GENOMIC VARIANTS

CROSS REFERENCE TO RELATED APPLICATION

[0001] This application claims the benefit of U.S. Provisional Application No. 63/408,417, filed September 20, 2022, the contents of which are hereby incorporated by reference in its entirety.

FIELD OF THE DISCLOSURE

[0002] The present disclosure relates generally to methods and systems for analyzing genomic profiling data, and more specifically to methods and systems for assigning a functional status to one or more genomic variants using genomic profiling data.

BACKGROUND

[0003] Interpreting the functional status of genomic variants in a sample from an individual can provide insight into the mechanisms that can drive disease pathology in the individual. Genomic variants that occur relatively frequently in the population may be associated with a particular structural label, e.g., CD74-ROS1 fusion, and a known functional status, e.g., activating fusion. The known functional status may correspond to a functional status group identified in scientific literature. For example, the functional status group may correspond to an activating variant, an inactivating variant, a functionally unknown variant, or a wildtype. The functional status group may be associated with specific prognosis predictions, treatment recommendations, treatment outcomes, and the like. Accordingly, assigning a genomic variant to an appropriate functional status and determining a label for the genomic variant may allow a healthcare provider to efficiently determine a recommended course of treatment based on specific mechanisms that drive disease.

[0004] However, not all genomic variants have been classified or well- studied. In some instances, genomic variants in a sample from an individual may correspond to novel alterations or rearrangement events. The novel alteration or rearrangement events may not be associated with a known label and may not clearly map to a functional status group. Moreover, the process of assigning a genomic variant to a functional status group and labeling the genomic variant is typically performed manually. This manual process can be time intensive and subject to human error. Accordingly, there is a need to provide an automatic process that assigns a genomic variant to a functional status group and labels the genomic variant, where the process can assign and label novel genomic variants that are not well classified in the art.

BRIEF SUMMARY

[0005] Disclosed herein are methods and systems for automatically assigning a genomic variant from a sample to a functional status group. The functional status assignment of the genomic variant may be used to identify a treatment for the individual providing the sample. The disclosed methods have the potential to improve healthcare outcomes for individuals (e.g., cancer patients) by providing healthcare providers with treatment recommendations based on an accurate identification of a functional status group for both novel and frequently occurring genomic variants. In one or more examples, methods and systems can further label the genomic variant and provide the label to a healthcare provider (e.g., in a report). In such embodiments, the healthcare provider may further base the treatment recommendation based on the label. Accordingly, embodiments of the present disclosure provide improvements to patient care by providing an automatic process that assigns a genomic variant to a functional status group and labels the genomic variant, where the process can assign and label novel genomic variants that are not well classified in the art.

[0006] Embodiments of the present disclosure provide systems and methods for determining a functional status group for a genomic variant and identifying treatment for an individual based on the functional status group. In one or more examples, the method can comprise: providing a plurality of nucleic acid molecules obtained from a sample from a subject, ligating one or more adapters onto one or more nucleic acid molecules from the plurality of nucleic acid molecules; amplifying the one or more ligated nucleic acid molecules from the plurality of nucleic acid molecules; capturing amplified nucleic acid molecules from the amplified nucleic acid molecules; sequencing, by a sequencer, the captured nucleic acid molecules to obtain a plurality of sequence reads that represent the captured nucleic acid molecules; receiving, at one or more processors, sequence read data for the plurality of sequence reads; determining, using the one or more processors, a breakpoint of a genomic variant and a location of the breakpoint based on the sequence read data, the breakpoint associated with a rearrangement event and further associated with a first gene and a second gene of the sample, the first gene corresponding to the genomic variant; if the breakpoint of the genomic variant is located outside a sequence encoding a predetermined protein domain, performing, using the one or more processors: a first determination of whether the rearrangement event is in-strand; and a second determination of whether the sequence encoding the predetermined protein domain is impacted by the rearrangement event; assigning, using the one or more processors, the genomic variant to a functional status group based on the first determination and the second determination; and identifying, using the one or more processors, a treatment based on the assigned functional status group of the genomic variant.

[0007] In one or more examples, the method can further comprise: in accordance with a determination that the rearrangement event is not in-strand, assigning the genomic variant to a first functional status group corresponding to a functionally unknown rearrangement group; and in accordance with a determination that the rearrangement event is in- strand and the sequence encoding the predetermined protein domain is in sequence read data corresponding to the rearrangement event, assigning the genomic variant to a second functional status group corresponding to a functionally unknown fusion group.

[0008] In one or more examples, the method can further comprise performing, using the one or more processors, a third determination of whether the first gene and the second gene are known partner genes. In such examples, whether the first gene and the second gene are known partner genes is determined based on a predetermined association between the first gene and the second gene described in scientific literature. In one or more examples, determining whether the first gene and the second gene are known partner genes comprises comparing the second gene to a lookup table associated with the first gene. In one or more examples, the method can further comprise in accordance with a determination that the rearrangement event is in-strand, the sequence encoding the predetermined protein domain impacted by the rearrangement event, and the first gene is a known partner of the second gene, assigning the genomic variant to a third functional status group corresponding to an activating fusion group. [0009] In one or more examples, the subject is suspected of having or is determined to have cancer. In such examples, the cancer is a B cell cancer (multiple myeloma), a melanoma, breast cancer, lung cancer, bronchus cancer, colorectal cancer, prostate cancer, pancreatic cancer, stomach cancer, ovarian cancer, urinary bladder cancer, brain cancer, central nervous system cancer, peripheral nervous system cancer, esophageal cancer, cervical cancer, uterine cancer, endometrial cancer, cancer of an oral cavity, cancer of a pharynx, liver cancer, kidney cancer, testicular cancer, biliary tract cancer, small bowel cancer, appendix cancer, salivary gland cancer, thyroid gland cancer, adrenal gland cancer, osteosarcoma, chondrosarcoma, a cancer of hematological tissue, an adenocarcinoma, an inflammatory myofibroblastic tumor, a gastrointestinal stromal tumor (GIST), colon cancer, multiple myeloma (MM), myelodysplastic syndrome (MDS), myeloproliferative disorder (MPD), acute lymphocytic leukemia (ALL), acute myelocytic leukemia (AML), chronic myelocytic leukemia (CML), chronic lymphocytic leukemia (CLL), polycythemia Vera, Hodgkin lymphoma, non-Hodgkin lymphoma (NHL), soft- tissue sarcoma, fibrosarcoma, myxosarcoma, liposarcoma, osteogenic sarcoma, chordoma, angiosarcoma, endotheliosarcoma, lymphangiosarcoma, lymphangioendotheliosarcoma, synovioma, mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinomas, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatoma, bile duct carcinoma, choriocarcinoma, seminoma, embryonal carcinoma, Wilms' tumor, bladder carcinoma, epithelial carcinoma, glioma, astrocytoma, medulloblastoma, craniopharyngioma, ependymoma, pinealoma, hemangioblastoma, acoustic neuroma, oligodendroglioma, meningioma, neuroblastoma, retinoblastoma, follicular lymphoma, diffuse large B-cell lymphoma, mantle cell lymphoma, hepatocellular carcinoma, thyroid cancer, gastric cancer, head and neck cancer, small cell cancer, essential thrombocythemia, agnogenic myeloid metaplasia, hypereosinophilic syndrome, systemic mastocytosis, familiar hypereosinophilia, chronic eosinophilic leukemia, neuroendocrine cancers, or a carcinoid tumor.

[0010] In one or more examples, the cancer comprises acute lymphoblastic leukemia (Philadelphia chromosome positive), acute lymphoblastic leukemia (precursor B-cell), acute myeloid leukemia (FLT3+), acute myeloid leukemia (with an IDH2 mutation), anaplastic large cell lymphoma, basal cell carcinoma, B-cell chronic lymphocytic leukemia, bladder cancer, breast cancer (HER2 overexpressed/amplified), breast cancer (HER2+), breast cancer (HR+, HER2-), cervical cancer, cholangiocarcinoma, chronic lymphocytic leukemia, chronic lymphocytic leukemia (with 17p deletion), chronic myelogenous leukemia, chronic myelogenous leukemia (Philadelphia chromosome positive), classical Hodgkin lymphoma, colorectal cancer, colorectal cancer (dMMR/MSI-H), colorectal cancer (KRAS wild type), cryopyrin-associated periodic syndrome, a cutaneous T-cell lymphoma, dermatofibrosarcoma protuberans, a diffuse large B-cell lymphoma, fallopian tube cancer, a follicular B-cell non-Hodgkin lymphoma, a follicular lymphoma, gastric cancer, gastric cancer (HER2+), gastroesophageal junction (GEJ) adenocarcinoma, a gastrointestinal stromal tumor, a gastrointestinal stromal tumor (KIT+), a giant cell tumor of the bone, a glioblastoma, granulomatosis with polyangiitis, a head and neck squamous cell carcinoma, a hepatocellular carcinoma, Hodgkin lymphoma, juvenile idiopathic arthritis, lupus erythematosus, a mantle cell lymphoma, medullary thyroid cancer, melanoma, a melanoma with a BRAF V600 mutation, a melanoma with a BRAF V600E or V600K mutation, Merkel cell carcinoma, multicentric Castleman's disease, multiple hematologic malignancies including Philadelphia chromosome-positive ALL and CML, multiple myeloma, myelofibrosis, a non-Hodgkin’ s lymphoma, a nonresectable subependymal giant cell astrocytoma associated with tuberous sclerosis, a non-small cell lung cancer, a non-small cell lung cancer (ALK+), a non-small cell lung cancer (PD-L1+), a non-small cell lung cancer (with ALK fusion or ROS1 gene alteration), a non-small cell lung cancer (with BRAF V600E mutation), a non-small cell lung cancer (with an EGFR exon 19 deletion or exon 21 substitution (L858R) mutations), a non- small cell lung cancer (with an EGFR T790M mutation), ovarian cancer, ovarian cancer (with a BRCA mutation), pancreatic cancer, a pancreatic, gastrointestinal, or lung origin neuroendocrine tumor, a pediatric neuroblastoma, a peripheral T-cell lymphoma, peritoneal cancer, prostate cancer, a renal cell carcinoma, rheumatoid arthritis, a small lymphocytic lymphoma, a soft tissue sarcoma, a solid tumor (MSLH/dMMR), a squamous cell cancer of the head and neck, a squamous non-small cell lung cancer, thyroid cancer, a thyroid carcinoma, urothelial cancer, a urothelial carcinoma, or Waldenstrom's macroglobulinemia.

[0011] In one or more examples, the method can further comprise treating the subject with an anti-cancer therapy. In one or more examples, the anti-cancer therapy comprises a targeted anti- cancer therapy. In such examples, targeted anti-cancer therapy comprises abemaciclib (Verzenio), abiraterone acetate (Zytiga), acalabrutinib (Calquence), ado-trastuzumab emtansine (Kadcyla), afatinib dimaleate (Gilotrif), aldesleukin (Proleukin), alectinib (Alecensa), alemtuzumab (Campath), alitretinoin (Panretin), alpelisib (Piqray), amivantamab-vmjw (Rybrevant), anastrozole (Arimidex), apalutamide (Erleada), asciminib hydrochloride (Scemblix), atezolizumab (Tecentriq), avapritinib (Ayvakit), avelumab (Bavencio), axicabtagene ciloleucel (Yescarta), axitinib (Inlyta), belantamab mafodotin-blmf (Blenrep), belimumab (Benlysta), belinostat (Beleodaq), belzutifan (Welireg), bevacizumab (Avastin), bexarotene (Targretin), binimetinib (Mektovi), blinatumomab (Blincyto), bortezomib (Velcade), bosutinib (Bosulif), brentuximab vedotin (Adcetris), brexucabtagene autoleucel (Tecartus), brigatinib (Alunbrig), cabazitaxel (Jevtana), cabozantinib (Cabometyx), cabozantinib (Cabometyx, Cometriq), canakinumab (Haris), capmatinib hydrochloride (Tabrecta), carfilzomib (Kyprolis), cemiplimab-rwlc (Libtayo), ceritinib (LDK378/Zykadia), cetuximab (Erbitux), cobimetinib (Cotellic), copanlisib hydrochloride (Aliqopa), crizotinib (Xalkori), dabrafenib (Tafinlar), dacomitinib (Vizimpro), daratumumab (Darzalex), daratumumab and hyaluronidase-fihj (Darzalex Faspro), darolutamide (Nubeqa), dasatinib (Sprycel), denileukin diftitox (Ontak), denosumab (Xgeva), dinutuximab (Unituxin), dostarlimab-gxly (Jemperli), durvalumab (Imfinzi), duvelisib (Copiktra), elotuzumab (Empliciti), enasidenib mesylate (Idhifa), encorafenib (Braftovi), enfortumab vedotin-ejfv (Padcev), entrectinib (Rozlytrek), enzalutamide (Xtandi), erdafitinib (Balversa), erlotinib (Tarceva), everolimus (Afinitor), exemestane (Aromasin), fam-trastuzumab deruxtecan-nxki (Enhertu), fedratinib hydrochloride (Inrebic), fulvestrant (Faslodex), gefitinib (Iressa), gemtuzumab ozogamicin (Mylotarg), gilteritinib (Xospata), glasdegib maleate (Daurismo), hyaluronidase-zzxf (Phesgo), ibrutinib (Imbruvica), ibritumomab tiuxetan (Zevalin), idecabtagene vicleucel (Abecma), idelalisib (Zydelig), imatinib mesylate (Gleevec), infigratinib phosphate (Truseltiq), inotuzumab ozogamicin (Besponsa), iobenguane 1131 (Azedra), ipilimumab (Yervoy), isatuximab-irfc (Sarclisa), ivosidenib (Tibsovo), ixazomib citrate (Ninlaro), lanreotide acetate (Somatuline Depot), lapatinib (Tykerb), larotrectinib sulfate (Vitrakvi), lenvatinib mesylate (Lenvima), letrozole (Femara), lisocabtagene maraleucel (Breyanzi), loncastuximab tesirine-lpyl (Zynlonta), lorlatinib (Lorbrena), lutetium Lu 177-dotatate (Lutathera), margetuximab-cmkb (Margenza), midostaurin (Rydapt), mobocertinib succinate (Exkivity), mogamulizumab-kpkc (Poteligeo), moxetumomab pasudotox-tdfk (Lumoxiti), naxitamab-gqgk (Danyelza), necitumumab (Portrazza), neratinib maleate (Nerlynx), nilotinib (Tasigna), niraparib tosylate monohydrate (Zejula), nivolumab (Opdivo), obinutuzumab (Gazyva), ofatumumab (Arzerra), olaparib (Lynparza), olaratumab (Lartruvo), osimertinib (Tagrisso), palbociclib (Ibrance), panitumumab (Vectibix), panobinostat (Farydak), pazopanib (Votrient), pembrolizumab (Keytruda), pemigatinib (Pemazyre), pertuzumab (Perjeta), pexidartinib hydrochloride (Turalio), polatuzumab vedotin-piiq (Polivy), ponatinib hydrochloride (Iclusig), pralatrexate (Folotyn), pralsetinib (Gavreto), radium 223 dichloride (Xofigo), ramucirumab (Cyramza), regorafenib (Stivarga), ribociclib (Kisqali), ripretinib (Qinlock), rituximab (Rituxan), rituximab and hyaluronidase human (Rituxan Hycela), romidepsin (Istodax), rucaparib camsylate (Rubraca), ruxolitinib phosphate (Jakafi), sacituzumab govitecan-hziy (Trodelvy), seliciclib, selinexor (Xpovio), selpercatinib (Retevmo), selumetinib sulfate (Koselugo), siltuximab (Sylvant), sipuleucel-T (Provenge), sirolimus protein-bound particles (Fyarro), sonidegib (Odomzo), sorafenib (Nexavar), sotorasib (Lumakras), sunitinib (Sutent), tafasitamab-cxix (Monjuvi), tagraxofusp-erzs (Elzonris), talazoparib tosylate (Talzenna), tamoxifen (Nolvadex), tazemetostat hydrobromide (Tazverik), tebentafusp-tebn (Kimmtrak), temsirolimus (Torisel), tepotinib hydrochloride (Tepmetko), tisagenlecleucel (Kymriah), tisotumab vedotin-tftv (Tivdak), tocilizumab (Actemra), tofacitinib (Xeljanz), tositumomab (Bexxar), trametinib (Mekinist), trastuzumab (Herceptin), tretinoin (Vesanoid), tivozanib hydrochloride (Fotivda), toremifene (Fareston), tucatinib (Tukysa), umbralisib tosylate (Ukoniq), vandetanib (Caprelsa), vemurafenib (Zelboraf), venetoclax (Venclexta), vismodegib (Erivedge), vorinostat (Zolinza), zanubrutinib (Brukinsa), ziv-aflibercept (Zaltrap), or any combination thereof.

[0012] In one or more examples, the method can further comprise obtaining the sample from the subject. In one or more examples the sample comprises a tissue biopsy sample, a liquid biopsy sample, or a normal control. In such examples, the sample is a liquid biopsy sample and comprises blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva. In one or more examples, the sample is a liquid biopsy sample and comprises circulating tumor cells (CTCs). In one or more examples, the sample is a liquid biopsy sample and comprises cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof. [0013] In one or more examples, the plurality of nucleic acid molecules comprises a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules. In such examples, the tumor nucleic acid molecules are derived from a tumor portion of a heterogeneous tissue biopsy sample, and the non-tumor nucleic acid molecules are derived from a normal portion of the heterogeneous tissue biopsy sample. In one or more examples, the sample comprises a liquid biopsy sample, and wherein the tumor nucleic acid molecules are derived from a circulating tumor DNA (ctDNA) fraction of the liquid biopsy sample, and the non-tumor nucleic acid molecules are derived from a non-tumor, cell-free DNA (cfDNA) fraction of the liquid biopsy sample.

[0014] In one or more examples, the one or more adapters comprise amplification primers, flow cell adaptor sequences, substrate adapter sequences, or sample index sequences. In one or more examples, the captured nucleic acid molecules are captured from the amplified nucleic acid molecules by hybridization to one or more bait molecules. In such examples, the one or more bait molecules comprise one or more nucleic acid molecules, each comprising a region that is complementary to a region of a captured nucleic acid molecule.

[0015] In one or more examples, amplifying nucleic acid molecules comprises performing a polymerase chain reaction (PCR) amplification technique, a non-PCR amplification technique, or an isothermal amplification technique. In one or more examples, the sequencing comprises use of a massively parallel sequencing (MPS) technique, whole genome sequencing (WGS), whole exome sequencing, targeted sequencing, direct sequencing, or Sanger sequencing technique. In such examples, the sequencing comprises massively parallel sequencing, and the massively parallel sequencing technique comprises next generation sequencing (NGS).

[0016] In one or more examples, the sequencer comprises a next generation sequencer. In one or more examples, one or more of the plurality of sequencing reads overlap one or more gene loci within one or more subgenomic intervals in the sample. In such examples, the one or more gene loci comprises between 10 and 20 loci, between 10 and 40 loci, between 10 and 60 loci, between 10 and 80 loci, between 10 and 100 loci, between 10 and 150 loci, between 10 and 200 loci, between 10 and 250 loci, between 10 and 300 loci, between 10 and 350 loci, between 10 and 400 loci, between 10 and 450 loci, between 10 and 500 loci, between 20 and 40 loci, between 20 and 60 loci, between 20 and 80 loci, between 20 and 100 loci, between 20 and 150 loci, between 20 and 200 loci, between 20 and 250 loci, between 20 and 300 loci, between 20 and 350 loci, between 20 and 400 loci, between 20 and 500 loci, between 40 and 60 loci, between 40 and 80 loci, between 40 and 100 loci, between 40 and 150 loci, between 40 and 200 loci, between 40 and 250 loci, between 40 and 300 loci, between 40 and 350 loci, between 40 and 400 loci, between 40 and 500 loci, between 60 and 80 loci, between 60 and 100 loci, between 60 and 150 loci, between 60 and 200 loci, between 60 and 250 loci, between 60 and 300 loci, between 60 and 350 loci, between 60 and 400 loci, between 60 and 500 loci, between 80 and 100 loci, between 80 and 150 loci, between 80 and 200 loci, between 80 and 250 loci, between 80 and 300 loci, between 80 and 350 loci, between 80 and 400 loci, between 80 and 500 loci, between 100 and 150 loci, between 100 and 200 loci, between 100 and 250 loci, between 100 and 300 loci, between 100 and 350 loci, between 100 and 400 loci, between 100 and 500 loci, between 150 and 200 loci, between 150 and 250 loci, between 150 and 300 loci, between 150 and 350 loci, between 150 and 400 loci, between 150 and 500 loci, between 200 and 250 loci, between 200 and 300 loci, between 200 and 350 loci, between 200 and 400 loci, between 200 and 500 loci, between 250 and 300 loci, between 250 and 350 loci, between 250 and 400 loci, between 250 and 500 loci, between 300 and 350 loci, between 300 and 400 loci, between 300 and 500 loci, between 350 and 400 loci, between 350 and 500 loci, or between 400 and 500 loci.

[0017] In one or more examples, the one or more gene loci comprise ABL1, ACVR1B, AKT1, AKT2, AKT3, ALK, ALOX12B, AMER1, APC, AR, ARAF, ARFRP1, ARID1A, ASXL1, ATM, ATR, ATRX, AURKA, AURKB, AXIN1, AXL, BAP1, BARD1, BCL2, BCL2L1, BCL2L2, BCL6, BCOR, BCORL1, BCR, BRAF, BRCA1, BRCA2, BRD4, BRIP1, BTG1, BTG2, BTK, CALR, CARD11, CASP8, CBFB, CBL, CCND1, CCND2, CCND3, CCNE1, CD22, CD274, CD70, CD74, CD79A, CD79B, CDC73, CDH1, CDK12, CDK4, CDK6, CDK8, CDKN1A, CDKN1B, CDKN2A, CDKN2B, CDKN2C, CEBPA, CHEK1, CHEK2, CIC, CREBBP, CRKL, CSF1R, CSF3R, CTCF, CTNNA1, CTNNB1, CUL3, CUL4A, CXCR4, CYP17A1, DAXX, DDR1, DDR2, DIS3, DNMT3A, DOT1L, EED, EGFR, EMSY (Cl lorf30), EP300, EPHA3, EPHB 1, EPHB4, ERBB2, ERBB3, ERBB4, ERCC4, ERG, ERRFI1, ESRI, ETV4, ETV5, ETV6, EWSR1, EZH2, EZR, FAM46C, FANCA, FANCC, FANCG, FANCL, FAS, FBXW7, FGF10, FGF12, FGF14, FGF19, FGF23, FGF3, FGF4, FGF6, FGFR1, FGFR2, FGFR3, FGFR4, FH, FLCN, FLT1, FLT3, FOXL2, FUBP1, GABRA6, GAT A3, GATA4, GATA6, GID4 (C17orf39), GNA11, GNA13, GNAQ, GNAS, GRM3, GSK3B, H3F3A, HDAC1, HGF, HNF1A, HRAS, HSD3B1, ID3, IDH1, IDH2, IGF1R, IKBKE, IKZF1, INPP4B, IRF2, IRF4, IRS2, JAK1, JAK2, JAK3, JUN, KDM5A, KDM5C, KDM6A, KDR, KEAP1, KEL, KIT, KLHL6, KMT2A (MLL), KMT2D (MLL2), KRAS, LTK, LYN, MAF, MAP2K1, MAP2K2, MAP2K4, MAP3K1, MAP3K13, MAPK1, MCL1, MDM2, MDM4, MED12, MEF2B, MEN1, MERTK, MET, MITF, MKNK1, MLH1, MPL, MRE11A, MSH2, MSH3, MSH6, MST1R, MTAP, MTOR, MUTYH, MYB, MYC, MYCL, MYCN, MYD88, NBN, NF1, NF2, NFE2L2, NFKBIA, NKX2-1, NOTCH1, NOTCH2, NOTCH3, NPM1, NRAS, NT5C2, NTRK1, NTRK2, NTRK3, NUTM1, P2RY8, PALB2, PARK2, PARP1, PARP2, PARP3, PAX5, PBRM1, PDCD1, PDCD1LG2, PDGFRA, PDGFRB, PDK1, PIK3C2B, PIK3C2G, PIK3CA, PIK3CB, PIK3R1, PIM1, PMS2, POLDI, POLE, PPARG, PPP2R1A, PPP2R2A, PRDM1, PRKAR1A, PRKCI, PTCHI, PTEN, PTPN11, PTPRO, QKI, RAC1, RAD21, RAD51, RAD51B, RAD51C, RAD51D, RAD52, RAD54L, RAFI, RARA, RBI, RBM10, REL, RET, RICTOR, RNF43, ROS1, RPTOR, RSPO2, SDC4, SDHA, SDHB, SDHC, SDHD, SETD2, SF3B1, SGK1, SLC34A2, SMAD2, SMAD4, SMARCA4, SMARCB1, SMO, SNCAIP, S0CS1, SOX2, SOX9, SPEN, SPOP, SRC, STAG2, STAT3, STK11, SUFU, SYK, TBX3, TEK, TERC, TERT, TET2, TGFBR2, TIPARP, TMPRSS2, TNFAIP3, TNFRSF14, TP53, TSC1, TSC2, TYRO3, U2AF1, VEGFA, VHL, WHSCI, WHSC1L1, WT1, XPO1, XRCC2, ZNF217, ZNF703, or any combination thereof.

[0018] In one or more examples, the one or more gene loci comprise ABL, ALK, ALL, B4GALNT1, BAFF, BCL2, BRAF, BRCA, BTK, CD19, CD20, CD3, CD30, CD319, CD38, CD52, CDK4, CDK6, CML, CRACC, CS1, CTLA-4, dMMR, EGFR, ERBB1, ERBB2, FGFR1- 3, FLT3, GD2, HDAC, HER1, HER2, HR, IDH2, IL-ip, IL-6, IL-6R, JAK1, JAK2, JAK3, KIT, KRAS, MEK, MET, MSLH, mTOR, PARP, PD-1, PDGFR, PDGFRa, PDGFRp, PD-L1, PI3K5, PIGF, PTCH, RAF, RANKL, RET, ROS1, SLAMF7, VEGF, VEGFA, VEGFB, or any combination thereof.

[0019] In one or more examples, the method can further comprise generating, by the one or more processors, a report indicating the functional status of the genomic variant. In such examples, the method can further comprise transmitting the report to a healthcare provider. In such examples, the report is transmitted via a computer network or a peer-to-peer connection.

[0020] Embodiments of the present disclosure further comprise a method for identifying a treatment based on a genomic variant in a sample from an individual. The method can comprise: receiving, at one or more processors, sequence read data associated with the genomic variant in the sample; determining, using the one or more processors, a breakpoint of the genomic variant and a location of the breakpoint based on the sequence read data, the breakpoint associated with a rearrangement event and further associated with a first gene and a second gene of the sample, the first gene corresponding to the genomic variant; if the breakpoint of the genomic variant is located outside a sequence encoding a predetermined protein domain, performing, using the one or more processors: a first determination of whether the rearrangement event is in-strand; and a second determination of whether the sequence encoding the predetermined protein domain is impacted by the rearrangement event; assigning, using the one or more processors, the genomic variant to a functional status group based on the first determination and the second determination; and identifying, using the one or more processors, the treatment based on the assigned functional status group of the genomic variant.

[0021] In one or more examples, the method can further comprise in accordance with a determination that the rearrangement event is not in-strand, assigning the genomic variant to a first functional status group; and in accordance with a determination that the rearrangement event is in-strand and the sequence encoding the predetermined protein domain is impacted by the rearrangement event, assigning the genomic variant to a second functional status group. In such examples, the first functional status group corresponds to a functionally unknown rearrangement group. In one or more examples, the method can further comprise comprising labeling the genomic variant as a rearrangement. In one or more examples, a functionally unknown rearrangement label is further based on an orientation of the first gene with respect to the second gene.

[0022] In one or more examples, the second functional status group corresponds to a functionally unknown fusion group. In such examples, the method can further comprise labeling the genomic variant as a reciprocal fusion. [0023] In one or more examples, the method can further comprise performing, using the one or more processors, a third determination of whether the first gene and the second gene are known partner genes. In such examples, whether the first gene and the second gene are known partner genes is determined based on a predetermined association between the first gene and the second gene described in scientific literature. In one or more examples, determining whether the first gene and the second gene are known partner genes is based on a lookup table associated with the first gene.

[0024] In one or more examples, the method can further comprise in accordance with a determination that the rearrangement event is in-strand, the sequence encoding the predetermined protein domain is impacted by the rearrangement event, and the first gene is a known partner of the second gene, assigning the genomic variant to a third functional status group. In such examples, the third functional status group corresponds to an activating fusion group. In one or more examples, the method can further comprise labeling the genomic variant as an activating fusion. In one or more examples, determining whether the sequence encoding the predetermined protein domain is impacted by the rearrangement event comprises: determining, whether a functional domain of the first gene is in the sequence read data corresponding to the rearrangement event.

[0025] In one or more examples, the method can further comprise in accordance with a determination that the rearrangement event is in-strand, and further that the sequence encoding the predetermined protein domain is impacted by the rearrangement event, and the first gene is not a known partner gene of the second gene, flagging the genomic variant for manual review and forgoing labeling the genomic variant. In one or more examples, the method can further comprise forgoing assigning the genomic variant to the functional status group if the breakpoint of the genomic variant is located outside a predetermined sequence encoding a protein domain.

[0026] In one or more examples, the method can further comprise determining, using the one or more processors, whether the first gene is an oncogene.

[0027] In one or more examples, the first gene is ABL1, ALK, BRAF, FGFR1, FGFR2, FGFR3, MET, NTRK1, NTRK2, NTRK3, PDGFRA, PDGFRB, ROS1, RET, or RAFI. In one or more examples, the predetermined protein domain is a functional domain. In one or more examples, the predetermined protein domain is a kinase domain.

[0028] In one or more examples, the method can further comprise performing, using the one or more processors, a fourth determination of whether the second gene is a coding gene.

[0029] In one or more examples, the rearrangement event comprises a fusion event. In one or more examples, the method can further comprise determining the second gene is a known partner of the first gene in accordance with a determination that the first gene is ROS 1 and the second gene is one of CD74, CLIP1, EZR, GOPC, LRIG3, MY05A, PPFIBP1, PWWP2A, SLC34A2, SDC4, SHTN1 (KIAA1598), TPM3, and ZCCHC8.

[0030] In one or more examples, the sequence read data for the individual is based on a targeted exome sequencing panel. In one or more examples, the sequence read data for the individual is derived from a single biopsy sample. In one or more examples, the sequence read data for the individual is derived from multiple biopsy samples. In one or more examples, the sequence read data for the individual is derived from single cell sequencing. In one or more examples, the sequence read data for the individual is derived from circulating tumor DNA in a liquid biopsy sample.

[0031] In one or more examples, the method can further comprise assigning, using the one or more processors, a therapy for the individual based on the functional status group. In one or more examples, the method can further comprise administering, using the one or more processors, a treatment to the individual based on the functional status group. In one or more examples, the method can further comprise associating, using the one or more processors, the individual with a clinical trial based on the functional status group. In one or more examples, the method can further comprise monitoring, using the one or more processors, a prognosis of the individual based on the functional status group. In one or more examples, the method can further comprise predicting, using the one or more processors, one or more clinical outcomes based on the functional status group.

[0032] Embodiments of the present disclosure further provide systems for identifying a treatment for an individual based on a genomic variant. In one or more examples, the system can comprise: one or more processors; and a memory communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to perform a method comprising: receiving, at one or more processors, sequence read data associated with a genomic variant in a sample from an individual; determining, using the one or more processors, a breakpoint of the genomic variant and a location of the breakpoint based on the sequence read data, the breakpoint associated with a rearrangement event and further associated with a first gene and a second gene of the sample, the first gene corresponding to the genomic variant; if the breakpoint of the genomic variant is located outside a sequence encoding a predetermined protein domain, performing, using the one or more processors: a first determination of whether the rearrangement event is in-strand; and a second determination of whether the sequence encoding the predetermined protein domain is impacted by the rearrangement event; assigning, using the one or more processors, the genomic variant to a functional status group based on the first determination and the second determination; and identifying, using the one or more processors, the treatment based on the assigned functional status group of the genomic variant.

[0033] Embodiments of the present disclosure further provide non-transitory computer-readable storage mediums for identifying a treatment for an individual based on a genomic variant. In one or more examples, the non-transitory computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of a system, cause the system to: receive, at one or more processors, sequence read data associated with a genomic variant in a sample from an individual; determine, using the one or more processors, a breakpoint of the genomic variant and a location of the breakpoint based on the sequence read data, the breakpoint associated with a rearrangement event and further associated with a first gene and a second gene of the sample, the first gene corresponding to the genomic variant; if the breakpoint of the genomic variant is located outside a sequence encoding a predetermined protein domain, perform, using the one or more processors: a first determination of whether the rearrangement event is in-strand; and a second determination of whether the sequence encoding the predetermined protein domain is impacted by the rearrangement event; assign, using the one or more processors, the genomic variant to a functional status group based on the first determination and the second determination; and identify, using the one or more processors, the treatment based on the assigned functional status group of the genomic variant.

[0034] Embodiments of the present disclosure provide systems and methods for determining a functional status group for a genomic variant and identifying treatment for an individual based a genomic variant in a sample from the individual. In one or more examples, the method can comprise: receiving, at one or more processors, sequence read data associated with the genomic variant in the sample; determining, using the one or more processors, one or more breakpoints of the genomic variant and a location of the breakpoint based on the sequence read data, the breakpoint associated with a rearrangement event; if the one or more breakpoints of the genomic variant are located outside of a sequence encoding a predetermined protein domain, performing, using the one or more processors: a determination of whether the sequence encoding the predetermined protein domain is in the rearrangement event; assigning, using the one or more processors, the genomic variant to a functional status group based on the determination; and identifying, using the one or more processors, the treatment based on the assigned functional status group of the genomic variant.

[0035] In one or more examples, the rearrangement event is an intragenic event. In one or more examples, the method can further comprise performing a second determination to identify a rearrangement type, wherein assigning the genomic variant to the functional status group is based on the rearrangement type. In such examples, the rearrangement type comprises a deletion, a duplication, or an inversion.

[0036] In one or more examples, the method can further comprise in accordance with a determination that the sequence encoding the predetermined protein domain is in the rearrangement event, assigning the genomic variant to a first functional status group. In such examples, assigning the genomic variant to a functional status group is further based on the genomic variant. In one or more examples, the method can further comprise forgoing assigning the genomic variant to the functional status group if the breakpoint of the genomic variant is located outside a predetermined sequence encoding a protein domain. [0037] In one or more examples, the method can further comprise determining, using the one or more processors, whether the first gene is an oncogene. In one or more examples, a gene corresponding to the genomic variant is EGFR, BRAF, FGFR1, FGFR2, MET, or PDGFRA. In one or more examples, the predetermined protein domain is a functional domain.

[0038] In one or more examples, the sequence read data for the individual is based on a targeted exome sequencing panel. In one or more examples, the sequence read data for the individual is derived from a single biopsy sample. In one or more examples, the sequence read data for the individual is derived from multiple biopsy samples. In one or more examples, the sequence read data for the individual is derived from single cell sequencing. In one or more examples, the sequence read data for the individual is derived from circulating tumor DNA in a liquid biopsy sample.

[0039] In one or more examples, the method can further comprise assigning, using the one or more processors, a therapy for the individual based on the functional status group. In one or more examples, the method can further comprise administering, using the one or more processors, a treatment to the individual based on the functional status group. In one or more examples, the method can further comprise associating, using the one or more processors, the individual with a clinical trial based on the functional status group. In one or more examples, the method can further comprise monitoring, using the one or more processors, a prognosis of the individual based on the functional status group. In one or more examples, the method can further comprise predicting, using the one or more processors, one or more clinical outcomes based on the functional status group.

[0040] Embodiments of the present disclosure further provide systems for identifying a treatment for an individual based on a genomic variant. In one or more examples, the system can comprise: one or more processors; and a memory communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to perform a method comprising: receiving, at one or more processors, sequence read data associated with a genomic variant in a sample from an individual; determining, using the one or more processors, one or more breakpoints of the genomic variant and a location of the one or more breakpoints based on the sequence read data, the one or more breakpoints associated with a rearrangement event; if the one or more breakpoints of the genomic variant are located outside of a sequence encoding a predetermined protein domain, performing, using the one or more processors: a determination of whether the sequence encoding the predetermined protein domain is in the rearrangement event; assigning, using the one or more processors, the genomic variant to a functional status group based on the determination; and identifying, using the one or more processors, the treatment based on the assigned functional status group of the genomic variant.

[0041] Embodiments of the present disclosure further provide non-transitory computer-readable storage mediums for identifying a treatment for an individual based on a genomic variant. In one or more examples, the non-transitory computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of a system, cause the system to: receive, at one or more processors, sequence read data associated with a genomic variant in a sample from an individual; determine, using the one or more processors, one or more breakpoints of the genomic variant and a location of the one or more breakpoints based on the sequence read data, the one or more breakpoints associated with a rearrangement event; if the one or more breakpoints of the genomic variant are located outside of a sequence encoding a predetermined protein domain, perform, using the one or more processors: a determination of whether the sequence encoding the predetermined protein domain is in the rearrangement event; assign, using the one or more processors, the genomic variant to a functional status group based on the determination; and identify, using the one or more processors, the treatment based on the assigned functional status group of the genomic variant.

[0042] Embodiments of the present disclosure further provide methods for diagnosing a disease, the method comprising: diagnosing that a subject has the disease based on a determination of a functional status group of a genomic variant in a sample from the subject, wherein the functional status group of the genomic variant is determined according to any of the methods described above.

[0043] Embodiments of the present disclosure further provide methods of selecting an anticancer therapy, the method comprising: responsive to determining a functional status group of a genomic variant in a sample from a subject, selecting an anti-cancer therapy for the subject, wherein the functional status group of the genomic variant is determined according to any of the methods described above.

[0044] Embodiments of the present disclosure further provide methods of treating a cancer in a subject, comprising: responsive to determining a functional status group of a genomic variant in a sample from the subject, administering an effective amount of an anti-cancer therapy to the subject, wherein the functional status group of the genomic variant is determined according to any of the methods described above.

[0045] Embodiments of the present disclosure further provide methods for monitoring cancer progression or recurrence in a subject, the method comprising: determining a first functional status group of a genomic variant in a first sample obtained from the subject at a first time point according to any of the methods described above; determining a second functional status group of a genomic variant in a second sample obtained from the subject at a second time point; and comparing the first functional status group to the second functional status group, thereby monitoring the cancer progression or recurrence. In such embodiments, the second functional status group of the genomic variant for the second sample is determined according to any of the methods described above.

[0046] In one or more examples, the methods for monitoring cancer progression or recurrence can further comprise selecting an anti-cancer therapy for the subject in response to the cancer progression. In one or more examples, the methods for monitoring cancer progression or recurrence can further comprise administering an anti-cancer therapy to the subject in response to the cancer progression. In one or more examples, the methods for monitoring cancer progression or recurrence can further comprise adjusting an anti-cancer therapy for the subject in response to the cancer progression. In one or more examples, the methods for monitoring cancer progression or recurrence can further comprise a dosage of the anti-cancer therapy or selecting a different anti-cancer therapy in response to the cancer progression. In such examples, the method can further comprise administering the adjusted anti-cancer therapy to the subject.

[0047] In one or more examples of the methods for monitoring cancer progression or recurrence, the first time point is before the subject has been administered an anti-cancer therapy, and wherein the second time point is after the subject has been administered the anti-cancer therapy. In one or more examples of the methods for monitoring cancer progression or recurrence, the subject has a cancer, is at risk of having a cancer, is being routine tested for cancer, or is suspected of having a cancer. In one or more examples of the methods for monitoring cancer progression or recurrence, the cancer is a solid tumor. In one or more examples of the methods for monitoring cancer progression or recurrence, the cancer is a hematological cancer. In one or more examples of the methods for monitoring cancer progression or recurrence, the anti-cancer therapy comprises chemotherapy, radiation therapy, immunotherapy, a targeted therapy, or surgery.

[0048] Embodiments according to the methods described above further comprise determining, identifying, or applying the functional status group of the genomic variant of the sample as a diagnostic value associated with the sample.

[0049] Embodiments according to the methods described above further comprise generating a genomic profile for the subject based on the determination of the functional status group of the genomic variant. In such embodiments, the genomic profile for the subject further comprises results from a comprehensive genomic profiling (CGP) test, a gene expression profiling test, a cancer hotspot panel test, a DNA methylation test, a DNA fragmentation test, an RNA fragmentation test, or any combination thereof. In one or more examples, the genomic profile for the subject further comprises results from a nucleic acid sequencing-based test. In one or more examples, methods according to the present disclosure further comprise selecting an anticancer therapy, administering an anti-cancer therapy, or applying an anti-cancer therapy to the subject based on the generated genomic profile.

[0050] In one or more examples of the methods described above the determination of a functional status group of a genomic variant in a sample is used in making suggested treatment decisions for the subject. In one or more examples of the methods described above the determination of a functional status group of a genomic variant in a sample is used in applying or administering a treatment to the subject. INCORPORATION BY REFERENCE

[0051] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference in their entirety to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference in its entirety. In the event of a conflict between a term herein and a term in an incorporated reference, the term herein controls.

BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Various aspects of the disclosed methods, devices, and systems are set forth with particularity in the appended claims. A better understanding of the features and advantages of the disclosed methods, devices, and systems will be obtained by reference to the following detailed description of illustrative embodiments and the accompanying drawings, of which:

[0053] FIG. 1A provides a non-limiting example of an exemplary process for assigning a functional status to a genomic variant of a sample from an individual and identifying a treatment for the individual, according to embodiments of the present disclosure.

[0054] FIG. IB provides a non-limiting example of an exemplary process for assigning a functional status to a genomic variant of a sample from an individual and identifying a treatment for the individual, according to embodiments of the present disclosure.

[0055] FIG. 2A provides a non-limiting example of an exemplary pair of reference genes.

[0056] FIG. 2B provides a non-limiting example of an exemplary transcript of the pair of reference genes.

[0057] FIG. 2C provides a non-limiting example of an exemplary transcript of the pair of reference genes.

[0058] FIG. 2D provides a non-limiting example of an exemplary transcript of the pair of reference genes. [0059] FIG. 2E provides a non-limiting example of an exemplary transcript of the pair of reference genes.

[0060] FIG. 3 provides a non-limiting example of a process for assigning a functional status to a genomic variant of a sample from an individual, according to embodiments of the present disclosure.

[0061] FIG. 4 provides a non-limiting example of a process for assigning a functional status to a genomic variant of a sample from an individual, according to embodiments of the present disclosure.

[0062] FIG. 5 provides a non-limiting example of a process for assigning a functional status to a genomic variant of a sample from an individual, according to embodiments of the present disclosure.

[0063] FIG. 6 provides a non-limiting example of a process for identifying a treatment for an individual based on the functional status of a genomic variant of a sample from an individual, according to embodiments of the present disclosure.

[0064] FIG. 7 depicts an exemplary computing device or system, according to embodiments of the present disclosure.

[0065] FIG. 8 depicts an exemplary computer system or computer network, according to embodiments of the present disclosure.

DETAILED DESCRIPTION

[0066] Disclosed herein are methods and systems for automatically assigning a genomic variant from a sample to a functional status group. The functional status group may correspond to an activating variant, an inactivating variant, a functionally unknown variant, or a wildtype. The activating variants may be associated with promoting pathway activations. The inactivating variants may be associated with inhibiting a pathway. The activating variants and the inactivating variants may be determined to be a pathogenic variant or a likely pathogenic variant. Functionally unknown variants or functionally uncharacterized variants may correspond to variants (e.g., fusions or other rearrangements) that are not yet characterized in the literature. In some examples, functionally unknown variants may be determined to be a pathogenic or likely pathogenic variant. In some instances, the functionally unknown variant may be determined to have an uncharacterized pathogenic status. The wildtype variants may correspond to a variant that behaves as if there were no alteration. In some examples, the wildtype variants may be determined to be a benign variant.

[0067] The functional status assignment of the genomic variant may be used to identify a treatment for the individual providing the sample. The disclosed methods have the potential to improve healthcare outcomes for individuals (e.g., cancer patients) by providing healthcare providers with treatment recommendations based on an accurate identification of a functional status group for both novel and frequently occurring genomic variants. In one or more examples, methods and systems can further label the genomic variant and provide the label to a healthcare provider (e.g., in a report). In such embodiments, the healthcare provider may further base the treatment recommendation based on the label. Accordingly, embodiments of the present disclosure provide improvements to patient care by providing an automatic process that assigns a genomic variant to a functional status group and labels the genomic variant, where the process can assign and label novel genomic variants that are not well classified in the art.

[0068] Embodiments of the present disclosure further comprise a method for identifying a treatment based on a genomic variant in a sample from an individual. The method can comprise: receiving, at one or more processors, sequence read data associated with the genomic variant in the sample; determining, using the one or more processors, a breakpoint of the genomic variant and a location of the breakpoint based on the sequence read data, the breakpoint associated with a rearrangement event and further associated with a first gene and a second gene of the sample, the first gene corresponding to the genomic variant; if the breakpoint of the genomic variant is located outside a sequence encoding a predetermined protein domain, performing, using the one or more processors: a first determination of whether the rearrangement event is in-strand; and a second determination of whether the sequence encoding the predetermined protein domain is impacted by the rearrangement event; assigning, using the one or more processors, the genomic variant to a functional status group based on the first determination and the second determination; and identifying, using the one or more processors, the treatment based on the assigned functional status group of the genomic variant.

[0069] In one or more examples, the method can further comprise in accordance with a determination that the rearrangement event is not in-strand, assigning the genomic variant to a first functional status group; and in accordance with a determination that the rearrangement event is in-strand and the sequence encoding the predetermined protein domain is impacted by the rearrangement event, assigning the genomic variant to a second functional status group. In such examples, assigning the genomic variant to the first functional status group comprises associating the genomic variant with a functionally unknown rearrangement group. In one or more examples, the method can further comprise comprising labeling the genomic variant as a rearrangement. In one or more examples, a functionally unknown rearrangement label is further based on an orientation of the first gene with respect to the second gene.

[0070] In one or more examples, the method can further comprise assigning the genomic variant to the second functional status group comprises associating the genomic variant with a functionally unknown fusion group. In such examples, the method can further comprise labeling the genomic variant as a reciprocal fusion.

[0071] In one or more examples, the method can further comprise performing, using the one or more processors, a third determination of whether the first gene and the second gene are known partner genes. In such examples, whether the first gene and the second gene are known partner genes is determined based on a predetermined association between the first gene and the second gene described in scientific literature. In one or more examples, determining whether the first gene and the second gene are known partner genes is based on a lookup table associated with the first gene.

[0072] In one or more examples, the method can further comprise in accordance with a determination that the rearrangement event is in-strand, the sequence encoding the predetermined protein domain is impacted by the rearrangement event, and the first gene is a known partner of the second gene, assigning the genomic variant to a third functional status group. In such examples, assigning the genomic variant to the third functional status group comprises associating the genomic variant with an activating fusion group. In one or more examples, the method can further comprise labeling the genomic variant as an activating fusion. In one or more examples, determining whether the sequence encoding the predetermined protein domain is impacted by the rearrangement event comprises: determining, whether a functional domain of the first gene is in the sequence read data corresponding to the rearrangement event.

[0073] In one or more examples, the method can further comprise in accordance with a determination that the rearrangement event is in-strand, and further that the sequence encoding the predetermined protein domain is impacted by the rearrangement event, and the first gene is not a known partner gene of the second gene, flagging the genomic variant for manual review and forgoing labeling the genomic variant. In one or more examples, the method can further comprise forgoing assigning the genomic variant to the functional status group if the breakpoint of the genomic variant is located outside a predetermined sequence encoding a protein domain.

[0074] In one or more examples, the method can further comprise determining, using the one or more processors, whether the first gene is an oncogene.

[0075] In one or more examples, the first gene is ABL1, ALK, BRAF, FGFR1, FGFR2, FGFR3, MET, NTRK1, NTRK2, NTRK3, PDGFRA, PDGFRB, ROS1, RET, or RAFI. In one or more examples, the predetermined protein domain is a functional domain. In one or more examples, the predetermined protein domain is a kinase domain.

[0076] The disclosed methods and systems can improve patient outcomes, based on, for example, targeted treatment recommendations based on the functional status of one or more genomic variants identified in a patient’ s sample. For example, embodiments of the present disclosure provide improvements to patient care by providing an automatic process for assigning a genomic variant to a functional status group and labeling the genomic variant that can handle novel genomic variants that are not well classified in the art. In such examples, a healthcare provider may be able to base treatment decisions, make predictions regarding a patient’s response to treatment, make predictions regarding a patient’s prognosis, and the like based on the functional status and/or the label assigned the genomic variant. Definitions

[0077] Unless otherwise defined, all of the technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art in the field to which this disclosure belongs.

[0078] As used in this specification and the appended claims, the singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise. Any reference to “or” herein is intended to encompass “and/or” unless otherwise stated.

[0079] “About” and “approximately” shall generally mean an acceptable degree of error for the quantity measured given the nature or precision of the measurements. Exemplary degrees of error are within 20 percent (%), typically, within 10%, and more typically, within 5% of a given value or range of values.

[0080] As used herein, the terms "comprising" (and any form or variant of comprising, such as "comprise" and "comprises"), "having" (and any form or variant of having, such as "have" and "has"), "including" (and any form or variant of including, such as "includes" and "include"), or "containing" (and any form or variant of containing, such as "contains" and "contain"), are inclusive or open-ended and do not exclude additional, un-recited additives, components, integers, elements, or method steps.

[0081] As used herein, the terms “individual,” “patient,” or “subject” are used interchangeably and refer to any single animal, e.g., a mammal (including such non-human animals as, for example, dogs, cats, horses, rabbits, zoo animals, cows, pigs, sheep, and non-human primates) for which treatment is desired. In particular embodiments, the individual, patient, or subject herein is a human.

[0082] The terms “cancer” and “tumor” are used interchangeably herein. These terms refer to the presence of cells possessing characteristics typical of cancer-causing cells, such as uncontrolled proliferation, immortality, metastatic potential, rapid growth and proliferation rate, and certain characteristic morphological features. Cancer cells are often in the form of a tumor, but such cells can exist alone within an animal, or can be a non-tumorigenic cancer cell, such as a leukemia cell. These terms include a solid tumor, a soft tissue tumor, or a metastatic lesion. As used herein, the term “cancer” includes premalignant, as well as malignant cancers.

[0083] As used herein, “treatment” (and grammatical variations thereof such as “treat” or “treating”) refers to clinical intervention (e.g., administration of an anti-cancer agent or anticancer therapy) in an attempt to alter the natural course of the individual being treated, and can be performed either for prophylaxis or during the course of clinical pathology. Desirable effects of treatment include, but are not limited to, preventing occurrence or recurrence of disease, alleviation of symptoms, diminishment of any direct or indirect pathological consequences of the disease, preventing metastasis, decreasing the rate of disease progression, amelioration or palliation of the disease state, and remission or improved prognosis.

[0084] As used herein, the term “subgenomic interval” (or “subgenomic sequence interval”) refers to a portion of a genomic sequence.

[0085] As used herein, the term "subject interval" refers to a subgenomic interval or an expressed subgenomic interval (e.g., the transcribed sequence of a subgenomic interval).

[0086] As used herein, the terms “variant sequence” or “variant” are used interchangeably and refer to a modified nucleic acid sequence relative to a corresponding “normal” or “wild-type” sequence. In some instances, a variant sequence may be a “short variant sequence” (or “short variant”), i.e., a variant sequence of less than about 50 base pairs in length.

[0087] As used herein, the terms “allele frequency” and “allele fraction” are used interchangeably herein and refer to the fraction of sequence reads corresponding to a particular allele relative to the total number of sequence reads for a genomic locus.

[0088] As used herein, the terms “variant allele frequency” and “variant allele fraction” are used interchangeably herein and refer to the fraction of sequence reads corresponding to a particular variant allele relative to the total number of sequence reads for a genomic locus. [0089] As used herein, the term “pathogenic variant” may refer to a variant that has a known and documented relevance to disease, e.g., recognized in the scientific literature as being an oncogenic driver of disease.

[0090] As used herein, the term “likely pathogenic variant” may refer to a variant that is potentially relevant to pathogenicity, e.g., as supported by scientific literature, research, and or the genomic data from the one or more samples.

[0091] As used herein, the terms “variant of unknown significance” (VUS), “functionally unknown variant,” and/or “functionally uncharacterized variant” may refer to a variant that does not have a well-known or documented association with disease, e.g., there may not be sufficient evidence to identify the variant as a known pathogenic or likely pathogenic variant.

[0092] As used herein, the term “benign variant” may refer to a variant that is determined to not be relevant to disease.

[0093] As used herein, the term “activating variant” may refer to a variant that is associated with promoting pathway activations.

[0094] As used herein, the term “inactivating variant” may refer to a variant that is associated with inhibiting a pathway.

[0095] The section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described.

Methods for determining afunctional status assignment for genomic variants

[0096] The disclosed methods for assigning a genomic variant from a sample to a functional status group. The functional status assignment of the genomic variant may be used to identify a treatment for the individual providing the sample. The disclosed methods have the potential to improve healthcare outcomes for individuals (e.g., cancer patients) by providing healthcare providers with treatment recommendations based on an accurate identification of a functional status group for both novel and frequently occurring genomic variants. In one or more examples, methods and systems can further label the genomic variant and provide the label (e.g., a structural label) to a healthcare provider (e.g., in a report). In such embodiments, the healthcare provider may further base the treatment recommendation based on the label. Accordingly, embodiments of the present disclosure provide improvements to patient care by providing an automatic process that assigns a genomic variant to a functional status group and labels the genomic variant, where the process can assign and label novel genomic variants that are not well classified in the art.

[0097] FIG. 1A provides a non-limiting example of a process 100A for assigning a genomic variant to a functional status group and identifying a treatment based on the assignment. Process 100A can be performed, for example, using one or more electronic devices implementing a software platform. In some examples, process 100A is performed using a client-server system, and the blocks of process 100A are divided up in any manner between the server and a client device. In other examples, the blocks of process 100A are divided up between the server and multiple client devices. Thus, while portions of process 100A are described herein as being performed by particular devices of a client-server system, it will be appreciated that process 100A is not so limited. In other examples, process 100A is performed using only a client device or only multiple client devices. In process 100A, some blocks are, optionally, combined, the order of some blocks is, optionally, changed, and some blocks are, optionally, omitted. In some examples, additional steps may be performed in combination with the process 100A.

Accordingly, the operations as illustrated (and described in greater detail below) are exemplary by nature and, as such, should not be viewed as limiting. In one or more examples, process 100A may be performed for rearrangement events that are determined to be intergenic and affect more than one gene.

[0098] At block 102 of FIG. 1A, the system can receive sequence read data associated with a genomic variant in a sample from an individual. In some instances, the sequence read data may be derived from single region sequencing (e.g., sequencing of a single tissue biopsy sample collected from the tumor of the individual). In some instances, the genomic data comprising sequence read data may be derived from multi-region sequencing (e.g., sequencing of multiple tissue biopsy samples collected from the tumor of the individual). In some instances, the genomic data comprising sequence read data may be derived from single cell sequencing data as opposed to bulk tumor sequencing. In some instances, the genomic data comprising sequence read data may be derived from sequencing the circulating tumor DNA in a liquid biopsy sample.

[0099] In some instances, the genomic data comprising sequence read data may be derived from targeted sequencing, e.g., targeted exome sequencing. In some instances, the genomic data comprising sequence read data may be derived from, e.g., whole genome or whole exome sequencing, as opposed to targeted exome sequencing to increase the number of genomic features (e.g., the number of short variants) detected. In one or more examples, the sequence read data may be received by the system as a BAM file.

[0100] In one or more examples, the sequence read data may be indicative of a presence or absence of one or more short variants (SVs) in a patient sample. In one or more examples, the sequence read data may also be indicative of the presence or absence of genomic events, such as copy number alterations, rearrangements, insertions, deletions, fusions, chromosomal aneuploidy, whole genome doubling, Catalogue Of Somatic Mutations In Cancer (COSMIC) mutational signatures, or any combination thereof. In one or more examples, the sequence read data can be indicative of features associated with a genomic event such as a location of the genomic event, whether the genomic event is in- strand, an orientation of the genomic event, a directionality of the genomic event, genes involved in the genomic event, and the like.

[0101] At block 104 of FIG. 1A, the system can determine a breakpoint of the genomic variant and a location of the breakpoint based on the sequence read data, the breakpoint associated with a mutation event (e.g., a rearrangement event). The following embodiment is described with respect to a rearrangement event, however, process 100 may apply to other mutation events, e.g., an insertion event, a deletion event, and the like. In some instances, the rearrangement event may be associated with a primary gene and a secondary gene of the sample. In one or more examples, the primary gene may correspond to a gene associated with the genomic variant. For example, if the genomic variant is identified as ROS1, the primary gene would correspond to ROS1 and the secondary gene would correspond to the non-ROSl gene associated with the rearrangement event. In one or more examples, the genomic event may affect a single gene, which is described in greater detail with respect to FIG. 6. [0102] In one or more examples, the system may determine whether the breakpoint of the genomic variant is located outside of a predetermined sequence encoding a protein domain. For example, if the breakpoint is located within the predetermined sequence encoding the protein domain (e.g., the functional or pathologically activating domain), then the system may determine that genomic variant likely does not have a clinical effect on the patient’s health because the protein domain is disrupted. In such examples, (e.g., for oncogenes) the system may determine that the disruption of the protein domain deactivates the pathogenic functionality of the protein. If the breakpoint is located outside the predetermined sequence encoding the protein domain, then the system may determine that the genomic variant may have a clinical effect on the patient’s health because the protein’s functional (e.g., pathogenic) domain is intact.

[0103] In one or more examples, if the breakpoint is located within the predetermined sequence encoding the protein domain, the system may determine the genomic variant does have a clinical effect on the patient’s health. For example, for tumor suppressor genes, having the breakpoint located within the protein will inactivate the protein and lead to a pathogenic functional status due to the break in the protein’s functional domain.

[0104] In one or more examples the location of the breakpoint of the genomic variant may be obtained via a computational pipeline for processing nucleic acid sequencing data. In one or more examples, the location of the breakpoint of the genomic variant may be determined by the system based on information provided by the computational pipeline. For example, the computational pipeline may indicate the location of the breakpoint in the genomic data for the sample based on an identification of the position of the first or last intact amino acid codon. In one or more examples, the position of the breakpoint may correspond to the position at which the sequence read begins (e.g., for a 5’ disruption) and/or ends (e.g., for a 3’ disruption). In one or more examples, the location indicated by the pipeline may correspond to an integer value. In one or more examples, the location indicated by the pipeline may be compared to a predetermined threshold to determine whether the breakpoint is located inside or outside the sequence that encodes for a predetermined protein domain. While this example is described with respect to data obtained via a computational pipeline, a skilled artisan will understand that the location of the breakpoint of the genomic variant may be obtained using other methods known in the art. [0105] If the system determines that the breakpoint of the genomic variant is located outside a sequence that encodes a predetermined protein domain perform, the system can move to block 106 of FIG. 1A. At block 106, the system can perform a plurality of determinations regarding the genomic variant. While particular determinations are shown in the figure (e.g., block 108 and 110), a skilled artisan will understand that more or less determinations may be performed without departing from the scope of this disclosure. In one or more examples the determinations may be based on data obtained via the computational pipeline for analyzing sequence read data. In one or more examples, if the system determines that the breakpoint of the genomic variant is located in the sequence that encodes a predetermined protein domain, the system may flag the genomic variant for manual processing.

[0106] At block 108 of FIG. 1A, for example, the system can determine whether the rearrangement event is in-strand. For example, in an in-strand event the sequence reads of the genes will be oriented in a correct direction, while sequence reads of an out-of-strand event will be oriented in an incorrect direction.

[0107] For example, referring to FIGs. 2A-2E, FIG. 2A illustrates an exemplary pair of reference genes comprising gene 1 and gene 2. FIG. 2B illustrates an exemplary transcript (e.g., an amplified copy of the genomic region corresponding to the pair of reference genes) where the primary gene (gene 2) and the secondary gene (gene 1) correctly map to the reference pair of genes. As shown in the figure, the transcript comprises gene 1, gene 2, and breakpoint 202. In this example, the position of the genes are in the canonical orientation, such that the genes are in the correct relative location, e.g., gene 1 comes before gene 2. Additionally, the genes are instrand, such that the directionality of the sequence reads overlapping the pair of genes is correct. FIG. 2C illustrates an exemplary transcript with a non-canonical (e.g., reciprocal) orientation, in-strand event, where the positions of the two genes are incorrect such that gene 2 comes before gene 1; but the orientation of the sequence reads overlapping the pair of genes is correct (e.g., instrand). As used herein, a reciprocal event may refer to a non-canonical event. FIG. 2D illustrates an exemplary transcript with a canonical orientation, out-of-strand event, where the relative locations of the two genes are correct such that gene 1 comes before gene 2; but the directionality of the sequence reads of gene 2 are incorrect (e.g., out-of-strand). FIG. 2E illustrates another exemplary transcript with a canonical orientation, out-of-strand event, where the relative positions of the two genes are correct such that gene 1 comes before gene 2, but the directionality of the sequence reads of gene 1 are incorrect (e.g., out-of-strand).

[0108] In one or more examples the determination of whether the rearrangement event is instrand or out-of-strand may be obtained via the computational pipeline for analyzing sequence read data. For example, the computational pipeline may indicate whether the rearrangement event is in-strand or out-of-strand. However, a skilled artisan could use other methods known in the art to determine whether the rearrangement event is in-strand or out-of-strand, such as manual inspection of the directionality of the sequence reads.

[0109] At block 110 of FIG. 1A, the system can determine whether the sequence encoding the predetermined protein domain is impacted by the rearrangement event, e.g., based on the sequence read data corresponding to the rearrangement event. In one or more examples, the sequence read data corresponding to the rearrangement event may comprise the sequence encoding the predetermined protein domain (e.g., such that the sequence encoding the predetermined protein domain is in the sequence read data corresponding to the rearrangement event). For example, the system may determine whether the sequence encoding the functional domain of the protein is intact and retained in the sequence read data following the rearrangement event. In one or more examples, determining whether the sequence encoding the predetermined protein domain is impacted by the rearrangement event may be associated with determining whether the rearrangement event is in-frame, as discussed above with respect to, for example, FIG. 2B and FIG. 2C. For example, if gene 2 corresponds to the primary gene, FIG. 2B illustrates canonically oriented example where the sequence read data corresponding to the rearrangement event comprises the sequence encoding the predetermined protein domain, while FIG. 2C illustrates a non-canonically oriented example where the sequence read data corresponding to the rearrangement event may not comprise the sequence encoding the predetermined protein domain.

[0110] For example, determining whether an event is canonically or non-canonically oriented may allow the system to determine if the functional domain of the primary gene is retained in the fusion product of the rearrangement event. For example, for a ROS 1 gene, the functional element of the protein domain (e.g., kinase domain) is located at the downstream end of the protein. Accordingly, the system may need to determine whether the kinase domain is retained in the rearrangement event based on the location of the breakpoint. For example, the system may determine whether the protein domain is disrupted based on a location of the breakpoint with respect to the sequence encoding the protein domain.

[0111] In one or more examples, a determination of whether the sequence encoding the predetermined protein domain is included in the sequence read data corresponding to the rearrangement event may be obtained via the computational pipeline for analyzing sequence read data. For example, the computational pipeline may indicate whether the sequence encoding the protein domain is retained as an intact sequence in the rearrangement event. While this example relies on data obtained via the computational pipeline, a skilled artisan could use other methods known in the art to determine whether the protein domain is retained in the rearrangement event.

[0112] In one or more examples, additional determinations may be associated with block 106. For example, the system may further determine whether the primary gene and the secondary gene associated with the rearrangement event are known partner genes in the fusion event (e.g., block 112B in FIG. IB). As used herein, partner genes may refer to genes that drive expression of the target gene, encode proteins that facilitate oligomerization, recruit additional cofactors, and/or separate or disrupt the functional domain in the primary gene. In one or more examples, the partner genes may not be identified in literature to be considered functional/applicable to the fusion event.

[0113] In one or more examples, the known partner genes may be determined based on partner genes identified in research and scientific literature. For example, known partner genes of ROS 1 include, and is not limited to CD74, CLIP1, EZR, GOPC, LRIG3, MY05A, PPFIBP1, PWWP2A, SLC34A2, SDC4, SHTN1 (KIAA1598), TPM3, and ZCCHC8. In one or more examples, the partner genes may be determined based on a database or look-up table comprising the partner genes identified in research and scientific literature. For example, the system may access the database to determine whether the primary gene and the secondary gene are known partner genes. In one or more examples, the system may further determine whether the secondary gene is a coding gene (e.g., block 114B in FIG. IB). In one or more examples, determining whether a gene is a coding gene may be based on scientific literature. The number and types of determinations associated with block 106 is not intended to limit the scope of this disclosure and more or less determinations may be made by the system.

[0114] At block 116 of FIG. 1A, the system can assign the genomic variant to a functional status group based on blocks 108 and 110. In one or more examples, if the rearrangement event is not in-strand, then the system may assign the genomic variant to a functionally unknown rearrangement functional status group. In one or more examples, the functional status group may be associated with a first type of functionally unknown rearrangements. In one or more examples, the first type of functionally unknown rearrangements may be associated with a particular genomic event (e.g., insertion, deletion, etc.). The functionally unknown rearrangement group may be associated with one or more therapies and/or clinical trials.

[0115] In one or more examples, if the rearrangement event is in-strand and the rearrangement event includes the sequence encoding the predetermined protein domain, then the system may assign the genomic variant to a functionally unknown fusion functional status group associated with non-canonical fusions.

[0116] In one or more examples, if the rearrangement event is in-strand, the rearrangement event includes the sequence encoding the predetermined protein domain, and the primary gene associated with the genomic variant and the secondary gene associated with the variant are known partner genes, then the system may assign the genomic variant to an activating fusion functional status group associated with canonical fusions.

[0117] In one or more examples, if the rearrangement event is in-strand, the rearrangement event includes the sequence encoding the predetermined protein domain, and the secondary gene associated with the variant not known partner genes, then the system may not assign the genomic variant to a functional status group. Instead, the system may flag the genomic alteration for manual processing, whereby an individual may review the genomic alteration and determine the functional status group assignment for the genomic alteration. In one or more examples, the system may assign the genomic alteration to a functionally unknown fusion group because the primary and secondary genes are not known partner genes. [0118] In one or more examples, if the breakpoint of the genomic variant is located within the sequence encoding a predetermined protein domain (e.g., based on the determination at block 104), then the system may not assign the genomic variant to a functional status group. Instead, the system may flag the genomic alteration for manual processing, whereby an individual may review the genomic alteration and determine the functional status group assignment for the genomic alteration. In one or more examples, such a genomic variant may be determined to be functionally inactive and/or not pathogenic, e.g., such as for oncogenes. In one or more examples, the genomic variant may be determined to be functionally active and/or pathogenic, e.g., such as for tumor suppressor genes.

[0119] At block 118A of FIG. 1A, the system can identify a treatment based on the assigned functional status group of the genomic variant. For example, certain alterations may be associated with specific approved therapies. For example, a activating or canonical fusion, e.g., an in-strand fusion where the rearrangement event includes the sequence encoding the predetermined protein domain, of ROS 1 and CD74 as a 5’ partner may be associated with ROS1 inhibitors such as entrectinib, lorlatinib, rizotinib, ceritinib, cabozantinib, and brigatinib. As another example, a non-canonical fusion, e.g., an in-strand but reciprocal fusion where the rearrangement event involving ROS 1 does not include the ROS 1 sequence encoding the predetermined protein domain and an unknown 3’ partner may be provisionally associated with ROS 1 inhibitors such as entrectinib, lorlatinib, crizotinib, ceritinib, cabozantinib, and brigatinib. As another example, an out-of-strand fusion where the rearrangement event includes the sequence encoding the predetermined protein domain of ROS 1 and the known partner CD74 may not be associated with ROS1 inhibitors. While these examples are discussed with respect to specific genes, a skilled artisan will understand that other genes and associated therapies may be used without departing from the scope of this disclosure.

[0120] In one or more embodiments, the system can generate a label for the genomic variant based on blocks 104 and 106. In one or more examples, where the genomic variant is associated with functionally unknown rearrangements (e.g., where the rearrangement event is not in-strand), the system can label the genomic variant as a rearrangement. For example, for out-of-strand rearrangement event that includes the sequence encoding the predetermined protein domain, the system may label the genomic variant as secondary gene-primary gene rearrangement. In this example, if the primary gene is ROS1 and the secondary gene is KCNIP3, the out-of-strand rearrangement event that includes the sequence encoding the predetermined protein domain may be labeled as KCNIP3-ROS1 rearrangement. As another example, if the secondary gene is FLJ40288 (which is a non-coding partner), the variant may be labeled as FLJ40288-ROS1 rearrangement. As another example, for out-of-strand rearrangement event that does not include the sequence encoding the predetermined protein domain, the system may label the genomic variant as primary gene- secondary gene rearrangement. In this example, if the primary gene is ROS 1 and the secondary gene is LHFPL4, an out-of-strand rearrangement event that does not include the sequence encoding the predetermined protein domain may be labeled as ROS1- LHFPL4 rearrangement. While these examples are discussed with respect to specific genes, a skilled artisan will understand that other genes may be used without departing from the scope of this disclosure.

[0121] In one or more examples, if the genomic variant is associated with non-canonical or reciprocal fusions (e.g., the rearrangement event is determined to be in-strand and does not include the sequence encoding the predetermined protein domain), then the system can label the genomic variant as a non-canonical fusion. In such examples, the system may label the genomic variant as primary gene- secondary gene non-canonical fusion. In this example, if the primary gene is ROS1 and the secondary gene is TSPAN3, an in-strand rearrangement event that does not include the sequence encoding the predetermined protein domain, the rearrangement may be labeled as ROS1-TSPAN3 non-canonical fusion. In one or more examples, the genomic variant could alternatively be labeled as a reciprocal fusion.

[0122] In one or more examples, if the genomic variant is associated with a canonical fusion (e.g., the rearrangement event is in-strand and includes the sequence encoding the predetermined protein domain), and the primary gene and the secondary gene are known partner genes, then the system may label the genomic variant as a canonical fusion. In this example, if the primary gene is ROS1 and the secondary gene is CD74, an in-strand rearrangement event that includes the sequence encoding the predetermined protein domain with a known secondary gene may be labeled as CD74-ROS1 fusion or CD74-ROS1 canonical fusion. In one or more examples, the genomic variant could alternatively be labeled as an activating fusion.

[0123] In one or more examples, if the rearrangement event is in-strand and does not include the sequence encoding the predetermined protein domain, and the primary gene and the secondary gene associated with the variant are not known partner genes, then the system may label the genomic variant as a functionally unknown fusion. In some embodiments, the system may label forgo labeling the variant. Instead, the system may flag the genomic alteration for manual processing, whereby an individual may review the genomic alteration and determine an appropriate label.

[0124] In one or more examples, if the breakpoint of the genomic variant is located within the sequence encoding a predetermined protein domain, then the system may not label the genomic variant. Instead, the system may flag the genomic alteration for manual processing, whereby an individual may review the genomic alteration and determine an appropriate functional status and/or label, as necessary.

[0125] FIG. IB provides a non-limiting example of a process 100B for assigning a genomic variant to a functional status group. Process 100B can be performed, for example, using one or more electronic devices implementing a software platform. In some examples, process 100B is performed using a client-server system, and the blocks of process 100B are divided up in any manner between the server and a client device. In other examples, the blocks of process 100B are divided up between the server and multiple client devices. Thus, while portions of process 100B are described herein as being performed by particular devices of a client-server system, it will be appreciated that process 100B is not so limited. In other examples, process 100B is performed using only a client device or only multiple client devices. In process 100B, some blocks are, optionally, combined, the order of some blocks is, optionally, changed, and some blocks are, optionally, omitted. In some examples, additional steps may be performed in combination with the process 100B. Accordingly, the operations as illustrated (and described in greater detail below) are exemplary by nature and, as such, should not be viewed as limiting. In one or more examples, process 100B may be performed for rearrangement events that are determined to be intergenic and affect more than one gene. [0126] At block 102 of FIG. IB, the system can receive sequence read data associated with a genomic variant in a sample from an individual. In one or more examples block 102 of FIG. IB can correspond to block 102 of FIG. 1A. At block 104 of FIG. IB, the system can determine a breakpoint of the genomic variant and a location of the breakpoint based on the sequence read data, the breakpoint associated with a rearrangement event. In one or more embodiments, the breakpoint may further be associated with a primary gene and a secondary gene of the sample. In one or more examples block 104 of FIG. IB can correspond to block 104 of FIG. 1A.

[0127] If the system determines that the breakpoint of the genomic variant is located outside a sequence encoding a predetermined protein domain, the system can move to block 106 of FIG. IB. At block 106, the system can perform a plurality of determinations regarding the genomic variant. In one or more examples block 106 of FIG. IB can correspond to block 106 of FIG. 1A. At block 108 of FIG. IB, the system can determine whether the rearrangement event is instrand. In one or more examples block 108 of FIG. IB can correspond to block 108 of FIG. 1A. At block 110 of FIG. IB, the system can determine whether the sequence encoding the predetermined protein domain is impacted by the rearrangement event, e.g., based on the sequence read data corresponding to the rearrangement event. In one or more examples block 110 of FIG. IB can correspond to block 110 of FIG. 1A.

[0128] At block 112B of FIG. IB, the system may further determine whether the primary gene and the secondary gene associated with the rearrangement event are known partner genes. In one or more examples, the known partner genes may be determined based on partner genes identified in research and scientific literature. In one or more examples, the partner genes may be determined based on a database or look-up table comprising the partner genes identified in research and scientific literature. For example, the system may access the database to determine whether the primary gene and the secondary gene are known partner genes. In one or more examples, the partner genes may not be specifically identified in scientific literature.

[0129] At block 114B of FIG. IB, the system may determine whether the secondary gene is a coding gene. In one or more examples, if the secondary gene is a non-coding gene, then the secondary gene may not encode a protein. In such examples, the secondary gene may be determined to not encode a functional rearrangement event, e.g., the system may determine that the rearrangement event has an unknown pathogenic significance. The number and types of determinations associated with block 106 is not intended to limit the scope of this disclosure and more or less determinations may be made by the system with respect to the genomic variant.

[0130] At block 116 of FIG. IB, the system can assign the genomic variant to a functional status group based on blocks 108, 110, 112B, and 114B in FIG. IB, blocks 430, 440, 442, 444, and 446 of FIG. 4, and blocks 530, 540, 542, 544, and 546 of FIG. 5. In one or more examples block 116 of FIG. IB can correspond to block 116 of FIG. 1A. At block 118 of FIG. 1A, the system can identify a treatment based on the assigned functional status group of the genomic variant. In one or more examples block 118 of FIG. IB can correspond to block 118 of FIG. 1A.

[0131] In one or more embodiments, the system can generate a label for the genomic variant based on blocks 104 and 106, as discussed above with respect to FIG. 1A, as discussed below with respect to FIG. 4 and FIG. 5.

[0132] In one or more examples, the system may further perform process 300 shown in FIG. 3. In one or more examples, Process 300 may be performed prior to performing process 100A and/or 100B. For example, process 300 may be performed to determine whether a genomic variant should be assigned to a functional group based on the blocks described with respect to process 100A and/or 100B. Process 300 can be performed, for example, using one or more electronic devices implementing a software platform. In some examples, process 300 is performed using a client-server system, and the blocks of process 300 are divided up in any manner between the server and a client device. In other examples, the blocks of process 300 are divided up between the server and multiple client devices. Thus, while portions of process 300 are described herein as being performed by particular devices of a client-server system, it will be appreciated that process 300 is not so limited. In other examples, process 300 is performed using only a client device or only multiple client devices. In process 300, some blocks are, optionally, combined, the order of some blocks is, optionally, changed, and some blocks are, optionally, omitted. In some examples, additional steps may be performed in combination with the process 300. Accordingly, the operations as illustrated (and described in greater detail below) are exemplary by nature and, as such, should not be viewed as limiting. [0133] At block 302 of FIG. 3, the system can determine whether the mutation event is. The mutation event may be intragenic if it affects a single gene (e.g., both breakpoints are within the same transcript). In one or more examples intragenic events may be associated with deletions, duplications, and inversions. As shown in the figure, the intragenic event may affect gene A. In one or more examples, the system may determine whether the mutation event is intragenic based on information received from the computational pipeline (e.g., both breakpoints are within the same gene or transcript). If the system determines that the event is intragenic, the system may proceed to block 304. At block 304 of FIG. 3, the system may determine whether the mutation event affects a baited oncogene. For example, the system may determine whether the gene variant associated with the mutation event corresponds to a gene that was baited in test performed on the sample from the individual. If the system determines that the mutation event is associated with a baited oncogene from the individual’s sample, the system may proceed to block 306. At block 306 of FIG. 3, the system may perform an oncogene intragenic functional status group assignment process. In one or more examples, block 308 may correspond to process 600 described with respect to FIG. 6.

[0134] If the system determines that the event is not intragenic, the system may proceed to block 308. At block 308 of FIG. 3, the system may determine whether the mutation event affects more than one gene. For example, a first breakpoint may be within a first gene and a second breakpoint may be in a second, different gene or an intergenic space. If the system determines that the event is affects more than one gene, the system may proceed to block 310. At block 310, the system may determine whether the mutation event affects a baited oncogene, similar to the determination at block 304. If the system determines that the mutation event is associated with a baited oncogene from an individual’s sample, the system may proceed to block 312. At block 312, the system may perform an oncogene intergenic multigene functional status group assignment process. In one or more examples, block 312 may correspond to process 100A and/or 100B described with respect to FIG. 1A and FIG. IB, respectively. In one or more examples, block 312 may correspond to processes 400 described with respect to FIG. 4 and/or 500 described with respect to and FIG. 5. [0135] FIG. 4 and FIG. 5 illustrate process 400 and 500, respectively. For example, processes 400 and 500 may be performed to determine whether a genomic variant should be assigned to a functional group based on the blocks described with respect to process 400 and/or 500.

Processes 400 and 500 can be performed, for example, using one or more electronic devices implementing a software platform. In some examples, processes 400 and 500 are performed using a client-server system, and the blocks of processes 400 and 500 are divided up in any manner between the server and a client device. In other examples, the blocks of processes 400 and 500 are divided up between the server and multiple client devices. Thus, while portions of processes 400 and 500 are described herein as being performed by particular devices of a clientserver system, it will be appreciated that processes 400 and 500 are not so limited. In other examples, processes 400 and 500 are performed using only a client device or only multiple client devices. In processes 400 and 500, some blocks are, optionally, combined, the order of some blocks is, optionally, changed, and some blocks are, optionally, omitted. In some examples, additional steps may be performed in combination with the processes 400 and 500. Accordingly, the operations as illustrated (and described in greater detail below) are exemplary by nature and, as such, should not be viewed as limiting.

[0136] At block 402 of FIG. 4, the system can determine whether the rearrangement event impacts a genomic variant. In one or more examples, this determination may be based on sequence read data from a sample from an individual. The sequence read data may be obtained as described above with respect to block 104.

[0137] In one or more examples, the system may receive an indication of whether the rearrangement event impacts a genomic variant via a computational pipeline for analyzing sequence read data. For example, an output of the computational pipeline may indicate one or more alterations detected in a sample and whether the alteration corresponds to a rearrangement event that is intergenic. For example, the sequence read data may indicate whether the rearrangement event impacts a particular genomic variant (e.g., a primary gene) and a secondary gene. In one or more examples, the genomic variant may correspond to intergenic genomic variants, including but not limited to variants in ABL1, ALK, BRAF, FGFR1, FGFR2, FGFR3, MET, NTRK1, NTRK2, NTRK3, PDGFRA, PDGFRB, ROS1, RET, RAFI, and the like. [0138] At block 404 of FIG. 4, the system can determine whether the breakpoint of the genomic variant is located outside of a sequence encoding a predetermined protein domain. In one or more examples, the predetermined protein domain may be associated with a gene within which the genomic variant occurs. For example, ROS1 encodes a kinase domain. If the breakpoint is located within the sequence encoding a predetermined protein domain, such a breakpoint location can effectively disable the functionality of the protein domain. Accordingly, the rearrangement may be determined to not be oncogenic if the breakpoint is in the protein domain, e.g., because the functional domain of the protein is disabled. As shown in the figure, if the system determines that the breakpoint of the genomic variant is located outside of a sequence encoding a predetermined protein domain, the system may proceed to block 430, where the system may not label the rearrangement nor assign the rearrangement to a functional status group. In such examples, the system may flag the alteration for manual review.

[0139] In one or more examples the location of the breakpoint of the genomic variant may be obtained via a computational pipeline for analyzing sequence read data. In one or more examples, the location of the breakpoint of the genomic variant may be determined by the system based on information provided by the computational pipeline. For example, the computational pipeline may indicate the location of the breakpoint in the genomic data for the sample. In one or more examples, the location indicated by the pipeline may correspond to an integer value, a floating-point value, or any other suitable value type. In one or more examples, the location indicated by the pipeline may be compared to a predetermined threshold (e.g., a genomic location threshold) to determine whether the breakpoint is located inside or outside the sequence encoding a predetermined protein domain. While this example is described with respect to data obtained via a computational pipeline, a skilled artisan will understand that the location of the breakpoint of the genomic variant may be obtained using other methods known in the art.

[0140] If the breakpoint is determined to be located outside the sequence encoding a predetermined protein domain, the system may proceed to blocks 406 and 408. At block 406, the system can determine whether the secondary gene is a coding gene. In one or more examples, block 406 may correspond to block 114B described with respect to FIG. IB. At block 408, the system can determine if the rearrangement event is in-strand. In one or more examples, block 406 may correspond to block 108 described with respect to FIG. 1A. If the system determines that the partner gene is not a coding gene and/or that the rearrangement event is not in-strand, then the system may proceed to block 416.

[0141] At block 416, the system may determine whether the sequence read data corresponding to the rearrangement event comprises the sequence encoding the predetermined protein domain. In one or more examples, block 416 may correspond to block 110 described with respect to FIG. 1A. For example, the system may determine that the sequence read data corresponding to the rearrangement event comprises the sequence encoding the predetermined protein domain and the system may label the rearrangement at block 434. In one or more examples, the naming convention at block 434 may label the genomic variant as “secondary gene-primary gene rearrangement.” For example, if the primary gene is ROS1 and the secondary gene is FLJ40288 (which is a non-coding partner), the variant may be labeled as FLJ40288-ROS1 rearrangement. As another example, where the rearrangement event is an out-of- strand event, if the primary gene is ROS1 and the secondary gene is KCNIP3, the variant may be labeled as KCNIP3-ROS1 rearrangement.

[0142] The labeled genomic variant may be provided to a healthcare professional (e.g., in a report) who can use the label to inform treatment decisions. For example, the healthcare professional may understand based on the label that the FLJ40288-ROS1 rearrangement includes a rearrangement event with a non-coding partner gene, suggesting that the detected event lacks a partner that encodes a protein product with an oligomerization domain and that the detected event may not be an oncogenic driver in the tumor. As another example, the healthcare professional may understand based on the label that the ROS1-CD74 non-canonical fusion corresponds to a reciprocal event where the rearrangement event comprises the 5’ portion of ROS1 and 3’ portion of the known partner CD74, and that while the detected event may not be oncogenic, it may indicate that additional or orthogonal testing could detect a canonical ROS 1 fusion. As another example, the healthcare professional may understand based on the label that the KCNIP3-ROS1 rearrangement corresponds to an out-of-strand event where the rearrangement event comprises the ROS 1 kinase domain, such that the detected event may not be oncogenic and additional testing may be warranted depending on the clinical context.

[0143] If the sequence read data corresponding to the rearrangement event does not comprise the sequence encoding the predetermined protein domain, the system may label the rearrangement at block 432. In one or more examples, the naming convention at block 432 may label the genomic variant as “primary gene- secondary gene rearrangement.” For example, if the primary gene is ROS1 and the secondary gene is LHFPL4, for an out-of-strand rearrangement event that does not include the sequence encoding the predetermined protein domain, the system may label the rearrangement as ROS1-LHFPL4 rearrangement. The labeled genomic variant may be provided to a healthcare professional (e.g., in a report) who can use the label to inform treatment decisions. For example, the healthcare professional may understand based on the label that the genomic variant comprises an out-of-strand rearrangement, affecting ROS1 and LHFPL4 genes, where the rearrangement event does not comprise the protein domain. The label suggests that additional testing to identify the presence of an oncogenic fusion may be warranted, depending on the clinical context.

[0144] As shown in the figure, after labeling the genomic variant at either block 434 or 432, the system can proceed to block 440. At block 440, the system can assign a functional status group to the rearrangement. For example, the system may assign the genomic variant to a functionally unknown rearrangement functional status group, indicating that the functional implications of the genomic variant is not well-defined or well-known based on research and/or literature. In some examples, the functionally unknown rearrangement may be determined to be a potentially pathogenic variant. In some instances, the system may use the functional status to identify specific content, therapies, and clinical trials to be shared with a healthcare professional (e.g., via a report), and the healthcare professional can use the content provided in the report to inform treatment decisions.

[0145] Returning to blocks 406 and 408, if the system determines that the partner gene is a coding gene at block 406 and further determines that the rearrangement event is in-strand at block 408, the system can proceed to block 410. At block 410, the system may determine whether the sequence read data corresponding to the rearrangement event comprises the sequence encoding the predetermined protein domain. In one or more examples, block 410 may correspond to block 110 described with respect to FIG. 1A.

[0146] If the sequence read data corresponding to the rearrangement event does not comprise the sequence encoding the predetermined protein domain, the system may label the genomic variant at block 436. In one or more examples, the naming convention at block 436 may label the genomic variant as “primary gene- secondary gene non-canonical fusion.” For example, if the primary gene is ROS1 and the secondary gene is TSPAN3, an in-strand rearrangement that does not include the sequence encoding the predetermined protein domain may be labeled as ROS1- TSPAN3 non-canonical fusion. The labeled genomic variant may be provided to a healthcare professional (e.g., in a report) who can use the label to inform treatment decisions or determine if additional testing is necessary to detect if an oncogenic ROS1 fusion is present in the sample. In one or more examples, instead of labeling the genomic variant as a non-canonical fusion, the system may label the genomic variant as a reciprocal fusion or the like. A skilled artisan will understand that any suitable label may be used that is indicative that the rearrangement is a non- canonical fusion. As used herein, “non-canonical fusion” and “reciprocal fusion” may be used interchangeably.

[0147] As shown in the figure, after labeling a genomic variant at block 436, the system can proceed to block 442. At block 442, the system can assign a functional status group to the rearrangement. For example, the system may assign the genomic variant to a functionally unknown fusion (e.g., non-canonical fusion) functional status group. As used herein, a functionally unknown fusion may refer to an in-strand, non-canonical (reciprocal) orientation, that lacks a key functional domain (e.g., kinase domain), with an unknown partner lacking an oligomerization domain. In some instances, the functionally unknown fusion may be identified as potentially pathogenic. In some instances, the system may use the functional status to identify specific content, therapies, and clinical trials associated with the non-canonical fusion to a healthcare professional (e.g., via a report), and the healthcare professional can use the content provided in the report to inform treatment decisions.

[0148] Returning to block 410, if the sequence the system determines that the rearrangement event includes the sequence encoding the predetermined protein domain, the system may proceed to block 412. At block 412, the system may determine whether the secondary gene is a known partner gene. In one or more examples, the known partner genes may be determined based on partner genes identified in research and scientific literature. In one or more examples, the partner genes may be determined based on a database or look-up table comprising the partner genes identified in research and scientific literature. For example, at block 414, the system may access a database or look up table to determine whether the primary gene and the secondary gene are known partner genes. If the system determines that the secondary gene is not a known partner gene, the system may proceed to block 444. At block 444, the system may forgo labeling or assigning the genomic variant to a functional status group. In one or more examples, the system may instead flag the genomic variant for manual processing. In one or more embodiments, the system may assign the variant to a functionally unknown functional status group because the primary and secondary genes are not known partner genes.

[0149] Returning to block 412, if the system determines that the secondary gene is a known partner gene, the system may proceed to block 438. At block 438, the system may label the genomic variant accordingly. In one or more examples, the naming convention at block 438 may label the genomic variant as “secondary gene-primary gene canonical fusion.” For example, if the primary gene is ROS1 and the secondary gene is CD74, then the system would label the genomic variant as CD74- ROS1 canonical fusion. In one or more examples, the labeled genomic variant may be provided to a healthcare professional (e.g., in a report) who can use the label to inform treatment decisions. A skilled artisan will understand that any suitable label may be used that communicates the rearrangement is a canonical fusion.

[0150] As shown in the figure, after labeling a genomic variant at block 438, the system can proceed to block 446. At block 446, the system can assign a functional status group to the rearrangement. For example, the system may assign the genomic variant to an activating fusion (e.g., associated with a canonical fusion) functional status group. As used herein, an activating fusion or canonical fusion may refer to an in-strand, canonical orientation, inclusion of the key functional domain (e.g., kinase domain), known partner or partner capable of oligomerization. In one or more examples, the activating fusion functional status group may be determined to be pathogenic or likely pathogenic. In some instances, the system may use the functional status to provide specific content, therapies, and clinical trials associated with the canonical fusion to a healthcare professional (e.g., via a report), and the healthcare professional can use the content provided in the report to inform treatment decisions.

[0151] While the FIG. 4 has been described generally, a skilled artisan will understand that the process 400 can be used to assign various genomic variants to a functional group. In one or more examples, process 400 can be used to assign variants in ALK, MET, NTRK1, NTRK2, NTRK3, PDGFRA, PDGFRB, ROS1, RET, RAFI, and others to a functional group.

[0152] FIG. 5 provides an example of process 500 for assigning a genomic variant to a functional group with respect to a ROS1 genomic variant, e.g., where ROS1 corresponds to the primary gene. In one or more examples, one or more blocks of process 500 may correspond to one or more like-numbered blocks described above with respect to FIG. 4.

[0153] At block 502 of FIG. 5, the system can determine whether the rearrangement event impacts ROSE In one or more examples, this determination may be based on sequence read data from a sample from an individual. In one or more examples, the system may receive an indication from a computational pipeline regarding whether the sample includes a genomic rearrangement event that impacts ROSE

[0154] At block 504 of FIG. 5, the system can determine whether the breakpoint of the ROS 1 rearrangement event is located outside of a sequence encoding a kinase domain, where the kinase domain is the functional domain of ROSE If the breakpoint is within the sequence encoding the kinase domain, the breakpoint can effectively disable the functional element of the ROS 1 kinase. Accordingly, the ROS1 rearrangement may be determined to not be oncogenic, e.g., because the kinase domain is broken. As shown in the figure, if the system determines that the breakpoint of the genomic variant is located outside of a sequence encoding a predetermined protein domain, the system may proceed to block 530, where the system may not label the rearrangement nor assign the rearrangement to a functional status group. In such examples, the system may flag the alteration for manual review.

[0155] If the breakpoint is determined to be located outside the sequence encoding the kinase domain, the system may proceed to blocks 506 and 508. At block 506, the system can determine whether the secondary gene is a coding gene. In one or more examples, block 506 may correspond to block 114B described with respect to FIG. IB. At block 508, the system can determine if the ROS 1 rearrangement event is in-strand. In one or more examples, block 508 may correspond to block 108 described with respect to FIG. 1A. If the system determines that the partner gene is not a coding gene and/or that the ROS 1 rearrangement event is not in-strand, then the system may proceed to block 516.

[0156] At block 516, the system may determine whether the sequence read data corresponding to the ROS 1 rearrangement event comprises the sequence encoding the kinase domain. In one or more examples, block 516 may be correspond to block 110 described with respect to FIG. 1A. If the system determines that the ROS 1 rearrangement event includes the sequence encoding the predetermined protein domain, then the system may label the ROS1 rearrangement at block 534. In one or more examples, the naming convention at block 534 may label the ROS1 variant as “secondary gene-ROSl rearrangement.” For example, if the secondary gene is FLJ40288 (which is a non-coding partner), the variant may be labeled as FLJ40288-ROS1 rearrangement. As another example, where the rearrangement event is an out-of- strand event, if the secondary gene is KCNIP3, the variant may be labeled as KCNIP3-ROS1 rearrangement. The ROS1 variant may be provided to a healthcare professional (e.g., in a report) who can use the label to inform treatment decisions.

[0157] If the system determines that the rearrangement event does not include the sequence encoding the predetermined protein domain, then the system may label the ROS 1 rearrangement at block 532. In one or more examples, the naming convention at block 532 may label the ROS 1 variant as “ROS 1- secondary gene rearrangement.” For example, if the secondary gene is LHFPL4, an out-of-strand rearrangement that does not include the sequence encoding the predetermined protein domain may be labeled as ROS1-LHFPL4 rearrangement. The labeled genomic variant may be provided to a healthcare professional (e.g., in a report) who can use the label to inform treatment decisions. For example, the healthcare professional may understand based on the label that the genomic variant does not include the sequence encoding the predetermined protein domain affecting the ROS 1 and the secondary gene. The label suggests that additional testing may be warranted, depending on the clinical context. [0158] As shown in the figure, after labeling the ROS1 variant at either block 534 or 532, the system can proceed to block 540. At block 540, the system can assign a functional status group to the ROS1 rearrangement. For example, the system may assign the ROS1 variant to a functionally unknown functional status group, indicating that the functional implications of the ROS 1 variant is not well-defined or well-known based on research and/or literature. In one or more examples, the system may use the functional status to provide specific content, therapies, and clinical trials to a healthcare professional (e.g., via a report), and the healthcare professional can use the content provided in the report to inform treatment decisions.

[0159] Returning to blocks 506 and 508, if the system determines that the partner gene is a coding gene at block 506 and further determines that the ROS1 rearrangement event is in-strand at block 508, the system can proceed to block 510. At block 510, the system may determine whether the sequence read data corresponding to the ROS 1 rearrangement event comprises the sequence encoding the kinase domain. In one or more examples, block 510 may correspond to block 110 described with respect to FIG. 1A. For example, if the sequence read data corresponding to the ROS 1 rearrangement event does not comprise the sequence encoding the kinase domain, the system may label the ROS 1 variant at block 536. In one or more examples, the naming convention at block 536 may label the genomic variant as “ROS 1 -secondary gene non-canonical fusion.” In this example, if the primary gene is ROS 1 and the secondary gene is TSPAN3, an in-strand rearrangement that does not include the sequence encoding the predetermined protein domain may be labeled as ROS1-TSPAN3 non-canonical fusion. The labeled ROS1 variant may be provided to a healthcare professional (e.g., in a report) who can use the label to inform treatment decisions. In one or more examples, instead of labeling the genomic variant as a non-canonical fusion, the system may label the genomic variant as a reciprocal fusion or the like. A skilled artisan will understand that any suitable label may be used that is indicative that the ROS 1 rearrangement is a non-canonical fusion.

[0160] As shown in the figure, after labeling the ROS1 variant at block 536, the system can proceed to block 542. At block 542, the system can assign a functional status group to the ROS 1 rearrangement. For example, the system may assign the ROS 1 variant to a functionally unknown fusion functional status group. For example, the system may use the functional status to provide specific content, therapies, and clinical trials associated with the functionally unknown fusion group to a healthcare professional (e.g., via a report), and the healthcare professional can use the content provided in the report to inform treatment decisions.

[0161] Returning to block 510, if the sequence read data corresponding to the ROS1 rearrangement event comprises the sequence encoding the kinase domain, the system may proceed to block 512. At block 512, the system may determine whether the secondary gene is a known partner gene of ROS 1. In one or more examples, the known partner genes may be determined based on partner genes identified in research and scientific literature. In one or more examples, the partner genes may be determined based on a database or look-up table comprising the partner genes identified in research and scientific literature. For example, at block 514, the system may access a database to determine whether the secondary gene corresponds to known partner genes of ROS 1, including but not limited to, CD74, CLIP1, EZR, GOPC, LRIG3, MY05A, PPFIBP1, PWWP2A, SLC34A2, SDC4, SHTN1 (KIAA1598), TPM3, and ZCCHC8. If the system determines that the secondary gene is not a known partner gene, the system may proceed to block 544. At block 544, the system may forgo labeling or assigning the ROS 1 variant to a functional status group. In one or more examples, the system may instead flag the ROS 1 variant for manual processing.

[0162] Returning to block 512, if the system determines that the secondary gene is a known partner gene, the system may proceed to block 538. At block 538, the system may label the ROS1 variant accordingly. In one or more examples, the naming convention at block 538 may label the ROS 1 variant as “secondary gene-ROS 1 canonical fusion.” For example, if the secondary gene is CD74, the system may label the variant as CD74-ROS1 canonical fusion. In one or more examples, the labeled genomic variant may be provided to a healthcare professional (e.g., in a report) who can use the label to inform treatment decisions. A skilled artisan will understand that any suitable label may be used that communicates the ROS 1 rearrangement is a canonical fusion.

[0163] As shown in the figure, after labeling a genomic variant at block 538, the system can proceed to block 546. At block 546, the system can assign a functional status group to the ROS 1 rearrangement. For example, the system may assign the ROS 1 variant to an activating fusion functional status group. In one or more examples, the system may use the functional status to provide specific content, therapies, and clinical trials associated with the activating fusion to a healthcare professional (e.g., via a report), and the healthcare professional can use the content provided in the report to inform treatment decisions.

[0164] FIG. 6 provides a non-limiting example of a process 600 for assigning a genomic variant to a functional status group and identifying a treatment for an individual. In one or more examples, process 600 may correspond to block 306 of FIG. 3. For example, process 600 may be performed for intragenic rearrangement events. In one or more examples, process 600 may be performed for an intragenic rearrangement event that affects a baited oncogene.

[0165] Process 600 can be performed, for example, using one or more electronic devices implementing a software platform. In some examples, process 600 is performed using a clientserver system, and the blocks of process 600 are divided up in any manner between the server and a client device. In other examples, the blocks of process 600 are divided up between the server and multiple client devices. Thus, while portions of process 600 are described herein as being performed by particular devices of a client-server system, it will be appreciated that process 600 is not so limited. In other examples, process 600 is performed using only a client device or only multiple client devices. In process 600, some blocks are, optionally, combined, the order of some blocks is, optionally, changed, and some blocks are, optionally, omitted. In some examples, additional steps may be performed in combination with the process 600. Accordingly, the operations as illustrated (and described in greater detail below) are exemplary by nature and, as such, should not be viewed as limiting.

[0166] At block 602 of FIG. 6, the system can receive sequence read data associated with a sample from an individual. In some instances, the sequence read data may be derived from single region sequencing (e.g., sequencing of a single tissue biopsy sample collected from the tumor of the individual). In some instances, the genomic data comprising sequence read data may be derived from multi-region sequencing (e.g., sequencing of multiple tissue biopsy samples collected from the tumor of the individual). In some instances, the genomic data comprising sequence read data may be derived from single cell sequencing data as opposed to bulk tumor sequencing. In some instances, the genomic data comprising sequence read data may be derived from sequencing the circulating tumor DNA and/or RNA in a liquid biopsy sample.

[0167] In some instances, the genomic data comprising sequence read data may be derived from targeted sequencing, e.g., targeted exome sequencing. In some instances, the genomic data comprising sequence read data may be derived from, e.g., whole genome or whole exome sequencing, as opposed to targeted exome sequencing to increase the number of genomic features (e.g., the number of short variants) detected. In one or more examples, the sequence read data may be received by the system as a BAM file.

[0168] In one or more examples, the sequence read data may be indicative of a presence or absence of one or more short variants (SVs) in a patient sample. In one or more examples, the sequence read data may also be indicative of the presence or absence of genomic events, such as copy number alterations, rearrangements, insertions, deletions, fusions, chromosomal aneuploidy, whole genome doubling, Catalogue Of Somatic Mutations In Cancer (COSMIC) mutational signatures, or any combination thereof. In one or more examples, the sequence read data can be indicative of features associated with a genomic event such as a location of the genomic event, whether the genomic event is in- strand, an orientation of the genomic event, a directionality of the genomic event, genes involved in the genomic event, and the like.

[0169] At block 604 of FIG. 6, the system can determine one or more breakpoints of the genomic variant and a location of the one or more breakpoints based on the sequence read data, the one or more breakpoints associated with a rearrangement event. In one or more examples, the system may determine whether the one or more breakpoints of the genomic variant are located outside of a sequence encoding a predetermined protein domain.

[0170] In one or more examples the location of the breakpoint of the genomic variant may be obtained via a computational pipeline for analyzing sequence read data. In one or more examples, the location of the breakpoint of the genomic variant may be determined by the system based on information provided by the computational pipeline. For example, the computational pipeline may indicate the location of the breakpoint in the genomic data for the sample. In one or more examples, the location indicated by the pipeline may correspond to, for example, an integer value, floating point value, and the like. In one or more examples, the location indicated by the pipeline may be compared to a predetermined threshold to determine whether the breakpoint is located inside or outside the sequence encoding a predetermined protein domain. While this example is described with respect to data obtained via a computational pipeline, a skilled artisan will understand that the location of the breakpoint of the genomic variant may be obtained using other methods known in the art.

[0171] If the system determines that the one or more breakpoints of the genomic variant are located outside a sequence encoding a predetermined protein domain perform, the system can move to block 606 of FIG. 6. At block 606, the system can perform determinations regarding the genomic variant. While particular determinations are shown in the figures, a skilled artisan will understand that more or less determinations may be performed without departing from the scope of this disclosure. In one or more examples the determinations may be based on data obtained via the computational pipeline for analyzing sequence read data.

[0172] At block 610 of FIG. 6, the system can determine whether the sequence encoding the predetermined protein domain is impacted by the rearrangement event, e.g., based on the sequence read data corresponding to the rearrangement event. In one or more examples, the sequence read data corresponding to the rearrangement event may comprise the sequence encoding the predetermined protein domain (e.g., such that the sequence encoding the predetermined protein domain is in the sequence read data corresponding to the rearrangement event). For example, the system may determine whether the functional domain of the genomic variant is retained in the sequence read data following the rearrangement event. In one or more examples, a determination of whether the sequence encoding the predetermined protein domain is included in the sequence read data corresponding to the rearrangement event may be obtained via the computational pipeline for processing nucleic acid sequencing data. While this example relies on data obtained via the computational pipeline, a skilled artisan could use other methods known in the art to determine whether the rearrangement event is in-strand or out-of- strand.

[0173] In one or more examples, the system can further determine a rearrangement type associated with the genomic variant. For example, the system may determine whether the genomic variant corresponds to a deletion, a duplication, or an inversion. [0174] At block 616 of FIG. 6, the system can assign the genomic variant to a functional status group based on block 610. In one or more embodiments, the functional status group may further be based on a rearrangement type. In one or more examples, the label applied to the genomic variant may be based on block 610 and the rearrangement type.

[0175] In one or more examples, if the one or more breakpoints of the genomic variant are located within the sequence encoding a predetermined protein domain, then the system may not assign the genomic variant to a functional status group. Instead, the system may flag the genomic alteration for manual processing, whereby an individual may review the genomic alteration and determine the functional status group assignment for the genomic alteration. In one or more examples, such a genomic variant may be determined to be functionally inactive and/or not be pathogenic, e.g., such as for oncogenes.

[0176] At block 618 of FIG. 6, the system can identify a treatment based on the assigned functional status group of the genomic variant. For example, certain alterations may be associated with specific approved therapies. For example, an intragenic duplication of sequence encoding the FGFR2 kinase domain may be associated with kinase inhibitors such as pemigatinib. As another example, an intragenic deletion of sequence encoding a portion of the FGFR2 gene prior to the kinase domain may be associated with kinase inhibitors such as pemigatinib.

[0177] In one or more embodiments, the system can generate a label for the genomic variant based on the determinations associated with blocks 604 and 606. In one or more examples, an intragenic event involving the duplication of sequence encoding the BRAF kinase domain may be labeled kinase domain duplication. As another example, an intragenic event involving the deletion of sequence encoding the BRAF autoinhibitory domain may be labeled deletion exons 3-6.

[0178] In some instances, the disclosed methods may be used to identify variants in the ABL1, ACVR1B, AKT1, AKT2, AKT3, ALK, ALOX12B, AMER1, APC, AR, ARAF, ARFRP1, ARID1A, ASXL1, ATM, ATR, ATRX, AURKA, AURKB, AXIN1, AXL, BAP1, BARD1, BCL2, BCL2L1, BCL2L2, BCL6, BCOR, BCORL1, BCR, BRAF, BRCA1, BRCA2, BRD4, BRIP1, BTG1, BTG2, BTK, CALR, CARD11, CASP8, CBFB, CBL, CCND1, CCND2, CCND3, CCNE1, CD22, CD274, CD70, CD74, CD79A, CD79B, CDC73, CDH1, CDK12, CDK4, CDK6, CDK8, CDKN1A, CDKN1B, CDKN2A, CDKN2B, CDKN2C, CEBPA, CHEK1, CHEK2, CIC, CREBBP, CRKL, CSF1R, CSF3R, CTCF, CTNNA1, CTNNB1, CUL3, CUL4A, CXCR4, CYP17A1, DAXX, DDR1, DDR2, DIS3, DNMT3A, D0T1L, EED, EGFR, EMSY (Cllorf30), EP300, EPHA3, EPHB1, EPHB4, ERBB2, ERBB3, ERBB4, ERCC4, ERG, ERRFI1, ESRI, ETV4, ETV5, ETV6, EWSR1, EZH2, EZR, FAM46C, FANCA, FANCC, FANCG, FANCL, FAS, FBXW7, FGF10, FGF12, FGF14, FGF19, FGF23, FGF3, FGF4, FGF6, FGFR1, FGFR2, FGFR3, FGFR4, FH, FECN, FET1, FET3, FOXE2, FUBP1, GABRA6, GATA3, GATA4, GATA6, GID4 (C17orf39), GNA11, GNA13, GNAQ, GNAS, GRM3, GSK3B, H3F3A, HDAC1, HGF, HNF1A, HRAS, HSD3B1, ID3, IDH1, IDH2, IGF1R, IKBKE, IKZF1, INPP4B, IRF2, IRF4, IRS2, JAK1, JAK2, JAK3, JUN, KDM5A, KDM5C, KDM6A, KDR, KEAP1, KEF, KIT, KLHL6, KMT2A (MLL), KMT2D (MLL2), KRAS, ETK, LYN, MAF, MAP2K1, MAP2K2, MAP2K4, MAP3K1, MAP3K13, MAPK1, MCL1, MDM2, MDM4, MED12, MEF2B, MEN1, MERTK, MET, MITF, MKNK1, MLH1, MPL, MRE11A, MSH2, MSH3, MSH6, MST1R, MTAP, MTOR, MUTYH, MYB, MYC, MYCL, MYCN, MYD88, NBN, NF1, NF2, NFE2L2, NFKBIA, NKX2-1, NOTCH1, NOTCH2, NOTCH3, NPM1, NRAS, NT5C2, NTRK1, NTRK2, NTRK3, NUTM1, P2RY8, PALB2, PARK2, PARP1, PARP2, PARP3, PAX5, PBRM1, PDCD1, PDCD1LG2, PDGFRA, PDGFRB, PDK1, PIK3C2B, PIK3C2G, PIK3CA, PIK3CB, PIK3R1, PIM1, PMS2, POLDI, POLE, PPARG, PPP2R1A, PPP2R2A, PRDM1, PRKAR1A, PRKCI, PTCHI, PTEN, PTPN11, PTPRO, QKI, RAC1, RAD21, RAD51, RAD51B, RAD51C, RAD51D, RAD52, RAD54L, RAFI, RARA, RBI, RBM10, REL, RET, RICTOR, RNF43, ROS1, RPTOR, RSPO2, SDC4, SDHA, SDHB, SDHC, SDHD, SETD2, SF3B1, SGK1, SLC34A2, SMAD2, SMAD4, SMARCA4, SMARCB1, SMO, SNCAIP, SOCS1, SOX2, SOX9, SPEN, SPOP, SRC, STAG2, STAT3, STK11, SUFU, SYK, TBX3, TEK, TERC, TERT, TET2, TGFBR2, TIPARP, TMPRSS2, TNFAIP3, TNFRSF14, TP53, TSC1, TSC2, TYRO3, U2AF1, VEGFA, VHL, WHSCI, WHSC1L1, WT1, XPO1, XRCC2, ZNF217, or ZNF703 gene locus, or any combination thereof.

[0179] In some instances, the disclosed methods may be used to identify variants in the ABL,

ALK, ALL, B4GALNT1, BAFF, BCL2, BRAF, BRCA, BTK, CD19, CD20, CD3, CD30, CD319, CD38, CD52, CDK4, CDK6, CML, CRACC, CS1, CTLA-4, dMMR, EGFR, ERBB 1, ERBB2, FGFR1-3, FLT3, GD2, HDAC, HER1, HER2, HR, IDH2, IL-ip, IL-6, IL-6R, JAK1, JAK2, JAK3, KIT, KRAS, MEK, MET, MSLH, mTOR, PARP, PD-1, PDGFR, PDGFRa, PDGFRP, PD-L1, PI3K5, PIGF, PTCH, RAF, RANKL, RET, ROS1, SLAMF7, VEGF, VEGFA, or VEGFB gene locus, or any combination thereof.

Methods of use

[0180] In some instances, the disclosed methods may further comprise one or more of the steps of: (i) obtaining the sample from the subject (e.g., a subject suspected of having or determined to have cancer), (ii) extracting nucleic acid molecules (e.g., a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules) from the sample, (iii) ligating one or more adapters to the nucleic acid molecules extracted from the sample (e.g., one or more amplification primers, flow cell adaptor sequences, substrate adapter sequences, or sample index sequences), (iv) amplifying the nucleic acid molecules (e.g., using a polymerase chain reaction (PCR) amplification technique, a non-PCR amplification technique, or an isothermal amplification technique), (v) capturing nucleic acid molecules from the amplified nucleic acid molecules (e.g., by hybridization to one or more bait molecules, where the bait molecules each comprise one or more nucleic acid molecules that each comprising a region that is complementary to a region of a captured nucleic acid molecule), (vi) sequencing the nucleic acid molecules extracted from the sample (or library proxies derived therefrom) using, e.g., a next-generation (massively parallel) sequencing technique, a whole genome sequencing (WGS) technique, a whole exome sequencing technique, a targeted sequencing technique, a direct sequencing technique, or a Sanger sequencing technique) using, e.g., a next-generation (massively parallel) sequencer, and (vii) generating, displaying, transmitting, and/or delivering a report (e.g., an electronic, webbased, or paper report) to the subject (or patient), a caregiver, a healthcare provider, a physician, an oncologist, an electronic medical record system, a hospital, a clinic, a third-party payer, an insurance company, or a government office. In some instances, the report comprises output from the methods described herein. In some instances, all or a portion of the report may be displayed in the graphical user interface of an online or web-based healthcare portal. In some instances, the report is transmitted via a computer network or peer-to-peer connection. [0181] The disclosed methods may be used with any of a variety of samples. For example, in some instances, the sample may comprise a tissue biopsy sample, a liquid biopsy sample, or a normal control. In some instances, the sample may be a liquid biopsy sample and may comprise blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva. In some instances, the sample may be a liquid biopsy sample and may comprise circulating tumor cells (CTCs). In some instances, the sample may be a liquid biopsy sample and may comprise cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof.

[0182] In some instances, the nucleic acid molecules extracted from a sample may comprise a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules. In some instances, the tumor nucleic acid molecules may be derived from a tumor portion of a heterogeneous tissue biopsy sample, and the non-tumor nucleic acid molecules may be derived from a normal portion of the heterogeneous tissue biopsy sample. In some instances, the sample may comprise a liquid biopsy sample, and the tumor nucleic acid molecules may be derived from a circulating tumor DNA (ctDNA) fraction of the liquid biopsy sample while the non-tumor nucleic acid molecules may be derived from a non-tumor, cell-free DNA (cfDNA) fraction of the liquid biopsy sample.

[0183] In some instances, the disclosed methods for assigning a genomic variant to a functional status may be used to diagnose (or as part of a diagnosis of) the presence of disease or other condition (e.g., cancer, genetic disorders (such as Down Syndrome and Fragile X), neurological disorders, or any other disease type where detection of variants, e.g., copy number alternations, are relevant to diagnosing, treating, or predicting said disease) in a subject (e.g., a patient). In some instances, the disclosed methods may be applicable to diagnosis of any of a variety of cancers as described elsewhere herein.

[0184] In some instances, the disclosed methods for assigning a genomic variant to a functional status may be used to predict genetic disorders in fetal DNA. (e.g., for invasive or non-invasive prenatal testing). For example, sequence read data obtained by sequencing fetal DNA extracted from samples obtained using invasive amniocentesis, chorionic villus sampling (cVS), or fetal umbilical cord sampling techniques, or obtained using non-invasive sampling of cell-free DNA (cfDNA) samples (which comprises a mix of maternal cfDNA and fetal cfDNA), may be processed according to the disclosed methods to identify variants, e.g., copy number alterations, associated with, e.g., Down Syndrome (trisomy 21), trisomy 18, trisomy 13, and extra or missing copies of the X and Y chromosomes.

[0185] In some instances, the disclosed methods for assigning a genomic variant to a functional status may be used to select a subject (e.g., a patient) for a clinical trial based on the functional status determined for one or more gene loci. In some instances, patient selection for clinical trials based on, e.g., identification of the functional status at one or more gene loci, may accelerate the development of targeted therapies and improve the healthcare outcomes for treatment decisions.

[0186] In some instances, the disclosed methods for assigning a genomic variant to a functional status may be used to select an appropriate therapy or treatment (e.g., an anti-cancer therapy or anti-cancer treatment) for a subject. In some instances, for example, the anti-cancer therapy or treatment may comprise use of a poly (ADP-ribose) polymerase inhibitor (PARPi), a platinum compound, chemotherapy, radiation therapy, a targeted therapy (e.g., immunotherapy), surgery, or any combination thereof.

[0187] In some instances, the targeted therapy (or anti-cancer target therapy) may comprise abemaciclib (Verzenio), abiraterone acetate (Zytiga), acalabrutinib (Calquence), ado-trastuzumab emtansine (Kadcyla), afatinib dimaleate (Gilotrif), aldesleukin (Proleukin), alectinib (Alecensa), alemtuzumab (Campath), alitretinoin (Panretin), alpelisib (Piqray), amivantamab-vmjw (Rybrevant), anastrozole (Arimidex), apalutamide (Erleada), asciminib hydrochloride (Scemblix), atezolizumab (Tecentriq), avapritinib (Ayvakit), avelumab (Bavencio), axicabtagene ciloleucel (Yescarta), axitinib (Inlyta), belantamab mafodotin-blmf (Blenrep), belimumab (Benlysta), belinostat (Beleodaq), belzutifan (Welireg), bevacizumab (Avastin), bexarotene (Targretin), binimetinib (Mektovi), blinatumomab (Blincyto), bortezomib (Velcade), bosutinib (Bosulif), brentuximab vedotin (Adcetris), brexucabtagene autoleucel (Tecartus), brigatinib (Alunbrig), cabazitaxel (Jevtana), cabozantinib (Cabometyx), cabozantinib (Cabometyx, Cometriq), canakinumab (Haris), capmatinib hydrochloride (Tabrecta), carfilzomib (Kyprolis), cemiplimab-rwlc (Libtayo), ceritinib (LDK378/Zykadia), cetuximab (Erbitux), cobimetinib (Cotellic), copanlisib hydrochloride (Aliqopa), crizotinib (Xalkori), dabrafenib (Tafinlar), dacomitinib (Vizimpro), daratumumab (Darzalex), daratumumab and hyaluronidase-fihj (Darzalex Faspro), darolutamide (Nubeqa), dasatinib (Sprycel), denileukin diftitox (Ontak), denosumab (Xgeva), dinutuximab (Unituxin), dostarlimab-gxly (Jemperli), durvalumab (Imfinzi), duvelisib (Copiktra), elotuzumab (Empliciti), enasidenib mesylate (Idhifa), encorafenib (Braftovi), enfortumab vedotin-ejfv (Padcev), entrectinib (Rozlytrek), enzalutamide (Xtandi), erdafitinib (Balversa), erlotinib (Tarceva), everolimus (Afinitor), exemestane (Aromasin), fam-trastuzumab deruxtecan-nxki (Enhertu), fedratinib hydrochloride (Inrebic), fulvestrant (Faslodex), gefitinib (Iressa), gemtuzumab ozogamicin (Mylotarg), gilteritinib (Xospata), glasdegib maleate (Daurismo), hyaluronidase-zzxf (Phesgo), ibrutinib (Imbruvica), ibritumomab tiuxetan (Zevalin), idecabtagene vicleucel (Abecma), idelalisib (Zydelig), imatinib mesylate (Gleevec), infigratinib phosphate (Truseltiq), inotuzumab ozogamicin (Besponsa), iobenguane 1131 (Azedra), ipilimumab (Yervoy), isatuximab-irfc (Sarclisa), ivosidenib (Tibsovo), ixazomib citrate (Ninlaro), lanreotide acetate (Somatuline Depot), lapatinib (Tykerb), larotrectinib sulfate (Vitrakvi), lenvatinib mesylate (Lenvima), letrozole (Femara), lisocabtagene maraleucel (Breyanzi), loncastuximab tesirine-lpyl (Zynlonta), lorlatinib (Lorbrena), lutetium Lu 177-dotatate (Lutathera), margetuximab-cmkb (Margenza), midostaurin (Rydapt), mobocertinib succinate (Exkivity), mogamulizumab-kpkc (Poteligeo), moxetumomab pasudotox-tdfk (Lumoxiti), naxitamab-gqgk (Danyelza), necitumumab (Portrazza), neratinib maleate (Nerlynx), nilotinib (Tasigna), niraparib tosylate monohydrate (Zejula), nivolumab (Opdivo), obinutuzumab (Gazyva), ofatumumab (Arzerra), olaparib (Lynparza), olaratumab (Lartruvo), osimertinib (Tagrisso), palbociclib (Ibrance), panitumumab (Vectibix), panobinostat (Farydak), pazopanib (Votrient), pembrolizumab (Keytruda), pemigatinib (Pemazyre), pertuzumab (Perjeta), pexidartinib hydrochloride (Turalio), polatuzumab vedotin-piiq (Polivy), ponatinib hydrochloride (Iclusig), pralatrexate (Folotyn), pralsetinib (Gavreto), radium 223 dichloride (Xofigo), ramucirumab (Cyramza), regorafenib (Stivarga), ribociclib (Kisqali), ripretinib (Qinlock), rituximab (Rituxan), rituximab and hyaluronidase human (Rituxan Hycela), romidepsin (Istodax), rucaparib camsylate (Rubraca), ruxolitinib phosphate (Jakafi), sacituzumab govitecan-hziy (Trodelvy), seliciclib, selinexor (Xpovio), selpercatinib (Retevmo), selumetinib sulfate (Koselugo), siltuximab (Sylvant), sipuleucel-T (Provenge), sirolimus protein-bound particles (Fyarro), sonidegib (Odomzo), sorafenib (Nexavar), sotorasib (Lumakras), sunitinib (Sutent), tafasitamab-cxix (Monjuvi), tagraxofusp-erzs (Elzonris), talazoparib tosylate (Talzenna), tamoxifen (Nolvadex), tazemetostat hydrobromide (Tazverik), tebentafusp-tebn (Kimmtrak), temsirolimus (Torisel), tepotinib hydrochloride (Tepmetko), tisagenlecleucel (Kymriah), tisotumab vedotin-tftv (Tivdak), tocilizumab (Actemra), tofacitinib (Xeljanz), tositumomab (Bexxar), trametinib (Mekinist), trastuzumab (Herceptin), tretinoin (Vesanoid), tivozanib hydrochloride (Fotivda), toremifene (Fareston), tucatinib (Tukysa), umbralisib tosylate (Ukoniq), vandetanib (Caprelsa), vemurafenib (Zelboraf), venetoclax (Venclexta), vismodegib (Erivedge), vorinostat (Zolinza), zanubrutinib (Brukinsa), ziv-aflibercept (Zaltrap), or any combination thereof.

[0188] In some instances, the disclosed methods for assigning a genomic variant to a functional status may be used in treating a disease (e.g., a cancer) in a subject. For example, in response to determining the functional status of a genomic variant using any of the methods disclosed herein, an effective amount of an anti-cancer therapy or anti-cancer treatment may be administered to the subject.

[0189] In some instances, the disclosed methods for assigning a genomic variant to a functional status may be used for monitoring disease progression or recurrence (e.g., cancer or tumor progression or recurrence) in a subject. For example, in some instances, the methods may be used to determine a functional status in a first sample obtained from the subject at a first time point, and used to determine a functional status in a second sample obtained from the subject at a second time point, where comparison of the first determination of the functional status and the second determination of the functional status allows one to monitor disease progression or recurrence. In some instances, the first time point is chosen before the subject has been administered a therapy or treatment, and the second time point is chosen after the subject has been administered the therapy or treatment.

[0190] In some instances, the disclosed methods may be used for adjusting a therapy or treatment (e.g., an anti-cancer treatment or anti-cancer therapy) for a subject, e.g., by adjusting a treatment dose and/or selecting a different treatment in response to a change in the determination of the functional status. [0191] In some instances, the functional status determined using the disclosed methods may be used as a prognostic or diagnostic indicator associated with the sample. For example, in some instances, the prognostic or diagnostic indicator may comprise an indicator of the presence of a disease (e.g., cancer) in the sample, an indicator of the probability that a disease (e.g., cancer) is present in the sample, an indicator of the probability that the subject from which the sample was derived will develop a disease (e.g., cancer) (z.e., a risk factor), or an indicator of the likelihood that the subject from which the sample was derived will respond to a particular therapy or treatment.

[0192] In some instances, the disclosed methods for assigning a genomic variant to a functional status may be implemented as part of a genomic profiling process that comprises identification of the presence of variant sequences at one or more gene loci in a sample derived from a subject as part of detecting, monitoring, predicting a risk factor, or selecting a treatment for a particular disease, e.g., cancer. In some instances, the variant panel selected for genomic profiling may comprise the detection of variant sequences at a selected set of gene loci. In some instances, the variant panel selected for genomic profiling may comprise detection of variant sequences at a number of gene loci through comprehensive genomic profiling (CGP), which is a nextgeneration sequencing (NGS) approach used to assess hundreds of genes (including relevant cancer biomarkers) in a single assay. Inclusion of the disclosed methods for assigning a genomic variant to a functional status as part of a genomic profiling process (or inclusion of the output from the disclosed methods for assigning a genomic variant to a functional status as part of the genomic profile of the subject) can improve the validity of, e.g., disease detection calls and treatment decisions, made on the basis of the genomic profile by, for example, independently determining the functional status of a genomic variant in a given patient sample.

[0193] In some instances, a genomic profile may comprise information on the presence of genes (or variant sequences thereof), copy number variations, epigenetic traits, proteins (or modifications thereof), and/or other biomarkers in an individual’s genome and/or proteome, as well as information on the individual’s corresponding phenotypic traits and the interaction between genetic or genomic traits, phenotypic traits, and environmental factors. [0194] In some instances, a genomic profile for the subject may comprise results from a comprehensive genomic profiling (CGP) test, a nucleic acid sequencing-based test, a gene expression profiling test, a cancer hotspot panel test, a DNA methylation test, a DNA fragmentation test, an RNA fragmentation test, or any combination thereof.

[0195] In some instances, the method can further include administering or applying a treatment or therapy (e.g., an anti-cancer agent, anti-cancer treatment, or anti-cancer therapy) to the subject based on the generated genomic profile. An anti-cancer agent or anti-cancer treatment may refer to a compound that is effective in the treatment of cancer cells. Examples of anti-cancer agents or anti-cancer therapies include, but not limited to, alkylating agents, antimetabolites, natural products, hormones, chemotherapy, radiation therapy, immunotherapy, surgery, or a therapy configured to target a defect in a specific cell signaling pathway, e.g., a defect in a DNA mismatch repair (MMR) pathway.

Samples

[0196] The disclosed methods and systems may be used with any of a variety of samples (also referred to herein as specimens) comprising nucleic acids (e.g., DNA or RNA) that are collected from a subject (e.g., a patient). Examples of a sample include, but are not limited to, a tumor sample, a tissue sample, a biopsy sample (e.g., a tissue biopsy, a liquid biopsy, or both), a blood sample (e.g., a peripheral whole blood sample), a blood plasma sample, a blood serum sample, a lymph sample, a saliva sample, a sputum sample, a urine sample, a gynecological fluid sample, a circulating tumor cell (CTC) sample, a cerebral spinal fluid (CSF) sample, a pericardial fluid sample, a pleural fluid sample, an ascites (peritoneal fluid) sample, a feces (or stool) sample, or other body fluid, secretion, and/or excretion sample (or cell sample derived therefrom). In certain instances, the sample may be frozen sample or a formalin-fixed paraffin-embedded (FFPE) sample.

[0197] In some instances, the sample may be collected by tissue resection (e.g., surgical resection), needle biopsy, bone marrow biopsy, bone marrow aspiration, skin biopsy, endoscopic biopsy, fine needle aspiration, oral swab, nasal swab, vaginal swab or a cytology smear, scrapings, washings or lavages (such as a ductal lavage or bronchoalveolar lavage), etc. [0198] In some instances, the sample is a liquid biopsy sample, and may comprise, e.g., whole blood, blood plasma, blood serum, urine, stool, sputum, saliva, or cerebrospinal fluid. In some instances, the sample may be a liquid biopsy sample and may comprise circulating tumor cells (CTCs). In some instances, the sample may be a liquid biopsy sample and may comprise cell- free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof.

[0199] In some instances, the sample may comprise one or more premalignant or malignant cells. Premalignant, as used herein, refers to a cell or tissue that is not yet malignant but is poised to become malignant. In certain instances, the sample may be acquired from a solid tumor, a soft tissue tumor, or a metastatic lesion. In certain instances, the sample may be acquired from a hematologic malignancy or pre-malignancy. In other instances, the sample may comprise a tissue or cells from a surgical margin. In certain instances, the sample may comprise tumor-infiltrating lymphocytes. In some instances, the sample may comprise one or more non- malignant cells. In some instances, the sample may be, or is part of, a primary tumor or a metastasis (e.g., a metastasis biopsy sample). In some instances, the sample may be obtained from a site (e.g., a tumor site) with the highest percentage of tumor (e.g., tumor cells) as compared to adjacent sites (e.g., sites adjacent to the tumor). In some instances, the sample may be obtained from a site (e.g., a tumor site) with the largest tumor focus (e.g., the largest number of tumor cells as visualized under a microscope) as compared to adjacent sites (e.g., sites adjacent to the tumor).

[0200] In some instances, the disclosed methods may further comprise analyzing a primary control (e.g., a normal tissue sample). In some instances, the disclosed methods may further comprise determining if a primary control is available and, if so, isolating a control nucleic acid (e.g., DNA) from said primary control. In some instances, the sample may comprise any normal control (e.g., a normal adjacent tissue (NAT)) if no primary control is available. In some instances, the sample may be or may comprise histologically normal tissue. In some instances, the method includes evaluating a sample, e.g., a histologically normal sample (e.g., from a surgical tissue margin) using the methods described herein. In some instances, the disclosed methods may further comprise acquiring a sub-sample enriched for non-tumor cells, e.g., by macro-dissecting non-tumor tissue from said NAT in a sample not accompanied by a primary control. In some instances, the disclosed methods may further comprise determining that no primary control and no NAT is available, and marking said sample for analysis without a matched control.

[0201] In some instances, samples obtained from histologically normal tissues (e.g., otherwise histologically normal surgical tissue margins) may still comprise a genetic alteration such as a variant sequence as described herein. The methods may thus further comprise re-classifying a sample based on the presence of the detected genetic alteration. In some instances, multiple samples (e.g., from different subjects) are processed simultaneously.

[0202] The disclosed methods and systems may be applied to the analysis of nucleic acids extracted from any of variety of tissue samples (or disease states thereof), e.g., solid tissue samples, soft tissue samples, metastatic lesions, or liquid biopsy samples. Examples of tissues include, but are not limited to, connective tissue, muscle tissue, nervous tissue, epithelial tissue, and blood. Tissue samples may be collected from any of the organs within an animal or human body. Examples of human organs include, but are not limited to, the brain, heart, lungs, liver, kidneys, pancreas, spleen, thyroid, mammary glands, uterus, prostate, large intestine, small intestine, bladder, bone, skin, etc.

[0203] In some instances, the nucleic acids extracted from the sample may comprise deoxyribonucleic acid (DNA) molecules. Examples of DNA that may be suitable for analysis by the disclosed methods include, but are not limited to, genomic DNA or fragments thereof, mitochondrial DNA or fragments thereof, cell-free DNA (cfDNA), and circulating tumor DNA (ctDNA). Cell-free DNA (cfDNA) is comprised of fragments of DNA that are released from normal and/or cancerous cells during apoptosis and necrosis, and circulate in the blood stream and/or accumulate in other bodily fluids. Circulating tumor DNA (ctDNA) is comprised of fragments of DNA that are released from cancerous cells and tumors that circulate in the blood stream and/or accumulate in other bodily fluids.

[0204] In some instances, DNA is extracted from nucleated cells from the sample. In some instances, a sample may have a low nucleated cellularity, e.g., when the sample is comprised mainly of erythrocytes, lesional cells that contain excessive cytoplasm, or tissue with fibrosis. In some instances, a sample with low nucleated cellularity may require more, e.g., greater, tissue volume for DNA extraction.

[0205] In some instances, the nucleic acids extracted from the sample may comprise ribonucleic acid (RNA) molecules. Examples of RNA that may be suitable for analysis by the disclosed methods include, but are not limited to, total cellular RNA, total cellular RNA after depletion of certain abundant RNA sequences (e.g., ribosomal RNAs), cell-free RNA (cfRNA), messenger RNA (mRNA) or fragments thereof, the poly(A)-tailed mRNA fraction of the total RNA, ribosomal RNA (rRNA) or fragments thereof, transfer RNA (tRNA) or fragments thereof, and mitochondrial RNA or fragments thereof. In some instances, RNA may be extracted from the sample and converted to complementary DNA (cDNA) using, e.g., a reverse transcription reaction. In some instances, the cDNA is produced by random-primed cDNA synthesis methods. In other instances, the cDNA synthesis is initiated at the poly(A) tail of mature mRNAs by priming with oligo(dT)-containing oligonucleotides. Methods for depletion, poly(A) enrichment, and cDNA synthesis are well known to those of skill in the art.

[0206] In some instances, the sample may comprise a tumor content (e.g., comprising tumor cells or tumor cell nuclei), or a non-tumor content (e.g., immune cells, fibroblasts, and other nontumor cells). In some instances, the tumor content of the sample may constitute a sample metric. In some instances, the sample may comprise a tumor content of at least 5-50%, 10-40%, 15-25%, or 20-30% tumor cell nuclei. In some instances, the sample may comprise a tumor content of at least 5%, at least 10%, at least 20%, at least 30%, at least 40%, or at least 50% tumor cell nuclei. In some instances, the percent tumor cell nuclei (e.g., sample fraction) is determined (e.g., calculated) by dividing the number of tumor cells in the sample by the total number of all cells within the sample that have nuclei. In some instances, for example when the sample is a liver sample comprising hepatocytes, a different tumor content calculation may be required due to the presence of hepatocytes having nuclei with twice, or more than twice, the DNA content of other, e.g., non-hepatocyte, somatic cell nuclei. In some instances, the sensitivity of detection of a genetic alteration, e.g., a variant sequence, or a determination of, e.g., micro satellite instability, may depend on the tumor content of the sample. For example, a sample having a lower tumor content can result in lower sensitivity of detection for a given size sample. [0207] In some instances, as noted above, the sample comprises nucleic acid (e.g., DNA, RNA (or a cDNA derived from the RNA), or both), e.g., from a tumor or from normal tissue. In certain instances, the sample may further comprise a non-nucleic acid component, e.g., cells, protein, carbohydrate, or lipid, e.g., from the tumor or normal tissue.

Subjects

[0208] In some instances, the sample is obtained (e.g., collected) from a subject (e.g., patient) with a condition or disease (e.g., a hyperproliferative disease or a non-cancer indication) or suspected of having the condition or disease. In some instances, the hyperproliferative disease is a cancer. In some instances, the cancer is a solid tumor or a metastatic form thereof. In some instances, the cancer is a hematological cancer, e.g., a leukemia or lymphoma.

[0209] In some instances, the subject has a cancer or is at risk of having a cancer. For example, in some instances, the subject has a genetic predisposition to a cancer (e.g., having a genetic mutation that increases his or her baseline risk for developing a cancer). In some instances, the subject has been exposed to an environmental perturbation (e.g., radiation or a chemical) that increases his or her risk for developing a cancer. In some instances, the subject is in need of being monitored for development of a cancer. In some instances, the subject is in need of being monitored for cancer progression or regression, e.g., after being treated with an anti-cancer therapy (or anti-cancer treatment). In some instances, the subject is in need of being monitored for relapse of cancer. In some instances, the subject is in need of being monitored for minimum residual disease (MRD). In some instances, the subject has been, or is being treated, for cancer. In some instances, the subject has not been treated with an anti-cancer therapy (or anti-cancer treatment).

[0210] In some instances, the subject (e.g., a patient) is being treated, or has been previously treated, with one or more targeted therapies. In some instances, e.g., for a patient who has been previously treated with a targeted therapy, a post-targeted therapy sample (e.g., specimen) is obtained (e.g., collected). In some instances, the post-targeted therapy sample is a sample obtained after the completion of the targeted therapy. [0211] In some instances, the patient has not been previously treated with a targeted therapy. In some instances, e.g., for a patient who has not been previously treated with a targeted therapy, the sample comprises a resection, e.g., an original resection, or a resection following recurrence (e.g., following a disease recurrence post-therapy).

Cancers

[0212] In some instances, the sample is acquired from a subject having a cancer. Exemplary cancers include, but are not limited to, B cell cancer (e.g., multiple myeloma), melanomas, breast cancer, lung cancer (such as non-small cell lung carcinoma or NSCLC), bronchus cancer, colorectal cancer, prostate cancer, pancreatic cancer, stomach cancer, ovarian cancer, urinary bladder cancer, brain or central nervous system cancer, peripheral nervous system cancer, esophageal cancer, cervical cancer, uterine or endometrial cancer, cancer of the oral cavity or pharynx, liver cancer, kidney cancer, testicular cancer, biliary tract cancer, small bowel or appendix cancer, salivary gland cancer, thyroid gland cancer, adrenal gland cancer, osteosarcoma, chondrosarcoma, cancer of hematological tissues, adenocarcinomas, inflammatory myofibroblastic tumors, gastrointestinal stromal tumor (GIST), colon cancer, multiple myeloma (MM), myelodysplastic syndrome (MDS), myeloproliferative disorder (MPD), acute lymphocytic leukemia (ALL), acute myelocytic leukemia (AML), chronic myelocytic leukemia (CML), chronic lymphocytic leukemia (CLL), polycythemia Vera, Hodgkin lymphoma, nonHodgkin lymphoma (NHL), soft-tissue sarcoma, fibrosarcoma, myxosarcoma, liposarcoma, osteogenic sarcoma, chordoma, angiosarcoma, endothelio sarcoma, lymphangiosarcoma, lymphangioendotheliosarcoma, synovioma, mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinomas, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatoma, bile duct carcinoma, choriocarcinoma, seminoma, embryonal carcinoma, Wilms' tumor, bladder carcinoma, epithelial carcinoma, glioma, astrocytoma, medulloblastoma, craniopharyngioma, ependymoma, pinealoma, hemangioblastoma, acoustic neuroma, oligodendroglioma, meningioma, neuroblastoma, retinoblastoma, follicular lymphoma, diffuse large B-cell lymphoma, mantle cell lymphoma, hepatocellular carcinoma, thyroid cancer, gastric cancer, head and neck cancer, small cell cancers, essential thrombocythemia, agnogenic myeloid metaplasia, hypereosinophilic syndrome, systemic mastocytosis, familiar hypereosinophilia, chronic eosinophilic leukemia, neuroendocrine cancers, carcinoid tumors, and the like.

[0213] In some instances, the cancer comprises acute lymphoblastic leukemia (Philadelphia chromosome positive), acute lymphoblastic leukemia (precursor B-cell), acute myeloid leukemia (FLT3+), acute myeloid leukemia (with an IDH2 mutation), anaplastic large cell lymphoma, basal cell carcinoma, B-cell chronic lymphocytic leukemia, bladder cancer, breast cancer (HER2 overexpressed/amplified), breast cancer (HER2+), breast cancer (HR+, HER2-), cervical cancer, cholangiocarcinoma, chronic lymphocytic leukemia, chronic lymphocytic leukemia (with 17p deletion), chronic myelogenous leukemia, chronic myelogenous leukemia (Philadelphia chromosome positive), classical Hodgkin lymphoma, colorectal cancer, colorectal cancer (dMMR and MSI-H), colorectal cancer (KRAS wild type), cryopyrin-associated periodic syndrome, a cutaneous T-cell lymphoma, dermato fibrosarcoma protuberans, a diffuse large B- cell lymphoma, fallopian tube cancer, a follicular B-cell non-Hodgkin lymphoma, a follicular lymphoma, gastric cancer, gastric cancer (HER2+), a gastroesophageal junction (GEJ) adenocarcinoma, a gastrointestinal stromal tumor, a gastrointestinal stromal tumor (KIT+), a giant cell tumor of the bone, a glioblastoma, granulomatosis with polyangiitis, a head and neck squamous cell carcinoma, a hepatocellular carcinoma, Hodgkin lymphoma, juvenile idiopathic arthritis, lupus erythematosus, a mantle cell lymphoma, medullary thyroid cancer, melanoma, a melanoma with a BRAF V600 mutation, a melanoma with a BRAF V600E or V600K mutation, Merkel cell carcinoma, multicentric Castleman's disease, multiple hematologic malignancies including Philadelphia chromosome-positive ALL and CML, multiple myeloma, myelofibrosis, a non-Hodgkin’ s lymphoma, a nonresectable subependymal giant cell astrocytoma associated with tuberous sclerosis, a non-small cell lung cancer, a non-small cell lung cancer (ALK+), a non-small cell lung cancer (PD-L1+), a non-small cell lung cancer (with ALK fusion or ROS1 gene alteration), a non-small cell lung cancer (with BRAF V600E mutation), a non-small cell lung cancer (with an EGFR exon 19 deletion or exon 21 substitution (L858R) mutations), a non- small cell lung cancer (with an EGFR T790M mutation), ovarian cancer, ovarian cancer (with a BRCA mutation), pancreatic cancer, a pancreatic, gastrointestinal, or lung origin neuroendocrine tumor, a pediatric neuroblastoma, a peripheral T-cell lymphoma, peritoneal cancer, prostate cancer, a renal cell carcinoma, rheumatoid arthritis, a small lymphocytic lymphoma, a soft tissue sarcoma, a solid tumor (MSI-H/dMMR), a squamous cell cancer of the head and neck, a squamous non-small cell lung cancer, thyroid cancer, a thyroid carcinoma, urothelial cancer, a urothelial carcinoma, or Waldenstrom's macroglobulinemia.

[0214] In some instances, the cancer is a hematologic malignancy (or premaligancy). As used herein, a hematologic malignancy refers to a tumor of the hematopoietic or lymphoid tissues, e.g., a tumor that affects blood, bone marrow, or lymph nodes. Exemplary hematologic malignancies include, but are not limited to, leukemia (e.g., acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), chronic myelogenous leukemia (CML), hairy cell leukemia, acute monocytic leukemia (AMoL), chronic myelomonocytic leukemia (CMML), juvenile myelomonocytic leukemia (JMML), or large granular lymphocytic leukemia), lymphoma (e.g., AIDS-related lymphoma, cutaneous T-cell lymphoma, Hodgkin lymphoma (e.g., classical Hodgkin lymphoma or nodular lymphocyte- predominant Hodgkin lymphoma), mycosis fungoides, non-Hodgkin lymphoma (e.g., B-cell non-Hodgkin lymphoma (e.g., Burkitt lymphoma, small lymphocytic lymphoma (CLL/SLL), diffuse large B-cell lymphoma, follicular lymphoma, immunoblastic large cell lymphoma, precursor B -lymphoblastic lymphoma, or mantle cell lymphoma) or T-cell non-Hodgkin lymphoma (mycosis fungoides, anaplastic large cell lymphoma, or precursor T-lymphoblastic lymphoma)), primary central nervous system lymphoma, Sezary syndrome, Waldenstrom macroglobulinemia), chronic myeloproliferative neoplasm, Langerhans cell histiocytosis, multiple myeloma/plasma cell neoplasm, myelodysplastic syndrome, or myelodysplastic/myeloproliferative neoplasm.

Nucleic acid extraction and processing

[0215] DNA or RNA may be extracted from tissue samples, biopsy samples, blood samples, or other bodily fluid samples using any of a variety of techniques known to those of skill in the art (see, e.g., Example 1 of International Patent Application Publication No. WO 2012/092426; Tan, et al. (2009), “DNA, RNA, and Protein Extraction: The Past and The Present”, J. Biomed. Biotech. 2009:574398; the technical literature for the Maxwell® 16 LEV Blood DNA Kit (Promega Corporation, Madison, WI); and the Maxwell 16 Buccal Swab LEV DNA Purification Kit Technical Manual (Promega Literature #TM333, January 1, 2011, Pro mega Corporation, Madison, WI)). Protocols for RNA isolation are disclosed in, e.g., the Maxwell® 16 Total RNA Purification Kit Technical Bulletin (Promega Literature #TB351, August 2009, Promega Corporation, Madison, WI).

[0216] A typical DNA extraction procedure, for example, comprises (i) collection of the fluid sample, cell sample, or tissue sample from which DNA is to be extracted, (ii) disruption of cell membranes (z.e., cell lysis), if necessary, to release DNA and other cytoplasmic components, (iii) treatment of the fluid sample or lysed sample with a concentrated salt solution to precipitate proteins, lipids, and RNA, followed by centrifugation to separate out the precipitated proteins, lipids, and RNA, and (iv) purification of DNA from the supernatant to remove detergents, proteins, salts, or other reagents used during the cell membrane lysis step.

[0217] Disruption of cell membranes may be performed using a variety of mechanical shear (e.g., by passing through a French press or fine needle) or ultrasonic disruption techniques. The cell lysis step often comprises the use of detergents and surfactants to solubilize lipids the cellular and nuclear membranes. In some instances, the lysis step may further comprise use of proteases to break down protein, and/or the use of an RNase for digestion of RNA in the sample.

[0218] Examples of suitable techniques for DNA purification include, but are not limited to, (i) precipitation in ice-cold ethanol or isopropanol, followed by centrifugation (precipitation of DNA may be enhanced by increasing ionic strength, e.g., by addition of sodium acetate), (ii) phenol-chloroform extraction, followed by centrifugation to separate the aqueous phase containing the nucleic acid from the organic phase containing denatured protein, and (iii) solid phase chromatography where the nucleic acids adsorb to the solid phase (e.g., silica or other) depending on the pH and salt concentration of the buffer.

[0219] In some instances, cellular and histone proteins bound to the DNA may be removed either by adding a protease or by having precipitated the proteins with sodium or ammonium acetate, or through extraction with a phenol-chloroform mixture prior to a DNA precipitation step.

[0220] In some instances, DNA may be extracted using any of a variety of suitable commercial DNA extraction and purification kits. Examples include, but are not limited to, the QIAamp (for isolation of genomic DNA from human samples) and DNAeasy (for isolation of genomic DNA from animal or plant samples) kits from Qiagen (Germantown, MD) or the Maxwell® and ReliaPrep™ series of kits from Promega (Madison, WI).

[0221] As noted above, in some instances the sample may comprise a formalin-fixed (also known as formaldehyde-fixed, or paraformaldehyde-fixed), paraffin-embedded (FFPE) tissue preparation. For example, the FFPE sample may be a tissue sample embedded in a matrix, e.g., an FFPE block. Methods to isolate nucleic acids (e.g., DNA) from formaldehyde- or paraformaldehyde-fixed, paraffin-embedded (FFPE) tissues are disclosed in, e.g., Cronin, et al., (2004) Am J Pathol. 164(l):35-42; Masuda, et al., (1999) Nucleic Acids Res. 27(22): 4436-4443; Specht, et al., (2001) Am J Pathol. 158(2):419-429; the Ambion RecoverAll™ Total Nucleic Acid Isolation Protocol (Ambion, Cat. No. AM1975, September 2008); the Maxwell® 16 FFPE Plus LEV DNA Purification Kit Technical Manual (Promega Literature #TM349, February 2011); the E.Z.N.A.® FFPE DNA Kit Handbook (OMEGA bio-tek, Norcross, GA, product numbers D3399-00, D3399-01, and D3399-02, June 2009); and the QIAamp® DNA FFPE Tissue Handbook (Qiagen, Cat. No. 37625, October 2007). For example, the RecoverAll™ Total Nucleic Acid Isolation Kit uses xylene at elevated temperatures to solubilize paraffin- embedded samples and a glass-fiber filter to capture nucleic acids. The Maxwell® 16 FFPE Plus LEV DNA Purification Kit is used with the Maxwell® 16 Instrument for purification of genomic DNA from 1 to 10 pm sections of FFPE tissue. DNA is purified using silica-clad paramagnetic particles (PMPs), and eluted in low elution volume. The E.Z.N.A.® FFPE DNA Kit uses a spin column and buffer system for isolation of genomic DNA. QIAamp® DNA FFPE Tissue Kit uses QIAamp® DNA Micro technology for purification of genomic and mitochondrial DNA.

[0222] In some instances, the disclosed methods may further comprise determining or acquiring a yield value for the nucleic acid extracted from the sample and comparing the determined value to a reference value. For example, if the determined or acquired value is less than the reference value, the nucleic acids may be amplified prior to proceeding with library construction. In some instances, the disclosed methods may further comprise determining or acquiring a value for the size (or average size) of nucleic acid fragments in the sample, and comparing the determined or acquired value to a reference value, e.g., a size (or average size) of at least 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 base pairs (bps). In some instances, one or more parameters described herein may be adjusted or selected in response to this determination.

[0223] After isolation, the nucleic acids are typically dissolved in a slightly alkaline buffer, e.g., Tris-EDTA (TE) buffer, or in ultra-pure water. In some instances, the isolated nucleic acids (e.g., genomic DNA) may be fragmented or sheared by using any of a variety of techniques known to those of skill in the art. For example, genomic DNA can be fragmented by physical shearing methods, enzymatic cleavage methods, chemical cleavage methods, and other methods known to those of skill in the art. Methods for DNA shearing are described in Example 4 in International Patent Application Publication No. WO 2012/092426. In some instances, alternatives to DNA shearing methods can be used to avoid a ligation step during library preparation.

Library preparation

[0224] In some instances, the nucleic acids isolated from the sample may be used to construct a library (e.g., a nucleic acid library as described herein). In some instances, the nucleic acids are fragmented using any of the methods described above, optionally subjected to repair of chain end damage, and optionally ligated to synthetic adapters, primers, and/or barcodes (e.g., amplification primers, sequencing adapters, flow cell adapters, substrate adapters, sample barcodes or indexes, and/or unique molecular identifier sequences), size-selected (e.g., by preparative gel electrophoresis), and/or amplified (e.g., using PCR, a non-PCR amplification technique, or an isothermal amplification technique). In some instances, the fragmented and adapter-ligated group of nucleic acids is used without explicit size selection or amplification prior to hybridization-based selection of target sequences. In some instances, the nucleic acid is amplified by any of a variety of specific or non-specific nucleic acid amplification methods known to those of skill in the art. In some instances, the nucleic acids are amplified, e.g., by a whole-genome amplification method such as random-primed strand-displacement amplification. Examples of nucleic acid library preparation techniques for next-generation sequencing are described in, e.g., van Dijk, et al. (2014), Exp. Cell Research 322: 12 - 20, and Illumina’s genomic DNA sample preparation kit. [0225] In some instances, the resulting nucleic acid library may contain all or substantially all of the complexity of the genome. The term “substantially all” in this context refers to the possibility that there can in practice be some unwanted loss of genome complexity during the initial steps of the procedure. The methods described herein also are useful in cases where the nucleic acid library comprises a portion of the genome, e.g., where the complexity of the genome is reduced by design. In some instances, any selected portion of the genome can be used with a method described herein. For example, in certain embodiments, the entire exome or a subset thereof is isolated. In some instances, the library may include at least 95%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10%, or 5% of the genomic DNA. In some instances, the library may consist of cDNA copies of genomic DNA that includes copies of at least 95%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10%, or 5% of the genomic DNA. In certain instances, the amount of nucleic acid used to generate the nucleic acid library may be less than 5 micrograms, less than 1 microgram, less than 500 ng, less than 200 ng, less than 100 ng, less than 50 ng, less than 10 ng, less than 5 ng, or less than 1 ng.

[0226] In some instances, a library (e.g., a nucleic acid library) includes a collection of nucleic acid molecules. As described herein, the nucleic acid molecules of the library can include a target nucleic acid molecule (e.g., a tumor nucleic acid molecule, a reference nucleic acid molecule and/or a control nucleic acid molecule; also referred to herein as a first, second and/or third nucleic acid molecule, respectively). The nucleic acid molecules of the library can be from a single subject or individual. In some instances, a library can comprise nucleic acid molecules derived from more than one subject (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30 or more subjects). For example, two or more libraries from different subjects can be combined to form a library having nucleic acid molecules from more than one subject (where the nucleic acid molecules derived from each subject are optionally ligated to a unique sample barcode corresponding to a specific subject). In some instances, the subject is a human having, or at risk of having, a cancer or tumor.

[0227] In some instances, the library (or a portion thereof) may comprise one or more subgenomic intervals. In some instances, a subgenomic interval can be a single nucleotide position, e.g., a nucleotide position for which a variant at the position is associated (positively or negatively) with a tumor phenotype. In some instances, a subgenomic interval comprises more than one nucleotide position. Such instances include sequences of at least 2, 5, 10, 50, 100, 150, 250, or more than 250 nucleotide positions in length. Subgenomic intervals can comprise, e.g., one or more entire genes (or portions thereof), one or more exons or coding sequences (or portions thereof), one or more introns (or portion thereof), one or more microsatellite region (or portions thereof), or any combination thereof. A subgenomic interval can comprise all or a part of a fragment of a naturally occurring nucleic acid molecule, e.g., a genomic DNA molecule. For example, a subgenomic interval can correspond to a fragment of genomic DNA which is subjected to a sequencing reaction. In some instances, a subgenomic interval is a continuous sequence from a genomic source. In some instances, a subgenomic interval includes sequences that are not contiguous in the genome, e.g., subgenomic intervals in cDNA can include exonexonjunctions formed as a result of splicing. In some instances, the subgenomic interval comprises a tumor nucleic acid molecule. In some instances, the subgenomic interval comprises a non-tumor nucleic acid molecule.

Targeting gene loci for analysis

[0228] The methods described herein can be used in combination with, or as part of, a method for evaluating a plurality or set of subject intervals (e.g., target sequences), e.g., from a set of genomic loci (e.g., gene loci or fragments thereof), as described herein.

[0229] In some instances, the set of genomic loci evaluated by the disclosed methods comprises a plurality of, e.g., genes, which in mutant form, are associated with an effect on cell division, growth or survival, or are associated with a cancer, e.g., a cancer described herein.

[0230] In some instances, the set of gene loci evaluated by the disclosed methods comprises at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, or more than 100 gene loci.

[0231] In some instances, the selected gene loci (also referred to herein as target gene loci or target sequences), or fragments thereof, may include subject intervals comprising non-coding sequences, coding sequences, intragenic regions, or intergenic regions of the subject genome. For example, the subject intervals can include a non-coding sequence or fragment thereof (e.g., a promoter sequence, enhancer sequence, 5’ untranslated region (5’ UTR), 3’ untranslated region (3’ UTR), or a fragment thereof), a coding sequence of fragment thereof, an exon sequence or fragment thereof, an intron sequence or a fragment thereof.

Target capture reagents

[0232] The methods described herein may comprise contacting a nucleic acid library with a plurality of target capture reagents in order to select and capture a plurality of specific target sequences (e.g., gene sequences or fragments thereof) for analysis. In some instances, a target capture reagent (i.e., a molecule which can bind to and thereby allow capture of a target molecule) is used to select the subject intervals to be analyzed. For example, a target capture reagent can be a bait molecule, e.g., a nucleic acid molecule (e.g., a DNA molecule or RNA molecule) which can hybridize to (i.e., is complementary to) a target molecule, and thereby allows capture of the target nucleic acid. In some instances, the target capture reagent, e.g., a bait molecule (or bait sequence), is a capture oligonucleotide (or capture probe). In some instances, the target nucleic acid is a genomic DNA molecule, an RNA molecule, a cDNA molecule derived from an RNA molecule, a microsatellite DNA sequence, and the like. In some instances, the target capture reagent is suitable for solution-phase hybridization to the target. In some instances, the target capture reagent is suitable for solid-phase hybridization to the target. In some instances, the target capture reagent is suitable for both solution-phase and solid-phase hybridization to the target. The design and construction of target capture reagents is described in more detail in, e.g., International Patent Application Publication No. WO 2020/236941, the entire content of which is incorporated herein by reference.

[0233] The methods described herein provide for optimized sequencing of a large number of genomic loci (e.g., genes or gene products (e.g., mRNA), micro satellite loci, etc.) from samples (e.g., cancerous tissue specimens, liquid biopsy samples, and the like) from one or more subjects by the appropriate selection of target capture reagents to select the target nucleic acid molecules to be sequenced. In some instances, a target capture reagent may hybridize to a specific target locus, e.g., a specific target gene locus or fragment thereof. In some instances, a target capture reagent may hybridize to a specific group of target loci, e.g., a specific group of gene loci or fragments thereof. In some instances, a plurality of target capture reagents comprising a mix of target- specific and/or group- specific target capture reagents may be used.

[0234] In some instances, the number of target capture reagents (e.g., bait molecules) in the plurality of target capture reagents (e.g., a bait set) contacted with a nucleic acid library to capture a plurality of target sequences for nucleic acid sequencing is greater than 10, greater than 50, greater than 100, greater than 200, greater than 300, greater than 400, greater than 500, greater than 600, greater than 700, greater than 800, greater than 900, greater than 1,000, greater than 1,250, greater than 1,500, greater than 1,750, greater than 2,000, greater than 3,000, greater than 4,000, greater than 5,000, greater than 10,000, greater than 25,000, or greater than 50,000.

[0235] In some instances, the overall length of the target capture reagent sequence can be between about 70 nucleotides and 1000 nucleotides. In one instance, the target capture reagent length is between about 100 and 300 nucleotides, 110 and 200 nucleotides, or 120 and 170 nucleotides, in length. In addition to those mentioned above, intermediate oligonucleotide lengths of about 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 300, 400, 500, 600, 700, 800, and 900 nucleotides in length can be used in the methods described herein. In some embodiments, oligonucleotides of about 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, or 230 bases can be used.

[0236] In some instances, each target capture reagent sequence can include: (i) a target- specific capture sequence (e.g., a gene locus or micro satellite locus-specific complementary sequence), (ii) an adapter, primer, barcode, and/or unique molecular identifier sequence, and (iii) universal tails on one or both ends. As used herein, the term "target capture reagent" can refer to the targetspecific target capture sequence or to the entire target capture reagent oligonucleotide including the target- specific target capture sequence.

[0237] In some instances, the target- specific capture sequences in the target capture reagents are between about 40 nucleotides and 1000 nucleotides in length. In some instances, the targetspecific capture sequence is between about 70 nucleotides and 300 nucleotides in length. In some instances, the target- specific sequence is between about 100 nucleotides and 200 nucleotides in length. In yet other instances, the target- specific sequence is between about 120 nucleotides and 170 nucleotides in length, typically 120 nucleotides in length. Intermediate lengths in addition to those mentioned above also can be used in the methods described herein, such as target-specific sequences of about 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 300, 400, 500, 600, 700, 800, and 900 nucleotides in length, as well as target- specific sequences of lengths between the above-mentioned lengths.

[0238] In some instances, the target capture reagent may be designed to select a subject interval containing one or more rearrangements, e.g., an intron containing a genomic rearrangement. In such instances, the target capture reagent is designed such that repetitive sequences are masked to increase the selection efficiency. In those instances where the rearrangement has a known juncture sequence, complementary target capture reagents can be designed to recognize the juncture sequence to increase the selection efficiency.

[0239] In some instances, the disclosed methods may comprise the use of target capture reagents designed to capture two or more different target categories, each category having a different target capture reagent design strategy. In some instances, the hybridization-based capture methods and target capture reagent compositions disclosed herein may provide for the capture and homogeneous coverage of a set of target sequences, while minimizing coverage of genomic sequences outside of the targeted set of sequences. In some instances, the target sequences may include the entire exome of genomic DNA or a selected subset thereof. In some instances, the target sequences may include, e.g., a large chromosomal region (e.g., a whole chromosome arm). The methods and compositions disclosed herein provide different target capture reagents for achieving different sequencing depths and patterns of coverage for complex sets of target nucleic acid sequences.

[0240] Typically, DNA molecules are used as target capture reagent sequences, although RNA molecules can also be used. In some instances, a DNA molecule target capture reagent can be single stranded DNA (ssDNA) or double- stranded DNA (dsDNA). In some instances, an RNA- DNA duplex is more stable than a DNA-DNA duplex and therefore provides for potentially better capture of nucleic acids. [0241] In some instances, the disclosed methods comprise providing a selected set of nucleic acid molecules (e.g., a library catch) captured from one or more nucleic acid libraries. For example, the method may comprise: providing one or a plurality of nucleic acid libraries, each comprising a plurality of nucleic acid molecules (e.g., a plurality of target nucleic acid molecules and/or reference nucleic acid molecules) extracted from one or more samples from one or more subjects; contacting the one or a plurality of libraries (e.g., in a solution-based hybridization reaction) with one, two, three, four, five, or more than five pluralities of target capture reagents (e.g., oligonucleotide target capture reagents) to form a hybridization mixture comprising a plurality of target capture reagent/nucleic acid molecule hybrids; separating the plurality of target capture reagent/nucleic acid molecule hybrids from said hybridization mixture, e.g., by contacting said hybridization mixture with a binding entity that allows for separation of said plurality of target capture reagent/nucleic acid molecule hybrids from the hybridization mixture, thereby providing a library catch (e.g., a selected or enriched subgroup of nucleic acid molecules from the one or a plurality of libraries).

[0242] In some instances, the disclosed methods may further comprise amplifying the library catch (e.g., by performing PCR). In other instances, the library catch is not amplified.

[0243] In some instances, the target capture reagents can be part of a kit which can optionally comprise instructions, standards, buffers or enzymes or other reagents.

Hybridization conditions

[0244] As noted above, the methods disclosed herein may include the step of contacting the library (e.g., the nucleic acid library) with a plurality of target capture reagents to provide a selected library target nucleic acid sequences (z.e., the library catch). The contacting step can be effected in, e.g., solution-based hybridization. In some instances, the method includes repeating the hybridization step for one or more additional rounds of solution-based hybridization. In some instances, the method further includes subjecting the library catch to one or more additional rounds of solution-based hybridization with the same or a different collection of target capture reagents. [0245] In some instances, the contacting step is effected using a solid support, e.g., an array. Suitable solid supports for hybridization are described in, e.g., Albert, T.J. et al. (2007) Nat. Methods 4(l l):903-5; Hodges, E. et al. (2007) Nat. Genet. 39(12): 1522-7; and Okou, D.T. et al. (2007) Nat. Methods 4(11):907-9, the contents of which are incorporated herein by reference in their entireties.

[0246] Hybridization methods that can be adapted for use in the methods herein are described in the art, e.g., as described in International Patent Application Publication No. WO 2012/092426. Methods for hybridizing target capture reagents to a plurality of target nucleic acids are described in more detail in, e.g., International Patent Application Publication No. WO 2020/236941, the entire content of which is incorporated herein by reference.

Sequencing methods

[0247] The methods and systems disclosed herein can be used in combination with, or as part of, a method or system for sequencing nucleic acids (e.g., a next-generation sequencing system) to generate a plurality of sequence reads that overlap one or more gene loci within a subgenomic interval in the sample and thereby determine, e.g., gene allele sequences at a plurality of gene loci. “Next-generation sequencing” (or “NGS”) as used herein may also be referred to as “massively parallel sequencing” (or “MPS”), and refers to any sequencing method that determines the nucleotide sequence of either individual nucleic acid molecules (e.g., as in single molecule sequencing) or clonally expanded proxies for individual nucleic acid molecules in a high throughput fashion (e.g., wherein greater than 10 ³ , 10 ⁴ , 10 ⁵ or more than 10 ⁵ molecules are sequenced simultaneously).

[0248] Next-generation sequencing methods are known in the art, and are described in, e.g., Metzker, M. (2010) Nature Biotechnology Reviews 11:31-46, which is incorporated herein by reference. Other examples of sequencing methods suitable for use when implementing the methods and systems disclosed herein are described in, e.g., International Patent Application Publication No. WO 2012/092426. In some instances, the sequencing may comprise, for example, whole genome sequencing (WGS), whole exome sequencing, targeted sequencing, or direct sequencing. In some instances, sequencing may be performed using, e.g., Sanger sequencing. In some instances, the sequencing may comprise a paired-end sequencing technique that allows both ends of a fragment to be sequenced and generates high-quality, alignable sequence data for detection of, e.g., genomic rearrangements, repetitive sequence elements, gene fusions, and novel transcripts.

[0249] The disclosed methods and systems may be implemented using sequencing platforms such as the Roche 454, Illumina Solexa, ABI-SOLiD, ION Torrent, Complete Genomics, Pacific Bioscience, Helicos, and/or the Polonator platform. In some instances, sequencing may comprise Illumina MiSeq sequencing. In some instances, sequencing may comprise Illumina HiSeq sequencing. In some instances, sequencing may comprise Illumina NovaSeq sequencing. Optimized methods for sequencing a large number of target genomic loci in nucleic acids extracted from a sample are described in more detail in, e.g., International Patent Application Publication No. WO 2020/236941, the entire content of which is incorporated herein by reference.

[0250] In certain instances, the disclosed methods comprise one or more of the steps of: (a) acquiring a library comprising a plurality of normal and/or tumor nucleic acid molecules from a sample; (b) simultaneously or sequentially contacting the library with one, two, three, four, five, or more than five pluralities of target capture reagents under conditions that allow hybridization of the target capture reagents to the target nucleic acid molecules, thereby providing a selected set of captured normal and/or tumor nucleic acid molecules (z.e., a library catch); (c) separating the selected subset of the nucleic acid molecules (e.g., the library catch) from the hybridization mixture, e.g., by contacting the hybridization mixture with a binding entity that allows for separation of the target capture reagent/nucleic acid molecule hybrids from the hybridization mixture, (d) sequencing the library catch to acquiring a plurality of reads (e.g., sequence reads) that overlap one or more subject intervals (e.g., one or more target sequences) from said library catch that may comprise a mutation (or alteration), e.g., a variant sequence comprising a somatic mutation or germline mutation; (e) aligning said sequence reads using an alignment method as described elsewhere herein; and/or (f) assigning a nucleotide value for a nucleotide position in the subject interval (e.g., calling a mutation using, e.g., a Bayesian method or other method described herein) from one or more sequence reads of the plurality. [0251] In some instances, acquiring sequence reads for one or more subject intervals may comprise sequencing at least 1, at least 5, at least 10, at least 20, at least 30, at least 40, at least 50, at least 100, at least 150, at least 200, at least 250, at least 300, at least 350, at least 400, at least 450, at least 500, at least 550, at least 600, at least 650, at least 700, at least 750, at least 800, at least 850, at least 900, at least 950, at least 1,000, at least 1,250, at least 1,500, at least 1,750, at least 2,000, at least 2,250, at least 2,500, at least 2,750, at least 3,000, at least 3,500, at least 4,000, at least 4,500, or at least 5,000 loci, e.g., genomic loci, gene loci, microsatellite loci, etc. In some instances, acquiring a sequence read for one or more subject intervals may comprise sequencing a subject interval for any number of loci within the range described in this paragraph, e.g., for at least 2,850 gene loci.

[0252] In some instances, acquiring a sequence read for one or more subject intervals comprises sequencing a subject interval with a sequencing method that provides a sequence read length (or average sequence read length) of at least 20 bases, at least 30 bases, at least 40 bases, at least 50 bases, at least 60 bases, at least 70 bases, at least 80 bases, at least 90 bases, at least 100 bases, at least 120 bases, at least 140 bases, at least 160 bases, at least 180 bases, at least 200 bases, at least 220 bases, at least 240 bases, at least 260 bases, at least 280 bases, at least 300 bases, at least 320 bases, at least 340 bases, at least 360 bases, at least 380 bases, or at least 400 bases. In some instances, acquiring a sequence read for the one or more subject intervals may comprise sequencing a subject interval with a sequencing method that provides a sequence read length (or average sequence read length) of any number of bases within the range described in this paragraph, e.g., a sequence read length (or average sequence read length) of 56 bases.

[0253] In some instances, acquiring a sequence read for one or more subject intervals may comprise sequencing with at least lOOx or more coverage (or depth) on average. In some instances, acquiring a sequence read for one or more subject intervals may comprise sequencing with at least lOOx, at least 150x, at least 200x, at least 250x, at least 500x, at least 750x, at least l,000x, at least 1,500 x, at least 2,000x, at least 2,500x, at least 3,000x, at least 3,500x, at least 4,000x, at least 4,500x, at least 5,000x, at least 5,500x, or at least 6,000x or more coverage (or depth) on average. In some instances, acquiring a sequence read for one or more subject intervals may comprise sequencing with an average coverage (or depth) having any value within the range of values described in this paragraph, e.g., at least 160x.

[0254] In some instances, acquiring a read for the one or more subject intervals comprises sequencing with an average sequencing depth having any value ranging from at least lOOx to at least 6,000x for greater than about 90%, 92%, 94%, 95%, 96%, 97%, 98%, or 99% of the gene loci sequenced. For example, in some instances acquiring a read for the subject interval comprises sequencing with an average sequencing depth of at least 125x for at least 99% of the gene loci sequenced. As another example, in some instances acquiring a read for the subject interval comprises sequencing with an average sequencing depth of at least 4,100x for at least 95% of the gene loci sequenced.

[0255] In some instances, the relative abundance of a nucleic acid species in the library can be estimated by counting the relative number of occurrences of their cognate sequences (e.g., the number of sequence reads for a given cognate sequence) in the data generated by the sequencing experiment.

[0256] In some instances, the disclosed methods and systems provide nucleotide sequences for a set of subject intervals (e.g., gene loci), as described herein. In certain instances, the sequences are provided without using a method that includes a matched normal control (e.g., a wild-type control) and/or a matched tumor control (e.g., primary versus metastatic).

[0257] In some instances, the level of sequencing depth as used herein (e.g., an X-fold level of sequencing depth) refers to the number of reads (e.g., unique reads) obtained after detection and removal of duplicate reads (e.g., PCR duplicate reads). In other instances, duplicate reads are evaluated, e.g., to support detection of copy number alteration (CNAs).

Alignment

[0258] Alignment is the process of matching a read with a location, e.g., a genomic location or locus. In some instances, NGS reads may be aligned to a known reference sequence (e.g., a wild-type sequence). In some instances, NGS reads may be assembled de novo. Methods of sequence alignment for NGS reads are described in, e.g., Trapnell, C. and Salzberg, S.L. Nature Biotech., 2009, 27:455-457. Examples of de novo sequence assemblies are described in, e.g., Warren R., et al., Bioinformatics, 2007, 23:500-501; Butler, J. et al., Genome Res., 2008, 18:810-820; and Zerbino, D.R. and Birney, E., Genome Res., 2008, 18:821-829. Optimization of sequence alignment is described in the art, e.g., as set out in International Patent Application Publication No. WO 2012/092426. Additional description of sequence alignment methods is provided in, e.g., International Patent Application Publication No. WO 2020/236941, the entire content of which is incorporated herein by reference.

[0259] Misalignment (e.g., the placement of base-pairs from a short read at incorrect locations in the genome), e.g., misalignment of reads due to sequence context (e.g., the presence of repetitive sequence) around an actual cancer mutation can lead to reduction in sensitivity of mutation detection, can lead to a reduction in sensitivity of mutation detection, as reads for the alternate allele may be shifted off the histogram peak of alternate allele reads. Other examples of sequence context that may cause misalignment include short-tandem repeats, interspersed repeats, low complexity regions, insertions - deletions (indels), and paralogs. If the problematic sequence context occurs where no actual mutation is present, misalignment may introduce artifactual reads of “mutated” alleles by placing reads of actual reference genome base sequences at the wrong location. Because mutation-calling algorithms for multigene analysis should be sensitive to even low-abundance mutations, sequence misalignments may increase false positive discovery rates and/or reduce specificity.

[0260] In some instances, the methods and systems disclosed herein may integrate the use of multiple, individually-tuned, alignment methods or algorithms to optimize base-calling performance in sequencing methods, particularly in methods that rely on massively parallel sequencing (MPS) of a large number of diverse genetic events at a large number of diverse genomic loci. In some instances, the disclosed methods and systems may comprise the use of one or more global alignment algorithms. In some instances, the disclosed methods and systems may comprise the use of one or more local alignment algorithms. Examples of alignment algorithms that may be used include, but are not limited to, the Burrows-Wheeler Alignment (BWA) software bundle (see, e.g., Li, et al. (2009), “Fast and Accurate Short Read Alignment with Burrows-Wheeler Transform”, Bioinformatics 25: 1754-60; Li, et al. (2010), Fast and Accurate Long-Read Alignment with Burrows-Wheeler Transform”, Bioinformatics epub.

PMID: 20080505), the Smith- Waterman algorithm (see, e.g., Smith, et al. (1981), "Identification of Common Molecular Subsequences", J. Molecular Biology 147(1): 195-197), the Striped Smith- Waterman algorithm (see, e.g., Farrar (2007), “Striped Smith-Waterman Speeds Database Searches Six Times Over Other SIMD Implementations”, Bioinformatics 23(2): 156-161), the Needleman-Wunsch algorithm (Needleman, et al. (1970) "A General Method Applicable to the Search for Similarities in the Amino Acid Sequence of Two Proteins", J. Molecular Biology 48(3):443-53), or any combination thereof.

[0261] In some instances, the methods and systems disclosed herein may also comprise the use of a sequence assembly algorithm, e.g., the Arachne sequence assembly algorithm (see, e.g., Batzoglou, et al. (2002), “ARACHNE: A Whole-Genome Shotgun Assembler”, Genome Res. 12: 177-189).

[0262] In some instances, the alignment method used to analyze sequence reads is not individually customized or tuned for detection of different variants (e.g., point mutations, insertions, deletions, and the like) at different genomic loci. In some instances, different alignment methods are used to analyze reads that are individually customized or tuned for detection of at least a subset of the different variants detected at different genomic loci. In some instances, different alignment methods are used to analyze reads that are individually customized or tuned to detect each different variant at different genomic loci. In some instances, tuning can be a function of one or more of: (i) the genetic locus (e.g., gene loci, micro satellite locus, or other subject interval) being sequenced, (ii) the tumor type associated with the sample, (iii) the variant being sequenced, or (iv) a characteristic of the sample or the subject. The selection or use of alignment conditions that are individually tuned to a number of specific subject intervals to be sequenced allows optimization of speed, sensitivity, and specificity. The method is particularly effective when the alignment of reads for a relatively large number of diverse subject intervals are optimized. In some instances, the method includes the use of an alignment method optimized for rearrangements in combination with other alignment methods optimized for subject intervals not associated with rearrangements. [0263] In some instances, the methods disclosed herein further comprise selecting or using an alignment method for analyzing, e.g., aligning, a sequence read, wherein said alignment method is a function of, is selected responsive to, or is optimized for, one or more of: (i) tumor type, e.g., the tumor type in the sample; (ii) the location (e.g., a gene locus) of the subject interval being sequenced; (iii) the type of variant (e.g., a point mutation, insertion, deletion, substitution, copy number variation (CNV), rearrangement, or fusion) in the subject interval being sequenced; (iv) the site (e.g., nucleotide position) being analyzed; (v) the type of sample (e.g., a sample described herein); and/or (vi) adjacent sequence(s) in or near the subject interval being evaluated (e.g., according to the expected propensity thereof for misalignment of the subject interval due to, e.g., the presence of repeated sequences in or near the subject interval).

[0264] In some instances, the methods disclosed herein allow for the rapid and efficient alignment of troublesome reads, e.g., a read having a rearrangement. Thus, in some instances where a read for a subject interval comprises a nucleotide position with a rearrangement, e.g., a translocation, the method can comprise using an alignment method that is appropriately tuned and that includes: (i) selecting a rearrangement reference sequence for alignment with a read, wherein said rearrangement reference sequence aligns with a rearrangement (in some instances, the reference sequence is not identical to the genomic rearrangement); and (ii) comparing, e.g., aligning, a read with said rearrangement reference sequence.

[0265] In some instances, alternative methods may be used to align troublesome reads. These methods are particularly effective when the alignment of reads for a relatively large number of diverse subject intervals is optimized. By way of example, a method of analyzing a sample can comprise: (i) performing a comparison (e.g., an alignment comparison) of a read using a first set of parameters (e.g., using a first mapping algorithm, or by comparison with a first reference sequence), and determining if said read meets a first alignment criterion (e.g., the read can be aligned with said first reference sequence, e.g., with less than a specific number of mismatches); (ii) if said read fails to meet the first alignment criterion, performing a second alignment comparison using a second set of parameters, (e.g., using a second mapping algorithm, or by comparison with a second reference sequence); and (iii) optionally, determining if said read meets said second criterion (e.g., the read can be aligned with said second reference sequence, e.g., with less than a specific number of mismatches), wherein said second set of parameters comprises use of, e.g., said second reference sequence, which, compared with said first set of parameters, is more likely to result in an alignment with a read for a variant (e.g., a rearrangement, insertion, deletion, or translocation).

[0266] In some instances, the alignment of sequence reads in the disclosed methods may be combined with a mutation calling method as described elsewhere herein. As discussed herein, reduced sensitivity for detecting actual mutations may be addressed by evaluating the quality of alignments (manually or in an automated fashion) around expected mutation sites in the genes or genomic loci (e.g., gene loci) being analyzed. In some instances, the sites to be evaluated can be obtained from databases of the human genome (e.g., the HG19 human reference genome) or cancer mutations (e.g., COSMIC). Regions that are identified as problematic can be remedied with the use of an algorithm selected to give better performance in the relevant sequence context, e.g., by alignment optimization (or re-alignment) using slower, but more accurate alignment algorithms such as Smith- Waterman alignment. In cases where general alignment algorithms cannot remedy the problem, customized alignment approaches may be created by, e.g., adjustment of maximum difference mismatch penalty parameters for genes with a high likelihood of containing substitutions; adjusting specific mismatch penalty parameters based on specific mutation types that are common in certain tumor types (e.g. C~ T in melanoma); or adjusting specific mismatch penalty parameters based on specific mutation types that are common in certain sample types (e.g. substitutions that are common in FFPE).

[0267] Reduced specificity (increased false positive rate) in the evaluated subject intervals due to misalignment can be assessed by manual or automated examination of all mutation calls in the sequencing data. Those regions found to be prone to spurious mutation calls due to misalignment can be subjected to alignment remedies as discussed above. In cases where no algorithmic remedy is found possible, “mutations” from the problem regions can be classified or screened out from the panel of targeted loci. Mutation calling

[0268] Base calling refers to the raw output of a sequencing device, e.g., the determined sequence of nucleotides in an oligonucleotide molecule. Mutation calling refers to the process of selecting a nucleotide value, e.g., A, G, T, or C, for a given nucleotide position being sequenced. Typically, the sequence reads (or base calling) for a position will provide more than one value, e.g., some reads will indicate a T and some will indicate a G. Mutation calling is the process of assigning a correct nucleotide value, e.g., one of those values, to the sequence. Although it is referred to as “mutation” calling, it can be applied to assign a nucleotide value to any nucleotide position, e.g., positions corresponding to mutant alleles, wild-type alleles, alleles that have not been characterized as either mutant or wild-type, or to positions not characterized by variability.

[0269] In some instances, the disclosed methods may comprise the use of customized or tuned mutation calling algorithms or parameters thereof to optimize performance when applied to sequencing data, particularly in methods that rely on massively parallel sequencing (MPS) of a large number of diverse genetic events at a large number of diverse genomic loci (e.g., gene loci, micro satellite regions, etc.) in samples, e.g., samples from a subject having cancer. Optimization of mutation calling is described in the art, e.g., as set out in International Patent Application Publication No. WO 2012/092426.

[0270] Methods for mutation calling can include one or more of the following: making independent calls based on the information at each position in the reference sequence (e.g., examining the sequence reads; examining the base calls and quality scores; calculating the probability of observed bases and quality scores given a potential genotype; and assigning genotypes (e.g., using Bayes’ rule)); removing false positives (e.g., using depth thresholds to reject SNPs with read depth much lower or higher than expected; local realignment to remove false positives due to small indels); and performing linkage disequilibrium (LD)/imputation- based analysis to refine the calls.

[0271] Equations used to calculate the genotype likelihood associated with a specific genotype and position are described in, e.g., Li, H. and Durbin, R. Bioinformatics, 2010; 26(5): 589-95. The prior expectation for a particular mutation in a certain cancer type can be used when evaluating samples from that cancer type. Such likelihood can be derived from public databases of cancer mutations, e.g., Catalogue of Somatic Mutation in Cancer (COSMIC), HGMD (Human Gene Mutation Database), The SNP Consortium, Breast Cancer Mutation Data Base (BIC), and Breast Cancer Gene Database (BCGD).

[0272] Examples of LD/imputation based analysis are described in, e.g., Browning, B.L. and Yu, Z. Am. J. Hum. Genet. 2009, 85(6):847-61. Examples of low-coverage SNP calling methods are described in, e.g., Li, Y., et al., Annu. Rev. Genomics Hum. Genet. 2009, 10:387-406.

[0273] After alignment, detection of substitutions can be performed using a mutation calling method (e.g., a Bayesian mutation calling method) which is applied to each base in each of the subject intervals, e.g., exons of a gene or other locus to be evaluated, where presence of alternate alleles is observed. This method will compare the probability of observing the read data in the presence of a mutation with the probability of observing the read data in the presence of basecalling error alone. Mutations can be called if this comparison is sufficiently strongly supportive of the presence of a mutation.

[0274] An advantage of a Bayesian mutation detection approach is that the comparison of the probability of the presence of a mutation with the probability of base-calling error alone can be weighted by a prior expectation of the presence of a mutation at the site. If some reads of an alternate allele are observed at a frequently mutated site for the given cancer type, then presence of a mutation may be confidently called even if the amount of evidence of mutation does not meet the usual thresholds. This flexibility can then be used to increase detection sensitivity for even rarer mutations/lower purity samples, or to make the test more robust to decreases in read coverage. The likelihood of a random base-pair in the genome being mutated in cancer is ~le-6. The likelihood of specific mutations occurring at many sites in, for example, a typical multigenic cancer genome panel can be orders of magnitude higher. These likelihoods can be derived from public databases of cancer mutations (e.g., COSMIC).

[0275] Indel calling is a process of finding bases in the sequencing data that differ from the reference sequence by insertion or deletion, typically including an associated confidence score or statistical evidence metric. Methods of indel calling can include the steps of identifying candidate indels, calculating genotype likelihood through local re-alignment, and performing LD-based genotype inference and calling. Typically, a Bayesian approach is used to obtain potential indel candidates, and then these candidates are tested together with the reference sequence in a Bayesian framework.

[0276] Algorithms to generate candidate indels are described in, e.g., McKenna, A., et al., Genome Res. 2010; 20(9): 1297-303; Ye, K., et al., Bioinformatics, 2009; 25(21):2865-71; Lunter, G., and Goodson, M., Genome Res. 2011; 21(6):936-9; and Li, H., et al. (2009), Bioinformatics 25(16):2078-9.

[0277] Methods for generating indel calls and individual-level genotype likelihoods include, e.g., the Dindel algorithm (Albers, C.A., et al., Genome Res. 2011;21(6):961-73). For example, the Bayesian EM algorithm can be used to analyze the reads, make initial indel calls, and generate genotype likelihoods for each candidate indel, followed by imputation of genotypes using, e.g., QCALL (Le S.Q. and Durbin R. Genome Res. 2011;21(6):952-60). Parameters, such as prior expectations of observing the indel can be adjusted {e.g., increased or decreased), based on the size or location of the indels.

[0278] Methods have been developed that address limited deviations from allele frequencies of 50% or 100% for the analysis of cancer DNA. (see, e.g., SNVMix -Bioinformatics. 2010 March 15; 26(6): 730-736.) Methods disclosed herein, however, allow consideration of the possibility of the presence of a mutant allele at frequencies (or allele fractions) ranging from 1% to 100% (z.e., allele fractions ranging from 0.01 to 1.0), and especially at levels lower than 50%. This approach is particularly important for the detection of mutations in, for example, low-purity FFPE samples of natural (multi-clonal) tumor DNA.

[0279] In some instances, the mutation calling method used to analyze sequence reads is not individually customized or fine-tuned for detection of different mutations at different genomic loci. In some instances, different mutation calling methods are used that are individually customized or fine-tuned for at least a subset of the different mutations detected at different genomic loci. In some instances, different mutation calling methods are used that are individually customized or fine-tuned for each different mutant detected at each different genomic loci. The customization or tuning can be based on one or more of the factors described herein, e.g., the type of cancer in a sample, the gene or locus in which the subject interval to be sequenced is located, or the variant to be sequenced. This selection or use of mutation calling methods individually customized or fine-tuned for a number of subject intervals to be sequenced allows for optimization of speed, sensitivity and specificity of mutation calling.

[0280] In some instances, a nucleotide value is assigned for a nucleotide position in each of X unique subject intervals using a unique mutation calling method, and X is at least 2, at least 3, at least 4, at least 5, at least 10, at least 15, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, at least 200, at least 300, at least 400, at least 500, at least 1000, at least 1500, at least 2000, at least 2500, at least 3000, at least 3500, at least 4000, at least 4500, at least 5000, or greater. The calling methods can differ, and thereby be unique, e.g., by relying on different Bayesian prior values.

[0281] In some instances, assigning said nucleotide value is a function of a value which is or represents the prior (e.g., literature) expectation of observing a read showing a variant, e.g., a mutation, at said nucleotide position in a tumor of type.

[0282] In some instances, the method comprises assigning a nucleotide value (e.g., calling a mutation) for at least 10, 20, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1,000 nucleotide positions, wherein each assignment is a function of a unique value (as opposed to the value for the other assignments) which is or represents the prior (e.g., literature) expectation of observing a read showing a variant, e.g., a mutation, at said nucleotide position in a tumor of type.

[0283] In some instances, assigning said nucleotide value is a function of a set of values which represent the probabilities of observing a read showing said variant at said nucleotide position if the variant is present in the sample at a specified frequency (e.g., 1%, 5%, 10%, etc.) and/or if the variant is absent (e.g., observed in the reads due to base-calling error alone).

[0284] In some instances, the mutation calling methods described herein can include the following: (a) acquiring, for a nucleotide position in each of said X subject intervals: (i) a first value which is or represents the prior (e.g., literature) expectation of observing a read showing a variant, e.g., a mutation, at said nucleotide position in a tumor of type X; and (ii) a second set of values which represent the probabilities of observing a read showing said variant at said nucleotide position if the variant is present in the sample at a frequency (e.g., 1%, 5%, 10%, etc.) and/or if the variant is absent (e.g., observed in the reads due to base-calling error alone); and (b) responsive to said values, assigning a nucleotide value (e.g., calling a mutation) from said reads for each of said nucleotide positions by weighing, e.g., by a Bayesian method described herein, the comparison among the values in the second set using the first value (e.g., computing the posterior probability of the presence of a mutation), thereby analyzing said sample.

[0285] Additional description of mutation calling methods is provided in, e.g., International Patent Application Publication No. WO 2020/236941, the entire content of which is incorporated herein by reference.

Systems

[0286] Also disclosed herein are systems designed to implement any of the disclosed methods for assigning a genomic variant to a functional status based on a sample from a subject. The systems may comprise, e.g., one or more processors, and a memory unit communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to perform a method comprising: receiving, at one or more processors, sequence read data associated with a genomic variant in a sample from an individual; determining, using the one or more processors, a breakpoint of the genomic variant and a location of the breakpoint based on the sequence read data, the breakpoint associated with a rearrangement event and further associated with a first gene and a second gene of the sample, the first gene corresponding to the genomic variant; if the breakpoint of the genomic variant is located outside a sequence encoding a predetermined protein domain, performing, using the one or more processors: a first determination of whether the rearrangement event is in-strand; and a second determination of whether the sequence encoding the predetermined protein domain is impacted by the rearrangement event; assigning, using the one or more processors, the genomic variant to a functional status group based on the first determination and the second determination; and identifying, using the one or more processors, the treatment based on the assigned functional status group of the genomic variant. [0287] In some instances, the disclosed systems may further comprise a sequencer, e.g., a next generation sequencer (also referred to as a massively parallel sequencer). Examples of next generation (or massively parallel) sequencing platforms include, but are not limited to, Roche/454’s Genome Sequencer (GS) FLX system, Illumina/Solexa’s Genome Analyzer (GA), Illumina’s HiSeq® 2500, HiSeq® 3000, HiSeq® 4000 and NovaSeq® 6000 sequencing systems, Life/APG’s Support Oligonucleotide Ligation Detection (SOLiD) system, Polonator’s G.007 system, Helicos BioSciences’ HeliScope Gene Sequencing system, ThermoFisher Scientific’s Ion Torrent Genexus system, or Pacific Biosciences’ PacBio® RS system.

[0288] In some instances, the disclosed systems may be used for assigning a genomic variant to a functional status in any of a variety of samples as described herein (e.g., a tissue sample, biopsy sample, hematological sample, or liquid biopsy sample derived from the subject).

[0289] In some instances, the plurality of gene loci for which sequencing data is processed to assign a genomic variant to the functional status may comprise at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more than 10 gene loci.

[0290] In some instance, the nucleic acid sequence data is acquired using a next generation sequencing technique (also referred to as a massively parallel sequencing technique) having a read-length of less than 400 bases, less than 300 bases, less than 200 bases, less than 150 bases, less than 100 bases, less than 90 bases, less than 80 bases, less than 70 bases, less than 60 bases, less than 50 bases, less than 40 bases, or less than 30 bases.

[0291] In some instances, the assigning a genomic variant to a functional status is used to select, initiate, adjust, or terminate a treatment for cancer in the subject (e.g., a patient) from which the sample was derived, as described elsewhere herein.

[0292] In some instances, the disclosed systems may further comprise sample processing and library preparation workstations, microplate-handling robotics, fluid dispensing systems, temperature control modules, environmental control chambers, additional data storage modules, data communication modules (e.g., Bluetooth®, WiFi, intranet, or internet communication hardware and associated software), display modules, one or more local and/or cloud-based software packages (e.g., instrument / system control software packages, sequencing data analysis software packages), etc., or any combination thereof. In some instances, the systems may comprise, or be part of, a computer system or computer network as described elsewhere herein.

Computer systems and networks

[0293] FIG. 7 illustrates an example of a computing device or system in accordance with one embodiment. Device 700 can be a host computer connected to a network. Device 700 can be a client computer or a server. As shown in FIG. 7, device 700 can be any suitable type of microprocessor-based device, such as a personal computer, workstation, server or handheld computing device (portable electronic device) such as a phone or tablet. The device can include, for example, one or more processor(s) 710, input devices 720, output devices 730, memory or storage devices 740, communication devices 760, and nucleic acid sequencers 770. Software 750 residing in memory or storage device 740 may comprise, e.g., an operating system as well as software for executing the methods described herein. Input device 720 and output device 730 can generally correspond to those described herein, and can either be connectable or integrated with the computer.

[0294] Input device 720 can be any suitable device that provides input, such as a touch screen, keyboard or keypad, mouse, or voice-recognition device. Output device 730 can be any suitable device that provides output, such as a touch screen, haptics device, or speaker.

[0295] Storage 740 can be any suitable device that provides storage (e.g., an electrical, magnetic or optical memory including a RAM (volatile and non-volatile), cache, hard drive, or removable storage disk). Communication device 760 can include any suitable device capable of transmitting and receiving signals over a network, such as a network interface chip or device. The components of the computer can be connected in any suitable manner, such as via a wired media (e.g., a physical system bus 780, Ethernet connection, or any other wire transfer technology) or wirelessly (e.g., Bluetooth®, Wi-Fi®, or any other wireless technology).

[0296] Software module 750, which can be stored as executable instructions in storage 740 and executed by processor(s) 710, can include, for example, an operating system and/or the processes that embody the functionality of the methods of the present disclosure (e.g., as embodied in the devices as described herein). [0297] Software module 750 can also be stored and/or transported within any non-transitory computer-readable storage medium for use by or in connection with an instruction execution system, apparatus, or device, such as those described herein, that can fetch instructions associated with the software from the instruction execution system, apparatus, or device and execute the instructions. In the context of this disclosure, a computer-readable storage medium can be any medium, such as storage 740, that can contain or store processes for use by or in connection with an instruction execution system, apparatus, or device. Examples of computer- readable storage media may include memory units like hard drives, flash drives and distribute modules that operate as a single functional unit. Also, various processes described herein may be embodied as modules configured to operate in accordance with the embodiments and techniques described above. Further, while processes may be shown and/or described separately, those skilled in the art will appreciate that the above processes may be routines or modules within other processes.

[0298] Software module 750 can also be propagated within any transport medium for use by or in connection with an instruction execution system, apparatus, or device, such as those described above, that can fetch instructions associated with the software from the instruction execution system, apparatus, or device and execute the instructions. In the context of this disclosure, a transport medium can be any medium that can communicate, propagate or transport programming for use by or in connection with an instruction execution system, apparatus, or device. The transport readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic or infrared wired or wireless propagation medium.

[0299] Device 700 may be connected to a network (e.g., network 804, as shown in FIG. 8 and/or described below), which can be any suitable type of interconnected communication system. The network can implement any suitable communications protocol and can be secured by any suitable security protocol. The network can comprise network links of any suitable arrangement that can implement the transmission and reception of network signals, such as wireless network connections, T1 or T3 lines, cable networks, DSE, or telephone lines.

[0300] Device 700 can be implemented using any operating system, e.g., an operating system suitable for operating on the network. Software module 750 can be written in any suitable programming language, such as C, C++, Java or Python. In various embodiments, application software embodying the functionality of the present disclosure can be deployed in different configurations, such as in a client/server arrangement or through a Web browser as a Web-based application or Web service, for example. In some embodiments, the operating system is executed by one or more processors, e.g., processor(s) 710.

[0301] Device 700 can further include a sequencer 770, which can be any suitable nucleic acid sequencing instrument.

[0302] FIG. 8 illustrates an example of a computing system in accordance with one embodiment. In system 800, device 700 (e.g., as described above and illustrated in FIG. 7) is connected to network 804, which is also connected to device 806. In some embodiments, device 806 is a sequencer. Exemplary sequencers can include, without limitation, Roche/454’s Genome Sequencer (GS) FLX System, Illumina/Solexa’s Genome Analyzer (GA), Illumina’s HiSeq® 2500, HiSeq® 3000, HiSeq® 4000 and NovaSeq® 6000 Sequencing Systems, Life/APG’s Support Oligonucleotide Ligation Detection (SOLiD) system, Polonator’s G.007 system, Helicos BioSciences’ HeliScope Gene Sequencing system, or Pacific Biosciences’ PacBio® RS system.

[0303] Devices 700 and 806 may communicate, e.g., using suitable communication interfaces via network 804, such as a Local Area Network (LAN), Virtual Private Network (VPN), or the Internet. In some embodiments, network 804 can be, for example, the Internet, an intranet, a virtual private network, a cloud network, a wired network, or a wireless network. Devices 700 and 806 may communicate, in part or in whole, via wireless or hardwired communications, such as Ethernet, IEEE 802.1 lb wireless, or the like. Additionally, devices 700 and 806 may communicate, e.g., using suitable communication interfaces, via a second network, such as a mobile/cellular network. Communication between devices 700 and 806 may further include or communicate with various servers such as a mail server, mobile server, media server, telephone server, and the like. In some embodiments, Devices 700 and 806 can communicate directly (instead of, or in addition to, communicating via network 804), e.g., via wireless or hardwired communications, such as Ethernet, IEEE 802.11b wireless, or the like. In some embodiments, devices 700 and 806 communicate via communications 808, which can be a direct connection or can occur via a network (e.g., network 804). [0304] One or all of devices 700 and 806 generally include logic (e.g., http web server logic) or are programmed to format data, accessed from local or remote databases or other sources of data and content, for providing and/or receiving information via network 804 according to various examples described herein.

EXEMPLARY IMPLEMENTATIONS

[0305] Exemplary implementations of the methods and systems described herein include:

1. A method comprising: providing a plurality of nucleic acid molecules obtained from a sample from a subject; ligating one or more adapters onto one or more nucleic acid molecules from the plurality of nucleic acid molecules; amplifying the one or more ligated nucleic acid molecules from the plurality of nucleic acid molecules; capturing amplified nucleic acid molecules from the amplified nucleic acid molecules; sequencing, by a sequencer, the captured nucleic acid molecules to obtain a plurality of sequence reads that represent the captured nucleic acid molecules; receiving, at one or more processors, sequence read data for the plurality of sequence reads; determining, using the one or more processors, a breakpoint of a genomic variant and a location of the breakpoint based on the sequence read data, the breakpoint associated with a rearrangement event and further associated with a first gene and a second gene of the sample, the first gene corresponding to the genomic variant; if the breakpoint of the genomic variant is located outside a sequence encoding a predetermined protein domain, performing, using the one or more processors: a first determination of whether the rearrangement event is in-strand; and a second determination of whether the sequence encoding the predetermined protein domain is impacted by the rearrangement event; assigning, using the one or more processors, the genomic variant to a functional status group based on the first determination and the second determination; and identifying, using the one or more processors, a treatment based on the assigned functional status group of the genomic variant.

2. The method of clause 1, further comprising: in accordance with a determination that the rearrangement event is not in-strand, assigning the genomic variant to a first functional status group corresponding to a functionally unknown rearrangement group; and in accordance with a determination that the rearrangement event is in- strand and the sequence encoding the predetermined protein domain is in sequence read data corresponding to the rearrangement event, assigning the genomic variant to a second functional status group corresponding to a functionally unknown fusion group.

3. The method of any of clauses 1 to 2, further comprising performing, using the one or more processors, a third determination of whether the first gene and the second gene are known partner genes.

4. The method of clause 3, wherein whether the first gene and the second gene are known partner genes is determined based on a predetermined association between the first gene and the second gene described in scientific literature.

5. The method of any of clauses 3 to 4, wherein determining whether the first gene and the second gene are known partner genes comprises comparing the second gene to a lookup table associated with the first gene.

6. The method of any of clauses 3 to 5, in accordance with a determination that the rearrangement event is in-strand, the sequence encoding the predetermined protein domain impacted by the rearrangement event, and the first gene is a known partner of the second gene, assigning the genomic variant to a third functional status group corresponding to an activating fusion group. 7. The method of any one of clauses 1 to 6, wherein the subject is suspected of having or is determined to have cancer.

8. The method of clause 7, wherein the cancer is a B cell cancer (multiple myeloma), a melanoma, breast cancer, lung cancer, bronchus cancer, colorectal cancer, prostate cancer, pancreatic cancer, stomach cancer, ovarian cancer, urinary bladder cancer, brain cancer, central nervous system cancer, peripheral nervous system cancer, esophageal cancer, cervical cancer, uterine cancer, endometrial cancer, cancer of an oral cavity, cancer of a pharynx, liver cancer, kidney cancer, testicular cancer, biliary tract cancer, small bowel cancer, appendix cancer, salivary gland cancer, thyroid gland cancer, adrenal gland cancer, osteosarcoma, chondrosarcoma, a cancer of hematological tissue, an adenocarcinoma, an inflammatory myofibroblastic tumor, a gastrointestinal stromal tumor (GIST), colon cancer, multiple myeloma (MM), myelodysplastic syndrome (MDS), myeloproliferative disorder (MPD), acute lymphocytic leukemia (ALL), acute myelocytic leukemia (AML), chronic myelocytic leukemia (CML), chronic lymphocytic leukemia (CLL), polycythemia Vera, Hodgkin lymphoma, nonHodgkin lymphoma (NHL), soft-tissue sarcoma, fibrosarcoma, myxosarcoma, liposarcoma, osteogenic sarcoma, chordoma, angiosarcoma, endotheliosarcoma, lymphangiosarcoma, lymphangioendotheliosarcoma, synovioma, mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinomas, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatoma, bile duct carcinoma, choriocarcinoma, seminoma, embryonal carcinoma, Wilms' tumor, bladder carcinoma, epithelial carcinoma, glioma, astrocytoma, medulloblastoma, craniopharyngioma, ependymoma, pinealoma, hemangioblastoma, acoustic neuroma, oligodendroglioma, meningioma, neuroblastoma, retinoblastoma, follicular lymphoma, diffuse large B-cell lymphoma, mantle cell lymphoma, hepatocellular carcinoma, thyroid cancer, gastric cancer, head and neck cancer, small cell cancer, essential thrombocythemia, agnogenic myeloid metaplasia, hypereosinophilic syndrome, systemic mastocytosis, familiar hypereosinophilia, chronic eosinophilic leukemia, neuroendocrine cancers, or a carcinoid tumor. 9. The method of clause 7, wherein the cancer comprises acute lymphoblastic leukemia (Philadelphia chromosome positive), acute lymphoblastic leukemia (precursor B-cell), acute myeloid leukemia (FLT3+), acute myeloid leukemia (with an IDH2 mutation), anaplastic large cell lymphoma, basal cell carcinoma, B-cell chronic lymphocytic leukemia, bladder cancer, breast cancer (HER2 overexpressed/amplified), breast cancer (HER2+), breast cancer (HR+, HER2-), cervical cancer, cholangiocarcinoma, chronic lymphocytic leukemia, chronic lymphocytic leukemia (with 17p deletion), chronic myelogenous leukemia, chronic myelogenous leukemia (Philadelphia chromosome positive), classical Hodgkin lymphoma, colorectal cancer, colorectal cancer (dMMR/MSI-H), colorectal cancer (KRAS wild type), cryopyrin-associated periodic syndrome, a cutaneous T-cell lymphoma, dermatofibrosarcoma protuberans, a diffuse large B-cell lymphoma, fallopian tube cancer, a follicular B-cell non-Hodgkin lymphoma, a follicular lymphoma, gastric cancer, gastric cancer (HER2+), gastroesophageal junction (GEJ) adenocarcinoma, a gastrointestinal stromal tumor, a gastrointestinal stromal tumor (KIT+), a giant cell tumor of the bone, a glioblastoma, granulomatosis with polyangiitis, a head and neck squamous cell carcinoma, a hepatocellular carcinoma, Hodgkin lymphoma, juvenile idiopathic arthritis, lupus erythematosus, a mantle cell lymphoma, medullary thyroid cancer, melanoma, a melanoma with a BRAF V600 mutation, a melanoma with a BRAF V600E or V600K mutation, Merkel cell carcinoma, multicentric Castleman's disease, multiple hematologic malignancies including Philadelphia chromosome-positive ALL and CML, multiple myeloma, myelofibrosis, a non-Hodgkin’ s lymphoma, a nonresectable subependymal giant cell astrocytoma associated with tuberous sclerosis, a non-small cell lung cancer, a non-small cell lung cancer (ALK+), a non-small cell lung cancer (PD-L1+), a non-small cell lung cancer (with ALK fusion or ROS1 gene alteration), a non-small cell lung cancer (with BRAF V600E mutation), a non-small cell lung cancer (with an EGFR exon 19 deletion or exon 21 substitution (L858R) mutations), a non- small cell lung cancer (with an EGFR T790M mutation), ovarian cancer, ovarian cancer (with a BRCA mutation), pancreatic cancer, a pancreatic, gastrointestinal, or lung origin neuroendocrine tumor, a pediatric neuroblastoma, a peripheral T-cell lymphoma, peritoneal cancer, prostate cancer, a renal cell carcinoma, rheumatoid arthritis, a small lymphocytic lymphoma, a soft tissue sarcoma, a solid tumor (MSLH/dMMR), a squamous cell cancer of the head and neck, a squamous non-small cell lung cancer, thyroid cancer, a thyroid carcinoma, urothelial cancer, a urothelial carcinoma, or Waldenstrom's macroglobulinemia.

10. The method of clause 9, further comprising treating the subject with an anti-cancer therapy.

11. The method of clause 10, wherein the anti-cancer therapy comprises a targeted anticancer therapy.

12. The method of clause 11, wherein the targeted anti-cancer therapy comprises abemaciclib (Verzenio), abiraterone acetate (Zytiga), acalabrutinib (Calquence), ado-trastuzumab emtansine (Kadcyla), afatinib dimaleate (Gilotrif), aldesleukin (Proleukin), alectinib (Alecensa), alemtuzumab (Campath), alitretinoin (Panretin), alpelisib (Piqray), amivantamab-vmjw (Rybrevant), anastrozole (Arimidex), apalutamide (Erleada), asciminib hydrochloride (Scemblix), atezolizumab (Tecentriq), avapritinib (Ayvakit), avelumab (Bavencio), axicabtagene ciloleucel (Yescarta), axitinib (Inlyta), belantamab mafodotin-blmf (Blenrep), belimumab (Benlysta), belinostat (Beleodaq), belzutifan (Welireg), bevacizumab (Avastin), bexarotene (Targretin), binimetinib (Mektovi), blinatumomab (Blincyto), bortezomib (Velcade), bosutinib (Bosulif), brentuximab vedotin (Adcetris), brexucabtagene autoleucel (Tecartus), brigatinib (Alunbrig), cabazitaxel (Jevtana), cabozantinib (Cabometyx), cabozantinib (Cabometyx, Cometriq), canakinumab (Haris), capmatinib hydrochloride (Tabrecta), carfilzomib (Kyprolis), cemiplimab-rwlc (Libtayo), ceritinib (LDK378/Zykadia), cetuximab (Erbitux), cobimetinib (Cotellic), copanlisib hydrochloride (Aliqopa), crizotinib (Xalkori), dabrafenib (Tafinlar), dacomitinib (Vizimpro), daratumumab (Darzalex), daratumumab and hyaluronidase-fihj (Darzalex Faspro), darolutamide (Nubeqa), dasatinib (Sprycel), denileukin diftitox (Ontak), denosumab (Xgeva), dinutuximab (Unituxin), dostarlimab-gxly (Jemperli), durvalumab (Imfinzi), duvelisib (Copiktra), elotuzumab (Empliciti), enasidenib mesylate (Idhifa), encorafenib (Braftovi), enfortumab vedotin-ejfv (Padcev), entrectinib (Rozlytrek), enzalutamide (Xtandi), erdafitinib (Balversa), erlotinib (Tarceva), everolimus (Afinitor), exemestane (Aromasin), fam-trastuzumab deruxtecan-nxki (Enhertu), fedratinib hydrochloride (Inrebic), fulvestrant (Faslodex), gefitinib (Iressa), gemtuzumab ozogamicin (Mylotarg), gilteritinib (Xospata), glasdegib maleate (Daurismo), hyaluronidase-zzxf (Phesgo), ibrutinib (Imbruvica), ibritumomab tiuxetan (Zevalin), idecabtagene vicleucel (Abecma), idelalisib (Zydelig), imatinib mesylate (Gleevec), infigratinib phosphate (Truseltiq), inotuzumab ozogamicin (Besponsa), iobenguane 1131 (Azedra), ipilimumab (Yervoy), isatuximab-irfc (Sarclisa), ivosidenib (Tibsovo), ixazomib citrate (Ninlaro), lanreotide acetate (Somatuline Depot), lapatinib (Tykerb), larotrectinib sulfate (Vitrakvi), lenvatinib mesylate (Lenvima), letrozole (Femara), lisocabtagene maraleucel (Breyanzi), loncastuximab tesirine-lpyl (Zynlonta), lorlatinib (Lorbrena), lutetium Lu 177-dotatate (Lutathera), margetuximab-cmkb (Margenza), midostaurin (Rydapt), mobocertinib succinate (Exkivity), mogamulizumab-kpkc (Poteligeo), moxetumomab pasudotox-tdfk (Lumoxiti), naxitamab-gqgk (Danyelza), necitumumab (Portrazza), neratinib maleate (Nerlynx), nilotinib (Tasigna), niraparib tosylate monohydrate (Zejula), nivolumab (Opdivo), obinutuzumab (Gazyva), ofatumumab (Arzerra), olaparib (Lynparza), olaratumab (Lartruvo), osimertinib (Tagrisso), palbociclib (Ibrance), panitumumab (Vectibix), panobinostat (Farydak), pazopanib (Votrient), pembrolizumab (Keytruda), pemigatinib (Pemazyre), pertuzumab (Perjeta), pexidartinib hydrochloride (Turalio), polatuzumab vedotin-piiq (Polivy), ponatinib hydrochloride (Iclusig), pralatrexate (Folotyn), pralsetinib (Gavreto), radium 223 dichloride (Xofigo), ramucirumab (Cyramza), regorafenib (Stivarga), ribociclib (Kisqali), ripretinib (Qinlock), rituximab (Rituxan), rituximab and hyaluronidase human (Rituxan Hycela), romidepsin (Istodax), rucaparib camsylate (Rubraca), ruxolitinib phosphate (Jakafi), sacituzumab govitecan-hziy (Trodelvy), seliciclib, selinexor (Xpovio), selpercatinib (Retevmo), selumetinib sulfate (Koselugo), siltuximab (Sylvant), sipuleucel-T (Provenge), sirolimus protein-bound particles (Fyarro), sonidegib (Odomzo), sorafenib (Nexavar), sotorasib (Lumakras), sunitinib (Sutent), tafasitamab-cxix (Monjuvi), tagraxofusp-erzs (Elzonris), talazoparib tosylate (Talzenna), tamoxifen (Nolvadex), tazemetostat hydrobromide (Tazverik), tebentafusp-tebn (Kimmtrak), temsirolimus (Torisel), tepotinib hydrochloride (Tepmetko), tisagenlecleucel (Kymriah), tisotumab vedotin-tftv (Tivdak), tocilizumab (Actemra), tofacitinib (Xeljanz), tositumomab (Bexxar), trametinib (Mekinist), trastuzumab (Herceptin), tretinoin (Vesanoid), tivozanib hydrochloride (Fotivda), toremifene (Fareston), tucatinib (Tukysa), umbralisib tosylate (Ukoniq), vandetanib (Caprelsa), vemurafenib (Zelboraf), venetoclax (Venclexta), vismodegib (Erivedge), vorinostat (Zolinza), zanubrutinib (Brukinsa), ziv-aflibercept (Zaltrap), or any combination thereof.

13. The method of any one of clauses 1 to 12, further comprising obtaining the sample from the subject.

14. The method of any one of clauses 1 to 13, wherein the sample comprises a tissue biopsy sample, a liquid biopsy sample, or a normal control.

15. The method of clause 14, wherein the sample is a liquid biopsy sample and comprises blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva.

16. The method of clause 14, wherein the sample is a liquid biopsy sample and comprises circulating tumor cells (CTCs).

17. The method of clause 14, wherein the sample is a liquid biopsy sample and comprises cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof.

18. The method of any one of clauses 1 to 17, wherein the plurality of nucleic acid molecules comprises a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules.

19. The method of clause 18, wherein the tumor nucleic acid molecules are derived from a tumor portion of a heterogeneous tissue biopsy sample, and the non-tumor nucleic acid molecules are derived from a normal portion of the heterogeneous tissue biopsy sample.

20. The method of clause 18, wherein the sample comprises a liquid biopsy sample, and wherein the tumor nucleic acid molecules are derived from a circulating tumor DNA (ctDNA) fraction of the liquid biopsy sample, and the non-tumor nucleic acid molecules are derived from a non-tumor, cell-free DNA (cfDNA) fraction of the liquid biopsy sample. 21. The method of any one of clauses 1 to 20, wherein the one or more adapters comprise amplification primers, flow cell adaptor sequences, substrate adapter sequences, or sample index sequences.

22. The method of any one of clauses 1 to 21, wherein the captured nucleic acid molecules are captured from the amplified nucleic acid molecules by hybridization to one or more bait molecules.

23. The method of clause 22, wherein the one or more bait molecules comprise one or more nucleic acid molecules, each comprising a region that is complementary to a region of a captured nucleic acid molecule.

24. The method of any one of clauses 1 to 23, wherein amplifying nucleic acid molecules comprises performing a polymerase chain reaction (PCR) amplification technique, a non-PCR amplification technique, or an isothermal amplification technique.

25. The method of any one of clauses 1 to 24, wherein the sequencing comprises use of a massively parallel sequencing (MPS) technique, whole genome sequencing (WGS), whole exome sequencing, targeted sequencing, direct sequencing, or Sanger sequencing technique.

26. The method of clause 25, wherein the sequencing comprises massively parallel sequencing, and the massively parallel sequencing technique comprises next generation sequencing (NGS).

27. The method of any one of clauses 1 to 26, wherein the sequencer comprises a next generation sequencer.

28. The method of any one of clauses 1 to 27, wherein one or more of the plurality of sequencing reads overlap one or more gene loci within one or more subgenomic intervals in the sample. 29. The method of clause 28, wherein the one or more gene loci comprises between 10 and 20 loci, between 10 and 40 loci, between 10 and 60 loci, between 10 and 80 loci, between 10 and 100 loci, between 10 and 150 loci, between 10 and 200 loci, between 10 and 250 loci, between 10 and 300 loci, between 10 and 350 loci, between 10 and 400 loci, between 10 and 450 loci, between 10 and 500 loci, between 20 and 40 loci, between 20 and 60 loci, between 20 and 80 loci, between 20 and 100 loci, between 20 and 150 loci, between 20 and 200 loci, between 20 and 250 loci, between 20 and 300 loci, between 20 and 350 loci, between 20 and 400 loci, between 20 and 500 loci, between 40 and 60 loci, between 40 and 80 loci, between 40 and 100 loci, between 40 and 150 loci, between 40 and 200 loci, between 40 and 250 loci, between 40 and 300 loci, between 40 and 350 loci, between 40 and 400 loci, between 40 and 500 loci, between 60 and 80 loci, between 60 and 100 loci, between 60 and 150 loci, between 60 and 200 loci, between 60 and 250 loci, between 60 and 300 loci, between 60 and 350 loci, between 60 and 400 loci, between 60 and 500 loci, between 80 and 100 loci, between 80 and 150 loci, between 80 and 200 loci, between 80 and 250 loci, between 80 and 300 loci, between 80 and 350 loci, between 80 and 400 loci, between 80 and 500 loci, between 100 and 150 loci, between 100 and 200 loci, between 100 and 250 loci, between 100 and 300 loci, between 100 and 350 loci, between 100 and 400 loci, between 100 and 500 loci, between 150 and 200 loci, between 150 and 250 loci, between 150 and 300 loci, between 150 and 350 loci, between 150 and 400 loci, between 150 and 500 loci, between 200 and 250 loci, between 200 and 300 loci, between 200 and 350 loci, between 200 and 400 loci, between 200 and 500 loci, between 250 and 300 loci, between 250 and 350 loci, between 250 and 400 loci, between 250 and 500 loci, between 300 and 350 loci, between 300 and 400 loci, between 300 and 500 loci, between 350 and 400 loci, between 350 and 500 loci, or between 400 and 500 loci.

30. The method of any of clauses 28 to 29, wherein the one or more gene loci comprise ABL1, ACVR1B, AKT1, AKT2, AKT3, ALK, ALOX12B, AMER1, APC, AR, ARAF, ARFRP1, ARID1A, ASXL1, ATM, ATR, ATRX, AURKA, AURKB, AXIN1, AXL, BAP1, BARD1, BCL2, BCL2L1, BCL2L2, BCL6, BCOR, BCORL1, BCR, BRAF, BRCA1, BRCA2, BRD4, BRIP1, BTG1, BTG2, BTK, CALR, CARD11, CASP8, CBFB, CBL, CCND1, CCND2, CCND3, CCNE1, CD22, CD274, CD70, CD74, CD79A, CD79B, CDC73, CDH1, CDK12, CDK4, CDK6, CDK8, CDKN1A, CDKN1B, CDKN2A, CDKN2B, CDKN2C, CEBPA, CHEK1, CHEK2, CIC, CREBBP, CRKL, CSF1R, CSF3R, CTCF, CTNNA1, CTNNB1, CUL3, CUL4A, CXCR4, CYP17A1, DAXX, DDR1, DDR2, DIS3, DNMT3A, DOT1L, EED, EGFR, EMSY (Cllorf30), EP300, EPHA3, EPHB1, EPHB4, ERBB2, ERBB3, ERBB4, ERCC4, ERG, ERRFI1, ESRI, ETV4, ETV5, ETV6, EWSR1, EZH2, EZR, FAM46C, FANCA, FANCC, FANCG, FANCL, FAS, FBXW7, FGF10, FGF12, FGF14, FGF19, FGF23, FGF3, FGF4, FGF6, FGFR1, FGFR2, FGFR3, FGFR4, FH, FECN, FET1, FET3, FOXE2, FUBP1, GABRA6, GATA3, GATA4, GATA6, GID4 (C17orf39), GNA11, GNA13, GNAQ, GNAS, GRM3, GSK3B, H3F3A, HDAC1, HGF, HNF1A, HRAS, HSD3B1, ID3, IDH1, IDH2, IGF1R, IKBKE, IKZF1, INPP4B, IRF2, IRF4, IRS2, JAK1, JAK2, JAK3, JUN, KDM5A, KDM5C, KDM6A, KDR, KEAP1, KEF, KIT, KLHL6, KMT2A (MLL), KMT2D (MLL2), KRAS, ETK, LYN, MAF, MAP2K1, MAP2K2, MAP2K4, MAP3K1, MAP3K13, MAPK1, MCL1, MDM2, MDM4, MED12, MEF2B, MEN1, MERTK, MET, MITF, MKNK1, MLH1, MPL, MRE11A, MSH2, MSH3, MSH6, MST1R, MTAP, MTOR, MUTYH, MYB, MYC, MYCL, MYCN, MYD88, NBN, NF1, NF2, NFE2L2, NFKBIA, NKX2-1, NOTCH1, NOTCH2, NOTCH3, NPM1, NRAS, NT5C2, NTRK1, NTRK2, NTRK3, NUTM1, P2RY8, PALB2, PARK2, PARP1, PARP2, PARP3, PAX5, PBRM1, PDCD1, PDCD1LG2, PDGFRA, PDGFRB, PDK1, PIK3C2B, PIK3C2G, PIK3CA, PIK3CB, PIK3R1, PIM1, PMS2, POLDI, POLE, PPARG, PPP2R1A, PPP2R2A, PRDM1, PRKAR1A, PRKCI, PTCHI, PTEN, PTPN11, PTPRO, QKI, RAC1, RAD21, RAD51, RAD51B, RAD51C, RAD51D, RAD52, RAD54L, RAFI, RARA, RBI, RBM10, REL, RET, RICTOR, RNF43, ROS1, RPTOR, RSPO2, SDC4, SDHA, SDHB, SDHC, SDHD, SETD2, SF3B1, SGK1, SLC34A2, SMAD2, SMAD4, SMARCA4, SMARCB1, SMO, SNCAIP, S0CS1, SOX2, SOX9, SPEN, SPOP, SRC, STAG2, STAT3, STK11, SUFU, SYK, TBX3, TEK, TERC, TERT, TET2, TGFBR2, TIPARP, TMPRSS2, TNFAIP3, TNFRSF14, TP53, TSC1, TSC2, TYRO3, U2AF1, VEGFA, VHL, WHSCI, WHSC1L1, WT1, XPO1, XRCC2, ZNF217, ZNF703, or any combination thereof.

31. The method of any of clauses 28 to 30, wherein the one or more gene loci comprise ABL, ALK, ALL, B4GALNT1, BAFF, BCL2, BRAF, BRCA, BTK, CD19, CD20, CD3, CD30, CD319, CD38, CD52, CDK4, CDK6, CML, CRACC, CS1, CTLA-4, dMMR, EGFR, ERBB 1, ERBB2, FGFR1-3, FLT3, GD2, HDAC, HER1, HER2, HR, IDH2, IL-ip, IL-6, IL-6R, JAK1, JAK2, JAK3, KIT, KRAS, MEK, MET, MSLH, mTOR, PARP, PD-1, PDGFR, PDGFRa, PDGFRP, PD-L1, PI3K5, PIGF, PTCH, RAF, RANKL, RET, R0S1, SLAMF7, VEGF, VEGFA, VEGFB, or any combination thereof.

32. The method of any one of clauses 1 to 31, further comprising generating, by the one or more processors, a report indicating the functional status of the genomic variant.

33. The method of clause 32, further comprising transmitting the report to a healthcare provider.

34. The method of clause 33, wherein the report is transmitted via a computer network or a peer-to-peer connection.

35. A method for identifying a treatment based on a genomic variant in a sample from an individual, the method comprising: receiving, at one or more processors, sequence read data associated with the genomic variant in the sample; determining, using the one or more processors, a breakpoint of the genomic variant and a location of the breakpoint based on the sequence read data, the breakpoint associated with a rearrangement event and further associated with a first gene and a second gene of the sample, the first gene corresponding to the genomic variant; if the breakpoint of the genomic variant is located outside a sequence encoding a predetermined protein domain, performing, using the one or more processors: a first determination of whether the rearrangement event is in-strand; and a second determination of whether the sequence encoding the predetermined protein domain is impacted by the rearrangement event; assigning, using the one or more processors, the genomic variant to a functional status group based on the first determination and the second determination; and identifying, using the one or more processors, the treatment based on the assigned functional status group of the genomic variant.

36. The method of clause 35, further comprising: in accordance with a determination that the rearrangement event is not in-strand, assigning the genomic variant to a first functional status group; and in accordance with a determination that the rearrangement event is in- strand and the sequence encoding the predetermined protein domain is impacted by the rearrangement event, assigning the genomic variant to a second functional status group.

37. The method of clause 36, wherein the first functional status group corresponds to a functionally unknown rearrangement group.

38. The method of clause 37, further comprising labeling the genomic variant as a rearrangement.

39. The method of clause 38, wherein the rearrangement label is further based on an orientation of the first gene with respect to the second gene.

40. The method of any of clauses 36 to 39, wherein the second functional status group corresponds to a functionally unknown fusion group.

41. The method of clause 40, further comprising labeling the genomic variant as a reciprocal fusion.

42. The method of any of clauses 35 to 41, further comprising performing, using the one or more processors, a third determination of whether the first gene and the second gene are known partner genes. 43. The method of clause 42, wherein whether the first gene and the second gene are known partner genes is determined based on a predetermined association between the first gene and the second gene described in scientific literature.

44. The method of any of clauses 42 to 43, wherein determining whether the first gene and the second gene are known partner genes is based on a lookup table associated with the first gene.

45. The method of any of clauses 42 to 44, in accordance with a determination that the rearrangement event is in-strand, the sequence encoding the predetermined protein domain is impacted by the rearrangement event, and the first gene is a known partner of the second gene, assigning the genomic variant to a third functional status group.

46. The method clause 45, wherein the third functional status group corresponds to an activating fusion group.

47. The method of clause 46, further comprising labeling the genomic variant as an activating fusion.

48. The method of any of clauses 42 to 47, wherein determining whether the sequence encoding the predetermined protein domain is impacted by the rearrangement event comprises: determining, whether a functional domain of the first gene is in the sequence read data corresponding to the rearrangement event.

49. The method of any of clauses 42 to 48, further comprising in accordance with a determination that the rearrangement event is in-strand, and further that the sequence encoding the predetermined protein domain is impacted by the rearrangement event, and the first gene is not a known partner gene of the second gene, flagging the genomic variant for manual review and forgoing labeling the genomic variant. 50. The method of any of clauses 35 to 49, further comprising forgoing assigning the genomic variant to the functional status group if the breakpoint of the genomic variant is located outside a predetermined sequence encoding a protein domain.

51. The method of any of clauses 35 to 50, further comprising determining, using the one or more processors, whether the first gene is an oncogene.

52. The method of any of clauses 35 to 51, wherein the first gene is ABL1, ALK, BRAF, FGFR1, FGFR2, FGFR3, MET, NTRK1, NTRK2, NTRK3, PDGFRA, PDGFRB, ROS1, RET, or RAFI.

53. The method of any of clauses 35 to 52, wherein the predetermined protein domain is a functional domain.

54. The method of any of clauses 35 to 53, wherein the predetermined protein domain is a kinase domain.

55. The method of any of clauses 35 to 54, further comprising performing, using the one or more processors, a fourth determination of whether the second gene is a coding gene.

56. The method of any of clauses 35 to 55, wherein the rearrangement event comprises a fusion event.

57. The method of any of clauses 35 to 56, further comprising determining the second gene is a known partner of the first gene in accordance with a determination that the first gene is ROS 1 and the second gene is one of CD74, CLIP1, EZR, GOPC, LRIG3, MY05A, PPFIBP1, PWWP2A, SLC34A2, SDC4, SHTN1 (KIAA1598), TPM3, and ZCCHC8.

58. The method any of clauses 35 to 57, wherein the sequence read data for the individual is based on a targeted exome sequencing panel. 59. The method any of clauses 35 to 58, wherein the sequence read data for the individual is derived from a single biopsy sample.

60. The method any of clauses 35 to 59, wherein the sequence read data for the individual is derived from multiple biopsy samples.

61. The method any of clauses 35 to 60, wherein the sequence read data for the individual is derived from single cell sequencing.

62. The method any of clauses 35 to 61, wherein the sequence read data for the individual is derived from circulating tumor DNA in a liquid biopsy sample.

63. The method any of clauses 35 to 62, further comprising assigning, using the one or more processors, a therapy for the individual based on the functional status group.

64. The method any of clauses 35 to 63, further comprising administering, using the one or more processors, a treatment to the individual based on the functional status group.

65. The method any of clauses 35 to 64, further comprising associating, using the one or more processors, the individual with a clinical trial based on the functional status group.

66. The method any of clauses 35 to 65, further comprising monitoring, using the one or more processors, a prognosis of the individual based on the functional status group.

67. The method any of clauses 35 to 66, further comprising predicting, using the one or more processors, one or more clinical outcomes based on the functional status group.

68. A method for diagnosing a disease, the method comprising: diagnosing that a subject has the disease based on a determination of a functional status group of a genomic variant in a sample from the subject, wherein the functional status group of the genomic variant is determined according to the method of any one of clauses 35 to 67.

69. A method of selecting an anti-cancer therapy, the method comprising: responsive to determining a functional status group of a genomic variant in a sample from a subject, selecting an anti-cancer therapy for the subject, wherein the functional status group of the genomic variant is determined according to the method of any one of clauses 35 to 67.

70. A method of treating a cancer in a subject, comprising: responsive to determining a functional status group of a genomic variant in a sample from the subject, administering an effective amount of an anti-cancer therapy to the subject, wherein the functional status group of the genomic variant is determined according to the method of any one of clauses 35 to 67.

71. A method for monitoring cancer progression or recurrence in a subject, the method comprising: determining a first functional status group of a genomic variant in a first sample obtained from the subject at a first time point according to the method of any one of clauses 35 to 67; determining a second functional status group of a genomic variant in a second sample obtained from the subject at a second time point; and comparing the first functional status group to the second functional status group, thereby monitoring the cancer progression or recurrence.

72. The method of clause 71, wherein the second functional status group of the genomic variant for the second sample is determined according to the method of any one of clauses 35 to 67.

73. The method of any of clauses 71 to 72, further comprising selecting an anti-cancer therapy for the subject in response to the cancer progression.

I l l 74. The method of any of clauses 71 to 72, further comprising administering an anti-cancer therapy to the subject in response to the cancer progression.

75. The method of any of clauses 71 to 72, further comprising adjusting an anti-cancer therapy for the subject in response to the cancer progression.

76. The method of any of clauses 73 to 75, further comprising adjusting a dosage of the anticancer therapy or selecting a different anti-cancer therapy in response to the cancer progression.

77. The method of clause 76, further comprising administering the adjusted anti-cancer therapy to the subject.

78. The method of any one of clauses 71 to 77, wherein the first time point is before the subject has been administered an anti-cancer therapy, and wherein the second time point is after the subject has been administered the anti-cancer therapy.

79. The method of any one of clauses 71 to 78, wherein the subject has a cancer, is at risk of having a cancer, is being routine tested for cancer, or is suspected of having a cancer.

80. The method of any one of clauses 71 to 79, wherein the cancer is a solid tumor.

81. The method of any one of clauses 71 to 79, wherein the cancer is a hematological cancer.

82. The method of any one of clauses 73 to 81, wherein the anti-cancer therapy comprises chemotherapy, radiation therapy, immunotherapy, a targeted therapy, or surgery.

83. The method of any one of clauses 35 to 67, further comprising determining, identifying, or applying the functional status group of the genomic variant of the sample as a diagnostic value associated with the sample. 84. The method of any one of clauses 35 to 67, further comprising generating a genomic profile for the subject based on the determination of the functional status group of the genomic variant.

85. The method of clause 84, wherein the genomic profile for the subject further comprises results from a comprehensive genomic profiling (CGP) test, a gene expression profiling test, a cancer hotspot panel test, a DNA methylation test, a DNA fragmentation test, an RNA fragmentation test, or any combination thereof.

86. The method of any of clauses 84 to 85, wherein the genomic profile for the subject further comprises results from a nucleic acid sequencing-based test.

87. The method of any of clauses 84 to 86, further comprising selecting an anti-cancer therapy, administering an anti-cancer therapy, or applying an anti-cancer therapy to the subject based on the generated genomic profile.

88. The method of any one of clauses 35 to 67, wherein the determination of a functional status group of a genomic variant in a sample is used in making suggested treatment decisions for the subject.

89. The method of any one of clauses 35 to 67, wherein the determination of a functional status group of a genomic variant in a sample is used in applying or administering a treatment to the subject.

90. A system comprising: one or more processors; and a memory communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to perform a method comprising: receiving, at one or more processors, sequence read data associated with a genomic variant in a sample from an individual; determining, using the one or more processors, a breakpoint of the genomic variant and a location of the breakpoint based on the sequence read data, the breakpoint associated with a rearrangement event and further associated with a first gene and a second gene of the sample, the first gene corresponding to the genomic variant; if the breakpoint of the genomic variant is located outside a sequence encoding a predetermined protein domain, performing, using the one or more processors: a first determination of whether the rearrangement event is in-strand; and a second determination of whether the sequence encoding the predetermined protein domain is impacted by the rearrangement event; assigning, using the one or more processors, the genomic variant to a functional status group based on the first determination and the second determination; and identifying, using the one or more processors, the treatment based on the assigned functional status group of the genomic variant.

91. A non-transitory computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of a system, cause the system to: receive, at one or more processors, sequence read data associated with a genomic variant in a sample from an individual; determine, using the one or more processors, a breakpoint of the genomic variant and a location of the breakpoint based on the sequence read data, the breakpoint associated with a rearrangement event and further associated with a first gene and a second gene of the sample, the first gene corresponding to the genomic variant; if the breakpoint of the genomic variant is located outside a sequence encoding a predetermined protein domain, perform, using the one or more processors: a first determination of whether the rearrangement event is in-strand; and a second determination of whether the sequence encoding the predetermined protein domain is impacted by the rearrangement event; assign, using the one or more processors, the genomic variant to a functional status group based on the first determination and the second determination; and identify, using the one or more processors, the treatment based on the assigned functional status group of the genomic variant.

92. A method for identifying a treatment based on a genomic variant in a sample from an individual, the method comprising: receiving, at one or more processors, sequence read data associated with the genomic variant in the sample; determining, using the one or more processors, one or more breakpoints of the genomic variant and a location of the breakpoint based on the sequence read data, the breakpoint associated with a rearrangement event; if the one or more breakpoints of the genomic variant are located outside of a sequence encoding a predetermined protein domain, performing, using the one or more processors: a determination of whether the sequence encoding the predetermined protein domain is in the rearrangement event; assigning, using the one or more processors, the genomic variant to a functional status group based on the determination; and identifying, using the one or more processors, the treatment based on the assigned functional status group of the genomic variant.

93. The method of clause 92, wherein the rearrangement event is an intragenic event.

94. The method of any of clauses 92 to 93, further comprising performing a second determination to identify a rearrangement type, wherein assigning the genomic variant to the functional status group is based on the rearrangement type.

95. The method of clause 94, wherein the rearrangement type comprises a deletion, a duplication, or an inversion. 96. The method of any of clauses 92 to 95, further comprising in accordance with a determination that the sequence encoding the predetermined protein domain is in the rearrangement event, assigning the genomic variant to a first functional status group.

97. The method of any of clauses 92 to 96, wherein the first functional status group is further based on the genomic variant.

98. The method of any of clauses 92 to 97, further comprising forgoing assigning the genomic variant to the functional status group if the breakpoint of the genomic variant is located outside a predetermined sequence encoding a protein domain.

99. The method of any of clauses 92 to 98, further comprising determining, using the one or more processors, whether the first gene is an oncogene.

100. The method of any of clauses 92 to 99, wherein a gene corresponding to the genomic variant is EGFR, BRAF, FGFR1, FGFR2, MET, or PDGFRA.

101. The method of any of clauses 92 to 100, wherein the predetermined protein domain is a functional domain.

102. The method any of clauses 92 to 101, wherein the sequence read data for the individual is based on a targeted exome sequencing panel.

103. The method any of clauses 92 to 102, wherein the sequence read data for the individual is derived from a single biopsy sample.

104. The method any of clauses 92 to 103, wherein the sequence read data for the individual is derived from multiple biopsy samples. 105. The method any of clauses 92 to 104, wherein the sequence read data for the individual is derived from single cell sequencing.

106. The method any of clauses 92 to 105, wherein the sequence read data for the individual is derived from circulating tumor DNA in a liquid biopsy sample.

107. The method any of clauses 92 to 106, further comprising assigning, using the one or more processors, a therapy for the individual based on the functional status group.

108. The method any of clauses 92 to 107, further comprising administering, using the one or more processors, a treatment to the individual based on the functional status group.

109. The method any of clauses 92 to 108, further comprising associating, using the one or more processors, the individual with a clinical trial based on the functional status group.

110. The method any of clauses 92 to 109, further comprising monitoring, using the one or more processors, a prognosis of the individual based on the functional status group.

111. The method any of clauses 92 to 110, further comprising predicting, using the one or more processors, one or more clinical outcomes based on the functional status group.

112. A method for diagnosing a disease, the method comprising: diagnosing that a subject has the disease based on a determination of a functional status group of a genomic variant in a sample from the subject, wherein the functional status group of the genomic variant is determined according to the method of any one of clauses 92 to 111.

113. A method of selecting an anti-cancer therapy, the method comprising: responsive to determining a functional status group of a genomic variant in a sample from a subject, selecting an anti-cancer therapy for the subject, wherein the functional status group of the genomic variant is determined according to the method of any one of clauses 92 to 111. 114. A method of treating a cancer in a subject, comprising: responsive to determining a functional status group of a genomic variant in a sample from the subject, administering an effective amount of an anti-cancer therapy to the subject, wherein the functional status group of the genomic variant is determined according to the method of any one of clauses 92 to 111.

115. A method for monitoring cancer progression or recurrence in a subject, the method comprising: determining a first functional status group of a genomic variant in a first sample obtained from the subject at a first time point according to the method of any one of clauses 92 to 111; determining a second functional status group of a genomic variant in a second sample obtained from the subject at a second time point; and comparing the first functional status group to the second functional status group, thereby monitoring the cancer progression or recurrence.

116. The method of clause 115, wherein the second functional status group of the genomic variant for the second sample is determined according to the method of any one of clauses 92 to 111.

117. The method of any of clauses 115 to 116, further comprising selecting an anti-cancer therapy for the subject in response to the cancer progression.

118. The method of any of clauses 115 to 116, further comprising administering an anti-cancer therapy to the subject in response to the cancer progression.

119. The method of any of clauses 115 to 116, further comprising adjusting an anti-cancer therapy for the subject in response to the cancer progression. 120. The method of any of clauses 117 to 119, further comprising adjusting a dosage of the anti-cancer therapy or selecting a different anti-cancer therapy in response to the cancer progression.

121. The method of clause 120, further comprising administering the adjusted anti-cancer therapy to the subject.

122. The method of any one of clauses 115 to 121, wherein the first time point is before the subject has been administered an anti-cancer therapy, and wherein the second time point is after the subject has been administered the anti-cancer therapy.

123. The method of any one of clauses 115 to 122, wherein the subject has a cancer, is at risk of having a cancer, is being routine tested for cancer, or is suspected of having a cancer.

124. The method of any one of clauses 115 to 123, wherein the cancer is a solid tumor.

125. The method of any one of clauses 115 to 123, wherein the cancer is a hematological cancer.

126. The method of any one of clauses 117 to 125, wherein the anti-cancer therapy comprises chemotherapy, radiation therapy, immunotherapy, a targeted therapy, or surgery.

127. The method of any one of clauses 92 to 111, further comprising determining, identifying, or applying the functional status group of the genomic variant of the sample as a diagnostic value associated with the sample.

128. The method of any one of clauses 92 to 111, further comprising generating a genomic profile for the subject based on the determination of the functional status group of the genomic variant. 129. The method of clause 128, wherein the genomic profile for the subject further comprises results from a comprehensive genomic profiling (CGP) test, a gene expression profiling test, a cancer hotspot panel test, a DNA methylation test, a DNA fragmentation test, an RNA fragmentation test, or any combination thereof.

130. The method of any of clauses 128 to 129, wherein the genomic profile for the subject further comprises results from a nucleic acid sequencing-based test.

131. The method of any of clauses 128 to 130, further comprising selecting an anti-cancer therapy, administering an anti-cancer therapy, or applying an anti-cancer therapy to the subject based on the generated genomic profile.

132. The method of any one of clauses 92 to 111, wherein the determination of a functional status group of a genomic variant in a sample is used in making suggested treatment decisions for the subject.

133. The method of any one of clauses 92 to 111, wherein the determination of a functional status group of a genomic variant in a sample is used in applying or administering a treatment to the subject.

134. A system comprising: one or more processors; and a memory communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to perform a method comprising: receiving, at one or more processors, sequence read data associated with a genomic variant in a sample from an individual; determining, using the one or more processors, one or more breakpoints of the genomic variant and a location of the one or more breakpoints based on the sequence read data, the one or more breakpoints associated with a rearrangement event; if the one or more breakpoints of the genomic variant are located outside of a sequence encoding a predetermined protein domain, performing, using the one or more processors: a determination of whether the sequence encoding the predetermined protein domain is in the rearrangement event; assigning, using the one or more processors, the genomic variant to a functional status group based on the determination; and identifying, using the one or more processors, the treatment based on the assigned functional status group of the genomic variant.

135. A non-transitory computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of a system, cause the system to: receive, at one or more processors, sequence read data associated with a genomic variant in a sample from an individual; determine, using the one or more processors, one or more breakpoints of the genomic variant and a location of the one or more breakpoints based on the sequence read data, the one or more breakpoints associated with a rearrangement event; if the one or more breakpoints of the genomic variant are located outside of a sequence encoding a predetermined protein domain, perform, using the one or more processors: a determination of whether the sequence encoding the predetermined protein domain is in the rearrangement event; assign, using the one or more processors, the genomic variant to a functional status group based on the determination; and identify, using the one or more processors, the treatment based on the assigned functional status group of the genomic variant.

[0306] It should be understood from the foregoing that, while particular implementations of the disclosed methods and systems have been illustrated and described, various modifications can be made thereto and are contemplated herein. It is also not intended that the invention be limited by the specific examples provided within the specification. While the invention has been described with reference to the aforementioned specification, the descriptions and illustrations of the preferable embodiments herein are not meant to be construed in a limiting sense. Furthermore, it shall be understood that all aspects of the invention are not limited to the specific depictions, configurations or relative proportions set forth herein which depend upon a variety of conditions and variables. Various modifications in form and detail of the embodiments of the invention will be apparent to a person skilled in the art. It is therefore contemplated that the invention shall also cover any such modifications, variations and equivalents.