GENETIC MARKERS PREDICTIVE OF RESPONSE TO GLATIRAMER ACETATE

This application claims the priority of U.S. Provisional Application No. 61/893,807, filed October 21, 2013, U.S. Provisional Application No. 62/048,127, filed September 9, 2014, and U.S. Provisional Application No. 62/048,641, filed September 10, 2014, the contents of which are hereby incorporated by reference.

Throughout this application various publications are referenced. The disclosures of these publications in their entireties are hereby incorporated by reference into this application in order to more fully describe the state of the art to which this invention pertains .

BACKGROUND OF THE INVENTION

Multiple Sclerosis

Multiple sclerosis (MS) is a chronic, debilitating autoimmune disease of the central nervous system (CNS) with either relapsing- remitting (RR) or progressive course leading to neurologic deterioration and disability. At time of initial diagnosis, RRMS is the most common form of the disease (1) which is characterized by unpredictable acute episodes of neurological dysfunction (relapses) , followed by variable recovery and periods of clinical stability. The vast majority of RRMS patients eventually develop secondary progressive (SP) disease with or without superimposed relapses. Around 15% of patients develop a sustained deterioration of their neurological function from the beginning; this form is called primary progressive (PP) MS. Patients who have experienced a single clinical event (Clinically Isolated Syndrome or "CIS") and who show lesion dissemination on subsequent magnetic resonance imaging (MRI) scans according to McDonald's criteria, are also considered as having relapsing MS. (2)

With a prevalence that varies considerably around the world, MS is the most common cause of chronic neurological disability in young adults. ( 3 , 4 ) Anderson et al . estimated that there were about 350 , 000 physician-diagnosed patients with MS in the United States in 1990 (approx. 140 per 100 , 000 population) . ( 5 ) It is estimated that about 2 . 5 million individuals are affected worldwide .( 6 ) In general, there has been a trend toward an increasing prevalence and incidence of MS worldwide, but the reasons for this trend are not fully understood. ( 5 )

Current therapeutic approaches consist of i) symptomatic treatment ii) treatment of acute relapses with corticosteroids and iii) treatment aimed to modify the course of the disease. Currently approved therapies target the inflammatory processes of the disease. Most of them are considered to act as immunomodulators but their mechanisms of action have not been completely elucidated. Immunosuppressants or cytotoxic agents are also used in some patients after failure of conventional therapies. Several medications have been approved and clinically ascertained as efficacious for the treatment of RR-MS; including BETASERON®, AVONEX® and REBIF®, which are derivatives of the cytokine interferon beta (IFNB) , whose mechanism of action in MS is generally attributed to its immunomodulatory effects, antagonizing pro-inflammatory reactions and inducing suppressor cells. (7) Other approved drugs for the treatment of MS include Mitoxantrone and Natalizumab. Glatiramer Acetate

Glatiramer acetate (GA) is the active substance in Copaxone®, a marketed product indicated for reduction of the frequency of relapses in patients with RRMS. Its effectiveness in reducing relapse rate and disability accumulation in RR-MS is comparable to that of other available immunomodulating treatments .( 8 , 9 , 10 ) Glatiramer acetate consists of the acetate salts of synthetic polypeptides containing four naturally occurring amino acids: L- glutamic acid, L-alanine, L-tyrosine and L-lysine. The average molecular weight of glatiramer acetate is between 5 , 000 and 9 , 000 Daltons. At a daily standard dose of 20 mg, GA is generally well tolerated, however response to the drug is variable. In various clinical trials, GA reduced relapse rates and progression of disability in patients with RR-MS . The therapeutic effect of GA is supported by the results of magnetic resonance imaging (MRI) findings from various clinical centers (11) , however there are no validated predictive biomarkers of response to GA treatment.

A possible initial mode of action of GA is associated with binding to MHC molecules and consequent competition with various myelin antigens for their presentation to T cells. (12) A further aspect of its mode of action is the potent induction of T helper 2 (Th2) type cells that presumably can migrate to the brain and lead to in situ bystander suppression .( 13 ) It has been shown that GA treatment in MS results in the induction of GA-specific T cells with predominant Th2 phenotype both in response to GA and cross-reactive myelin antigens .( 13 , 14 ) Furthermore, the ability of GA-specific infiltrating cells to express anti-inflammatory cytokines such as IL-10 and transforming growth factor-beta (TGF-β) together with brain-derived neurotrophic factor (BDNF) seem to correlate with the therapeutic activity of GA in EAE. (15, 16, 17)

Clinical experience with GA consists of information obtained from completed and ongoing clinical trials and from pos -marketing experience. The clinical program includes three double-blind, placebo-controlled studies in RRMS subjects treated with GA 20 mg/day. ( 18 , 19 , 20 ) A significant reduction in the number of relapses, compared with placebo, was seen. In the largest controlled study, the relapse rate was reduced by 32% from 1.98 under placebo to 1.34 under GA 20 mg. GA 20 mg has also demonstrated beneficial effects over placebo on MRI parameters relevant to RRMS. A significant effect in median cumulative number of Gd-enhancing lesions over 9 months of treatment (11 lesions in the 20 mg group compared to 17 lesions under placebo) was demonstrated. The clinical program with GA also includes one double-blind study in chronic-progressive MS subjects, (21) one double-blind placebo- controlled study in primary progressive patients , (22 ) one double- blind placebo-controlled study in CIS patients (23) and numerous open-label and compassionate use studies, mostly in RRMS . The clinical use of GA has been extensively reviewed and published in the current literature (24,25,26,27).

U.S. Patent No. 7,855,176 discloses administering glatiramer acetate to patients afflicted with relapsing-remitting multiple sclerosis (RRMS) by subcutaneous injection of 0.5 ml of an aqueous pharmaceutical solution which contains in solution 20 mg glatiramer acetate and 20 mg mannitol (34) . U.S. Patent Application Publication No. US 2011-0046065 Al discloses administering glatiramer acetate to patients suffering from relapsing-remitting multiple sclerosis by three subcutaneous injections of a therapeutically effective dose of glatiramer acetate over a period of seven days with at least one day between every subcutaneous injection (35) .

Pharmacogenomics

Pharmacogenomics is the methodology which associates genetic variability with physiological responses to drug. Pharmacogenetics is a subset of pharmacogenomics and is defined as "the study of variations in DNA sequence as related to drug response" (ICH El5; fda. gov/downloads/Regulatorylnformation/Guidances/ucml29296.pdf . Pharmacogenetics focuses on genetic polymorphism in genes related to drug metabolism, drug mechanism of action, disease type, and side effects. Pharmacogenetics is the cornerstone of Personalized Medicine which allows the development of more individualized drug therapies to obtain more effective and safe treatment.

Pharmacogenetics has become a core component of many drug development programs, being used to explain variability in drug response among subjects in clinical trials, to address unexpected emerging clinical issues, such as adverse events, to determine eligibility for a clinical trial (pre-screening) to optimize trial yield, to develop drug-linked diagnostic tests to identify patients who are more likely or less likely to benefit from treatment or who may be at risk of adverse events, to provide information in drug labels to guide physician treatment decisions, to better understand the mechanism of action or metabolism of new and existing drugs, and to provide better understanding of disease mechanisms.

Generally, Pharmacogenetics analyses are performed in either of two methodology approaches: Candidate genes research technique, and Genome Wide Association Study (GWAS) . Candidate genes research technique is based on the detection of polymorphism in candidate genes pre-selected using the knowledge on the disease, the drug mode of action, toxicology or metabolism of drug. The Genome Wide Association Study (GWAS) enables the detection of more than 1 M (one million) polymorphisms across the genome. This approach is used when related genes are unknown. DNA arrays used for GWAS can be also analyzed per gene as in candidate gene approach.

Pharmacogenetic studies

Various pharmacogenetic studies were done in MS patients. For example, a Genome-Wide Association study by Byun et al . (36) focused on extreme clinical phenotypes in order to maximize the ability to detect genetic differences between responders and non-responders to interferon-beta . A multi-analytical approach detected significant associations between several SNPs and treatment response. Responders and Non-Responders had significantly different genotype frequencies for SNPs located in many genes, including glypican 5, collagen type XXV al, hyaluronan proteoglycan link protein, calpastatin, and neuronal PAS domain protein 3. Other studies used pharmacogenetic analyses in order to characterize the genomic profile and gene expression profile of IFN responders and non-responders. Other pharmacogenetic studies analyzed the genetic background associated with response to Glatiramer Acetate. For examples, Fusco C et al (37) assessed a possible relationship between HLA alleles and response to GA (N=83 RR S) . DRB1*1501 allele frequency was increased in MS patients compared to healthy controls (10.8% vs 2.7%; p= 0.001). In DRB1*1501 carriers the response rate was 81.8% compared to 39.4% in non-carriers of DRBl*1501 and to 50 % in the whole study population. Grossman et al (38) genotyped HLA-DRB1*1501 and 61 SNPs within a total of 27 other candidate genes, on DNA from two clinical trial cohorts. The study revealed no association between HLA-DRB1*1501 and response to GA. The results of the study are disclosed in the international application published as WO2006/116602 (39) . Pharmacogenetics is the cornerstone of personalized medicine which allows the development of more individualized drug therapies to obtain more effective and safe treatment. Multiple Sclerosis is a complex disease with clinical heterogeneity. In patients afflicted with multiple sclerosis or a single clinical attack consistent with multiple sclerosis, the ability to determine the likelihood of treatment success would be an important tool improving the therapeutic management of the patients . As the therapeutic options for MS and CIS increase, the importance of being able to determine who will respond favorably to therapy and specifically to GA, has become of increasing significance.

SUMMARY OF THE INVENTION

INDEPENDENT EMBODIMENTS

The present invention provides a method for treating a human subject afflicted with multiple sclerosis or a single clinical attack consistent with multiple sclerosis with a pharmaceutical composition comprising glatiramer acetate and a pharmaceutically acceptable carrier, comprising the steps of:

(i) determining a genotype of the subject at a location corresponding to the location of one or more single nucleotide polymorphisms (SNPs) selected from the group consisting of: kgpl0090631, kgpl009249, kgpl0152733. kgpl0224254, kgpl0305127, kgpl0351364. kgpl0372946, kgpl0404633, kgpl0412303, kgpl0523170, kgpl054273, kgpl0558725. kgpl0564659, kgpl0591989, kgpl0594414, kgpl0619195, kgpl0620244, kgpl0632945. kgpl0633631, kgpl0679353, kgpl0788130, kgpl0826273 ₍ kgpl0910719, kgpl0922969, kgpl0948564, kgpl0967046, kgpl0974833, kgpl098237, kgpll002881, kgpll010680, kgpll077373, kgplll41512, kgpll206453, kgpll210903. kgpll24492, kgpll281589, kgpll285862, kgpll328629. kgpll356379, kgpll407560, kgpll453406, kgpll467007, kgpll514107, kgpll543962, kgpll580695, kgpll627530, kgpll633966, kgpll686146, kgpll702474, kgpll711524, kgpll768533, kgpll804835, kgpll843177, kgpl2008955, kgpl2083934. kgpl2182745, kgpl2230354, kgpl224440, kgpl2371757 ', kgpl24162, kgpl2426624, kgpl2557319, kgpl285441, kgpl3161760, kgpl355977, kgpl371881, kgpl5390522, kgpl683448, kgpl688752, kgpl699628, kgpl753445, kgpl779254, kgpl786079. kgpl8379774, kgpl8432055. kgpl8525257. kgpl912531. kgpl9568724, kgp20163979, kgp2023214, kgp2045074, kgp20478926, kgp2092817, kgp21171930, kgp2245775, kgp2262166, kgp22778566, kgp22793211, kgp22811918, kgp22823022, kgp2282938, kgp2299675, kgp23298674, kgp2356388, kgp23672937 ₍ kgp23737989, kgp2388352, kgp2391411. kgp24131116. kgp24415534, kgp2446153, kgp2451249, kgp2465184, kgp24729706, kgp24753470, kgp25191871, kgp25216186, kgp25543811, kgp25921291, kgp25952891, kgp26026546, kgp26271158, kgp2638591, kgp26528455, kgp26533576, kgp2688306, kgp26995430, kgp270001, kgp2709692, kgp2715873, kgp27500525, kgp27571222, kgp27640141, kgp2788291, kgp279772, kgp28532436, kgp28586329, kgp28687699, kgp28817122, kgp2923815, kgp29367521, kgp293787, kgp2958113, kgp2959751, kgp297178, kgp29794723, kgp30282494, kgp3048169, kgp304921, kgp3182607, kgp3202939, kgp3205849, kgp3218351, kgp3267884, kgp3276689, kgp337461, kgp3418770, kgp3450875, kgp345301, kgp3477351, kgp3496814, kgp355027, kgp355723, kgp3593828, kgp3598409, kgp3651767, kgp3669685, kgp3730395, kgp3812034, kgp3854180, kgp3933330, kgp3951463, kgp3984567, kgp3991733, kgp4011779, kgp4056892, kgp4096263, kgp4127859, kgp4155998, kgp4162414, kgp4223880, kgp4346717, kgp4370912, kgp4418535, kgp4420791 ₍ kgp4479467, kgp4524468, kgp4543470, kgp4559907, kgp4573213, kgp4634875, kgp4705854, kgp4734301, kgp4755147, kgp4812831, kgp4842590, kgp485316, kgp487328, kgp4898179, kgp5002011, kgp5014707, kgp5017029, kgp5053636, kgp5068397, kgp512180, kgp5144181, kgp5159037, kgp5216209, kgp5292386, kgp5334779, kgp5388938, kgp5409955, kgp5440506, kgp5441587, kgp5483926, kgp55646, kgp5564995, kgp5579170, kgp5680955, kgp5869992, kgp5908616, kgp6023196, kgp6032617, kgp6038357, kgp6076976, kgp6091119, kgp6127371, kgp61811, kgp6190988, kgp6214351, kgp6228750, kgp6236949, kgp6469620, kgp6505544, kgp6507761, kgp652534, kgp6539666, kgp6567154, kgp6599438, kgp6603796, kgp6666134, kgp6700691, kgp6737096, kgp6768546, kgp6772915, kgp6835138, kgp6959492, kgp6996560, kgp7059449, kgp7063887, kgp7077322, kgp7092772, kgp7117398, kgp7121374, kgp7178233, kgp7181058, kgp7186699, kgp7189498, kgp7242489, kgp7331172, kgp7416024, kgp7481870, kgp7506434 ₍ kgp7521990, kgp759150, kgp767200, kgp7714238, kgp7730397, kgp7747883, kgp7792268, kgp7802182, kgp7804623, kgp7924485, kgp8030775, kgp8036704, kgp8046214, kgp8106690, kgp8107491, kgp8110667, kgp8169636 ₍ kgp8174785, kgp8178358, kgp8183049, kgp8192546, kgp8200264, kgp8303520, kgp8335515, kgp8372910, kgp841428, kgp8437961, kgp8440036, kgp85534, kgp8599417, kgp8602316, kgp8615910, kgp8767692, kgp8777935, kgp8793915. kgp8796185, kgp8817856, kgp8869954, kgp8990121, kgp9018750, kgp9071686. kgp9078300, kgp9320791, kgp9354462, kgp9354820, kgp9368119, kgp9410843, kgp9421884, kgp9450430, kgp9530088, kgp9551947, kgp9601362, kgp9627338. kgp9627406, kgp9669946, kgp9699754, kgp971582, kgp97310, kgp974569, ]kgp9795732, kgp9806386, kgp9854133, kgp9884626, rsl0049206. rsl0124492, rsl0125298, rsl0162089, rsl0201643, rsl0203396, rsl0251797, rsl0278591, rsl0489312, rsl0492882, rsl0498793, rsl0501082, rsl0510774, rsl0512340, rsl079303, rsl0815160, rsl0816302, rsl0841322, rsl0841337, rsl0954782, rsll002051, rsll022778, rsll029892, rsll029907, rsll029928. rsll083404, rsll085044, rslll36970, rslll47439, rslll92461. rslll92469. rsll559024, rsll57449. rsll648129, rsll691553, rsl2013377. rsl2494712, rsl2943140, rsl3002663, rsl3394010, rsl3415334, rsl3419758. rsl380706, rsl387768, rsl410779, rsl478682, rsl508102. rsl532365, rsl544352, rsl545223, rsl579771, rsl604169. rsl621509, rsl644418, rsl6886004. rsl6895510, rsl6901784, rsl6927077, rsl6930057, rsl7029538, rsl7224858, rsl7238927, rsl7329014, rsl7400875. rsl7449018, rsl7577980, rsl7638791, rsl858973, rsl886214, rsl894406, rsl894407, rsl894408, rsl96295, rsl96341. rsl96343, rsl97523, rsl979992, rsl979993, rs2043136, rs2058742, rs2071469, rs2071470, rs2071472, rs2074037, rs2136408. rs2139612. rs2175121, rs2241883, rs2309760, rs2325911, rs241435 ₍ rs241440, rs241442, rs241443, rs241444, rs241445, rs241446, rs241447, rs241449, rs241451, rs241452, rs241453, rs241454, rs241456, rs2453478. rs2598360. rs2621321, rs2621323, rs2660214. rs2816838. rs2824070, rs2839117, rs2845371, rs2857101, rs2857103, rs2857104, rs2926455, rs2934491, rs3135388, rs3218328, rs343087, rs343092, rs3767955, rs3792135, rs3799383, rs3803277, rs3815822, rs3818675, rs3829539, rs3885907, rs3899755, rs4075692, rs4143493, rs419132. rs423239, rs4254166, rs4356336, rs4360791, rs4449139, rs4584668, rs4669694, rs4709792, rs4738738, rs4769060, rs4780822, rs4782279, rs4822644, rs484482, rs4894701, rs5024722, rs502530, rs543122. rs6032205, rs6032209. rs6110157, rs623011, rs6497396, rs6535882, rs6687976, rs6718758, rs6835202, rs6840089, rs6845927, rs6895094, rs6899068, rs7020402. rs7024953. rs7028906, rs7029123, rs7062312, rs714342, rs7187976, rs7191155, rs720176, rs7217872, rs7228827, rs7348267. rs7496451, rs7524868, rs7563131, rs7579987, rs759458, rs7666442, rs7670525, rs7672014, rs7677801, rs7725112, rs7844274, rs7850, rs7860748, rs7862565, rs7864679, rs7928078, rs7948420, rs8035826, rs8050872, rs8053136, rs8055485, rs823829, rs858341, rs9315047, rs931570, rs9346979, rs9376361, rs9393727, rs9501224, rs9508832, rs950928, rs9579566, rs9597498, rs9670531, rs9671124, rs9671182, rs9817308. rs9834010, rs9876830, rs9913349, rs9931167 and rs9931211 (hereinafter Group 1), ) identifying the subject as a predicted responder to glatiramer acetate if the genotype of the subject contains one or more A alleles at the location of kgpl0152733, kgpl0224254, kgpl0305127, kgpl0351364, kgpl0372946, kgpl0404633, kgpl0564659, kgpl0591989, kgpl0594414, kgpl0619195, kgpl0620244, kgpl0633631, kgpl0974833, kgpll002881, kgpll285862, kgpll328629, kgpll407560, kgpll514107, kgpll627530, kgpll702474, kgpll711524, kgpll768533, kgpll804835, kgpl2083934, kgpl2182745, kgpl2230354, kgpl224440, kgpl24162, kgpl2557319, kgpl371881, kgpl699628, kgpl753445, kgpl779254, kgpl786079, kgpl8379774, kgpl8525257, kgp20163979, kgp2023214, kgp20478926, kgp21171930, kgp2262166, kgp22778566, kgp2465184, kgp24753470, kgp25191871, kgp25216186, kgp25952891, kgp26026546, kgp26533576, kgp27500525, kgp27571222, kgp28532436, kgp28586329, kgp28817122, kgp2958113, kgp29794723, kgp30282494, kgp304921, kgp3205849, kgp3218351, kgp3276689, kgp337461, kgp345301, kgp355027, kgp355723, kgp3593828, kgp3812034, kgp3951463, kgp4162414, kgp4223880, kgp4418535, kgp4543470, kgp4573213, kgp4634875, kgp4755147, kgp4842590, kgp485316, kgp5068397, kgp5334779, kgp5483926, kgp5564995 , kgp5869992, kgp5908616, kgp6032617, kgp6038357, kgp6076976 , kgp6091119, kgp6127371, kgp61811, kgp6214351, kgp6228750 , kgp6236949, kgp6469620, kgp6505544, kgp6507761, kgp6666134 , kgp6700691, kgp6772915, kgp6959492, kgp7077322, kgp7117398 , kgp7178233, kgp7186699, kgp7506434, kgp759150, kgp7730397 , kgp7802182, kgp7804623, kgp7924485, kgp8030775, kgp8036704 , kgp8046214, kgp8106690, kgp8110667, kgp8178358, kgp8200264 , kgp8372910. kgp841428, kgp8602316. kgp8615910, kgp8793915 , kgp8796185. kgp8990121, kgp9018750, kgp9354462. kgp9368119 , kgp9410843, kgp9450430. kgp9530088, kgp9627338, kgp9669946 kgp97310, kgp974569, kgp9806386. kgp9884626, rsl0049206 , rsl0124492, rsl0125298, rsl0162089, rsl0203396, rsl0251797 , rsl0278591. rsl0489312, rsl0492882, rsl0498793. rsl0501082 , rsl0510774. rsl0512340, rsl0815160, rsl0816302, rsl0841337 , rsll029892, rsll029928, rslll92469, rsll559024. rsll648129 , rsl2013377, rsl3394010. rsl3415334, rsl478682, rsl544352, rsl545223, rsl604169, rsl621509, rsl644418, rsl7029538 , rsl7400875, rsl7449018, rsl7577980, rsl858973, rsl894406, rsl894407, rsl97523. rs2058742, rs2071469, rs2071472, rs2139612, rs2241883. rs2309760, rs241440, rs241442. rs241444, rs241445, rs241446, rs241449 , rs241453, rs241456, rs2453478, rs2660214, rs2824070, rs2845371, rs2857103, rs2926455, rs343087, rs343092, rs3767955, rs3792135, rs3829539, rs3899755, rs4075692. rs4143493. rs423239, rs4254166. rs4356336. rs4584668, rs4780822, rs4782279, rs5024722, rs6032209, rs6110157, rs623011, rs6497396, rs6845927. rs6895094. rs6899068, rs7024953, rs7028906, rs7029123. rs7062312, rs7187976, rs7191155, rs720176, rs7228827, rs7496451, rs7563131, rs759458, rs7666442. rs7670525, rs7677801. rs7725112 rs7850, rs7862565, rs7948420, rs8035826, rs8053136, rs8055485, rs823829, rs9315047, rs9501224, rs9508832, rs950928, rs9597498, rs9670531, rs9671124, rs9817308. rs9834010, rs9876830 or rs9931211 (hereinafter Group 2),

one or more C alleles at the location of kgpl0910719, kgpll077373, kgpll453406, kgpl2426624, kgp2045074, kgp22811918, kgp23298674, kgp2709692, kgp28687699, kgp3496814, kgp3669685, kgp3730395, kgp4056892, kgp4370912, kgp5053636, kgp5216209, kgp5292386, kgp6023196, kgp652534, kgp7059449, kgp7189498, kgp7521990, kgp7792268, kgp8303520, kgp9320791, kgp9795732, rsl0201643, rsll022778, rslll36970, rslll47439, rsll691553, rsl579771, rsl6901784, rs2136408, rs2325911, rs241443, rs2857104, rs3803277, rs3885907, rs4738738, rs4894701, rs502530, rs6032205, rs6687976, rs6718758, rs6835202, rs714342, rs7524868, rs7844274, rs9393727 or rs9671182 (hereinafter Group 3),

one or more G alleles at the location of kgpl0090631, kgpl009249, kgpl0412303, kgpl0523170, kgpl054273, kgpl0558725, kgpl0632945, kgpl0679353, kgpl0788130, kgpl0826273, kgpl0922969, kgpl0948564, kgpl0967046, kgpl098237, kgpll010680, kgplll41512, kgpll206453, kgpll210903, kgpll24492, kgpll281589, kgpll356379, kgpll467007, kgpll543962, kgpll580695, kgpll633966, kgpll686146, kgpll843177, kgpl2008955. kgpl2371757, kgpl285441, kgpl3161760, kgpl355977, kgpl5390522, kgpl683448, kgpl688752, kgpl912531, kgpl9568724, kgp2092817, kgp2245775, kgp22793211, kgp22823022, kgp2282938, kgp2299675, kgp2356388, kgp23672937, kgp23737989, kgp2388352, kgp2391411, kgp24131116, kgp24415534, kgp2446153, kgp2451249, kgp24729706, kgp25543811, kgp25921291, kgp26271158, kgp2638591, kgp26528455, kgp2688306, kgp26995430 , kgp270001, kgp2715873, kgp27640141, kgp2788291, kgp2923815, kgp29367521, kgp293787, kgp2959751, kgp297178, kgp3048169, kgp3182607, kgp3202939, kgp3267884, kgp3418770, kgp3450875, kgp3477351, kgp3598409, kgp3651767, kgp3854180, kgp3933330, kgp3984567, kgp4011779, kgp4096263, kgp4127859, kgp4155998, kgp4346717, kgp4420791, kgp4479467, kgp4524468, kgp4559907, kgp4705854, kgp4734301, kgp4812831, kgp487328, kgp4898179, kgp5002011, kgp5014707, kgp5017029, kgp512180, kgp5144181, kgp5159037, kgp5388938, kgp5409955, kgp5440506, kgp5441587, kgp55646, kgp5579170, kgp5680955, kgp6190988, kgp6539666, kgp6567154, kgp6599438, kgp6603796, kgp6737096, kgp6768546, kgp6835138, kgp6996560, kgp7063887, kgp7092772, kgp7121374, kgp7181058, kgp7331172, kgp7416024, kgp7481870, kgp767200, kgp7714238, kgp7747883, kgp8107491, kgp8169636, kgp8174785, kgp8183049, kgp8192546, kgp8335515. kgp8437961, kgp8440036 kgp85534, kgp8599417, kgp8767692, kgp8777935, kgp8817856, kgp8869954, kgp9071686, kgp9078300, kgp9354820, kgp9421884, kgp9551947, kgp9601362. kgp9627406, kgp9699754, kgp971582, kgp9854133, rsl079303, rsl0841322, rsl0954782, rsll002051, rsll029907, rsll083404, rsll085044, rslll92461, rsll57449, rsl2494712, rsl2943140, rsl3002663. rsl3419758. rsl380706, rsl387768, rsl410779. rsl508102, rsl532365, rsl6886004, rsl6895510, rsl6927077, rsl6930057, rsl7224858, rsl7238927, rsl7329014, rsl7638791, rsl886214, rsl894408. rsl96295, rsl96341, rsl96343, rsl979992, rsl979993, rs2043136, rs2071470, rs2074037, rs2175121, rs241435, rs241447, rs24145 1, rs241452, rs241454, rs2598360, rs2621321, rs2621323, rs2816838. rs2839117, rs2857101, rs2934491. rs3135388, rs3218328. rs3799383, rs3815822, rs3818675, rs419132, rs4360791, rs4449139, rs4669694, rs4709792, rs4769060, rs4822644, rs484482, rs543122, rs6535882, rs6840089, rs7020402, rs7217872, rs7348267, rs7579987, rs7672014, rs7860748, rs7864679, rs7928078, rs8050872, rs858341, rs931570, rs9346979, rs9376361, rs9579566, rs9913349 or rs9931167

(hereinafter Group 4) , or

one or more T alleles at the location of kgpl8432055, kgp279772, kgp3991733 or kgp7242489; and

(iii) administering the pharmaceutical composition comprising glatiramer acetate and a pharmaceutically acceptable carrier to the subject only if the subject is identified as a predicted responder to glatiramer acetate.

The present invention also provides a method of identifying a human subject afflicted with multiple sclerosis or a single clinical attack consistent with multiple sclerosis as a predicted responder or as a predicted non-responder to glatiramer acetate, the method comprising determining the genotype of the subject at a location corresponding to the location of one or more single nucleotide polymorphisms (SNPs) selected from the group consisting of Group 1, and identifying the human subject as a predicted responder to glatiramer acetate if the genotype of the subject contains

one or more A alleles at the location of Group 2,

one or more C alleles at the location of Group 3,

one or more G alleles at the location of Group 4, or

one or more T alleles at the location of kgpl8432055, kgp279772, kgp3991733 or kgp7242489, or identifying the human subject as a predicted non-responder to glatiramer acetate if the genotype of the subject contains no A alleles at the location of Group 2,

no C alleles at the location of Group 3,

no G alleles at the location of Group 4, or

no T alleles at the location of kgpl8432055, kgp279772, kgp3991733 or kgp7242489.

The present invention also provides a kit for identifying a human subject afflicted with multiple sclerosis or a single clinical attack consistent with multiple sclerosis as a predicted responder or as a predicted non-responder to glatiramer acetate, the kit comprising at least one probe specific for the location of a SNP selected from the group consisting of Group 1. The present invention also provides a kit for identifying a human subject afflicted with multiple sclerosis or a single clinical attack consistent with multiple sclerosis as a predicted responder or as a predicted non-responder to glatiramer acetate, the kit comprising at least one pair of PCR primers designed to amplify a DNA segment which includes the location of a SNP selected from the group consisting of Group 1.

The present invention also provides a kit for identifying a human subject afflicted with multiple sclerosis or a single clinical attack consistent with multiple sclerosis as a predicted responder or as a predicted non-responder to glatiramer acetate, the kit comprising a reagent for performing a method selected from the group consisting of restriction fragment length polymorphism (RFLP) analysis, sequencing, single strand conformation polymorphism analysis (SSCP) , chemical cleavage of mismatch (CCM) , gene chip and denaturing high performance liquid chromatography (DHPLC) for determining the genotype of the subject at a location corresponding to the location of at least one SNP selected from the group consisting of Group 1.

The present invention also provides a kit for identifying a human subject afflicted with multiple sclerosis or a single clinical attack consistent with multiple sclerosis as a predicted responder or as a predicted non-responder to glatiramer acetate, the kit comprising reagents for TaqMan Open Array assay designed for determining the genotype of the subject at a location corresponding to the location of at least one SNP selected from the group consisting of Group 1.

The present invention also provides a kit for identifying a human subject afflicted with multiple sclerosis or a single clinical attack consistent with multiple sclerosis as a predicted responder or as a predicted non-responder to glatiramer acetate, the kit comprising

a) at least one probe specific for a location corresponding to the location of at least one SNP;

b) at least one pair of PCR primers designed to amplify a DNA segment which includes a location corresponding to the location of at least one SNP;

c) at least one pair of PCR primers designed to amplify a DNA segment which includes a location corresponding to the location of at least one SNP and at least one probe specific for a location corresponding to the location of at least one SNP;

d) a reagent for performing a method selected from the group consisting of restriction fragment length polymorphism (RFLP) analysis, sequencing, single strand conformation polymorphism analysis (SSCP) , chemical cleavage of mismatch (CCM) , gene chip and denaturing high performance liquid chromatography (DHPLC) for determining the identity of at least one SNP; or e) reagents for TaqMan Open Array assay designed for determining the genotype at a location corresponding to the location of at least one SNP,

wherein the at least one SNP is selected from the group consisting of Group 1.

The present invention also provides a probe for identifying the genotype of a location corresponding to the location of a SNP selected from the group consisting of kgpl0090631, kgpl009249, kgpl0148554, kgpl0152733, kgpl0215554, kgpl0224254, kgpl0305127, kgpl0351364, kgpl0372946, kgpl0404633, kgpl0412303, kgpl0523170, kgpl054273, kgpl0558725. kgpl0564659, kgpl0591989, kgpl0594414, kgpl0619195, kgpl0620244, kgpl0632945, kgpl0633631, kgpl0679353, kgpl0762962. kgpl0788130, kgpl0826273, kgpl0836214, kgpl0910719, kgpl0922969, kgpl0948564, kgpl0967046. kgpl0974833. kgpl098237, kgpl0989246, kgpll002881, kgpll010680, kgpll077373, kgplll41512, kgpll206453. kgpll210903, kgpll24492, kgpll281589, kgpll285862, kgpll285883, kgpll328629, kgpll356379, kgpll407560, kgpll453406. kgpll467007, kgpll514107, kgpll543962, kgpll580695, kgpll604017, kgpll627530, kgpll633966, kgpll686146. kgpll702474, kgpll711524, kgpll755256, kgpll768533, kgpll804835. kgpll843177, kgpl2008955, kgpl2083934, kgpl211163 ₍ kgpl2182745, kgpl2230354, kgpl224440, kgpl2253568, kgpl2371757, kgpl24162, kgpl2426624, kgpl2557319, kgpl2562255. kgpl285441, kgpl3161760, kgpl355977, kgpl371881. kgpl432800. kgpl5390522. kgpl682126, kgpl683448, kgpl688752. kgpl699628. kgpl753445. kgpl758575, kgpl779254, kgpl786079, kgpl8379774, kgpl8432055, kgpl8525257, kgpl912531, kgpl9568724, kgp20163979, kgp2023214, kgp2045074, kgp20478926, kgp2092817, kgp21171930, kgp2176915, kgp2245775, kgp2262166, kgp22778566, kgp22793211. kgp22811918, kgp22823022, kgp2282938, kgp22839559, kgp2299675, kgp23298674, kgp2356388, kgp23672937, kgp23737989, kgp2388352. kgp2391411, kgp24131116, kgp24415534, kgp2446153, kgp2451249, kgp24521552, kgp2465184, kgp24729706, kgp24753470, kgp25191871, kgp25216186, kgp25543811 ₍ kgp25921291. kgp25952891, kgp26026546, kgp26271158, kgp2638591, kgp26528455, kgp26533576, kgp2688306, kgp26995430, kgp270001, kgp2709692, kgp2715873, kgp27500525, kgp27571222, kgp27640141, kgp2788291, kgp279772, kgp28532436, kgp28586329, kgp28687699 ₍ kgp2877482, kgp28817122. kgp2920925, kgp2923815, kgp29367521, kgp293787, kgp2958113, kgp2959751. kgp2 7178, kgp29794723, kgp2993366, kgp30282494, kgp3048169, kgp304921, kgp3182607, kgp3188, kgp3202939, kgp3205849 ₍ kgp3218351, kgp3267884, kgp3276689, kgp3287349, kgp337461, kgp3418770, kgp3420309, kgp3450875, kgp345301, kgp3477351, kgp3488270, kgp3496814, kgp355027, kgp355723. kgp3593828, kgp3598409, kgp3598966, kgp3624014, kgp3651767, kgp3669685, kgp3697615, kgp3730395, kgp3812034. kgp3854180, kgp3933330, kgp394638, kgp3951463, kgp3984567, kgp3991733. kgp4011779, kgp4037661, kgp4056892, kgp4096263, kgp4127859, kgp4137144, kgp4155998, kgp4162414, kgp4223880, kgp433351, kgp4346717, kgp4370912, kgp4418535, kgp4420791, kgp4456934, kgp4479467, kgp4524468, kgp4543470, kgp4559907. kgp4573213, kgp4575797, kgp4591145, kgp4634875, kgp4705854. kgp4734301, kgp4755147, kgp4812831, kgp4842590, kgp485316, kgp487328, kgp4892427, kgp4898179, kgp4970670, kgp4985243. kgp5002011, kgp5014707, kgp5017029, kgp5053636, kgp5068397, kgp512180, kgp5144181, kgp5159037, kgp5216209, kgp5252824, kgp5292386, kgp5326762, kgp5334779, kgp5388938, kgp5409955, kgp541892, kgp5440506, kgp5441587, kgp5483926, kgp55646 , kgp5564995, kgp5579170, kgp5680955, kgp5691690, kgp5747456, kgp5869992, kgp5894351. kgp5908616. kgp5924341, kgp5949515, kgp6023196, kgp6032617, kgp6038357, kgp6042557, kgp6076976, kgp6081880, kgp6091119, kgp6127371, kgp61811, kgp6190988, kgp6194428, kgp6213972, kgp6214351, kgp6228750, kgp6236949, kgp625941, kgp6301155. kgp6429231, kgp6469620, kgp6505544, kgp6507761, kgp652534, kgp6539666, kgp6567154, kgp6599438, kgp6603796, kgp6666134, kgp6700691, kgp6737096, kgp6768546, kgp6772915, kgp6828277, kgp6835138, kgp6889327, kgp6959492, kgp6990559, kgp6996560, kgp7006201, kgp7059449, kgp7063887, kgp7077322, kgp7092772, kgp7117398, kgp7121374, kgp7151153, kgp7161038, kgp7178233, kgp7181058, kgp7186699, kgp7189498, kgp7242489, kgp7331172, kgp7416024, kgp7481870, kgp7506434, kgp7521990, kgp759150, kgp7653470, kgp767200, kgp7714238, kgp7730397, kgp7747883, kgp7778345, kgp7792268, kgp7802182, kgp7804623, kgp7924485, kgp7932108, kgp8030775, kgp8036704, kgp8046214, kgp8106690, kgp8107491, kgp8110667, kgp8145845, kgp8169636, kgp8174785, kgp8178358, kgp8183049, kgp8192546, kgp8200264, kgp8303520, kgp8335515, kgp8372910, kgp841428, kgp8437961, kgp8440036, kgp85534, kgp8599417, kgp8602316, kgp8615910, kgp8644305, kgp8767692, kgp8777935, kgp8793915, kgp8796185, kgp8817856, kgp8847137, kgp8869954, kgp8990121, kgp9018750, kgp9071686, kgp9078300, kgp9143704, kgp9320791, kgp9354462, kgp9354820, kgp9368119, kgp9409440, kgp9410843, kgp9421884, kgp9450430, kgp9530088, kgp9551947, kgp956070, kgp9601362, kgp9627338, kgp9627406, kgp9669946, kgp9699754, kgp971582, kgp97310, kgp974569, kgp9795732, kgp9806386, kgp9854133, kgp9884626, kgp9909702, kgp9927782, Pl_M_061510 _. 11_106_M, P1_M_061510_18_342_P, P1_M_061510_6_159_P, rsl0038844, rsl0049206, rsl0124492, rsl0125298, rsl0162089, rsl0201643, rsl0203396, rsl0251797, rsl026894, rsl0278591, rsl0489312, rsl0492882, rsl0495115, rsl0498793, rsl0501082, rsl0510774, rsl0512340, rsl079303, rsl0815160, rsl0816302, rsl0841322, rsl0841337, rsl0954782, rsll002051, rsll022778, rsll029892, rsll029907, rsll029928, rsll083404, rsll085044, rslll36970, rslll47439, rslll92461, rslll92469, rsll559024, rsll562998, rsll563025, rsll57449, rsll648129, rsll691553, rsll750747, rsll947777, rsl2013377, rsl2043743, rsl2233980, rsl2341716, rsl2472695, rsl2494712, rsl2881439, rsl2943140, rsl3002663, rsl3168893, rsl3386874, rsl3394010, rsl3415334. rsl3419758, rsl357718, rsl380706, rsl387768, rsl393037, rsl393040, rsl397481, rsl410779, rsl474226, rsl478682, rsl508102, rsl508515, rsl532365, rsl534647, rsl544352, rsl545223, rsl579771, rsl604169, rsl621509, rsl644418, rsl6846161, rsl6886004, rsl6895510, rsl6901784, rsl6927077, rsl6930057, rsl7029538, rsl715441, rsl7187123, rsl7224858, rsl7238927, rsl7245674, rsl7329014, rsl7400875, rsl7419416, rsl7449018, rsl7577980, rsl7638791, rsl793174, rsl858973, rsl883448, rsl886214, rsl894406, rsl894407, rsl894408, rsl905248, rsl96295, rsl96341, rsl96343, rsl97523, rsl979992, rsl979993, rs2043136, rs2058742, rs2071469, rs2071470, rs2071472, rs2074037, rs209568, rs2136408, rs2139612, rs2175121, rs2241883. rs2309760, rs2325911. rs2354380, rs241435, rs241440, rs241442, rs241443, rs241444, rs241445, rs241446, rs241447, rs241449, rs241451, rs241452, rs241453, rs241454, rs241456, rs2453478, rs2598360, rs2618065, rs2621321. rs2621323, rs263247, rs2660214, rs2662, rs2816838, rs2824070, rs2839117, rs2845371. rs2857101. rs2857103. rs2857104, rs28993969, rs2926455, rs2934491. rs3135388, rs3218328. rs343087, rs343092, rs34647183, rs35615951, rs3767955, rs3768769, rs3792135, rs3799383, rs3803277, rs3815822, rs3818675, rs3829539, rs3847233, rs3858034, rs3858035, rs3858036, rs3858038. rs3885907. rs3894712, rs3899755, rs4075692, rs4143493, rs419132, rs423239, rs4254166, rs4356336, rs4360791, rs4449139, rs4584668. rs4669694. rs4709792, rs4738738, rs4740708, rs4769060, rs4780822, rs4782279, rs4797764, rs4822644, rs484482, rs4894701, rs4978567, rs5024722, rs502530, rs528065, rs543122. rs6032205, rs6032209, rs6110157, rs623011. rs6459418, rs6497396, rs6535882, rs6577395. rs6687976, rs6718758. rs6811337, rs6835202, rs6840089, rs6845927. rs6895094, rs6899068, rs7020402, rs7024953, rs7028906, rs7029123, rs7062312, rs7119480, rs7123506, rs714342, rs7187976, rs7191155, rs720176, rs7217872, rs7228827, rs7231366, rs7348267, rs7496451, rs7524868, rs7563131. rs7579987, rs759458, rs7666442, rs7670525. rs7672014, rs7677801. rs7680970, rs7684006, rs7696391, rs7698655. rs7725112, rs7819949 rs7844274 rs7846783, rs7850, rs7860748, rs7862565, rs7864679, rs7928078, rs7948420, rs7949751, rs7961005. rs8000689, rs8018807, rs8035826, rs8050872, rs8053136. rs8055485, rs823829. rs858341, rs9315047, rs931570. rs9346979, rs9376361, rs9393727. rs9501224, rs9508832, rs950928. rs9579566, rs9597498, rs961090, rs9670531, rs9671124, rs9671182. rs967616, rs9817308, rs9834010, rs9876830, rs9913349, rs9931167, rs9931211 ₍ rs9948620 and rs995327 4 (hereinafter Group 5) .

The present invention also provides glatiramer acetate or a pharmaceutical composition comprising glatiramer acetate for use in treating a human subject afflicted with multiple sclerosis or a single clinical attack consistent with multiple sclerosis which human subject is identified as a predicted responder to glatiramer acetate by:

a) determining a genotype of the subject at a location corresponding to the location of one or more single nucleotide polymorphisms (SNPs) selected from the group consisting of: Group 1 , and

b) identifying the subject as a predicted responder to glatiramer acetate if the genotype of the subject contains

one or more A alleles at the location of Group 2, one or more C alleles at the location of Group 3, one or more G alleles at the location of Group 4, or one or more T alleles at the location of kgpl8432055, kgp279772, kgp3991733 or kgp7242489. The present invention also provides a method of determining the genotype of a human subject comprising identifying whether the genotype of a human subject contains

one or more A alleles at the location of Group 2,

one or more C alleles at the location of Group 3,

one or more G alleles at the location of Group 4, or

one or more T alleles at the location of kgpl8432055, kgp279772, kgp3991733 or kgp7242489.

The present invention also provides a method of identifying a human subject afflicted with multiple sclerosis or a single clinical attack consistent with multiple sclerosis who is predicted to have a slower course of disease progression, comprising the steps of:

(i) determining a genotype of the subject at a location corresponding to the location of one or more single nucleotide polymorphisms (SNPs) selected from the group consisting of: kgpl0148554, kgpl0215554, kgpl0762962, kgpl0836214, kgpl0989246, kgpll285883, kgpll604017, kgpll755256, kgpl211163, kgpl2253568, kgpl2562255, kgpl432800, kgpl682126, kgpl758575, kgp2176915, kgp22839559, kgp24521552, kgp2877482 ₍ kgp2920925, kgp2993366, kgp3188, kgp3287349, kgp3420309, kgp3488270, kgp3598966, kgp3624014, kgp3697615, kgp394638, kgp4037661, kgp4137144, kgp433351, kgp4456934, kgp4575797, kgp4591145, kgp4892427, kgp4970670, kgp4985243, kgp5252824, kgp5326762, kgp541892, kgp5691690, kgp5747456. kgp5894351, kgp5924341, kgp5949515, kgp6042557, kgp6081880, kgp6194428, kgp6213972, kgp625941, kgp6301155, kgp6429231. kgp6828277, kgp6889327 ₍ kgp6990559, kgp7006201. kgp7151153, kgp7161038, kgp7653470, kgp7778345. kgp7932108, kgp8145845, kgp8644305. kgp8847137, kgp9143704, kgp9409440, kgp956070, kgp9909702, kgp9927782, rsl0038844, rsl026894, rsl0495115, rsll562998. rsll563025, rsll750747, rsll947777, rsl2043743, rsl2233980. rsl2341716, rsl2472695, rsl2881439, rsl3168893, rsl3386874, rsl357718. rsl393037, rsl393040, rsl397481, rsl474226, rsl508515, rsl534647, rsl6846161, rsl715441. rsl7187123, rsl7245674, rsl7419416, rsl793174, rsl883448, rsl905248, . rs209568, rs2354380, rs2618065, rs263247, rs2662, rs28993969, rs34647183, rs35615951, rs3768769, rs3847233, rs3858034, rs3858035. rs3858036, rs3858038, rs3894712, rs4740708, rs4797764, rs4978567, rs528065, rs6459418, rs6577395, rs6811337, rs7119480, rs7123506, rs7231366, rs7680970, rs7684006, rs7696391, rs7698655, rs7819949, rs7846783, rs7949751, rs7961005, rs8000689, rs8018807, rs961090, rs967616, rs9948620 and rs9953274 (hereinafter Group 6), and

(ii) identifying the subject as predicted to have a slower course of disease progression if the genotype of the subject contains

one or more A alleles at the location of kgpl0762962, kgpll285883, kgpll604017, kgpl211163, kgpl2253568, kgpl2562255, kgp2176915, kgp24521552, kgp2877482, kgp2993366, kgp3188, kgp3624014, kgp394638, kgp4037661, kgp433351, kgp4456934, kgp4575797, kgp4591145, kgp4892427, kgp4970670, kgp4985243, kgp5252824, kgp5326762, kgp541892, kgp5747456, kgp5894351, kgp6042557, kgp6081880, kgp6194428, kgp6429231, kgp7006201, kgp7151153, kgp7161038, kgp7653470, kgp8145845, kgp8644305, kgp9143704, kgp9409440, kgp9909702, kgp9927782, rsl0038844, rsl0495115, rsll750747, rsl2341716, rsl2881439, rsl3168893, rsl393040, rsl474226, rsl534647, rsl715441, rsl7187123, rsl7245674, rsl7419416, rsl793174, rsl883448, rsl905248, rs263247, rs34647183, rs35615951, rs3847233, rs3858038, rs4740708, rs528065, rs6459418, rs6577395, rs6811337, rs7680970, rs7684006, rs7698655, rs7961005, rs8018807, rs9948620 or rs9953274 (hereinafter Group 7), one or more C alleles at the location of kgpl0836214, kgpl432800, kgp22839559, kgp6301155, kgp6828277, rs2354380, rs2662, rs3858035, rs3894712, rs4797764 or rs7696391 (hereinafter Group 8) ,

one or more G alleles at the location of kgpl0148554, kgpl0215554, kgpl0989246, kgpll755256, kgpl682126, kgpl758575, kgp2920925, kgp3287349, kgp3420309, kgp3488270, kgp3598966, kgp3697615, kgp4137144, kgp5691690, kgp5924341, kgp5949515, kgp6213972, kgp625941, kgp6889327, kgp6990559, kgp7778345, kgp7932108, kgp8847137, kgp956070, rsl026894, rsll562998, rsll563025, rsll947777, rsl2233980, rsl2472695, rsl3386874, rsl357718, rsl393037, rsl397481, rsl508515, rsl6846161, rs209568, rs2618065, rs28993969, rs3768769, rs3858034, rs3858036 ₍ rs4978567, rs7119480, rs7123506, rs7231366, rs7819949, rs7846783, rs7949751, rs8000689, rs961090 or rs967616 (hereinafter Group 9), or one or more T alleles at the location of rsl2043743.

The present invention also provides a kit for identifying a human subject afflicted with multiple sclerosis or a single clinical attack consistent with multiple sclerosis who is predicted to have a slower course of disease progression, the kit comprising

a) at least one probe specific for a location corresponding to the location of at least one SNP; b) at least one pair of PCR primers designed to amplify a DNA segment which includes a location corresponding to the location of at least one SNP;

c) at least one pair of PCR primers designed to amplify a DNA segment which includes a location corresponding to the location of at least one SNP and at least one probe specific for a location corresponding to the location of at least one SNP;

d) a reagent for performing a method selected from the group consisting of restriction fragment length polymorphism (RFLP) analysis, sequencing, single strand conformation polymorphism analysis (SSCP) , chemical cleavage of mismatch (CCM) , gene chip and denaturing high performance liquid chromatography (DHPLC) for determining the identity of at least one SNP; or e) reagents for TaqMan Open Array assay designed for determining the genotype at a location corresponding to the location of at least one SNP,

wherein the at least one SNP is selected from the group consisting of Group 6.

BRIEF DESCRIPTION OF THE DRAWINGS

Figure 1. Figure 1 shows Receiver Operating Characteristics for optimization of test threshold.

Figure 2. Figure 2 shows Response Rate of Predicted Responders (green line) and Response Rate of Predicted Non-Responders (red line) by predictive test threshold.

Figure 3. Figure 3 shows overall percent of Predicted Responders by predictive test threshold.

Figure 4. Figure 4 shows chi square P-values (-Log P-value) of different test thresholds in the ability of the test to differentiate between cases and controls. A threshold of 0.71 demonstrated the most significant p-value.

Figure 5. Figure 5 shows overall Response to glatiramer acetate as Predicted by Model (model 3, threshold 0.71) for Predicted Responders (left panel) and Predicted Non-Responders (right panel) .

Figure 6. Figure 6 shows GALA and FORTE patients were stratified by clearly defined response. High Response: improved ARR (ARR change <(-l), during study versus prior 2 years). Low Response: no change or worsening of ARR (ARR change ≥0, during study versus previous 2 years) .

Figure 7. Figure 7 shows predictive model building for GALA and FORTE cohorts .

Figure 8■ Figure 8 shows the algorithm and calculation of values for all genotyped patients of the Gala and FORTE cohorts, based on the predictive model (11 SNPs and 2 clinical variables) .

Figure 9. Figure 9 shows the algorithm and calculation of values for all genotyped patients of the Gala and FORTE cohorts, based on the 11 SNPs in the predictive model, without including the clinical variables, and using a threshold at -30% of the population classified as "predicted responders". DETAILED DESCRIPTION OF THE INVENTION

Embodiments of the Invention

The present invention provides a method for treating a human subject afflicted with multiple sclerosis or a single clinical attack consistent with multiple sclerosis with a pharmaceutical composition comprising glatiramer acetate and a pharmaceutically acceptable carrier, comprising the steps of:

(i) determining a genotype of the subject at a location corresponding to the location of one or more single nucleotide polymorphisms (SNPs) selected from the group consisting of: Group 1,

(ii) identifying the subject as a predicted responder to glatiramer acetate if the genotype of the subject contains one or more A alleles at the location of Group 2,

one or more C alleles at the location of Group 3,

one or more G alleles at the location of Group 4, or

one or more T alleles at the location of kgpl8432055, kgp279772, kgp3991733 or kgp7242489; and

(iii) administering the pharmaceutical composition comprising glatiramer acetate and a pharmaceutically acceptable carrier to the subject only if the subject is identified as a predicted responder to glatiramer acetate.

In some embodiments step (i) further comprises determining a genotype of the subject at a location corresponding to the location of one or more single nucleotide polymorphisms (SNPs) selected from the group consisting of: rsl0988087, rsl573706, rsl7575455, rs2487896, rs3135391, rs6097801 and rs947603, and wherein step (ii) further comprises identifying the subject as a predicted responder to glatiramer acetate if the genotype of the subject contains one or more A alleles at the location of rsl0988087, one or more C alleles at the location of rsl7575455, or one or more G alleles at the location of rsl573706, rs2487896, rs3135391, rs6097801 or rs947603.

In some embodiments administering the pharmaceutical composition comprising glatiramer acetate and a pharmaceutically acceptable carrier comprises administering to the human subject three subcutaneous injections of the pharmaceutical composition over a period of seven days with at least one day between every subcutaneous injection. In some embodiments the pharmaceutical composition is a unit dose of a 1 ml aqueous solution comprising 40 mg of glatiramer acetate.

In some embodiments the pharmaceutical composition is a unit dose of a 1 ml aqueous solution comprising 20 mg of glatiramer acetate.

In some embodiments the pharmaceutical composition is a unit dose of a 0.5 ml aqueous solution comprising 20 mg of glatiramer acetate.

In some embodiments the pharmaceutical composition comprising glatiramer acetate and a pharmaceutically acceptable carrier is administered as a monotherapy.

In some embodiments the pharmaceutical composition comprising glatiramer acetate and a pharmaceutically acceptable carrier is administered in combination with at least one other multiple sclerosis drug.

The present invention also provides a method of identifying a human subject afflicted with multiple sclerosis or a single clinical attack consistent with multiple sclerosis as a predicted responder or as a predicted non-responder to glatiramer acetate, the method comprising determining the genotype of the subject at a location corresponding to the location of one or more single nucleotide polymorphisms (SNPs) selected from the group consisting of Group 1, and

identifying the human subject as a predicted responder to glatiramer acetate if the genotype of the subject contains

one or more A alleles at the location of Group 2,

one or more C alleles at the location of Group 3,

one or more G alleles at the location of Group 4, or

one or more T alleles at the location of kgpl8432055, kgp279772, kgp3991733 or kgp7242489, or identifying the human subject as a predicted non-responder to glatiramer acetate if the genotype of the subject contains no A alleles at the location of Group 2,

no C alleles at the location of Group 3 ,

no G alleles at the location of Group 4, or

no T alleles at the location of kgpl8432055, kgp279772, kgp3991733 or kgp7242489.

In some embodiments the methods further comprise determining a genotype of the subject at a location corresponding to the location of one or more SNPs selected from the group consisting of: rsl0988087, rsl573706, rsl7575455, rs2487896, rs3135391, rs6097801 and rs947603, and identifying the human subject as a predicted responder to glatiramer acetate if the genotype of the subject contains one or more A alleles at the location of rsl0988087, one or more C alleles at the location of rsl7575455, or one or more G alleles at the location of rsl573706, rs2487896, rs3135391, rs6097801 or rs947603, or identifying the human subject as a predicted non-responder to glatiramer acetate if the genotype of the subject contains no A alleles at the location of rsl0988087, no C alleles at the location of rsl7575455, or no G alleles at the location of rsl573706, rs2487896, rs3135391, rs6097801 or rs947603. In some embodiments the genotype is determined from a nucleic acid- containing sample that has been obtained from the subject. In some embodiments determining the genotype comprises using a method selected from the group consisting of restriction fragment length polymorphism (RFLP) analysis, sequencing, single strand conformation polymorphism analysis (SSCP) , chemical cleavage of mismatch (CCM) , denaturing high performance liquid chromatography (DHPLC) , Polymerase Chain Reaction (PCR) and an array, or a combination thereof.

In some embodiment, applying the algorithm depicted in Figure 8 or in Figure 9 to identify the subject as a predicted responder or as a predicted non-responder to glatiramer acetate. In some embodiments the genotype is determined using at least one pair of PCR primers and at least one probe.

In some embodiments the array is selected from the group consisting of a gene chip, and a TaqMan Open Array. In some embodiments the gene chip is selected from the group consisting of a DNA array, a DNA microarray, a DNA chip, and a whole genome genotyping array.

In some embodiments the array is a TaqMan Open Array.

In some embodiments the gene chip is a whole genome genotyping array.

In some embodiments determining the genotype of the subject at the location corresponding to the location of the said one or more SNPs comprises :

(i) obtaining DNA from a sample that has been obtained from the subject;

(ii) optionally amplifying the DNA; and

(iii) subjecting the DNA or the amplified DNA to a method selected from the group consisting of restriction fragment length polymorphism (RFLP) analysis, sequencing, single strand conformation polymorphism analysis (SSCP) , chemical cleavage of mismatch (CCM) , denaturing high performance liquid chromatography (DHPLC) , Polymerase Chain Reaction (PCR) and an array, or a combination thereof, for determining the identity the one or more SNPs. In some embodiments the array comprises a plurality of probes suitable for determining the identity of the one or more SNPs.

In some embodiments the array is a gene chip.

In some embodiments the gene chip is a whole genome genotyping array . some embodiments the human subject is a naive patient In some embodiments the human subject has been previously administered glatiramer acetate.

In some embodiments the human subject has been previously administered a multiple sclerosis drug other than glatiramer acetate.

In some embodiments the genotype is determined at locations corresponding to the locations of 2 , 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or more single nucleotide polymorphisms (SNPs) .

In some embodiments the one or more SNPs is selected from the group consisting of kgp24415534, kgp6214351, kgp6599438, kgp7747883, kgp8110667, kgp8817856, rsl0162089, rsl6886004, rsl894408 and rs759458 (hereinafter Group 10) .

In some embodiments the one or more SNPs is selected from the group consisting of kgp24415534, kgp6214351, kgp6599438, kgp7747883, kgp8110667, kgp8817856, rsl0162089, rsl6886004, rsl894408, rs3135391, and rs759458.

In some embodiments the one or more SNPs is selected from the group consisting of kgp24415534, kgp6214351, kgp6599438, kgp8110667, kgp8817856, rsl0162089, rsl6886004, rsl894408, rs3135391, and rs759458.

In some embodiments, if rs3135391 is the at least one SNP selected, then selecting at least one SNP other than rs3135391.

In some embodiments the one or more SNPs comprise 2, 3, 4, 5, 6, 7, 8, 9 or 10 of the SNPs selected from the group consisting of Group 10.

In some embodiments the one or more SNPs further comprise rs3135391.

In some embodiments the one or more SNPs comprise 2, 3, 4, 5, 6, 7, 8, 9, 10, or 11 of the SNPs selected from the group consisting of kgp24415534, kgp6214351, kgp6599438, kgp7747883, kgp8110667, kgp8817856, rsl0162089, rsl6886004, rsl894408, rs3135391 and rs759458.

In some embodiments the one or more single nucleotide polymorphisms (SNPs) further comprise 2, 3, 4, 5, 6, 7, 8, 9 or 10 of the SNPs selected from the group consisting of kgp24415534 , kgp6214351, kgp6599438, kgp8110667, kgp8817856, rsl0162089, rsl6886004, rsl894408, rs3135391 and rs759458.

In some embodiments the genotype of the subject at the location corresponding to the location of one or more of the SNPs is determined indirectly by determining the genotype of the subject at a location corresponding to the location of at least one SNP that is in linkage disequilibrium with the one or more SNPs.

In some embodiments the genotype of the subject at the location corresponding to the location of the one or more SNPs is determined by indirect genotyping.

In some embodiments the indirect genotyping allows identification of the genotype of the subject at the location corresponding to the location of the one or more SNPs with a probability of at least 85%.

In some embodiments the indirect genotyping allows identification of the genotype of the subject at the location corresponding to the location of the one or more SNPs with a probability of at least 90% .

In some embodiments the indirect genotyping allows identification of the genotype of the subject at the location corresponding to the location of the one or more SNPs with a probability of at least 99 % . In some embodiments the methods further comprise the step of determining the log number of relapses in the last two years for the human subj ect .

In some embodiments the methods further comprise the step of determining the baseline Expanded Disability Status Scale (EDSS) score for the human subject. In some embodiments the methods further comprise determining the genotype of the subject at a location corresponding to the location of one or more single nucleotide polymorphisms (SNPs) selected from the group consisting of: Group 6, and identifying the human subject as a predicted responder to glatiramer acetate if the genotype of the subject contains

one or more A alleles at the location of Group 7,

one or more C alleles at the location of Group 8,

one or more G alleles at the location of Group 9, or

one or more T alleles at the location of rsl2043743.

The present invention also provides a kit for identifying a human subject afflicted with multiple sclerosis or a single clinical attack consistent with multiple sclerosis as a predicted responder or as a predicted non-responder to glatiramer acetate, the kit comprising at least one probe specific for the location of a SNP selected from the group consisting of Group 1.

The present invention also provides a kit for identifying a human subject afflicted with multiple sclerosis or a single clinical attack consistent with multiple sclerosis as a predicted responder or as a predicted non-responder to glatiramer acetate, the kit comprising at least one pair of PCR primers designed to amplify a DNA segment which includes the location of a SNP selected from the group consisting of Group 1.

The present invention also provides a kit for identifying a human subject afflicted with multiple sclerosis or a single clinical attack consistent with multiple sclerosis as a predicted responder or as a predicted non-responder to glatiramer acetate, the kit comprising a reagent for performing a method selected from the group consisting of restriction fragment length polymorphism (RFLP) analysis, sequencing, single strand conformation polymorphism analysis (SSCP) , chemical cleavage of mismatch (CCM) , gene chip and denaturing high performance liquid chromatography (DHPLC) for determining the genotype of the subject at a location corresponding to the location of at least one SNP selected from the group consisting of Group 1.

In some embodiments the gene chip is a whole genome genotyping array . In some embodiments the kit comprises

(i) at least one pair of PCR primers designed to amplify a DNA segment which includes the location a SNP selected from the group consisting of Group 1, and

(ii) at least one probe specific for the location of a SNP selected from the group consisting of Group 1.

The present invention also provides a kit for identifying a human subject afflicted with multiple sclerosis or a single clinical attack consistent with multiple sclerosis as a predicted responder or as a predicted non-responder to glatiramer acetate, the kit comprising reagents for TaqMan Open Array assay designed for determining the genotype of the subject at a location corresponding to the location of at least one SNP selected from the group consisting of Group 1.

In some embodiments the kit further comprises instructions for use of the kit for identifying a human subject afflicted with multiple sclerosis or a single clinical attack consistent with multiple sclerosis as a predicted responder or as a predicted non-responder to glatiramer acetate.

In some embodiments the one or more single nucleotide polymorphisms (SNPs) are selected from the group consisting of Group 10.

In some embodiments the one or more single nucleotide polymorphisms (SNPs) comprise 2, 3, 4, 5, 6, 7, 8, 9 or 10 of the SNPs selected from the group consisting of Group 10.

In some embodiments the one or more SNPs further comprise rs3135391. The present invention also provides a kit for identifying a human subject afflicted with multiple sclerosis or a single clinical attack consistent with multiple sclerosis as a predicted responder or as a predicted non-responder to glatiramer acetate, the kit comprising

a) at least one probe specific for a location corresponding to the location of at least one SNP;

b) at least one pair of PCR primers designed to amplify a DNA segment which includes a location corresponding to the location of at least one SNP;

c) at least one pair of PCR primers designed to amplify a DNA segment which includes a location corresponding to the location of at least one SNP and at least one probe specific for a location corresponding to the location of at least one SNP;

d) a reagent for performing a method selected from the group consisting of restriction fragment length polymorphism (RFLP) analysis, sequencing, single strand conformation polymorphism analysis (SSCP) , chemical cleavage of mismatch (CC ) , gene chip and denaturing high performance liquid chromatography (DHPLC) for determining the identity of at least one SNP; or e) reagents for TaqMan Open Array assay designed for determining the genotype at a location corresponding to the location of at least one SNP,

wherein the at least one SNP is selected from the group consisting of Group 1. In some embodiments the at least one single nucleotide polymorphisms (SNPs) are selected from the group consisting of Group 10, preferably wherein the kit further comprises instructions for use of the kit for identifying a human subject afflicted with multiple sclerosis or a single clinical attack consistent with multiple sclerosis as a predicted responder or as a predicted non-responder to glatiramer acetate.

In some embodiments the at least one single nucleotide polymorphisms (SNPs) comprise 2, 3, 4, 5, 6, 7, 8, 9 or 10 of the SNPs selected from the group consisting of Group 10. In some embodiments the at least one single nucleotide polymorphisms (SNPs) further comprise rs3135391.

In some embodiments the genotype of the subject at the location corresponding to the location of one or more of the SNPs is determined by indirect genotyping.

In some embodiments the genotype of the subject at the location corresponding to the location of one or more of the SNPs is determined indirectly by determining the genotype of the subject at a location corresponding to the location of at least one SNP that is in linkage disequilibrium with the one or more SNPs.

In some embodiments the kit comprises

a) at least one probe specific for a location corresponding to the location of at least one SNP;

b) at least one pair of PCR primers designed to amplify a DNA segment which includes a location corresponding to the location of at least one SNP;

c) at least one pair of PCR primers designed to amplify a DNA segment which includes a location corresponding to the location of at least one SNP and at least one probe specific for a location corresponding to the location of at least one

SNP;

d) a reagent for performing a method selected from the group consisting of restriction fragment length polymorphism (RFLP) analysis, sequencing, single strand conformation polymorphism analysis (SSCP) , chemical cleavage of mismatch (CCM) , gene chip and denaturing high performance liquid chromatography (DHPLC) for determining the identity of at least one SNP; or e) reagents for TaqMan Open Array assay designed for determining the genotype at a location corresponding to the location of at least one SNP,

wherein the at least one SNP is in linkage disequilibrium with the one or more SNPs . In some embodiments determining the genotype of the subject at a location corresponding to the location of at least one SNP that is in linkage disequilibrium with the one or more SNPs allows identification of the genotype of the subject at the location corresponding to the location of the one or more SNPs with a probability of at least 85%.

In some embodiments determining the genotype of the subject at a location corresponding to the location of at least one SNP that is in linkage disequilibrium with the one or more SNPs allows identification of the genotype of the subject at the location corresponding to the location of the one or more SNPs with a probability of at least 90%.

In some embodiments determining the genotype of the subject at a location corresponding to the location of at least one SNP that is in linkage disequilibrium with the one or more SNPs allows identification of the genotype of the subject at the location corresponding to the location of the one or more SNPs with a probability of at least 99%.

The present invention also provides a probe for identifying the genotype of a location corresponding to the location of a SNP selected from the group consisting of Group 5.

The present invention also provides a probe for identifying the genotype of a location corresponding to the location of a SNP selected from the group consisting of kgp24415534, kgp6214351, kgp6599438, kgp7747883, kgp8110667, kgp8817856, rsl0162089, rsl6886004, rsl894408, rs3135391 ₍ and rs759458.

In some embodiments the SNP is in linkage disequilibrium with the one or more SNPs . The present invention also provides glatiramer acetate or a pharmaceutical composition comprising glatiramer acetate for use in treating a human subject afflicted with multiple sclerosis or a single clinical attack consistent with multiple sclerosis which human subject is identified as a predicted responder to glatiramer acetate by:

a) determining a genotype of the subject at a location corresponding to the location of one or more single nucleotide polymorphisms (SNPs) selected from the group consisting of:

Group 1 , and

b) identifying the subject as a predicted responder to glatiramer acetate if the genotype of the subject contains

one or more A alleles at the location of Group 2, one or more C alleles at the location of Group 3, one or more G alleles at the location of Group 4, or one or more T alleles at the location of kgpl8432055, kgp279772, kgp3991733 or kgp7242489.

In some embodiments the genotype of the subject at the location corresponding to the location of one or more of the SNPs is determined indirectly by determining the genotype of the subject at a location corresponding to the location of at least one SNP that is in linkage disequilibrium with the one or more SNPs.

The present invention also provides a method of determining the genotype of a human subject comprising identifying whether the genotype of a human subject contains

one or more A alleles at the location of Group 2,

one or more C alleles at the location of Group 3,

one or more G alleles at the location of Group 4, or

one or more T alleles at the location of kgpl8432055, kgp279772, kgp3991733 or kgp7242489.

In some embodiments identifying whether the genotype of a human subject contains

one or more A alleles at the location of Group 2,

one or more C alleles at the location of Group 3,

one or more G alleles at the location of Group 4, or

one or more T alleles at the location of kgpl8432055, kgp279772, kgp3991733 or kgp7242489 is determined indirectly by determining the genotype of the subject at a location corresponding to the location of at least one SNP that is in linkage disequilibrium with the one or more SNPs .

The present invention also provides a method of determining the genotype of a human subject comprising identifying the genotype of a human subject at the location of kgp6214351, kgp6599438, kgp7747883, kgp8110667, kgp8817856, rsl0162089, rsl6886004, rsl894408, rs3135391, or rs759458.

The present invention also provides a method of identifying a human subject afflicted with multiple sclerosis or a single clinical attack consistent with multiple sclerosis who is predicted to have a slower course of disease progression, comprising the steps of:

(i) determining a genotype of the subject at a location corresponding to the location of one or more single nucleotide polymorphisms (SNPs) selected from the group consisting of: Group 6, and

(ii) identifying the subject as predicted to have a slower course of disease progression if the genotype of the subject contains

one or more A alleles at the location of Group 7, one or more C alleles at the location of Group 8, one or more G alleles at the location of Group 9, or one or more T alleles at the location of rsl2043743.

In some embodiments the genotype of the subject at the location corresponding to the location of one or more of the SNPs is determined indirectly by determining the genotype of the subject at a location corresponding to the location of at least one SNP that is in linkage disequilibrium with the one or more SNPs.

The present invention also provides a kit for identifying a human subject afflicted with multiple sclerosis or a single clinical attack consistent with multiple sclerosis who is predicted to have a slower course of disease progression, the kit comprising

a) at least one probe specific for a location corresponding to the location of at least one SNP; b) at least one pair of PCR primers designed to amplify a DNA segment which includes a location corresponding to the location of at least one SNP;

c) at least one pair of PCR primers designed to amplify a DNA segment which includes a location corresponding to the location of at least one SNP and at least one probe specific for a location corresponding to the location of at least one SNP;

d) a reagent for performing a method selected from the group consisting of restriction fragment length polymorphism (RFLP) analysis, sequencing, single strand conformation polymorphism analysis (SSCP) , chemical cleavage of mismatch (CCM) , gene chip and denaturing high performance liquid chromatography (DHPLC) for determining the identity of at least one SNP; or e) reagents for TaqMan Open Array assay designed for determining the genotype at a location corresponding to the location of at least one SNP,

wherein the at least one SNP is selected from the group consisting of Group 6. For the foregoing embodiments, each embodiment disclosed herein is contemplated as being applicable to each of the other disclosed embodiments. Thus, all combinations of the various elements described herein are within the scope of the invention.

Definitions

As used herein, a genetic marker refers to a DNA sequence that has a known location on a chromosome. Several non-limiting examples of classes of genetic markers include SNP (single nucleotide polymorphism) , STR (short tandem repeat) , and SFP (single feature polymorphism) . VNTR (variable number tandem repeat) , microsatellite polymorphism, insertions and deletions. The genetic markers associated with the invention are SNPs . As used herein a SNP or "single nucleotide polymorphism" refers to a specific site in the genome where there is a difference in DNA base between individuals. In some embodiments the SNP is located in a coding region of a gene. In other embodiments the SNP is located in a noncoding region of a gene. In still other embodiments the SNP is located in an intergenic region.

Several non-limiting examples of databases from which information on SNPs or genes that are associated with human disease can be retrieved include: NCBI resources, The SNP Consortium LTD, NCBI dbSNP database, International HapMap Project, 1000 Genomes Project, Glovar Variation Browser, SNPStats, PharmGKB, GEN-SniP, and SNPedia.

SNPs are identified herein using the rs identifier numbers in accordance with the NCBI dbSNP database, which is publically available at: ncbi.nlm.nih.gov/projects/SNP/ or using the kgp identifier numbers, which were created by Illumina. Genotype at the kgp SNPs can be obtained by using the Illumina genotyping arrays. In addition, SNPs can be identified by the specific location on the chromosome indicated for the specific SNP.

Additional information about identifying SNPs can be obtained from the NCBI database SNP FAQ archive located at ncbi.nlm.nih.gov/books/NBK3848/ or from literature available on the Illumina website located at illumina. com/applications/genotyping/literature . ilmn.

In some embodiments, SNPs in linkage disequilibrium with the SNPs associated with the invention are useful for obtaining similar results. As used herein, linkage disequilibrium refers to the non- random association of SNPs at one loci . Techniques for the measurement of linkage disequilibrium are known in the art. As two SNPs are in linkage disequilibrium if they are inherited together, the information they provide is correlated to a certain extent. SNPs in linkage disequilibrium with the SNPs included in the models can be obtained from databases such as HapMap or other related databases, from experimental setups run in laboratories or from computer-aided in-silico experiments. Determining the genotype of a subject at a position of SNP as specified herein, e.g. as specified by NCBI dbSNP rs identifier, may comprise "direct genotyping", e.g. by determining the identity of the nucleotide of each allele at the locus of SNP, and/or "indirect genotyping", defined herein as evaluating/determining the identity of an allele at one or more loci that are in linkage disequilibrium with the SNP in question, allowing one to infer the identity of the allele at the locus of SNP in question with a substantial degree of confidence. In some cases, indirect genotyping may comprise determining the identity of each allele at one or more loci that are in sufficiently high linkage disequilibrium with the SNP in question so as to allow one to infer the identity of each allele at the locus of SNP in question with a probability of at least 85%, at least 90% or at least 99% certainty. A genotype at a position of SNP (genotype "at a" SNP) may be represented by a single letter which corresponds to the identity of the nucleotide at the SNP, where A represents adenine, T represents thymine, C represents cytosine, and G represents guanine. The identity of two alleles at a single SNP may be represented by a two letter combination of A, T, C, and G, where the first letter of the two letter combination represents one allele and the second letter represents the second allele, and where A represents adenine, T represents thymine, C represents cytosine, and G represents guanine. Thus, a two allele genotype at a SNP can be represented as, for example, AA, AT, AG, AC, TT, TG, TC, GG, GC, or CC. It is understood that AT, AG, AC, TG, TC, and GC are equivalent to TA, GA, CA, GT, CT, and CG, respectively.

The SNPs of the invention can be used as predictive indicators of the response to GA in subjects afflicted with multiple sclerosis or a single clinical attack consistent with multiple sclerosis. Aspects of the invention relate to determining the presence of SNPs through obtaining a patient DNA sample and evaluating the patient sample for the presence of one or more SNPs, or for a certain set of SNPs. It should be appreciated that a patient DNA sample can be extracted, and a SNP can be detected in the sample, through any means known to one of ordinary skill in art. Some non- limiting examples of known techniques include detection via restriction fragment length polymorphism (RFLP) analysis, arrays including but not limited to planar microarrays or bead arrays, sequencing, single strand conformation polymorphism analysis (SSCP) , chemical cleavage of mismatch (CC ) , Polymerase chain reaction (PCR) and denaturing high performance liquid chromatography (DHPLC) .

In some embodiments, the genotyping array is a whole genome genotyping array. In some embodiments, the Whole-genome genotyping arrays as defined here are arrays that contain hundreds of thousands to millions of genetic sequences (which may also be named "probes"). In some embodiments, Whole-genome genotyping arrays contain 500,000 probes or more. In some embodiments, Whole-genome genotyping arrays contain 1 million probes or more. In some embodiments, Whole-genome genotyping arrays contain 5 million probes or more.

In some embodiments, a SNP is detected through PCR amplification and sequencing of the DNA region comprising the SNP. In some embodiments SNPs are detected using arrays, exemplified by gene chip, including but not limited to DNA arrays or microarrays, DNA chips, and whole genome genotyping arrays, all of which may be for example planar arrays or bead arrays, or a TaqMan open Array. Arrays/Microarrays for detection of genetic polymorphisms, changes or mutations (in general, genetic variations) such as a SNP in a DNA sequence, may comprise a solid surface, typically glass, on which a high number of genetic sequences are deposited (the probes) , complementary to the genetic variations to be studied. Using standard robotic printers to apply probes to the array a high density of individual probe features can be obtained, for example probe densities of 600 features per cm ² or more can be typically achieved. The positioning of probes on an array is precisely controlled by the printing device (robot, inkjet printer, photolithographic mask etc) and probes are aligned in a grid. The organization of probes on the array facilitates the subsequent identification of specific probe-target interactions. Additionally it is common, but not necessary, to divide the array features into smaller sectors, also grid-shaped, that are subsequently referred to as sub-arrays . Sub-arrays typically comprise 32 individual probe features although lower (e.g. 16) or higher (e.g. 64 or more) features can comprise each sub- array. In some arrays the probes are connected to beads instead of the solid support. Such arrays are called "bead arrays" or "bead CHIPS" .

In some embodiments, detection of genetic variation such as the presence of a SNP involves hybridization to sequences which specifically recognize the normal and the mutant allele in a fragment of DNA derived from a test sample. Typically, the fragment has been amplified, e.g. by using the polymerase chain reaction (PCR) , and labeled e.g. with a fluorescent molecule. A laser can be used to detect bound labeled fragments on the chip and thus an individual who is homozygous for the normal allele can be specifically distinguished from heterozygous individuals (in the case of autosomal dominant conditions then these individuals are referred to as carriers) or those who are homozygous for the mutant allele. In some embodiments, the amplification reaction and/or extension reaction is carried out on the microarray or bead itself. For differential hybridization based methods there are a number of methods for analyzing hybridization data for genotyping: Increase in hybridization level: The hybridization levels of probes complementary to the normal and mutant alleles are compared. Decrease in hybridization level: Differences in the sequence between a control sample and a test sample can be identified by a decrease in the hybridization level of the totally complementary oligonucleotides with a reference sequence. A loss approximating 100% is produced in mutant homozygous individuals while there is only an approximately 50% loss in heterozygotes . In Microarrays for examining all the bases of a sequence of "n" nucleotides ("oligonucleotide") of length in both strands, a minimum of " 2n" oligonucleotides that overlap with the previous oligonucleotide in all the sequence except in the nucleotide are necessary. Typically the size of the oligonucleotides is about 25 nucleotides. However it should be appreciated that the oligonucleotide can be any length that is appropriate as would be understood by one of ordinary skill in the art. The increased number of oligonucleotides used to reconstruct the sequence reduces errors derived from fluctuation of the hybridization level. However, the exact change in sequence cannot be identified with this method; in some embodiments this method is combined with sequencing to identify the mutation. Where amplification or extension is carried out on the microarray or bead itself, three methods are presented by way of example: In the inisequencing strategy, a mutation specific primer is fixed on the slide and after an extension reaction with fluorescent dideoxynucleotides, the image of the Microarray is captured with a scanner. In the Primer extension strategy, two oligonucleotides are designed for detection of the wild type and mutant sequences respectively. The extension reaction is subsequently carried out with one fluorescently labeled nucleotide and the remaining nucleotides unlabelled. In either case the starting material can be either an R A sample or a DNA product amplified by PCR. In the Tag arrays strategy, an extension reaction is carried out in solution with specific primers, which carry a determined 5 ¹ sequence or "tag". The use of Microarrays with oligonucleotides complementary to these sequences or "tags" allows the capture of the resultant products of the extension. Examples of this include the high density Microarray "Flex-flex" (Affymetrix) . In the Illumina 1M Dou BeadChip array

(illumina. com/products/humanlm_duo_dna_analysis_beadchip_kits . ilmn) , SNP genotypes are generated from fluorescent intensities using the manufacturer's default cluster settings.

In some aspects of the invention measurement of clinical variables comprises part of the prediction model predicting response to GA along with the genetic variables. Some non-limiting examples are age of the patient (in years) , gender of patient, clinical manifestations, MRI parameter, country, ancestry, and years of exposure to treatment) "Clinical manifestations" include but are not limited to EDSS score such as baseline EDSS score, log of number of relapses in last 2 Years and relapse rate. "MRI parameters" include but are not limited to the volume and/or number of Tl enhancing lesions and/or T2 enhancing lesions; exemplified by baseline volume of T2 lesion, number of Gd-Tl lesions at baseline. In certain aspect of the invention, the clinical variables taken into account are as measured at the time of the decision about the treatment suitable for the patient, or measured at a time point determined by the physician, researcher or other professional involved in the decision .

The identification of a patient as a responder or as a non-responder to GA based on the presence of at least one SNP from tables 2-32 and 34-44, a set of SNPs from tables 2-32 and 34-44, or the combination of a SNP or a set of SNPs from tables 2-32 and 34-44 with one or more clinical variables described above, may be used for predicting response to GA.

Also within the scope of the invention are kits and instructions for their use. In some embodiments kits associated with the invention are kits for identifying one or more SNPs within a patient sample. In some embodiments a kit may contain primers for amplifying a specific genetic locus. In some embodiments, a kit may contain a probe for hybridizing to a specific SNP. The kit of the invention can include reagents for conducting each of the following assays including but not limited to restriction fragment length polymorphism (RFLP) analysis, arrays including but not limited to planar microarrays or bead arrays, sequencing, single strand conformation polymorphism analysis (SSCP) , chemical cleavage of mismatch (CCM) , and denaturing high performance liquid chromatography (DHPLC) , PCR amplification and sequencing of the DNA region comprising the SNP. A kit of the invention can include a description of use of the contents of the kit for participation in any biological or chemical mechanism disclosed herein. A kit can include instructions for use of the kit components alone or in combination with other methods or compositions for assisting in screening or diagnosing a sample and/or determining whether a subject is a responder or a non-responder to GA.

Forms of Multiple Sclerosis:

There are five distinct disease stages and/or types of MS:

1) benign multiple sclerosis;

2) relapsing-remitting multiple sclerosis (RRMS) ;

3) secondary progressive multiple sclerosis (SPMS) ; 4 ) progressive relapsing multiple sclerosis (PRMS) ; and

5 ) primary progressive multiple sclerosis (PPMS) .

Benign multiple sclerosis is a retrospective diagnosis which is characterized by 1-2 exacerbations with complete recovery, no lasting disability and no disease progression for 10-15 years after the initial onset. Benign multiple sclerosis may, however, progress into other forms of multiple sclerosis.

Patients suffering from RRMS experience sporadic exacerbations or relapses, as well as periods of remission. Lesions and evidence of axonal loss may or may not be visible on MRI for patients with RRMS. SPMS may evolve from RRMS. Patients afflicted with SPMS have relapses, a diminishing degree of recovery during remissions, less frequent remissions and more pronounced neurological deficits than RRMS patients. Enlarged ventricles, which are markers for atrophy of the corpus callosum, midline center and spinal cord, are visible on MRI of patients with SPMS.

PPMS is characterized by a steady progression of increasing neurological deficits without distinct attacks or remissions. Cerebral lesions, diffuse spinal cord damage and evidence of axonal loss are evident on the MRI of patients with PPMS. PPMS has periods of acute exacerbations while proceeding along a course of increasing neurological deficits without remissions. Lesions are evident on MRI of patients suffering from PRMS. ( 28 )

A clinically isolated syndrome (CIS) is a single monosymptomatic attack compatible with MS, such as optic neuritis, brain stem symptoms, and partial myelitis. Patients with CIS that experience a second clinical attack are generally considered to have clinically definite multiple sclerosis (CDMS) . Over 80 percent of patients with a CIS and MRI lesions go on to develop MS, while approximately 20 percent have a self-limited process . ( 29 , 30 ) Patients who experience a single clinical attack consistent with MS may have at least one lesion consistent with multiple sclerosis prior to the development of clinically definite multiple sclerosis.

Multiple sclerosis may present with optic neuritis, blurring of vision, diplopia, involuntary rapid eye movement, blindness, loss of balance, tremors, ataxia, vertigo, clumsiness of a limb, lack of coordination, weakness of one or more extremity, altered muscle tone, muscle stiffness, spasms, tingling, paraesthesia, burning sensations, muscle pains, facial pain, trigeminal neuralgia, stabbing sharp pains, burning tingling pain, slowing of speech, slurring of words, changes in rhythm of speech, dysphagia, fatigue, bladder problems (including urgency, frequency, incomplete emptying and incontinence) , bowel problems (including constipation and loss of bowel control) , impotence, diminished sexual arousal, loss of sensation, sensitivity to heat, loss of short term memory, loss of concentration, or loss of judgment or reasoning.

Relapsing Form of Multiple Sclerosis:

The term relapsing MS includes:

1) patients with RRMS;

2) patients with SPMS and superimposed relapses; and

3) patients with CIS who show lesion dissemination on subsequent MRI scans according to McDonald's criteria.

As used herein, relapsing forms of multiple sclerosis include: Relapsing-remitting multiple sclerosis (RRMS) , characterized by unpredictable acute episodes of neurological dysfunction (relapses) , followed by variable recovery and periods of clinical stability;

Secondary Progressive MS (SPMS) , wherein patients having RRMS develop sustained deterioration with or without relapses superimposed; and

Primary progressive-relapsing multiple sclerosis (PPRMS) or progressive-relapsing multiple sclerosis (PRMS) , an uncommon form wherein patients developing a progressive deterioration from the beginning can also develop relapses later on.

Kurtzke Expanded Disability Status Scale (EDSS) :

The Kurtzke Expanded Disability Status Scale (EDSS) is a method of quantifying disability in multiple sclerosis. The EDSS replaced the previous Disability Status Scales which used to bunch people with MS in the lower brackets. The EDSS quantifies disability in eight Functional Systems (FS) and allows neurologists to assign a Functional System Score (FSS) in each of these. The Functional Systems are: pyramidal, cerebellar, brainstem, sensory, bowel and bladder, visual & cerebral (according to mult- sclerosis . org/expandeddisabilitystatusscale) .

Clinical Relapse:

A clinical relapse, which may also be used herein as "relapse," "confirmed relapse, " or "clinically defined relapse, " is defined as the appearance of one or more new neurological abnormalities or the reappearance of one or more previously observed neurological abnormalities .

This change in clinical state must last at least 48 hours and be immediately preceded by a relatively stable or improving neurological state of at least 30 days. This criterion is different from the clinical definition of exacerbation "at least 24 hours duration of symptoms," (31) as detailed in the section "relapse evaluation . "

An event is counted as a relapse only when the subject's symptoms are accompanied by observed objective neurological changes, consistent with:

a) an increase of at least 0.5 in the EDSS score or one grade in the score of two or more of the seven FS (32) ; or,

b) two grades in the score of one of FS as compared to the previous evaluation .

The subject must not be undergoing any acute metabolic changes such as fever or other medical abnormality. A change in bowel/bladder function or in cognitive function must not be entirely responsible for the changes in EDSS or FS scores .

As used herein, a "multiple sclerosis drug" is a drug or an agent intended to treat clinically defined MS, CIS, any form of neurodegenerative or demyelinating diseases, or symptoms of any of the above mentioned diseases. "Multiple sclerosis drugs" may include but are not limited to antibodies, immunosuppressants, antiinflammatory agents, immunomodulators , cytokines, cytotoxic agents and steroids and may include approved drugs, drugs in clinical trial, or alternative treatments, intended to treat clinically defined MS, CIS or any form of neurodegenerative or demyelinating diseases. "Multiple sclerosis drugs" include but are not limited to Interferon and its derivatives (including BETASERON®, AVONEX® and REBIF®) , Mitoxantrone and Natalizumab. Agents approved or in-trial for the treatment of other autoimmune diseases, but used in a MS or CIS patient to treat MS or CIS are also defined as multiple sclerosis drugs .

As used herein, a "naive patient" is a subject that has not been treated with any multiple sclerosis drugs as defined in the former paragraph .

The administration of glatiramer acetate may be oral, nasal, pulmonary, parenteral, intravenous, intra-articular , transdermal, intradermal, subcutaneous, topical, intramuscular, rectal, intrathecal, intraocular, buccal or by gavage.

As used herein, "GALA" is a phase 3 clinical trial entitled "A Study in Subjects With Relapsing-Remitting Multiple Sclerosis (RRMS) to Assess the Efficacy, Safety and Tolerability of Glatiramer Acetate (GA) Injection 40 mg Administered Three Times a Week Compared to Placebo (GALA)." The GALA trial has the ClinicalTrials.gov Identifier NCT01067521, and additional information about the trial can be found at clinicaltrials.gov/ct2/show/NCT01067521.

As used herein, "FORTE" is a phase 3 clinical trial entitled "Clinical Trial Comparing Treatment of Relapsing-Remitting Multiple Sclerosis (RR-MS) With Two Doses of Glatiramer Acetate (GA) . " The FORTE trial has the ClinicalTrials.gov Identifier NCT00337779 and additional information, including study results can be found at clinicaltrials.gov/ct2/show/NCT00337779.

As used herein, "about" with regard to a stated number encompasses a range of +10 percent to -10 percent of the stated value. By way of example, about 100 mg/kg therefore includes the range 90-100 mg/kg and therefore also includes 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 018, 109 and 110 mg/kg. Accordingly, about 100 mg/kg includes, in an embodiment, 100 mg/kg.

It is understood that where a parameter range is provided, all integers within that range, tenths thereof, and hundredths thereof, are also provided by the invention. For example, "0.2-5 mg/kg" is a disclosure of 0.2 mg/kg, 0.21 mg/kg, 0.22 mg/kg, 0.23 mg/kg etc. up to 0.3 mg/kg, 0.31 mg/kg, 0.32 mg/kg, 0.33 mg/kg etc. up to 0.4 mg/kg, 0.5 mg/kg, 0.6 mg/kg etc. up to 5.0 mg/kg.

All combinations of the various elements described herein are within the scope of the invention.

This invention will be better understood by reference to the Experimental Details which follow, but those skilled in the art will readily appreciate that the specific experiments detailed are only illustrative of the invention as described more fully in the claims which follow thereafter.

Experimental Details

Description of the Study

Copaxone® (Glatiramer acetate) is a leading drug for the treatment of MS that is marketed by TEVA. Glatiramer acetate significantly improves patient outcomes, but glatiramer acetate treatment is not equally effective in all patients. Individual differences between patients, including inherited genetic factors, can account for significant differences in individual responses to medications. A consequence of this diversity is that no single medication is effective in all patients. Clinical and genetic factors are predictive of patient response to glatiramer acetate.

In the following Examples, predictive genetic factors of glatiramer acetate treatment response are identified and a diagnostic model is demonstrated to help guide MS drug therapy to significantly improve patient outcomes. Examples

SSxaxs l© 1 Patient Populations

Response definitions were received from patients from two large glatiramer acetate clinical trial cohorts (GALA, FORTE) and patients were categorized as responder, non-responder, extreme-responder, or extreme non-responde according to the criteria set forth in Table

Table 1. Definition of Glatiramer Acetate Responders and Non Res onders and population representation in GALA and FORTE cohorts.

Example 2 Patient Genotyping

DNA samples from categorized patients were subject to quality control analysis followed by genotyping with the Illumina 0MNI-5M genome wide array. This array tests 4,301,331 variants with a median marker spacing of 360 bp. The array includes 84,004 non-synonymous SNPs including 43,904 variants in the MHC region. Over 800 patients were genotyped.

Genotyping Quality Control

An Illumina-derived algorithm of SNP cluster definitions (i.e., the specific parameters used to determine specific genotypes of each SNP) was used to determine the 4,301,331 genotypes for each of the genotyped samples. For genotyping QC, SNPs were evaluated as either pass, fail, or the SNP cluster calling definitions were revised and the SNP was re-evaluated as pass or fail.

Evaluation of SNPs with poor cluster separation values (i.e., the location of SNP calling clusters were very close together) identified 126 SNPs for which SNP clustering was manually corrected. Evaluation of SNPs that were not in Hardy-Weinburg equilibrium identified 1,000 SNPs for which SNP clustering was manually corrected. Evaluation of SNPs with low GC scores (GC score: an Illumina-developed score of overall SNP performance) identified 10,000 SNPs for which SNP clustering was manually corrected. Evaluation of SNPs with low GC scores also identified 160,000 SNPs for which SNP clustering was revised using Illumina GenomeStudio software to re-define SNP cluster calling definitions. A total of 524 SNPs were scored as "failed" and removed from further analyses due to poor SNP clustering that could not be manually corrected.

In addition, SNPs with low call rates (i.e., a low number of genotype calls were generated from a particular SNP test) were scored as "fail" and removed from further analyses. Applying a "call rate" threshold of >85% to the 4,301,331 SNPs tested (i.e., for each SNP, the % of samples for which a genotype was called) resulted in "fails" for 4,384 SNPs, yielding a total of 4,296,423 SNPs available for subsequent analyses (99.89% of variants tested). Finally, samples with call rates less than 94% (i.e., samples for which less than 94% of the genotyped SNPs produced genotype calls) were removed. This resulted in the removal of 31 samples with call rates of 49-93%, and resulted in a final cohort of 776 samples for subsequent analyses. Notably, of these 31 excluded samples, 18 (58%) had very low (<1 ng/ul) DNA concentrations and 12 of the other 13 excluded samples had low DNA quality (OD 260/280 ratio <1.8 or >2.0), or low DNA volumes.

For the final 776 samples, the overall median sample genotype call rate was 99.88% (min. 94.26%, max. 99.96%) indicative of high quality genotype data for these samples .

Example 3 Overview of Genetic Analysis

Genotype data was merged with selected clinical data (Responder/Non- Responder status, country, age, gender, ancestry, log of number of relapses in last 2 Years, baseline EDSS score, baseline volume of T2 lesion, number of Gd-Tl lesions at baseline, and years of exposure to treatment) . Association and regression analyses were conducted using SVS7 software.

Analyses were conducted using standard association analyses and regression analyses. To maximize the statistical power for high priority variants, the analyses began with focused list of candidate variants (35), then expanded to a larger number of variants in 30 genes, then expanded to variants in 180 candidate genes, and finally expanded to the entire genome-wide analysis.

For each stage of association analyses, results were calculated to identify genetic associations using three genetic models:

1. Allelic Model (chi-square, chi-square -lOLogP, fisher exact, fisher exact -lOLogP, values for fisher and chi-square with Bonferoni correction, Odds Ratios and Confidence Bounds, Regression P-value, Regression -loglO P, Call Rate (Cases) , Call Rate (Controls) , Minor Allele Frequency, Allele Freq. (Cases) , Allele Freq. (Controls), Major Allele Frequency, Allele Freq. (Cases), Allele Freq. (Controls), Genotype Counts for cases and controls, Missing Genotype Counts, Allele Counts for cases and controls) .

2. Additive Model (Cochrane-Armitage Trend Test P-value, Exact for of Cochrane Armitage Trend Test, -loglO P-values, Correlation/Trend test P-value, Correlation/Trend -loglO P, Call Rate, Call Rate (Cases), Call Rate (Controls), Minor Allele Frequency, Allele Freq. (Cases) , Allele Freq. (Controls) .

3. Genotypic Model (chi-square, chi-square -lOLogP, fisher exact, fisher exact -lOLogP, values for fisher and chi-square with Bonferoni correction, Odds Ratios and Confidence Bounds, Regression P-value, Regression -loglO P, Call Rate (Cases) , Call Rate (Controls), Minor Allele Frequency, Allele Freq. (Cases), Allele Freq. (Controls), Major Allele Frequency, Allele Freq. (Cases), Allele Freq. (Controls) , Genotype Counts for cases and controls, Missing Genotype Counts, Allele Counts for Cases and controls). For each stage of regression analyses, results were calculated to identify genetic associations using an additive genetic model.

Example 4 Stages of Analysis

Stage 1. Discovery Cohort (n = 318: 198 R vs. 120 NR) - In the first stage of analysis, the discovery cohort (GALA) was analyzed to identify variants associated with good response vs. poor response.

Stage 2. Replication Cohort (n = 262: 201 R vs . 61 NR) - In the second stage of each analysis, variants selected in the discovery cohort were analyzed to identify replicating associations in the FORTE replication cohort associated with good response vs. poor response.

Stage 3. Combined Cohorts (n = 580: 399 R vs. Ill NR) - In the third stage of the analysis, the combined GALA and FORTE cohorts were analyzed.

Stage 4. Placebo Cohort (n = 196: 95 R vs . 101 NR) In the fourth stage of the analysis, the placebo cohort (GALA placebo) was analyzed to identify variants associated with placebo response/non- response. These results will be used to confirm whether significantly associated variants are specific to glatiramer acetate drug response versus disease severity.

An overview of these analyses is presented in Table A. For each stage a step-wise analysis was performed in order to maximize study power .

Table A: Overview of the analyses used to identify genetic markers predictive of response to glatiramer acetate.

Combined Cohorts for Re lication Cohort Com a vt,Ef I« S

StOB 1 Csndkiate SH s (3S) Can idate S Ps (3S) Candidate SNP¾ {35}

:«rs ~M»S . Atefe, Osseins. f½s?<»$»ft -A<s¾siy«, ASsSc, Sssoi^ss, R8$ps$s»«

Can idate SMFs, Extreme Caric ite SNPs, Extreme Candi ate SNP& _> £Χ$Γ*«Μ?

- A<fci¾v<s, AM*, S¾s§.<*i<s>i>«

Step 2 Csndi ste Q o (30) Can i ate Genes (30) andidate G«««s (30)

Candidate Genes, Extreme Candidate Genes, Extreme Cas & ss Getm, treme

Step 3 Candidate Genes (180) Ca idate Genes (180) Candidate G«r>«s (180)

Candidate Genes, Extreme Ca didate Genes, Extreme Ca idate nes, E t eme

Genome-wide, Extreme Geno e-wfcie, Extreme enome-wide, Extreme

^• >C«if«4 $« !(».' SfSCS! SCvSriiS!SS

«- CiV!SviSii tor Wf> 8!*

Example 5 Analysis Part 1 - Analysis of Candidate Variants

The initial analysis was limited to 35 genetic variants identified in high priority genes. Power (80%) with Bonferroni statistical correction for multiple testing to identify significant genetic associations with an odds ratio >3, for variants with an allele frequency greater than 10%. (Or rare alleles (2.5%) with an odds ratio >7 ) .

Results for Standard Response Definition, Candidate Variants Selected a priori for Additive, Allelic and Genotypic models are presented in tables 2-4, respectively. Table 2. Additive Model, Candidate Variants (Gala, Forte, and Combined cohorts)

! BALA FORTE COMBINED

Allele Allele

Allele Freq. Allele Freq, Allele Freq.

1 .rmitage Qdds Freq. (Non- Armitage Odds Freq. {Won- Armitage Odds Freq. (Non-

Source Name Ch Gene P walue Ratio Resp ) Resp ) P- alue Ratio ( Res ) J¾esp,j P-value Ratio (Resp.) Resp.) Mutation

Top Priority Tchelet GWAS rs3135391 6 HLA-DRBl Ψ 0.040 0.66 17% l iiii:ffi0499 0.641 23% l¾llli 0.70 20% 27% *1501, T118T

Top Priority Tchelet GWAS rs3135388 6 HLA-DRB1 0.047 0.67 : 17% iiilf 0.0499 0 64 Ϊ - ^■■ lilll con 0.70 20% 27% *1501

Tchelet GWAS rs947603 10 CEP55 0.054 1.48 I 7t>- 1') .16 1,45 ¾il 1.42 24% 18%

Top Priority Tsareva 2011 rs 1800629 6 TNF 0.088 0.68 12% r Ο.·Λ OM [7 . liiii 0.09 0.75 12% 16%

Tchelet GWAS rs4344916 2 AC083939.il 0.32 1.20 30% 0,$7 34% l 0.70 1.05 34% 33%

Tchelet GWAS rs 10950359 7 AC074389.l| 0.32 0.85 29% 33% α« 123 25 liiii 0.51 0.91 27% 29%

Tchelet GWAS rs 12256889 10 CYP26C1 0.33 0.84 31%

Top Priority Comi ii °- ⁷⁷ 1.04 32% 31%

rs974060 7 TAC1 0.34 1.18 !7 i 2l8l% ιιιι .56 33% lii

0ι,7ι8ι-¾ 1

0,94 27 llill 0.67 1.06 30% 28%

Top Priority Tchelet GWAS rs 17771939 8 ACOl 6885.1 0.36 1.18 29% 26% 0.003 0.54 M l lll 0.35 0.88 29% 31%

Top Priority Tsareva 2011 rs6897932 5 IL7Ra 0.39 0.84 20% 23% Q.63 0.9O 24% llill 0.44 0.89 22% 24% Missense T244l

Tchelet GWAS rs 11599624 10 P11-655H13 0.39 1,23 15% 13% o.so 1,2? 11 lilll 0.43 1.17 13% 12%

Top Priority Comi rs 1558896 7 TAC1 0.40 1.15 ;i 30% |||f|l|| [l 7" llill 0.73 1.05 31% 30%

Tchelet GWAS rsll617134 13 I'l l 6291 74 Q.41 0.7$ 5% 7 0.95 li>? T ^> li ll 0.64 0.89 6% 7%

Tchelet GWAS rsl7575455 2 AC078940.2 0.41 0.87 33% 11 ₁ !! 0.0062 0.56 § il liiii 0.019 0.73 32% 39% in

Top Priority Grossman 2007 rs946685 1 IL12RB2 1 0.43 1.19 17% 15% 0,22 1,4?

Tchelet GWAS llill °- ¹⁷ 1.28 18% 14%

rs4343256 15 CRT( 0.47 1.27 8% 6% 0,71 0.S6 lilll llill 0.72 1.09 8% 7%

Tchelet GWAS rs6097801 20 CYP24A11 0.47 0.84 11% 13% 0,27 0.75 n

Tchelet GWAS liiii °- ²⁹ 0.83 13% 15%

rsl007328 15 ACOl 2109.1 0 50 0.89 48% illlliilll _¾ 0.661 47% liiii °- ⁰⁸ 0.80 48% 53%

Top Priority Tsareva 2011 rs231775 2 CTLA4 1 0.51 1.12 12 39% lllei 1,11 4P% llill 0.46 1.10 41% 38% Missense T17A

Top Priority Tchelet GWAS rs9944913 18 NOL4 ₁ 0.52 0.84 10% 12% 0.054 0.55 ₁ !§¾! |¾1 0.08 0.71 9% 13%

Tchelet GWAS rs 1573706 20 PTPRT 1 0.55 0.88 17% 19% 0,15 QM t? lliil 0.19 0.80 17% 20%

Top Priority Grossman 2007 rs 1415148 1 CTSS 0.56 0.90 39% 41% 0.38 120 41% llill 0.88 1.02 40% 39%

Tchelet GWAS rs2487896 10 HPSi ; 0.57 0,88 14% 16% 0.0006 0.39 1? lliil 0.01J 0.65 13% 19%

Tchelet GWAS rsl0931091 2 AC074182.1S 0.58 1.17 11% 9% 0.53 aw 7..' llill 0.88 0.97 9% 9%

Top Priority Comi rs269976 18 SLC14A21 0.59 118 8% 7% ύ,η. 1.75 h ^" . i i 0.26 132 8% 6%

Top Priority Grossman 2007 rs2275235 1 CTSS 0.62 0.91 34% l?h liiii 0.66 1.06 35% 34%

Tchelet GWAS rs4369324 10 P11-655H13 0.64 1.10 25% « r, 13?

iority Tchelet GWAS rs4148871 6 TAP2 0.69 0.93 21% 22% 0,3? 1 77 ? : ι |ΐ1¾ϋΐ .11 22% 20%

Top Pr ^ο 0 ^λ .7 ⁹ 1

9 1.04 22% 21%

Grossman 2007 rs2001791 3 CD86 0.74 1.08 14% 0 58 n . lliil 0.36 0.85 14% 16%

Tchelet GWAS rs 10988087 9 SET 0.77 0.89 4% 4% 0.0062 0.381 4% llill 0.065 0.61 4% 7%

Grossman 2007 rs 1129055 3 CD86 0.83 1.04 26% 25% OM 0,84 lilll 0.97 0.99 28% 28% Missense A228 ^"

Tchelet GWAS rsl0853605 18 MEX3C 1 0.91 1.02 Ί '< 43% 0,34 1,22 44% l iil 0.49 1.09 44% 41%

Top Priority Comi rs4890535 18 SLC14A2 ₁ 0.94 1.02 9% 9% iiiiF 1.31 liiii lii 0.66 1.10 9% 9%

Tchelet GWAS rs2177073 18 DTNA 1 0.95 1.02 11% |||l|||i.:033 0.551 llill 0.21 0.79 11% 13%

Top Priority Tchelet GWAS rs2521644 7 NPY 0.&? 0.99 -i ; 43% iiiiiii no 71I liiii 0.67 106 45% 44%

able 3. Allelic Model, Candidate Variants (Gala, Forte, and Combined cohorts)

Table 4. Genotypic Model, Candidate Variants (Gala, Forte, and Combined cohorts)

GALA 1 ^: ORTE COMBINED

Allele Α1|Ι||| Allele

Allele Freq. Allele Allele Freq. DD Dd dd

Fisher's Freq. (Non- f isher's Freq (Non- Fisher's Freq. (Non- DD (Contr Dd (Contr dd (Contr

Source Name Ch Gene (Resp ) Resp ) Exact P (Resp ) ftesp.) Exact P (Resp.) Resp.) (Cases) ols) (Cases) ols) (Cases) ols)

Top Priority Comi rsl558896 7 TAC1 1111:0043 34% 30% 0,3$ 2¾¾ ll 0.0079 31% 30% 53 11 142 87 203 83

Top Priority Comi rs974O60 7 TAC1 llllliia 32% 28% 1 D.ffi 27% lllll 0.047 30% 28% 46 11 145 81 208 89

Previous GWAS rs947603 10 CEP55 0.055 Λ 19% 1 0,43 23% 0.050 24% 18% 22 8 148 50 228 123

Previous GWAS rsl0853605 18 MEX3C 0.11 43% Q.54 44% !ifll 0.31 44% 41% 79 27 190 96 130 58

Top Priority Tchelet GWAS rs3135391 6 HLA-DRB1 0.11 17¾ 24% 0.0093 23% lllll "-"Ί' ¹ 20% 27% 20 10 122 77 257 94

Top Priority Tsareva 2011 rsl800629 6 TNF 0.13 12% 17% 1 0,33 3.2% lliil aw 12% 16% 10 6 77 46 310 129

Top Priority Tchelet GWAS rs3135388 6 HLA-DRB1 0.14 17% 24% 0.0093 lBl 32¾|: 0.018 20% 27% 20 10 122 76 257 94

Previous GWAS rsl0950359 7 AC074389.1 0.15 29% 33% 1 0,54 25% ll l 0.18 27% 29% 39 14 138 77 222 90

Grossman 2007 rs2001791 3 CD86 a 19 14% lllll 0.052 13% 0.56 14% 16% 11 5 87 47 301 129

Previous GWAS rs6097801 20 CYP24A1 11% 13% § 0,19 14% lllil 0.047 13% 15% 14 3 72 48 313 130

Previous GWAS rsl0931091 2 AC074182.1 11% 9% 1 iiiiiiiiiiil lllll 0.54 9% 9% 3 3 65 27 331 150

Previous GWAS rsl007328 15 AC012409.1 0.29 48% 51% 1 CU& 47% lllll 0.20 48% 53% 88 48 204 96 107 37

Previous GWAS rsl7575455 2 AC078940.2 33% 36% 0.018 31% lllil 0.0'.·. 32% 39% 42 26 170 89 186 66 in

-J

Top Priority Comi rs4890535 18 SLC14A2 0.33 9% 9% % Q.91 9¾> lllll 0.37 9% 9% 5 4 65 23 329 154

Previous GWAS rsl573706 20 PTPRT 0.37 17% 19% 1 0,33 17% lllll 0.24 17% 20% 7 7 120 58 272 116

Previous GWAS rsl2256889 10 CYP26C1 0.41 31% o.o2i||||il lllil 0.75 32% 31% 39 19 178 75 182 87

Top Priority Grossman 2007 rsl415148 1 CTSS (XAZ 39% 41% 1 . ₍₁ t> n ■ 0.99 40% 39% 60 27 195 88 142 66

Previous GWAS rs4344916 2 AC083939.1 0.51 »l . 30% 0.»/ w. ilfl 0.49 34% 33% 52 18 167 83 179 80

Top Priority Grossman 2007 rs2275235 1 CTSS a 54 34% 35% 1 37% 31% 0.90 35% 34% 47 19 187 85 164 77

Top Priority Comi rs269976 18 SLC14A2 0.55 8% 7% 1 0,47 m lllll 0.7'. 8% 6% 3 2 60 19 336 160

Previous GWAS rsll617134 13 RP11-629E24.2 0.57 5% 7% 1 Q.9Q 7% 111111 0.82 6% 7% 3 1 41 22 355 158

Top Priority Grossman 2007 rs946685 1 IL12RB2 0.65 17% 15% 1 0A7 16¾ iiiii 0.40 18% 14% 10 2 119 48 268 131

Top Priority Tchelet GWAS rsl7771939 8 AC016885.1 0.67 29% 26% 0.0095 m* ^' iiiii 0. 29% 31% 35 22 159 70 205 89

Top Priority Tsareva 2011 rs6897932 5 IL7Ra 0.68 20% r- ο.3δ te% lllll 0.62 22% 24% 21 13 135 62 242 106

Top Priority Tchelet GWAS rs4148871 6 TAP2 0.69 21% ?> 0,73 23% lllil .66 22% 21% 18 10 137 56 244 115

Previous GWAS rs2487896 10 HPSE2 a 71 14% 16% 0.0019 12% lliillli|||iiii! 13% 19% 6 6 92 55 301 119

Previous GWAS rsll599624 10 RP11-655H13.1 n /f 15% 13% n .n i i -. lllll 069 13% 12% 7 3 92 36 300 142

Previous GWAS rs2177073 18 DTNA ii.,',' 11% 11%: 0.053 t lll l 0.23 11% 13% 4 5 77 38 317 138

Grossman 2007 rsll29055 3 CD86 0.78 26% 25% 1 0.61 30% iiiii 0.67 28% 28% 33 12 158 78 208 91

Top Priority Tsareva 2011 rs231775 2 CTLA4 0.81 ■17 39% 1 0,32 40% lllll o. // 41% 38% 68 28 189 83 142 70

Previous GWAS rsl0988087 9 SET • i.s ; 4% 4% 0.015 4¾ 11 0.15 4% 7% 1 1 31 22 367 156

Previous GWAS rs4343256 15 CRTC3 0.84 8% 6% 1 0,S9 7% lllll 0.93 8% 7% 1 0 58 25 340 155

Top Priority Tchelet GWAS rs9944913 18 NOL4 0.84 10% 12% 1 0,073 θ¾ 111 0.14 9% 13% 2 3 71 40 326 138

Previous GWAS rs4369324 10 RP11-655H13.2 0.86 25% r- 0.J9 20% iii 0.82 22% 20% 23 9 132 56 244 116

Top Priority Tchelet GWAS rs2521644 7 NPY 1,00 43% 43% 1 0,63 47% ϋϋΐ 0.76 45% 44% 83 33 193 92 123 56

In some embodiments genetic markers presented in Tables 2 , 3 and 4 are identified as predictive of response to glatiramer acetate if the p-value for the GALA cohort is less than about 0.12, less than about 0.08, less than about 0.05, less than about 0.01 or less than about 0.005.

In some embodiments genetic markers presented in Tables 2 , 3 and 4 are identified as predictive of response to glatiramer acetate if the p-value for the FORTE cohort is less than about 0.12, less than about 0.08, less than about 0.05, less than about 0.01, less than about 0.005 or less than about 0.001.

In some embodiments genetic markers presented in Tables 2 , 3 and 4 are identified as predictive of response to glatiramer acetate if the p-value for the Combined cohort is less than about 0.12, less than about 0.08, less than about 0.05, less than about 0.01, less than about 0.005 or less than about 0.001.

Example 6 Analysis Part 2 - Analysis of Candidate Genes (30)

The second analysis was limited to a selected set of genetic variants in 30 priority candidate genes (4,012 variants). Power (80%) to identify significant genetic associations with an odds ratio >4, for variants with an allele frequency greater than 10%. (Or rare alleles (5%) with an odds ratio >6) .

Results for Standard Response Definition, Top 30 Candidate Genes Selected a priori for Additive, Allelic and Genotypic models are presented in tables 5-7, respectively.

In some embodiments genetic markers presented in Tables 5, 6 and 7 are identified as predictive of response to glatiramer acetate if the p-value for the GALA cohort is less than about 0.05, less than about 0.01 or less than about 0.005.

In some embodiments genetic markers presented in Tables 5 , 6 and 7 are identified as predictive of response to glatiramer acetate if the p-value for the FORTE cohort is less than about 0.10, less than about 0.05, less than about 0.01, less than about 0.005 or less than about 0.001. able 5. Additive Model, Analysis of Candidate Genes (30) (Gala, Forte, and Combined cohorts)

able 5b. Additive Model, Analysis of Candidate Genes (30) replicated p<0.05 (Gala, Forte, and Combined cohorts)

._- er ^w S ^ - ^■ ΐ ^:

able 6b. Allelic Model, Analysis of Candidate Genes (30) - replicated p<0.05 (Gala, Forte, and Combined cohorts)

able 7. Genotypic Model, Analysis of Candidate Genes (30) (Gala, Forte, and Combined cohorts)

Table 7b. Genotypic Model, Analysis of Candidate Genes (30) - replicated p<0.05 (Gala, Forte, and Combined cohorts)

In some embodiments genetic markers presented in Tables 5 , 6 and 7 are identified as predictive of response to glatiramer acetate if the p-value for the Combined cohort is less than about 0.05, less than about 0.01, less than about 0.005, less than about 0.001, less than about 0.0005 or less than about 10 ^~4 .

Example 7 Analysis Part 3 - Analysis of Candidate Genes (180)

The third analysis was limited to a selected set of genetic variants in 180 priority candidate genes (25,461 variants).

Results for Standard Response Definition, 180 Candidate Genes Selected a priori for Additive, Allelic and Genotypic models are presented in tables 8-10, respectively.

In some embodiments genetic markers presented in Tables 8, 9 and 10 are identified as predictive of response to glatiramer acetate if the p-value for the GALA cohort is less than about 0.05, less than about 0.01, less than about 0.005, less than about 0.001, less than about 0.0005 or less than about 10 ^"4 .

In some embodiments genetic markers presented in Tables 8, 9 and 10 are identified as predictive of response to glatiramer acetate if the p-value for the FORTE cohort is less than about 0.05, less than about 0.01 or less than about 0.005.

In some embodiments genetic markers presented in Tables 8, 9 and 10 are identified as predictive of response to glatiramer acetate if the p-value for the Combined cohort is less than about 0.05, less than about 0.01, less than about 0.005, less than about 0.001, less than about 0.0005 or less than about 10 ^~4 . - 66 -

Table 8. Additive Model, Analysis of Candidate Genes (180) (Gala, Forte, and Combined cohorts)

T C

Table 10. Genotypic Model, Analysis of Candidate Genes (180) (Gala, Forte, and Combined cohorts)

Example 8 Analysis Part 4 - Genome Wide Analysis

A full genome-wide analysis was then conducted (4 variants) . Power (80%) with Bonferroni statistical correction to identify significant genetic associations with an odds ratio >7, for variants with an allele frequency greater than 10%. (Or rare alleles (5%) with an odds ratio >11) . Approximately 4,200 variants were selected for analysis in stage 2 (replication) (P < 0.001).

Replication Cohort (n = 262: 201 R vs . 61 NR) - In the second stage of analysis, variants selected in the discovery cohort were analyzed to identify replicating associations in the FORTE replication cohort associated with good response vs. poor response. Based upon an analysis of an estimated 4,200 variants, there is statistical power (80%) with Bonferroni correction to identify significant genetic associations with an odds ratio >6.5, for variants with an allele frequency greater than 5%.

Combined Cohorts (n = 580: 399 R vs. Ill NR) - In the third stage of the analysis, the combined GALA and FORTE cohorts were analyzed identify variants associated with response/non-response using a full genome-wide analysis (4 M variants) .

Results for Standard Response Definition, Genome Wide Analysis for Additive, Allelic and Genotypic models are presented in tables 11-13, respectively.

In some embodiments genetic markers presented in Tables 11, 12 and 13 are identified as predictive of response to glatiramer acetate if the p-value for the GALA cohort is less than about 0.001, less than about 0.0005, less than about 10 ^"4 or less than about 5 * 10 ^~5 .

In some embodiments genetic markers presented in Tables 11, 12 and 13 are identified as predictive of response to glatiramer acetate if the p-value for the FORTE cohort is less than about 0.05, less than about 0.01, less than about 0.005, less than about 0.001 or less than about 0.0005. able 11. Additive Model, Genome Wide Analysis, p-value sorted by GALA cohort (Gala, Forte, and Combined cohorts)

Table lib. Additive Model, Genome Wide Analysis, p-value sorted by Combined cohort (Gala, Forte, and Combine cohorts )

able 12 Allelic Model, Genome Wide Analysis, p-value sorted by GALA cohort (Gala, Forte, and Combined cohorts)

Table 12b. Allelic Model, Genome Wide Analysis, p-value sorted by Combined cohort (Gala, Forte, and Combine cohorts )

Table 13. Genotypic Model, Genome Wide Analysis, p-value sorted by GALA cohort (Gala, Forte, and Combined cohorts)

Table 13b. Genotypic Model, Genome Wide Analysis, p-value sorted by Combined cohort (Gala, Forte, and Combine cohorts )

In some embodiments genetic markers presented in Tables 11, 12 and 13 are identified as predictive of response to glatiramer acetate if the p-value for the Combined cohort is less than about 0.05, less than about 0.01, less than about 0.005, less than about 0.001, or less than about 0.0005, less than about 10 ^~4 , less than about 5 * 10 ^~5 , less than about 10 ^"5 , less than about 5 * 10 ^~6 , less than about 10 ^~6 or less than about 5 * 10 ^"7 .

In the fourth stage of the analysis, the placebo cohort (n = 196: 95 R vs. 101 NR) (GALA placebo) was analyzed to identify variants associated with placebo response/non-response. These results will be used to confirm whether significantly associated variants are specific to glatiramer acetate drug response versus disease severity.

Overlap with Placebo Cohort Results:

An analysis to investigate whether any of the highly associated variants (P<0.0001) from the combined cohorts in the additive association analysis showed a similar significant association in the placebo cohort was conducted. This analysis identified two overlapping associations with the placebo associations, which include the 132 ^nd top associated variant in the combined cohorts (variant kpg5144181) and the 242 ^nd top associated variant in the combined cohort (kpg7063887) .

Results for Standard Response Definition, Placebo Cohort Results for Additive, Allelic and Genotypic models are presented in tables 14- 16, respectively.

Table 14. Additive Model, Genome Wide Placebo Cohort Analysis

GALA PLACEBO cohort

Allele

Gene Allele Freq 1 ⁾ 1 ⁾ l¾) l)d Drl rl l

Locations Arm ita !B Ke *SIO x: Freq. (Con rols (Case 1 Con t ( »asi i; Con t (Cast ίί ton-

Name Chr Position Gene(s) Mutation (s) P Odds Ratio (Cases) ) rols) s) rols) s) rols) rsl2472695 2 65804266: ? :? ? 2. 1f 05 0.38 31% 51% i 10 21 39 62 46 i 18 kgp3188 2 65804244; ? ;? ? 2.W 05 0.39 36% 56% i 13 25 41 63 40 i 13 kgp5747456 ; 2 23932556: ? :? ? 3.241 05 Infinity 8% 0% i o 0 15 0 80 ; loi rsll562998 2 51814215: ? ;? S3iW-E*05: 6.52 14% 2% i 2 0 23 5 70 i 96 rsll563025 2 51864372: ? i? 5.411. 05 6.52 14% 2% i 2 0 23 5 70 i 96 rsl6846161 2 2.12E+08: ERBB4,ERESilent,Sile INTRON i ::::3:;:72E¾Si 12.04 12% 1% i 2 0 18 2 74 i 97 kgp22839559 i ? ;? ? .5. /l ^: 0!> 2.82 34% 16% i 10 2 44 28 40 i 70 kgpl2562255 ; 1 2.01E+08: ? :? ? 4.21f 05 21.79 9% 0% i o 0 17 1 78 i 100 kgp6990559 i 1 7014101: CAMTA1 iSilent INTRON,E:: 4.491 05 0.44 35% i 58% i 15 35 36 : 42 43 i 20 rs6577395 1 6991925; CAMTA1 iSilent IN I RON.I 5. 541. 05 0.45 37% 59% i 16 38 37 43 41 i 20 kgp4456934 i 2 2.18E+08 DIRC3 iSilent INTRON ; 5.681: 05 3.79 21% 7% i 4 0 31 13 60 i 87 rsl0495115 1 2.19E+08; ? i? 6.041 05 2.90 30% 13% i 7 2 43 23 45 i 76 kgp4137144 ; 1 2.19E+08; ? :? ? 6.1. if 05 6.19 14% 3% i 2 0 22 5 70 i 95 rs3768769 2 1.14E+08; iL36A iSilent INTRON ί ;.21l ^: 05 4.30 17% 5% i 2 0 29 : 10 64 i 91 kgp3488270 i 1 20335423; ? i? ? /.30I. 05 0.27 6% 21% i 1 4 10 33 84 i 63 rs2354380 2 51826155: ? ;? /.48i: 05 5.49 14% 3% i 2 0 23 6 69 i 95 kgp7151153 i 3 79590648; ROBOl iSilent INTRON : /.86(. 05 3.98 18% 5% i 4 1 27 8 64 i 92 rs28993969 2 1.14E+08: ? :? 8.511: 05 3.67 20% 6% i 4 0 30 13 61 i 88 rsl2043743 1 1.97E+08; KCNT2 iSilent INTRON ί 8.61l ^: 05 0.16 3% 13% i 0 0 5 26 90 i 75 kgp24521552 ; 2 1.44E+08;ARHGAPi:Silent INTRON ¾ 8.861. 05 4.22 17% 5% i 0 25 9 66 i 91 kgpll755256 i 2 42245135: ? ;? ? 8.991 05 0.38 14% 32% i 1 14 25 37 68 i 50 rs528065 2 23859449; KLHL29 iSilent INTRON ί 9.241. 05 2.45 44% 26% i 19 3 46 46 30 i 52 rsl3386874 2 51820543; ? :? 9.251: 05 2.64 32% 15% i 12 1 37 28 46 i 72 kgp956070 2 2.06E+08: PARD3B,P:Silent,Sile INTRON ί 9. 591 05 0.37 14% 32% i 2 11 23 41 70 i 48 rs35615951 2 1.34E+08: NCKAP5,IVSilent,Sile INTRON i 9.41f 05 2.32 48% 28% i 22 8 46 41 26 i 52 kgpl2253568 i 3 79428265; ROBOl iSilent INTRON ^; 9.551 05 4.29 17% 4% i 4 1 24 : 6 67 i 94 rsl397481 2 2.06E+08: PARD3B,PSilent,Sile INTRON i 9.56t. 05 0.37 14% 31% i 2 10 23 43 70 i 48 kgp7161038 i 2 53521025: ? ;? 9. /01: 05 0.09 1% 10% i 0 0 2 20 92 i 81 rsl534647 2 62038088; ? ;? 9.721 05 3.34 22% 8% i 5 0 32 16 58 i 85 kgp7799142 ; 3 13902000; WNT7A iSilent INTRON ; 1.041: 04 0.12 2% 11% i 0 0 3 22 91 i 79 kgp6029 2 1.69E+08; ? ;? ? 1.0/l ^: 04 0.37 13% 30% i 2 11 21 39 72 i 51 kgp8142606 ; 2 1.74E+08; ? :? ? l.lOt. 04 0.22 4% 17% i o 3 8 27 87 i 70 rs6737616 2 51807660: ? ;? ? 1.181 04 5.98 13% 2% i 1 0 22 5 72 i 96 kgp7713264 i 2 2.42E+08:GPR35,GP:Silent,Sile INTRON ί 1.181. 04 0.45 30% 51% i 10 27 37 47 47 i 26 kgp8055964 ; 3 1.73E+08:SPATA16 iSilent INTRON : 1.191: 04 Infinity 7% 0% i 0 0 13 0 82 i 101 rsl2712821 2 42238864; ? i? 1.191 04 0.39 15% 32% i 1 14 26 37 68 i 50 rsl3424176 2 42239532; ? :? ? 1.19f 04 0.39 15% 32% i 1 14 26 37 68 i 50 kgp9777128 i 2 42242872; ? ;? ? 1.191 04 0.39 15% : 32% i 1 14 26 : 37 68 i 50 rsl0195970 2 42249643; ? i? 1.191. 04 0.39 15% 32% i 1 14 26 37 68 i 50 rsl0177811 2 42263580: ? ;? 1.191: 04 0.39 15% 32% i 1 14 26 37 68 i 50

Table 15. Allelic Model, Genome Wide Placebo Cohort Analysis

GALA PLACEBO cohort

Gene Odds Ratio Allele Allele DD DD Dd Dd dd dd Locations Fisher's (Minor Freq. Freq. (Cas (Con (Cas (Con (Cas

Name Chr Position Gene(s) Mutation (s) Exact P Allele) (Cases) (Controls) es) trols) es) trols) es) trols) kgp5471255 ; 11; 57870219: OR9Q1 iSilent I I RON, 1 1.161 06 0.25 9% 29% 5 ; 25 7 j 7 81 68 kgp 11285883 ; 9 2953403; ? j? ? iii!H!ll 2.79 46% 23% 26 j 5 35 j 37 34 59 kgp433351 : 8 41496314:? j? ? iili!l!l 0.35 23% 46% 6 i 19 32 i 55 57 27 kgp 10148554 4 89767803; FAM13A jSilent INTRON i!i!l!l 7.19 15% 2% 3 i 0 23 j 5 68 96 rs3858038 i ⁹ 2988280:? j? ? iiillllil 2.63 53% 30% 33 j 7 34 i 46 28 48 kgp2877482 6 1644677iGMDS,G iSilent,Sile lNTRON iiillllili 8.20 14% 2% 0 i 0 27 i 4 68 97 kgp6042557 3 1.94E+O8j LOC10050 ^' jSilent INTRON iiillllili 0.08 1% 12% 0 j 1 2 i 22 93 77 kgp22755512 ;x 27326117: ? ? ? iiiiiii ? 8% 0% 3 i ⁰ 10 i 0 82 101 kgp 10989246 4 89761443i FAM13A jSilent INTRON ill lili 7.11 15% 3% 3 i 0 23 j 5 68 95 rs7698655 4 89756076: FAM13A jSilent INTRON iiiiiii 7.10 15% 2% 3 i o 23 i 5 69 96 kgp9409440 4 89759159: FAM13A jSilent IN I RON ills!!! 7.10 15% 2% 3 0 23 j 5 69 96 kgp6889327 4 89766553j FAM13A jSilent INTRON iillll!li 7.10 15% 2% 3 i 0 23 j 5 69 96 rs7696391 4 89789287: FAM13A jSilent INTRON iiillllil 7.10 15% 2% 3 i o 23 j 5 69 96 rsll947777 4 89768744j FAM13A jSilent INTRON 7.02 15% 3% 3 i 0 23 j 5 69 95 kgp6301155 4 89766647j FAM13A jSilent INTRON 6.95 15% 3% 3 j 0 23 j 5 69 94 rsl6846161 2 2.12E+08: ERBB4,ER[jSilent,Sile INTRON 12.99 12% 1% 2 i ⁰ 18 j 2 74 97 kgp7778345 i 9 2965090j? j? ? 2.59 49% 27% 27 i 6 38 i 42 29 52 kgp6990559 1 7014101jCAMTAl jSilent INTRON, E iiillllili 0.40 35% 58% 15 i 35 36 i 42 43 20 rsl393040 i 9 2985743 i? i? ? 2.57 48% 27% 28 i 6 35 i 42 31 53 rs6577395 1 6991925iCAMTAl jSilent INTRON, E 0.40 37% 59% 16 i 38 37 i 43 41 20 rs7846783 i 9 2958182:? j? ? iiillllili 2.58 45% 24% 25 i ⁶ 36 i 37 34 58 kgp5747456 2 23932556 ? j? ? iiiiiiiiii ? 8% 0% 0 i 0 15 i o 80 101 kgp6429231 i 15 62931802 j MGC1588!: Silent INTRON ? 8% 0% 0 i o 15 i 0 80 101 kgp30689515 X 56022365: ? j? ? iiillllili ? 8% 0% 4 0 7 j 0 84 101 kgp 1682126 5 2047397 j? j? ? 0.05 1% 10% 0 j 1 1 i ¹⁸ 94 82 kgp2920925 i ¹⁷ 39694480: ? j? ? iiillllili 0.30 10% 27% 0 i ⁶ 19 i 43 76 52 rs3894712 5 73973651j ? j? ? iiillllili 0.29 9% 25% 3 j 5 11 i 41 81 55 rs7119480 j 1 1 84247636: DLG2,DLGjSilent,Sile INTRON, E iiiiiii ii 0.34 14% 33% 1 i 9 25 i 48 69 44 rs3858035 : 9 2968044:? j? ? iiillllili 2.51 48% 27% 27 j 7 37 i 41 30 53 rs3847233 i 9 2987835 j? j? ? iiillllili 2.49 52% 30% 31 j 7 34 j 46 28 47 kgp 12253568 3 79428265: ROBOl jSilent INTRON iiiiiiiiii 4.91 17% 4% 4 j 1 24 i 6 67 94 kgp 1977942 i 9 2938757 i? i? ? iiillllili 2.52 46% 26% 28 i 7 32 i 37 35 56 kgp22744690 ix 83601713j HDX,HDX,jSilent,Sile INTRON 7.50 13% 2% 7 j 0 1 1 j 4 77 97 rs8000689 : 13; 41043438; ; Silent, Sile lNTRON iiillllili 0.42 38% 60% 14 j 40 45 i 41 36 20 kgp4892427 i 9 2995617 j? j? ? iiillllili 2.46 52% 30% 31 j 7 36 i 47 28 47 rsll562998 2 51814215: ? j? ? iiillllili 6.53 14% 2% 2 i o 23 j 5 70 96 rsll563025 2 51864372j ? i? ? iiillllili 6.53 14% 2% 2 i 0 23 j 5 70 96 rs7680970 4 89772301j FAM13A j Missense EXON iiillllili 5.88 15% 3% 3 i o 23 i ⁶ 69 95 kgp22836129 ;x 1.45E+08:? j? ? iiillllili 5.84 15% 3% 5 i ⁰ 19 i 6 70 93 kgp 11604017 i 11 1.18E+08jAMICAl,AiSilent,Sile lNTRON ilia!!! 2.69 38% 18% 11 j 3 48 i 31 34 67 rs961090 i 15 40617414: ? j? ? iilllll 2.97 31% 13% 9 i 2 40 i 22 46 77 kgp22760557 ; X 3520721:? j? ? ii!H!ii 2.97 31% 13% 16 j 5 26 i 16 53 80 rsl393037 i 9 2968451j? j? ? 2.50 48% 27% 27 j 7 37 j 40 30 52 rs4978567 i 9 1. 1 /1 - 08 ? j? ? iiillllili 0.41 32% 54% 10 i 27 41 i 52 44 20 Table 16. Genotypic Model, Genome Wide Placebo Cohort Analysis

Example 9 Analysis for Extreme Responders vs. Extreme Non-Responders Part 1 - Analysis of Candidate Variants

The initial analysis was analyzed to 35 genetic variants in high priority genes. Power (80%) with Bonferroni statistical correction for multiple testing to identify significant genetic associations with an odds ratio >4, for variants with an allele frequency greater than 10%.

Results for Extreme Response Definition, Candidate Variants Selected a priori for Additive, Allelic and Genotypic models are presented in tables 17-19, respectively.

In some embodiments genetic markers presented in Tables 17-19 are identified as predictive of response to glatiramer acetate if the p- value for the GALA cohort is less than about 0.15, less than about 0.13, less than about 0.07 or less than about 0.06.

In some embodiments genetic markers presented in Tables 17-19 are identified as predictive of response to glatiramer acetate if the p- value for the FORTE cohort is less than about 0.10, less than about 0.05, less than about 0.01, less than about 0.005 or less than about 0.001.

In some embodiments genetic markers presented in Tables 17-19 are identified as predictive of response to glatiramer acetate if the p- value for the Combined cohort is less than about 0.10, less than about 0.05, less than about 0.01, less than about 0.005 or less than about 0.001.

able 17. Additive Model, Extreme Response Definition, Candidate Variants (Gala, Forte, and Combined cohorts)

Table 19. Genotypic Model, Extreme Response Definition, Candidate Variants (Gala, Forte, and Combined cohorts)

Fisher's i Fisher's Fisher's (Case (Cont (Case (Cont (Case (Cont

Source Name Ch Gene Exact P ¾ Exact P s) rols) s) rols) s) rols)

Comi rs ₄ 890535 18 SLC1 ₄ A2 0.051225; mmmm 0.130308 1 3 26 12 128 106

Tchelet GWAS rs 1085360 18 MEX3C 0.09938 28 17 68 69 59 35

Comi rs 1558896 7 TAC1 0.108585; &3S4 07 0.129289 18 9 54 56 83 56

Comi rs269976 18 SLC1 ₄ A2 0.131319; 0.068507 0 1 22 9 133 111

Grossman 2007 rs2001791 3 CD86 0.163623; 0.466835 6 2 35 32 114 87

Tchelet GWAS rs9 ₄ 7603 10 CEP55 0.175968 0.018504 0.03493 7 6 59 29 88 86

Tchelet GWAS rs3135391 6 HLA-DRB1 mmmm 0,063.953 0.019534 6 9 47 52 102 60

Tchelet GWAS rs 1225688 10 CYP26C1 0.211437: G.112S9? 1 15 11 71 56 69 54

Tchelet GWAS rs3135388 6 HLA-DRB1 0.229549; 0.023584 6 9 47 51 102 60

Tchelet GWAS rs 1098808 9 SET 0.2701 1 0.(X)3153 0.009817 0 1 9 18 146 101

Tchelet GWAS rs 1095035 7 AC07 ₄ 389.1 0.290815;mmmm 0.194248 21 10 54 53 80 58

Tchelet GWAS rsll59962 10 RP11-655H13.1 0.319145; 0,620619 0.808936 2 1 28 26 125 94

Tsareva 2011 rs231775 2 CTLA ₄ 0.323348; ;:0 Θ§¾8 0.641801 26 18 71 51 58 52

Tchelet GWAS rs2487896 10 HPSE2 0. 9:¾68 0.00344 0.016573 3 6 29 37 123 77 00 in

Tchelet GWAS rs 1007328 15 AC012 ₄ 09.1 0.342331; ;«&Jii 0.490428 39 32 77 66 39 23

Tchelet GWAS rsl093109 2 AC07 ₄ 182.1 0.399287; mmmm 0.674319 2 3 22 14 131 104

Tchelet GWAS rs 1573706 20 PTPRT 0.421405 0.00156 0.022921 2 7 39 41 114 73

Tchelet GWAS rs ₄ 148871 6 TAP2 0.500007; &72523S 0.328473 4 7 55 37 96 77

Comi rs974O60 7 TAC1 0.511178; D, 294494 0.443883 14 9 56 53 85 59

Grossman 2007 rs9 ₄ 6685 1 IL12RB2 0.545787; 0.760198 0.755453 5 2 44 36 105 83

Tchelet GWAS rs2521644 7 NPY 0.549577; mmmm 0.759357 28 22 82 59 45 40

Tchelet GWAS rs6097801 20 CYP2 ₄ A1 0.583023 0.(X)01 4 0.(X)1719 7 3 20 36 128 82

Tsareva 2011 rs 1800629 6 TNF 0.5881 ; mmmm 0.420205 6 4 27 29 122 88

Tchelet GWAS rs ₄ 344916 2 AC083939.1 0.607658 0.028396 0.025091 19 11 49 58 86 52

Tchelet GWAS rsl777193 8 AC016885.1 0.737475 2.951: 05 0.096795 9 16 62 48 84 57

Tsareva 2011 rs6897932 5 IL7Ra 0.803461; mmmm 0.938197 8 7 55 44 92 70

Grossman 2007 rsl ₄ 15148 1 CTSS 0.848296; D, 504217 0.708954 25 16 71 61 57 44

Grossman 2007 rs2275235 1 CTSS 0.886713; t SSi 0.515363 21 11 70 58 63 52

Tchelet GWAS rs 1757545 2 AC0789 ₄ 0.2 0.911321; 0-066946 0.276505 15 17 63 55 76 49

Tchelet GWAS rs9944913 18 NOW 0.914654; 0,33.2493 0.314049 0 2 33 27 122 92

Tchelet GWAS rs ₄ 36932 ₄ 10 RP11-655H13.2 0.953211; m mm 0.88185 6 5 52 37 97 79

Tchelet GWAS rsll61713 13 RP11-629E24.2 1 0.005284 0.060266 1 1 10 17 144 103

Tchelet GWAS rs ₄ 3 ₄ 3256 15 CRTC3 mm m mmmm 0.463072 0 0 17 17 138 103

Tchelet GWAS rs2177073 18 DTNA mmmm mmmm 0.470055 2 3 29 28 124 90

Grossman 2007 rsll29055 3 CD86 i ; ;:;:;:a;gS82¾ 0.877801 12 8 63 53 80 60

Example 10 Analysis for Extreme Responders vs. Extreme Non- Responders Part 2 - Analysis of Candidate Genes (30)

The second analysis was analyzed to a selected set of genetic variants in 30 priority candidate genes (4,012 variants). Power (80%) to identify significant genetic associations with an odds ratio >7 ₍ for variants with an allele frequency greater than 10%.

Results for Extreme Response Definition, Analysis of Candidate Genes (30) Selected a priori for Additive, Allelic and Genotypic models are presented in tables 20-22, respectively. No variants replicated in both cohorts (P<0.05). Less stringent (P<0.10 + P<0.05) values were used.

In some embodiments genetic markers presented in Tables 20-22 are identified as predictive of response to glatiramer acetate if the p- value for the GALA cohort is less than about 0.10, less than about 0.09, less than about 0.08, less than about 0.07 or less than about 0.02.

In some embodiments genetic markers presented in Tables 20-22 are identified as predictive of response to glatiramer acetate if the p- value for the FORTE cohort is less than about 0.05, less than about 0.02, less than about 0.01 or less than about 0.005.

In some embodiments genetic markers presented in Tables 20-22 are identified as predictive of response to glatiramer acetate if the p- value for the Combined cohort is less than about 0.05, less than about 0.01 or less than about 0.005.

Table 20. Additive Model, Extreme Response Definition Analysis of Candidate Genes (30) (Gala, Forte, and Combine cohorts )

Table 21. Allelic Model, Extreme Response Definition, Analysis of Candidate Genes (30) (Gala, Forte, and Combine cohorts )

Table 22. Genotypic Model, Extreme Response Definition, Analysis of Candidate Genes (30) (Gala, Forte, and Combine cohorts

GALA FORTE COMBINED

Allele Allele

Allele Freq. Allele FftSf. Allele Freq. DD DD Dd Dd dd dd Fisher's Freq. (Contro s Fisher's Freq. tCOfttWii Fisher's Freq. (Controls (Cases (Contr (Cases (Contr (Cases (Contr

Columns ' Gene Chr Positioi Exact F "· (Cases) ) Exact P (Cast >$) ' ) ' Exact F ' ^■ (Cases) ' ) LI ) L ols) ) LL ois) : : ) LI ols) kgpll964392 18 74768870 0.0135 45% 49% 0.0288 !!§i 0.0050 49% 48% 30 34 91 48 32 39 kgp776593 11 1.21E+08 0.0711 11% 6% 0.0391 III! 0-0074 10% 5% 0 1 30 10 125 110 kgp8702370 3 1.32E+08 0.0747 17% 11% 0.0024111 iiiiiii iiiiiiiiiiii 0.0033 is% 9% 7 0 43 21 105 99 rsl894407 HLA-DOB/TAP2 6 32787036 0.0923 39% 30% 0.0155111 0.0063 40% 28% 21 10 82 48 51 63

Example 11 Analysis for Extreme Responders vs. Extreme Non- Responders Part 3 - Analysis of Candidate Genes (180)

The third analysis was analyzed to a selected set of genetic variants in 180 priority candidate genes (25,461 variants). Power (80%) to identify significant genetic associations with an odds ratio >7, for variants with an allele frequency greater than 10%.

Results for Extreme Response Definition, Analysis of Candidate Genes (180) Selected a priori for Additive, Allelic and Genotypic models are presented in tables 23-25, respectively.

In some embodiments genetic markers presented in Tables 23-25 are identified as predictive of response to glatiramer acetate if the p- value for the GALA cohort is less than about 0.05, less than about 0.01, less than about 0.005, less than about 0.001, less than about 0.0005 or less than about 10 ^"4 .

In some embodiments genetic markers presented in Tables 23-25 are identified as predictive of response to glatiramer acetate if the p- value for the FORTE cohort is less than about 0.05, less than about 0.01, less than about 0.005 or less than about 0.001.

In some embodiments genetic markers presented in Tables 23-25 are identified as predictive of response to glatiramer acetate if the p- value for the Combined cohort is less than about 0.05, less than about 0.01, less than about 0.005, less than about 0.001, less than about 0.0005 or less than about 10 ^~4 .

Table 23. Additive Model, Extreme Response Definition Analysis of Candidate Genes (180) (Gala, Forte, and Combine cohorts )

Table 25 Genotypic Model, Extreme Response Definition, Analysis of Candidate Genes (180) (Gala, Forte, and Combine cohorts )

Example 12 Analysis for Extreme Responders vs. Extreme Non- Responders Part 4 - Genome Wide Analysis

A full genome-wide analysis (4 M variants) was then conducted. Power (80%) with Bonferroni statistical correction to identify significant genetic associations with an odds ratio >11, for variants with an allele frequency greater than 10%. Approximately 4200 variants were selected for analysis in stage 2 (replication) (P < 0.001).

Results for Extreme Response Definition, Genome Wide Analysis for Additive, Allelic and Genotypic models are presented in tables 23- 25, respectively.

In some embodiments genetic markers presented in Tables 26-28 are identified as predictive of response to glatiramer acetate if the p- value for the GALA cohort is less than about 0.05, less than about 0.01, less than about 0.001, less than about 0.0005, less than about 10 ^"4 or less than about 5 * 10 ^"5 .

In some embodiments genetic markers presented in Tables 26-28 are identified as predictive of response to glatiramer acetate if the p- value for the FORTE cohort is less than about 0.05, less than about 0.01, less than about 0.001, less than about 0.0005, less than about 10 ^"4 or less than about 5 * 10 ^"5 .

In some embodiments genetic markers presented in Tables 26-28 are identified as predictive of response to glatiramer acetate if the p- value for the Combined cohort is less than about 10 ^"4 , less than about 5 * 10 ^~5 , less than about 10 ^~5 , less than about 5 * 10 ^~6 , less than about 10 ^"6 or less than about 5 * 10 ^"7 .

Stage 4. Placebo Cohort (n = 102: 23 R vs. 79 NR) - The placebo cohort (GALA placebo) was analyzed to identify variants associated with placebo response/non-response.

Results for Standard Response Definition, Placebo Cohort Results for Additive, Allelic and Genotypic models are presented in tables 29- 31, respectively. able 26. Additive Model, Extreme Response Definition, Genome Wide Analysis (Gala, Forte, and Combined cohorts)

Table 27. Allelic Model, Extreme Response Definition, Genome Wide Analysis (Gala, Forte, and Combined cohorts)

GALA liiii lOMBINED

Odds Odds Allele

Gene Ratio Freq Ratio Allele Freq. DD DD Dd Dd dd dd

Chromos Locations Fisher's iilll Freq !!!i Fishe s (Minor illi! Fisher's (Minor Freq. (Contr (Case (Cont (Case (Cont (Case (Cont

Name I ome ; ' : Positioii . Gene(si ;' : Mutatfc j (s) Allel (Case.;'. ols: " Hi mm ^■ 0)S ' Exact : Alleli " (Case ols ' s)i ' roli " ' roll " s)i ' roli - kgp621435 11 75546691 UVRAG Silent INTRON 3.08E-03 0.21 3% 13% 2.29E- 04 0,14 1.51 E 0(. 0.18 3% 14% 0 1 9 32 145 88 rs759458 2 65245365 SLC1A4,SL ⁽ Missense_ EXON 8.18E-05 2.97 36% 16% 4.98E- 02 1.93 2.38E Of. 2.61 36% 18% 20 3 71 37 63 81 rsl97523 21 19337261 CHODL,CH Silent,Silen I NTRON 5.54E-05 2.94 40% 19% 3.61E- 02 im 63% 77% 2.67E 06 2.52 38% 20% 26 5 67 38 62 78 rs7844274 8 72411302 ? ? ? 1.13E-03 0.42 21% 39% 1.57E- 02 8,45 2.87E-06 0.40 19% 37% 4 18 50 53 100 50 kgpl07881 12 13898682 GRIN2B Silent INTRON 1.51E-03 0,00 0% 7% 7.96E- 04 8,09 m 89% 5.83E 06 0.07 1% 8% 0 1 2 18 153 102 rs5918137 X 41113080 ? ? ? 1.69E-03 2.74 24% 10% 4.13E- 02 2.05 £8¾ 81¾ 6.58E 06 2.72 29% 13% 22 8 44 15 88 98 kgpl9125. 2 1.38E+08 THSD7B Silent I NTRON 1.04E-03 2.85 25% 10% 4.44E- 03 3.45 76% m 6.88E 06 2.98 25% 10% 13 3 50 18 91 100 kgp96273. 17 90155 RPH3AL,RF Silent,Silen I NTRON 1.46E-03 0.32 8% 22% 1.58E- 03 O.30 -89% 71% 7.24E-06 0.34 10% 24% 1 7 28 44 125 70 rsl96343 10 1.21E+08 BAG3 Silent INTRON S.14E-05 0.30 11% 30% 7.18E- 03 0,42 82¾ 66¾ 7.88E-06 0.39 15% 31% 4 15 39 45 112 60 rsl96295 10 1.21E+08 BAG3 Synonymo EXON 7.94E-05 0.30 11% 30% 1.09E- 02 0.43 8¾¾ 66 8.19E 06 0.39 15% 31% 4 15 39 46 111 60 rs343087 12 66260924 HMGA2,Hf Silent,Silen I NTRON, Εί 3.77E-03 2.96 18% 7% 5.26E- 03 3 JO 33% 8.46E 06 3.40 20% 7% 12 0 39 17 103 104 kgpl8432C 9 1.09E+08 TMEM38B Silent UTR 3.76E-04 3.72 20% 6% 1.08E- 02 4,35 S 96% 1.53E-05 3.56 18% 6% 5 0 46 14 104 106 rs7725112 5 1.74E+08 ? ? ? 4.85E-04 3,59 21% 7% 3.71E- 02 2,61 % 91% 1.76E 05 3.17 20% 7% 5 1 53 16 97 104 rs7028906 9 1.08E+08 ? ? ? 8.10E-05 4.36 21% 6% 2.72E- 02 3.65 96¾ 1.76E 05 3.63 17% 5% 4 0 45 13 106 108 rs9579566 13 30980265 ? ? ? 3.90E-04 0.00 0% 8% 1.68E- 02 0.18 98% 91% 1.80E 05 0.11 1% 8% 0 1 3 18 152 102 kgp239141 2 43425645 ? ? ? 7.83E-04 0,39 18% 36% 4.73E- 03 0.41 76% 57% 1.81E-05 0.44 21% 38% 8 20 49 52 96 49 kgp32029; 12 13859947 GRIN2B Silent INTRON 8.78E-03 0.10 1% 7% 6.75E- 04 0.Q9 99¾ 88¾ 1.87E 05 0.11 1% 8% 0 1 3 18 150 101 kgp988462 2 2.07E+08 ? ? ? 5.91E-03 0.00 0% 6% 2.23E- 02 0.QQ 1QQ% 96% 1.93E 05 0.00 0% 5% 0 0 0 13 154 108 kgp227932 X 92601576 ? ? ? 1.68E-03 0.47 34% 52% 4.17E- 03 0.44 65% 44% 1.93E 05 0.47 35% 53% 32 41 42 47 78 33 kgp56809E 6 1.64E+08 ? ? ? 6.18E-04 0,43 29% 48% 2.57E- 02 8,51 7W> 54% 1.94E-05 0.46 30% 48% 13 27 65 61 76 33 kgp81074S 6 1.64E+08 ? ? ? 1.11E-03 0,45 35% 54% 2.91E- 02 8,52 66% 50% 1.96E-05 0.47 34% 53% 17 32 72 64 65 25 kgpll467C 5 1.73E+08 STC2 Silent I NTRON 6.44E-04 0.16 2% 13% 4.17E- 03 0.26 3 ¾ 83¾ 1.96E 05 0.25 4% 14% 0 2 12 30 143 89 kgpl0948E 20 44082511 ? ? ? 9.59E-03 0.49 20% 33% 4.00E- 03 0.37 S5& 69¾ 2.02E 05 0.42 17% 33% 4 8 44 63 107 50 rs7217872 17 88988 RPH3AL,RF Silent,Silen I NTRON 2.31E-03 0.33 8% 22% 1.77E- 03 0.32 i 71% 2.O5E 05 0.36 10% 24% 1 7 29 43 125 71 rs343092 12 66250940 HMGA2,Hf Silent,Silen I NTRON, Εί 6.14E-03 2,81 17% 7% 8.40E- 03 3.53 79% 93% 2.18E-05 3.24 20% 7% 11 0 39 17 105 104 rs7948420 11 27276450 ? ? ? 4.80E-05 0.35 20% 42% 8.59E- 03 0.46 67¾ 49¾ 2.27E 05 0.46 27% 45% 12 24 61 61 82 36 rs9913349 17 68260070 ? ? ? 2.63E-03 2.30 32% 17% 3.61E- 02 1.99 6¾% ??% 2.31 E 05 2.34 35% 19% 15 5 78 35 62 81 rs6718758 2 60328802 ? ? ? 6.51E-03 0.51 31% 47% 1.42E- 02 8,48 7 53% 2.36E 05 0.46 28% 45% 10 27 66 56 79 38

able 28. Genotype Model, Extreme Response Definition, Genome Wide Analysis (Gala, Forte, and Combined cohorts)

Table 29. Additive Model, Extreme Response Definition, Genome Wide Placebo Cohort Analysis

Table 30. Allelic Model, Extreme Response Definition, Genome Wide Placebo Cohort Analysis

Table 31. Genotype Model, Extreme Response Definition, Genome Wide Placebo Cohort Analysis

Placebo

Gene Allele Freq. DD DD Dd Dd dd dd

Locations Fisher's Freq. (Controls (Case (Cont (Case (Cont (Case (Cont

Name Chr Position Gene(s) Mutation (s) Exact P (Cases) s| rols) s) rols) s) rols) rsl978721 19 : 30966217;ZNF536 Silent INTRON w m 24% 1% ; o o ; 11 2 ; 12 77 kgp7344529 19 : 30967564:ZNF536 Silent INTRON m a 24% 1% ; o o ; 11 2 ; 12 77 rs7252241 19 : 30967836:ZNF536 Silent INTRON m a 24% 1% ; o o ; 11 2 ; 12 77 rsl978720 19 : 30968371:ZNF536 Silent INTRON m a 24% 1% ; o o ; 11 2 ; 12 77 kgpl46166 19 i 30965980 ;ZNF536 Silent INTRON 1.91K 06 30% 5% ; o o ; 14 8 ; 9 71 rs8112863 19 : 30965063;ZNF536 Silent INTRON 2.19K 06 30% 5% ; o o ; 14 8 ; 9 70 rs7565256 2 ; 79227275;? ? ? 2.25F 06 48% 14% ; 4 o ; 14 22 ; 5 56 kgp6295377 19 ; 30953846;ZNF536 Silent INTRON 3.08F 06 20% 2% ; o l ; 9 l ; 14 77 rs4477500 12 ; 128645821;? ? ? 3.54F 06 64% 26% ; 9 2 ; 10 37 ; 3 39 rsll705401 22 ; 37678096;? ? ? S:3:;:97:Es06:s 13% 37% ; 2 4 ; 2 so ; 19 25 kgp2536097 6 ; 25181978;? ? ? :S:5:;:S8:EH06s: 74% 41% ; i4 8 ; 6 48 ; 3 23 rs2109066 19 ; 28835327;? ? ? mm 9% 31% ; 2 5 ; 0 39 ; 21 35 kgp2877482 6 1644677 :GMDS,GM Silent,Siler INTRON msmmsi 24% 3% ; o o ; 11 4 ; 12 75 kgp9775757 1 ; 23068465 ;EPHB2,EPhSilent,Siler INTRON mmrnrn 46% 14% ; 3 o ; 15 22 ; 5 57 kgp6601755 19 ; 28886975;? ? ? 6.761 06 11% 33% ; 2 3 ; 1 45 ; 19 30 kgp8598661 6 1627678 ;GMDS,GM Silent,Siler INTRON 8.891 06 28% 4% ; l o ; 11 6 ; 11 73 rs2159327 19 ; 28835571;? ? ? 9.7 1 06 9% 31% ; 2 5 ; 0 39 ; 20 35 kgp2151888 2 ; 79295288;? ? ? 9.911 06 43% 12% ; 3 o ; 13 19 ; 6 60 rs2289333 15 ; 40617209;? ? ? 1.011 05 24% 2% ; l o ; 9 3 ; 13 76 kgp2471573 15 ; 40633138 ;cl5orf52 Synonymo EXON 1.011 05 24% 2% ; l o ; 9 3 ; 13 76 kgp6870400 2 ; 79278036;? ? ? mmmm 46% 14% ; o ; 13 22 ; 6 57 kgp6850713 19 ; 28885593;? ? ? mmmm 7% 32% ; l 3 ; 1 44 ; 20 32 kgp7851536 15 ; 27960322;? ? ? mmmm 15% 0% ; o o ; 7 o ; 16 79 kgp9348779 15 ; 101900592 ;PCSK6,PCSSilent,Siler INTRON m m 15% 0% ; o o ; 7 o ; 16 79 rs995834 19 ; 28866596:? ? ? m mm 11% 32% ; 2 4 ; 1 43 ; 20 32 rsl773631 10 ; 25665449 ;GPR158 Silent INTRON 1.43K 05 27% 7% ; o 2 ; 12 7 ; 10 70 kgp8034516 8 ; 97282138 iPTDSSl Silent INTRON 1.46K 05 20% 3% ; o l ; 9 2 ; 14 76 rsl3280716 8 ; 97282560 iPTDSSl Silent INTRON 1.46K 05 20% 3% ; o l ; 9 2 ; 14 76 kgp303315 8 ; 97283313 iPTDSSl Silent INTRON 1.46F 05 20% 3% ; o l ; 9 2 ; 14 76 kgp5433489 8 ; 97302091 iPTDSSl Silent INTRON 1.46F 05 20% 3% ; o l ; 9 2 ; 14 76 rsl7707686 8 ; 97312442 iPTDSSl Silent INTRON S:l:;:46Es05:s 20% 3% ; o l ; 9 2 ; 14 76 rs727047 22 ; 37677719;? ? ? S:l:;:48:Es05:s 13% 35% ; 2 4 ; 2 48 ; 19 27 kgp7521451 8 ; 97297894 iPTDSSl Silent INTRON mmmm 20% 4% ; o 2 ; 9 2 ; 13 74 rs2056136 12 ; 20867893 ;SLC01Cl,SSilent,Siler INTRON mmmm 35% 9% ; l l ; 14 12 ; 8 66 rsl0746192 12 ; 81942162 : PPFIA2,PPI Silent,Siler INTRON mmmm 67% 35% ; 8 5 ; 15 45 ; 0 29 rs2555629 4 ; 175430288 H GD.HPO Silont.Silor INTRON, 1.57E 05 61% 25% ; 11 4 ; 6 32 ; 6 43 kgpl2537012 8 ; 97285429 ;PTDSS1 Silent INTRON 1.601 05 20% 3% ; o l i 9 2 ; 14 75 rs9969509 8 ; 97293953 ;PTDSS1 Silent INTRON 1.601 05 20% 3% ; o 1 : 9 2 ; 14 75 kgp6535349 15 ; 40614200;? ? ? 1.601 05 20% 1% ; o o ; 9 2 ; 14 76 rsl6846841 2 ; 197063250;? ? ? 1.731 05 17% 1% ; o o ; 8 l ; 15 78 kgpl2396787 22 ; 27267611;? ? ? 1.871 05 17% 1% ; o o ; 8 l ; 15 77 kgp4985243 7 ; 136556162 ;CHRM2,Ch Silent,Siler INTRON mmmm 33% 7% ; l o ; 13 11 ; 9 68 rs4935590 10 ; 57059483;? ? ? mmmm 35% 8% ; 2 o ; 12 12 ; 9 67 rsl6907220 10 ; 57059690;? ? ? mmmm 35% 8% ; 2 o ; 12 12 ; 9 67 rsl073665 10 ; 57061057:? ? ? mmmm 35% 8% ; 2 o ; 12 12 ; 9 67 rs2292275 1 : 163292217 ; N U Γ2.Ν U F Silont.Silor INTRON, i: 2.1 It 05 57% 27% ; 4 6 ; 18 30 ; 1 42 rs7962380 12 ; 128643018;? ? ? 2.27K 05 67% 34% ; 11 5 ; 9 44 ; 3 30 kgpl0638512 3 ; 196573166;? ? ? 2.34K 05 7% 44% ; o 17 ; 3 35 ; 20 27 Example 13 Association Analyses Corrected for Ancestry

A Principal Components Analysis (PCA) was performed in order to investigate potential population stratification among cases and controls. Sample-specific Eigen values were calculated to produce an output of 1st and 2nd Principal Components which can be used to infer patient ancestry.

An association analysis was performed using an Additive Genetic Model with Principal Components Analysis correction for population stratification; results are presented in Table 32.

Table 32. Regression, Additive Model, Corrected for ancestry by PCA

Example 14 Regression Analysis

Regression analysis was conducted using an additive genetic model to identify additional clinical and genetic variants that are highly associated with response after correction for the most significantly associated variables.

For clinical factors, regression analyses revealed two highly associated clinical covariates : "Log number of relapses in the last two years" significantly associated with response to glatiramer acetate (combined cohorts p-value 3.6xl0 ^"32 , odds ratio 14.5 (95%CI 8.6-24.4)) and "Baseline Expanded Disability Status Scale (EDSS) Score" (combined cohorts p-value 5.9xl0 ^"10 , odds ratio 0.62 (95%CI 8.6-24.4)) with higher baseline EDSS scores (increased MS disability) associated with increased likelihood of non-response to glatiramer acetate. Importantly, these clinical factors were significantly associated with glatiramer acetate response in both the GALA and FORTE patient cohorts.

Table 33. Clinical co-variates associated with response to glatiramer acetate.

Results of regression analyses for the Additive Models are presented in Tables 34-37.

In some embodiments, all of the genetic markers presented in Tables 34-37 are identified as predictive of response to glatiramer acetate . Table 34. Regression Analysis, Additive Model (Gala cohort)

COMBINED

Predictor . " : Chf I Positior ^ Gene ; j P-Value ^r Odds RatK "

rsl6886004 7 78021500 MAG 12 3.10E-07 2.79

kgp26026546 13 79972606 7.45E-07 0.03

rsl0251797 7 78025427 MAG 12 7.93E-07 2.67

kgp8110667 22 32716792 9.48E-07 Infinity

kgpll210241 3 38537237 9.74E-07 Infinity

rsl7687961 22 32716927 9.74E-07 Infinity

kgpl2008955 2 73759636 1.08E-06 0.08

kgp24415534 2 1.74E+08 1.08E-06 0.08

kgp5976729 22 32675303 2.12E-06 Infinity

kgp25952891 13 80027089 2.38E-06 0.04

rs543122 3 1.24E+08 2.81E-06 0.54

kgp6236949 2 60301030 3.70E-06 0.54

rs9817308 3 1.24E+08 4.60E-06 0.55

kgpl0372946 10 1.34E+08 4.62E-06 10.53

kgp8817856 6 32744440 4.90E-06 0.53

kgpll328629 10 1.21E+08 5.02E-06 2.95

kgp4705854 12 19907696 5.19E-06 0.55

rs4143493 6 51829939 5.24E-06 4.21

kgp3450875 16 57268931 5.60E-06 0.12

kgpl688752 21 43016736 5.79E-06 0.33

kgp9627338 17 90155 6.00E-06 0.45

rsl7577980 6 32359821 6.15E-06 2.36

kgp3418770 10 59425598 6.31E-06 10.31

kgp2299675 20 16933074 6.55E-06 0.19

rs6718758 2 60328802 6.61E-06 0.55

rs7579987 2 60307009 6.71E-06 0.55

kgpl0594414 1 2.16E+08 6.80E-06 0.14

rsl0498793 6 51829707 7.20E-06 4.14

rs2816838 10 52714759 7.32E-06 0.46

kgpl2230354 5 27037978 7.64E-06 0.19

rsl3394010 2 60302746 8.16E-06 0.56

rsll029892 11 27269546 8.23E-06 1.94

kgp2356388 16 19771577 8.28E-06 0.46

rsll691553 2 60303554 8.55E-06 0.56

kgp5564995 6 26414060 9.08E-06 2.88

rs6895094 5 1.41E+08 9.18E-06 0.57

kgpl0352965 7 30647900 9.40E-06 7.44

kgp26116630 6 48158833 9.42E-06 16.38

kgp7059449 2 41255455 9.98E-06 4.93

rsl0203396 2 60305110 1.01E-05 0.56

kgpll843177 11 27316568 1.01E-05 1.95

rs9579566 13 30980265 1.04E-05 0.26

kgplll41512 20 35283733 1.06E-05 0.21 able 35. Regression Analysis, Additive Model Corrected for Log Relapse and EDSS (Gala, Forte, Combined cohorts)

able 36. Regression Analysis, Additive Model Corrected for top SNP rsl686004 (Gala, Forte, and Combined cohorts)

Table 37. Regression Analysis, Additive Model Corrected for rsl686004 + Log Relapse + EDSS (Gala, Forte, Combined cohorts )

Example 15 Selection of Genetic Markers Predictive of Response to Glatiramer Acetate.

Based on the analyses above, genetic markers were selected as Predictive of Response to Glatiramer Acetate based on the following p-value thresholds: Priority candidate variants: P<0.05 (combined cohorts); Priority Genes: Replicated P<0.05 in both cohorts; GWAS: P<10-4 (combined cohorts) ; and Placebo P<10-4 (placebo cohort) .

The selected genetic markers are presented in Tables 38-41. Alleles associated with response are highlighted.

.17-

SUBSTITUTE SHEET (RLILE 26)

. 3

-138-

^■ 141-

-144-

Example 16 Selection of Genetic Markers for Predictive Models

A total of 11 genetic variants were selected for inclusion in a preliminary multi-marker risk prediction model. Importantly, many of the identified genes have been previously implicated in MS and/or glatiramer acetate response (i.e., MAGI2, HLA-DOB/TAP2 region, MBP, AL0X5AP, and the HLA-DRB1-15 : 01 polymorphism).

Variants were identified and selected using a multi-step approach, beginning with the selection of replicated variants from a priority list of 35 candidate variants. This led to one variant selected for inclusion into the model: rs3135391, a marker of HLA-DRBl*1501 , P<0.05 in Gala, P<0.05 in Forte, P = 0.014 combined, odds ratio 1.6) .

This was followed by selection of three replicated variants from a list of 4,012 variants in 30 priority genes (kgp8817856 in HLA- DQB2/D0B, p<0.001 in Gala, p<0.001 in Forte, p-value 5.33E-06, odds ratio 0.53; rsl894408 in HLA-D0B/TAP2 , p<0.01 in Gala, p<0.01 in Forte, p-value 0.000098, odds ratio 1.7; and kgp7747883 in MBP, p<0.05 in Gala, p<0.01 in Forte, p-value 0.00086, odds ratio 0.64).

This was followed by a selection of two variants from a list of 25,000 candidate variants in 180 second priority genes (kgp6599438 in PTPRT, p<0.01 in Gala, p<0.05 in Forte, p-value 0.00025, odds ratio 0.26; and rsl0162089 in AL0X5AP, p<0.01 in Gala, p<0.05 in Forte, p-value 0.0014, odds ratio 1.5).

Finally, three variants were selected from the entire genome-wide panel (rsl6886004 in MAGI2, p<0.005 in Gala, p<0.00005 in Forte, p- value 0.00000098combined, odds ratio 2.8; kgp24415534 in the ZA /CDCA7 gene region, p<0.00005 in Gala, p<0.05 in Forte, p-value 0.000000398, odds ratio 0.08; and kgp8110667 in the RFPL3 /SLC5A4 region, p<0.01 in Gala, p<0.05 in Forte, p-value 0.00014, odds ratio: infinity).

In addition, two variants were selected from the entire genome-wide panel using an extreme phenotype definition (kgp6214351 in the UVRAG gene, combined p-value 0.0000055, odds ratio 0.35; and rs759458 in SLC1A4, combined p-value 0.002; odds ratio 1.6). The statistics of the selected 11 SNPs are shown for the additive, allelic, and genotypic genetic models. The statistics of the selected 11 SNPs are shown for the additive, allelic, and genotypic genetic models (Tables 42, 43 and 44a and 44b, respectively) .

Table 42. Additive Model Characteristics of Individual SNPs in Model

Table 43. Allelic Model Characteristics of Individual SNPs in Model

Table 44a. Genotypic Model Characteristics of Individual SNPs in Model

Table 44b. Genotypic Model Characteristics of Individual SNPs in Model

Example 17 Preliminary Predictive Model: Clinical and Genetic Factors Combined

A predictive model was generated based on the 11 SNPs shown in tables 42, 43, 44a and 44b and the two Clinical co-variants shown in table 33.

Receiver Operating Characteristic (ROC) analysis was performed using the actual value (case or control) and predicted value for each sample from the multi-marker regression model (Figure 1) . For these preliminary analyses, two risk groups were defined using the predicted values from the multi-marker regression model. The predictive threshold value was set at 0.71 (termed "model 3") based on a variety of factors after consultation with the Teva team and Teva MS clinical experts.

Ultimately, a threshold that best differentiated between responders and non-responders (minimum positive predictive value of 90% or higher) (Figure 2), while maximizing the number of predicted responders (predicted responders >60%) (Figure 3) was selected. This threshold also coincided with the lowest p-value of all the thresholds examined (Chi square p-value 6.1 x 10 ^"46 , odds ratio 19.9) (Figure 4) . The positive predictive value (% of all predicted responders to be true responders) was 91.1%, sensitivity (% of all true responders detected) was 80.2%; specificity (% of all true non- responders classified as non-responders) was 83.1%; and the negative predictive value (% of all true non-responders classified as non- responders) was 65.9%.

Example 18 Patient Responses Predicted by the Preliminary Predictive Model

For the genotyped patients of the Gala and Forte cohorts, based on the predictive model, 60% of patients were classified as "predicted responders" with a response rate of 91.1% (as defined by the a priori definition of responders and non-responders) . While 40% of patients were classified as "predicted non-responders" with an overall response rate of 34% (Figure 5) .

Compared to the "predicted non-responders", the "predicted responders" exhibited a 2.7-fold improved response rate (91% vs. 34%) (P < lO ^"40 ); and the "predicted responders" had a 34% improvement in response rate compared to the overall cohort (68% vs. 91%) .

The annualized relapse rate (ARR) of the "predicted responders" (0.21 ± 0.03 standard error of the mean) was reduced (improved) by 60% compared to the overall patient cohort (0.53 ± 0.04), and reduced (improved) by 80% compared to the "predicted non-responders" (1.04 ± 0.08) (p-value 2.2 x 10 ^"25 ) .

The number of confirmed relapses (nrelapse) of the "predicted responders" (0.19 ± 0.03 standard error of the mean) was reduced (improved) by 58% compared to the overall patient cohort (0.46 ± 0.03), and reduced (improved) by 78% compared to the "predicted non- responders" (0.88 ± 0.06) (p-value 7.70 x 10 ^"32 ) .

The number of Tl enhancing lesions at month 12 was significantly reduced (improved) by 47% in the "predicted responders" compared to the "predicted non-responders" (0.91 ± 0.18 versus 1.70 ± 0.38; p- value 0.043). Similarly, EDSS progression was significantly delayed (improved) by 72% in the "predicted responders" versus the "predicted non-responders" (0.03 ± 0.01 vs. 0.10 ± 0.02; p-value 0.00095), and showed a strong trend with a 49% reduced progression compared to the overall cohort (value 0.057, p-value 0.08).

Predictive Modeling

A predictive model based on the identified markers was developed and tested in the full cohorts, including intermediate responders. Additional independent cohorts are used to evaluate and confirm the predictive model.

DNA was collected from consenting RRMS patients in one year GALA study (40mg Copaxone TIW, or placebo) and one year FORTE study (20mg Copaxone or 40mg Copaxone daily) ("PGx population") (Table 45) The PGx (i.e. the population studied for genetic analyses) and ITT (intent to treat) populations did not differ on baseline characteristics .

To identify genetic markers associated with high response to Copaxone® comprising the following characteristics: (1) high response as measured by ARR reductions, (2) predictive, not prognostic, markers: associated with response only in Copaxone®- treated patients, and not in the placebo group, (3) markers that are confirmed in an independent cohort, and (4) a subset of GALA and FORTE studies' patients with clarly defined response phenotypes (high responders versus low responders) (Figure 6) Patient DNA samples were genotyped for 4.3 million genetic variants (Illumina HumanOmni5 array) .

Association analysis, using a tiered candidate-marker and genome- wide approach, was conducted in the GALA cohort to identify GA- specific response-associated SNPs . SNPs that were not associated with placebo response and that replicated in the FORTE cohort, were selected for modeling.

Regression analysis was applied, with the threshold for distinguishing responders from non-responders was selected by analysis of receiver-operator curves. Intermediate responders were genotyped by either Illumina 5M array or focused taqman-based SNP genotyping and Sanger sequencing.

The SNP-signature was evaluated in the full GALA/FORTE population including intermediate patients (Figure 7) . In the high reponse/low response subgroups of both GALA and FORTE, the SNP signature exhibited highly predictive characteristics (OR 6 to 8, p-value<10 ^{" 11} ) (Table 46) . Validation of the identified model can be applied to additional independent cohorts .

P and at least one probe specific for a location corresponding to onse since 129 placebo-treated patients were predicted to be high Copaxone®-responders based on the signature. These patients fo not show ARR reduction when treated with placebo (3% ARR reduction versus remaining placebo patients who provided DNA samples (n=252)) The SNP signature was significantly associated with high response to Copaxone in both GALA and FORTE (OR of 1.9 to 3.8, p<0.002 including sensitivity analysis) and not in placebo (OR of 0.9 to 1.2, NS) .

Genetic association with response to Copaxone®, and not placebo, was identified. In Copaxone® naive RRMS patients, the 11 SNP signature identifies high Copaxone® responders who exhibit significantly greater reductions in ARR compared to the average response observed in Copaxone® clinical trials.

Table 45. Baseline characteristics of PGx and ITT populations

Table 46. Genes of the 11 SNP Signature

Example 19

Additional genotyping of the 11 SNPs of the predictive model (rs3135391, rsl894408, kgp7747883, kpg6599438, rsl0162089, rsl6886004, kgp8110667, kgp8817856, kgp24415534, kgp6214351, rs759458) was conducted on the remaining portion of the patients from the GALA and FORTE cohorts, for which DNA was available (Figure 8) .

When analysis was conducted for all genotyped patients of the Gala and FORTE cohorts, based on the predictive model (11 SNPs and 2 clinical variables) , 34% of GALA, and 42% of FORTE- patients were classified as "predicted responders".

In the GALA Copaxone treated patients, the annualized relapse rate (ARR) of the "predicted responders" (0.18510.032 standard error of the mean) was reduced (improved) by 51% compared to the "predicted non-responders" (0.37410.038) (p-value=0.0028) and by 64% compared to the placebo (0.51010.062) (p-value<0.0001 ) . In the FORTE Copaxone treated patients, the annualized relapse rate (ARR) of the "predicted responders" (0.102+0.020 standard error of the mean) was reduced (improved) by 72% compared to the "predicted non-responders" ( 0.368±0.039) (p-value<0.0001 ) . In some embodiments, the at least one single nucleotide polymorphisms (SNPs) are selected from the group consisting of kgp24415534, kgp6214351, kgp6599438, kgp7747883, kgp8110667, kgp8817856, rsl0162089, rsl6886004, rsl894408, rs3135391, and rs759458. In some embodiments, the at least one single nucleotide polymorphisms (SNPs) comprise 2, 3, 4, 5, 6, 7, 8, 9 or 10 of the SNPs selected from the group consisting of kgp24415534, kgp6214351, kgp6599438, kgp7747883, kgp8110667, kgp8817856, rsl0162089, rsl6886004, rsl894408, rs3135391 and rs759458. In some embodiments, the at least one single nucleotide polymorphisms (SNPs) are selected from the group consisting of kgp24415534, kgp6214351, kgp6599438, kgp7747883, kgp8110667, kgp8817856, rsl0162089, rsl6886004, rsl894408, and rs759458.

In some embodiments, the at least one single nucleotide polymorphisms (SNPs) comprise 2, 3, 4, 5, 6, 7, 8, 9 or 10 of the SNPs selected from the group consisting of kgp24415534, kgp6214351, kgp6599438, kgp7747883, kgp8110667, kgp8817856, rsl0162089, rsl6886004, rsl894408 and rs759458.

In some embodiments, the at least one SNPs is selected from the group further comprising- rs3135391.

In some embodiments, if rs3135391 is the at least one SNP selected, then selecting at least one SNP other than rs3135391.

In some embodiments, the genotype of the subject at the location corresponding to the location of one or more of the SNPs is determined by indirect genotyping. In some embodiments, the genotype of the subject at the location corresponding to the location of one or more of the SNPs is determined indirectly by determining the genotype of the subject at a location corresponding to the location of at least one SNP that is in linkage disequilibrium with the one or more SNPs.

In some embodiments, the genotype is determined at locations corresponding to the locations of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or more SNPs. In some embodiments, one or more SNPs is selected from the group consisting of kgp24415534, kgp6214351, kgp6599438, kgp7747883, kgp8110667, kgp8817856, rsl0162089, rsl6886004, rsl894408, rs3135391, and rs759458.

In some embodiments, one or more SNPs comprise 2, 3, 4, 5, 6, 7, 8, 9 or 10 of the SNPs selected from the group consisting of kgp24415534, kgp6214351, kgp6599438, kgp7747883, kgp8110667, kgp8817856, rsl0162089, rsl6886004, rsl894408, rs3135391, and rs759458.

In some embodiments, one or more SNPs is selected from the group consisting of kgp24415534, kgp6214351, kgp6599438, kgp7747883, kgp8110667, kgp8817856, rsl0162089, rsl6886004, rsl894408, and rs759458.

In some embodiments, one or more SNPs comprise 2, 3, 4, 5, 6, 7, 8, 9 or 10 of the SNPs selected from the group consisting of kgp24415534, kgp6214351, kgp6599438, kgp7747883, kgp8110667, kgp8817856, rsl0162089, rsl6886004, rsl894408, and rs759458.

In some embodiments, the one or more SNPs is selected from the group further comprising rs3135391.

In some embodiments, if rs3135391 is the one SNP selected, then selecting at least one SNP other than rs3135391. In some embodiments, the method further comprising applying the algorithm depicted in Figure 8 or Figure 9 to identify the subject as a predicted responder or as a predicted non-responder to glatiramer acetate. In some embodiments the kit for identifying a human subject afflicted with multiple sclerosis or a single clinical attack consistent with multiple sclerosis as a predicted responder or as a predicted non-responder to glatiramer acetate, or for identifying a human subject afflicted with multiple sclerosis or a single clinical attack consistent with multiple sclerosis who is predicted to have a slower course of disease progression, the kit comprising a) at least one probe specific for a location corresponding to the location of at least one SNP; b) at least one pair of PCR primers designed to amplify a DNA segment which includes a location corresponding to the location of at least one SNP; c) at least one pair of PCR primers designed to amplify a DNA segment which includes a location corresponding to the location of at least one SNP and at least one probe specific for a location corresponding to the location of at least one

SNP; d) a reagent for performing a method selected from the group consisting of restriction fragment length polymorphism (RFLP) analysis, sequencing, single strand conformation polymorphism analysis (SSCP) , chemical cleavage of mismatch (CCM) , gene chip and denaturing high performance liquid chromatography (DHPLC) for determining the identity of at least one SNP; or e) reagents for TaqMan Open Array assay designed for determining the genotype at a location corresponding to the location of at least one SNP, wherein the at least one SNP is selected from the group consisting of kgp24415534 , kgp6214351, kgp6599438, kgp7747883, kgp8110667, kgp8817856, rsl0162089, rsl6886004, rsl894408, rs3135391, and rs759458; or wherein the at least one SNP is selected from the group consisting of kgp24415534, kgp6214351, kgp6599438, kgp7747883, kgp8110667, kgp8817856, rsl0162089, rsl6886004, rsl894408, and rs759458. In some embodiments, the kit further comprising applying the algorithm depicted in Figure 8 or Figure 9 to identify the subject as a predicted responder or as a predicted non-responder to glatiramer acetate.

Example 20

Analysis was conducted for all genotyped patients of the Gala and FORTE cohorts, based on the 11 SNPs in the predictive model, but without including the clinical variables, and using a threshold at ~30% of the population classified as "predicted responders" (Figure 9) .

In the GALA Copaxone treated patients, the annualized relapse rate (ARR) of the "predicted responders" (0.131±0.026 standard error of the mean) was reduced (improved) by 62% compared to the "predicted non-responders" ( 0.382±0.037 ) (p-value<0.0001 ) and by 71% compared to the placebo ( 0.488±0.058 ) (p-value<0.0001 ) .

In the FORTE Copaxone treated patients, the annualized relapse rate (ARR) of the "predicted responders" (0.145+0.029 standard error of the mean) was reduced (improved) by 50% compared to the "predicted non-responders" (0.29010.03) (p-value=0.0113 ) .

In some embodiments, the method further comprising applying the algorithm depicted in Figure 9 to identify the subject as a predicted responder or as a predicted non-responder to glatiramer acetate . In some embodiments, the method further comprising applying the algorithm depicted in Figure 8 or Figure 9 to identify the subject as a predicted responder or as a predicted non-responder to glatiramer acetate. In some embodiments, the kit further comprising applying the algorithm depicted in Figure 8 or Figure 9 to identify the subject as a predicted responder or as a predicted non-responder to glatiramer acetate.

Example 21

Additional genotyping of 10 SNPs of the predictive model (rs3135391, rsl894408, kpg6599438, rsl0162089, rsl6886004, kgp8110667, kgp8817856, kgp24415534, kgp6214351, rs759458) was conducted on the remaining portion of the patients from the GALA and FORTE cohorts, for which DNA was available.

When analysis was conducted for all genotyped patients of the Gala and FORTE cohorts, based on the 10 SNPs and 2 clinical variables, 34% of GALA, and 42% of FORTE- patients were classified as "predicted responders".

In the GALA Copaxone treated patients, the annualized relapse rate (ARR) of the "predicted responders" (0.185+0.032 standard error of the mean) was reduced (improved) by 51% compared to the "predicted non-responders" ( 0.374±0.038 ) (p-value=0.0028 ) and by 64% compared to the placebo ( 0.510±0.062 ) (p-value<0.0001 ) .

In the FORTE Copaxone treated patients, the annualized relapse rate (ARR) of the "predicted responders" (0.102±0.020 standard error of the mean) was reduced (improved) by 72% compared to the "predicted non-responders" ( 0.368±0.039) (p-value<0.0001) . In some embodiments, the genotype is determined at locations corresponding to the locations of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or more SNPs .

In some embodiments, one or more SNPs is selected from the group consisting of kgp24415534, kgp6214351, kgp6599438, kgp8110667, kgp8817856, rsl0162089, rsl6886004, rsl894408, rs3135391, and rs759458.

In some embodiments, one or more SNPs is selected from the group consisting of kgp24415534, kgp6214351, kgp6599438, kgp8110667, kgp8817856, rsl0162089, rsl6886004, rsl894408, and rs759458.

In some embodiments, one or more SNPs is selected from the group further comprising rs3135391.

In some embodiments, one or more SNPs comprise 2, 3, 4, 5, 6, 7, 8, 9 or 10 of the SNPs selected from the group consisting of kgp24415534, kgp6214351, kgp6599438, kgp8110667, kgp8817856, rsl0162089, rsl6886004, rsl894408, rs3135391, and rs759458.

In some embodiments, if rs3135391 is the one SNP selected, then selecting at least one SNP other than rs3135391.

In some embodiments, the at least one single nucleotide polymorphisms (SNPs) comprise 2, 3, 4, 5, 6, 7, 8, 9 or 10 of the SNPs selected from the group consisting of kgp24415534, kgp6214351, kgp6599438, kgp8110667, kgp8817856, rsl0162089, rsl6886004, rsl894408, rs3135391 and rs759458.

In some embodiments, if rs3135391 is the at least one SNP selected, then selecting at least one SNP other than rs3135391.

In some embodiments, the at least one SNP is selected from the group further comprising rs3135391. In some embodiments, the genotype of the subject at the location corresponding to the location of one or more of the SNPs is determined by indirect genotyping.

In some embodiments, the genotype of the subject at the location corresponding to the location of one or more of the SNPs is determined indirectly by determining the genotype of the subject at a location corresponding to the location of at least one SNP that is in linkage disequilibrium with the one or more SNPs.

In some embodiments the kit for identifying a human subject afflicted with multiple sclerosis or a single clinical attack consistent with multiple sclerosis as a predicted responder or as a predicted non-responder to glatiramer acetate, or for identifying a human subject afflicted with multiple sclerosis or a single clinical attack consistent with multiple sclerosis who is predicted to have a slower course of disease progression, the kit comprising a) at least one probe specific for a location corresponding to the location of at least one SNP; b) at least one pair of PCR primers designed to amplify a DNA segment which includes a location corresponding to the location of at least one SNP; c) at least one pair of PCR primers designed to amplify a DNA segment which includes a location corresponding to the location of at least one SNP and at least one probe specific for a location corresponding to the location of at least one SNP; d) a reagent for performing a method selected from the group consisting of restriction fragment length polymorphism (RFLP) analysis, sequencing, single strand conformation polymorphism analysis (SSCP) , chemical cleavage of mismatch (CCM) , gene chip and denaturing high performance liquid chromatography

(DHPLC) for determining the identity of at least one SNP; or e) reagents for TaqMan Open Array assay designed for determining the genotype at a location corresponding to the location of at least one SNP, wherein the at least one SNP is selected from the group consisting of kgp24415534, kgp6214351, kgp6599438, kgp8110667, kgp8817856, rsl0162089, rsl6886004, rsl894408, rs3135391, and rs759458; or wherein the at least one SNP is selected from the group consisting of kgp24415534, kgp6214351, kgp6599438, kgp8110667, kgp8817856, rsl0162089, rsl6886004, rsl894408, and rs759458.

In some embodiments, the method further comprising applying the algorithm depicted in Figure 8 or Figure 9 to identify the subject as a predicted responder or as a predicted non-responder to glatiramer acetate.

In some embodiments, the kit further comprising applying the algorithm depicted in Figure 8 or Figure 9 to identify the subject as a predicted responder or as a predicted non-responder to glatiramer acetate.

Biology of High Response to Copaxone®

Identified genes are associated with Copaxone® (glatiramer acetate, or GA) mechanism of action. These genes include: (1) Myelin Basic Protein (MBP) , which is associated with Copaxone® response (38) , and Copaxone® designed to mimic MBP; (2) MHC region (3 SNPs) , including HLA-DRB1*15 : 01 (37) involved in antigen processing and presentation and is associated with Copaxone® response and MS susceptibility or severity; and (3) arachidonate 5-lipoxygenase-activating protein, involved in synthesis of leukotrienes (inflammation) and associated with Copaxone® response (40) . Identified genes are also associated with MS severity and/or the brain. These genes include: (1) Membrane-associated guanylate kinase, a synaptic junction scaffold molecule exclusively expressed in brain and shown to modulate MS severity; (2) Glutamate/neutral amino acid transporter, which transports glutamate and alanine (2 of the 4 amino acid components of Copaxone®) , as well as serine, cysteine, and threonine and has highest expression in brain; (3) Radiation resistance-associated gene protein, which is highly expressed in brain and has a role in axis formation and autophagy; and (4) Receptor-tyrosine protein phosphatase, associated with Copaxone® response, and tyrosine phosphorylation involved in myelin formation, differentiation of oligodendrocytes and Schwann cells, and recovery from demyelinating lesions.

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