA

994 CG018 0.37 AAATGTTTCCTTGTGCCTGCTCCTG

995 CD3D 0.32 TCCTCCATCACCTGAAACACTGGAC

996 T3JAM 0.41 TGCCTGCTCCTGTACTTGTCCTCAG

997 FNBPl 0.36 TCCTGTACTTGTCCTCAGCTTGGGC

998 CD6 0.36 AAGCCTATACGTTTCTGTGGAGTAA

999 ZAP70 0.36 TGGACCCCACTGGCTGAGAATCTGG

1000 LSTl 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC

1001 GPR65 0.42 TTGGACATCTCTAGTGTAGCTGCCA

1002 PRFl 0.41 GCCCCACTGGACAACACTGATTCCT

1003 AIFl 0.32 GCCCCACTGGACAACACTGATTCCT

1004 FLJ20331 0.42 TCCTCCATCACCTGAAACACTGGAC

1005 RAG2 0.31 CACCCAGCTGGTCCTGTGGATGGGA

1006 WDR45 0.37 TCCTGTACTTGTCCTCAGCTTGGGC

1007 CDlE 0.31 TCCTGTACTTGTCCTCAGCTTGGGC

1008 CYFIP2 0.4 TCCTCCATCACCTGAAACACTGGAC

1009 TARP 0.36 CACCCAGCTGGTCCTGTGGATGGGA

1010 TRIM 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC

1011 RPLlOL 0.3 TTGGACATCTCTAGTGTAGCTGCCA

1012 GLTSCR2 0.46 CACCCAGCTGGTCCTGTGGATGGGA

1013 GIMAP5 0.32 AAGCCTATACGTTTCTGTGGAGTAA

1014 ARHGAP15 0.36 TGCCTGCTCCTGTACTTGTCCTCAG

1015 NOTCHl 0.34 GCCCCACTGGACAACACTGATTCCT

1016 BIN2 0.36 TGGACCCCACTGGCTGAGAATCTGG 1017 C13orfl8 0.34 TCCTTGTGCCTGCTCCTGTACTTGT 1018 CECRl 0.32 TCCTGTACTTGTCCTCAGCTTGGGC 1019 BCLIlB 0.32 TCCTTGTGCCTGCTCCTGTACTTGT 1020 GIMAP6 0.3 TCCTTGTGCCTGCTCCTGTACTTGT 1021 STAG3 0.58 TTGGACATCTCTAGTGTAGCTGCCA 1022 TM6SF1 0.31 TCCTGTACTTGTCCTCAGCTTGGGC 1023 HSD17B7 0.32 GCCCCACTGGACAACACTGATTCCT 1024 UBASH3A 0.3 ACTTGTCCTCAGCTTGGGCTTCTTC 1025 MGC5566 0.45 TCCTCCATCACCTGAAACACTGGAC 1026 FLJ22457 0.39 AAGCCTATACGTTTCTGTGGAGTAA 1027 TPKl 0.33 TGCCTGCTCCTGTACTTGTCCTCAG 1028 PHFIl 0.3 AAATGTTTCCTTGTGCCTGCTCCTG 1029 DKFZP434B0335 0.4 TCCTTGTGCCTGCTCCTGTACTTGT

Table 47, IL4-PR38 fusion protein biomarkers

SEQIDNO Gene Correlation Medianprobe

1030 MCLl 0.3 TCCTCCATCACCTGAAACACTGGAC

1031 DDX23 0.35 CACCCAGCTGGTCCTGTGGATGGGA

1032 JUNB 0.31 TGCCTGCTCCTGTACTTGTCCTCAG

1033 ZFP36 0.33 CACCCAGCTGGTCCTGTGGATGGGA

1034 IFITMl 0.32 ACTTGTCCTCAGCTTGGGCTTCTTC

1035 CKSlB 0.3 TGGACCCCACTGGCTGAGAATCTGG

1036 SERPINAl 0.31 GCCCCACTGGACAACACTGATTCCT

1037 IL4R 0.3 ACTTGTCCTCAGCTTGGGCTTCTTC

1038 CLDN3 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC

1039 ARL4A 0.33 AAATGTTTCCTTGTGCCTGCTCCTG

1040 HMMR 0.31 TCCTTGTGCCTGCTCCTGTACTTGT

1041 FLJ12671 0.42 TCCTTGTGCCTGCTCCTGTACTTGT

1042 ANKHDl 0.42 GCCCCACTGGACAACACTGATTCCT

1043 KIF2C 0.37 ACTTGTCCTCAGCTTGGGCTTCTTC

1044 RPA3 0.34 CACCCAGCTGGTCCTGTGGATGGGA

1045 MCCC2 0.34 TGCCTGCTCCTGTACTTGTCCTCAG

1046 CDH17 0.32 TCCTTGTGCCTGCTCCTGTACTTGT

1047 LSM5 0.33 TTGGACATCTCTAGTGTAGCTGCCA

1048 PRFl 0.32 GCCCCACTGGACAACACTGATTCCT

1049 RODl 0.34 TCCTCCATCACCTGAAACACTGGAC

1050 FLJ12666 0.37 TCCTCCATCACCTGAAACACTGGAC

1051 SUV420H1 0.31 TTGGACATCTCTAGTGTAGCTGCCA

1052 MUC13 0.36 TCCTCCATCACCTGAAACACTGGAC

1053 C13orfl8 0.35 GCCCCACTGGACAACACTGATTCCT

1054 CDCA8 0.35 TGCCTGCTCCTGTACTTGTCCTCAG

Table 48 . Valproic acid (VPA) biomarkers.

SEQIDNO Gene Correlation Medianprobe

1055 STOM 0.32 AAATGTTTCCTTGTGCCTGCTCCTG

1056 TNFAIP3 0.32 TGGACCCCACTGGCTGAGAATCTGG

1057 ASNS 0.31 GCCCCACTGGACAACACTGATTCCT

1058 GARS 0.37 TGCCTGCTCCTGTACTTGTCCTCAG

1059 CXCR4 0.32 AAGCCTATACGTTTCTGTGGAGTAA

1060 EGLN3 0.31 TGGACCCCACTGGCTGAGAATCTGG

1061 LBH 0.35 TCCTGTACTTGTCCTCAGCTTGGGC

1062 GDFl5 0.3 TGCCTGCTCCTGTACTTGTCCTCAG

Table 49. All-trans retinoic acid (ATRA) biomarkers. SEQIDNO Gene Correlation Medianprobe

1063 PPIB 0.31 AAGCCTATACGTTTCTGTGGAGTAA

1064 ZFP36L2 0.48 AAGCCTATACGTTTCTGTGGAGTAA

1065 IFI30 0.46 ACTTGTCCTCAGCTTGGGCTTCTTC

1066 USP7 0.35 TCCTCCATCACCTGAAACACTGGAC

1067 SRM 0.43 TCCTCCATCACCTGAAACACTGGAC

1068 SH3BP5 0.32 TGCCTGCTCCTGTACTTGTCCTCAG

1069 ALDOC 0.41 TTGGACATCTCTAGTGTAGCTGCCA

1070 FADS2 0.33 ACTTGTCCTCAGCTTGGGCTTCTTC

1071 GUSB 0.38 TTGGACATCTCTAGTGTAGCTGCCA

1072 PSCDl 0.48 TCCTGTACTTGTCCTCAGCTTGGGC

1073 IQGAP2 0.34 TCCTGTACTTGTCCTCAGCTTGGGC

1074 STS 0.34 GCCCCACTGGACAACACTGATTCCT

1075 MFNG 0.36 TGGACCCCACTGGCTGAGAATCTGG

1076 FLIl 0.33 ACTTGTCCTCAGCTTGGGCTTCTTC

1077 PIM2 0.35 TGGACCCCACTGGCTGAGAATCTGG

1078 INPP4A 0.54 TCCTGTACTTGTCCTCAGCTTGGGC

1079 LRMP 0.51 GCCCCACTGGACAACACTGATTCCT

1080 ICAM2 0.3 AAATGTTTCCTTGTGCCTGCTCCTG

1081 EVI2A 0.33 CACCCAGCTGGTCCTGTGGATGGGA

1082 MAL 0.46 AAATGTTTCCTTGTGCCTGCTCCTG

1083 BTN3A3 0.43 TTGGACATCTCTAGTGTAGCTGCCA

1084 PTPN7 0.4 TTGGACATCTCTAGTGTAGCTGCCA

1085 ILlORA 0.42 TTGGACATCTCTAGTGTAGCTGCCA

1086 SPIl 0.41 AAGCCTATACGTTTCTGTGGAGTAA

1087 TRAFl 0.3 TGCCTGCTCCTGTACTTGTCCTCAG

1088 ITGB7 0.33 TCCTTGTGCCTGCTCCTGTACTTGT

1089 ARHGAP6 0.32 TGGACCCCACTGGCTGAGAATCTGG

1090 MAP4K1 0.52 GCCCCACTGGACAACACTGATTCCT

1091 CD28 0.34 AAGCCTATACGTTTCTGTGGAGTAA

1092 PTP4A3 0.3 TCCTCCATCACCTGAAACACTGGAC

1093 LTB 0.32 ACTTGTCCTCAGCTTGGGCTTCTTC

1094 Clorf38 0.4 TGCCTGCTCCTGTACTTGTCCTCAG

1095 WBSCR22 0.53 TCCTCCATCACCTGAAACACTGGAC

1096 CD8B1 0.35 TCCTCCATCACCTGAAACACTGGAC

1097 LCPl 0.35 ACTTGTCCTCAGCTTGGGCTTCTTC

1098 FLJ13052 0.31 TCCTCCATCACCTGAAACACTGGAC

1099 MEF2C 0.71 TTGGACATCTCTAGTGTAGCTGCCA

1100 PSCDBP 0.41 AAATGTTTCCTTGTGCCTGCTCCTG

1101 IL16 0.51 TGGACCCCACTGGCTGAGAATCTGG

1102 SELPLG 0.53 TGCCTGCTCCTGTACTTGTCCTCAG

1103 MAGEA9 0.6 AAATGTTTCCTTGTGCCTGCTCCTG

1104 LAIRl 0.43 TCCTCCATCACCTGAAACACTGGAC

1105 TNFRSF25 0.53 TCCTCCATCACCTGAAACACTGGAC

1106 EVI2B 0.42 ACTTGTCCTCAGCTTGGGCTTCTTC

1107 IGJ 0.37 TCCTTGTGCCTGCTCCTGTACTTGT

1108 PDCD4 0.47 AAATGTTTCCTTGTGCCTGCTCCTG

1109 RASA4 0.52 CACCCAGCTGGTCCTGTGGATGGGA

1110 HA-I 0.73 AAGCCTATACGTTTCTGTGGAGTAA

1111 PLCL2 0.47 TCCTGTACTTGTCCTCAGCTTGGGC

1112 RNASE6 0.31 AAGCCTATACGTTTCTGTGGAGTAA

1113 WBSCR20C 0.35 TTGGACATCTCTAGTGTAGCTGCCA

1114 NUP210 0.36 AAGCCTATACGTTTCTGTGGAGTAA

1115 RPLlOL 0.39 ACTTGTCCTCAGCTTGGGCTTCTTC

1116 Cllorf2 0.33 TGGACCCCACTGGCTGAGAATCTGG

1117 CABCl 0.32 TGCCTGCTCCTGTACTTGTCCTCAG

1118 ARHGEF3 0.37 TCCTGTACTTGTCCTCAGCTTGGGC

1119 TAPBPL 0.42 TGCCTGCTCCTGTACTTGTCCTCAG

1120 CHST12 0.35 AAATGTTTCCTTGTGCCTGCTCCTG

1121 FKBPIl 0.54 TGCCTGCTCCTGTACTTGTCCTCAG

1122 FLJ35036 0.42 TTGGACATCTCTAGTGTAGCTGCCA

1123 MYLIP 0.38 CACCCAGCTGGTCCTGTGGATGGGA

1124 TXNDC5 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC

1125 PACAP 0.3 TCCTCCATCACCTGAAACACTGGAC

1126 TOSO 0.34 TCCTGTACTTGTCCTCAGCTTGGGC

1127 PNAS-4 0.37 TGGACCCCACTGGCTGAGAATCTGG

1128 IL21R 0.57 AAGCCTATACGTTTCTGTGGAGTAA

1129 TCF4 0.64 TCCTTGTGCCTGCTCCTGTACTTGT

Table 50. Cytoxan biomarkers .

SEQIDNO Gene Correlation Medianprobe

1130 C6orf29 0.31 AAGCCTATACGTTTCTGTGGAGTAA

1131 TRIM31 0.31 AAATGTTTCCTTGTGCCTGCTCCTG

1132 CD69 0.37 GCCCCACTGGACAACACTGATTCCT

1133 LRRN3 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC

1134 GPR35 0.41 TCCTCCATCACCTGAAACACTGGAC

1135 CDW52 0.48 TTGGACATCTCTAGTGTAGCTGCCA

Table 51. Topotecan (Hycamtin) biomarkers. SEQIDNO Gene Correlation Medianprobe

1136 K-ALPHA-] L 0.32 AAGCCTATACGTTTCTGTGGAGTAA

1137 CSDA 0.32 AAGCCTATACGTTTCTGTGGAGTAA

1138 UCHLl 0.32 TTGGACATCTCTAGTGTAGCTGCCA

1139 NAPlLl 0.3 TCCTCCATCACCTGAAACACTGGAC

1140 ATP5G2 0.3 TCCTGTACTTGTCCTCAGCTTGGGC

1141 HDGFRP3 0.3 AAGCCTATACGTTTCTGTGGAGTAA

1142 IFI44 0.3 GCCCCACTGGACAACACTGATTCCT

Table 52 . Suberoyl Lanili

Zolinza) biomarkers .

SEQIDNO Gene C Coorrrreelation Medianprobe

1143 NOL5A 0.35 TCCTTGTGCCTGCTCCTGTACTTGT

1144 STOM 0.35 TGCCTGCTCCTGTACTTGTCCTCAG

1145 SIATl 0.36 AAATGTTTCCTTGTGCCTGCTCCTG

1146 CUGBP2 0.39 GCCCCACTGGACAACACTGATTCCT

1147 GUSB 0.33 TGGACCCCACTGGCTGAGAATCTGG

1148 ITM2A 0.34 TCCTTGTGCCTGCTCCTGTACTTGT

1149 JARID2 0.32 ACTTGTCCTCAGCTTGGGCTTCTTC

1150 RUNX3 0.32 CACCCAGCTGGTCCTGTGGATGGGA

1151 ICAM2 0.35 TGCCTGCTCCTGTACTTGTCCTCAG

1152 PTPN7 0.37 AAGCCTATACGTTTCTGTGGAGTAA

1153 VAVl 0.35 TTGGACATCTCTAGTGTAGCTGCCA

1154 PTP4A3 0.42 AAGCCTATACGTTTCTGTGGAGTAA

1155 MCAM 0.35 ACTTGTCCTCAGCTTGGGCTTCTTC

1156 MEF2C 0.32 ACTTGTCCTCAGCTTGGGCTTCTTC

1157 IDH3B 0.3 TGCCTGCTCCTGTACTTGTCCTCAG

1158 RFP 0.31 TCCTCCATCACCTGAAACACTGGAC

1159 SEPT6 0.31 TCCTGTACTTGTCCTCAGCTTGGGC

1160 SLC43A3 0.34 GCCCCACTGGACAACACTGATTCCT

1161 WBSCR20C 0.46 TGGACCCCACTGGCTGAGAATCTGG

1162 SHMT2 0.34 TCCTGTACTTGTCCTCAGCTTGGGC

1163 GLTSCR2 0.31 TCCTGTACTTGTCCTCAGCTTGGGC

1164 CABCl 0.33 TCCTGTACTTGTCCTCAGCTTGGGC

12

1165 FLJ20859 0.42 ACTTGTCCTCAGCTTGGGCTTCTTC 1166 FLJ20010 0.51 TCCTGTACTTGTCCTCAGCTTGGGC 1167 MGC10993 0.33 TCCTTGTGCCTGCTCCTGTACTTGT 1168 FKBPIl 0.31 TCCTCCATCACCTGAAACACTGGAC

Table 53. Depsipeptide (FR901228) biomarkers.

SEQIDNO Gene Correlation Medianprobe

1169 ZFP36L2 0.32 AAATGTTTCCTTGTGCCTGCTCCTG

1170 TRIB2 0.35 TGCCTGCTCCTGTACTTGTCCTCAG

1171 LCP2 0.37 ACTTGTCCTCAGCTTGGGCTTCTTC

1172 C6orf32 0.35 TGCCTGCTCCTGTACTTGTCCTCAG

1173 ILl 6 0.34 CACCCAGCTGGTCCTGTGGATGGGA

1174 CACNAlG 0.31 AAGCCTATACGTTTCTGTGGAGTAA

1175 SPDEF 0.31 GCCCCACTGGACAACACTGATTCCT

1176 HABl 0.39 TCCTCCATCACCTGAAACACTGGAC

1177 TOSO 0.31 TGGACCCCACTGGCTGAGAATCTGG

1178 ARHGAP25 0.33 AAGCCTATACGTTTCTGTGGAGTAA

Table 54. Bortezomib biomarkers.

SEQIDNO Gene Correlation Medianprobe

1179 PLEKHB2 0.32 A AAAAATTGGTTTTTTCCCCTTTGTGCCTGCTCCTG

1180 ARPClB 0.32 TTGGGGAACCCCCCCCAACCTTGGCTGAGAATCTGG

1181 MXl 0.39 TTCCCCTTTTGGTTGGCCCCTTGCTCCTGTACTTGT

1182 CUGBP2 0.37 AAAAGGCCCCTTAATTAACCGGTTTCTGTGGAGTAA

1183 IFI16 0.33 AAAAGGCCCCTTAATTAACCGGTTTCTGTGGAGTAA

1184 TNFRSF14 0.3 AAAAAATTGGTTTTTTCCCCTTTGTGCCTGCTCCTG

1185 SPIlO 0.39 TTGGGGAACCCCCCCCAACCTTGGCTGAGAATCTGG

1186 ELFl 0.33 TTGGGGAACCCCCCCCAACCTTGGCTGAGAATCTGG

1187 LPXN 0.33 TTCCCCTTGGTTAACCTTTTGGTCCTCAGCTTGGGC

1188 IFRG28 0.31 TTCCCCTTGGTTAACCTTTTGGTCCTCAGCTTGGGC

1189 LEFl 0.33 GGCCCCCCCCAACCTTGGGGAACAACACTGATTCCT

1190 PYCARD 0.31 TTCCCCTTGGTTAACCTTTTGGTCCTCAGCTTGGGC

Table 55. Leukeran biomarkers .

SEQIDNO Gene Correlation M Meeddiiaannpprroobbe

^' 1191 SSRPl 0.31 G GCCCCCCCCAACCTTGGGGACAACACTGATTCCT

1192 ALDOC 0.36 A AAAAATTGGTTTTTTCCCCTTGTGCCTGCTCCTG

1193 ClQRl 0.31 T TGGCCCCTTGGCCTTCCCCTGTACTTGTCCTCAG

1194 TTFl 0.31 T TCCCCTTGGTTAACCTTTTGTCCTCAGCTTGGGC

1195 PRIMl 0.31 G GCCCCCCCCAACCTTGGGGACAACACTGATTCCT

1196 USP34 0.38 T TCCCCTTCCCCAATTCC^ACCTGAAACACTGGAC

1197 TK2 0.33 T TCCCCTTGGTTAACCTTTTGTCCTCAGCTTGGGC

1198 GOLGIN-67 0.31 T TGGCCCCTTGGCCTTCCCCTGTACTTGTCCTCAG

1199 NPD014 0.35 A ACCTTTTGGTTCCCCTTCCAGCTTGGGCTTCTTC

1200 KIAA0220 0.31 T TCCCCTTCCCCAATTCC^ACCTGAAACACTGGAC

1201 SLC43A3 0.3 T TTTGGGGAACCAATTCCTTCTAGTGTAGCTGCCA

1202 WBSCR20C 0.3 C CAACCCCCCAAGGCCTTGGGTCCTGTGGATGGGA

1203 ICAM2 0.3 T TGGCCCCTTGGCCTTCCCCTGTACTTGTCCTCAG

1204 TEXlO 0.32 T TGGGGAACCCCCCCCAACCTGGCTGAGAATCTGG

1205 CHD7 0.3 A ACCTTTTGGTTCCCCTTCCAGCTTGGGCTTCTTC

1206 SAMSNl 0.34 T TTTGGGGAACCAATTCCTTCTAGTGTAGCTGCCA

1207 TPRT 0.35 A ACCTTTTGGTTCCCCTTCCAGCTTGGGCTTCTTC

Table 56. Fludarabine biomarkers.

SEQIDNO Gene Correlation Medianprobe

1208 HLA-E 0.3 ACTTGTCCTCAGCTTGGGCTTCTTC

1209 BAT3 0.34 TGCCTGCTCCTGTACTTGTCCTCAG

1210 ENO2 0.37 TGGACCCCACTGGCTGAGAATCTGG

1211 UBE2L6 0.3 TCCTGTACTTGTCCTCAGCTTGGGC

1212 CUGBP2 0.35 TGCCTGCTCCTGTACTTGTCCTCAG

1213 ITM2A 0.32 GCCCCACTGGACAACACTGATTCCT

1214 PALM2-AKAP2 0.41 GCCCCACTGGACAACACTGATTCCT

1215 JARID2 0.33 GCCCCACTGGACAACACTGATTCCT

1216 DGKA 0.33 TGGACCCCACTGGCTGAGAATCTGG

1217 SLC7A6 0.4 AAGCCTATACGTTTCTGTGGAGTAA

1218 TFDP2 0.35 AAATGTTTCCTTGTGCCTGCTCCTG

1219 ADA 0.41 TGCCTGCTCCTGTACTTGTCCTCAG

1220 EDGl 0.33 TGCCTGCTCCTGTACTTGTCCTCAG

1221 ICAM2 0.46 AAGCCTATACGTTTCTGTGGAGTAA

1222 PTPN7 0.33 TCCTCCATCACCTGAAACACTGGAC

1223 CXorf9 0.35 AAGCCTATACGTTTCTGTGGAGTAA

1224 RHOH 0.31 CACCCAGCTGGTCCTGTGGATGGGA

1225 MX2 0.32 AAATGTTTCCTTGTGCCTGCTCCTG

1226 ZNFNlAl 0.31 TCCTCCATCACCTGAAACACTGGAC

1227 COCH 0.33 TGGACCCCACTGGCTGAGAATCTGG

1228 LCP2 0.34 TGGACCCCACTGGCTGAGAATCTGG

1229 CLGN 0.31 TCCTCCATCACCTGAAACACTGGAC

1230 BNCl 0.38 GCCCCACTGGACAACACTGATTCCT

1231 FLNC 0.3 TCCTGTACTTGTCCTCAGCTTGGGC

1232 HLA-DRB3 0.32 TGCCTGCTCCTGTACTTGTCCTCAG

1233 UCP2 0.34 TGGACCCCACTGGCTGAGAATCTGG

1234 HLA-DRBl 0.3 GCCCCACTGGACAACACTGATTCCT

1235 GATA3 0.37 TCCTTGTGCCTGCTCCTGTACTTGT

1236 PRKCQ 0.39 AAATGTTTCCTTGTGCCTGCTCCTG

1237 SH2D1A 0.37 ACTTGTCCTCAGCTTGGGCTTCTTC

1238 NFATC3 0.33 ACTTGTCCTCAGCTTGGGCTTCTTC

1239 TRB@ 0.35 AAATGTTTCCTTGTGCCTGCTCCTG

1240 FNBPl 0.34 TCCTCCATCACCTGAAACACTGGAC

1241 SEPT6 0.33 ACTTGTCCTCAGCTTGGGCTTCTTC

1242 NME4 0.32 AAGCCTATACGTTTCTGTGGAGTAA

1243 DKFZP434C171 0.3 TCCTTGTGCCTGCTCCTGTACTTGT

1244 ZC3HAV1 0.32 TCCTGTACTTGTCCTCAGCTTGGGC

1245 SLC43A3 0.37 AAATGTTTCCTTGTGCCTGCTCCTG

1246 CD3D 0.31 AAATGTTTCCTTGTGCCTGCTCCTG

1247 AIFl 0.35 TCCTCCATCACCTGAAACACTGGAC

1248 SPTANl 0.34 TCCTCCATCACCTGAAACACTGGAC

1249 CDlE 0.31 TCCTTGTGCCTGCTCCTGTACTTGT

1250 TRIM 0.31 AAGCCTATACGTTTCTGTGGAGTAA

1251 DATFl 0.31 TCCTTGTGCCTGCTCCTGTACTTGT

1252 FHODl 0.37 TCCTGTACTTGTCCTCAGCTTGGGC

1253 ARHGAPl 5 0.3 CACCCAGCTGGTCCTGTGGATGGGA

1254 STAG3 0.34 AAGCCTATACGTTTCTGTGGAGTAA

1255 SAP130 0.31 TCCTGTACTTGTCCTCAGCTTGGGC

1256 CYLD 0.3 ACTTGTCCTCAGCTTGGGCTTCTTC

Table 57. Vinblastine biomarkers . SEQIDNO Gene Correlation Medianprobe

1257 CD99 0.33 ACTTGTCCTCAGCTTGGGCTTCTTC

Table 58. Busulfan biomarkers.

SEQIDNO Gene Correlation Medianprobe

1258 RPLP2 0.37 TCCTCCATCACCTGAAACACTGGAC

1259 BTGl 0.36 ACTTGTCCTCAGCTTGGGCTTCTTC 1260 CSDA 0.31 TGCCTGCTCCTGTACTTGTCCTCAG 1261 ARHGDIB 0.38 AAGCCTATACGTTTCTGTGGAGTAA 1262 INSIGl 0.41 TCCTCCATCACCTGAAACACTGGAC 1263 ALDOC 0.36 TTGGACATCTCTAGTGTAGCTGCCA 1264 WASPIP 0.31 TCCTCCATCACCTGAAACACTGGAC 1265 ClQRl 0.46 TCCTGTACTTGTCCTCAGCTTGGGC 1266 EDEMl 0.36 TGGACCCCACTGGCTGAGAATCTGG 1267 SLA 0.35 TCCTTGTGCCTGCTCCTGTACTTGT 1268 MFNG 0.4 TCCTTGTGCCTGCTCCTGTACTTGT 1269 GPSM3 0.75 GCCCCACTGGACAACACTGATTCCT 1270 ADA 0.53 ACTTGTCCTCAGCTTGGGCTTCTTC 1271 LRMP 0.31 TCCTGTACTTGTCCTCAGCTTGGGC 1272 EVI2A 0.52 TCCTCCATCACCTGAAACACTGGAC 1273 FMNLl 0.45 ACTTGTCCTCAGCTTGGGCTTCTTC 1274 PTPN7 0.3 ACTTGTCCTCAGCTTGGGCTTCTTC 1275 RHOH 0.39 ACTTGTCCTCAGCTTGGGCTTCTTC 1276 ZNFNlAl 0.36 AAGCCTATACGTTTCTGTGGAGTAA 1277 CENTBl 0.33 TTGGACATCTCTAGTGTAGCTGCCA 1278 MAP4K1 0.31 TGGACCCCACTGGCTGAGAATCTGG 1279 CD28 0.51 TCCTGTACTTGTCCTCAGCTTGGGC 1280 SPIlO 0.38 TCCTTGTGCCTGCTCCTGTACTTGT 1281 NAPlLl 0.31 TGCCTGCTCCTGTACTTGTCCTCAG 1282 IFI16 0.35 TCCTCCATCACCTGAAACACTGGAC 1283 ARHGEF6 0.42 AAATGTTTCCTTGTGCCTGCTCCTG 1284 SELPLG 0.45 TCCTGTACTTGTCCTCAGCTTGGGC 1285 CD3Z 0.35 CACCCAGCTGGTCCTGTGGATGGGA 1286 SH2D1A 0.38 CACCCAGCTGGTCCTGTGGATGGGA 1287 LAIRl 0.34 TGCCTGCTCCTGTACTTGTCCTCAG 1288 RAFTLIN 0.36 GCCCCACTGGACAACACTGATTCCT 1289 HA-I 0.61 ACTTGTCCTCAGCTTGGGCTTCTTC 1290 DOCK2 0.4 TGCCTGCTCCTGTACTTGTCCTCAG 1291 CD3D 0.31 GCCCCACTGGACAACACTGATTCCT 1292 T3JAM 0.35 ACTTGTCCTCAGCTTGGGCTTCTTC 1293 ZAP70 0.36 TGGACCCCACTGGCTGAGAATCTGG 1294 GPR65 0.32 TCCTCCATCACCTGAAACACTGGAC 1295 CYFIP2 0.58 CACCCAGCTGGTCCTGTGGATGGGA 1296 LPXN 0.34 TTGGACATCTCTAGTGTAGCTGCCA 1297 RPLlOL 0.41 TCCTGTACTTGTCCTCAGCTTGGGC 1298 GLTSCR2 0.33 AAATGTTTCCTTGTGCCTGCTCCTG 1299 ARHGAP15 0.47 CACCCAGCTGGTCCTGTGGATGGGA 1300 BCLIlB 0.31 TGCCTGCTCCTGTACTTGTCCTCAG 1301 TM6SF1 0.39 AAGCCTATACGTTTCTGTGGAGTAA 1302 PACAP 0.33 ACTTGTCCTCAGCTTGGGCTTCTTC 1303 TCF4 0.32 TGGACCCCACTGGCTGAGAATCTGG

Table 59, Dacarbazine biomarkers.

SEQIDNO Gene Correlation Medianprobe

1304 ARHGDIB 0.31 TCCTTGTGCCTGCTCCTGTACTTGT

1305 ITM2A 0.4 TCCTCCATCACCTGAAACACTGGAC

1306 SSBP2 0.33 CACCCAGCTGGTCCTGTGGATGGGA

1307 PIM2 0.39 GCCCCACTGGACAACACTGATTCCT

1308 SELL 0.31 GCCCCACTGGACAACACTGATTCCT

1309 ICAM2 0.43 TCCTGTACTTGTCCTCAGCTTGGGC

1310 EVI2A 0.32 AAGCCTATACGTTTCTGTGGAGTAA

1311 MAL 0.32 TTGGACATCTCTAGTGTAGCTGCCA

1312 PTPN7 0.34 ACTTGTCCTCAGCTTGGGCTTCTTC

1313 ZNFNlAl 0 .32 TCCTTGTGCCTGCTCCTGTACTTGT

1314 LCP2 0 .3 GCCCCACTGGACAACACTGATTCCT

1315 ARHGAP6 0 .33 TGGACCCCACTGGCTGAGAATCTGG

1316 CD28 0 .33 ACTTGTCCTCAGCTTGGGCTTCTTC

1317 CD8B1 0 .32 TCCTCCATCACCTGAAACACTGGAC

1318 LCPl 0 .34 TCCTTGTGCCTGCTCCTGTACTTGT

1319 NPD014 0 .31 TGCCTGCTCCTGTACTTGTCCTCAG

1320 CD69 0 .32 AAGCCTATACGTTTCTGTGGAGTAA

1321 NFATC3 0 .32 AAGCCTATACGTTTCTGTGGAGTAA

1322 TRB@ 0 .32 AAATGTTTCCTTGTGCCTGCTCCTG

1323 IGJ 0 .33 AAGCCTATACGTTTCTGTGGAGTAA

1324 SLC43A3 0 .3 TTGGACATCTCTAGTGTAGCTGCCA

1325 DOCK2 0 .36 TCCTCCATCACCTGAAACACTGGAC

1326 FHODl 0 .33 TGGACCCCACTGGCTGAGAATCTGG

1327 PACAP 0 .31 AAGCCTATACGTTTCTGTGGAGTAA

Table 60 . Oxaliplatin biomark

SEQIDNO Gene Correlation Medianprobe

1328 RPL18 0.38 TTGGACATCTCTAGTGTAGCTGCCA

1329 RPLlOA 0.32 AAATGTTTCCTTGTGCCTGCTCCTG

1330 RPS3A 0.34 TGGACCCCACTGGCTGAGAATCTGG

1331 EEF1B2 0.39 CACCCAGCTGGTCCTGTGGATGGGA

1332 GOT2 0.32 AAGCCTATACGTTTCTGTGGAGTAA

1333 RPL13A 0.33 AAATGTTTCCTTGTGCCTGCTCCTG

1334 RPS15 0.41 GCCCCACTGGACAACACTGATTCCT

1335 NOL5A 0.37 TGCCTGCTCCTGTACTTGTCCTCAG

1336 RPLP2 0.36 TGCCTGCTCCTGTACTTGTCCTCAG

1337 SLC9A3R1 0.43 TGGACCCCACTGGCTGAGAATCTGG

1338 EIF3S3 0.43 GCCCCACTGGACAACACTGATTCCT

1339 MTHFD2 0.33 TGCCTGCTCCTGTACTTGTCCTCAG

1340 IMPDH2 0.34 ACTTGTCCTCAGCTTGGGCTTCTTC

1341 ALDOC 0.44 TGCCTGCTCCTGTACTTGTCCTCAG

1342 FABP5 0.33 CACCCAGCTGGTCCTGTGGATGGGA

1343 ITM2A 0.35 TCCTTGTGCCTGCTCCTGTACTTGT

1344 PCK2 0.36 ACTTGTCCTCAGCTTGGGCTTCTTC

1345 MFNG 0.33 GCCCCACTGGACAACACTGATTCCT

1346 GCHl 0.37 TGGACCCCACTGGCTGAGAATCTGG

1347 PIM2 0.39 CACCCAGCTGGTCCTGTGGATGGGA

1348 ADA 0.32 TCCTTGTGCCTGCTCCTGTACTTGT

1349 ICAM2 0.31 TCCTCCATCACCTGAAACACTGGAC

1350 TTFl 0.47 TTGGACATCTCTAGTGTAGCTGCCA

1351 MYB 0.36 TGCCTGCTCCTGTACTTGTCCTCAG

1352 PTPN7 0.37 CACCCAGCTGGTCCTGTGGATGGGA

1353 RHOH 0.42 TCCTCCATCACCTGAAACACTGGAC

1354 ZNFNlAl 0.39 ACTTGTCCTCAGCTTGGGCTTCTTC

1355 PRIMl 0.36 TCCTTGTGCCTGCTCCTGTACTTGT

1356 FHIT 0.48 TCCTCCATCACCTGAAACACTGGAC

1357 ASS 0.45 TGGACCCCACTGGCTGAGAATCTGG

1358 SYK 0.31 TGCCTGCTCCTGTACTTGTCCTCAG

1359 OXAlL 0.32 TTGGACATCTCTAGTGTAGCTGCCA

1360 LCPl 0.31 TGCCTGCTCCTGTACTTGTCCTCAG

1361 DDX18 0.32 AAGCCTATACGTTTCTGTGGAGTAA

1362 NOLA2 0.35 AAATGTTTCCTTGTGCCTGCTCCTG

1363 KIAA0922 0.41 TCCTCCATCACCTGAAACACTGGAC

1364 PRKCQ 0.34 TCCTTGTGCCTGCTCCTGTACTTGT

1365 NFATC3 0.32 TCCTGTACTTGTCCTCAGCTTGGGC

1366 ANAPC5 0.34 TCCTCCATCACCTGAAACACTGGAC

1367 TRB@ 0.4 TGGACCCCACTGGCTGAGAATCTGG

1368 CXCR4 0.32 TCCTTGTGCCTGCTCCTGTACTTGT

1369 FNBP4 0.3 TCCTGTACTTGTCCTCAGCTTGGGC

1370 SEPT6 0.53 TTGGACATCTCTAGTGTAGCTGCCA

1371 RPS2 0.35 TCCTCCATCACCTGAAACACTGGAC

1372 MDNl 0.41 ACTTGTCCTCAGCTTGGGCTTCTTC

1373 PCCB 0.32 AAGCCTATACGTTTCTGTGGAGTAA

1374 RASA4 0.33 TGGACCCCACTGGCTGAGAATCTGG

1375 WBSCR20C 0.31 CACCCAGCTGGTCCTGTGGATGGGA

1376 SFRS7 0.32 TTGGACATCTCTAGTGTAGCTGCCA

1377 WBSCR20A 0.3 TGCCTGCTCCTGTACTTGTCCTCAG

1378 NUP210 0.43 TGGACCCCACTGGCTGAGAATCTGG

1379 SHMT2 0.36 TCCTTGTGCCTGCTCCTGTACTTGT

1380 RPLPO 0.33 TTGGACATCTCTAGTGTAGCTGCCA

1381 MAP4K1 0.31 CACCCAGCTGGTCCTGTGGATGGGA

1382 HNRPAl 0.37 TCCTCCATCACCTGAAACACTGGAC

1383 CYFIP2 0.3 GCCCCACTGGACAACACTGATTCCT

1384 RPLlOL 0.32 TCCTCCATCACCTGAAACACTGGAC

1385 GLTSCR2 0.39 TGGACCCCACTGGCTGAGAATCTGG

1386 MRPL16 0.38 TCCTGTACTTGTCCTCAGCTTGGGC

1387 MRPS2 0.34 GCCCCACTGGACAACACTGATTCCT

1388 FLJ12270 0.31 AAGCCTATACGTTTCTGTGGAGTAA

1389 CDK5RAP3 0.32 TTGGACATCTCTAGTGTAGCTGCCA

1390 ARHGAPl5 0.32 TCCTGTACTTGTCCTCAGCTTGGGC

1391 CUTC 0.33 TCCTTGTGCCTGCTCCTGTACTTGT

1392 FKBPIl 0.32 ACTTGTCCTCAGCTTGGGCTTCTTC

1393 ADPGK 0.41 AAGCCTATACGTTTCTGTGGAGTAA

1394 FLJ22457 0.32 GCCCCACTGGACAACACTGATTCCT

1395 PUS3 0.31 TCCTTGTGCCTGCTCCTGTACTTGT

1396 PACAP 0.36 TGCCTGCTCCTGTACTTGTCCTCAG

1397 CALML4 0.31 TCCTGTACTTGTCCTCAGCTTGGGC

Table 61 . Hydroxyurea biomark

SEQIDNO Gene Correlation Medianprobe

1398 CSDA 0.31 TCCTCCATCACCTGAAACACTGGAC

1399 INSIGl 0.38 AAGCCTATACGTTTCTGTGGAGTAA

1400 UBE2L6 0.33 CACCCAGCTGGTCCTGTGGATGGGA

1401 PRGl 0.36 GCCCCACTGGACAACACTGATTCCT

1402 ITM2A 0.3 ACTTGTCCTCAGCTTGGGCTTCTTC

1403 DGKA 0.31 CACCCAGCTGGTCCTGTGGATGGGA

1404 SLA 0.47 CACCCAGCTGGTCCTGTGGATGGGA

1405 PCBP2 0.51 TGGACCCCACTGGCTGAGAATCTGG

1406 IL2RG 0.42 ACTTGTCCTCAGCTTGGGCTTCTTC

1407 ALOX5AP 0.31 AAGCCTATACGTTTCTGTGGAGTAA

1408 PSMB9 0.33 GCCCCACTGGACAACACTGATTCCT

1409 LRMP 0.36 TTGGACATCTCTAGTGTAGCTGCCA

1410 ICAM2 0.31 TGGACCCCACTGGCTGAGAATCTGG

1411 PTPN7 0.36 TCCTCCATCACCTGAAACACTGGAC

1412 CXorf9 0.38 TCCTTGTGCCTGCTCCTGTACTTGT

1413 RHOH 0.41 TGCCTGCTCCTGTACTTGTCCTCAG

1414 ZNFNlAl 0.31 AAATGTTTCCTTGTGCCTGCTCCTG

1415 CENTBl 0.36 TTGGACATCTCTAGTGTAGCTGCCA

1416 LCP2 0.37 CACCCAGCTGGTCCTGTGGATGGGA

1417 STAT4 0.32 GCCCCACTGGACAACACTGATTCCT

1418 CCR7 0.31 TCCTTGTGCCTGCTCCTGTACTTGT

1419 CD3G 0.33 AAATGTTTCCTTGTGCCTGCTCCTG

1420 SPIlO 0.32 TCCTGTACTTGTCCTCAGCTTGGGC

1421 TNFAIP8 0.31 TCCTCCATCACCTGAAACACTGGAC

1422 IFI16 0.4 TGGACCCCACTGGCTGAGAATCTGG

1423 CXCR4 0.37 ACTTGTCCTCAGCTTGGGCTTCTTC

1424 ARHGEF6 0.37 TTGGACATCTCTAGTGTAGCTGCCA

1425 SELPLG 0.3 TCCTCCATCACCTGAAACACTGGAC

1426 CD3Z 0.38 TCCTCCATCACCTGAAACACTGGAC

1427 PRKCQ 0.3 TGCCTGCTCCTGTACTTGTCCTCAG

1428 SH2D1A 0.3 TCCTTGTGCCTGCTCCTGTACTTGT

1429 CDlA 0.31 TTGGACATCTCTAGTGTAGCTGCCA

1430 NFATC3 0.33 TGGACCCCACTGGCTGAGAATCTGG

1431 LAIRl 0.34 TCCTCCATCACCTGAAACACTGGAC

1432 TRB@ 0.3 CACCCAGCTGGTCCTGTGGATGGGA

1433 SEPT6 0.34 CACCCAGCTGGTCCTGTGGATGGGA

1434 RAFTLIN 0.3 TCCTTGTGCCTGCTCCTGTACTTGT

1435 DOCK2 0.32 TGGACCCCACTGGCTGAGAATCTGG

1436 CD3D 0.37 TGCCTGCTCCTGTACTTGTCCTCAG

1437 CD6 0.42 AAGCCTATACGTTTCTGTGGAGTAA

1438 AIFl 0.4 TGCCTGCTCCTGTACTTGTCCTCAG

1439 CDlE 0.41 GCCCCACTGGACAACACTGATTCCT

1440 CYFIP2 0.35 TCCTGTACTTGTCCTCAGCTTGGGC

1441 TARP 0.38 AAATGTTTCCTTGTGCCTGCTCCTG

1442 ADA 0.33 AAGCCTATACGTTTCTGTGGAGTAA

1443 ARHGAP15 0.32 TGGACCCCACTGGCTGAGAATCTGG

1444 GIMAP6 0.34 GCCCCACTGGACAACACTGATTCCT

1445 STAG3 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC

1446 FLJ22457 0.31 AAGCCTATACGTTTCTGTGGAGTAA

1447 PACAP 0.35 AAGCCTATACGTTTCTGTGGAGTAA

1448 TCF4 0.4 TCCTGTACTTGTCCTCAGCTTGGGC

Table 62 . Tegafur biomarkers .

SEQIDNO Gene Correlation Medianprobe

1449 RPLIl 0.31 GCCCCACTGGACAACACTGATTCCT

1450 RPL17 0.38 TGCCTGCTCCTGTACTTGTCCTCAG

1451 ANAPC5 0.34 CACCCAGCTGGTCCTGTGGATGGGA

1452 RPL13A 0.34 TCCTGTACTTGTCCTCAGCTTGGGC

1453 STOM 0.37 TCCTCCATCACCTGAAACACTGGAC

1454 TUFM 0.38 GCCCCACTGGACAACACTGATTCCT

1455 SCARBl 0.35 TCCTGTACTTGTCCTCAGCTTGGGC

1456 FABP5 0.33 CACCCAGCTGGTCCTGTGGATGGGA

1457 KIAA0711 0.35 TCCTTGTGCCTGCTCCTGTACTTGT

1458 IL6R 0.33 TCCTCCATCACCTGAAACACTGGAC

1459 WBSCR22 0.3 AAATGTTTCCTTGTGCCTGCTCCTG

1460 UCK2 0.4 TGCCTGCTCCTGTACTTGTCCTCAG

1461 GZMB 0.3 AAGCCTATACGTTTCTGTGGAGTAA

1462 Clorf38 0.32 CACCCAGCTGGTCCTGTGGATGGGA

1463 PCBP2 0.31 TCCTGTACTTGTCCTCAGCTTGGGC

1464 GPR65 0.44 TGCCTGCTCCTGTACTTGTCCTCAG

1465 GLTSCR2 0.38 TCCTTGTGCCTGCTCCTGTACTTGT

1466 FKBPIl 0.38 TGGACCCCACTGGCTGAGAATCTGG

Table 63. Daunorubicin biomarkers.

SEQIDNO Gene Correlation Medianprobe

1467 ALDOC 0.41 TGCCTGCTCCTGTACTTGTCCTCAG

1468 ITM2A 0.32 GCCCCACTGGACAACACTGATTCCT

1469 SLA 0.41 TCCTTGTGCCTGCTCCTGTACTTGT

1470 SSBP2 0.31 TCCTTGTGCCTGCTCCTGTACTTGT

1471 IL2RG 0.31 TGGACCCCACTGGCTGAGAATCTGG

1472 MFNG 0.47 TTGGACATCTCTAGTGTAGCTGCCA

1473 SELL 0.33 TCCTCCATCACCTGAAACACTGGAC

1474 STCl 0.31 AAATGTTTCCTTGTGCCTGCTCCTG

1475 LRMP 0.33 AAGCCTATACGTTTCTGTGGAGTAA

1476 MYB 0.41 GCCCCACTGGACAACACTGATTCCT

1477 PTPN7 0.31 AAATGTTTCCTTGTGCCTGCTCCTG

1478 CXorf9 0.38 TGGACCCCACTGGCTGAGAATCTGG

1479 RHOH 0.32 AAATGTTTCCTTGTGCCTGCTCCTG

1480 ZNFNlAl 0.36 CACCCAGCTGGTCCTGTGGATGGGA

1481 CENTBl 0.37 TGGACCCCACTGGCTGAGAATCTGG

1482 MAP4K1 0.32 TGGACCCCACTGGCTGAGAATCTGG

1483 CCR7 0.3 TCCTGTACTTGTCCTCAGCTTGGGC

1484 CD3G 0.33 AAATGTTTCCTTGTGCCTGCTCCTG

1485 CCR9 0.33 TGGACCCCACTGGCTGAGAATCTGG

1486 CBFA2T3 0.31 CACCCAGCTGGTCCTGTGGATGGGA

1487 CXCR4 0.41 AAATGTTTCCTTGTGCCTGCTCCTG

1488 ARHGEF6 0.4 TCCTGTACTTGTCCTCAGCTTGGGC

1489 SELPLG 0.45 TCCTTGTGCCTGCTCCTGTACTTGT

1490 SEC31L2 0.38 TCCTGTACTTGTCCTCAGCTTGGGC

1491 CD3Z 0.33 CACCCAGCTGGTCCTGTGGATGGGA

1492 SH2D1A 0.33 TCCTGTACTTGTCCTCAGCTTGGGC

1493 CDlA 0.35 TGGACCCCACTGGCTGAGAATCTGG

1494 SCN3A 0.33 CACCCAGCTGGTCCTGTGGATGGGA

1495 LAIRl 0.33 TGCCTGCTCCTGTACTTGTCCTCAG

1496 TRBg 0.3 AAATGTTTCCTTGTGCCTGCTCCTG

1497 DOCK2 0.35 AAGCCTATACGTTTCTGTGGAGTAA

1498 WBSCR20C 0.38 CACCCAGCTGGTCCTGTGGATGGGA

1499 CD3D 0.32 TGCCTGCTCCTGTACTTGTCCTCAG

1500 T3JAM 0.34 CACCCAGCTGGTCCTGTGGATGGGA

1501 CD6 0.33 ACTTGTCCTCAGCTTGGGCTTCTTC

1502 ZAP70 0.32 TGCCTGCTCCTGTACTTGTCCTCAG

1503 GPR65 0.33 TCCTGTACTTGTCCTCAGCTTGGGC

1504 AIFl 0.3 GCCCCACTGGACAACACTGATTCCT

1505 WDR45 0.3 TCCTCCATCACCTGAAACACTGGAC

1506 CDlE 0.3 TCCTTGTGCCTGCTCCTGTACTTGT

1507 CYFIP2 0.39 AAGCCTATACGTTTCTGTGGAGTAA

1508 TARP 0.38 TTGGACATCTCTAGTGTAGCTGCCA

1509 TRIM 0.34 TCCTGTACTTGTCCTCAGCTTGGGC

1510 ARHGAP15 0.37 ACTTGTCCTCAGCTTGGGCTTCTTC

1511 NOTCHl 0.39 AAGCCTATACGTTTCTGTGGAGTAA

1512 STAG3 0.35 AAGCCTATACGTTTCTGTGGAGTAA

1513 UBASH3A 0.31 TGCCTGCTCCTGTACTTGTCCTCAG

1514 MGC5566 0.33 TGCCTGCTCCTGTACTTGTCCTCAG

1515 PACAP 0.33 TCCTTGTGCCTGCTCCTGTACTTGT

Table 64. Bleomycin biomarkers.

SEQIDNO Gene Correlation Medianprobe

1516 PFNl 0.32 CACCCAGCTGGTCCTGTGGATGGGA

1517 CALU 0.32 ACTTGTCCTCAGCTTGGGCTTCTTC

1518 ZYX 0.34 CACCCAGCTGGTCCTGTGGATGGGA

1519 PSMD2 0.36 GCCCCACTGGACAACACTGATTCCT

1520 RAPlB 0.32 TCCTGTACTTGTCCTCAGCTTGGGC

1521 EPASl 0.35 TCCTGTACTTGTCCTCAGCTTGGGC

1522 PGAMl 0.36 GCCCCACTGGACAACACTGATTCCT

1523 STATl 0.38 TGCCTGCTCCTGTACTTGTCCTCAG

1524 CKAP4 0.38 GCCCCACTGGACAACACTGATTCCT

1525 DUSPl 0.32 TCCTGTACTTGTCCTCAGCTTGGGC

1526 RCNl 0.32 TCCTCCATCACCTGAAACACTGGAC

1527 UCHLl 0.44 TGGACCCCACTGGCTGAGAATCTGG

1528 ITGA5 0.33 AAATGTTTCCTTGTGCCTGCTCCTG

1529 NFKBIA 0.32 TGCCTGCTCCTGTACTTGTCCTCAG

1530 LAMBl 0.4 GCCCCACTGGACAACACTGATTCCT

1531 TGFBI 0.37 TTGGACATCTCTAGTGTAGCTGCCA

1532 FHLl 0.31 GCCCCACTGGACAACACTGATTCCT

1533 GJAl 0.32 TCCTCCATCACCTGAAACACTGGAC

1534 PRGl 0.33 CACCCAGCTGGTCCTGTGGATGGGA

1535 EXTl 0.35 CACCCAGCTGGTCCTGTGGATGGGA

1536 MVP 0.32 TCCTGTACTTGTCCTCAGCTTGGGC

1537 NNMT 0.38 TGGACCCCACTGGCTGAGAATCTGG

1538 TAPl 0.37 TCCTCCATCACCTGAAACACTGGAC

1539 CRIMl 0.41 TGGACCCCACTGGCTGAGAATCTGG

1540 PLOD2 0.36 GCCCCACTGGACAACACTGATTCCT

1541 RPS19 0.34 TCCTGTACTTGTCCTCAGCTTGGGC

1542 AXL 0.43 GCCCCACTGGACAACACTGATTCCT

1543 PALM2-AKAP2 0.42 TCCTCCATCACCTGAAACACTGGAC

1544 IL8 0.32 TCCTGTACTTGTCCTCAGCTTGGGC

1545 LOXL2 0.3 TGCCTGCTCCTGTACTTGTCCTCAG

1546 PAPSS2 0.31 CACCCAGCTGGTCCTGTGGATGGGA

1547 CAVl 0.31 TCCTTGTGCCTGCTCCTGTACTTGT

1548 F2R 0.32 ACTTGTCCTCAGCTTGGGCTTCTTC

1549 PSMB9 0.38 CACCCAGCTGGTCCTGTGGATGGGA

1550 LOX 0.36 TGGACCCCACTGGCTGAGAATCTGG

1551 Clorf29 0.36 TCCTGTACTTGTCCTCAGCTTGGGC

1552 STCl 0.32 TTGGACATCTCTAGTGTAGCTGCCA

1553 LIF 0.34 TCCTTGTGCCTGCTCCTGTACTTGT

1554 KCNJ8 0.46 GCCCCACTGGACAACACTGATTCCT

1555 SMAD3 0.32 TGCCTGCTCCTGTACTTGTCCTCAG

1556 HPCALl 0.45 AAATGTTTCCTTGTGCCTGCTCCTG

1557 WNT5A 0.34 TCCTTGTGCCTGCTCCTGTACTTGT

1558 BDNF 0.33 TGCCTGCTCCTGTACTTGTCCTCAG

1559 TNFRSFlA 0.38 TCCTGTACTTGTCCTCAGCTTGGGC

1560 NCOR2 0.45 CACCCAGCTGGTCCTGTGGATGGGA

1561 FLNC 0.44 TTGGACATCTCTAGTGTAGCTGCCA

1562 HMGA2 0.41 AAATGTTTCCTTGTGCCTGCTCCTG

1563 HLA-B 0.42 AAGCCTATACGTTTCTGTGGAGTAA

1564 FLOTl 0.3 AAATGTTTCCTTGTGCCTGCTCCTG

1565 PTRF 0.36 CACCCAGCTGGTCCTGTGGATGGGA

1566 IFI16 0.32 TCCTTGTGCCTGCTCCTGTACTTGT

1567 MGC4083 0.34 TCCTCCATCACCTGAAACACTGGAC

1568 TNFRSFlOB 0.4 ACTTGTCCTCAGCTTGGGCTTCTTC

1569 PNMA2 0.38 TCCTGTACTTGTCCTCAGCTTGGGC

1570 TFPI 0.32 TCCTTGTGCCTGCTCCTGTACTTGT

1571 CLECSF2 0.33 TGCCTGCTCCTGTACTTGTCCTCAG

1572 SPIlO 0.34 GCCCCACTGGACAACACTGATTCCT

1573 PLAUR 0.35 ACTTGTCCTCAGCTTGGGCTTCTTC

1574 ASPH 0.42 TCCTTGTGCCTGCTCCTGTACTTGT

1575 FSCNl 0.38 TGCCTGCTCCTGTACTTGTCCTCAG

1576 HIC 0.46 TCCTCCATCACCTGAAACACTGGAC

1577 HLA-C 0.34 TGGACCCCACTGGCTGAGAATCTGG

1578 COL6A1 0.34 TCCTCCATCACCTGAAACACTGGAC

1579 IL6ST 0.45 AAATGTTTCCTTGTGCCTGCTCCTG

1580 IFITM3 0.36 GCCCCACTGGACAACACTGATTCCT

1581 MAPlB 0.31 TCCTCCATCACCTGAAACACTGGAC

1582 FLJ46603 0.3 ACTTGTCCTCAGCTTGGGCTTCTTC

1583 RAFTLIN 0.32 GCCCCACTGGACAACACTGATTCCT

1584 FTL 0.37 CACCCAGCTGGTCCTGTGGATGGGA

1585 KIAA0877 0.43 TCCTCCATCACCTGAAACACTGGAC

1586 MTlE 0.41 TGGACCCCACTGGCTGAGAATCTGG

1587 CDClO 0.32 TGCCTGCTCCTGTACTTGTCCTCAG

1588 ZNF258 0.31 AAATGTTTCCTTGTGCCTGCTCCTG

1589 BCATl 0.39 TTGGACATCTCTAGTGTAGCTGCCA

1590 IFI44 0.36 AAATGTTTCCTTGTGCCTGCTCCTG

1591 SOD2 0.36 GCCCCACTGGACAACACTGATTCCT

1592 TMSBlO 0.33 TCCTCCATCACCTGAAACACTGGAC

1593 FLJ10350 0.3 TTGGACATCTCTAGTGTAGCTGCCA

1594 Clorf24 0.34 CACCCAGCTGGTCCTGTGGATGGGA

1595 EFHD2 0.36 AAATGTTTCCTTGTGCCTGCTCCTG

1596 RPS27L 0.33 AAGCCTATACGTTTCTGTGGAGTAA

1597 TNFRSF12A 0.43 CACCCAGCTGGTCCTGTGGATGGGA

1598 FAD104 0.38 TTGGACATCTCTAGTGTAGCTGCCA

1599 RAB7L1 0.58 ACTTGTCCTCAGCTTGGGCTTCTTC

1600 NME7 0.36 TTGGACATCTCTAGTGTAGCTGCCA

1601 TMEM22 0.34 TTGGACATCTCTAGTGTAGCTGCCA

1602 TPKl 0.31 GCCCCACTGGACAACACTGATTCCT

1603 ELK3 0.36 TGGACCCCACTGGCTGAGAATCTGG

1604 CYLD 0.3 AAGCCTATACGTTTCTGTGGAGTAA

1605 AMIGO2 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC

1606 ADAMTSl 0.43 ACTTGTCCTCAGCTTGGGCTTCTTC

1607 ACTB 0.36 ACTTGTCCTCAGCTTGGGCTTCTTC

Table 65. Estramustine biomarkers. SEQIDNO Gene Correlation Medianprobe

1608 HSPCB 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC

1609 LDHA 0.42 TGCCTGCTCCTGTACTTGTCCTCAG

1610 TM4SF7 0.32 TCCTGTACTTGTCCTCAGCTTGGGC

Table 66 . Chlorambucil

SEQIDNO Gene C Coorrrrelation Medianprobe

1611 CSDA 0.33 TGGACCCCACTGGCTGAGAATCTGG

1612 INSIGl 0.32 TCCTTGTGCCTGCTCCTGTACTTGT

1613 UBE2L6 0.39 TGGACCCCACTGGCTGAGAATCTGG

1614 PRGl 0.37 TCCTTGTGCCTGCTCCTGTACTTGT

1615 ITM2A 0.3 GCCCCACTGGACAACACTGATTCCT

1616 DGKA 0.38 TCCTTGTGCCTGCTCCTGTACTTGT

1617 TFDP2 0.32 AAATGTTTCCTTGTGCCTGCTCCTG

1618 SLA 0.32 TCCTCCATCACCTGAAACACTGGAC

1619 IL2RG 0.44 AAGCCTATACGTTTCTGTGGAGTAA

1620 ALOX5AP 0.45 GCCCCACTGGACAACACTGATTCCT

1621 GPSM3 0.34 TTGGACATCTCTAGTGTAGCTGCCA

1622 PSMB9 0.36 ACTTGTCCTCAGCTTGGGCTTCTTC

1623 SELL 0.42 TGCCTGCTCCTGTACTTGTCCTCAG

1624 ADA 0.35 ACTTGTCCTCAGCTTGGGCTTCTTC

1625 EDGl 0.33 AAATGTTTCCTTGTGCCTGCTCCTG

1626 FMNLl 0.3 TCCTCCATCACCTGAAACACTGGAC

1627 PTPN7 0.5 TCCTTGTGCCTGCTCCTGTACTTGT

1628 CXorf9 0.41 TGGACCCCACTGGCTGAGAATCTGG

1629 RHOH 0.35 TTGGACATCTCTAGTGTAGCTGCCA

1630 ZNFNlAl 0.32 TCCTTGTGCCTGCTCCTGTACTTGT

1631 CENTBl 0.47 TCCTGTACTTGTCCTCAGCTTGGGC

1632 LCP2 0.37 TCCTCCATCACCTGAAACACTGGAC

1633 CDlD 0.36 AAGCCTATACGTTTCTGTGGAGTAA

1634 STAT4 0.31 AAATGTTTCCTTGTGCCTGCTCCTG

1635 VAVl 0.35 AAATGTTTCCTTGTGCCTGCTCCTG

1636 MAP4K1 0.36 TTGGACATCTCTAGTGTAGCTGCCA

1637 CCR7 0.31 TCCTGTACTTGTCCTCAGCTTGGGC

1638 PDE4C 0.42 GCCCCACTGGACAACACTGATTCCT

1639 CD3G 0.41 AAGCCTATACGTTTCTGTGGAGTAA

1640 CCR9 0.43 AAGCCTATACGTTTCTGTGGAGTAA

1641 SPIlO 0.43 TTGGACATCTCTAGTGTAGCTGCCA

1642 TNFAIP8 0.48 TTGGACATCTCTAGTGTAGCTGCCA

1643 LCPl 0.31 AAATGTTTCCTTGTGCCTGCTCCTG

1644 IFI16 0.5 TCCTCCATCACCTGAAACACTGGAC

1645 CXCR4 0.37 ACTTGTCCTCAGCTTGGGCTTCTTC

1646 ARHGEF6 0.37 ACTTGTCCTCAGCTTGGGCTTCTTC

1647 SELPLG 0.43 TTGGACATCTCTAGTGTAGCTGCCA

1648 SEC31L2 0.32 TGGACCCCACTGGCTGAGAATCTGG

1649 CD3Z 0.3 AAGCCTATACGTTTCTGTGGAGTAA

1650 PRKCQ 0.31 GCCCCACTGGACAACACTGATTCCT

1651 SH2D1A 0.47 ACTTGTCCTCAGCTTGGGCTTCTTC

1652 GZMB 0.48 TGGACCCCACTGGCTGAGAATCTGG

1653 CDlA 0.3 AAGCCTATACGTTTCTGTGGAGTAA

1654 LAIRl 0.32 TTGGACATCTCTAGTGTAGCTGCCA

1655 AFlQ 0.41 TTGGACATCTCTAGTGTAGCTGCCA

1656 TRB@ 0.35 TCCTCCATCACCTGAAACACTGGAC

1657 SEPT6 0.35 TGGACCCCACTGGCTGAGAATCTGG

1658 DOCK2 0.39 AAGCCTATACGTTTCTGTGGAGTAA

1659 RPS19 0.41 TTGGACATCTCTAGTGTAGCTGCCA

1660 CD3D 0.4 TTGGACATCTCTAGTGTAGCTGCCA

1661 T3JAM 0.32 TGGACCCCACTGGCTGAGAATCTGG

1662 FNBPl 0.31 GCCCCACTGGACAACACTGATTCCT

1663 CD6 0.33 TGGACCCCACTGGCTGAGAATCTGG

1664 ZAP70 0.52 CACCCAGCTGGTCCTGTGGATGGGA

1665 LSTl 0.34 AAATGTTTCCTTGTGCCTGCTCCTG

1666 BCATl 0.35 AAGCCTATACGTTTCTGTGGAGTAA

1667 PRFl 0.4 AAGCCTATACGTTTCTGTGGAGTAA

1668 AIFl 0.3 TTGGACATCTCTAGTGTAGCTGCCA

1669 RAG2 0.38 TGGACCCCACTGGCTGAGAATCTGG

1670 CDlE 0.35 ACTTGTCCTCAGCTTGGGCTTCTTC

1671 CYFIP2 0.38 TTGGACATCTCTAGTGTAGCTGCCA

1672 TARP 0.3 TGGACCCCACTGGCTGAGAATCTGG

1673 TRIM 0.36 CACCCAGCTGGTCCTGTGGATGGGA

1674 GLTSCR2 0.37 TCCTCCATCACCTGAAACACTGGAC

1675 GIMAP5 0.3 ACTTGTCCTCAGCTTGGGCTTCTTC

1676 ARHGAP15 0.32 AAGCCTATACGTTTCTGTGGAGTAA

1677 NOTCHl 0.31 CACCCAGCTGGTCCTGTGGATGGGA

1678 BCLIlB 0.3 TCCTTGTGCCTGCTCCTGTACTTGT

1679 GIMAP6 0.34 TTGGACATCTCTAGTGTAGCTGCCA

1680 STAG3 0.4 TCCTGTACTTGTCCTCAGCTTGGGC

1681 TM6SF1 0.39 TTGGACATCTCTAGTGTAGCTGCCA

1682 UBASH3A 0.37 TCCTGTACTTGTCCTCAGCTTGGGC

1683 MGC5566 0.36 CACCCAGCTGGTCCTGTGGATGGGA

1684 FLJ22457 0.31 TCCTCCATCACCTGAAACACTGGAC

1685 TPKl 0.33 AAATGTTTCCTTGTGCCTGCTCCTG

Table 67 . Mechlore :tham

SEQIDNO Gene C Coorrrrelation Medianprobe

1686 PRGl 0.37 GCCCCACTGGACAACACTGATTCCT

1687 SLC2A3 0.35 ACTTGTCCTCAGCTTGGGCTTCTTC

1688 RPS19 0.32 ACTTGTCCTCAGCTTGGGCTTCTTC

1689 PSMBlO 0.34 TGCCTGCTCCTGTACTTGTCCTCAG

1690 ITM2A 0.33 TGCCTGCTCCTGTACTTGTCCTCAG

1691 DGKA 0.37 TGCCTGCTCCTGTACTTGTCCTCAG

1692 SEMA4D 0.34 TCCTCCATCACCTGAAACACTGGAC

1693 SLA 0.33 TGCCTGCTCCTGTACTTGTCCTCAG

1694 IL2RG 0.3 TGGACCCCACTGGCTGAGAATCTGG

1695 MFNG 0.42 AAGCCTATACGTTTCTGTGGAGTAA

1696 ALOX5AP 0.31 AAGCCTATACGTTTCTGTGGAGTAA

1697 GPSM3 0.34 AAGCCTATACGTTTCTGTGGAGTAA

1698 PSMB9 0.36 ACTTGTCCTCAGCTTGGGCTTCTTC

1699 SELL 0.34 CACCCAGCTGGTCCTGTGGATGGGA

1700 ADA 0.35 AAATGTTTCCTTGTGCCTGCTCCTG

1701 FMNLl 0.4 CACCCAGCTGGTCCTGTGGATGGGA

1702 MYB 0.34 TGGACCCCACTGGCTGAGAATCTGG

1703 PTPN7 0.43 AAGCCTATACGTTTCTGTGGAGTAA

1704 CXorf9 0.32 TGCCTGCTCCTGTACTTGTCCTCAG

1705 RHOH 0.33 TTGGACATCTCTAGTGTAGCTGCCA

1706 ZNFNlAl 0.31 TCCTCCATCACCTGAAACACTGGAC

1707 CENTBl 0.43 TCCTCCATCACCTGAAACACTGGAC

1708 FXYD2 0.35 TTGGACATCTCTAGTGTAGCTGCCA

1709 CDlD 0.4 TTGGACATCTCTAGTGTAGCTGCCA

1710 STAT4 0.44 TTGGACATCTCTAGTGTAGCTGCCA

1711 MAP4K1 0.34 GCCCCACTGGACAACACTGATTCCT

1712 CCR7 0.39 TGGACCCCACTGGCTGAGAATCTGG

1713 PDE4C 0.33 TCCTTGTGCCTGCTCCTGTACTTGT

1714 CD3G 0.4 GCCCCACTGGACAACACTGATTCCT

1715 CCR9 0.34 TGGACCCCACTGGCTGAGAATCTGG

1716 SPIlO 0.31 AAATGTTTCCTTGTGCCTGCTCCTG

1717 TK2 0.33 TCCTGTACTTGTCCTCAGCTTGGGC

1718 TNFAIP8 0.34 GCCCCACTGGACAACACTGATTCCT

1719 NAPlLl 0.35 TCCTGTACTTGTCCTCAGCTTGGGC

1720 SELPLG 0.35 TCCTGTACTTGTCCTCAGCTTGGGC

1721 SEC31L2 0.38 TGCCTGCTCCTGTACTTGTCCTCAG

1722 CD3Z 0.44 TTGGACATCTCTAGTGTAGCTGCCA

1723 PRKCQ 0.37 TCCTGTACTTGTCCTCAGCTTGGGC

1724 SH2D1A 0.41 GCCCCACTGGACAACACTGATTCCT

1725 GZMB 0.43 TGGACCCCACTGGCTGAGAATCTGG

1726 CDlA 0.39 TGGACCCCACTGGCTGAGAATCTGG

1727 LAIRl 0.35 TGGACCCCACTGGCTGAGAATCTGG

1728 TRB@ 0.33 TTGGACATCTCTAGTGTAGCTGCCA

1729 SEPT6 0.3 CACCCAGCTGGTCCTGTGGATGGGA

1730 DOCK2 0.34 TGGACCCCACTGGCTGAGAATCTGG

1731 CG018 0.33 TGGACCCCACTGGCTGAGAATCTGG

1732 WBSCR20C 0.34 TCCTGTACTTGTCCTCAGCTTGGGC

1733 CD3D 0.33 ACTTGTCCTCAGCTTGGGCTTCTTC

1734 CD6 0.32 AAGCCTATACGTTTCTGTGGAGTAA

1735 LSTl 0.33 TTGGACATCTCTAGTGTAGCTGCCA

1736 GPR65 0.42 AAGCCTATACGTTTCTGTGGAGTAA 1737 PRFl 0.34 CACCCAGCTGGTCCTGTGGATGGGA 1738 ALMSl 0.41 TCCTGTACTTGTCCTCAGCTTGGGC 1739 AIFl 0.31 GCCCCACTGGACAACACTGATTCCT 1740 CDlE 0.31 CACCCAGCTGGTCCTGTGGATGGGA 1741 CYFIP2 0.33 GCCCCACTGGACAACACTGATTCCT 1742 TARP 0.31 AAATGTTTCCTTGTGCCTGCTCCTG 1743 GLTSCR2 0.31 AAGCCTATACGTTTCTGTGGAGTAA 1744 FLJ12270 0.34 TGGACCCCACTGGCTGAGAATCTGG 1745 ARHGAP15 0.33 GCCCCACTGGACAACACTGATTCCT 1746 NAP1L2 0.32 GCCCCACTGGACAACACTGATTCCT 1747 CECRl 0.34 TCCTTGTGCCTGCTCCTGTACTTGT 1748 GIMAP6 0.35 TCCTGTACTTGTCCTCAGCTTGGGC 1749 STAG3 0.33 CACCCAGCTGGTCCTGTGGATGGGA 1750 TM6SF1 0.3 CACCCAGCTGGTCCTGTGGATGGGA 1751 C15orf25 0.36 TTGGACATCTCTAGTGTAGCTGCCA 1752 MGC5566 0.32 TCCTGTACTTGTCCTCAGCTTGGGC 1753 FLJ22457 0.34 AAATGTTTCCTTGTGCCTGCTCCTG 1754 ET 0.32 CACCCAGCTGGTCCTGTGGATGGGA 1755 TPKl 0.34 CACCCAGCTGGTCCTGTGGATGGGA 1756 PHFIl 0.36 TTGGACATCTCTAGTGTAGCTGCCA

Table 68. Streptozocin biomarkers .

SEQIDNO Gene Correlation Medianprobe

1757 PGKl 0.32 TGCCTGCTCCTGTACTTGTCCTCAG

1758 SCD 0.31 TGGACCCCACTGGCTGAGAATCTGG

1759 INSIGl 0.3 TGCCTGCTCCTGTACTTGTCCTCAG

1760 IGBPl 0.39 TCCTCCATCACCTGAAACACTGGAC

1761 TNFAIP3 0.31 TCCTCCATCACCTGAAACACTGGAC

1762 TNFSFlO 0.31 CACCCAGCTGGTCCTGTGGATGGGA

1763 ABCAl 0.34 TGCCTGCTCCTGTACTTGTCCTCAG

1764 AGA 0.31 TGGACCCCACTGGCTGAGAATCTGG

1765 ABCA8 0.31 CACCCAGCTGGTCCTGTGGATGGGA

1766 DBCl 0.31 TGCCTGCTCCTGTACTTGTCCTCAG

1767 PTGER2 0.32 ACTTGTCCTCAGCTTGGGCTTCTTC

1768 UGT1A3 0.32 TCCTCCATCACCTGAAACACTGGAC

1769 ClOorflO 0.3 CACCCAGCTGGTCCTGTGGATGGGA

1770 TM4SF13 0.34 TGGACCCCACTGGCTGAGAATCTGG

1771 CGI-90 0.31 TCCTTGTGCCTGCTCCTGTACTTGT

1772 LXN 0.31 AAGCCTATACGTTTCTGTGGAGTAA

1773 DNAJC12 0.35 TTGGACATCTCTAGTGTAGCTGCCA

1774 HIPK2 0.31 CACCCAGCTGGTCCTGTGGATGGGA

1775 C9orf95 0.36 ACTTGTCCTCAGCTTGGGCTTCTTC

Table 69. Carmustine biomarkers .

SEQIDNO Gene Correlation Medianprobe

1776 RPLP2 0.34 TGCCTGCTCCTGTACTTGTCCTCAG

1777 CD99 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC

1778 IFITMl 0.36 TCCTCCATCACCTGAAACACTGGAC

1779 INSIGl 0.31 TCCTCCATCACCTGAAACACTGGAC

1780 ALDOC 0.4 TGCCTGCTCCTGTACTTGTCCTCAG

1781 ITM2A 0.33 TCCTTGTGCCTGCTCCTGTACTTGT

1782 SERPINAl 0.39 TTGGACATCTCTAGTGTAGCTGCCA

1783 ClQRl 0.35 AAGCCTATACGTTTCTGTGGAGTAA

1784 STAT5A 0.39 TTGGACATCTCTAGTGTAGCTGCCA

1785 INPP5D 0.44 TCCTTGTGCCTGCTCCTGTACTTGT

1786 SATBl 0.36 AAATGTTTCCTTGTGCCTGCTCCTG

1787 VPS16 0.31 AAATGTTTCCTTGTGCCTGCTCCTG

1788 SLA 0.37 TCCTGTACTTGTCCTCAGCTTGGGC

1789 IL2RG 0.45 TCCTCCATCACCTGAAACACTGGAC

1790 MFNG 0.33 TCCTCCATCACCTGAAACACTGGAC

1791 SELL 0.38 AAGCCTATACGTTTCTGTGGAGTAA

1792 LRMP 0.41 GCCCCACTGGACAACACTGATTCCT

1793 ICAM2 0.54 TCCTCCATCACCTGAAACACTGGAC

1794 MYB 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC

1795 PTPN7 0.3 TGCCTGCTCCTGTACTTGTCCTCAG

1796 ARHGAP25 0.42 TTGGACATCTCTAGTGTAGCTGCCA

1797 LCK 0.41 TGGACCCCACTGGCTGAGAATCTGG

1798 CXorf9 0.35 TGGACCCCACTGGCTGAGAATCTGG

1799 RHOH 0.41 AAATGTTTCCTTGTGCCTGCTCCTG

1800 ZNFNlAl 0.37 TGCCTGCTCCTGTACTTGTCCTCAG

1801 CENTBl 0.59 ACTTGTCCTCAGCTTGGGCTTCTTC

1802 ADD2 0.34 TCCTTGTGCCTGCTCCTGTACTTGT

1803 LCP2 0.33 TTGGACATCTCTAGTGTAGCTGCCA

1804 SPIl 0.39 TGCCTGCTCCTGTACTTGTCCTCAG

1805 DBT 0.42 ACTTGTCCTCAGCTTGGGCTTCTTC

1806 GZMA 0.34 CACCCAGCTGGTCCTGTGGATGGGA

1807 CD2 0.36 TGGACCCCACTGGCTGAGAATCTGG

1808 BATF 0.38 ACTTGTCCTCAGCTTGGGCTTCTTC

1809 HIST1H4C 0.31 TCCTGTACTTGTCCTCAGCTTGGGC

1810 ARHGAP6 0.4 TCCTCCATCACCTGAAACACTGGAC

1811 VAVl 0.42 TGGACCCCACTGGCTGAGAATCTGG

1812 MAP4K1 0.31 TCCTTGTGCCTGCTCCTGTACTTGT

1813 CCR7 0.31 TCCTTGTGCCTGCTCCTGTACTTGT

1814 PDE4C 0.57 TCCTCCATCACCTGAAACACTGGAC

1815 CD3G 0.44 AAGCCTATACGTTTCTGTGGAGTAA

1816 CCR9 0.37 AAATGTTTCCTTGTGCCTGCTCCTG

1817 SP140 0.48 TCCTGTACTTGTCCTCAGCTTGGGC

1818 TK2 0.31 TGGACCCCACTGGCTGAGAATCTGG

1819 LCPl 0.38 TCCTCCATCACCTGAAACACTGGAC

1820 IFI16 0.34 GCCCCACTGGACAACACTGATTCCT

1821 CXCR4 0.42 GCCCCACTGGACAACACTGATTCCT

1822 ARHGEF6 0.45 AAATGTTTCCTTGTGCCTGCTCCTG

1823 PSCDBP 0.42 TCCTGTACTTGTCCTCAGCTTGGGC

1824 SELPLG 0.52 TGGACCCCACTGGCTGAGAATCTGG

1825 SEC31L2 0.42 TCCTGTACTTGTCCTCAGCTTGGGC

1826 CD3Z 0.34 TCCTCCATCACCTGAAACACTGGAC

1827 PRKCQ 0.46 TCCTTGTGCCTGCTCCTGTACTTGT

1828 SH2D1A 0.46 TGCCTGCTCCTGTACTTGTCCTCAG

1829 GZMB 0.55 TTGGACATCTCTAGTGTAGCTGCCA

1830 CDlA 0.34 TCCTTGTGCCTGCTCCTGTACTTGT

1831 GATA2 0.41 TTGGACATCTCTAGTGTAGCTGCCA

1832 LY9 0.54 TCCTCCATCACCTGAAACACTGGAC

1833 LAIRl 0.3 TTGGACATCTCTAGTGTAGCTGCCA

1834 TRB@ 0.33 TCCTCCATCACCTGAAACACTGGAC

1835 SEPT6 0.32 ACTTGTCCTCAGCTTGGGCTTCTTC

1836 HA-I 0.32 TCCTCCATCACCTGAAACACTGGAC

1837 SLC43A3 0.32 TCCTGTACTTGTCCTCAGCTTGGGC

1838 DOCK2 0.31 TCCTCCATCACCTGAAACACTGGAC

1839 CG018 0.42 ACTTGTCCTCAGCTTGGGCTTCTTC

1840 MLCl 0.33 TGCCTGCTCCTGTACTTGTCCTCAG

1841 CD3D 0.35 TCCTGTACTTGTCCTCAGCTTGGGC

1842 T3JAM 0.34 CACCCAGCTGGTCCTGTGGATGGGA

1843 CD6 0.43 TCCTGTACTTGTCCTCAGCTTGGGC

1844 ZAP70 0.43 GCCCCACTGGACAACACTGATTCCT

1845 DOK2 0.3 TCCTGTACTTGTCCTCAGCTTGGGC

1846 LSTl 0.36 TCCTGTACTTGTCCTCAGCTTGGGC

1847 GPR65 0.31 TCCTGTACTTGTCCTCAGCTTGGGC

1848 PRFl 0.32 TCCTTGTGCCTGCTCCTGTACTTGT

1849 ALMSl 0.38 TTGGACATCTCTAGTGTAGCTGCCA

1850 AIFl 0.31 TCCTTGTGCCTGCTCCTGTACTTGT

1851 PRDX2 0.48 GCCCCACTGGACAACACTGATTCCT

1852 FLJ12151 0.36 AAATGTTTCCTTGTGCCTGCTCCTG

1853 FBXW12 0.37 TGCCTGCTCCTGTACTTGTCCTCAG

1854 CDlE 0.34 AAGCCTATACGTTTCTGTGGAGTAA

1855 CYFIP2 0.3 TCCTTGTGCCTGCTCCTGTACTTGT

1856 TARP 0.33 TCCTGTACTTGTCCTCAGCTTGGGC

1857 TRIM 0.38 CACCCAGCTGGTCCTGTGGATGGGA

1858 RPLlOL 0.43 AAATGTTTCCTTGTGCCTGCTCCTG

1859 GLTSCR2 0.43 CACCCAGCTGGTCCTGTGGATGGGA

1860 CKIP-I 0.33 TCCTTGTGCCTGCTCCTGTACTTGT

1861 NRNl 0.3 TCCTTGTGCCTGCTCCTGTACTTGT

1862 ARHGAP15 0.4 TCCTCCATCACCTGAAACACTGGAC

1863 NOTCHl 0.32 TGCCTGCTCCTGTACTTGTCCTCAG

1864 PSCD4 0.4 CACCCAGCTGGTCCTGTGGATGGGA

1865 C13orfl8 0.31 AAGCCTATACGTTTCTGTGGAGTAA

1866 BCLIlB 0.35 ACTTGTCCTCAGCTTGGGCTTCTTC

1867 GIMAP6 0.33 ACTTGTCCTCAGCTTGGGCTTCTTC

1868 STAG3 0.31 TCCTGTACTTGTCCTCAGCTTGGGC

1869 NARF 0.34 TCCTGTACTTGTCCTCAGCTTGGGC

1870 TM6SF1 0.48 TCCTCCATCACCTGAAACACTGGAC

1871 C15orf25 0.32 TCCTGTACTTGTCCTCAGCTTGGGC

1872 FLJ11795 0.35 GCCCCACTGGACAACACTGATTCCT

1873 SAMSNl 0.37 GCCCCACTGGACAACACTGATTCCT

1874 UBASH3A 0.4 TCCTGTACTTGTCCTCAGCTTGGGC

1875 PACAP 0.31 TGCCTGCTCCTGTACTTGTCCTCAG

1876 LEFl 0.3 CACCCAGCTGGTCCTGTGGATGGGA

1877 IL21R 0.34 AAGCCTATACGTTTCTGTGGAGTAA

1878 TCF4 0.41 GCCCCACTGGACAACACTGATTCCT

1879 DKFZP434B0335 0.33 TCCTGTACTTGTCCTCAGCTTGGGC

Table 70 . Lomustine bic ^marker

SEQIDNO Gene Correlation Medianprobe

1880 RPS15 0. 43 TCCTGTACTTGTCCTCAGCTTGGGC

1881 INSIGl 0. 31 TGGACCCCACTGGCTGAGAATCTGG

1882 ALDOC 0. 39 TGCCTGCTCCTGTACTTGTCCTCAG

1883 ITM2A 0. 32 TCCTCCATCACCTGAAACACTGGAC

1884 ClQRl 0. 33 TGCCTGCTCCTGTACTTGTCCTCAG

1885 STAT5A 0. 37 TGCCTGCTCCTGTACTTGTCCTCAG

1886 INPP5D 0. 32 TCCTGTACTTGTCCTCAGCTTGGGC

1887 VPS16 0. 32 TGCCTGCTCCTGTACTTGTCCTCAG

1888 SLA 0. 32 TGCCTGCTCCTGTACTTGTCCTCAG

1889 USP20 0. 41 ACTTGTCCTCAGCTTGGGCTTCTTC

1890 IL2RG 0. 31 TCCTGTACTTGTCCTCAGCTTGGGC

1891 MFNG 0. 4 ACTTGTCCTCAGCTTGGGCTTCTTC

1892 LRMP 0. 43 GCCCCACTGGACAACACTGATTCCT

1893 EVI2A 0. 35 ACTTGTCCTCAGCTTGGGCTTCTTC

1894 PTPN7 0. 35 TCCTTGTGCCTGCTCCTGTACTTGT

1895 ARHGAP25 0.39 TCCTGTACTTGTCCTCAGCTTGGGC

1896 RHOH 0.31 AAGCCTATACGTTTCTGTGGAGTAA

1897 ZNFNlAl 0.31 TCCTGTACTTGTCCTCAGCTTGGGC

1898 CENTBl 0.35 TCCTGTACTTGTCCTCAGCTTGGGC

1899 LCP2 0.41 TGGACCCCACTGGCTGAGAATCTGG

1900 SPIl 0.35 TGCCTGCTCCTGTACTTGTCCTCAG

1901 ARHGAP6 0.33 TTGGACATCTCTAGTGTAGCTGCCA

1902 MAP4K1 0.34 CACCCAGCTGGTCCTGTGGATGGGA

1903 CCR7 0.35 TCCTCCATCACCTGAAACACTGGAC

1904 LY96 0.35 GCCCCACTGGACAACACTGATTCCT

1905 C6orf32 0.32 ACTTGTCCTCAGCTTGGGCTTCTTC

1906 MAGEAl 0.31 AAATGTTTCCTTGTGCCTGCTCCTG

1907 SP140 0.35 TTGGACATCTCTAGTGTAGCTGCCA

1908 LCPl 0.36 TCCTCCATCACCTGAAACACTGGAC

1909 IFI16 0.39 TGCCTGCTCCTGTACTTGTCCTCAG

1910 ARHGEF6 0.33 TCCTCCATCACCTGAAACACTGGAC

1911 PSCDBP 0.43 AAGCCTATACGTTTCTGTGGAGTAA

1912 SELPLG 0.3 ACTTGTCCTCAGCTTGGGCTTCTTC

1913 CD3Z 0.35 AAGCCTATACGTTTCTGTGGAGTAA

1914 PRKCQ 0.4 TCCTTGTGCCTGCTCCTGTACTTGT

1915 GZMB 0.31 AAGCCTATACGTTTCTGTGGAGTAA

1916 LAIRl 0.38 TGGACCCCACTGGCTGAGAATCTGG

1917 SH2D1A 0.36 TCCTGTACTTGTCCTCAGCTTGGGC

1918 TRB@ 0.39 TTGGACATCTCTAGTGTAGCTGCCA

1919 RFP 0.35 TGCCTGCTCCTGTACTTGTCCTCAG

1920 SEPT6 0.41 TCCTCCATCACCTGAAACACTGGAC

1921 HA-I 0.43 TCCTGTACTTGTCCTCAGCTTGGGC

1922 SLC43A3 0.4 ACTTGTCCTCAGCTTGGGCTTCTTC

1923 CD3D 0.32 TCCTTGTGCCTGCTCCTGTACTTGT

1924 T3JAM 0.3 TGGACCCCACTGGCTGAGAATCTGG

1925 GPR65 0.34 GCCCCACTGGACAACACTGATTCCT

1926 PRFl 0.36 TGCCTGCTCCTGTACTTGTCCTCAG

1927 AIFl 0.33 TGGACCCCACTGGCTGAGAATCTGG

1928 LPXN 0.38 AAATGTTTCCTTGTGCCTGCTCCTG

1929 RPLlOL 0.3 TGGACCCCACTGGCTGAGAATCTGG

1930 SITPEC 0.36 CACCCAGCTGGTCCTGTGGATGGGA

1931 ARHGAP15 0.33 TGGACCCCACTGGCTGAGAATCTGG

1932 C13orfl8 0.32 TCCTGTACTTGTCCTCAGCTTGGGC

1933 NARF 0.35 TGGACCCCACTGGCTGAGAATCTGG

1934 TM6SF1 0.34 TCCTTGTGCCTGCTCCTGTACTTGT

1935 PACAP 0.31 AAGCCTATACGTTTCTGTGGAGTAA

1936 TCF4 0.33 TCCTTGTGCCTGCTCCTGTACTTGT

Table 71 . Mercaptopurin

SEQIDNO Gene C Coorrrrelation Medianprobe

1937 SSRPl 0.31 GCCCCACTGGACAACACTGATTCCT

1938 ALDOC 0.36 AAATGTTTCCTTGTGCCTGCTCCTG

1939 ClQRl 0.31 TGCCTGCTCCTGTACTTGTCCTCAG

1940 TTFl 0.31 TCCTGTACTTGTCCTCAGCTTGGGC

1941 PRIMl 0.31 GCCCCACTGGACAACACTGATTCCT

1942 USP34 0.38 TCCTCCATCACCTGAAACACTGGAC

1943 TK2 0.33 TCCTGTACTTGTCCTCAGCTTGGGC

1944 GOLGIN-67 0.31 TGCCTGCTCCTGTACTTGTCCTCAG

1945 NPD014 0.35 ACTTGTCCTCAGCTTGGGCTTCTTC

1946 KIAA0220 0.31 TCCTCCATCACCTGAAACACTGGAC

1947 SLC43A3 0.3 TTGGACATCTCTAGTGTAGCTGCCA

1948 WBSCR20C 0.3 CACCCAGCTGGTCCTGTGGATGGGA

1949 ICAM2 0.3 TGCCTGCTCCTGTACTTGTCCTCAG

1950 TEXlO 0.32 TGGACCCCACTGGCTGAGAATCTGG

1951 CHD7 0.3 ACTTGTCCTCAGCTTGGGCTTCTTC

1952 SAMSNl 0.34 TTGGACATCTCTAGTGTAGCTGCCA

1953 TPRT 0.35 ACTTGTCCTCAGCTTGGGCTTCTTC

Table 72 . Teniposide biomarke

SEQIDNO Gene Correlation Medianprobe

1954 CD99 0.35 TGCCTGCTCCTGTACTTGTCCTCAG

1955 INSIGl 0.35 AAGCCTATACGTTTCTGTGGAGTAA

1956 PRGl 0.36 TGCCTGCTCCTGTACTTGTCCTCAG

1957 ALDOC 0.3 ACTTGTCCTCAGCTTGGGCTTCTTC

1958 ITM2A 0.33 AAGCCTATACGTTTCTGTGGAGTAA

1959 SLA 0.43 GCCCCACTGGACAACACTGATTCCT

1960 SSBP2 0.31 TCCTTGTGCCTGCTCCTGTACTTGT

1961 IL2RG 0.37 AAATGTTTCCTTGTGCCTGCTCCTG

1962 MFNG 0.32 TTGGACATCTCTAGTGTAGCTGCCA

1963 ALOX5AP 0.32 TCCTCCATCACCTGAAACACTGGAC

1964 Clorf29 0.3 TCCTCCATCACCTGAAACACTGGAC

1965 SELL 0.33 CACCCAGCTGGTCCTGTGGATGGGA

1966 STCl 0.47 TGGACCCCACTGGCTGAGAATCTGG

1967 LRMP 0.33 TCCTCCATCACCTGAAACACTGGAC

1968 MYB 0.33 TGCCTGCTCCTGTACTTGTCCTCAG

1969 PTPN7 0.34 AAGCCTATACGTTTCTGTGGAGTAA

1970 CXorf9 0.42 TTGGACATCTCTAGTGTAGCTGCCA

1971 RHOH 0.31 AAGCCTATACGTTTCTGTGGAGTAA

1972 ZNFNlAl 0.34 CACCCAGCTGGTCCTGTGGATGGGA

1973 CENTBl 0.37 TGGACCCCACTGGCTGAGAATCTGG

1974 ADD2 0.31 TGCCTGCTCCTGTACTTGTCCTCAG

1975 CDlD 0.37 ACTTGTCCTCAGCTTGGGCTTCTTC

1976 BATF 0.32 TCCTGTACTTGTCCTCAGCTTGGGC

1977 MAP4K1 0.3 GCCCCACTGGACAACACTGATTCCT

1978 CCR7 0.48 TCCTGTACTTGTCCTCAGCTTGGGC

1979 PDE4C 0.33 TGGACCCCACTGGCTGAGAATCTGG

1980 CD3G 0.33 TTGGACATCTCTAGTGTAGCTGCCA

1981 CCR9 0.36 ACTTGTCCTCAGCTTGGGCTTCTTC

1982 SPIlO 0.34 AAATGTTTCCTTGTGCCTGCTCCTG

1983 TNFAIP8 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC

1984 NAPlLl 0.32 AAGCCTATACGTTTCTGTGGAGTAA

1985 CXCR4 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC

1986 ARHGEF6 0.31 TCCTGTACTTGTCCTCAGCTTGGGC

1987 GATA3 0.36 TCCTGTACTTGTCCTCAGCTTGGGC

1988 SELPLG 0.38 AAGCCTATACGTTTCTGTGGAGTAA

1989 SEC31L2 0.46 TGGACCCCACTGGCTGAGAATCTGG

1990 CD3Z 0.35 GCCCCACTGGACAACACTGATTCCT

1991 SH2D1A 0.45 AAATGTTTCCTTGTGCCTGCTCCTG

1992 GZMB 0.35 TCCTTGTGCCTGCTCCTGTACTTGT

1993 CDlA 0.45 GCCCCACTGGACAACACTGATTCCT

1994 SCN3A 0.31 TCCTTGTGCCTGCTCCTGTACTTGT

1995 LAIRl 0.31 TCCTTGTGCCTGCTCCTGTACTTGT

1996 AFlQ 0.3 TCCTCCATCACCTGAAACACTGGAC

1997 TRB@ 0.32 AAGCCTATACGTTTCTGTGGAGTAA

1998 DOCK2 0.33 TCCTGTACTTGTCCTCAGCTTGGGC

1999 MLCl 0.31 TCCTCCATCACCTGAAACACTGGAC

2000 CD3D 0.31 TGGACCCCACTGGCTGAGAATCTGG

2001 T3JAM 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC 2002 CD6 0.38 TGCCTGCTCCTGTACTTGTCCTCAG 2003 ZAP70 0.34 TCCTCCATCACCTGAAACACTGGAC 2004 IFI44 0.37 TCCTTGTGCCTGCTCCTGTACTTGT 2005 GPR65 0.34 TCCTGTACTTGTCCTCAGCTTGGGC 2006 PRFl 0.34 TCCTCCATCACCTGAAACACTGGAC 2007 AIFl 0.33 ACTTGTCCTCAGCTTGGGCTTCTTC 2008 WDR45 0.41 GCCCCACTGGACAACACTGATTCCT 2009 CDlE 0.31 AAGCCTATACGTTTCTGTGGAGTAA 2010 CYFIP2 0.32 TGGACCCCACTGGCTGAGAATCTGG 2011 TARP 0.42 CACCCAGCTGGTCCTGTGGATGGGA 2012 TRIM 0.33 TGGACCCCACTGGCTGAGAATCTGG 2013 ARHGAP15 0.38 AAGCCTATACGTTTCTGTGGAGTAA 2014 NOTCHl 0.32 TGCCTGCTCCTGTACTTGTCCTCAG 2015 STAG3 0.32 GCCCCACTGGACAACACTGATTCCT 2016 NARF 0.31 AAGCCTATACGTTTCTGTGGAGTAA 2017 TM6SF1 0.33 GCCCCACTGGACAACACTGATTCCT 2018 UBASH3A 0.33 TCCTGTACTTGTCCTCAGCTTGGGC 2019 MGC5566 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC

Table 73. . Dactinomycin biomarkers.

SEQIDNO Gene Correlation Medianprobe

2020 ALDOC 0.37 GCCCCACTGGACAACACTGATTCCT

2021 ClQRl 0.36 TGGACCCCACTGGCTGAGAATCTGG

2022 SLA 0.31 TCCTGTACTTGTCCTCAGCTTGGGC

2023 WBSCR20A 0.31 AAGCCTATACGTTTCTGTGGAGTAA

2024 MFNG 0. ,33 ACTTGTCCTCAGCTTGGGCTTCTTC

2025 SELL 0.,33 GCCCCACTGGACAACACTGATTCCT

2026 MYB 0.,3366 TTGGACATCTCTAGTGTAGCTGCCA

2027 RHOH 0 0..3322 TCCTTGTGCCTGCTCCTGTACTTGT

2028 ZNFNlAl 0.3 AAATGTTTCCTTGTGCCTGCTCCTG

2029 LCP2 0.3 CACCCAGCTGGTCCTGTGGATGGGA

2030 MAP4K1 0 0..3344 AAGCCTATACGTTTCTGTGGAGTAA

2031 CBFA2T3 00..3355 TCCTTGTGCCTGCTCCTGTACTTGT

2032 LCPl 00..3322 GCCCCACTGGACAACACTGATTCCT

2033 SELPLG 00..3333 ACTTGTCCTCAGCTTGGGCTTCTTC

2034 CD3Z 00..3355 GCCCCACTGGACAACACTGATTCCT

2035 LAIRl 00..3333 TGGACCCCACTGGCTGAGAATCTGG

2036 WBSCR20C 0.3 AAGCCTATACGTTTCTGTGGAGTAA

2037 CD3D 0.35 CACCCAGCTGGTCCTGTGGATGGGA

2038 GPR65 0.32 AAATGTTTCCTTGTGCCTGCTCCTG

2039 ARHGAP15 0.32 TCCTCCATCACCTGAAACACTGGAC

2040 FLJ10178 0.36 TCCTTGTGCCTGCTCCTGTACTTGT

2041 NARF 0.35 TCCTCCATCACCTGAAACACTGGAC

2042 PUS3 0.32 TCCTGTACTTGTCCTCAGCTTGGGC

Table 74 Tretinoin biomarkers.

SEQIDNO Gene Correlation Medianprobe

2043 PPIB 0.31 AAGCCTATACGTTTCTGTGGAGTAA

2044 ZFP36L2 0.48 AAGCCTATACGTTTCTGTGGAGTAA

2045 IFI 30 0.46 ACTTGTCCTCAGCTTGGGCTTCTTC

2046 USP7 0.35 TCCTCCATCACCTGAAACACTGGAC

2047 SRM 0.43 TCCTCCATCACCTGAAACACTGGAC

2048 SH3BP5 0.32 TGCCTGCTCCTGTACTTGTCCTCAG

2049 ALDOC 0.41 TTGGACATCTCTAGTGTAGCTGCCA

2050 FADS2 0.33 ACTTGTCCTCAGCTTGGGCTTCTTC

2051 GUSB 0.38 TTGGACATCTCTAGTGTAGCTGCCA

2052 PSCDl 0.48 TCCTGTACTTGTCCTCAGCTTGGGC

2053 IQGAP2 0.34 TCCTGTACTTGTCCTCAGCTTGGGC

2054 STS 0.34 GCCCCACTGGACAACACTGATTCCT

2055 MFNG 0.36 TGGACCCCACTGGCTGAGAATCTGG

2056 FLIl 0.33 ACTTGTCCTCAGCTTGGGCTTCTTC

2057 PIM2 0.35 TGGACCCCACTGGCTGAGAATCTGG

2058 INPP4A 0.54 TCCTGTACTTGTCCTCAGCTTGGGC

2059 LRMP 0.51 GCCCCACTGGACAACACTGATTCCT

2060 ICAM2 0.3 AAATGTTTCCTTGTGCCTGCTCCTG

2061 EVI2A 0.33 CACCCAGCTGGTCCTGTGGATGGGA

2062 MAL 0.46 AAATGTTTCCTTGTGCCTGCTCCTG

2063 BTN3A3 0.43 TTGGACATCTCTAGTGTAGCTGCCA

2064 PTPN7 0.4 TTGGACATCTCTAGTGTAGCTGCCA

2065 ILlORA 0.42 TTGGACATCTCTAGTGTAGCTGCCA

2066 SPIl 0.41 AAGCCTATACGTTTCTGTGGAGTAA

2067 TRAFl 0.3 TGCCTGCTCCTGTACTTGTCCTCAG

2068 ITGB7 0.33 TCCTTGTGCCTGCTCCTGTACTTGT

2069 ARHGAP6 0.32 TGGACCCCACTGGCTGAGAATCTGG

2070 MAP4K1 0.52 GCCCCACTGGACAACACTGATTCCT

2071 CD28 0.34 AAGCCTATACGTTTCTGTGGAGTAA

2072 PTP4A3 0.3 TCCTCCATCACCTGAAACACTGGAC

2073 LTB 0.32 ACTTGTCCTCAGCTTGGGCTTCTTC

2074 Clorf38 0.4 TGCCTGCTCCTGTACTTGTCCTCAG

2075 WBSCR22 0.53 TCCTCCATCACCTGAAACACTGGAC

2076 CD8B1 0.35 TCCTCCATCACCTGAAACACTGGAC

2077 LCPl 0.35 ACTTGTCCTCAGCTTGGGCTTCTTC

2078 FLJ13052 0.31 TCCTCCATCACCTGAAACACTGGAC

2079 MEF2C 0.71 TTGGACATCTCTAGTGTAGCTGCCA

2080 PSCDBP 0.41 AAATGTTTCCTTGTGCCTGCTCCTG

2081 IL16 0.51 TGGACCCCACTGGCTGAGAATCTGG

2082 SELPLG 0.53 TGCCTGCTCCTGTACTTGTCCTCAG

2083 MAGEA9 0.6 AAATGTTTCCTTGTGCCTGCTCCTG

2084 LAIRl 0.43 TCCTCCATCACCTGAAACACTGGAC

2085 TNFRSF25 0.53 TCCTCCATCACCTGAAACACTGGAC

2086 EVI2B 0.42 ACTTGTCCTCAGCTTGGGCTTCTTC

2087 IGJ 0.37 TCCTTGTGCCTGCTCCTGTACTTGT

2088 PDCD4 0.47 AAATGTTTCCTTGTGCCTGCTCCTG

2089 RASA4 0.52 CACCCAGCTGGTCCTGTGGATGGGA

2090 HA-I 0.73 AAGCCTATACGTTTCTGTGGAGTAA

2091 PLCL2 0.47 TCCTGTACTTGTCCTCAGCTTGGGC

2092 RNASE6 0.31 AAGCCTATACGTTTCTGTGGAGTAA

2093 WBSCR20C 0.35 TTGGACATCTCTAGTGTAGCTGCCA

2094 NUP210 0.36 AAGCCTATACGTTTCTGTGGAGTAA

2095 RPLlOL 0.39 ACTTGTCCTCAGCTTGGGCTTCTTC

2096 Cllorf2 0.33 TGGACCCCACTGGCTGAGAATCTGG

2097 CABCl 0.32 TGCCTGCTCCTGTACTTGTCCTCAG

2098 ARHGEF3 0.37 TCCTGTACTTGTCCTCAGCTTGGGC

2099 TAPBPL 0.42 TGCCTGCTCCTGTACTTGTCCTCAG

2100 CHST12 0.35 AAATGTTTCCTTGTGCCTGCTCCTG

2101 FKBPIl 0.54 TGCCTGCTCCTGTACTTGTCCTCAG

2102 FLJ35036 0.42 TTGGACATCTCTAGTGTAGCTGCCA

2103 MYLIP 0.38 CACCCAGCTGGTCCTGTGGATGGGA

2104 TXNDC5 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC

2105 PACAP 0.3 TCCTCCATCACCTGAAACACTGGAC

2106 TOSO 0.34 TCCTGTACTTGTCCTCAGCTTGGGC

2107 PNAS-4 0.37 TGGACCCCACTGGCTGAGAATCTGG

2108 IL21R 0.57 AAGCCTATACGTTTCTGTGGAGTAA

2109 TCF4 0.64 TCCTTGTGCCTGCTCCTGTACTTGT

Table 75 . Ifosfamide b

SEQIDNO Gene C Coorrrrelation Medianprobe

2110 ARHGDIB 0.36 TGGACCCCACTGGCTGAGAATCTGG

2111 ZFP36L2 0.45 TGGACCCCACTGGCTGAGAATCTGG

2112 ITM2A 0.39 AAGCCTATACGTTTCTGTGGAGTAA

2113 LGALS9 0.54 AAATGTTTCCTTGTGCCTGCTCCTG

2114 INPP5D 0.53 TCCTGTACTTGTCCTCAGCTTGGGC

2115 SATBl 0.35 TTGGACATCTCTAGTGTAGCTGCCA

2116 TFDP2 0.32 AAGCCTATACGTTTCTGTGGAGTAA

2117 IL2RG 0.32 TGCCTGCTCCTGTACTTGTCCTCAG

2118 CD48 0.5 ACTTGTCCTCAGCTTGGGCTTCTTC

2119 SELL 0.32 ACTTGTCCTCAGCTTGGGCTTCTTC

2120 ADA 0.32 TGCCTGCTCCTGTACTTGTCCTCAG

2121 LRMP 0.34 GCCCCACTGGACAACACTGATTCCT

2122 RIMS3 0.37 AAGCCTATACGTTTCTGTGGAGTAA

2123 LCK 0.37 TCCTGTACTTGTCCTCAGCTTGGGC

2124 CXorf9 0.4 CACCCAGCTGGTCCTGTGGATGGGA

2125 RHOH 0.3 GCCCCACTGGACAACACTGATTCCT

2126 ZNFNlAl 0.31 TTGGACATCTCTAGTGTAGCTGCCA

2127 LCP2 0.37 TCCTGTACTTGTCCTCAGCTTGGGC

2128 CDlD 0.49 TCCTCCATCACCTGAAACACTGGAC

2129 CD2 0.42 CACCCAGCTGGTCCTGTGGATGGGA

2130 ZNF91 0.45 AAATGTTTCCTTGTGCCTGCTCCTG

2131 MAP4K1 0.32 TCCTTGTGCCTGCTCCTGTACTTGT

2132 CCR7 0.44 TTGGACATCTCTAGTGTAGCTGCCA

2133 IGLLl 0.43 TGCCTGCTCCTGTACTTGTCCTCAG

2134 CD3G 0.3 TCCTCCATCACCTGAAACACTGGAC

2135 ZNF430 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC

2136 CCR9 0.31 TTGGACATCTCTAGTGTAGCTGCCA

2137 CXCR4 0.37 ACTTGTCCTCAGCTTGGGCTTCTTC

2138 KIAA0922 0.31 AAGCCTATACGTTTCTGTGGAGTAA

2139 TARP 0.31 GCCCCACTGGACAACACTGATTCCT

2140 FYN 0.35 TCCTGTACTTGTCCTCAGCTTGGGC

2141 SH2D1A 0.34 TTGGACATCTCTAGTGTAGCTGCCA

2142 CDlA 0.31 AAGCCTATACGTTTCTGTGGAGTAA

2143 LSTl 0.33 TTGGACATCTCTAGTGTAGCTGCCA

2144 LAIRl 0.36 ACTTGTCCTCAGCTTGGGCTTCTTC

2145 TRB@ 0.34 TGGACCCCACTGGCTGAGAATCTGG

2146 SEPT6 0.39 TTGGACATCTCTAGTGTAGCTGCCA

2147 CD3D 0.37 TCCTCCATCACCTGAAACACTGGAC

2148 CD6 0.32 AAATGTTTCCTTGTGCCTGCTCCTG

2149 AIFl 0.34 TGCCTGCTCCTGTACTTGTCCTCAG

2150 CDlE 0.31 TCCTGTACTTGTCCTCAGCTTGGGC

2151 TRIM 0.32 TCCTGTACTTGTCCTCAGCTTGGGC

2152 GLTSCR2 0.34 TCCTGTACTTGTCCTCAGCTTGGGC

2153 ARHGAP15 0.33 TCCTGTACTTGTCCTCAGCTTGGGC

2154 BIN2 0.33 TGCCTGCTCCTGTACTTGTCCTCAG

2155 SH3TC1 0.32 TGGACCCCACTGGCTGAGAATCTGG

2156 CECRl 0.36 TCCTCCATCACCTGAAACACTGGAC

2157 BCLIlB 0.38 TCCTCCATCACCTGAAACACTGGAC

2158 GIMAP6 0.32 GCCCCACTGGACAACACTGATTCCT

2159 STAG3 0.46 TTGGACATCTCTAGTGTAGCTGCCA

2160 GALNT6 0.32 ACTTGTCCTCAGCTTGGGCTTCTTC

2161 MGC5566 0.49 TCCTTGTGCCTGCTCCTGTACTTGT

2162 PACAP 0.48 TCCTGTACTTGTCCTCAGCTTGGGC

2163 LEFl 0.4 TGCCTGCTCCTGTACTTGTCCTCAG

Table 76 . Tamoxifen biomarkers

SEQIDNO Gene Correlation Medianprobe

2164 MLP 0.33 TCCTGTACTTGTCCTCAGCTTGGGC

2165 GLUL 0.33 TCCTTGTGCCTGCTCCTGTACTTGT

2166 SLC9A3R1 0.37 CACCCAGCTGGTCCTGTGGATGGGA

2167 ZFP36L2 0.33 TTGGACATCTCTAGTGTAGCTGCCA

2168 INSIGl 0.31 TCCTCCATCACCTGAAACACTGGAC

2169 TBLlX 0.36 TCCTGTACTTGTCCTCAGCTTGGGC

2170 NDUFABl 0.43 AAATGTTTCCTTGTGCCTGCTCCTG

2171 EBP 0.31 TGGACCCCACTGGCTGAGAATCTGG

2172 TRIM14 0.43 TTGGACATCTCTAGTGTAGCTGCCA

2173 SRPK2 0.41 GCCCCACTGGACAACACTGATTCCT

2174 PMM2 0.4 AAATGTTTCCTTGTGCCTGCTCCTG

2175 CLDN3 0.41 AAGCCTATACGTTTCTGTGGAGTAA

2176 GCHl 0.34 TTGGACATCTCTAGTGTAGCTGCCA

2177 IDIl 0.34 AAATGTTTCCTTGTGCCTGCTCCTG

2178 TTFl 0.46 TCCTTGTGCCTGCTCCTGTACTTGT

2179 MYB 0.39 CACCCAGCTGGTCCTGTGGATGGGA

2180 RASGRPl 0.32 CACCCAGCTGGTCCTGTGGATGGGA

2181 HIST1H3H 0.38 TGGACCCCACTGGCTGAGAATCTGG

2182 CBFA2T3 0.34 AAATGTTTCCTTGTGCCTGCTCCTG

2183 SRRM2 0.43 GCCCCACTGGACAACACTGATTCCT

2184 ANAPC5 0.31 TCCTGTACTTGTCCTCAGCTTGGGC

2185 MBD4 0.5 TCCTGTACTTGTCCTCAGCTTGGGC

2186 GATA3 0.32 TCCTCCATCACCTGAAACACTGGAC

2187 HIST1H2BG 0.32 AAGCCTATACGTTTCTGTGGAGTAA

2188 RAB14 0.31 TGGACCCCACTGGCTGAGAATCTGG

2189 PIK3R1 0.36 AAGCCTATACGTTTCTGTGGAGTAA

2190 MGC50853 0.37 CACCCAGCTGGTCCTGTGGATGGGA

2191 ELFl 0.35 GCCCCACTGGACAACACTGATTCCT

2192 ZRFl 0.32 TCCTTGTGCCTGCTCCTGTACTTGT

2193 ZNF394 0.31 AAATGTTTCCTTGTGCCTGCTCCTG

2194 S100A14 0.39 AAATGTTTCCTTGTGCCTGCTCCTG

2195 SLC6A14 0.31 CACCCAGCTGGTCCTGTGGATGGGA

2196 GALNT6 0.37 TCCTCCATCACCTGAAACACTGGAC

2197 SPDEF 0.44 AAATGTTTCCTTGTGCCTGCTCCTG

2198 TPRT 0.5 AAGCCTATACGTTTCTGTGGAGTAA

2199 CALML4 0.31 TTGGACATCTCTAGTGTAGCTGCCA

Table 77 . Floxuridine biomarke

SEQIDNO Gene Correlation

2200 CSDA 0.33 ACTTGTCCTCAGCTTGGGCTTCTTC

2201 F8A1 0.31 TGGACCCCACTGGCTGAGAATCTGG

2202 KYNU 0.32 TGGACCCCACTGGCTGAGAATCTGG

2203 PHF14 0.31 AAGCCTATACGTTTCTGTGGAGTAA

2204 SERPINB2 0.34 TCCTCCATCACCTGAAACACTGGAC

2205 OPHNl 0.31 GCCCCACTGGACAACACTGATTCCT

2206 HRMT1L2 0.31 AAGCCTATACGTTTCTGTGGAGTAA

2207 TNFRSFlA 0.3 GCCCCACTGGACAACACTGATTCCT

2208 PPP4C 0.31 AAGCCTATACGTTTCTGTGGAGTAA

2209 CESl 0.3 TCCTCCATCACCTGAAACACTGGAC

2210 TP53AP1 0.3 GCCCCACTGGACAACACTGATTCCT

2211 TM4SF4 0.32 GCCCCACTGGACAACACTGATTCCT

2212 RPL5 0.32 TGCCTGCTCCTGTACTTGTCCTCAG

2213 BC008967 0.32 TGGACCCCACTGGCTGAGAATCTGG

2214 TLK2 0.35 TTGGACATCTCTAGTGTAGCTGCCA

2215 COL4A6 0.31 TCCTTGTGCCTGCTCCTGTACTTGT

2216 PAK3 0.32 CACCCAGCTGGTCCTGTGGATGGGA

2217 RECK 0.34 TCCTTGTGCCTGCTCCTGTACTTGT

2218 LOC51321 0.32 AAGCCTATACGTTTCTGTGGAGTAA

2219 MST4 0.36 TCCTCCATCACCTGAAACACTGGAC

2220 DERP6 0.32 TGGACCCCACTGGCTGAGAATCTGG

2221 SCD4 0.33 TCCTTGTGCCTGCTCCTGTACTTGT

2222 FLJ22800 0.31 TGGACCCCACTGGCTGAGAATCTGG

Table 78 . Irinotecan biomarke

SEQIDNO Gene Correlation Medianprobe

2223 CSDA 0.32 TGCCTGCTCCTGTACTTGTCCTCAG

2224 UBE2L6 0.32 GCCCCACTGGACAACACTGATTCCT

2225 TAPl 0.44 TGGACCCCACTGGCTGAGAATCTGG

2226 RPS19 0.32 TGCCTGCTCCTGTACTTGTCCTCAG

2227 SERPINAl 0.32 ACTTGTCCTCAGCTTGGGCTTCTTC

2228 ClQRl 0.31 TTGGACATCTCTAGTGTAGCTGCCA

2229 SLA 0.33 CACCCAGCTGGTCCTGTGGATGGGA

2230 GPSM3 0.46 TGCCTGCTCCTGTACTTGTCCTCAG

2231 PSMB9 0.3 TGCCTGCTCCTGTACTTGTCCTCAG

2232 EDGl 0.34 TGCCTGCTCCTGTACTTGTCCTCAG

2233 FMNLl 0.4 GCCCCACTGGACAACACTGATTCCT

2234 PTPN7 0.39 TTGGACATCTCTAGTGTAGCTGCCA

2235 ZNFNlAl 0.32 AAGCCTATACGTTTCTGTGGAGTAA

2236 CENTBl 0.33 TTGGACATCTCTAGTGTAGCTGCCA

2237 BATF 0.41 ACTTGTCCTCAGCTTGGGCTTCTTC

2238 MAP4K1 0.39 AAATGTTTCCTTGTGCCTGCTCCTG

2239 PDE4C 0.31 AAGCCTATACGTTTCTGTGGAGTAA

2240 SPIlO 0.35 TCCTTGTGCCTGCTCCTGTACTTGT

2241 HLA-DRA 0.31 TGGACCCCACTGGCTGAGAATCTGG

2242 IFI 16 0.36 TTGGACATCTCTAGTGTAGCTGCCA

2243 HLA-DRBl 0.32 AAGCCTATACGTTTCTGTGGAGTAA

2244 ARHGEF6 0.43 ACTTGTCCTCAGCTTGGGCTTCTTC

2245 SELPLG 0.35 TCCTTGTGCCTGCTCCTGTACTTGT

2246 SEC31L2 0.35 CACCCAGCTGGTCCTGTGGATGGGA

2247 CD3Z 0.51 TCCTCCATCACCTGAAACACTGGAC

2248 PRKCQ 0.39 TTGGACATCTCTAGTGTAGCTGCCA

2249 SH2D1A 0.43 AAGCCTATACGTTTCTGTGGAGTAA

2250 GZMB 0.49 TCCTCCATCACCTGAAACACTGGAC

2251 TRB@ 0.43 ACTTGTCCTCAGCTTGGGCTTCTTC

2252 HLA-DPAl 0.47 ACTTGTCCTCAGCTTGGGCTTCTTC

2253 AIMl 0.36 TCCTTGTGCCTGCTCCTGTACTTGT

2254 DOCK2 0.39 TGGACCCCACTGGCTGAGAATCTGG

2255 CD3D 0.31 TCCTGTACTTGTCCTCAGCTTGGGC

2256 IFITMl 0.31 TTGGACATCTCTAGTGTAGCTGCCA

2257 ZAP70 0.31 GCCCCACTGGACAACACTGATTCCT

2258 PRFl 0.47 CACCCAGCTGGTCCTGTGGATGGGA

2259 Clorf24 0.39 GCCCCACTGGACAACACTGATTCCT

2260 ARHGAP15 0.48 TCCTCCATCACCTGAAACACTGGAC

2261 C13orfl8 0.33 CACCCAGCTGGTCCTGTGGATGGGA

2262 TM6SF1 0.37 TCCTTGTGCCTGCTCCTGTACTTGT

Table 79. Satraplatin biomarkers .

SEQIDNO Gene Correlation Medianprobe

2263 STATl 0.32 TCCTGTACTTGTCCTCAGCTTGGGC

2264 HSBPl 0.33 AAATGTTTCCTTGTGCCTGCTCCTG

2265 IFI30 0.35 AAGCCTATACGTTTCTGTGGAGTAA

2266 RIOK3 0.36 TCCTCCATCACCTGAAACACTGGAC

2267 TNFSFlO 0.31 ACTTGTCCTCAGCTTGGGCTTCTTC

2268 ALOX5AP 0.3 TCCTTGTGCCTGCTCCTGTACTTGT

2269 ADFP 0.33 TGGACCCCACTGGCTGAGAATCTGG

2270 IRS2 0.37 TCCTGTACTTGTCCTCAGCTTGGGC

2271 EFEMP2 0.31 TTGGACATCTCTAGTGTAGCTGCCA

2272 RIPK2 0.35 TGGACCCCACTGGCTGAGAATCTGG

2273 DKFZp564I1922 0.33 TCCTCCATCACCTGAAACACTGGAC

2274 MTlK 0.34 TCCTCCATCACCTGAAACACTGGAC

2275 RNASET2 0.38 ACTTGTCCTCAGCTTGGGCTTCTTC

2276 EFHD2 0.31 CACCCAGCTGGTCCTGTGGATGGGA

2277 TRIB3 0.33 GCCCCACTGGACAACACTGATTCCT

2278 ACSL5 0.42 AAATGTTTCCTTGTGCCTGCTCCTG

2279 IFIHl 0.37 ACTTGTCCTCAGCTTGGGCTTCTTC

2280 DNAPTP6 0.42 TGCCTGCTCCTGTACTTGTCCTCAG

Table 80. Vincristine microRNA biomarkers.

SEQIDNO Medianprobe Corr Sequence

2281 Hcd892 left 0.3 GAGGGCTGGAGAGGTTGGGTGCGCTTGTGCGTTTCACTTT

2282 Hcd678 right 0.27 GCCCTGAAGCTCCGGACTACAGCTCCCAGGCCTCTCCAAG

2283 mir-00: 7-1-prec 0.28 TGTTGGCCTAGTTCTGTGTGGAAGACTAGTGATTTTGTTG

2284 MPR243 left 0.25 GTATTTACCTAGTTGTAATGTGGGTTGCCATGGTGTTTTG

2285 Hcd654 left 0.25 AACGAGTAAAAGGCGTACATGGGAGCGCGGGGCGGCAGAG

2286 mir-487Nol 0.26 TTATGACGAATCATACAGGGACATCCAGTTTTTCAGTATC

2287 Hcd794 right 0.35 GGCCACCACAGACACCAACAAGTTCAGTCCGTTTCTGCAG

2288 Hcd739 right 0.32 TATTAGCTGAGGGAGGGCTGGAGGCGGCTGCATTCCGACT

2289 Hcd562 right 0.28 CGCATGTCCTGGCCCTCGTCCTTCCATGGCACTGGCACCG

Table 81. Cisplatm microRNA biomarkers.

SEQIDNO Medianprobe Corr Sequence

2290 HUMTRF 0.34 GATCTAAAGGTCCCTGGTTCGATCCCGGGTTTCGGCACCA

2291 HPR187 right 0.25 TATTTATTACAAGGTCCTTCTTCCCCGTAAAACTTTGTCC

2292 mir-450-1 0.26 AACGATACTAAACTGTTTTTGCGATGTGTTCCTAATATGC

2293 mir-155-prec 0.31 TTAATGCTAATCGTGATAGGGGTTTTTGCCTCCAACTGAC

2294 mir-515-15p 0.25 GATCTCATGCAGTCATTCTCCAAAAGAAAGCACTTTCTGT

2295 mir-181b-precNo2 0.25 ACCATCGACCGTTGATTGTACCCTATGGCTAACCATCATC

2296 mir-124a-l-precl 0.26 ATACAATTAAGGCACGCGGTGAATGCCAAGAATGGGGCTG

2297 mir-450-2Nol 0.3 GAAAGATGCTAAACTATTTTTGCGATGTGTTCCTAATATG

2298 Hcd923 right 0.31 CTGGAGATAATGATTCTGCATTTCTAATTAACTCCCAGGT

2299 mir-342Nol 0.31 GTCTCACACAGAAATCGCACCCGTCACCTTGGCCTACTTA

2300 mir-142-prec 0.27 CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG

2301 mir-223-prec 0.26 GAGTGTCAGTTTGTCAAATACCCCAAGTGCGGCACATGCT

2302 Hcd754 left 0.38 TCCTCCTCCTCCTTTTCGTTCCGGCTCCCTGGCTGGCTCC

2303 Hcd213 HPR182 left 0.3 CTGTTTCATACTTGAGGAGAAATTATCCTTGGTGTGTTCG

Table 82 Azaguamne microRNA biomarkers.

SEQIDNO Medianprobe Corr Sequence

2304 MPR121 left 0.3 CACCTGGCTCTGAGAACTGAATTCCATAGGCTGTGAGCTC

2305 HUMTRS 0.26 TCTAGCGACAGAGTGGTTCAATTCCACCTTTCGGGCGCCA

2306 mir-213-precNol 0.26 AACATTCATTGCTGTCGGTGGGTTGAACTGTGTGGACAAG

2307 mir-155-prec 0.4 TTAATGCTAATCGTGATAGGGGTTTTTGCCTCCAACTGAC

2308 mir-147-prec 0.47 GACTATGGAAGCCAGTGTGTGGAAATGCTTCTGCTAGATT

2309 mir-lOONol 0.26 CCTGTTGCCACAAACCCGTAGATCCGAACTTGTGGTATTA

2310 mir-138-l-prec 0.29 AGCTGGTGTTGTGAATCAGGCCGTTGCCAATCAGAGAACG

2311 mir-140No2 0.38 TTCTACCACAGGGTAGAACCACGGACAGGATACCGGGGCA

2312 mir-146-prec 0.51 TGAGAACTGAATTCCATGGGTTGTGTCAGTGTCAGACCTC

2313 mir-509Nol 0.25 ATTAAAAATGATTGGTACGTCTGTGGGTAGAGTACTGCAT

2314 mir-146bNol 0.33 CACCTGGCACTGAGAACTGAATTCCATAGGCTGTGAGCTC

2315 Hcd514 right 0.26 ATTAGAGACTCGTTAAGAGAAGGTGAGAAGGGCTCAGTAA 2316 Hcd397 left 0.34 GTGTGTATACTTATGTGTGTGTATGTGTGAGTGTGAATAT 2317 Hcd731 left 0.27 AATTGTGACAACTGAGTGGGAGGTTTGTGTGATGATTATC 2318 mir-034-precNo2 0.32 AGTAAGGAAGCAATCAGCAAGTATACTGCCCTAGAAGTGC 2319 mir-100-l/2-prec 0.3 TGAGGCCTGTTGCCACAAACCCGTAGATCCGAACTTGTGG

Table 83. Etoposide microRNA biomarkers.

SEQIDNO Medianprobe Corr Sequence

2320 Hcd415 right 0.28 GATGTTTGGGAAACAATGGGAGTGAGAGAATGGGAGAGCT

2321 Hcd768 right 0.37 GCCCTGGCGGAACGCTGAGAAGACAGTCGAACTTGACTAT

2322 HUMTRF 0.38 GATCTAAAGGTCCCTGGTTCGATCCCGGGTTTCGGCACCA

2323 Hcd866 right 0.26 GTCATGCTGCCACCAGCAGGCAGAGAAGAAGCAGAAGAAC

2324 Hcdl45 left 0.33 AAAAATCCCAGCGGCCACCTTTCCTCCCTGCCCCATTGGG

2325 HUMTRAB 0.29 ATGGTAGAGCGCTCGCTTTGCTTGCGAGAGGTAGCGGGAT

2326 Hcd913 right 0.36 CAAACATCATGTGACGTCTGTGGAGCGGCGGCGGCGGCGG

2327 HPR163 left 0.29 GCTGCCCCCTCCCTTAGCAACGTGGCCCCGGCGTTCCAAA

2328 Hcd697 right 0.27 GGCCTCATGCTGCCAAGGGCTGGCAAGAAGTCCCTGCTTG

2329 Hcd755 left 0.26 GGAAGTGGAGCAAATGGATGGAAAGCAATTTTTGGAAGAT

2330 Hcd716 right 0.25 CAATAAATGTGCCTATAAAGGCGCCGGCTCCGGGGCGCGG

2331 MPR207 right 0.33 AACAACTTTGTGCTGGTGCCGGGGAAGTTTGTGTCTCCTA

2332 HSTRNL 0.26 TCCGGATGGAGCGTGGGTTCGAATCCCACTTCTGACACCA

2333 HPR206 left 0.29 CTATATTGGACCGCAGCGCTGAGAGCTTTTGTGTTTAATG

2334 MPR243 left 0.27 GTATTTACCTAGTTGTAATGTGGGTTGCCATGGTGTTTTG

2335 Hcd654 left 0.4 AACGAGTAAAAGGCGTACATGGGAGCGCGGGGCGGCAGAG

2336 MPR130 left 0.28 AGGCCAAGGTGACGGGTGCGATTTCTGTGTGAGACAATTC

2337 Hcd782 left 0.26 GGAGCCCTGTCTGCAAAGAGTGGTGCGTGTGCGTGTGTGA

2338 Hcd794 right 0.26 GGCCACCACAGACACCAACAAGTTCAGTCCGTTTCTGCAG

2339 Hcd739 right 0.3 TATTAGCTGAGGGAGGGCTGGAGGCGGCTGCATTCCGACT

2340 mir-142-prec 0.29 CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG

2341 HSHELAOl 0.29 GGCCGCAGCAACCTCGGTTCGTATCCGAGTCACGGCACCA

2342 HUMTRVlA 0.29 ACGCGAAAGGTCCCCGGTTCGAAACCGGGCGGAAACACCA

2343 Hcd754 left 0.34 TCCTCCTCCTCCTTTTCGTTCCGGCTCCCTGGCTGGCTCC

Table 84. Carboplatin microRNA biomarkers.

SEQIDNO Medianprobe Corr Sequence

2344 Hcd829 right 0.27 AAAATGGCGGCGGGAAAAGCGAGCGGCGAGAGCGAGGAGG

2345 HUMTRF 0.26 GATCTAAAGGTCCCTGGTTCGATCCCGGGTTTCGGCACCA

2346 HPR187 left 0.29 TGTGTGTTGCGGGGGTGGGGGCCGGTGAAAGTGATTTGAT

2347 Hcd210_HPR205 right 0.32 CGAAACATTCGCGGTGCACTTCTTTTTCAGTATCCTATTC

2348 mir-379Nol 0.26 TTCCGTGGTTCCTGAAGAGATGGTAGACTATGGAACGTAG

2349 mir-213-precNol 0.26 AACATTCATTGCTGTCGGTGGGTTGAACTGTGTGGACAAG

2350 mir-4325p 0.29 CCAGGTCTTGGAGTAGGTCATTGGGTGGATCCTCTATTTC

2351 mir-450-1 0.3 AACGATACTAAACTGTTTTTGCGATGTGTTCCTAATATGC

2352 mir-155-prec 0.25 TTAATGCTAATCGTGATAGGGGTTTTTGCCTCCAACTGAC

2353 Hcd28_HPR39right 0.26 AAGCTCCCAAATTAGCTTTTTAAATAGAAGCTGAGAGTTA

2354 MPR244 right 0.27 TAAACATAGAGGAAATTTCACGTTTTCAGTGTCAAATGCT

2355 mir-409-3p 0.3 GACGAATGTTGCTCGGTGAACCCCTTTTCGGTATCAAATT

2356 mir-124a-l-precl 0.28 ATACAATTAAGGCACGCGGTGAATGCCAAGAATGGGGCTG

2357 mir-154-preclNol 0.26 GTGGTACTTGAAGATAGGTTATCCGTGTTGCCTTCGCTTT

2358 mir-495Nol 0.32 GTGACGAAACAAACATGGTGCACTTCTTTTTCGGTATCAA

2359 mir-515-23p 0.25 CAGAGTGCCTTCTTTTGGAGCGTTACTGTTTGAGAAAAAC

2360 Hcd43 8right 0.27 GTGTTTATTTGAATCTCACATCGCTCATAAGAATACACGC

2361 Hcd770 left 0.3 CCAGTATACAATCCGTTTTTCAGTTTAGCTTGAGATCAGA

2362 mir-382 0.32 GGTACTTGAAGAGAAGTTGTTCGTGGTGGATTCGCTTTAC

2363 mir-223-prec 0.3 GAGTGTCAGTTTGTCAAATACCCCAAGTGCGGCACATGCT

2364 Hcd754 left 0.48 TCCTCCTCCTCCTTTTCGTTCCGGCTCCCTGGCTGGCTCC

2365 Hcd213_HPR182 left 0.31 CTGTTTCATACTTGAGGAGAAATTATCCTTGGTGTGTTCG

Table 85, Adriamycin microRNA biomarkers .

SEQIDNO Medianprobe Corr Sequence

2366 Hcd768 right 0.25 GCCCTGGCGGAACGCTGAGAAGACAGTCGAACTTGACTAT

2367 mir-483Nol 0.28 ATCACGCCTCCTCACTCCTCTCCTCCCGTCTTCTCCTCTC

2368 Hcdl45 left 0.28 AAAAATCCCAGCGGCCACCTTTCCTCCCTGCCCCATTGGG

2369 mir-197-prec 0.25 TAAGAGCTCTTCACCCTTCACCACCTTCTCCACCCAGCAT

2370 mir-212-precNol 0.27 CCTCAGTAACAGTCTCCAGTCACGGCCACCGACGCCTGGC

2371 HPR163 left 0.3 GCTGCCCCCTCCCTTAGCAACGTGGCCCCGGCGTTCCAAA

2372 Hcd654 left 0.26 AACGAGTAAAAGGCGTACATGGGAGCGCGGGGCGGCAGAG

2373 mir-342Nol 0.32 GTCTCACACAGAAATCGCACCCGTCACCTTGGCCTACTTA

2374 Hcd794 right 0.32 GGCCACCACAGACACCAACAAGTTCAGTCCGTTTCTGCAG

2375 mir-142-prec 0.38 CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG 2376 Hcd754 left 0.28 TCCTCCTCCTCCTTTTCGTTCCGGCTCCCTGGCTGGCTCC

Table 86. Aclarubicm microRNA biomarkers.

SEQIDNO Medianprobe Corr Sequence

2377 mir-092-prec-X=092-2 0.32 GTTCTATATAAAGTATTGCACTTGTCCCGGCCTGTGGAAG

2378 mir-096-prec-7No2 0.29 TGGCCGATTTTGGCACTAGCACATTTTTGCTTGTGTCTCT

2379 Hcd605 left 0.26 ATTACTAGCAGTTAATGATTGGTTTGTTAGTTAATGGCCC

2380 mir-007-2-precNo2 0.34 GGACCGGCTGGCCCCATCTGGAAGACTAGTGATTTTGTTG

2381 mir-019b-2-prec 0.28 GTGGCTGTGCAAATCCATGCAAAACTGATTGTGATAATGT

2382 MPR216 left 0.26 GATCCTAGTAGTGCCAAAGTGCTCATAGTGCAGGTAGTTT

2383 mir-019b-l-prec 0.25 TTCTGCTGTGCAAATCCATGCAAAACTGACTGTGGTAGTG

2384 mir-135-2-prec 0.26 CACTCTAGTGCTTTATGGCTTTTTATTCCTATGTGATAGT

2385 HSTRNL 0.26 TCCGGATGGAGCGTGGGTTCGAATCCCACTTCTGACACCA

2386 mir-025-prec 0.31 ACGCTGCCCTGGGCATTGCACTTGTCTCGGTCTGACAGTG

2387 mir-007-l-prec 0.4 TGTTGGCCTAGTTCTGTGTGGAAGACTAGTGATTTTGTTG

2388 mir-019a-prec 0.26 TGTAGTTGTGCAAATCTATGCAAAACTGATGGTGGCCTGC

2389 mir-380-5p 0.31 AGGTACCTGAAAAGATGGTTGACCATAGAACATGCGCTAT

2390 irur-093-prec-7.1=093- 1 0.37 CCAAAGTGCTGTTCGTGCAGGTAGTGTGATTACCCAACCT

2391 mir-106-prec-X 0.37 CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT

2392 mir-142-prec 0.32 CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG

2393 mir-018-prec 0.31 TAAGGTGCATCTAGTGCAGATAGTGAAGTAGATTAGCATC

2394 mir-020-prec 0.36 TAAAGTGCTTATAGTGCAGGTAGTGTTTAGTTATCTACTG

Table 87 Mitoxantrone microRNA biomarkers.

SEQIDNO Medianprobe Corr Sequence

2395 Hcd768 left 0.26 GATGGTTTAGTGAGGCCCTCGGATCAGCCCGCTGGGTCAG

2396 HUMTRF 0.31 GATCTAAAGGTCCCTGGTTCGATCCCGGGTTTCGGCACCA

2397 mir-213-precNol 0.28 AACATTCATTGCTGTCGGTGGGTTGAACTGTGTGGACAAG

2398 mir-181b-precNol 0.26 TGAGGTTGCTTCAGTGAACATTCAACGCTGTCGGTGAGTT

2399 MPR244 right 0.27 TAAACATAGAGGAAATTTCACGTTTTCAGTGTCAAATGCT

2400 mir-409-3p 0.29 GACGAATGTTGCTCGGTGAACCCCTTTTCGGTATCAAATT

2401 HSTRNL 0.33 TCCGGATGGAGCGTGGGTTCGAATCCCACTTCTGACACCA

2402 mir-382 0.34 GGTACTTGAAGAGAAGTTGTTCGTGGTGGATTCGCTTTAC

2403 mir-342Nol 0.3 GTCTCACACAGAAATCGCACCCGTCACCTTGGCCTACTTA

2404 mir-142-prec 0.27 CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG

2405 Hcd200 right 0.29 CAATTAGCCAATTGTGGGTATAATTAGCTGCATGTAGAAT

Table 88. Mitomycin microRNA biomarkers.

SEQIDNO Medianprobe Corr Sequence

2406 HUMTRF 0.26 GATCTAAAGGTCCCTGGTTCGATCCCGGGTTTCGGCACCA

2407 Hcdl48_HPR2251eft 0.27 AATTAATGACCAAAATGTCAGATGTGTCCACAGCTAATTA 2408 Hcd938 right 0.26 ATTCCCTGCATCACTCTCATGAAATGGCTGAGAAAGTGAG 2409 MPR174 left 0.32 GAGCCGGTCTCTTTACATCTCAAATACCAGGTATTTAGGT 2410 mir-4323p 0.29 CCTTACGTGGGCCACTGGATGGCTCCTCCATGTCTTGGAG

Table 89, Paclitaxel (Taxol) microRNA biomarkers.

SEQIDNO Medianprobe Corr Sequence

2411 mir-092-prec-X=092-2 0.29 GTTCTATATAAAGTATTGCACTTGTCCCGGCCTGTGGAAG

2412 mir-096-prec-7Nol 0.36 CTCCGCTCTGAGCAATCATGTGCAGTGCCAATATGGGAAA

2413 mir-lOl-prec-9 0.38 GCTGTATATCTGAAAGGTACAGTACTGTGATAACTGAAGA

2414 mir-20bNol 0.28 AGTACCAAAGTGCTCATAGTGCAGGTAGTTTTGGCATGAC

2415 mir-019b-2-prec 0.28 GTGGCTGTGCAAATCCATGCAAAACTGATTGTGATAATGT

2416 mir-032-precNo2 0.29 GGAGATATTGCACATTACTAAGTTGCATGTTGTCACGGCC

2417 MPR156 left 0.25 TCCCTCACTTGAACTGACTGCCAGAGTTCACAGACAGCTG

2418 mir-019b-l-prec 0.28 TTCTGCTGTGCAAATCCATGCAAAACTGACTGTGGTAGTG

2419 mir-135-2-prec 0.36 CACTCTAGTGCTTTATGGCTTTTTATTCCTATGTGATAGT

2420 mir-025-prec 0.36 ACGCTGCCCTGGGCATTGCACTTGTCTCGGTCTGACAGTG

2421 mir-007-l-prec 0.27 TGTTGGCCTAGTTCTGTGTGGAAGACTAGTGATTTTGTTG

2422 mir-361Nol 0.29 GGATTTGGGAGCTTATCAGAATCTCCAGGGGTACTTTATA

2423 mir-093-prec-7.1=093-1 0.37 CCAAAGTGCTGTTCGTGCAGGTAGTGTGATTACCCAACCT

2424 mir-106-prec-X 0.38 CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT

2425 mir-098-prec-X 0.29 TGAGGTAGTAAGTTGTATTGTTGTGGGGTAGGGATATTAG

2426 mir-142-prec 0.27 CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG

2427 HPR169 right 0.26 GTTTCTTTCTCACGGTAACTGGCAGCCTCGTTGTGGGCTG

2428 mir-018-prec 0.4 TAAGGTGCATCTAGTGCAGATAGTGAAGTAGATTAGCATC

2429 mir-020-prec 0.36 TAAAGTGCTTATAGTGCAGGTAGTGTTTAGTTATCTACTG

Table 90. Gemcitabine (Gemzar) microRNA biomarkers.

SEQIDNO Medianprobe Corr Sequence

2430 mir-123-precNo2 0.27 TGTGACACTTCAAACTCGTACCGTGAGTAATAATGCGCCG

2431 Hcd257 right 0.29 CTTGGTTTTTGCAATAATGCTAGCAGAGTACACACAAGAA

2432 mir-155-prec 0.35 TTAATGCTAATCGTGATAGGGGTTTTTGCCTCCAACTGAC

2433 ath-MIR180aNo2 0.26 TGAGAATCTTGATGATGCTGCATCGGCAATCAACGACTAT

2434 Hcd448 left 0.33 TGTAATTCCATTGAGGGTTTCTGGTGACTCCAGCTTCGTA

2435 HSTRNL 0.31 TCCGGATGGAGCGTGGGTTCGAATCCCACTTCTGACACCA

2436 MPR174 right 0.29 CATTAGGGACACGTGTGAGTGTGCCAGGCTCATTCCTGAG

2437 Hcd200 right 0.29 CAATTAGCCAATTGTGGGTATAATTAGCTGCATGTAGAAT

2438 mir-4323p 0.26 CCTTACGTGGGCCACTGGATGGCTCCTCCATGTCTTGGAG

2439 HPR244 right 0.3 TAGTTCATGGCGTCCAGCAGCAGCTTCTGGCAGACCGGGT

Table 91. Taxotere (docetaxel) microRNA biomarkers. SEQIDNO Medianprobe Corr Sequence

2440 mir-096-prec-7Nol 0.28 CTCCGCTCTGAGCAATCATGTGCAGTGCCAATATGGGAAA

2441 mir-095-prec-4 0.27 CGTTACATTCAACGGGTATTTATTGAGCACCCACTCTGTG 2442 HSTRNL 0.26 TCCGGATGGAGCGTGGGTTCGAATCCCACTTCTGACACCA 2443 mir-007-l-prec 0.37 TGTTGGCCTAGTTCTGTGTGGAAGACTAGTGATTTTGTTG

Table 392 Dexamethasone microRNA biomarkers.

SEQIDNO Medianprobe Corr Sequence

2444 MPR141 left 0.42 CTCAGTCGTGCCCTAGCAGCGGGAACAGTACTGCAGTGAG

2445 mir-424No2 0.35 GTTCAAAACGTGAGGCGCTGCTATACCCCCTCGTGGGGAA

2446 Hcd690 right 0.26 GGACAAGGGAGGAGACACGCAGAGGTGACAGAAAGGTTAG

2447 Hcd783 left 0.26 CAGGCTCACACCTCCCTCCCCCAACTCTCTGGAATGTATA

2448 mir-150-prec 0.38 CTCCCCATGGCCCTGTCTCCCAACCCTTGTACCAGTGCTG

2449 Hcd266 left 0.37 AAGGTCTTTGGTCTTGGAGGAAGGTGTGCTACTGGAAGAG

2450 mir-503Nol 0.34 CTCAGCCGTGCCCTAGCAGCGGGAACAGTTCTGCAGTGAG

2451 mir-128b-precNol 0.29 TCACAGTGAACCGGTCTCTTTCCCTACTGTGTCACACTCC

2452 Hcd397 left 0.26 GTGTGTATACTTATGTGTGTGTATGTGTGAGTGTGAATAT

2453 mir-484 0.38 GTCAGGCTCAGTCCCCTCCCGATAAACCCCTAAATAGGGA

Table 93 Ara-C (Cytarabme hydrochloride) microRNA biomarkers.

SEQIDNO Medianprobe Corr Sequence

2454 HUMTRF 0.33 GATCTAAAGGTCCCTGGTTCGATCCCGGGTTTCGGCACCA

2455 mir-155-prec 0.28 TTAATGCTAATCGTGATAGGGGTTTTTGCCTCCAACTGAC

2456 mir-515-15p 0.27 GATCTCATGCAGTCATTCTCCAAAAGAAAGCACTTTCTGT

2457 Hcd938 right 0.26 ATTCCCTGCATCACTCTCATGAAATGGCTGAGAAAGTGAG

2458 Hcd642 right 0.25 TCAGGGTTTATGAAGTTATCAAAGCCCCTTGATGGAATTA

2459 Hcdl20 left 0.26 CTTGGTGTGTTCTCGGTAGCTATGGAAATCCCAGGGTTTC

2460 mir-380-5p 0.25 AGGTACCTGAAAAGATGGTTGACCATAGAACATGCGCTAT

2461 mir-342Nol 0.25 GTCTCACACAGAAATCGCACCCGTCACCTTGGCCTACTTA

2462 mir-142-prec 0.27 CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG

2463 mir-223-prec 0.31 GAGTGTCAGTTTGTCAAATACCCCAAGTGCGGCACATGCT

2464 mir-4323p 0.28 CCTTACGTGGGCCACTGGATGGCTCCTCCATGTCTTGGAG

Table 94 Methylprednisolone microRNA biomarkers .

SEQIDNO Medianprobe Corr Sequence

2465 Hcd544 left 0.26 TTCCAGGTGTCCACCAAGGACGTGCCGCTGGCGCTGATGG

2466 mir-181c-precNol 0.28 TGCCAAGGGTTTGGGGGAACATTCAACCTGTCGGTGAGTT

2467 Hcd517 left 0.25 TTAAAGCAGGAGAGGTGAGAGGAAGAATTAATGTGTGCTC

2468 MPR151 left 0.27 GGGATTAATGACCAGCTGGGGGAGTTGATAGCCCTCAGTG

2469 mir-213-precNol 0.34 AACATTCATTGCTGTCGGTGGGTTGAACTGTGTGGACAAG

2470 mir-181b-precNo2 0.36 ACCATCGACCGTTGATTGTACCCTATGGCTAACCATCATC

2471 mir-150-prec 0.27 CTCCCCATGGCCCTGTCTCCCAACCCTTGTACCAGTGCTG

2472 mir-153-l-precl 0.28 CAGTTGCATAGTCACAAAAGTGATCATTGGCAGGTGTGGC

2473 mir-128b-precNol 0.48 TCACAGTGAACCGGTCTCTTTCCCTACTGTGTCACACTCC

2474 Hcd812 left 0.25 CTGTGGGATCTGGTTCTGTAGCTGAGAGCACATCGCTAAA

2475 mir-195-prec 0.3 TCTAGCAGCACAGAAATATTGGCACAGGGAAGCGAGTCTG

2476 mir-342Nol 0.38 GTCTCACACAGAAATCGCACCCGTCACCTTGGCCTACTTA

2477 mir-370Nol 0.28 TTACACAGCTCACGAGTGCCTGCTGGGGTGGAACCTGGTC

2478 mir-142-prec 0.32 CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG

2479 mir-223-prec 0.36 GAGTGTCAGTTTGTCAAATACCCCAAGTGCGGCACATGCT

2480 mir-484 0.36 GTCAGGCTCAGTCCCCTCCCGATAAACCCCTAAATAGGGA

Table 95. Methotrexate microRNA biomarkers. SEQIDNO Medianprobe Corr Sequence

2481 mir-092-prec-X=092-2 0.37 GTTCTATATAAAGTATTGCACTTGTCCCGGCCTGTGGAAG

2482 mir-096-prec-7Nol 0.33 CTCCGCTCTGAGCAATCATGTGCAGTGCCAATATGGGAAA

2483 mir-123-precNol 0 , 25 GACGGGACATTATTACTTTTGGTACGCGCTGTGACACTTC

2484 Hcd250 left 0.26 GTTCTGTTGCTAAGACAACAGGATGCTAGCAGGCATATGC

2485 mir-518e/526c 0 , 3 TCTCAGGCTGTGACCCTCTAGAGGGAAGCGCTTTCTGTTG

2486 HPR232 right 0 , 3 TGAATTATTGCACAATAAATTCATGCCCTGTTGTGTCTTA

2487 Hcd263 left 0 ,29 GAGCATTAAGATTTCCTATTCTTTGAGGCAAATATTGACC

2488 mir-516-33p 0 , 35 GTGAAAGAAAGTGCTTCCTTTCAGAGGGTTACTCTTTGAG

2489 Hcd605 left 0 , 27 ATTACTAGCAGTTAATGATTGGTTTGTTAGTTAATGGCCC

2490 Hcd373 right 0.25 CCTGAAAGGTCTGGTGTTAAGCAAATACTCGGTGACCAGA

2491 MPR254 right 0.28 GTTCACAGTGGGAGAAATATGCTTCGTATTACTCTTTCTC

2492 MPR215 left 0.3 CAGCTATGTGGACTCTAGCTGCCAAAGGCGCTTCTCCTTC

2493 HUMTRF 0.28 GATCTAAAGGTCCCTGGTTCGATCCCGGGTTTCGGCACCA

2494 mir-106aNol 0.27 CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT

2495 mir-20bNol 0.37 AGTACCAAAGTGCTCATAGTGCAGGTAGTTTTGGCATGAC

2496 Hcd361 right 0.28 AACTTGGCTACAAGGCTCTTTCCCTCTCTATGAAGGACAG

2497 Hcd412 left 0.25 AGTTGGGGAGAACTTTATGATTATTCTCATGCATCATCTT

2498 Hcd781 left 0.26 GAGTGTGGATCTAATCTTCAGCTGATTAAATGTCCCTCAT

2499 mir-019b-2-prec 0.33 GTGGCTGTGCAAATCCATGCAAAACTGATTGTGATAATGT

2500 HPR214 right 0.29 AGCAAAAGCTATTATTTGCCCTTGATGAGCCAATCAGATG

2501 Hcd807 right 0.26 GCGCTGACAAATCTTGCCTGATTCTGTATGATCCATGAGA

2502 Hcd817 left 0.37 TAATGAGAATTATGTTTGCACATTGAGGCAGGATAAATCC

2503 Hcd788 left 0 ,25 GACAAACATGCAGGAAAAATTATCCCCTGGGGATTCTACA

2504 Hcd970 left 0 , 31 TTGTGGGTCAGCTGCCCAGCTATCGGCTGGATTAGTGAAT

2505 Hcdl48 HPR2251eft 0.26 AATTAATGACCAAAATGTCAGATGTGTCCACAGCTAATTA

2506 Hcdl02 left 0.27 ACTGGAATTATGTTTTATCTTAAGTCCACACTGGATCCTC

2507 Hcd246 right 0.29 TAAAGTGAGTTATGGAGGTTACTCTCCTGTGAGAGGAAAT

2508 HPR199 right 0.28 TACACCTAAGGCATGTACTGTATTAATGAACCAATAAAAC

2509 HPR233 right 0.27 CATGATGGGGTGGGGTGAGATGGGGAGCGAAGACTATTAC

2510 Hcd383 left 0.28 GCCCGGGCATGCATTTTATCTAGCACCATGTGTTTCAGCT

2511 MPR224 right 0.29 TGAATTATTGCACAATAAATTCATGCCCTGTTGTGTCTTA

2512 HPR172 right 0.26 GTTTAAACAGCCAGTGCAAACATTTAGATCTGAGTCAAAA

2513 MPR216 left 0.34 GATCCTAGTAGTGCCAAAGTGCTCATAGTGCAGGTAGTTT

2514 mir-321No2 0.25 CAGGGATTGTGGGTTCGAGTCCCACCCGGGGTAAAGAAAG

2515 Hcd586 right 0.28 GAACTGTTTGCTTTGGATGGGCTTGGTCCTCATTGGCTGA

2516 Hcd587 right 0.3 AAATAATGACTGGCCATAAGATCAAGACAAGTGTCCAAAG

2517 Hcd249 right 0.39 CAGGTACATGTTGATCAGCAGGGGCTGGGAGGCGCCGCTC

2518 Hcd279 right 0.27 CTCACGGCGTTGCCATGGAGACAACTCCGGGGCTGGGGCTC

2519 HPR159 left 0.3 TCCGTCACTTGAACTGGCTGCCAGCGTTCACAGACAGCTG

2520 Hcd689 right 0.28 GTACATCTGGATGTAGTTGTGCTGCAGCTGCTTCTGGTAG

2521 Hcd691 right 0.32 CGGCAAAAACCTCTGTCAGAACAAAATTAGGTGATCTATC

2522 mir-019b-l-prec 0.32 TTCTGCTGTGCAAATCCATGCAAAACTGACTGTGGTAGTG

2523 Hcd413 right 0.26 CACAAAAAGGCATAAGCAGACATCTTGCCCTTTGGTTTCT

2524 Hcd581 right 0.26 AGGAGATATGCCAAGATATATTCACAGCTTTATATACACA

2525 Hcd536 HPR104 right 0.28 GCTGCTCTGCTGAGGGGCTGGACTCTGTCCAGAAGCACCA

2526 Hcd230 left 0.28 CATTCTCTACAAGCATATGGCCTTGGGACATTAAGATGGC

2527 HPR154 left 0.28 AACATCAAGATCTATTGACCTGAGAGGTAAATATTGACCG

2528 Hcd270 right 0.31 AAATGTTGTTATAGTATCCCACCTACCCTGATGTATCTTT

2529 Hcd649 right 0.26 GAACAGGCTTCAAGGTTCTTGGCAGGAATATTCCGTGTAG

2530 Hcd889 right 0.27 ATGCCTTGTGCTCTGTGCTAATTCAGAAGAATAAGCCTGT

2531 Hcd938 left 0.36 CTTGTCGACTAGCCAGTTATGAACAGAGGAGGATGTTCTC

2532 HPR266 right 0.32 GGAGATCCCTTCAAGGTACTTAGTTTTAAATGAGTGCTCT

2533 mir-025-prec 0.39 ACGCTGCCCTGGGCATTGCACTTGTCTCGGTCTGACAGTG

2534 Hcd355 HPR190 left 0.25 TTGTGCACTGCACAACCCTAGTGGCGCCATTCAATTATAG

2535 MPR162 left 0.26 CTCTCTTTTTCCTGCTTGATTTGCCTAATGGAAGCTGACA

2536 Hcd923 right 0.34 CTGGAGATAATGATTCTGCATTTCTAATTAACTCCCAGGT

2537 MPR237 left 0.32 AGCACATCCCATGATCACAGTAATGTTCTTTGGAGATGTA

2538 MPR174 left 0.32 GAGCCGGTCTCTTTACATCTCAAATACCAGGTATTTAGGT

2539 mir-019a-prec 0.31 TGTAGTTGTGCAAATCTATGCAAAACTGATGGTGGCCTGC

2540 hsa mir 490 Hcd20 right 0.25 ACCAACCTGGAGGACTCCATGCTGTTGAGCTGTTCACAAG

2541 mir-380-5p 0.36 AGGTACCTGAAAAGATGGTTGACCATAGAACATGCGCTAT

2542 mir-093-prec-7.1=093-1 0.38 CCAAAGTGCTGTTCGTGCAGGTAGTGTGATTACCCAACCT

2543 mir-106-prec-X 0.45 CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT

2544 Hcd627 left 0.3 GCATTAGGGAGAATAGTTGATGGATTACAAATCTCTGCAT

2545 mir-142-prec 0.27 CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG

2546 HPR169 right 0.29 GTTTCTTTCTCACGGTAACTGGCAGCCTCGTTGTGGGCTG

2547 mir-OOlb-2-prec 0.28 TAAGCTATGGAATGTAAAGAAGTATGTATCTCAGGCCGGG

2548 mir-018-prec 0.4 TAAGGTGCATCTAGTGCAGATAGTGAAGTAGATTAGCATC

2549 mir-020-prec 0.48 TAAAGTGCTTATAGTGCAGGTAGTGTTTAGTTATCTACTG

2550 Hcd404 left 0.29 TGCTGCTGTTAATGCCATTAGGATGACTATTTATATCACC

2551 mir-384 0.25 CATAAGTCATTCCTAGAAATTGTTCATAATGCCTGTAACA 2552 mir-4323p 0.4 CCTTACGTGGGCCACTGGATGGCTCCTCCATGTCTTGGAG

Table 96. Bleomycin microRNA biomarkers.

SEQIDNO Medianprobe Corr Sequence

2553 mir-376aNol 0.27 AATCATAGAGGAAAATCCACGTTTTCAGTATCAAATGCTG

2554 mir-155-prec 0.35 TTAATGCTAATCGTGATAGGGGTTTTTGCCTCCAACTGAC

2555 mir-409-3p 0.28 GACGAATGTTGCTCGGTGAACCCCTTTTCGGTATCAAATT

2556 mir-495Nol 0.29 GTGACGAAACAAACATGGTGCACTTCTTTTTCGGTATCAA

2557 Hcd498 right 0.28 CACGAAGAAGTTCAGCAACCAGGAGACCAGGTGGGGGCCG

2558 mir-199a-2-prec 0.41 TCGCCCCAGTGTTCAGACTACCTGTTCAGGACAATGCCGT

2559 mir-382 0.3 GGTACTTGAAGAGAAGTTGTTCGTGGTGGATTCGCTTTAC

2560 HPR271 right 0.27 AATTGAGCAAACAGTGCAATTTTCTGTAATTATGCCAGTG

2561 mir-145-prec 0.31 CCTCACGGTCCAGTTTTCCCAGGAATCCCTTAGATGCTAA

2562 mir-199a-l-prec 0.35 GCCAACCCAGTGTTCAGACTACCTGTTCAGGAGGCTCTCA

Table 97. Methyl-GAG (methyl glyoxal bis amidinohydrazone dihydrochloride) microRNA biomarkers .

SEQIDNO Medianprobe Corr Sequence

2563 mir-092-prec-X=092-2 0.32 GTTCTATATAAAGTATTGCACTTGTCCCGGCCTGTGGAAG

2564 mir-lOl-prec-9 0.3 GCTGTATATCTGAAAGGTACAGTACTGTGATAACTGAAGA

2565 mir-144-precNo2 0.29 CCCTGGCTGGGATATCATCATATACTGTAAGTTTGCGATG

2566 mir-519a-l/526c 0.29 TCAGGCTGTGACACTCTAGAGGGAAGCGCTTTCTGTTGTC

2567 mir-519b 0.33 GAAAAGAAAGTGCATCCTTTTAGAGGTTTACTGTTTGAGG

2568 mir-015b-precNo2 0.26 TGCTACAGTCAAGATGCGAATCATTATTTGCTGCTCTAGA

2569 itur-106aNol 0.27 CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT

2570 mir-16-lNol 0.26 GTCAGCAGTGCCTTAGCAGCACGTAAATATTGGCGTTAAG

2571 mir-181dNol 0.27 GAGGTCACAATCAACATTCATTGTTGTCGGTGGGTTGTGA

2572 mir-017-precNo2 0.31 GTCAGAATAATGTCAAAGTGCTTACAGTGCAGGTAGTGAT

2573 mir-019b-2-prec 0.32 GTGGCTGTGCAAATCCATGCAAAACTGATTGTGATAATGT

2574 mir-192No2 0.26 TGCCAATTCCATAGGTCACAGGTATGTTCGCCTCAATGCC

2575 mir-213-precNol 0.25 AACATTCATTGCTGTCGGTGGGTTGAACTGTGTGGACAAG

2576 mir-215-precNo2 0.3 CATTTCTTTAGGCCAATATTCTGTATGACTGTGCTACTTC

2577 mir-107Nol 0.28 GGCATGGAGTTCAAGCAGCATTGTACAGGGCTATCAAAGC

2578 mir-200bNol 0.28 GTCTCTAATACTGCCTGGTAATGATGACGGCGGAGCCCTG

2579 mir-103-prec-5=103-l 0.3 TATGGATCAAGCAGCATTGTACAGGGCTATGAAGGCATTG

2580 mir-519a-l/526c 0.37 TCAGGCTGTGACACTCTAGAGGGAAGCGCTTTCTGTTGTC

2581 MPR216 left 0.28 GATCCTAGTAGTGCCAAAGTGCTCATAGTGCAGGTAGTTT

2582 mir-019b-l-prec 0.31 TTCTGCTGTGCAAATCCATGCAAAACTGACTGTGGTAGTG

2583 mir-107-prec-lO 0.29 GGCATGGAGTTCAAGCAGCATTGTACAGGGCTATCAAAGC

2584 mir-135-2-prec 0.39 CACTCTAGTGCTTTATGGCTTTTTATTCCTATGTGATAGT

2585 mir-103-2-prec 0.29 GTAGCATTCAGGTCAAGCAACATTGTACAGGGCTATGAAA

2586 mir-519a-2No2 0.29 TCTCAGGCTGTGTCCCTCTACAGGGAAGCGCTTTCTGTTG

2587 mir-025-prec 0.33 ACGCTGCCCTGGGCATTGCACTTGTCTCGGTCTGACAGTG

2588 mir-16-2Nol 0.33 GTTCCACTCTAGCAGCACGTAAATATTGGCGTAGTGAAAT

2589 MPR95 left 0.28 TTGTTGGACACTCTTTCCCTGTTGCACTACTGTGGGCCTC

2590 mir-016b-chr3 0.29 GTTCCACTCTAGCAGCACGTAAATATTGGCGTAGTGAAAT

2591 Hcd948 right 0.27 TGATATAAATAGTCATCCTAATGGCATTAACAGCAGCACT

2592 mir-195-prec 0.35 TCTAGCAGCACAGAAATATTGGCACAGGGAAGCGAGTCTG

2593 mir-093-prec-7.1=093-1 0.38 CCAAAGTGCTGTTCGTGCAGGTAGTGTGATTACCCAACCT

2594 mir-106-prec-X 0.42 CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT

2595 mir-142-prec 0.37 CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG

2596 mir-519c/526c 0.27 TCTCAGCCTGTGACCCTCTAGAGGGAAGCGCTTTCTGTTG

2597 mir-200a-prec 0.29 GTCTCTAATACTGCCTGGTAATGATGACGGCGGAGCCCTG

2598 mir-016a-chrl3 0.29 CAATGTCAGCAGTGCCTTAGCAGCACGTAAATATTGGCGT

2599 mir-018-prec 0.41 TAAGGTGCATCTAGTGCAGATAGTGAAGTAGATTAGCATC

2600 mir-020-prec 0.39 TAAAGTGCTTATAGTGCAGGTAGTGTTTAGTTATCTACTG

Table 98 . pXDIOl HDAC inhibitors microRNA biomarkers. SEQIDNO Medianprobe Corr Sequence

2601 mir-092-prec-X=092-2 0.42 GTTCTATATAAAGTATTGCACTTGTCCCGGCCTGTGGAAG

2602 mir-123-precNo2 0.31 TGTGACACTTCAAACTCGTACCGTGAGTAATAATGCGCCG

2603 mir-106aNol 0.36 CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT

2604 mir-20bNol 0.36 AGTACCAAAGTGCTCATAGTGCAGGTAGTTTTGGCATGAC

2605 mir-017-precNo2 0.32 GTCAGAATAATGTCAAAGTGCTTACAGTGCAGGTAGTGAT

2606 mir-019b-2-prec 0.42 GTGGCTGTGCAAATCCATGCAAAACTGATTGTGATAATGT

2607 mir-033-prec 0.3 GTGGTGCATTGTAGTTGCATTGCATGTTCTGGTGGTACCC

2608 mir-092-prec-13=092-lNo2 0.31 TCTGTATGGTATTGCACTTGTCCCGGCCTGTTGAGTTTGG

2609 mir-122a-prec 0.29 CCTTAGCAGAGCTGTGGAGTGTGACAATGGTGTTTGTGTC

2610 Hcd783 left 0.27 CAGGCTCACACCTCCCTCCCCCAACTCTCTGGAATGTATA

2611 MPR216 left 0.29 GATCCTAGTAGTGCCAAAGTGCTCATAGTGCAGGTAGTTT

2612 mir-019b-l-prec 0.41 TTCTGCTGTGCAAATCCATGCAAAACTGACTGTGGTAGTG

2613 mir-135-2-prec 0.46 CACTCTAGTGCTTTATGGCTTTTTATTCCTATGTGATAGT

2614 mir-128b-precNol 0.39 TCACAGTGAACCGGTCTCTTTCCCTACTGTGTCACACTCC

2615 mir-025-prec 0.45 ACGCTGCCCTGGGCATTGCACTTGTCTCGGTCTGACAGTG

2616 Hcdδll right 0.26 TACCTCAGAAGCCTCACTCAACCCTCTCCCGCTGAGTCTC

2617 mir-093-prec-7.1=093-1 0.45 CCAAAGTGCTGTTCGTGCAGGTAGTGTGATTACCCAACCT

2618 mir-106-prec-X 0.5 CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT

2619 mir-142-prec 0.5 CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG

2620 HPR169 right 0.26 GTTTCTTTCTCACGGTAACTGGCAGCCTCGTTGTGGGCTG

2621 mir-223-prec 0.26 GAGTGTCAGTTTGTCAAATACCCCAAGTGCGGCACATGCT

2622 mir-018-prec 0.48 TAAGGTGCATCTAGTGCAGATAGTGAAGTAGATTAGCATC

2623 mir-020-prec 0.52 TAAAGTGCTTATAGTGCAGGTAGTGTTTAGTTATCTACTG

Table 99 . 55--FFlluuoorroouurraacciill mmiiccrrooRRNI JAA bbiiomarkers.

SEQIDNO Medianprobe Corr Sequence

2624 mir-096-prec-7No2 0.27 TGGCCGATTTTGGCACTAGCACATTTTTGCTTGTGTCTCT

2625 mir-429Nol 0.25 CTAATACTGTCTGGTAAAACCGTCCATCCGCTGCCTGATC

2626 Hcd693 right 0.25 AGGCTTTGTGCGCGCATTAAAGCTCGCCGGACCCCCGACC

2627 HPR214 right 0.27 AGCAAAAGCTATTATTTGCCCTTGATGAGCCAATCAGATG

2628 Hcd586 left 0.26 GTCCTGTCTAAAGGAAGAAGTTTGTTCTACTGTAAACAGT

2629 Hcd249 right 0.26 CAGGTACATGTTGATCAGCAGGGGCTGGGAGGCGCCGCTC

2630 Hcd689 right 0.27 GTACATCTGGATGTAGTTGTGCTGCAGCTGCTTCTGGTAG

2631 mir-194-2Nol 0.25 TGGTTCCCGCCCCCTGTAACAGCAACTCCATGTGGAAGTG

2632 Hcd581 right 0.26 AGGAGATATGCCAAGATATATTCACAGCTTTATATACACA

2633 Hcd270 right 0.3 AAATGTTGTTATAGTATCCCACCTACCCTGATGTATCTTT

2634 mir-025-prec 0.27 ACGCTGCCCTGGGCATTGCACTTGTCTCGGTCTGACAGTG

2635 Hcd340 left 0.27 GGACAATTCAACAGTGGTGAGTCACTTCGCCACTTTTCAG

2636 mir-007-l-prec 0.27 TGTTGGCCTAGTTCTGTGTGGAAGACTAGTGATTTTGTTG

2637 mir-093-prec-7.1=093-1 0.25 CCAAAGTGCTGTTCGTGCAGGTAGTGTGATTACCCAACCT

2638 mir-106-prec-X 0.26 CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT

2639 Hcd794 right 0.27 GGCCACCACAGACACCAACAAGTTCAGTCCGTTTCTGCAG

2640 mir-020-prec 0.26 TAAAGTGCTTATAGTGCAGGTAGTGTTTAGTTATCTACTG

2641 mir-4323p 0.26 CCTTACGTGGGCCACTGGATGGCTCCTCCATGTCTTGGAG

Table 100. Radiation microRNA biomarkers.

SEQIDNO Medianprobe Corr Sequence

2642 mir-136-precNo2 0.3 TGAGCCCTCGGAGGACTCCATTTGTTTTGATGATGGATTC

2643 Hcd570 right 0.26 GCCCAACAGAACAACTTGTTTCTCCAGAGCCTGAGGTTTA

2644 Hcd873 left 0.26 TCTTCTGACAATGAAGGTAGGCGGACAACGAGGAGATTGC

2645 Hcd282PO right 0.26 GAAGACGGACTTGGTTCCGTTTGACCAGCCAGAGCAGGGG

2646 Hcd799 left 0.25 GTCCGGCGCGAGTGGAGCTGTTGTAAAATGGCGGCCGAAG

2647 Hcd829 right 0.39 AAAATGGCGGCGGGAAAAGCGAGCGGCGAGAGCGAGGAGG

2648 Hcd210_HPR205 right 0.32 CGAAACATTCGCGGTGCACTTCTTTTTCAGTATCCTATTC

2649 mir-219-prec 0.26 ATTGTCCAAACGCAATTCTCGAGTCTATGGCTCCGGCCGA

2650 mir-202* 0.31 CCGCCCGCCGTTCCTTTTTCCTATGCATATACTTCTTTGA

2651 mir-429No2 0.42 CACCGCCGGCCGATGGGCGTCTTACCAGACATGGTTAGAC

2652 Hcd693 right 0.32 AGGCTTTGTGCGCGCATTAAAGCTCGCCGGACCCCCGACC

2653 mir-022-prec 0.34 TGTCCTGACCCAGCTAAAGCTGCCAGTTGAAGAACTGTTG

2654 MPR88 right 0.32 CTTACCCTGGTGCGTGGGGCCGCAGGGCTAACACCAAAAA

2655 mir-198-prec 0.39 TCATTGGTCCAGAGGGGAGATAGGTTCCTGTGATTTTTCC

2656 mir-199b-precNol 0.29 GTCTGCACATTGGTTAGGCTGGGCTGGGTTAGACCCTCGG

2657 Hcdl45 left 0.26 AAAAATCCCAGCGGCCACCTTTCCTCCCTGCCCCATTGGG

2658 mir-124a-2-prec 0.34 TTAAGGCACGCGGTGAATGCCAAGAGCGGAGCCTACGGCT

2659 mir-138-2-prec 0.39 AGCTGGTGTTGTGAATCAGGCCGACGAGCAGCGCATCCTC

2660 Hcd960 left 0.29 CTCAGTCTGCGGGCCCCGAGGAGGGTTGTGGGCCCTTTTT

2661 Hcd869 left 0.31 CGAGAGGCACTTTGTACTTCTGCCAGGAGACCATATGATA

2662 Hcd384 left 0.41 TTACCCAGCCGGGCCGCCAACACCAGATCCTTCTCCTTCT

2663 mir-027b-prec 0.31 CCGCTTTGTTCACAGTGGCTAAGTTCTGCACCTGAAGAGA

2664 Hcd444 right 0.31 GTATATGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGT

2665 mir-194-2Nol 0.3 TGGTTCCCGCCCCCTGTAACAGCAACTCCATGTGGAAGTG

2666 mir-197-prec 0.44 TAAGAGCTCTTCACCCTTCACCACCTTCTCCACCCAGCAT

2667 Hcd913 right 0.39 CAAACATCATGTGACGTCTGTGGAGCGGCGGCGGCGGCGG

2668 HPR163 left 0.39 GCTGCCCCCTCCCTTAGCAACGTGGCCCCGGCGTTCCAAA

2669 mir-138-l-prec 0.25 AGCTGGTGTTGTGAATCAGGCCGTTGCCAATCAGAGAACG

2670 mir-OlOa-precNol 0.25 GTCTGTCTTCTGTATATACCCTGTAGATCCGAATTTGTGT

2671 mir-023b-prec 0.34 AATCACATTGCCAGGGATTACCACGCAACCACGACCTTGG

2672 mir-193bNo2 0.35 CTGTGGTCTCAGAATCGGGGTTTTGAGGGCGAGATGAGTT

2673 Hcd654 left 0.43 AACGAGTAAAAGGCGTACATGGGAGCGCGGGGCGGCAGAG

2674 Hcd542 left 0.26 ATCTCAGTAGCCAATATTTTTCTCTGCTGGTATCAAATGA

2675 mir-199a-2-prec 0.28 TCGCCCCAGTGTTCAGACTACCTGTTCAGGACAATGCCGT

2676 mir-214-prec 0.43 TGTACAGCAGGCACAGACAGGCAGTCACATGACAACCCAG

2677 Hcd608 right 0.31 CTTGTGTTTTCACAGCAGCCACAGGCCCTACATCCTTCCT

2678 Hcd684 right 0.28 AGAAGGCGCTCCCTGCTAGCCCGGCTCTGTTCTAATTATA

2679 mir-145-prec 0.4 CCTCACGGTCCAGTTTTCCCAGGAATCCCTTAGATGCTAA

2680 mir-023a-prec 0.37 TCCTGTCACAAATCACATTGCCAGGGATTTCCAACCGACC

2681 mir-024-2-prec 0.32 AGTTGGTTTGTGTACACTGGCTCAGTTCAGCAGGAACAGG

2682 mir-199a-l-prec 0.29 GCCAACCCAGTGTTCAGACTACCTGTTCAGGAGGCTCTCA

Table 101 5-Aza-2 ' -deoxycytidme (decitabme) microRNA biomarkers.

SEQIDNO Medianprobe Corr Sequence

2683 mir-096-prec-7Nol 0.36 CTCCGCTCTGAGCAATCATGTGCAGTGCCAATATGGGAAA

2684 Hcd605 right 0.25 GGTTAAGACTCTAACAAACGAGTTGTGAATTGTAGCAATG

2685 mir-20bNol 0.3 AGTACCAAAGTGCTCATAGTGCAGGTAGTTTTGGCATGAC

2686 miR-373*Nol 0.26 GGGATACTCAAAATGGGGGCGCTTTCCTTTTTGTCTGTAC

2687 HUMTRAB 0.3 ATGGTAGAGCGCTCGCTTTGCTTGCGAGAGGTAGCGGGAT

2688 mir-019b-l-prec 0.25 TTCTGCTGTGCAAATCCATGCAAAACTGACTGTGGTAGTG

2689 HPR163 left 0.31 GCTGCCCCCTCCCTTAGCAACGTGGCCCCGGCGTTCCAAA

2690 mir-371Nol 0.25 ACTTTCTGCTCTCTGGTGAAAGTGCCGCCATCTTTTGAGT

2691 mir-025-prec 0.29 ACGCTGCCCTGGGCATTGCACTTGTCTCGGTCTGACAGTG

2692 mir-18bNo2 0.27 AGCAGCTTAGAATCTACTGCCCTAAATGCCCCTTCTGGCA

2693 mir-093-prec-7.1=093-1 0.28 CCAAAGTGCTGTTCGTGCAGGTAGTGTGATTACCCAACCT

2694 mir-106-prec-X 0.29 CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT

2695 mir-142-prec 0.29 CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG

2696 mir-020-prec 0.29 TAAAGTGCTTATAGTGCAGGTAGTGTTTAGTTATCTACTG

Table 102 Idarubicin microRNA biomarkers .

SEQIDNO Medianprobe Corr Sequence

2697 HUMTRF 0.33 GATCTAAAGGTCCCTGGTTCGATCCCGGGTTTCGGCACCA

2698 mir-483Nol 0.3 ATCACGCCTCCTCACTCCTCTCCTCCCGTCTTCTCCTCTC

2699 MPR74 left 0.27 CAAAGGTCACAATTAACATTCATTGTTGTCGGTGGGTTGT

2700 mir-122a-prec 0.27 CCTTAGCAGAGCTGTGGAGTGTGACAATGGTGTTTGTGTC

2701 ath-MIR180aNo2 0.29 TGAGAATCTTGATGATGCTGCATCGGCAATCAACGACTAT

2702 mir-128b-precNol 0.26 TCACAGTGAACCGGTCTCTTTCCCTACTGTGTCACACTCC

2703 Hcd923 left 0.25 TGGGAACCTTGTTAAAATGCAGATTCTGATTCTCAGGTCT

2704 mir-106-prec-X 0.25 CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT

2705 mir-342Nol 0.36 GTCTCACACAGAAATCGCACCCGTCACCTTGGCCTACTTA

2706 mir-142-prec 0.34 CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG

2707 HPR169 right 0.25 GTTTCTTTCTCACGGTAACTGGCAGCCTCGTTGTGGGCTG

2708 mir-223-prec 0.36 GAGTGTCAGTTTGTCAAATACCCCAAGTGCGGCACATGCT

2709 Hcd754 left 0.26 TCCTCCTCCTCCTTTTCGTTCCGGCTCCCTGGCTGGCTCC

2710 mir-020-prec 0.29 TAAAGTGCTTATAGTGCAGGTAGTGTTTAGTTATCTACTG

Table 103. Melphalan microRNA biomarkers . SEQIDNO Medianprobe Corr Sequence

2711 mir-124a-3-prec 0.32 TTAAGGCACGCGGTGAATGCCAAGAGAGGCGCCTCCGCCG

2712 mir-lδla-precNol 0.28 TCAGAGGACTCCAAGGAACATTCAACGCTGTCGGTGAGTT

2713 Hcd773 left 0.26 CTTCCTCCCTGGGCATCTCTAGCACAGGGGATCCCCAAAC

2714 Hcd683 left 0.25 CTATGACAGAAGGTACTCTGTGGGAGGGAGGAGATAATAG

2715 Hcd796 left 0.29 GGTGGGATTACCCGGCTGCCGCTGTCGCCTGGATGGTCTC

2716 HUMTRF 0.44 GATCTAAAGGTCCCTGGTTCGATCCCGGGTTTCGGCACCA

2717 HUMTRS 0.27 TCTAGCGACAGAGTGGTTCAATTCCACCTTTCGGGCGCCA

2718 mir-181b-2Nol 0.25 CTGATGGCTGCACTCAACATTCATTGCTGTCGGTGGGTTT

2719 Hcd294 left 0.26 TTATCATAAAATAATCACAGCCCTCAGGTGCTGTGAGGCA

2720 mir-20bNol 0.27 AGTACCAAAGTGCTCATAGTGCAGGTAGTTTTGGCATGAC

2721 mir-181dNol 0.27 GAGGTCACAATCAACATTCATTGTTGTCGGTGGGTTGTGA

2722 mir-213-precNol 0.4 AACATTCATTGCTGTCGGTGGGTTGAACTGTGTGGACAAG

2723 Hcdl48_HPR2251eft 0.29 AATTAATGACCAAAATGTCAGATGTGTCCACAGCTAATTA

2724 rair-515-15p 0.34 GATCTCATGCAGTCATTCTCCAAAAGAAAGCACTTTCTGT

2725 mir-181b-precNol 0.43 TGAGGTTGCTTCAGTGAACATTCAACGCTGTCGGTGAGTT

2726 Hcd783 left 0.26 CAGGCTCACACCTCCCTCCCCCAACTCTCTGGAATGTATA 2727 HUMTRAB 0.29 ATGGTAGAGCGCTCGCTTTGCTTGCGAGAGGTAGCGGGAT

2728 HUMTRN 0.27 CAATCGGTTAGCGCGTTCGGCTGTTAACCGAAAGGTTGGT

2729 mir- 181b- lNol 0.31 TTTAAAAGGTCACAATCAACATTCATTGCTGTCGGTGGGT

2730 mir- 124 a- l-precl 0.31 ATACAATTAAGGCACGCGGTGAATGCCAAGAATGGGGCTG

2731 mir-367Nol 0.26 TCTGTTGAATATAAATTGGAATTGCACTTTAGCAATGGTG

2732 mir- 128b-precNol 0.38 TCACAGTGAACCGGTCTCTTTCCCTACTGTGTCACACTCC

2733 Hcd43 8 πght 0.25 GTGTTTATTTGAATCTCACATCGCTCATAAGAATACACGC

2734 mir- 025-prec 0.3 ACGCTGCCCTGGGCATTGCACTTGTCTCGGTCTGACAGTG

2735 mir-216-precNol 0.35 CTGGGATTATGCTAAACAGAGCAATTTCCTAGCCCTCACG

2736 Hcd731 left 0.26 AATTGTGACAACTGAGTGGGAGGTTTGTGTGATGATTATC

2737 mir- 093-prec-7 . 1=093-1 0.25 CCAAAGTGCTGTTCGTGCAGGTAGTGTGATTACCCAACCT

2738 mir- 106-prec-X 0.27 CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT

2739 mir- 342Nol 0.36 GTCTCACACAGAAATCGCACCCGTCACCTTGGCCTACTTA

2740 mir- 142-prec 0.53 CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG

2741 HSHELAOl 0.32 GGCCGCAGCAACCTCGGTTCGTATCCGAGTCACGGCACCA 2742 HUMTRVlA 0.25 ACGCGAAAGGTCCCCGGTTCGAAACCGGGCGGAAACACCA

2743 mi r-223-prec 0.46 GAGTGTCAGTTTGTCAAATACCCCAAGTGCGGCACATGCT

2744 Hcd754 left 0.45 TCCTCCTCCTCCTTTTCGTTCCGGCTCCCTGGCTGGCTCC

2745 mi r-020-prec 0.3 TAAAGTGCTTATAGTGCAGGTAGTGTTTAGTTATCTACTG

Table 104. IL4-PR38 fusion protein microRNA biomarkers.

SEQIDNO Medianprobe Corr Sequence

2746 Hcd829 right 0.28 AAAATGGCGGCGGGAAAAGCGAGCGGCGAGAGCGAGGAGG

2747 mir-197-prec 0.28 TAAGAGCTCTTCACCCTTCACCACCTTCTCCACCCAGCAT

2748 HPR163 left 0.28 GCTGCCCCCTCCCTTAGCAACGTGGCCCCGGCGTTCCAAA

2749 mir-150-prec 0.47 CTCCCCATGGCCCTGTCTCCCAACCCTTGTACCAGTGCTG

Table 105 . Valproic acid (VPA) microRNA biomarkers.

SEQIDNO Medianprobe Corr Sequence

2750 mir-034precNol 0.26 GAGTGTTTCTTTGGCAGTGTCTTAGCTGGTTGTTGTGAGC

2751 Hcd255 left 0.28 CTAGCTCCGTTCGTGATCCGGGAGCCTGGTGCCAGCGAGA

2752 Hcd712 right 0.27 GAAGATCGGTTGTCATCTGGTCTGGTCAGCCCGGCCCCGA

2753 Hcd965 left 0.26 TGTTAAGTGGAAAAGCCTCCAGGAACGTGGCAGAAAAAGG

2754 Hcd891 right 0.29 GCAACGGCCTGATTCACAACACCAGCTGCCCCACCACACC

2755 Hcd210 HPR205 πght 0.31 CGAAACATTCGCGGTGCACTTCTTTTTCAGTATCCTATTC

2756 mir-429No2 0.33 CACCGCCGGCCGATGGGCGTCTTACCAGACATGGTTAGAC

2757 Hcd753 left 0.27 GACCTGATTCCCATCTTTGTATTTGGCGACCACCCGACTG

2758 Hcd693 right 0.38 AGGCTTTGTGCGCGCATTAAAGCTCGCCGGACCCCCGACC

2759 MPR203 left 0.25 CTATATTGGACCGCAGCGCTGAGAGCTTTTGTGTTTAATG

2760 Hcd704 left 0.4 TCTGTATTTAATTTGGCTCAGCCGGGAAGATTTTTGGCTC

2761 Hcd863PO right 0.3 TTGCAGAGCCTAAGACACAGGCCCAGAGAGGCAGTGATCG

2762 mir-122a-prec 0.29 CCTTAGCAGAGCTGTGGAGTGTGACAATGGTGTTTGTGTC

2763 Hcd760 left 0.35 TGTGGTCACGTTTCTCCCTCTCTGCTGGCCCCCATCTGTC

2764 Hcd338 left 0.35 CTTCTCCTCCTGTTCGCCGCAGGCGCCCGTCCCAGTAGTC

2765 HPR213 right 0.33 AACAACTTTGTGCTGGTGCCGGGGAAGTTTGTGTCTCCAA

2766 Hcd852 right 0.26 AAAAGTAAACAACAATTTGCCGCTGCCAGCCTCCCATTAG

2767 Hcd366 left 0.28 ATACTAGATTAAATTTCAGCCCCGGGCCAATCTGTCAAAG

2768 MPR103 right 0.27 GAGGTGTTTGTGCTCCACTCGGCTCCCTTGGTTACATAAC

2769 Hcd669 right 0.27 ATGTTTAACAGTCCAGGTTTTGTAGAATATGTGGTGGACC

2770 mir-188-prec 0.27 TCACATCCCTTGCATGGTGGAGGGTGAGCTTTCTGAAAAC

Table 106 All-trans retinoic acid (ATRA) microRNA biomarkers.

SEQIDNO Medianprobe Corr Sequence

2771 Hcd257 left 0.42 CTTCTTGTATAAGCACTGTGCTAAAATTGCAGACACTAGG

2772 mir-148-prec 0.45 TGAGTATGATAGAAGTCAGTGCACTACAGAACTTTGTCTC

2773 Hcd512 left 0.28 CTGCGCTCTCGGAAATGACTCGCTCCAATCCCGCTTCGCG

2774 HPR227 right 0.25 CAGTGCAATGATATTGTCAAAGCATCTGGGACCAGCCTTG

2775 Hcd421 right 0.37 AGTAAACAATGTCGGCTTTCCGCCTCCTCCCCTGCCATCC

2776 MPR203 left 0.39 CTATATTGGACCGCAGCGCTGAGAGCTTTTGTGTTTAATG

2777 mir-017-precNol 0.26 GCATCTACTGCAGTGAAGGCACTTGTAGCATTATGGTGAC

2778 mir-219-2Nol 0.26 CTCAGGGGCTTCGCCACTGATTGTCCAAACGCAATTCTTG

2779 mir-328Nol 0.3 GAAAGTGCATACAGCCCCTGGCCCTCTCTGCCCTTCCGTC

2780 Hcd783 left 0.31 CAGGCTCACACCTCCCTCCCCCAACTCTCTGGAATGTATA

2781 Hcdl81 left 0.32 TTGGCGTCCTTGTCTCTCTCTCCCCTGCCCAGTGGCCTCC

2782 HPR213 right 0.3 AACAACTTTGTGCTGGTGCCGGGGAAGTTTGTGTCTCCAA

2783 mir-191-prec 0.31 CAACGGAATCCCAAAAGCAGCTGTTGTCTCCAGAGCATTC

2784 mir-375 0.31 TTTTGTTCGTTCGGCTCGCGTGAGGCAGGGGCGGCCTCTC

2785 mir-212-precNo2 0.26 CGGACAGCGCGCCGGCACCTTGGCTCTAGACTGCTTACTG

2786 Hcd913 right 0.34 CAAACATCATGTGACGTCTGTGGAGCGGCGGCGGCGGCGG

2787 Hcd716 right 0.48 CAATAAATGTGCCTATAAAGGCGCCGGCTCCGGGGCGCGG

2788 MPR207 right 0.3 AACAACTTTGTGCTGGTGCCGGGGAAGTTTGTGTCTCCTA

2789 HPR206 left 0.26 CTATATTGGACCGCAGCGCTGAGAGCTTTTGTGTTTAATG

2790 mir-016b-chr3 0.29 GTTCCACTCTAGCAGCACGTAAATATTGGCGTAGTGAAAT

2791 Hcd654 left 0.34 AACGAGTAAAAGGCGTACATGGGAGCGCGGGGCGGCAGAG

2792 mir-195-prec 0.3 TCTAGCAGCACAGAAATATTGGCACAGGGAAGCGAGTCTG

2793 Hcd425 left 0.25 GGTTCTACTCTCTTACCCCTCCCCCACGTGGTTGTTGCTG

2794 mir-148aNol 0.35 TGAGTATGATAGAAGTCAGTGCACTACAGAACTTTGTCTC

2795 mir-142-prec 0.36 CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG

2796 mir-016a-chrl3 0.25 CAATGTCAGCAGTGCCTTAGCAGCACGTAAATATTGGCGT

Table 107. Cytoxan microRNA biomarkers.

SEQIDNO Medianprobe Corr Sequence

2797 Hcd99 right 0.25 CAATCCTGGCTGCAGGCATATTTGCATATTGGATGCTGTG

2798 mir-520c/526a 0.32 TCTCAGGCTGTCGTCCTCTAGAGGGAAGCACTTTCTGTTG

2799 mir-191-prec 0.32 CAACGGAATCCCAAAAGCAGCTGTTGTCTCCAGAGCATTC

2800 mir-205-prec 0.35 TCCTTCATTCCACCGGAGTCTGTCTCATACCCAACCAGAT

2801 mir-375 0.33 TTTTGTTCGTTCGGCTCGCGTGAGGCAGGGGCGGCCTCTC

2802 mir-423Nol 0.29 CAAAAGCTCGGTCTGAGGCCCCTCAGTCTTGCTTCCTAAC

2803 mir-449Nol 0.39 TGTGATGAGCTGGCAGTGTATTGTTAGCTGGTTGAATATG

2804 mir-196-2-precNo2 0.26 GCTGATCTGTGGCTTAGGTAGTTTCATGTTGTTGGGATTG

Table 108. Topotecan (Hycamtin) microRNA biomarkers. SEQIDNO Medianprobe Corr Sequence

2805 HUMTRF 0.26 GATCTAAAGGTCCCTGGTTCGATCCCGGGTTTCGGCACCA 2806 MPR74 lleefftt 0.29 CAAAGGTCACAATTAACATTCATTGTTGTCGGTGGGTTGT 2807 mir-213-precNol 0.28 AACATTCATTGCTGTCGGTGGGTTGAACTGTGTGGACAAG 2808 mir-155-prec 0.31 TTAATGCTAATCGTGATAGGGGTTTTTGCCTCCAACTGAC 2809 mir-181b-precNol 0.31 TGAGGTTGCTTCAGTGAACATTCAACGCTGTCGGTGAGTT 2810 mir-342Nol 0.33 GTCTCACACAGAAATCGCACCCGTCACCTTGGCCTACTTA 2811 mir-4323p 0.28 CCTTACGTGGGCCACTGGATGGCTCCTCCATGTCTTGGAG

Table 109. Suberoylanilide hydroxamic acid (SAHA, vorinostat, Zolinza) microRNA biomarkers.

SEQIDNO Medianprobe Corr Sequence

2812 mir-092-prec-X=092-2 0.38 GTTCTATATAAAGTATTGCACTTGTCCCGGCCTGTGGAAG

2813 mir-123-precNol 0.31 GACGGGACATTATTACTTTTGGTACGCGCTGTGACACTTC

2814 mir-514-lNo2 0.29 TGTCTGTGGTACCCTACTCTGGAGAGTGACAATCATGTAT

2815 mir-lOl-prec-9 0.25 GCTGTATATCTGAAAGGTACAGTACTGTGATAACTGAAGA

2816 mir-148-prec 0.36 TGAGTATGATAGAAGTCAGTGCACTACAGAACTTTGTCTC

2817 mir-106aNol 0.34 CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT

2818 mir-20bNol 0.41 AGTACCAAAGTGCTCATAGTGCAGGTAGTTTTGGCATGAC

2819 Hcd781 right 0.32 AGTTTCTTTAATTAATGAAGTTTTTGGGTCTGCTCCACTT

2820 mir-017-precNo2 0.29 GTCAGAATAATGTCAAAGTGCTTACAGTGCAGGTAGTGAT

2821 mir-019b-2-prec 0.42 GTGGCTGTGCAAATCCATGCAAAACTGATTGTGATAATGT

2822 mir-033-prec 0.27 GTGGTGCATTGTAGTTGCATTGCATGTTCTGGTGGTACCC

2823 mir-092-prec-13=092-1NO2 0.28 TCTGTATGGTATTGCACTTGTCCCGGCCTGTTGAGTTTGG

2824 mir-107Nol 0.29 GGCATGGAGTTCAAGCAGCATTGTACAGGGCTATCAAAGC

2825 mir-103-prec-5=103-l 0.32 TATGGATCAAGCAGCATTGTACAGGGCTATGAAGGCATTG 2826 MPR216 left 0.29 GATCCTAGTAGTGCCAAAGTGCTCATAGTGCAGGTAGTTT 2827 mir-29b-2=102prec7.1=7 .2 0.27 AGTGATTGTCTAGCACCATTTGAAATCAGTGTTCTTGGGG 2828 mir-019b-l-prec 0.4 TTCTGCTGTGCAAATCCATGCAAAACTGACTGTGGTAGTG 2829 mir-107-prec-10 0.3 GGCATGGAGTTCAAGCAGCATTGTACAGGGCTATCAAAGC 2830 mir-135-2-prec 0.37 CACTCTAGTGCTTTATGGCTTTTTATTCCTATGTGATAGT 2831 Hcd581 right 0.28 AGGAGATATGCCAAGATATATTCACAGCTTTATATACACA 2832 mir-103-2-prec 0.29 GTAGCATTCAGGTCAAGCAACATTGTACAGGGCTATGAAA 2833 Hcd230 left 0.27 CATTCTCTACAAGCATATGGCCTTGGGACATTAAGATGGC 2834 inir-025-prec 0.4 ACGCTGCCCTGGGCATTGCACTTGTCTCGGTCTGACAGTG 2835 mir-208-prec 0.31 ACCTGATGCTCACGTATAAGACGAGCAAAAAGCTTGTTGG 2836 mir-18bNo2 0.31 AGCAGCTTAGAATCTACTGCCCTAAATGCCCCTTCTGGCA 2837 mir-093-prec-7.1=093-1 0.39 CCAAAGTGCTGTTCGTGCAGGTAGTGTGATTACCCAACCT 2838 mir-106-prec-X 0.48 CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT 2839 mir-142-prec 0.37 CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG 2840 HPR169 right 0.28 GTTTCTTTCTCACGGTAACTGGCAGCCTCGTTGTGGGCTG 2841 mir-018-prec 0.44 TAAGGTGCATCTAGTGCAGATAGTGAAGTAGATTAGCATC 2842 mir-020-prec 0.48 TAAAGTGCTTATAGTGCAGGTAGTGTTTAGTTATCTACTG

Table 110. Depsipeptide (FR901228) microRNA biomarkers. SEQIDNO Medianprobe Corr Sequence

2843 Hcd415 right 0.27 GATGTTTGGGAAACAATGGGAGTGAGAGAATGGGAGAGCT 2844 mir-147-prec 0.27 GACTATGGAAGCCAGTGTGTGGAAATGCTTCTGCTAGATT 2845 mir-033b-prec 0.34 GTGCATTGCTGTTGCATTGCACGTGTGTGAGGCGGGTGCA 2846 Hcd778 right 0.34 CAGAGGGGAGGCCCAGAGGAGAGGGAAGCTTGGGCAAAGG 2847 mir-127-prec 0.25 TCGGATCCGTCTGAGCTTGGCTGGTCGGAAGTCTCATCAT 2848 mir-324Nol 0.28 TGGAGACCCACTGCCCCAGGTGCTGCTGGGGGTTGTAGTC 2849 Hcd794 right 0.35 GGCCACCACAGACACCAACAAGTTCAGTCCGTTTCTGCAG 2850 Hcd634 left 0.27 CTGCTCCGCTCAGAGCCTTTTCCTCTCCACTTCCTGTTCA

Table 111 . Bortezomib microRNA biomarkers.

SEQIDNO Medianprobe Corr Sequence

2851 MPR121 left 0.31 CACCTGGCTCTGAGAACTGAATTCCATAGGCTGTGAGCTC

2852 Hcdll5 left 0.27 CTCTGTGGCCATTTCGGTTTTTCCAGTCCGATGCCCCTGA

2853 Hcd693 right 0.28 AGGCTTTGTGCGCGCATTAAAGCTCGCCGGACCCCCGACC

2854 Hcd704 left 0.25 TCTGTATTTAATTTGGCTCAGCCGGGAAGATTTTTGGCTC

2855 HPRlOO right 0.28 GGTGTTTGTGCTCCACTCAGCTCCCTTGGTTACATAACAG

2856 Hcd760 left 0.26 TGTGGTCACGTTTCTCCCTCTCTGCTGGCCCCCATCTGTC

2857 mir-147-prec 0.3 GACTATGGAAGCCAGTGTGTGGAAATGCTTCTGCTAGATT

2858 mir-033b-prec 0.29 GTGCATTGCTGTTGCATTGCACGTGTGTGAGGCGGGTGCA

2859 mir-146-prec 0.33 TGAGAACTGAATTCCATGGGTTGTGTCAGTGTCAGACCTC

2860 Hcdl42 right 0.3 TAAATGTGTAATTTCTCCCTTGACGGCCCCCGGCCGCTGG

2861 mir-501No2 0.33 ATGCAATGCACCCGGGCAAGGATTCTGAGAGGGTGAGCCC

2862 Hcd716 right 0.26 CAATAAATGTGCCTATAAAGGCGCCGGCTCCGGGGCGCGG

2863 MPR207 right 0.27 AACAACTTTGTGCTGGTGCCGGGGAAGTTTGTGTCTCCTA

2864 Hcd777 left 0.26 CAGGTGGGTGCTGAGGCCGCGTTGTTGCTTGAAGCTAGCC

2865 mir-204-precNo2 0.27 AGGCTGGGAAGGCAAAGGGACGTTCAATTGTCATCACTGG

2866 mir-146bNol 0.26 CACCTGGCACTGAGAACTGAATTCCATAGGCTGTGAGCTC

2867 Hcd511 right 0.29 TACCTCAGAAGCCTCACTCAACCCTCTCCCGCTGAGTCTC

2868 Hcd397 left 0.28 GTGTGTATACTTATGTGTGTGTATGTGTGAGTGTGAATAT

2869 MPR130 right 0.33 CAATCACAGATAGCACCCCTCACCTTGAGCCCATTTTCAC

2870 Hcd782 left 0.28 GGAGCCCTGTCTGCAAAGAGTGGTGCGTGTGCGTGTGTGA

2871 mir-324No2 0.28 CTGACTATGCCTCCCCGCATCCCCTAGGGCATTGGTGTAA

2872 Hcd794 right 0.34 GGCCACCACAGACACCAACAAGTTCAGTCCGTTTCTGCAG

2873 Hcd739 right 0.29 TATTAGCTGAGGGAGGGCTGGAGGCGGCTGCATTCCGACT

Table 112 Leukeran microRNA biomarkers .

SEQIDNO Medianprobe Corr Sequence

2874 mir-092-prec-X=092-2 0.39 GTTCTATATAAAGTATTGCACTTGTCCCGGCCTGTGGAAG

2875 mir-096-prec-7Nol 0.26 CTCCGCTCTGAGCAATCATGTGCAGTGCCAATATGGGAAA

2876 mir-123-precNo2 0.32 TGTGACACTTCAAACTCGTACCGTGAGTAATAATGCGCCG

2877 MPR249 left 0.26 TCGGTTTGGTTCAGCTGGTATGCTTTCCAGTATCTCATTC

2878 HPR232 right 0.28 TGAATTATTGCACAATAAATTCATGCCCTGTTGTGTCTTA

2879 mir-lOl-prec-9 0.4 GCTGTATATCTGAAAGGTACAGTACTGTGATAACTGAAGA

2880 mir-106aNol 0.31 CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT

2881 mir-20bNol 0.38 AGTACCAAAGTGCTCATAGTGCAGGTAGTTTTGGCATGAC

2882 Hcd861 right 0.25 AAGGTCTGGATTGATCGTACTGCTTTCTGAAAGGTAAAAA

2883 mir-017-precNo2 0.26 GTCAGAATAATGTCAAAGTGCTTACAGTGCAGGTAGTGAT

2884 mir-019b-2-prec 0.33 GTGGCTGTGCAAATCCATGCAAAACTGATTGTGATAATGT

2885 mir-033-prec 0.3 GTGGTGCATTGTAGTTGCATTGCATGTTCTGGTGGTACCC

2886 HcdlO2 left 0.26 ACTGGAATTATGTTTTATCTTAAGTCCACACTGGATCCTC

2887 MPR216 left 0.32 GATCCTAGTAGTGCCAAAGTGCTCATAGTGCAGGTAGTTT

2888 Hcd975 left 0.25 GGTTTTGTGTTTTTGTAAACAGCAGAAGGTATTAGTCCAT

2889 mir-019b-l-prec 0.3 TTCTGCTGTGCAAATCCATGCAAAACTGACTGTGGTAGTG

2890 mir-135-2-prec 0.38 CACTCTAGTGCTTTATGGCTTTTTATTCCTATGTGATAGT

2891 Hcd581 right 0.26 AGGAGATATGCCAAGATATATTCACAGCTTTATATACACA

2892 Hcd536_HPR104 right 0.25 GCTGCTCTGCTGAGGGGCTGGACTCTGTCCAGAAGCACCA

2893 mir-128b-precNo2 0.25 GGGGGCCGATACACTGTACGAGAGTGAGTAGCAGGTCTCA

2894 HSTRNL 0.37 TCCGGATGGAGCGTGGGTTCGAATCCCACTTCTGACACCA

2895 mir-025-prec 0.47 ACGCTGCCCTGGGCATTGCACTTGTCTCGGTCTGACAGTG

2896 mir-18bNo2 0.27 AGCAGCTTAGAATCTACTGCCCTAAATGCCCCTTCTGGCA

2897 HPR262 left 0.26 TCAGTTTGGTTCAGCTGGTATGCTTTCCAGTATCTCATTC

2898 Hcd923 right 0.33 CTGGAGATAATGATTCTGCATTTCTAATTAACTCCCAGGT

2899 Hcd434 right 0.3 CACTTTTTCCTTTGTGGAAATCCTGGGTGACATCACCTCC

2900 Hcd658 right 0.28 GACTGCAGAGCAAAAGACACGATGGGTGTCTATTGTTTTC

2901 HPR129 left 0.29 TTTTCCTGCTTGATTTGCTTAATGGAAGCTGACAGTGAAG

2902 mir-380-5p 0.32 AGGTACCTGAAAAGATGGTTGACCATAGAACATGCGCTAT

2903 mir-093-prec-7.1=093-1 0.45 CCAAAGTGCTGTTCGTGCAGGTAGTGTGATTACCCAACCT

2904 mir-106-prec-X 0.5 CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT

2905 Hcd627 left 0.31 GCATTAGGGAGAATAGTTGATGGATTACAAATCTCTGCAT

2906 mir-142-prec 0.33 CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG

2907 mir-018-prec 0.46 TAAGGTGCATCTAGTGCAGATAGTGAAGTAGATTAGCATC

2908 mir-020-prec 0.5 TAAAGTGCTTATAGTGCAGGTAGTGTTTAGTTATCTACTG

Table 113. Fludarabine microRNA biomarkers . SEQIDNO Medianprobe Corr Sequence

2909 Hcd773 left 0.26 CTTCCTCCCTGGGCATCTCTAGCACAGGGGATCCCCAAAC

2910 Hcd248 right 0.33 CATTATGCAAATGGTATGAGAGGAAAATTAGGCAATAAGG

2911 mir-181dNol 0.34 GAGGTCACAATCAACATTCATTGTTGTCGGTGGGTTGTGA

2912 MPR74 left 0.3 CAAAGGTCACAATTAACATTCATTGTTGTCGGTGGGTTGT

2913 mir-213-precNol 0.37 AACATTCATTGCTGTCGGTGGGTTGAACTGTGTGGACAAG

2914 mir-155-prec 0.32 TTAATGCTAATCGTGATAGGGGTTTTTGCCTCCAACTGAC

2915 MPR197 right 0.29 TATTTATTACAAGGTCCTTCTTCCCCGTAAAACTTTGTCC

2916 mir-181b-precNol 0.26 TGAGGTTGCTTCAGTGAACATTCAACGCTGTCGGTGAGTT

2917 mir-29b-2=102prec7.1=7.2 0.32 AGTGATTGTCTAGCACCATTTGAAATCAGTGTTCTTGGGG

2918 mir-029c-prec 0.33 TTTTGTCTAGCACCATTTGAAATCGGTTATGATGTAGGGG

2919 Hcd318 right 0.32 CAAGTGGTTAATTGAGCCCACAAGTGACCTACTCAATCAG

2920 mir-128b-precNol 0.25 TCACAGTGAACCGGTCTCTTTCCCTACTGTGTCACACTCC

2921 mir-130a-precNo2 0.27 TGTCTGCACCTGTCACTAGCAGTGCAATGTTAAAAGGGCA

2922 mir-140No2 0.26 TTCTACCACAGGGTAGAACCACGGACAGGATACCGGGGCA

2923 mir-16-2Nol 0.31 GTTCCACTCTAGCAGCACGTAAATATTGGCGTAGTGAAAT

2924 mir-526a-2Nol 0.26 GATCTCGTGCTGTGACCCTCTAGAGGGAAGCACTTTCTGT

2925 mir-016b-chr3 0.3 GTTCCACTCTAGCAGCACGTAAATATTGGCGTAGTGAAAT

2926 mir-195-prec 0.34 TCTAGCAGCACAGAAATATTGGCACAGGGAAGCGAGTCTG

2927 mir-216-precNol 0.25 CTGGGATTATGCTAAACAGAGCAATTTCCTAGCCCTCACG

2928 mir-342Nol 0.26 GTCTCACACAGAAATCGCACCCGTCACCTTGGCCTACTTA

2929 mir-29b-lNol 0.34 AGTGATTGTCTAGCACCATTTGAAATCAGTGTTCTTGGGG

2930 Hcd627 left 0.33 GCATTAGGGAGAATAGTTGATGGATTACAAATCTCTGCAT

2931 mir-102-prec-l 0.33 TCTTTGTATCTAGCACCATTTGAAATCAGTGTTTTAGGAG

2932 mir-142-prec 0.32 CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG

2933 mir-223-prec 0.34 GAGTGTCAGTTTGTCAAATACCCCAAGTGCGGCACATGCT

2934 let-7f-2-prec2 0.26 TGAGGTAGTAGATTGTATAGTTTTAGGGTCATACCCCATC

2935 mir-016a-chrl3 0.36 CAATGTCAGCAGTGCCTTAGCAGCACGTAAATATTGGCGT

Table 114. Vinblastine microRNA biomarkers. SEQIDNO Medianprobe Corr Sequence

2936 Hcd794 right 0.33 GGCCACCACAGACACCAACAAGTTCAGTCCGTTTCTGCAG

2937 Hcd754 left 0.25 TCCTCCTCCTCCTTTTCGTTCCGGCTCCCTGGCTGGCTCC

Table 115. Busulfan microRNA biomarkers.

SEQIDNO Medianprobe Corr Sequence

2938 mir-096-prec-7No2 0.27 TGGCCGATTTTGGCACTAGCACATTTTTGCTTGTGTCTCT

2939 mir-124a-3-prec 0.25 TTAAGGCACGCGGTGAATGCCAAGAGAGGCGCCTCCGCCG

2940 mir-101-prec-9 0.25 GCTGTATATCTGAAAGGTACAGTACTGTGATAACTGAAGA

2941 Hcd712 right 0.27 GAAGATCGGTTGTCATCTGGTCTGGTCAGCCCGGCCCCGA

2942 Hcd693 right 0.26 AGGCTTTGTGCGCGCATTAAAGCTCGCCGGACCCCCGACC

2943 mir-219-2Nol 0.25 CTCAGGGGCTTCGCCACTGATTGTCCAAACGCAATTCTTG

2944 Hcdl45 left 0.29 AAAAATCCCAGCGGCCACCTTTCCTCCCTGCCCCATTGGG

2945 mir-155-prec 0.29 TTAATGCTAATCGTGATAGGGGTTTTTGCCTCCAACTGAC

2946 HPR213 right 0.3 AACAACTTTGTGCTGGTGCCGGGGAAGTTTGTGTCTCCAA

2947 mir-212-precNo2 0.34 CGGACAGCGCGCCGGCACCTTGGCTCTAGACTGCTTACTG

2948 Hcd913 right 0.33 CAAACATCATGTGACGTCTGTGGAGCGGCGGCGGCGGCGG

2949 Hcd716 right 0.51 CAATAAATGTGCCTATAAAGGCGCCGGCTCCGGGGCGCGG

2950 MPR207 right 0.26 AACAACTTTGTGCTGGTGCCGGGGAAGTTTGTGTCTCCTA

2951 Hcd559 right 0.33 TTCTTTGTCTATACATTTCCTAGATTTCTATGCAGTTGGG

2952 Hcd654 left 0.28 AACGAGTAAAAGGCGTACATGGGAGCGCGGGGCGGCAGAG

2953 Hcd739 right 0.27 TATTAGCTGAGGGAGGGCTGGAGGCGGCTGCATTCCGACT

2954 mir-142-prec 0.4 CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG

Table 116. Dacarbazme microRNA biomarkers. SEQIDNO Medianprobe Corr Sequence

2955 mir-092-prec-X=092-2 0. 25 GTTCTATATAAAGTATTGCACTTGTCCCGGCCTGTGGAAG 2956 mir-123-precNo2 0.28 TGTGACACTTCAAACTCGTACCGTGAGTAATAATGCGCCG 2957 mir-lOl-prec-9 0. 29 GCTGTATATCTGAAAGGTACAGTACTGTGATAACTGAAGA 2958 Hcd517 right 0. 3 GAGGGATTACAGATTAACTCCCACTTCTCCAGACTCAGAA 2959 Hcd796 left 0.37 GGTGGGATTACCCGGCTGCCGCTGTCGCCTGGATGGTCTC 2960 Hcd749 right 0.28 CGAGGAGGAGGTGACTGCTGTGGATGGTTATGAGACAGAC 2961 Hcd674 left 0.25 CTCCAGTGTGGTGTGCCTGCCCCCTTCCGTCATTGCTGTG 2962 mir-019b-2-prec 0. 27 GTGGCTGTGCAAATCCATGCAAAACTGATTGTGATAATGT 2963 mir-033-prec 0.29 GTGGTGCATTGTAGTTGCATTGCATGTTCTGGTGGTACCC 2964 mir-092-prec-13=092-lNo2 0.33 TCTGTATGGTATTGCACTTGTCCCGGCCTGTTGAGTTTGG 2965 mir-124a-2-prec 0. 29 TTAAGGCACGCGGTGAATGCCAAGAGCGGAGCCTACGGCT 2966 mir-143-prec 0. 36 CTGGTCAGTTGGGAGTCTGAGATGAAGCACTGTAGCTCAG 2967 mir-516-43p 0. 28 AAAGAAAAGAAAGTGCTTCCTTTCAGAGGGTTACTCTTTG 2968 mir-216-precNol 0. 31 CTGGGATTATGCTAAACAGAGCAATTTCCTAGCCCTCACG 2969 Hcd731 left 0.26 AATTGTGACAACTGAGTGGGAGGTTTGTGTGATGATTATC 2970 mir-106-prec-X 0.26 CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT 2971 mir-142-prec 0. 48 CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG 2972 mir-223-prec 0.48 GAGTGTCAGTTTGTCAAATACCCCAAGTGCGGCACATGCT 2973 Hcd754 left 0. 32 TCCTCCTCCTCCTTTTCGTTCCGGCTCCCTGGCTGGCTCC 2974 mir-018-prec 0.27 TAAGGTGCATCTAGTGCAGATAGTGAAGTAGATTAGCATC

Table 117 Oxaliplatin microRNA biomarkers .

SEQIDNO Medianprobe Corr Sequence

2975 mir-092-prec-X=092-2 0.36 GTTCTATATAAAGTATTGCACTTGTCCCGGCCTGTGGAAG

2976 mir-148-prec 0.27 TGAGTATGATAGAAGTCAGTGCACTACAGAACTTTGTCTC

2977 mir-20bNol 0.27 AGTACCAAAGTGCTCATAGTGCAGGTAGTTTTGGCATGAC

2978 mir-007-2-precNo2 0.28 GGACCGGCTGGCCCCATCTGGAAGACTAGTGATTTTGTTG

2979 mir-017-precNo2 0.28 GTCAGAATAATGTCAAAGTGCTTACAGTGCAGGTAGTGAT

2980 mir-019b-2-prec 0.32 GTGGCTGTGCAAATCCATGCAAAACTGATTGTGATAATGT

2981 Hcd760 left 0.27 TGTGGTCACGTTTCTCCCTCTCTGCTGGCCCCCATCTGTC

2982 Hcd783 left 0.36 CAGGCTCACACCTCCCTCCCCCAACTCTCTGGAATGTATA

2983 MPR216 left 0.26 GATCCTAGTAGTGCCAAAGTGCTCATAGTGCAGGTAGTTT

2984 mir-375 0.33 TTTTGTTCGTTCGGCTCGCGTGAGGCAGGGGCGGCCTCTC

2985 mir-019b-l-prec 0.36 TTCTGCTGTGCAAATCCATGCAAAACTGACTGTGGTAGTG

2986 mir-135-2-prec 0.32 CACTCTAGTGCTTTATGGCTTTTTATTCCTATGTGATAGT

2987 mir-150-prec 0.25 CTCCCCATGGCCCTGTCTCCCAACCCTTGTACCAGTGCTG

2988 mir-128b-precNol 0.33 TCACAGTGAACCGGTCTCTTTCCCTACTGTGTCACACTCC

2989 mir-499No2 0.26 GTGAACATCACAGCAAGTCTGTGCTGCTTCCCGTCCCTAC

2990 mir-025-prec 0.38 ACGCTGCCCTGGGCATTGCACTTGTCTCGGTCTGACAGTG

2991 mir-007-l-prec 0.32 TGTTGGCCTAGTTCTGTGTGGAAGACTAGTGATTTTGTTG

2992 mir-019a-prec 0.33 TGTAGTTGTGCAAATCTATGCAAAACTGATGGTGGCCTGC

2993 mir-093-prec-7.1=093-1 0.46 CCAAAGTGCTGTTCGTGCAGGTAGTGTGATTACCCAACCT

2994 mir-106-prec-X 0.45 CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT

2995 mir-142-prec 0.41 CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG

2996 HPR169 right 0.34 GTTTCTTTCTCACGGTAACTGGCAGCCTCGTTGTGGGCTG

2997 mir-018-prec 0.4 TAAGGTGCATCTAGTGCAGATAGTGAAGTAGATTAGCATC

2998 mir-020-prec 0.44 TAAAGTGCTTATAGTGCAGGTAGTGTTTAGTTATCTACTG

2999 mir-484 0.33 GTCAGGCTCAGTCCCCTCCCGATAAACCCCTAAATAGGGA

Table 118. Hydroxyurea microRNA biomarkers.

SEQIDNO Medianprobe Corr Sequence

3000 Hcd257 left 0.34 CTTCTTGTATAAGCACTGTGCTAAAATTGCAGACACTAGG

3001 Hcd768 right 0.26 GCCCTGGCGGAACGCTGAGAAGACAGTCGAACTTGACTAT

3002 Hcd796 left 0.25 GGTGGGATTACCCGGCTGCCGCTGTCGCCTGGATGGTCTC

3003 HUMTRF 0.48 GATCTAAAGGTCCCTGGTTCGATCCCGGGTTTCGGCACCA

3004 HUMTRS 0.3 TCTAGCGACAGAGTGGTTCAATTCCACCTTTCGGGCGCCA

3005 MPR74 left 0.28 CAAAGGTCACAATTAACATTCATTGTTGTCGGTGGGTTGT

3006 mir-213-precNol 0.29 AACATTCATTGCTGTCGGTGGGTTGAACTGTGTGGACAAG

3007 mir-155-prec 0.35 TTAATGCTAATCGTGATAGGGGTTTTTGCCTCCAACTGAC

3008 Hcd763 right 0.25 GGTGCACTCTAAATTCCTGTCCCTGCGGAAGGCTGACTAA

3009 mir-181b-precNol 0.28 TGAGGTTGCTTCAGTGAACATTCAACGCTGTCGGTGAGTT

3010 ath-MIR180aNo2 0.26 TGAGAATCTTGATGATGCTGCATCGGCAATCAACGACTAT

3011 mir-216-precNol 0.37 CTGGGATTATGCTAAACAGAGCAATTTCCTAGCCCTCACG

3012 mir-342Nol 0.31 GTCTCACACAGAAATCGCACCCGTCACCTTGGCCTACTTA

3013 mir-142-prec 0.49 CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG

3014 HSHELAOl 0.31 GGCCGCAGCAACCTCGGTTCGTATCCGAGTCACGGCACCA

3015 HUMTRVlA 0.26 ACGCGAAAGGTCCCCGGTTCGAAACCGGGCGGAAACACCA

3016 mir-223-prec 0.59 GAGTGTCAGTTTGTCAAATACCCCAAGTGCGGCACATGCT

3017 Hcd754 left 0.46 TCCTCCTCCTCCTTTTCGTTCCGGCTCCCTGGCTGGCTCC

3018 mir-020-prec 0.26 TAAAGTGCTTATAGTGCAGGTAGTGTTTAGTTATCTACTG

Table 119. Tegafur microRNA bio

SEQIDNO Medianprobe Corr Sequence

3019 Hcd257 right 0.26 CTTGGTTTTTGCAATAATGCTAGCAGAGTACACACAAGAA

3020 Hcd946 left 0 26 CACAGGATTTCAGGGGAGAAACGGTGGATTTTCACAAGAG

3021 Hcd503 left 0 .3 GAGATGAGGTAGCTGCCAGGTGCCATGGGGGTATAGGTGA

3022 mir-429Nol 0.25 CTAATACTGTCTGGTAAAACCGTCCATCCGCTGCCTGATC

3023 Hcd693 right 0 32 AGGCTTTGTGCGCGCATTAAAGCTCGCCGGACCCCCGACC

3024 miR-373*Nol 0 33 GGGATACTCAAAATGGGGGCGCTTTCCTTTTTGTCTGTAC

3025 Hcd738 left 0.28 GAAAAACTTAAGATTCCCTCTCGGCCCTCATTTTTAGCTG

3026 mir-328Nol 0.3333 GAAAGTGCATACAGCCCCTGGCCCTCTCTGCCCTTCCGTC

3027 Hcd783 left 0.36 CAGGCTCACACCTCCCTCCCCCAACTCTCTGGAATGTATA

3028 Hcdl81 right 0.34 GCTCACTGGGCAGGAGCCCTAATCGGATTCGACAGCTGAG

3029 Hcd631 left 0.38 CAGATATTTTCTCAGGCAATCCTCAGCCACAGCCTTCTTG

3030 Hcd279 left 0.25 CGGACTAACACTCCGCGGGTGTTTCCATGGAGACCGAGGC

3031 mir-194-2Nol 0.3 TGGTTCCCGCCCCCTGTAACAGCAACTCCATGTGGAAGTG

3032 mir-197-prec 0.38 TAAGAGCTCTTCACCCTTCACCACCTTCTCCACCCAGCAT

3033 HPR163 left 0.39 GCTGCCCCCTCCCTTAGCAACGTGGCCCCGGCGTTCCAAA

3034 mir-150-prec 0.32 CTCCCCATGGCCCTGTCTCCCAACCCTTGTACCAGTGCTG

3035 Hcd323 left 0.26 GTTGTAGCATGTGGTTGTATTAATGAACGTTACAGGAGAG

3036 mir-103-2-prec 0.28 GTAGCATTCAGGTCAAGCAACATTGTACAGGGCTATGAAA

3037 Hcd243 right 0.27 TATTATACATCATTTCCCATCAATCGACGAACTAAAGCCT

3038 Hcd938 right 0.27 ATTCCCTGCATCACTCTCATGAAATGGCTGAGAAAGTGAG

3039 mir-025-prec 0.29 ACGCTGCCCTGGGCATTGCACTTGTCTCGGTCTGACAGTG

3040 mir-007-l-prec 0.36 TGTTGGCCTAGTTCTGTGTGGAAGACTAGTGATTTTGTTG

3041 MPR243 left 0.26 GTATTTACCTAGTTGTAATGTGGGTTGCCATGGTGTTTTG

3042 Hcd511 right 0.27 TACCTCAGAAGCCTCACTCAACCCTCTCCCGCTGAGTCTC

3043 Hcd654 left 0.26 AACGAGTAAAAGGCGTACATGGGAGCGCGGGGCGGCAGAG

3044 mir-199a-2-prec 0.3 TCGCCCCAGTGTTCAGACTACCTGTTCAGGACAATGCCGT

3045 mir-214-prec 0.27 TGTACAGCAGGCACAGACAGGCAGTCACATGACAACCCAG

3046 mir-093-prec-7. 1=093-1 0.33 CCAAAGTGCTGTTCGTGCAGGTAGTGTGATTACCCAACCT

3047 mir-106-prec-X 0.27 CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT

3048 Hcd794 right 0.41 GGCCACCACAGACACCAACAAGTTCAGTCCGTTTCTGCAG

3049 Hcd530 right 0.26 AAGGAAAATCAAACCCACAATGCTGAACACAACAATGACC

3050 HSHELAOl 0.34 GGCCGCAGCAACCTCGGTTCGTATCCGAGTCACGGCACCA

3051 Hcd754 left 0.29 TCCTCCTCCTCCTTTTCGTTCCGGCTCCCTGGCTGGCTCC

3052 mir-020-prec 0.29 TAAAGTGCTTATAGTGCAGGTAGTGTTTAGTTATCTACTG

Table 120. Daunorubicm microRN

SEQIDNO Medianprobe Corr Sequence

3053 Hcd768 right 0.25 GCCCTGGCGGAACGCTGAGAAGACAGTCGAACTTGACTAT

3054 HUMTRF 0.34 GATCTAAAGGTCCCTGGTTCGATCCCGGGTTTCGGCACCA

3055 Hcdl45 left 0.28 AAAAATCCCAGCGGCCACCTTTCCTCCCTGCCCCATTGGG

3056 Hcd923 right 0.27 CTGGAGATAATGATTCTGCATTTCTAATTAACTCCCAGGT

3057 mir-216-precNol 0.27 CTGGGATTATGCTAAACAGAGCAATTTCCTAGCCCTCACG

3058 mir-093-prec-7. 1=093-1 0.25 CCAAAGTGCTGTTCGTGCAGGTAGTGTGATTACCCAACCT

3059 mir-342Nol 0.33 GTCTCACACAGAAATCGCACCCGTCACCTTGGCCTACTTA

3060 Hcd794 right 0.28 GGCCACCACAGACACCAACAAGTTCAGTCCGTTTCTGCAG

3061 mir-142-prec 0.48 CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG

3062 HSHELAOl 0.3 GGCCGCAGCAACCTCGGTTCGTATCCGAGTCACGGCACCA

3063 mir-223-prec 0.33 GAGTGTCAGTTTGTCAAATACCCCAAGTGCGGCACATGCT

3064 Hcd754 left 0.32 TCCTCCTCCTCCTTTTCGTTCCGGCTCCCTGGCTGGCTCC

Table 121.. BBlleeoommyycciinn mmiiccrrooRRNt^AA bbiiomarkers .

SEQIDNO Medianprobe Corr Sequence

3065 mir-125b-2-precNo2 0.29 ACCAGACTTTTCCTAGTCCCTGAGACCCTAACTTGTGAGG

3066 mir-022-prec 0.26 TGTCCTGACCCAGCTAAAGCTGCCAGTTGAAGAACTGTTG

3067 mir-125b-1 0.29 TCCCTGAGACCCTAACTTGTGATGTTTACCGTTTAAATCC

3068 mir-155-prec 0.38 TTAATGCTAATCGTGATAGGGGTTTTTGCCTCCAACTGAC

3069 mir-lOONol 0.25 CCTGTTGCCACAAACCCGTAGATCCGAACTTGTGGTATTA

3070 mir-409-3p 0.27 GACGAATGTTGCTCGGTGAACCCCTTTTCGGTATCAAATT

3071 mir-495Nol 0.31 GTGACGAAACAAACATGGTGCACTTCTTTTTCGGTATCAA

3072 mir-199a-2-prec 0.29 TCGCCCCAGTGTTCAGACTACCTGTTCAGGACAATGCCGT

3073 mir-382 0.28 GGTACTTGAAGAGAAGTTGTTCGTGGTGGATTCGCTTTAC

3074 mir-lOO-l/2-prec 0.26 TGAGGCCTGTTGCCACAAACCCGTAGATCCGAACTTGTGG

Table 122. Estramustine microRNA biomarkers. SEQIDNO Medianprobe Corr Sequence

3075 Hcd338 left 0.32 CTTCTCCTCCTGTTCGCCGCAGGCGCCCGTCCCAGTAGTC

3076 mir-099b-prec-19Nol 0.25 GCCTTCGCCGCACACAAGCTCGTGTCTGTGGGTCCGTGTC

3077 mir-149-prec 0.34 CGAGCTCTGGCTCCGTGTCTTCACTCCCGTGCTTGTCCGA

Table 123. Chlorambucil microRNA biomarkers.

SEQIDNO Medianprobe Corr Sequence

3078 mir-181a-precNol 0.26 TCAGAGGACTCCAAGGAACATTCAACGCTGTCGGTGAGTT

3079 mir-181c-precNol 0.25 TGCCAAGGGTTTGGGGGAACATTCAACCTGTCGGTGAGTT

3080 HUMTRF 0.35 GATCTAAAGGTCCCTGGTTCGATCCCGGGTTTCGGCACCA

3081 mir-181dNol 0.26 GAGGTCACAATCAACATTCATTGTTGTCGGTGGGTTGTGA

3082 MPR74 left 0.28 CAAAGGTCACAATTAACATTCATTGTTGTCGGTGGGTTGT

3083 Hcd817 left 0.28 TAATGAGAATTATGTTTGCACATTGAGGCAGGATAAATCC

3084 mir-213-precNol 0.42 AACATTCATTGCTGTCGGTGGGTTGAACTGTGTGGACAAG

3085 mir-155-prec 0.33 TTAATGCTAATCGTGATAGGGGTTTTTGCCTCCAACTGAC

3086 Hcdl48_HPR2251eft 0.29 AATTAATGACCAAAATGTCAGATGTGTCCACAGCTAATTA

3087 mir-515-15p 0.27 GATCTCATGCAGTCATTCTCCAAAAGAAAGCACTTTCTGT

3088 mir-181b-precNol 0.41 TGAGGTTGCTTCAGTGAACATTCAACGCTGTCGGTGAGTT

3089 HUMTRN 0.27 CAATCGGTTAGCGCGTTCGGCTGTTAACCGAAAGGTTGGT

3090 mir-128b-precNol 0.37 TCACAGTGAACCGGTCTCTTTCCCTACTGTGTCACACTCC

3091 mir-450-2Nol 0.29 GAAAGATGCTAAACTATTTTTGCGATGTGTTCCTAATATG

3092 mir-216-precNol 0.29 CTGGGATTATGCTAAACAGAGCAATTTCCTAGCCCTCACG

3093 mir-342Nol 0.35 GTCTCACACAGAAATCGCACCCGTCACCTTGGCCTACTTA

3094 mir-142-prec 0.45 CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG

3095 mir-223-prec 0.39 GAGTGTCAGTTTGTCAAATACCCCAAGTGCGGCACATGCT

3096 Hcd754 left 0.37 TCCTCCTCCTCCTTTTCGTTCCGGCTCCCTGGCTGGCTCC

3097 mir-020-prec 0.28 TAAAGTGCTTATAGTGCAGGTAGTGTTTAGTTATCTACTG

Table 124 Mechlorethamme microRNA biomarkers.

SEQIDNO Medianprobe Corr Sequence

3098 mir-124a-3-prec 0.33 TTAAGGCACGCGGTGAATGCCAAGAGAGGCGCCTCCGCCG

3099 Hcd946 left 0.3 CACAGGATTTCAGGGGAGAAACGGTGGATTTTCACAAGAG

3100 Hcd683 left 0.29 CTATGACAGAAGGTACTCTGTGGGAGGGAGGAGATAATAG

3101 HPR264 right 0.25 CAAATGGCGCATCAATGACTATCGCTCTTACAAAGCTCTT

3102 MPR185 right 0.3 CAGAACATGCAATGCAACTACAATGCACCACAGCTGCCCG

3103 HUMTRF 0.37 GATCTAAAGGTCCCTGGTTCGATCCCGGGTTTCGGCACCA

3104 Hcd294 left 0.25 TTATCATAAAATAATCACAGCCCTCAGGTGCTGTGAGGCA

3105 Hcd503 left 0.27 GAGATGAGGTAGCTGCCAGGTGCCATGGGGGTATAGGTGA

3106 mir-20bNol 0.27 AGTACCAAAGTGCTCATAGTGCAGGTAGTTTTGGCATGAC

3107 MPR74 left 0.25 CAAAGGTCACAATTAACATTCATTGTTGTCGGTGGGTTGT

3108 MPR234 right 0.28 GCTGACGTCACGGGCAGAATTGTCCCATTTAGGGATCCCG

3109 Hcd447 right 0.26 CTCAGGCCATTAACCTCAGTTGGTCACTAATCCCTAGGAA

3110 Hcd817 right 0.3 GAATCTTGCCCTTGGATGCATACTGTAATTTCCATTAAAG

3111 Hcdl48_HPR2251eft 0.32 AATTAATGACCAAAATGTCAGATGTGTCCACAGCTAATTA

3112 mir-515-15p 0.29 GATCTCATGCAGTCATTCTCCAAAAGAAAGCACTTTCTGT

3113 Hcd383 right 0.25 CTGATAGTACACGGGGCCAAAATAGATGTATGCTTCTAAG

3114 mir-181b-precNo2 0.31 ACCATCGACCGTTGATTGTACCCTATGGCTAACCATCATC

3115 Hcd783 left 0.33 CAGGCTCACACCTCCCTCCCCCAACTCTCTGGAATGTATA

3116 MPR224 left 0.34 TGAGGCCCTCTAGGCCGTGAATTAATGTGTCATAACTCAC

3117 HPR172 right 0.28 GTTTAAACAGCCAGTGCAAACATTTAGATCTGAGTCAAAA

3118 MPR216 left 0.32 GATCCTAGTAGTGCCAAAGTGCTCATAGTGCAGGTAGTTT

3119 HUMTRN 0.28 CAATCGGTTAGCGCGTTCGGCTGTTAACCGAAAGGTTGGT

3120 mir-321Nol 0.3 TTGGCCTCCTAAGCCAGGGATTGTGGGTTCGAGTCCCACC

3121 HPR159 left 0.25 TCCGTCACTTGAACTGGCTGCCAGCGTTCACAGACAGCTG

3122 MPR228 left 0.29 TTTTTGCTCCCAGTCAGTAGGAAGATTGTTTCAAATCTGT

3123 ath-MIR180aNo2 0.31 TGAGAATCTTGATGATGCTGCATCGGCAATCAACGACTAT

3124 mir-197-prec 0.28 TAAGAGCTCTTCACCCTTCACCACCTTCTCCACCCAGCAT

3125 mir-124a-l-precl 0.26 ATACAATTAAGGCACGCGGTGAATGCCAAGAATGGGGCTG

3126 mir-128b-precNol 0.31 TCACAGTGAACCGGTCTCTTTCCCTACTGTGTCACACTCC

3127 Hcd28 HPR391eft 0.28 CTGACTTTCAGTTCCTATTTAAAATGTCTGAATTGGGAGC

3128 Hcd889 right 0.25 ATGCCTTGTGCTCTGTGCTAATTCAGAAGAATAAGCCTGT

3129 Hcd350 right 0.26 TAGCACTTAGCAGGTTGTATTATCATTGTCCGTGTCTATG

3130 mir-025-prec 0.31 ACGCTGCCCTGGGCATTGCACTTGTCTCGGTCTGACAGTG

3131 mir-208-prec 0.27 ACCTGATGCTCACGTATAAGACGAGCAAAAAGCTTGTTGG

3132 mir-450-2Nol 0.25 GAAAGATGCTAAACTATTTTTGCGATGTGTTCCTAATATG

3133 Hcd923 right 0.29 CTGGAGATAATGATTCTGCATTTCTAATTAACTCCCAGGT

3134 Hcd434 right 0.28 CACTTTTTCCTTTGTGGAAATCCTGGGTGACATCACCTCC

3135 HPR129 left 0.27 TTTTCCTGCTTGATTTGCTTAATGGAAGCTGACAGTGAAG

3136 HPR220 left 0.27 GGAGACACTGTAACAACATTTTACTCCTGACTGATTACAT

3137 mir-380-5p 0.3 AGGTACCTGAAAAGATGGTTGACCATAGAACATGCGCTAT

3138 mir-093-prec-7.1=093-1 0.29 CCAAAGTGCTGTTCGTGCAGGTAGTGTGATTACCCAACCT

3139 mir-106-prec-X 0.3 CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT

3140 mir-342Nol 0.28 GTCTCACACAGAAATCGCACCCGTCACCTTGGCCTACTTA

3141 mir-142-prec 0.45 CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG

3142 HSHELAOl 0.29 GGCCGCAGCAACCTCGGTTCGTATCCGAGTCACGGCACCA

3143 mir-223-prec 0.32 GAGTGTCAGTTTGTCAAATACCCCAAGTGCGGCACATGCT

3144 Hcd754 left 0.32 TCCTCCTCCTCCTTTTCGTTCCGGCTCCCTGGCTGGCTCC

3145 mir-020-prec 0.37 TAAAGTGCTTATAGTGCAGGTAGTGTTTAGTTATCTACTG

3146 mir-4323p 0.26 CCTTACGTGGGCCACTGGATGGCTCCTCCATGTCTTGGAG

Table 125. Streptozocin microRNA biomarkers . SEQIDNO Medianprobe Corr Sequence

3147 mir-483Nol 0.2 ATCACGCCTCCTCACTCCTCTCCTCCCGTCTTCTCCTCTC

3148 Hcd631 right 0.21 AAAACCAAATGGCTGGCTACTCATGTACTGTTGAATGTCT

3149 mir-212-precNol 0.24 CCTCAGTAACAGTCTCCAGTCACGGCCACCGACGCCTGGC

3150 Hcd938 right 0.21 ATTCCCTGCATCACTCTCATGAAATGGCTGAGAAAGTGAG

3151 MPR133 right 0.2 CTGTAGATACTTTCTCCCTGAGCCCCTCCTGCCCCCCTGC

3152 Hcd794 right 0.21 GGCCACCACAGACACCAACAAGTTCAGTCCGTTTCTGCAG

3153 Hcd438 left 0.24 GTTTATTTGAATGTGTGATGGGGAGGTCATCAAAATGAAC

3154 Hcd886 right 0.23 CTCCAGTTGGGGGTGGGGAGTTGGGAACAGTGTGAATGGG

Table 126. Carmustme microRNA biomarkers. SEQIDNO Medianprobe Corr Sequence

3155 mir-092-prec-X=092-2 0.33 GTTCTATATAAAGTATTGCACTTGTCCCGGCCTGTGGAAG

3156 Hcd517 right 0.33 GAGGGATTACAGATTAACTCCCACTTCTCCAGACTCAGAA

3157 Hcd796 left 0.28 GGTGGGATTACCCGGCTGCCGCTGTCGCCTGGATGGTCTC

3158 HUMTRF 0.33 GATCTAAAGGTCCCTGGTTCGATCCCGGGTTTCGGCACCA

3159 mir-20bNol 0.29 AGTACCAAAGTGCTCATAGTGCAGGTAGTTTTGGCATGAC

3160 mir-019b-2-prec 0.25 GTGGCTGTGCAAATCCATGCAAAACTGATTGTGATAATGT

3161 mir-033-prec 0.27 GTGGTGCATTGTAGTTGCATTGCATGTTCTGGTGGTACCC

3162 mir-092-prec-13=092-lNo2 0.33 TCTGTATGGTATTGCACTTGTCCCGGCCTGTTGAGTTTGG

3163 Hcdl48_HPR2251eft 0.27 AATTAATGACCAAAATGTCAGATGTGTCCACAGCTAATTA

3164 HUMTRAB 0.3 ATGGTAGAGCGCTCGCTTTGCTTGCGAGAGGTAGCGGGAT

3165 Hcd975 left 0.26 GGTTTTGTGTTTTTGTAAACAGCAGAAGGTATTAGTCCAT

3166 mir-135-2-prec 0.28 CACTCTAGTGCTTTATGGCTTTTTATTCCTATGTGATAGT

3167 mir-128b-precNol 0.27 TCACAGTGAACCGGTCTCTTTCCCTACTGTGTCACACTCC

3168 mir-143-prec 0.32 CTGGTCAGTTGGGAGTCTGAGATGAAGCACTGTAGCTCAG

3169 mir-025-prec 0.33 ACGCTGCCCTGGGCATTGCACTTGTCTCGGTCTGACAGTG

3170 mir-216-precNol 0.34 CTGGGATTATGCTAAACAGAGCAATTTCCTAGCCCTCACG

3171 mir-093-prec-7.1=093-1 0.3 CCAAAGTGCTGTTCGTGCAGGTAGTGTGATTACCCAACCT

3172 mir-106-prec-X 0.33 CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT

3173 mir-142-prec 0.61 CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG

3174 HSHELAOl 0.26 GGCCGCAGCAACCTCGGTTCGTATCCGAGTCACGGCACCA

3175 HUMTRVlA 0.26 ACGCGAAAGGTCCCCGGTTCGAAACCGGGCGGAAACACCA

3176 mir-223-prec 0.52 GAGTGTCAGTTTGTCAAATACCCCAAGTGCGGCACATGCT

3177 Hcd754 left 0.46 TCCTCCTCCTCCTTTTCGTTCCGGCTCCCTGGCTGGCTCC

3178 mir-018-prec 0.34 TAAGGTGCATCTAGTGCAGATAGTGAAGTAGATTAGCATC

3179 mir-020-prec 0.35 TAAAGTGCTTATAGTGCAGGTAGTGTTTAGTTATCTACTG

Table 127. Lomustine microRNA biomarkers. SEQIDNO Medianprobe Corr Sequence

3180 mir-lOl-prec-9 0.27 GCTGTATATCTGAAAGGTACAGTACTGTGATAACTGAAGA

3181 Hcd796 left 0.26 GGTGGGATTACCCGGCTGCCGCTGTCGCCTGGATGGTCTC

3182 mir-20bNol 0.28 AGTACCAAAGTGCTCATAGTGCAGGTAGTTTTGGCATGAC 3183 HUMTRAB 0.35 ATGGTAGAGCGCTCGCTTTGCTTGCGAGAGGTAGCGGGAT 3184 mir-135-2-prec 0.27 CACTCTAGTGCTTTATGGCTTTTTATTCCTATGTGATAGT 3185 mir-153-l-precl 0.32 CAGTTGCATAGTCACAAAAGTGATCATTGGCAGGTGTGGC 3186 mir-025-prec 0.29 ACGCTGCCCTGGGCATTGCACTTGTCTCGGTCTGACAGTG 3187 mir-093-prec-7.1=093-1 0.26 CCAAAGTGCTGTTCGTGCAGGTAGTGTGATTACCCAACCT 3188 mir-106-prec-X 0.31 CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT 3189 mir-142-prec 0.41 CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG 3190 HUMTRVlA 0.28 ACGCGAAAGGTCCCCGGTTCGAAACCGGGCGGAAACACCA 3191 Hcd754 left 0.35 TCCTCCTCCTCCTTTTCGTTCCGGCTCCCTGGCTGGCTCC 3192 mir-018-prec 0.27 TAAGGTGCATCTAGTGCAGATAGTGAAGTAGATTAGCATC 3193 mir-020-prec 0.28 TAAAGTGCTTATAGTGCAGGTAGTGTTTAGTTATCTACTG

Table 128 Mercaptopurme microRNA biomarkers .

SEQIDNO Medianprobe Corr Sequence

3194 mir-092-prec-X=092-2 0.39 GTTCTATATAAAGTATTGCACTTGTCCCGGCCTGTGGAAG

3195 mir-096-prec-7Nol 0.26 CTCCGCTCTGAGCAATCATGTGCAGTGCCAATATGGGAAA

3196 mir-123-precNo2 0.32 TGTGACACTTCAAACTCGTACCGTGAGTAATAATGCGCCG

3197 MPR249 left 0.26 TCGGTTTGGTTCAGCTGGTATGCTTTCCAGTATCTCATTC

3198 HPR232 right 0.28 TGAATTATTGCACAATAAATTCATGCCCTGTTGTGTCTTA

3199 mir-lOl-prec-9 0.4 GCTGTATATCTGAAAGGTACAGTACTGTGATAACTGAAGA

3200 mir-106aNol 0.31 CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT

3201 mir-20bNol 0.38 AGTACCAAAGTGCTCATAGTGCAGGTAGTTTTGGCATGAC

3202 Hcd861 right 0.25 AAGGTCTGGATTGATCGTACTGCTTTCTGAAAGGTAAAAA

3203 mir-017-precNo2 0.26 GTCAGAATAATGTCAAAGTGCTTACAGTGCAGGTAGTGAT

3204 mir-019b-2-prec 0.33 GTGGCTGTGCAAATCCATGCAAAACTGATTGTGATAATGT

3205 mir-033-prec 0.3 GTGGTGCATTGTAGTTGCATTGCATGTTCTGGTGGTACCC

3206 HcdlO2 left 0.26 ACTGGAATTATGTTTTATCTTAAGTCCACACTGGATCCTC

3207 MPR216 left 0.32 GATCCTAGTAGTGCCAAAGTGCTCATAGTGCAGGTAGTTT

3208 Hcd975 left 0.25 GGTTTTGTGTTTTTGTAAACAGCAGAAGGTATTAGTCCAT

3209 mir-019b-l-prec 0.3 TTCTGCTGTGCAAATCCATGCAAAACTGACTGTGGTAGTG

3210 mir-135-2-prec 0.38 CACTCTAGTGCTTTATGGCTTTTTATTCCTATGTGATAGT

3211 Hcd581 right 0.26 AGGAGATATGCCAAGATATATTCACAGCTTTATATACACA

3212 Hcd536_HPR104 right 0.25 GCTGCTCTGCTGAGGGGCTGGACTCTGTCCAGAAGCACCA

3213 mir-128b-precNo2 0.25 GGGGGCCGATACACTGTACGAGAGTGAGTAGCAGGTCTCA

3214 HSTRNL 0.37 TCCGGATGGAGCGTGGGTTCGAATCCCACTTCTGACACCA

3215 mir-025-prec 0.47 ACGCTGCCCTGGGCATTGCACTTGTCTCGGTCTGACAGTG

3216 mir-18bNo2 0.27 AGCAGCTTAGAATCTACTGCCCTAAATGCCCCTTCTGGCA

3217 HPR262 left 0.26 TCAGTTTGGTTCAGCTGGTATGCTTTCCAGTATCTCATTC

3218 Hcd923 right 0.33 CTGGAGATAATGATTCTGCATTTCTAATTAACTCCCAGGT

3219 Hcd434 right 0.3 CACTTTTTCCTTTGTGGAAATCCTGGGTGACATCACCTCC

3220 Hcd658 right 0.28 GACTGCAGAGCAAAAGACACGATGGGTGTCTATTGTTTTC

3221 HPR129 left 0.29 TTTTCCTGCTTGATTTGCTTAATGGAAGCTGACAGTGAAG

3222 mir-380-5p 0.32 AGGTACCTGAAAAGATGGTTGACCATAGAACATGCGCTAT

3223 mir-093-prec-7.1=093- 1 0.45 CCAAAGTGCTGTTCGTGCAGGTAGTGTGATTACCCAACCT

3224 mir-106-prec-X 0.5 CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT

3225 Hcd627 left 0.31 GCATTAGGGAGAATAGTTGATGGATTACAAATCTCTGCAT

3226 mir-142-prec 0.33 CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG

3227 mir-018-prec 0.46 TAAGGTGCATCTAGTGCAGATAGTGAAGTAGATTAGCATC

3228 mir-020-prec 0.5 TAAAGTGCTTATAGTGCAGGTAGTGTTTAGTTATCTACTG

Table 129. Teniposide microRNA biomarkers . SEQIDNO Medianprobe Corr Sequence

3229 mir-124a-3-prec 0.25 TTAAGGCACGCGGTGAATGCCAAGAGAGGCGCCTCCGCCG

3230 Hcd768 right 0.28 GCCCTGGCGGAACGCTGAGAAGACAGTCGAACTTGACTAT

3231 HUMTRF 0.28 GATCTAAAGGTCCCTGGTTCGATCCCGGGTTTCGGCACCA

3232 mir-213-precNol 0.25 AACATTCATTGCTGTCGGTGGGTTGAACTGTGTGGACAAG

3233 mir-181b-precNo2 0.28 ACCATCGACCGTTGATTGTACCCTATGGCTAACCATCATC

3234 Hcd783 left 0.28 CAGGCTCACACCTCCCTCCCCCAACTCTCTGGAATGTATA

3235 mir-212-precNo2 0.32 CGGACAGCGCGCCGGCACCTTGGCTCTAGACTGCTTACTG

3236 mir-124a-l-precl 0.25 ATACAATTAAGGCACGCGGTGAATGCCAAGAATGGGGCTG

3237 mir-342Nol 0.29 GTCTCACACAGAAATCGCACCCGTCACCTTGGCCTACTTA

3238 mir-142-prec 0.49 CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG

3239 HSHELAOl 0.3 GGCCGCAGCAACCTCGGTTCGTATCCGAGTCACGGCACCA

3240 mir-223-prec 0.27 GAGTGTCAGTTTGTCAAATACCCCAAGTGCGGCACATGCT

3241 Hcd754 left 0.29 TCCTCCTCCTCCTTTTCGTTCCGGCTCCCTGGCTGGCTCC

Table 130. Dactinomycm microRNA biomarkers. SEQIDNO Medianprobe Corr Sequence

3242 mir-025-prec 0.27 ACGCTGCCCTGGGCATTGCACTTGTCTCGGTCTGACAGTG 3243 mir-007-l-prec 0.28 TGTTGGCCTAGTTCTGTGTGGAAGACTAGTGATTTTGTTG 3244 mir-093-prec-7.1=093- 1 0.3 CCAAAGTGCTGTTCGTGCAGGTAGTGTGATTACCCAACCT 3245 Hcd794 right 0.33 GGCCACCACAGACACCAACAAGTTCAGTCCGTTTCTGCAG 3246 mir-142-prec 0.34 CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG

Table 131 Tretinoin microRNA biomarkers .

SEQIDNO Medianprobe Corr Sequence

3247 Hcd257 left 0.42 CTTCTTGTATAAGCACTGTGCTAAAATTGCAGACACTAGG

3248 mir-148-prec 0.45 TGAGTATGATAGAAGTCAGTGCACTACAGAACTTTGTCTC

3249 Hcd512 left 0.28 CTGCGCTCTCGGAAATGACTCGCTCCAATCCCGCTTCGCG

3250 HPR227 right 0.25 CAGTGCAATGATATTGTCAAAGCATCTGGGACCAGCCTTG

3251 Hcd421 right 0.37 AGTAAACAATGTCGGCTTTCCGCCTCCTCCCCTGCCATCC

3252 MPR203 left 0.39 CTATATTGGACCGCAGCGCTGAGAGCTTTTGTGTTTAATG

3253 mir-017-precNol 0.26 GCATCTACTGCAGTGAAGGCACTTGTAGCATTATGGTGAC

3254 mir-219-2Nol 0.26 CTCAGGGGCTTCGCCACTGATTGTCCAAACGCAATTCTTG

3255 mir-328Nol 0.3 GAAAGTGCATACAGCCCCTGGCCCTCTCTGCCCTTCCGTC

3256 Hcd783 left 0.31 CAGGCTCACACCTCCCTCCCCCAACTCTCTGGAATGTATA

3257 Hcdl81 left 0.32 TTGGCGTCCTTGTCTCTCTCTCCCCTGCCCAGTGGCCTCC

3258 HPR213 right 0.3 AACAACTTTGTGCTGGTGCCGGGGAAGTTTGTGTCTCCAA

3259 mir-191-prec 0.31 CAACGGAATCCCAAAAGCAGCTGTTGTCTCCAGAGCATTC

3260 mir-375 0.31 TTTTGTTCGTTCGGCTCGCGTGAGGCAGGGGCGGCCTCTC

3261 mir-212-precNo2 0.26 CGGACAGCGCGCCGGCACCTTGGCTCTAGACTGCTTACTG

3262 Hcd913 right 0.34 CAAACATCATGTGACGTCTGTGGAGCGGCGGCGGCGGCGG

3263 Hcd716 right 0.48 CAATAAATGTGCCTATAAAGGCGCCGGCTCCGGGGCGCGG

3264 MPR207 right 0.3 AACAACTTTGTGCTGGTGCCGGGGAAGTTTGTGTCTCCTA

3265 HPR206 left 0.26 CTATATTGGACCGCAGCGCTGAGAGCTTTTGTGTTTAATG

3266 mir-016b-chr3 0.29 GTTCCACTCTAGCAGCACGTAAATATTGGCGTAGTGAAAT

3267 Hcd654 left 0.34 AACGAGTAAAAGGCGTACATGGGAGCGCGGGGCGGCAGAG

3268 mir-195-prec 0.3 TCTAGCAGCACAGAAATATTGGCACAGGGAAGCGAGTCTG

3269 Hcd425 left 0.25 GGTTCTACTCTCTTACCCCTCCCCCACGTGGTTGTTGCTG

3270 mir-148aNol 0.35 TGAGTATGATAGAAGTCAGTGCACTACAGAACTTTGTCTC

3271 mir-142-prec 0.36 CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG

3272 mir-016a-chrl3 0.25 CAATGTCAGCAGTGCCTTAGCAGCACGTAAATATTGGCGT

Table 132 . Ifosfamide microRNA biomarkers.

SEQIDNO Medianprobe Corr Sequence

3273 mir-092-prec-X=092-2 0.28 GTTCTATATAAAGTATTGCACTTGTCCCGGCCTGTGGAAG

3274 mir-181b-2Nol 0.28 CTGATGGCTGCACTCAACATTCATTGCTGTCGGTGGGTTT

3275 Hcd417 right 0.28 GGATTTAATGAGAAATATTGAGCCCTTTGGTTCAGGAACT

3276 Hcd440 HPR257 right 0.28 GCTCTGTTGTGATAAATTGGCTGTGTGCTTCATTTGGACT

3277 mir-019b-2-prec 0.25 GTGGCTGTGCAAATCCATGCAAAACTGATTGTGATAATGT

3278 mir-213-precNol 0.39 AACATTCATTGCTGTCGGTGGGTTGAACTGTGTGGACAAG

3279 mir-033-prec 0.29 GTGGTGCATTGTAGTTGCATTGCATGTTCTGGTGGTACCC

3280 mir-092-prec-13=092-lNo2 0.3 TCTGTATGGTATTGCACTTGTCCCGGCCTGTTGAGTTTGG

3281 mir-181b-precNol 0.36 TGAGGTTGCTTCAGTGAACATTCAACGCTGTCGGTGAGTT

3282 mir-128b-precNol 0.46 TCACAGTGAACCGGTCTCTTTCCCTACTGTGTCACACTCC

3283 mir-526a-2No2 0.29 GAAAAGAACATGCATCCTTTCAGAGGGTTACTCTTTGAGA

3284 MPR95 left 0.25 TTGTTGGACACTCTTTCCCTGTTGCACTACTGTGGGCCTC

3285 HPR220 right 0.27 GAGCATCAGTATGTAGTGCAATCAGTCAGGAGAAAATGAG

3286 mir-133a-l 0.35 CCTCTTCAATGGATTTGGTCCCCTTCAACCAGCTGTAGCT

3287 mir-148aNol 0.3 TGAGTATGATAGAAGTCAGTGCACTACAGAACTTTGTCTC

3288 mir-142-prec 0.4 CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG

3289 HPR169 right 0.26 GTTTCTTTCTCACGGTAACTGGCAGCCTCGTTGTGGGCTG

3290 mir-223-prec 0.38 GAGTGTCAGTTTGTCAAATACCCCAAGTGCGGCACATGCT

3291 mir-018-prec 0.27 TAAGGTGCATCTAGTGCAGATAGTGAAGTAGATTAGCATC

3292 mir-020-prec 0.25 TAAAGTGCTTATAGTGCAGGTAGTGTTTAGTTATCTACTG

3293 mir-484 0.27 GTCAGGCTCAGTCCCCTCCCGATAAACCCCTAAATAGGGA

Table 133 Tamoxifen microRNA biomarkers .

SEQIDNO Medianprobe Corr Sequence

3294 mir-092-prec-X=092-2 0.31 GTTCTATATAAAGTATTGCACTTGTCCCGGCCTGTGGAAG

3295 Hcd547 left 0.27 AAAATCAGCTTTAATTAATTTGAGTGCCAGCTCTGTGTAT

3296 Hcd257 left 0.27 CTTCTTGTATAAGCACTGTGCTAAAATTGCAGACACTAGG

3297 mir-148-prec 0.27 TGAGTATGATAGAAGTCAGTGCACTACAGAACTTTGTCTC

3298 HUMTRS 0.25 TCTAGCGACAGAGTGGTTCAATTCCACCTTTCGGGCGCCA

3299 mir-033-prec 0.27 GTGGTGCATTGTAGTTGCATTGCATGTTCTGGTGGTACCC

3300 mir-092-prec-13=092-1NO2 0.25 TCTGTATGGTATTGCACTTGTCCCGGCCTGTTGAGTTTGG

3301 mir-375 0.46 TTTTGTTCGTTCGGCTCGCGTGAGGCAGGGGCGGCCTCTC

3302 mir -095-prec-4 0.28 CGTTACATTCAACGGGTATTTATTGAGCACCCACTCTGTG

3303 mir -025-prec 0.35 ACGCTGCCCTGGGCATTGCACTTGTCTCGGTCTGACAGTG

3304 mir -202-prec 0.34 GATCTGGCCTAAAGAGGTATAGGGCATGGGAAGATGGAGC

3305 mir -007-1-prec 0.26 TGTTGGCCTAGTTCTGTGTGGAAGACTAGTGATTTTGTTG

3306 mir -093-prec-7.1=093-1 0.44 CCAAAGTGCTGTTCGTGCAGGTAGTGTGATTACCCAACCT

3307 mir -106-prec-X 0.31 CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT

3308 mir -142-prec 0.25 CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG

3309 mir -223-prec 0.25 GAGTGTCAGTTTGTCAAATACCCCAAGTGCGGCACATGCT

3310 mir -018-prec 0.26 TAAGGTGCATCTAGTGCAGATAGTGAAGTAGATTAGCATC

Table 134 . . FFllooxxuurriiddiinnee mmiiccrrrooRRNNA biomarkers.

SEQIDNO Medianprobe Corr Sequence

3311 HUMTRF 0.27 GATCTAAAGGTCCCTGGTTCGATCCCGGGTTTCGGCACCA

3312 HUMTRN 0.27 CAATCGGTTAGCGCGTTCGGCTGTTAACCGAAAGGTTGGT

3313 mir-124a-l-precl 0.31 ATACAATTAAGGCACGCGGTGAATGCCAAGAATGGGGCTG

3314 mir-150-prec 0.33 CTCCCCATGGCCCTGTCTCCCAACCCTTGTACCAGTGCTG

3315 Hcd923 left 0.26 TGGGAACCTTGTTAAAATGCAGATTCTGATTCTCAGGTCT

3316 HPR181 left 0.28 GAAGAAACATCTCAAATCATGCTGACAGCATTTTCACTAT

3317 Hcd569 right 0.26 TTATTGCTTGAATGAGTTTCAGGGTATTGGCCTTCATAAA

3318 mir-199a-2-prec 0.25 TCGCCCCAGTGTTCAGACTACCTGTTCAGGACAATGCCGT

3319 Hcd754 left 0.28 TCCTCCTCCTCCTTTTCGTTCCGGCTCCCTGGCTGGCTCC

3320 mir-4323p 0.3 CCTTACGTGGGCCACTGGATGGCTCCTCCATGTCTTGGAG

Table 135. Irinotecan microRNA biomarkers.

SEQIDNO Medianprobe Corr Sequence

3321 HUMTRF 0.27 GATCTAAAGGTCCCTGGTTCGATCCCGGGTTTCGGCACCA

3322 mir-380-5p 0.27 AGGTACCTGAAAAGATGGTTGACCATAGAACATGCGCTAT 3323 mir-342Nol 0.25 GTCTCACACAGAAATCGCACCCGTCACCTTGGCCTACTTA 3324 mir-142-prec 0.35 CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG 3325 Hcd200 right 0.25 CAATTAGCCAATTGTGGGTATAATTAGCTGCATGTAGAAT

Table 136. Satraplatm microRNA biomarkers.

SEQIDNO Medianprobe Corr Sequence

3326 Hcd289 left 0.31 TTCCTCTCAGAGCATGTTGTCATAGAAGTAAATGAAAAGG

3327 Hcd939 right 0.25 CTCTCCTGCACATAATGAGGTCTGATTTACTGTGATCATT

3328 Hcd330 right 0.28 ATTAATGGTAATTATGTGCGTAAATCCCCATGCTCTCAAT

3329 HPR76 right 0.25 GAGCCGTTTAAATTTAGCGCTTTGGGCTGCCTGGAGCGAG

3330 Hcdlll left 0.29 GCAGGGGATTTGAGGGGTGGTTGTGTGATTTGTACAGCTG

3331 Hcd976 right 0.36 CTTCTCAGAGTTGGAGATGAAAGAAAGAGAAGGTGGCCAC

3332 mir-15aNol 0.29 CCTTGGAGTAAAGTAGCAGCACATAATGGTTTGTGGATTT

3333 mir-OOlb-1-precl 0.26 AATGCTATGGAATGTAAAGAAGTATGTATTTTTGGTAGGC

3334 mir-450-1 0.36 AACGATACTAAACTGTTTTTGCGATGTGTTCCTAATATGC

3335 mir-200bNo2 0.3 CCAGCTCGGGCAGCCGTGGCCATCTTACTGGGCAGCATTG

3336 Hcd578 right 0.3 AATGATTGTAGAGGGGCGGGGCATGAAGAGTGCCGTTCTG

3337 mir-200a-prec 0.28 GTCTCTAATACTGCCTGGTAATGATGACGGCGGAGCCCTG