US20180002753A1 - Genetic markers predictive of response to glatiramer acetate - Google Patents

Genetic markers predictive of response to glatiramer acetate Download PDF

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US20180002753A1
US20180002753A1 US15/410,091 US201715410091A US2018002753A1 US 20180002753 A1 US20180002753 A1 US 20180002753A1 US 201715410091 A US201715410091 A US 201715410091A US 2018002753 A1 US2018002753 A1 US 2018002753A1
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location
subject
genotype
alleles
glatiramer acetate
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Amir Tchelet
Michael Hayden
Liat Hayardeny
Colin James Douglas ROSS
Iris Grossman
David Ladkani
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Teva Pharmaceutical Industries Ltd
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Teva Pharmaceutical Industries Ltd
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K38/00—Medicinal preparations containing peptides
    • A61K38/02—Peptides of undefined number of amino acids; Derivatives thereof
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K38/00—Medicinal preparations containing peptides
    • A61K38/16—Peptides having more than 20 amino acids; Gastrins; Somatostatins; Melanotropins; Derivatives thereof
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P25/00—Drugs for disorders of the nervous system
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/106—Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/156—Polymorphic or mutational markers

Definitions

  • MS Multiple sclerosis
  • CNS central nervous system
  • RRMS relapsing-remitting
  • RRMS progressive course leading to neurologic deterioration and disability.
  • 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.
  • SP secondary progressive
  • SP secondary progressive
  • PP primary progressive
  • 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)
  • 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.
  • 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.
  • IFNB cytokine interferon beta
  • Other approved drugs for the treatment of MS include Mitoxantrone and Natalizumab.
  • Glatiramer acetate 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.
  • 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.
  • 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.
  • MRI magnetic resonance imaging
  • 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.
  • 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.
  • Th2 T helper 2
  • the ability of GA-specific infiltrating cells to express anti-inflammatory cytokines such as IL-10 and transforming growth factor-beta (TGF- ⁇ ) 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 post-marketing experience.
  • the clinical program includes three double-blind, placebo-controlled studies in RRMS subjects treated with GA 20 mg/day.(18,19,20)
  • 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. Pat. 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).
  • RRMS relapsing-remitting multiple sclerosis
  • U.S. Patent Application Publication No. US 2011-0046065 A1 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 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 E15; fda.gov/downloads/RegulatoryInformation/Guidances/ucm129296.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.
  • 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.
  • 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.
  • 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.
  • 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:
  • 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
  • SNPs single nucleotide polymorphisms
  • 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.
  • RFLP restriction fragment length polymorphism
  • SSCP single strand conformation polymorphism analysis
  • CCM chemical cleavage of mismatch
  • DPLC denaturing high performance liquid chromatography
  • 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
  • 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 kgp10090631, kgp1009249, kgp10148554, kgp10152733, kgp10215554, kgp10224254, kgp10305127, kgp10351364, kgp10372946, kgp10404633, kgp10412303, kgp10523170, kgp1054273, kgp10558725, kgp10564659, kgp10591989, kgp10594414, kgp10619195, kgp10620244, kgp10632945, kgp10633631, kgp10679353, kgp10762962, kgp10788130, kgp10826273, kgp10836214, kgp10910719, kgp10922969, kgp10948564, kgp10967046, kgp10974833, kgp1098237, kgp10989246, kgp11002881, kgp110106
  • 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:
  • 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
  • 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:
  • 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
  • FIG. 1 shows Receiver Operating Characteristics for optimization of test threshold.
  • FIG. 2 shows Response Rate of Predicted Responders (green line) and Response Rate of Predicted Non-Responders (red line) by predictive test threshold.
  • FIG. 3 shows overall percent of Predicted Responders by predictive test threshold.
  • FIG. 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.
  • FIG. 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).
  • FIG. 6 shows GALA and FORTE patients were stratified by clearly defined response.
  • High Response improved ARR (ARR change ⁇ ( ⁇ 1), during study versus prior 2 years).
  • Low Response no change or worsening of ARR (ARR change ⁇ 0, during study versus previous 2 years).
  • FIG. 7 shows predictive model building for GALA and FORTE cohorts.
  • FIG. 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).
  • FIG. 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”.
  • 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:
  • 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: rs10988087, rs1573706, rs17575455, 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 rs10988087, one or more C alleles at the location of rs17575455, or one or more G alleles at the location of rs1573706, rs2487896, rs3135391, rs6097801 or rs947603.
  • SNPs single nucleotide polymorphisms
  • 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.
  • the pharmaceutical composition is a unit dose of a 1 ml aqueous solution comprising 40 mg of glatiramer acetate.
  • the pharmaceutical composition is a unit dose of a 1 ml aqueous solution comprising 20 mg of glatiramer acetate.
  • the pharmaceutical composition is a unit dose of a 0.5 ml aqueous solution comprising 20 mg of glatiramer acetate.
  • the pharmaceutical composition comprising glatiramer acetate and a pharmaceutically acceptable carrier is administered as a monotherapy.
  • 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
  • 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: rs10988087, rs1573706, rs17575455, 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 rs10988087, one or more C alleles at the location of rs17575455, or one or more G alleles at the location of rs1573706, 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 rs10988087, no C all
  • 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.
  • RFLP restriction fragment length polymorphism
  • sequencing single strand conformation polymorphism analysis
  • CCM chemical cleavage of mismatch
  • DPLC denaturing high performance liquid chromatography
  • PCR Polymerase Chain Reaction
  • the genotype is determined using at least one pair of PCR primers and at least one probe.
  • the array is selected from the group consisting of a gene chip, and a TaqMan Open Array.
  • 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.
  • the array is a TaqMan Open Array.
  • the gene chip is a whole genome genotyping array.
  • the array comprises a plurality of probes suitable for determining the identity of the one or more SNPs.
  • the array is a gene chip.
  • the gene chip is a whole genome genotyping array.
  • the human subject is a na ⁇ ve patient.
  • the human subject has been previously administered glatiramer acetate.
  • the human subject has been previously administered a multiple sclerosis drug other than glatiramer acetate.
  • 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).
  • SNPs single nucleotide polymorphisms
  • the one or more SNPs is selected from the group consisting of kgp24415534, kgp6214351, kgp6599438, kgp7747883, kgp8110667, kgp8817856, rs10162089, rs16886004, rs1894408 and rs759458 (hereinafter Group 10).
  • the one or more SNPs is selected from the group consisting of kgp24415534, kgp6214351, kgp6599438, kgp7747883, kgp8110667, kgp8817856, rs10162089, rs16886004, rs1894408, rs3135391, and rs759458.
  • the one or more SNPs is selected from the group consisting of kgp24415534, kgp6214351, kgp6599438, kgp8110667, kgp8817856, rs10162089, rs16886004, rs1894408, rs3135391, and rs759458.
  • rs3135391 is the at least one SNP selected, then selecting at least one SNP other than rs3135391.
  • 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.
  • the one or more SNPs further comprise rs3135391.
  • 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, rs10162089, rs16886004, rs1894408, rs3135391 and rs759458.
  • the one or more single nucleotide polymorphisms 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, rs10162089, rs16886004, rs1894408, rs3135391 and rs759458.
  • 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 genotype of the subject at the location corresponding to the location of the one or more SNPs is determined by indirect genotyping.
  • 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%.
  • 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%.
  • 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%.
  • the methods further comprise the step of determining the log number of relapses in the last two years for the human subject.
  • the methods further comprise the step of determining the baseline Expanded Disability Status Scale (EDSS) score for the human subject.
  • EDSS Expanded Disability Status Scale
  • 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
  • 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.
  • RFLP restriction fragment length polymorphism
  • SSCP single strand conformation polymorphism analysis
  • CCM chemical cleavage of mismatch
  • DPLC denaturing high performance liquid chromatography
  • the gene chip is a whole genome genotyping array.
  • 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 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.
  • the one or more single nucleotide polymorphisms are selected from the group consisting of Group 10.
  • the one or more single nucleotide polymorphisms comprise 2, 3, 4, 5, 6, 7, 8, 9 or 10 of the SNPs selected from the group consisting of Group 10.
  • 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
  • the at least one single nucleotide polymorphisms are selected from the group consisting of Group 10,
  • 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.
  • the at least one single nucleotide polymorphisms comprise 2, 3, 4, 5, 6, 7, 8, 9 or 10 of the SNPs selected from the group consisting of Group 10.
  • the at least one single nucleotide polymorphisms further comprise rs3135391.
  • the genotype of the subject at the location corresponding to the location of one or more of the SNPs is determined by indirect genotyping
  • 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.
  • 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%.
  • 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%.
  • 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, rs10162089, rs16886004, rs1894408, rs3135391, and rs759458.
  • 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:
  • 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
  • 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, rs10162089, rs16886004, rs1894408, 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:
  • 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 genetic marker refers to a DNA sequence that has a known location on a chromosome.
  • 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.
  • a SNP or “single nucleotide polymorphism” refers to a specific site in the genome where there is a difference in DNA base between individuals.
  • the SNP is located in a coding region of a gene.
  • the SNP is located in a noncoding region of a gene.
  • the SNP is located in an intergenic region.
  • 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.
  • 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.
  • SNPs in linkage disequilibrium with the SNPs associated with the invention are useful for obtaining similar results.
  • 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.
  • 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 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.
  • 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.
  • RFLP restriction fragment length polymorphism
  • arrays including but not limited to planar microarrays or bead arrays, sequencing, single strand conformation polymorphism analysis (SSCP), chemical cleavage of mismatch (CCM), Polymerase chain reaction (PCR) and denaturing high performance liquid chromatography (DHPLC).
  • RFLP restriction fragment length polymorphism
  • SSCP single strand conformation polymorphism analysis
  • CCM chemical cleavage of mismatch
  • PCR Polymerase chain reaction
  • DPLC denaturing high performance liquid chromatography
  • the genotyping array is a whole genome genotyping array.
  • 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”).
  • Whole-genome genotyping arrays contain 500,000 probes or more.
  • Whole-genome genotyping arrays contain 1 million probes or more.
  • Whole-genome genotyping arrays contain 5 million probes or more.
  • a SNP is detected through PCR amplification and sequencing of the DNA region comprising the SNP.
  • 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.
  • 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.
  • 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.
  • the probes are connected to beads instead of the solid support. Such arrays are called “bead arrays” or “bead CHIPs”.
  • 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.
  • the fragment has been amplified, e.g. by using the polymerase chain reaction (PCR), and labeled e.g. with a fluorescent molecule.
  • PCR polymerase chain reaction
  • 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.
  • the amplification reaction and/or extension reaction is carried out on the microarray or bead itself.
  • 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.
  • oligonucleotide 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.
  • this method is combined with sequencing to identify the mutation.
  • amplification or extension is carried out on the microarray or bead itself, three methods are presented by way of example: In the Minisequencing 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.
  • the starting material can be either an RNA sample or a DNA product amplified by PCR.
  • 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).
  • SNP genotypes are generated from fluorescent intensities using the manufacturer's default cluster settings.
  • 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 T1 enhancing lesions and/or T2 enhancing lesions; exemplified by baseline volume of T2 lesion, number of Gd-T1 lesions at baseline.
  • 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.
  • kits and instructions for their use are kits for identifying one or more SNPs within a patient sample.
  • a kit may contain primers for amplifying a specific genetic locus.
  • 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.
  • RFLP restriction fragment length polymorphism
  • arrays including but not limited to planar microarrays or bead arrays
  • sequencing single strand conformation polymorphism analysis
  • CCM chemical cleavage of mismatch
  • DPLC denaturing high performance liquid chromatography
  • 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
  • MS MS
  • 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.
  • RRMS 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 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 co-ordination, 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 MS includes:
  • relapsing forms of multiple sclerosis include:
  • RRMS Relapsing-remitting multiple sclerosis
  • SPMS Secondary Progressive MS
  • PPM Primary progressive-relapsing multiple sclerosis
  • PRMS progressive-relapsing multiple sclerosis
  • EDSS Kurtzke Expanded Disability Status Scale
  • the Kurtzke Expanded Disability Status Scale 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).
  • 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:
  • 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.
  • 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, anti-inflammatory 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.
  • a “na ⁇ ve patient” is a subject that has not been treated with any multiple sclerosis drugs as defined in the former paragraph.
  • glatiramer acetate may be oral, nasal, pulmonary, parenteral, intravenous, intra-articular, transdermal, intradermal, subcutaneous, topical, intramuscular, rectal, intrathecal, intraocular, buccal or by gavage.
  • 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.
  • 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.
  • 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, 108, 109 and 110 mg/kg. Accordingly, about 100 mg/kg includes, in an embodiment, 100 mg/kg.
  • 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.
  • 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.
  • DNA samples from categorized patients were subject to quality control analysis followed by genotyping with the Illumina OMNI-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.
  • SNP cluster definitions i.e., the specific parameters used to determine specific genotypes of each SNP
  • 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
  • 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.
  • SNPs with low call rates 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).
  • samples with call rates less than 94% i.e., samples for which less than 94% of the genotyped SNPs produced genotype calls.
  • 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.
  • 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-T1 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.
  • Allelic Model (chi-square, chi-square ⁇ 10 Log P, fisher exact, fisher exact ⁇ 10 Log P, values for fisher and chi-square with Bonferoni correction, Odds Ratios and Confidence Bounds, Regression P-value, Regression ⁇ log 10 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.
  • Genotypic Model (chi-square, chi-square ⁇ 10 Log P, fisher exact, fisher exact ⁇ 10 Log P, values for fisher and chi-square with Bonferoni correction, Odds Ratios and Confidence Bounds, Regression P-value, Regression ⁇ log 10 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).
  • results were calculated to identify genetic associations using an additive genetic model.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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).
  • 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.
  • 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.
  • 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 .
  • the third analysis was limited to a selected set of genetic variants in 180 priority candidate genes (25,461 variants).
  • 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 .
  • 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.
  • 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 .
  • 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 .
  • 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.
  • 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 .
  • 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%.
  • Candidate Variants Selected a priori for Additive, Allelic and Genotypic models are presented in tables 17-19, respectively.
  • 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.
  • 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.
  • 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.
  • 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%.
  • 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.
  • 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.
  • 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.
  • 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%.
  • 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 .
  • 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.
  • 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 .
  • 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 .
  • 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 .
  • 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 .
  • PCA Principal Components 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.
  • 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 3.6 ⁇ 10 ⁇ 32 , odds ratio 14.5 (95% CI 8.6-24.4)) and “Baseline Expanded Disability Status Scale (EDSS) Score” (combined cohorts p-value 5.9 ⁇ 10 ⁇ 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.
  • EDSS Baseline Expanded Disability Status Scale
  • all of the genetic markers presented in Tables 34-37 are identified as predictive of response to glatiramer acetate.
  • the selected genetic markers are presented in Tables 38-41. Alleles associated with response are highlighted.
  • 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).
  • 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 ( FIG. 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.
  • a threshold that best differentiated between responders and non-responders (minimum positive predictive value of 90% or higher) ( FIG. 2 ), while maximizing the number of predicted responders (predicted responders >60%) ( FIG. 3 ) was selected.
  • This threshold also coincided with the lowest p-value of all the thresholds examined (Chi square p-value 6.1 ⁇ 10 ⁇ 46 , odds ratio 19.9) ( FIG. 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%.
  • the “predicted responders” Compared to the “predicted non-responders”, the “predicted responders” exhibited a 2.7-fold improved response rate (91% vs. 34%) (P ⁇ 10 ⁇ 40 ); and the “predicted responders” had a 34% improvement in response rate compared to the overall cohort (68% vs. 91%).
  • 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 ⁇ 10 ⁇ 32 ).
  • the number of T1 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).
  • 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).
  • 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.
  • the SNP-signature was evaluated in the full GALA/FORTE population including intermediate patients ( FIG. 7 ). In the high response/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.
  • 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).
  • the at least one single nucleotide polymorphisms are selected from the group consisting of kgp24415534, kgp6214351, kgp6599438, kgp7747883, kgp8110667, kgp8817856, rs10162089, rs16886004, rs1894408, rs3135391, and rs759458.
  • the at least one single nucleotide polymorphisms 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, rs10162089, rs16886004, rs1894408, rs3135391 and rs759458.
  • the at least one single nucleotide polymorphisms are selected from the group consisting of kgp24415534, kgp6214351, kgp6599438, kgp7747883, kgp8110667, kgp8817856, rs10162089, rs16886004, rs1894408, and rs759458.
  • the at least one single nucleotide polymorphisms 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, rs10162089, rs16886004, rs1894408 and rs759458.
  • the at least one SNPs is selected from the group further comprising rs3135391.
  • rs3135391 is the at least one SNP selected, then selecting at least one SNP other than rs3135391.
  • the genotype of the subject at the location corresponding to the location of one or more of the SNPs is determined by indirect genotyping.
  • 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 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.
  • one or more SNPs is selected from the group consisting of kgp24415534, kgp6214351, kgp6599438, kgp7747883, kgp8110667, kgp8817856, rs10162089, rs16886004, rs1894408, rs3135391, and rs759458.
  • 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, rs10162089, rs16886004, rs1894408, rs3135391, and rs759458.
  • one or more SNPs is selected from the group consisting of kgp24415534, kgp6214351, kgp6599438, kgp7747883, kgp8110667, kgp8817856, rs10162089, rs16886004, rs1894408, and rs759458.
  • 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, rs10162089, rs16886004, rs1894408, and rs759458.
  • the one or more SNPs is selected from the group further comprising rs3135391.
  • rs3135391 is the one SNP selected, then selecting at least one SNP other than rs3135391.
  • the method further comprising applying the algorithm depicted in FIG. 8 or FIG. 9 to identify the subject as a predicted responder or as a predicted non-responder to glatiramer acetate.
  • 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
  • the kit further comprising applying the algorithm depicted in FIG. 8 or FIG. 9 to identify the subject as a predicted responder or as a predicted non-responder to glatiramer acetate.
  • 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).
  • the method further comprising applying the algorithm depicted in FIG. 9 to identify the subject as a predicted responder or as a predicted non-responder to glatiramer acetate.
  • the method further comprising applying the algorithm depicted in FIG. 8 or FIG. 9 to identify the subject as a predicted responder or as a predicted non-responder to glatiramer acetate.
  • the kit further comprising applying the algorithm depicted in FIG. 8 or FIG. 9 to identify the subject as a predicted responder or as a predicted non-responder to glatiramer acetate.
  • 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.
  • one or more SNPs is selected from the group consisting of kgp24415534, kgp6214351, kgp6599438, kgp8110667, kgp8817856, rs10162089, rs16886004, rs1894408, rs3135391, and rs759458.
  • one or more SNPs is selected from the group consisting of kgp24415534, kgp6214351, kgp6599438, kgp8110667, kgp8817856, rs10162089, rs16886004, rs1894408, and rs759458.
  • one or more SNPs is selected from the group further comprising rs3135391.
  • 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, rs10162089, rs16886004, rs1894408, rs3135391, and rs759458.
  • rs3135391 is the one SNP selected, then selecting at least one SNP other than rs3135391.
  • the at least one single nucleotide polymorphisms 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, rs10162089, rs16886004, rs1894408, rs3135391 and rs759458.
  • rs3135391 is the at least one SNP selected, then selecting at least one SNP other than rs3135391.
  • the at least one SNP is selected from the group further comprising rs3135391.
  • the genotype of the subject at the location corresponding to the location of one or more of the SNPs is determined by indirect genotyping.
  • 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 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
  • the method further comprising applying the algorithm depicted in FIG. 8 or FIG. 9 to identify the subject as a predicted responder or as a predicted non-responder to glatiramer acetate.
  • the kit further comprising applying the algorithm depicted in FIG. 8 or FIG. 9 to identify the subject as a predicted responder or as a predicted non-responder to glatiramer acetate.
  • 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).
  • MBP Myelin Basic Protein
  • SNPs MHC region
  • HLA-DRB1*15:01 including HLA-DRB1*15:01 (37) involved in antigen processing and presentation and is associated with Copaxone® response and MS susceptibility or severity
  • arachidonate 5-lipoxygenase-activating protein involved in synthesis of leukotrien
  • 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.
  • Membrane-associated guanylate kinase a synaptic junction scaffold molecule exclusively expressed in brain and shown to modulate MS

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Abstract

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 kgp18432055, 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.

Description

  • This application claims the benefit of U.S. Provisional Application No. 61/893,807, filed Oct. 21, 2013, U.S. Provisional Application No. 62/048,127, filed Sep. 9, 2014, and U.S. Provisional Application No. 62/048,641, filed Sep. 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 post-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. Pat. 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 A1 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 E15; fda.gov/downloads/RegulatoryInformation/Guidances/ucm129296.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 RRMS). 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 DRB1*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: kgp10090631, kgp1009249, kgp10152733, kgp10224254, kgp10305127, kgp10351364, kgp10372946, kgp10404633, kgp10412303, kgp10523170, kgp1054273, kgp10558725, kgp10564659, kgp10591989, kgp10594414, kgp10619195, kgp10620244, kgp10632945, kgp10633631, kgp10679353, kgp10788130, kgp10826273, kgp10910719, kgp10922969, kgp10948564, kgp10967046, kgp10974833, kgp1098237, kgp11002881, kgp11010680, kgp11077373, kgp11141512, kgp11206453, kgp11210903, kgp1124492, kgp11281589, kgp11285862, kgp11328629, kgp11356379, kgp11407560, kgp11453406, kgp11467007, kgp11514107, kgp11543962, kgp11580695, kgp11627530, kgp11633966, kgp11686146, kgp11702474, kgp11711524, kgp11768533, kgp11804835, kgp11843177, kgp12008955, kgp12083934, kgp12182745, kgp12230354, kgp1224440, kgp12371757, kgp124162, kgp12426624, kgp12557319, kgp1285441, kgp13161760, kgp1355977, kgp1371881, kgp15390522, kgp1683448, kgp1688752, kgp1699628, kgp1753445, kgp1779254, kgp1786079, kgp18379774, kgp18432055, kgp18525257, kgp1912531, kgp19568724, 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, rs10049206, rs10124492, rs10125298, rs10162089, rs10201643, rs10203396, rs10251797, rs10278591, rs10489312, rs10492882, rs10498793, rs10501082, rs10510774, rs10512340, rs1079303, rs10815160, rs10816302, rs10841322, rs10841337, rs10954782, rs11002051, rs11022778, rs11029892, rs11029907, rs11029928, rs11083404, rs11085044, rs11136970, rs11147439, rs11192461, rs11192469, rs11559024, rs1157449, rs11648129, rs11691553, rs12013377, rs12494712, rs12943140, rs13002663, rs13394010, rs13415334, rs13419758, rs1380706, rs1387768, rs1410779, rs1478682, rs1508102, rs1532365, rs1544352, rs1545223, rs1579771, rs1604169, rs1621509, rs1644418, rs16886004, rs16895510, rs16901784, rs16927077, rs16930057, rs17029538, rs17224858, rs17238927, rs17329014, rs17400875, rs17449018, rs17577980, rs17638791, rs1858973, rs1886214, rs1894406, rs1894407, rs1894408, rs196295, rs196341, rs196343, rs197523, rs1979992, rs1979993, 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),
      • (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 kgp10152733, kgp10224254, kgp10305127, kgp10351364, kgp10372946, kgp10404633, kgp10564659, kgp10591989, kgp10594414, kgp10619195, kgp10620244, kgp10633631, kgp10974833, kgp11002881, kgp11285862, kgp11328629, kgp11407560, kgp11514107, kgp11627530, kgp11702474, kgp11711524, kgp11768533, kgp11804835, kgp12083934, kgp12182745, kgp12230354, kgp1224440, kgp124162, kgp12557319, kgp1371881, kgp1699628, kgp1753445, kgp1779254, kgp1786079, kgp18379774, kgp18525257, 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, rs10049206, rs10124492, rs10125298, rs10162089, rs10203396, rs10251797, rs10278591, rs10489312, rs10492882, rs10498793, rs10501082, rs10510774, rs10512340, rs10815160, rs10816302, rs10841337, rs11029892, rs11029928, rs11192469, rs11559024, rs11648129, rs12013377, rs13394010, rs13415334, rs1478682, rs1544352, rs1545223, rs1604169, rs1621509, rs1644418, rs17029538, rs17400875, rs17449018, rs17577980, rs1858973, rs1894406, rs1894407, rs197523, 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 kgp10910719, kgp11077373, kgp11453406, kgp12426624, kgp2045074, kgp22811918, kgp23298674, kgp2709692, kgp28687699, kgp3496814, kgp3669685, kgp3730395, kgp4056892, kgp4370912, kgp5053636, kgp5216209, kgp5292386, kgp6023196, kgp652534, kgp7059449, kgp7189498, kgp7521990, kgp7792268, kgp8303520, kgp9320791, kgp9795732, rs10201643, rs11022778, rs11136970, rs11147439, rs11691553, rs1579771, rs16901784, 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 kgp10090631, kgp1009249, kgp10412303, kgp10523170, kgp1054273, kgp10558725, kgp10632945, kgp10679353, kgp10788130, kgp10826273, kgp10922969, kgp10948564, kgp10967046, kgp1098237, kgp11010680, kgp11141512, kgp11206453, kgp11210903, kgp1124492, kgp11281589, kgp11356379, kgp11467007, kgp11543962, kgp11580695, kgp11633966, kgp11686146, kgp11843177, kgp12008955, kgp12371757, kgp1285441, kgp13161760, kgp1355977, kgp15390522, kgp1683448, kgp1688752, kgp1912531, kgp19568724, 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, rs1079303, rs10841322, rs10954782, rs11002051, rs11029907, rs11083404, rs11085044, rs11192461, rs1157449, rs12494712, rs12943140, rs13002663, rs13419758, rs1380706, rs1387768, rs1410779, rs1508102, rs1532365, rs16886004, rs16895510, rs16927077, rs16930057, rs17224858, rs17238927, rs17329014, rs17638791, rs1886214, rs1894408, rs196295, rs196341, rs196343, rs1979992, rs1979993, rs2043136, rs2071470, rs2074037, rs2175121, rs241435, rs241447, rs241451, 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 kgp18432055, 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 kgp18432055, 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 kgp18432055, 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 kgp10090631, kgp1009249, kgp10148554, kgp10152733, kgp10215554, kgp10224254, kgp10305127, kgp10351364, kgp10372946, kgp10404633, kgp10412303, kgp10523170, kgp1054273, kgp10558725, kgp10564659, kgp10591989, kgp10594414, kgp10619195, kgp10620244, kgp10632945, kgp10633631, kgp10679353, kgp10762962, kgp10788130, kgp10826273, kgp10836214, kgp10910719, kgp10922969, kgp10948564, kgp10967046, kgp10974833, kgp1098237, kgp10989246, kgp11002881, kgp11010680, kgp11077373, kgp11141512, kgp11206453, kgp11210903, kgp1124492, kgp11281589, kgp11285862, kgp11285883, kgp11328629, kgp11356379, kgp11407560, kgp11453406, kgp11467007, kgp11514107, kgp11543962, kgp11580695, kgp11604017, kgp11627530, kgp11633966, kgp11686146, kgp11702474, kgp11711524, kgp11755256, kgp11768533, kgp11804835, kgp11843177, kgp12008955, kgp12083934, kgp1211163, kgp12182745, kgp12230354, kgp1224440, kgp12253568, kgp12371757, kgp124162, kgp12426624, kgp12557319, kgp12562255, kgp1285441, kgp13161760, kgp1355977, kgp1371881, kgp1432800, kgp15390522, kgp1682126, kgp1683448, kgp1688752, kgp1699628, kgp1753445, kgp1758575, kgp1779254, kgp1786079, kgp18379774, kgp18432055, kgp18525257, kgp1912531, kgp19568724, 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, kgp297178, 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, P1_M_061510_11_106_M, P1_M_061510_18_342_P, P1_M_061510_6_159_P, rs10038844, rs10049206, rs10124492, rs10125298, rs10162089, rs10201643, rs10203396, rs10251797, rs1026894, rs10278591, rs10489312, rs10492882, rs10495115, rs10498793, rs10501082, rs10510774, rs10512340, rs1079303, rs10815160, rs10816302, rs10841322, rs10841337, rs10954782, rs11002051, rs11022778, rs11029892, rs11029907, rs11029928, rs11083404, rs11085044, rs11136970, rs11147439, rs11192461, rs11192469, rs11559024, rs11562998, rs11563025, rs1157449, rs11648129, rs11691553, rs11750747, rs11947777, rs12013377, rs12043743, rs12233980, rs12341716, rs12472695, rs12494712, rs12881439, rs12943140, rs13002663, rs13168893, rs13386874, rs13394010, rs13415334, rs13419758, rs1357718, rs1380706, rs1387768, rs1393037, rs1393040, rs1397481, rs1410779, rs1474226, rs1478682, rs1508102, rs1508515, rs1532365, rs1534647, rs1544352, rs1545223, rs1579771, rs1604169, rs1621509, rs1644418, rs16846161, rs16886004, rs16895510, rs16901784, rs16927077, rs16930057, rs17029538, rs1715441, rs17187123, rs17224858, rs17238927, rs17245674, rs17329014, rs17400875, rs17419416, rs17449018, rs17577980, rs17638791, rs1793174, rs1858973, rs1883448, rs1886214, rs1894406, rs1894407, rs1894408, rs1905248, rs196295, rs196341, rs196343, rs197523, rs1979992, rs1979993, 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 rs9953274 (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 kgp18432055, 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 kgp18432055, 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: kgp10148554, kgp10215554, kgp10762962, kgp10836214, kgp10989246, kgp11285883, kgp11604017, kgp11755256, kgp1211163, kgp12253568, kgp12562255, kgp1432800, kgp1682126, kgp1758575, 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, rs10038844, rs1026894, rs10495115, rs11562998, rs11563025, rs11750747, rs11947777, rs12043743, rs12233980, rs12341716, rs12472695, rs12881439, rs13168893, rs13386874, rs1357718, rs1393037, rs1393040, rs1397481, rs1474226, rs1508515, rs1534647, rs16846161, rs1715441, rs17187123, rs17245674, rs17419416, rs1793174, rs1883448, rs1905248, 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 kgp10762962, kgp11285883, kgp11604017, kgp1211163, kgp12253568, kgp12562255, 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, rs10038844, rs10495115, rs11750747, rs12341716, rs12881439, rs13168893, rs1393040, rs1474226, rs1534647, rs1715441, rs17187123, rs17245674, rs17419416, rs1793174, rs1883448, rs1905248, 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 kgp10836214, kgp1432800, kgp22839559, kgp6301155, kgp6828277, rs2354380, rs2662, rs3858035, rs3894712, rs4797764 or rs7696391 (hereinafter Group 8),
        • one or more G alleles at the location of kgp10148554, kgp10215554, kgp10989246, kgp11755256, kgp1682126, kgp1758575, kgp2920925, kgp3287349, kgp3420309, kgp3488270, kgp3598966, kgp3697615, kgp4137144, kgp5691690, kgp5924341, kgp5949515, kgp6213972, kgp625941, kgp6889327, kgp6990559, kgp7778345, kgp7932108, kgp8847137, kgp956070, rs1026894, rs11562998, rs11563025, rs11947777, rs12233980, rs12472695, rs13386874, rs1357718, rs1393037, rs1397481, rs1508515, rs16846161, 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 rs12043743.
  • 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
  • FIG. 1 shows Receiver Operating Characteristics for optimization of test threshold.
  • FIG. 2 shows Response Rate of Predicted Responders (green line) and Response Rate of Predicted Non-Responders (red line) by predictive test threshold.
  • FIG. 3 shows overall percent of Predicted Responders by predictive test threshold.
  • FIG. 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.
  • FIG. 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).
  • FIG. 6 shows GALA and FORTE patients were stratified by clearly defined response. High Response: improved ARR (ARR change <(−1), during study versus prior 2 years). Low Response: no change or worsening of ARR (ARR change ≧0, during study versus previous 2 years).
  • FIG. 7 shows predictive model building for GALA and FORTE cohorts.
  • FIG. 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).
  • FIG. 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 kgp18432055, 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: rs10988087, rs1573706, rs17575455, 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 rs10988087, one or more C alleles at the location of rs17575455, or one or more G alleles at the location of rs1573706, 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 kgp18432055, 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 kgp18432055, 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: rs10988087, rs1573706, rs17575455, 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 rs10988087, one or more C alleles at the location of rs17575455, or one or more G alleles at the location of rs1573706, 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 rs10988087, no C alleles at the location of rs17575455, or no G alleles at the location of rs1573706, 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 FIG. 8 or in FIG. 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.
  • In some embodiments the human subject is a naïve 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, rs10162089, rs16886004, rs1894408 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, rs10162089, rs16886004, rs1894408, rs3135391, and rs759458.
  • In some embodiments the one or more SNPs is selected from the group consisting of kgp24415534, kgp6214351, kgp6599438, kgp8110667, kgp8817856, rs10162089, rs16886004, rs1894408, 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, rs10162089, rs16886004, rs1894408, 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, rs10162089, rs16886004, rs1894408, 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 subject.
  • 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 rs12043743.
  • 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 (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.
  • 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, rs10162089, rs16886004, rs1894408, 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 kgp18432055, 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 kgp18432055, 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 kgp18432055, 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, rs10162089, rs16886004, rs1894408, 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 rs12043743.
  • 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 (CCM), 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 cm2 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 Minisequencing 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 RNA 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 51 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/human1m_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 T1 enhancing lesions and/or T2 enhancing lesions; exemplified by baseline volume of T2 lesion, number of Gd-T1 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 co-ordination, 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, anti-inflammatory 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 “naïve 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, 108, 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 Example 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-responder according to the criteria set forth in Table 1.
    • Example 2 Patient Genotyping
  • DNA samples from categorized patients were subject to quality control analysis followed by genotyping with the Illumina OMNI-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-T1 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 −10 Log P, fisher exact, fisher exact −10 Log P, values for fisher and chi-square with Bonferoni correction, Odds Ratios and Confidence Bounds, Regression P-value, Regression −log 10 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, −log 10 P-values, Correlation/Trend test P-value, Correlation/Trend −log 10 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 −10 Log P, fisher exact, fisher exact −10 Log P, values for fisher and chi-square with Bonferoni correction, Odds Ratios and Confidence Bounds, Regression P-value, Regression −log 10 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. 111 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
    Discovery Cohort Replication Cohort Comparative Parameters
    Step 1 Candidate SNPs (35) Candidate SNPs (35) Candidate SNPs (35)
    −Additive, Allelic, Genotypic, Regression −Additive, Allelic, Genotypic, Regression −Additive, Allelic, Genotypic, Regression
    Candidate SNPs, Extreme Candidate SNPs, Extreme Candidate SNPs, Extreme
    −Additive, Allelic, Genotypic, Regression −Additive, Allelic, Genotypic, Regression −Additive, Allelic, Genotypic, Regression
    Step 2 Candidate Genes (30) Candidate Genes (30) Candidate Genes (30)
    −Additive, Allelic, Genotypic, Regression −Additive, Allelic, Genotypic, Regression −Additive, Allelic, Genotypic, Regression
    Candidate Genes, Extreme Candidate Genes, Extreme Candidate Genes, Extreme
    −Additive, Allelic, Genotypic, Regression −Additive, Allelic, Genotypic, Regression −Additive, Allelic, Genotypic, Regression
    Step 3 Candidate Genes (180) Candidate Genes (180) Candidate Genes (180)
    −Additive, Allelic, Genotypic, Regression −Additive, Allelic, Genotypic, Regression −Additive, Allelic, Genotypic, Regression
    Candidate Genes, Extreme Candidate Genes, Extreme Candidate Genes, Extreme
    −Additive, Allelic, Genotypic, Regression −Additive, Allelic, Genotypic, Regression −Additive, Allelic, Genotypic, Regression
    Step 4 Genome-wide Genome-wide Genome-wide
    −Additive, Allelic, Genotypic, Regression −Additive, Allelic, Genotypic, Regression −Additive, Allelic, Genotypic, Regression
    +Corrected for ancestry +Corrected for ancestry +Corrected for ancestry
    +Corrected for clinical covariates +Corrected for clinical covariates +Corrected for clinical covariates
    +Corrected for top SNP +Corrected for top SNP +Corrected for top SNP
    Genome-wide, Extreme Genome-wide, Extreme Genome-wide, Extreme
    −Additive, Allelic, Genotypic, Regression −Additive, Allelic, Genotypic, Regression −Additive, Allelic, Genotypic, Regression
    +Corrected for clinical covariates +Corrected for clinical covariates +Corrected for clinical covariates
    +Corrected for top SNP +Corrected for top SNP +Corrected for top SNP
    • 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.
  • 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.
  • 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.
    • Example 8 Analysis Part 4—Genome Wide Analysis
  • A full genome-wide analysis was then conducted (4 M 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. 111 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.
  • 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 132nd top associated variant in the combined cohorts (variant kpg5144181) and the 242nd 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 Allele
    Gene Armitage Regression Freq. Freq. DD DD Dd Dd dd dd
    Name Chr Position Gene(s) Mutation Locations(s) P Odds Ratio (Cases) (Controls) (Cases) (Controls) (Cases) (Controls) (Cases) (Controls)
    rs12472695 2 65804266 ? ? ? 2.31E−05 0.38 31% 51% 10 21 39 62 46 18
    kgp3188 2 65804244 ? ? ? 2.99E−05 0.39 36% 56% 13 25 41 63 40 13
    kgp5747456 2 23932556 ? ? ? 3.24E−05 Infinity  8%  0% 0 0 15 0 80 101
    rs11562998 2 51814215 ? ? ? 3.41E−05 6.52 14%  2% 2 0 23 5 70 96
    rs11563025 2 51864372 ? ? ? 3.41E−05 6.52 14%  2% 2 0 23 5 70 96
    rs16846161 2 2.12E+08 ERBB4, ER
    Figure US20180002753A1-20180104-P00899
    		 Silent, Sile
    Figure US20180002753A1-20180104-P00899
    		 INTRON 3.72E−05 12.04  12%  1% 2 0 18 2 74 97
    kgp22839559 ? ? ? 3.97E−05 2.82 34% 16% 10 2 44 28 40 70
    kgp12562255 1 2.01E+08 ? ? ? 4.21E−05 21.79   9%  0% 0 0 17 1 78 100
    kgp6990559 1 7014101 CAMTA1 Silent INTRON, E
    Figure US20180002753A1-20180104-P00899
    		 4.49E−05 0.44 35% 58% 15 35 36 42 43 20
    rs6577395 1 6991925 CAMTA1 Silent INTRON, E
    Figure US20180002753A1-20180104-P00899
    		 5.34E−05 0.45 37% 59% 16 38 37 43 41 20
    kgp4456934 2 2.18E+08 DIRC3 Silent INTRON 5.68E−05 3.79 21%  7% 4 0 31 13 60 87
    rs10495115 1 2.19E+08 ? ? ? 6.04E−05 2.90 30% 13% 7 2 43 23 45 76
    kgp4137144 1 2.19E+08 ? ? ? 6.13E−05 6.19 14%  3% 2 0 22 5 70 95
    rs3768769 2 1.14E+08 IL36A Silent INTRON 7.21E−05 4.30 17%  5% 2 0 29 10 64 91
    kgp3488270 1 20335423 ? ? ? 7.30E−05 0.27  6% 21% 1 4 10 33 84 63
    rs2354380 2 51826155 ? ? ? 7.48E−05 5.49 14%  3% 2 0 23 6 69 95
    kgp7151153 3 79590648 ROBO1 Silent INTRON 7.86E−05 3.98 18%  5% 4 1 27 8 64 92
    rs28993969 2 1.14E+08 ? ? ? 8.51E−05 3.67 20%  6% 4 0 30 13 61 88
    rs12043743 1 1.97E+08 KCNT2 Silent INTRON 8.61E−05 0.16  3% 13% 0 0 5 26 90 75
    kgp24521552 2 1.44E+08 ARHGAP1
    Figure US20180002753A1-20180104-P00899
    		 Silent INTRON 8.86E−05 4.22 17%  5% 4 0 25 9 66 91
    kgp11755256 2 42245135 ? ? ? 8.99E−05 0.38 14% 32% 1 14 25 37 68 50
    rs528065 2 23859449 KLHL29 Silent INTRON 9.24E−05 2.45 44% 26% 19 3 46 46 30 52
    rs13386874 2 51820543 ? ? ? 9.25E−05 2.64 32% 15% 12 1 37 28 46 72
    kgp956070 2 2.06E+08 PARD3B, P
    Figure US20180002753A1-20180104-P00899
    		 Silent, Sile
    Figure US20180002753A1-20180104-P00899
    		 INTRON 9.39E−05 0.37 14% 32% 2 11 23 41 70 48
    rs35615951 2 1.34E+08 NCKAP5,
    Figure US20180002753A1-20180104-P00899
    		 Silent, Sile
    Figure US20180002753A1-20180104-P00899
    		 INTRON 9.41E−05 2.32 48% 28% 22 8 46 41 26 52
    kgp12253568 3 79428265 ROBO1 Silent INTRON 9.55E−05 4.29 17%  4% 4 1 24 6 67 94
    rs1397481 2 2.06E+08 PARD3B, P
    Figure US20180002753A1-20180104-P00899
    		 Silent, Sile
    Figure US20180002753A1-20180104-P00899
    		 INTRON 9.56E−05 0.37 14% 31% 2 10 23 43 70 48
    kgp7161038 2 53521025 ? ? ? 9.70E−05 0.09  1% 10% 0 0 2 20 92 81
    rs1534647 2 62038088 ? ? ? 9.72E−05 3.34 22%  8% 5 0 32 16 58 85
    kgp7799142 3 13902000 WNT7A Silent INTRON 1.04E−O4 0.12  2% 11% 0 0 3 22 91 79
    kgp6029 2 1.69E+08 ? ? ? 1.07E−04 0.37 13% 30% 2 11 21 39 72 51
    kgp8142606 2 1.74E+08 ? ? ? 1.10E−04 0.22  4% 17% 0 3 8 27 87 70
    rs6737616 2 51807660 ? ? ? 1.18E−04 5.98 13%  2% 1 0 22 5 72 96
    kgp7713264 2 2.42E+08 GPR35, GP
    Figure US20180002753A1-20180104-P00899
    		 Silent, Sile
    Figure US20180002753A1-20180104-P00899
    		 INTRON 1.18E−04 0.45 30% 51% 10 27 37 47 47 26
    kgp8055964 3 1.73E+08 SPATA16 Silent INTRON 1.19E−04 Infinity  7%  0% 0 0 13 0 82 101
    rs12712821 2 42238864 ? ? ? 1.19E−04 0.39 15% 32% 1 14 26 37 68 50
    rs13424176 2 42239532 ? ? ? 1.19E−04 0.39 15% 32% 1 14 26 37 68 50
    kgp9777128 2 42242872 ? ? ? 1.19E−04 0.39 15% 32% 1 14 26 37 68 50
    rs10195970 2 42249643 ? ? ? 1.19E−04 0.39 15% 32% 1 14 26 37 68 50
    rs10177811 2 42263580 ? ? ? 1.19E−04 0.39 15% 32% 1 14 26 37 68 50
    Figure US20180002753A1-20180104-P00899
    indicates data missing or illegible when filed
  • TABLE 15
    Allelic Model, Genome Wide Placebo Cohort Analysis
    GALA PLACEBO cohort
    Odds Ratio Allele Allele
    Gene Fisher's (Minor Freq. Freq. DD DD Dd Dd dd dd
    Name Chr Position Gene(s) Mutation Locations(s) Exact P Allele) (Cases) (Controls) (Cases) (Controls) (Cases) (Controls) (Cases) (Controls)
    kgp5471255 11 57870219 OR9Q1 Silent INTRON, E
    Figure US20180002753A1-20180104-P00899
    		 1.16E−06 0.25  9% 29% 5 25 7 7 81 63
    kgp11285883 9 2953403 ? ? ? 2.68E−06 2.79 46% 23% 26 5 35 37 34 59
    kgp433351 8 41496314 ? ? ? 2.70E−06 0.35 23% 46% 6 19 32 55 57 27
    kgp10148554 4 89767803 FAM13A Silent INTRON 3.69E−06 7.19 15%  2% 3 0 23 5 68 96
    rs3858038 9 2988280 ? ? ? 5.49E−06 2.63 53% 30% 33 7 34 46 28 48
    kgp2877482 6 1644677 GMDS, GM
    Figure US20180002753A1-20180104-P00899
    		 Silent, Sile
    Figure US20180002753A1-20180104-P00899
    		 INTRON 6.08E−06 8.20 14%  2% 0 0 27 4 68 97
    kgp6042557 3 1.94E+08 LOC10050
    Figure US20180002753A1-20180104-P00899
    		 Silent INTRON 6.53E−06 0.08  1% 12% 0 1 2 22 93 77
    kgp22755512 X 27326117 ? ? ? 6.61E−06 ?  8%  0% 3 0 10 0 82 101
    kgp10989246 4 89761443 FAM13A Silent INTRON 6.68E−06 7.11 15%  3% 3 0 23 5 68 95
    rs7698655 4 89756076 FAM13A Silent INTRON 6.76E−06 7.10 15%  2% 3 0 23 5 69 96
    kgp9409440 4 89759159 FAM13A Silent INTRON 6.76E−06 7.10 15%  2% 3 0 23 5 69 96
    kgp6889327 4 89766553 FAM13A Silent INTRON 6.76E−06 7.10 15%  2% 3 0 23 5 69 96
    rs7696391 4 89789287 FAM13A Silent INTRON 6.76E−06 7.10 15%  2% 3 0 23 5 69 96
    rs11947777 4 89768744 FAM13A Silent INTRON 6.92E−06 7.02 15%  3% 3 0 23 5 69 95
    kgp6301155 4 89766647 FAM13A Silent INTRON 7.20E−06 6.95 15%  3% 3 0 23 5 69 94
    rs16846161 2 2.12E+08 ERBB4, ER
    Figure US20180002753A1-20180104-P00899
    		 Silent, Sile
    Figure US20180002753A1-20180104-P00899
    		 INTRON 7.44E−06 12.99  12%  1% 2 0 18 2 74 97
    kgp7778345 9 2965090 ? ? ? 9.91E−06 2.59 49% 27% 27 6 38 42 29 52
    kgp6990559 1 7014101 CAMTA1 Silent INTRON, E
    Figure US20180002753A1-20180104-P00899
    		 1.01E−05 0.40 35% 58% 15 35 36 42 43 20
    rs1393040 9 2985743 ? ? ? 1.04E−05 2.57 48% 27% 28 6 35 42 31 53
    rs6577395 1 6991925 CAMTA1 Silent INTRON, E
    Figure US20180002753A1-20180104-P00899
    		 1.27E−05 0.40 37% 59% 16 38 37 43 41 20
    rs7846783 9 2958182 ? ? ? 1.28E−05 2.58 45% 24% 25 6 36 37 34 58
    kgp5747456 2 23932556 ? ? ? 1.42E−05 ?  8%  0% 0 0 15 0 80 101
    kgp6429231 15 62931802 MGC1588
    Figure US20180002753A1-20180104-P00899
    		 Silent INTRON 1.42E−05 ?  8%  0% 0 0 15 0 80 101
    kgp30689515 X 56022365 ? ? ? 1.42E−05 ?  8%  0% 4 0 7 0 84 101
    kgp1682126 5 2047397 ? ? ? 1.56E−05 0.05  1% 10% 0 1 1 18 94 82
    kgp2920925 17 39694480 ? ? ? 1.56E−05 0.30 10% 27% 0 6 19 43 76 52
    rs3894712 5 73973651 ? ? ? 1.70E−05 0.29  9% 25% 3 5 11 41 81 55
    rs7119480 11 84247636 DLG2, DLG
    Figure US20180002753A1-20180104-P00899
    		 Silent, Sile
    Figure US20180002753A1-20180104-P00899
    		 INTRON, E
    Figure US20180002753A1-20180104-P00899
    		 1.71E−05 0.34 14% 33% 1 9 25 48 69 44
    rs3858035 9 2968044 ? ? ? 1.72E−05 2.51 48% 27% 27 7 37 41 30 53
    rs3847233 9 2987835 ? ? ? 1.95E−05 2.49 52% 30% 31 7 34 46 28 47
    kgp12253568 3 79428265 ROBO1 Silent INTRON 2.10E−05 4.91 17%  4% 4 1 24 6 67 94
    kgp1977942 9 2938757 ? ? ? 2.17E−05 2.52 46% 26% 28 7 32 37 35 56
    kgp22744690 X 83601713 HDX, HDX,
    Figure US20180002753A1-20180104-P00899
    		 Silent, Sile
    Figure US20180002753A1-20180104-P00899
    		 INTRON 2.21E−05 7.50 13%  2% 7 0 11 4 77 97
    rs8000689 13 41043438 TTL, TTL, T
    Figure US20180002753A1-20180104-P00899
    		 Silent, Sile
    Figure US20180002753A1-20180104-P00899
    		 INTRON 2.22E−05 0.42 38% 60% 14 40 45 41 36 20
    kgp4892427 9 2995617 ? ? ? 2.27E−05 2.46 52% 30% 31 7 36 47 28 47
    rs11562998 2 51814215 ? ? ? 2.36E−05 6.53 14%  2% 2 0 23 5 70 96
    rs11563025 2 51864372 ? ? ? 2.36E−05 6.53 14%  2% 2 0 23 5 70 96
    rs7680970 4 89772301 FAM13A Missense
    Figure US20180002753A1-20180104-P00899
    		 EXON 2.37E−05 5.88 15%  3% 3 0 23 6 69 95
    kgp22836129 X 1.45E+08 ? ? ? 2.38E−05 5.84 15%  3% 5 0 19 6 70 93
    kgp11604017 11 1.18E+08 AMICA1, A
    Figure US20180002753A1-20180104-P00899
    		 Silent, Sile
    Figure US20180002753A1-20180104-P00899
    		 INTRON 2.39E−05 2.69 38% 18% 11 3 48 31 34 67
    rs961090 15 40617414 ? ? ? 2.40E−05 2.97 31% 13% 9 2 40 22 46 77
    kgp22760557 X 3520721 ? ? ? 2.40E−05 2.97 31% 13% 16 5 26 16 53 80
    rs1393037 9 2968451 ? ? ? 2.41E−05 2.50 48% 27% 27 7 37 40 30 52
    rs4978567 9 1.17E+08 ? ? ? 2.47E−05 0.41 32% 54% 10 27 41 52 44 20
    Figure US20180002753A1-20180104-P00899
    indicates data missing or illegible when filed
  • TABLE 16
    Genotypic Model, Genome Wide Placebo Cohort Analysis
    GALA PLACEBO cohort
    Allele Allele
    Gene Fisher's Freq. Freq. DD DD Dd Dd dd dd
    Name Chromosome Position Gene(s) Mutation Locations(s) Exact P (Cases) (Controls) (Cases) (Controls) (Cases) (Controls) (Cases) (Controls)
    kgp54189
    Figure US20180002753A1-20180104-P00899
    		 5 73992881 HEXB Missense
    Figure US20180002753A1-20180104-P00899
    		 EXON 8.76E−07  9% 25% 3 3 11 44 81 54
    kgp34948
    Figure US20180002753A1-20180104-P00899
    		 14 91731724 ? ? ? 1.53E−06  6% 17% 3 1 5 32 87 67
    kgp21160
    Figure US20180002753A1-20180104-P00899
    		 14 91744233 CCDC88C Silent INTRON 1.55E−06  6% 17% 3 1 5 32 86 67
    kgp28774
    Figure US20180002753A1-20180104-P00899
    		 6 1644677 GMDS, G
    Figure US20180002753A1-20180104-P00899
    		 Silent, Sil
    Figure US20180002753A1-20180104-P00899
    		 INTRON 2.43E−06 14%  2% 0 0 27 4 68 97
    rs1175074
    Figure US20180002753A1-20180104-P00899
    		 5 73973220 ? ? ? 2.71E−06  9% 25% 3 4 11 42 81 55
    rs1223398
    Figure US20180002753A1-20180104-P00899
    		 5 73975094 ? ? ? 2.71E−06  9% 25% 3 4 11 42 81 55
    rs1203094
    Figure US20180002753A1-20180104-P00899
    		 1 67701765 IL23R Silent INTRON 3.44E−06 36% 37% 20 5 29 64 46 32
    rs3894712
    Figure US20180002753A1-20180104-P00899
    		 5 73973651 ? ? ? 3.50E−06  9% 25% 3 5 11 41 81 55
    rs3858038
    Figure US20180002753A1-20180104-P00899
    		 9 2988280 ? ? ? 4.13E−06 53% 30% 33 7 34 46 28 48
    kgp62594
    Figure US20180002753A1-20180104-P00899
    		 5 73973306 ? ? ? 5.26E−06  9% 24% 3 4 11 41 81 56
    rs7159692
    Figure US20180002753A1-20180104-P00899
    		 14 91729406 ? ? ? 6.22E−06  7% 18% 3 1 7 34 85 66
    kgp43335
    Figure US20180002753A1-20180104-P00899
    		 8 41496314 ? ? ? 7.73E−06 23% 46% 6 19 32 55 57 27
    kgp60425
    Figure US20180002753A1-20180104-P00899
    		 3 1.94E+08 LOC10050
    Figure US20180002753A1-20180104-P00899
    		 Silent INTRON 8.38E−06  1% 12% 0 1 2 22 93 77
    kgp89109
    Figure US20180002753A1-20180104-P00899
    		 8 4818950 CSMD1 Silent INTRON 8.91E−06 45% 33% 27 5 32 57 36 39
    kgp48182
    Figure US20180002753A1-20180104-P00899
    		 14 86277089 ? ? ? 8.95E−06 45% 36% 10 18 66 36 19 47
    kgp66017
    Figure US20180002753A1-20180104-P00899
    		 19 28886975 ? ? ? 9.85E−06 19% 31% 7 3 21 55 65 42
    kgp57474
    Figure US20180002753A1-20180104-P00899
    		 2 23932556 ? ? ? 1.03E−05  8%  0% 0 0 15 0 80 101
    kgp64292
    Figure US20180002753A1-20180104-P00899
    		 15 62931802 MGC1588
    Figure US20180002753A1-20180104-P00899
    		 Silent INTRON 1.03E−05  8%  0% 0 0 15 0 80 101
    kgp82762
    Figure US20180002753A1-20180104-P00899
    		 14 91725476 ? ? ? 1.22E−05  7% 17% 3 1 7 33 85 67
    kgp68282
    Figure US20180002753A1-20180104-P00899
    		 9 8373943 PTPRD, PT
    Figure US20180002753A1-20180104-P00899
    		 Silent, Sil
    Figure US20180002753A1-20180104-P00899
    		 INTRON 1.23E−05 26% 10% 3 2 43 17 48 82
    rs3847233
    Figure US20180002753A1-20180104-P00899
    		 9 2987835 ? ? ? 1.32E−05 52% 30% 31 7 34 46 28 47
    kgp3188 2 65804244 ? ? ? 1.34E−05 36% 56% 13 25 41 63 40 13
    rs1890118
    Figure US20180002753A1-20180104-P00899
    		 6 82857479 ? ? ? 1.48E−05 26% 32% 13 4 23 56 59 41
    rs2282624
    Figure US20180002753A1-20180104-P00899
    		 11 57001911 APLNR, AP
    Figure US20180002753A1-20180104-P00899
    		 Silent, Sil
    Figure US20180002753A1-20180104-P00899
    		 INTRON, E
    Figure US20180002753A1-20180104-P00899
    		 1.54E−05 30% 35% 15 5 27 61 53 35
    kgp48924
    Figure US20180002753A1-20180104-P00899
    		 9 2995617 ? ? ? 1.54E−05 52% 30% 31 7 36 47 28 47
    kgp11285
    Figure US20180002753A1-20180104-P00899
    		 9 2953403 ? ? ? 1.66E−05 46% 23% 26 5 35 37 34 59
    rs4740708
    Figure US20180002753A1-20180104-P00899
    		 9 2993975 ? ? ? 1.67E−05 51% 30% 31 7 34 47 29 47
    rs695915 1 82664165 ? ? ? 1.90E−05 34% 28% 6 17 51 23 37 61
    rs2327006
    Figure US20180002753A1-20180104-P00899
    		 6 1.31E+08 EPB41L2,
    Figure US20180002753A1-20180104-P00899
    		 Silent, Sil
    Figure US20180002753A1-20180104-P00899
    		 INTRON 1.93E−05 22%  9% 1 2 39 13 55 84
    kgp93349
    Figure US20180002753A1-20180104-P00899
    		 6 1.31E+08 EPB41L2,
    Figure US20180002753A1-20180104-P00899
    		 Silent, Sil
    Figure US20180002753A1-20180104-P00899
    		 INTRON 2.05E−05 22%  8% 2 2 38 13 55 86
    rs193933 19 8331375 ? ? ? 2.07E−05 27% 46% 11 17 30 59 54 25
    kgp12475
    Figure US20180002753A1-20180104-P00899
    		 4 1.86E+08 ACSL1 Silent INTRON 2.11E−05 13%  3% 0 1 24 4 71 96
    rs1247269
    Figure US20180002753A1-20180104-P00899
    		 2 65804266 ? ? ? 2.11E−05 31% 51% 10 21 39 62 46 18
    rs1393040 9 2985743 ? ? ? 2.31E−05 48% 27% 28 6 35 42 31 53
    kgp29209
    Figure US20180002753A1-20180104-P00899
    		 17 39694480 ? ? ? 2.33E−05 10% 27% 0 6 19 43 76 52
    rs209568 8 17612639 MTUS1, M
    Figure US20180002753A1-20180104-P00899
    		 Synonym
    Figure US20180002753A1-20180104-P00899
    		 EXON 2.34E−05 27% 11% 4 0 44 22 47 79
    kgp12562
    Figure US20180002753A1-20180104-P00899
    		 1 2.01E+08 ? ? ? 2.42E−05  9%  0% 0 0 17 1 78 100
    kgp26263
    Figure US20180002753A1-20180104-P00899
    		 13 67483846 PCDH9, P
    Figure US20180002753A1-20180104-P00899
    		 Silent, Sil
    Figure US20180002753A1-20180104-P00899
    		 INTRON, E
    Figure US20180002753A1-20180104-P00899
    		 2.43E−05 34% 49% 4 28 56 43 34 30
    kgp16821
    Figure US20180002753A1-20180104-P00899
    		 5 2047397 ? ? ? 2.51E−05  1% 10% 0 1 1 18 94 82
    kgp10148
    Figure US20180002753A1-20180104-P00899
    		 4 89767803 FAM13A Silent INTRON 2.55E−05 15%  2% 3 0 23 5 68 96
    kgp57600
    Figure US20180002753A1-20180104-P00899
    		 6 1.31E+08 EPB41L2,
    Figure US20180002753A1-20180104-P00899
    		 Silent, Sil
    Figure US20180002753A1-20180104-P00899
    		 INTRON 2.61E−05 20%  7% 1 2 35 11 58 88
    kgp78398
    Figure US20180002753A1-20180104-P00899
    		 1 95321361 SLC44A3,
    Figure US20180002753A1-20180104-P00899
    		 Silent, Sil
    Figure US20180002753A1-20180104-P00899
    		 INTRON 2.67E−05 20% 20% 0 11 38 19 57 71
    rs1049917
    Figure US20180002753A1-20180104-P00899
    		 6 1.31E+08 EPB41L2,
    Figure US20180002753A1-20180104-P00899
    		 Silent, Sil
    Figure US20180002753A1-20180104-P00899
    		 INTRON 2.77E−05 19%  7% 1 2 35 11 59 88
    kgp37781
    Figure US20180002753A1-20180104-P00899
    		 19 28893126 ? ? ? 2.80E−05 19% 32% 7 4 23 56 65 41
    kgp76534
    Figure US20180002753A1-20180104-P00899
    		 17 39694186 ? ? ? 2.81E−05 10% 27% 0 5 19 44 76 52
    rs1684616
    Figure US20180002753A1-20180104-P00899
    		 2 2.12E+08 ERBB4, ER
    Figure US20180002753A1-20180104-P00899
    		 Silent, Sil
    Figure US20180002753A1-20180104-P00899
    		 INTRON 2.96E−05 12%  1% 2 0 18 2 74 97
    Figure US20180002753A1-20180104-P00899
    indicates data missing or illegible when filed
    • 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.
    • 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.
    • 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.
    • 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.
  • TABLE 29
    Additive Model, Extreme Response Definition, Genome Wide Placebo Cohort Analysis
    Placebo
    Regres- DD Dd dd
    Gene Armitage sion Odds DD (Con- Dd (Con- dd (Con-
    Name Chr Position Gene(s) Mutation Locations(s) P Ratio (Cases) trols) (Cases) trols) (Cases) trols)
    rs1978721 19 30966217 ZNF536 Silent INTRON 9.89E−09 35.3 0 0 11 2 12 77
    kgp7344529 19 30967564 ZNF536 Silent INTRON 9.89E−09 35.3 0 0 11 2 12 77
    rs7252241 19 30967836 ZNF536 Silent INTRON 9.89E−09 35.3 0 0 11 2 12 77
    rs1978720 19 30968371 ZNF536 Silent INIRON 9.89E−09 35.3 0 0 11 2 12 77
    kgp146166 19 30965980 ZNF536 Silent INTRON 1.92E−07 13.8 0 0 14 8 9 71
    rs8112863 19 30965063 ZNF536 Silent INTRON 2.37E−07 13.6 0 0 14 8 9 70
    kgp2877482 6 1644677 GMDS, GMDS Silent, Silent INTRON 3.47E−07 17.2 0 0 11 4 12 75
    kgp7851536 15 27960322 ? ? ? 3.76E−07 ? 0 0 7 0 16 79
    kgp9348779 15 101900592 PCSK6, PCSK6, PCSK6, Silent, Silent, Silent, Silent, INTRON 3.76E−07 ? 0 0 7 0 16 79
    PCSK6, PCSK6, PCSK6 Silent, Silent
    rs2289333 15 40617209 ? ? ? 5.68E−07 17.9 1 0 9 3 13 76
    kgp2471573 15 40633138 C15orf52 Synonymous_ASA EXON 5.68E−07 17.9 1 0 9 3 13 76
    kgp8598661 6 1627678 GMDS, GMDS Silent, Siltent INTRON 6.01E−07 12.5 1 0 11 6 11 73
    rs16846841 2 197063250 ? ? ? 6.12E−07 41.6 0 0 8 1 15 78
    rs7565256 2 79227275 ? ? ? 6.17E−07 9.1 4 0 14 22 5 56
    kgp12396787 22 27267611 ? ? ? 7.21E−07 41.1 0 0 8 1 15 77
    kgp6535349 15 40614200 ? ? ? 7.54E−07 24.4 0 0 9 2 14 76
    kgp9775757 1 23063465 EPHB2, EPHB2 Silent, Silent INTRON 1.13E−06 9.2 3 0 15 22 5 57
    kgp2151888 2 79295288 ? ? ? 1.87E−06 8.2 3 0 13 19 6 60
    kgp4985243 7 136556162 CHRM2, CHRM2, CHRM2, Silent, Silent, Silent, Silent, INTRON 2.25E−06 9.3 1 0 13 11 9 68
    CHRM2, CHRM2, CHRM2, Silent, Silent, Silent, Silent
    CHRM2, CHRM2
    kgp6870400
    2 79278036 ? ? ? 2.38E−06 7.4 4 0 13 22 6 57
    rs1077476 15 40619743 ? ? ? 2.53E−06 13.1 1 0 9 4 13 74
    kgp2136475 15 40623593 ? ? ? 2.53E−06 13.1 1 0 9 4 13 74
    rs4935590 10 57059483 ? ? ? 2.59E−06 8.2 2 0 12 12 9 67
    rs16907220 10 57059690 ? ? ? 2.59E−06 8.2 2 0 12 12 9 67
    rs1073665 10 57061057 ? ? ? 2.59E−06 8.2 2 0 12 12 9 67
    rs4477500 10 128645821 ? ? ? 2.62E−06 7.3 9 2 10 37 3 39
    kgp9016053 17 69386788 ? ? ? 2.87E−06 10.5 1 0 10 7 10 71
    kgp2617488 3 11849777 TAMM41 Silent INTRON 2.88E−06 ? 0 0 6 0 17 79
    kgp3537954 5 103927513 ? ? ? 2.88E−06 74935934087673200.0 0 0 6 0 17 79
    kgp9400093 5 104031832 ? ? ? 2.88E−06 74935934087673200.0 0 0 6 0 17 79
    kgp3681524 7 145920329 CNTNAP2 Silent INTRON 2.88E−06 45450941538370800.0 0 0 6 0 17 79
    kgp788303 10 23646459 ? ? ? 2.88E−06 74935934087672700.0 0 0 6 0 17 79
    kgp7824246 12 11333716 ? ? ? 2.88E−06 74935934087672700.0 0 0 6 0 17 79
    kgp27533766 12 65501698 WIF1 Silent INTRON 2.88E−06 74935934087672700.0 0 0 6 0 17 79
    kgp4089310 18 7309451 ? ? ? 2.88E−06 ? 0 0 6 0 17 79
    rs17225585 17 69370430 ? ? ? 3.05E−06 10.2 1 0 11 7 11 69
    rs13104183 4 113323634 ALPK1, ALPK1, ALPK1 Silent, Silent, Silent INTRON, EXON 3.43E−06 6.7 4 0 10 16 8 63
    kgp11962282 10 88223587 WAPAL Silent INTRON 3.61E−06 10.5 1 1 10 6 12 73
    rs3934982 2 242926558 ? ? ? 3.66E−06 11.5 1 0 9 5 12 74
    kgp896539 3 135473872 ? ? ? 3.77E−06 10.6 0 0 12 8 10 71
    rs6743255 2 205363596 ? ? ? 4.33E−06 7.7 2 0 13 15 8 64
    kgp5046752 2 179650234 TTN, TTN, TTN, TTN, TTN Silent, Silent, Silent, Silent, INTRON 4.67E−06 34.1 0 0 7 1 16 78
    Silent
    kgp3420885 13 112188913 ? ? ? 4.67E−06 34.1 0 0 7 1 16 78
    kgp3423367 19 54113722 ? ? ? 4.67E−06 34.1 0 0 7 1 16 78
    kgp9522435 19 30951753 ZNF536 Silent INTRON 4.71E−06 20.5 0 0 8 2 15 77
    kgp5544649 19 30958606 ZNF536 Silent INTRON 4.71E−06 20.5 0 0 8 2 15 77
    kgp3185857 22 27269249 ? ? ? 4.71E−06 20.5 0 0 8 2 15 77
    kgp5863276 22 27274898 ? ? ? 4.71E−06 20.5 0 0 8 2 15 77
    rs17825388 17 69380584 ? ? ? 4.74E−06 9.2 1 0 11 8 11 71
    rs1942396 18 69347308 ? ? ? 4.74E−08 9.2 1 0 11 8 11 71
    kgp2575625 2 218219226 DIRC3 Silent INTRON 5.23E−06 8.6 1 0 12 10 10 69
    kgp11688655 2 218219697 DIRC3 Silent INTRON 5.23E−06 8.6 1 0 12 10 10 69
    kgp3778675 2 218226516 DIRC3 Silent INTRON 5.23E−06 8.6 1 0 12 10 10 69
    rs10488907 4 113312105 ALPK1, ALPK1, ALPK1 Silent, Silent, Silent INTRON, EXON 5.36E−06 7.5 2 0 12 13 9 66
    kgp2832863 3 8820301 ? ? ? 5.38E−06 33.7 0 0 7 1 16 77
    kgp6643157 3 13145604 ? ? ? 5.46E−06 20.3 0 0 8 2 15 76
    kgp4292871 22 27274445 ? ? ? 5.46E−06 20.3 0 0 8 2 15 76
    rs6643055 X 111782861 ? ? ? 5.65E−06 18.3 1 0 7 2 15 77
    rs12005792 9 87236739 ? ? ? 6.46E−06 6.8 3 1 15 22 5 56
    rs882829 15 40607689 ? ? ? 6.98E−06 10.6 1 0 9 5 13 74
    kgp1305638 6 122195448 ? ? ? 7.74E−06 29.6 1 0 6 1 16 78
    rs6673115 1 23069649 EPHB2, EPHB2 Silent, Silent INTRON 8.25E−06 6.7 5 1 14 30 4 48
    kgp7380442 22 28746343 TTC28 Silent INTRON 8.80E−06 ? 1 0 5 0 17 79
    kgp4898364 22 29092726 CHEK2, CHEK2, CHEK2 Silent, Silent, Silent INTRON 8.80E−06 ? 1 0 5 0 17 79
    kgp9420863 1 105167334 ? ? ? 9.42E−06 9.0 0 0 13 10 10 69
    kgp100271 1 105186472 ? ? ? 9.42E−06 9.0 0 0 13 10 10 69
    kgp4009576 1 105189899 ? ? ? 9.42E−06 9.0 0 0 13 10 10 69
    kgp11130156 12 20871256 SLCO1C1, SLCO1C1, Silent, Silent, Silent, Silent INTRON 9.52E−06 6.4 2 1 13 13 8 65
    SLCO1C1, SLCO1C1
    rs10746192 12 81942162 PPFIA2, PPFIA2, PPFIA2, Silent, Silent, Silent, Silent, INTRON 9.87E−06 8.0 8 5 15 45 0 29
    PPFIA2, PPFIA2, PPFIA2, Silent, Silent, Silent
    PPF1A2
    kgp8919080
    7 84958459 ? ? ? 9.94E−06 8.9 1 0 10 7 12 72
  • TABLE 30
    Allelic Model, Extreme Response Definition, Genome Wide Placebo Cohort Analysis
    Placebo
    Odds Ratio Allele Allele
    Gene Fisher's (Minor Freq. Freq. DD DD Dd Dd dd dd
    Name Chr Position Gene(s) Mutation Location(s) Exact P Allele) (Cases) (Controls) (Cases) (Controls) (Cases) (Controls) (Cases) (Controls)
    kgp10638
    Figure US20180002753A1-20180104-P00899
    		 3 196573166 ? ? ? 1.00E−06 0.1  7% 44% 0 17 3 35 20 27
    rs1978721
    Figure US20180002753A1-20180104-P00899
    		 19 30966217 ZNF536 Silent INTRON 1.49E−06 24.5 24%  1% 0 0 11 2 12 77
    kgp734452
    Figure US20180002753A1-20180104-P00899
    		 19 30967564 ZNF536 Silent INTRON 1.49E−06 24.5 24%  1% 0 0 11 2 12 77
    rs7252241
    Figure US20180002753A1-20180104-P00899
    		 19 30967836 ZNF536 Silent INTRON 1.49E−06 24.5 24%  1% 0 0 11 2 12 77
    rs1978720
    Figure US20180002753A1-20180104-P00899
    		 19 30968371 ZNF536 Silent INTRON 1.49E−06 24.5 24%  1% 0 0 11 2 12 77
    kgp183404
    Figure US20180002753A1-20180104-P00899
    		 3 196579489 ? ? ? 2.26E−06 0.1  7% 42% 0 14 3 38 20 27
    kgp860737
    Figure US20180002753A1-20180104-P00899
    		 17 20459947 ? ? ? 4.36E−06 0.0  2% 35% 0 10 1 35 20 34
    rs2289333
    Figure US20180002753A1-20180104-P00899
    		 15 40617209 ? ? ? 5.79E−06 16.2 24%  2% 1 0 9 3 13 76
    kgp247157
    Figure US20180002753A1-20180104-P00899
    		 15 40633138 C15orf52 Synonymo
    Figure US20180002753A1-20180104-P00899
    		 EXON 5.79E−06 16.2 24%  2% 1 0 9 3 13 76
    rs7565256
    Figure US20180002753A1-20180104-P00899
    		 2 79227275 ? ? ? 7.04E−06 5.6 48% 14% 4 0 14 22 5 56
    kgp85986
    Figure US20180002753A1-20180104-P00899
    		 6 1627678 GMDS, GM
    Figure US20180002753A1-20180104-P00899
    		 Silent, Silen
    Figure US20180002753A1-20180104-P00899
    		 INTRON 8.30E−06 10.0 28%  4% 1 0 11 6 11 73
    rs1310418
    Figure US20180002753A1-20180104-P00899
    		 4 113323634 ALPK1, ALP
    Figure US20180002753A1-20180104-P00899
    		 Silent, Silen
    Figure US20180002753A1-20180104-P00899
    		 INTRON, E
    Figure US20180002753A1-20180104-P00899
    		 9.26E−06 6.1 41% 10% 4 0 10 16 8 63
    rs4477500
    Figure US20180002753A1-20180104-P00899
    		 12 128645821 ? ? ? 9.70E−06 4.9 64% 26% 9 2 10 37 3 39
    kgp35989
    Figure US20180002753A1-20180104-P00899
    		 4 7649861 SORCS2 Silent INTRON 9.97E−06 0.0  2% 31% 0 7 1 35 22 37
    kgp11164
    Figure US20180002753A1-20180104-P00899
    		 17 20459328 ? ? ? 1.07E−05 0.1  4% 35% 0 9 2 36 21 32
    kgp14616
    Figure US20180002753A1-20180104-P00899
    		 19 30965980 ZNF536 Silent INTRON 1.22E−05 8.2 30%  5% 0 0 14 8 9 71
    rs8112863
    Figure US20180002753A1-20180104-P00899
    		 19 30965063 ZNF536 Silent INTRON 1.38E−05 8.1 30%  5% 0 0 14 8 9 70
    rs2555629
    Figure US20180002753A1-20180104-P00899
    		 4 175430288 HPGD, HPG
    Figure US20180002753A1-20180104-P00899
    		 Silent, Silen
    Figure US20180002753A1-20180104-P00899
    		 INTRON, E
    Figure US20180002753A1-20180104-P00899
    		 1.40E−05 4.6 61% 25% 11 4 6 32 6 43
    kgp25188
    Figure US20180002753A1-20180104-P00899
    		 2 79295288 ? ? ? 1.44E−05 5.6 43% 12% 3 0 13 19 6 60
    kgp553777
    Figure US20180002753A1-20180104-P00899
    		 20 35531097 SAMHD1 Silent INTRON 1.68E−05 5.5 41% 11% 4 1 11 16 8 62
    kgp40047
    Figure US20180002753A1-20180104-P00899
    		 20 35539858 SAMHD1 Silent INTRON 1.68E−05 5.5 41% 11% 4 1 11 16 8 62
    kgp97757
    Figure US20180002753A1-20180104-P00899
    		 1 23068465 EPBB2, EP
    Figure US20180002753A1-20180104-P00899
    		 Silent, Silen
    Figure US20180002753A1-20180104-P00899
    		 INTRON 1.68E−05 5.2 46% 14% 3 0 15 22 5 57
    kgp68704
    Figure US20180002753A1-20180104-P00899
    		 2 79278036 ? ? ? 1.68E−05 5.2 46% 14% 4 0 13 22 6 57
    rs763318 4 12963574 ? ? ? 1.70E−05 5.4 83% 47% 15 20 8 33 0 25
    rs4935590
    Figure US20180002753A1-20180104-P00899
    		 10 57059483 ? ? ? 1.73E−05 6.5 35%  8% 2 0 12 12 9 67
    rs1690722
    Figure US20180002753A1-20180104-P00899
    		 10 57059690 ? ? ? 1.73E−05 6.5 35%  8% 2 0 12 12 9 67
    rs1073665
    Figure US20180002753A1-20180104-P00899
    		 10 57061057 ? ? ? 1.73E−05 6.5 35%  8% 2 0 12 12 9 67
    kgp59692
    Figure US20180002753A1-20180104-P00899
    		 4 12976777 ? ? ? 1.73E−05 4.5 70% 34% 11 10 10 33 2 36
    kgp28774
    Figure US20180002753A1-20180104-P00899
    		 6 1644677 GMDS, GM
    Figure US20180002753A1-20180104-P00899
    		 Silent, Silen
    Figure US20180002753A1-20180104-P00899
    		 INTRON 1.81E−05 12.1 24%  3% 0 0 11 4 12 75
    rs4916561
    Figure US20180002753A1-20180104-P00899
    		 3 196576109 ? ? ? 1.84E−05 0.1  7% 38% 0 12 3 36 20 30
    kgp22823
    Figure US20180002753A1-20180104-P00899
    		 X 31244702 DMD, DMD
    Figure US20180002753A1-20180104-P00899
    		 Silent, Silen
    Figure US20180002753A1-20180104-P00899
    		 INTRON 1.91E−05 0.1  2% 30% 0 17 1 14 22 48
    rs1077476
    Figure US20180002753A1-20180104-P00899
    		 15 40619743 ? ? ? 2.00E−05 11.9 24%  3% 1 0 9 4 13 74
    kgp21364
    Figure US20180002753A1-20180104-P00899
    		 15 40623593 ? ? ? 2.00E−05 11.9 24%  3% 1 0 9 4 13 74
    kgp785153
    Figure US20180002753A1-20180104-P00899
    		 15 27960322 ? ? ? 2.04E−05 ? 15%  0% 0 0 7 0 16 79
    kgp934877
    Figure US20180002753A1-20180104-P00899
    		 15 101900592 PCSK6, PCS
    Figure US20180002753A1-20180104-P00899
    		 Silent, Silen
    Figure US20180002753A1-20180104-P00899
    		 INTRON 2.04E−05 ? 15%  0% 0 0 7 0 16 79
    Figure US20180002753A1-20180104-P00899
    indicates data missing or illegible when filed
  • TABLE 31
    Genotype Model, Extreme Response Definition, Genome Wide Placebo Cohort Analysis
    Placebo
    Allele Allele
    Gene Fisher's Freq. Freq. DD DD Dd Dd dd dd
    Name Chr Position Gene(s) Mutation Locations(s) Exact P (Cases) (Controls) (Cases) (Controls) (Cases) (Controls) (Cases) (Controls)
    rs1978721 19 30966217 ZNF536 Silent INTRON 4.57E−07 24% 1% 0 0 11 2 12 77
    kgp7344529 19 30967564 ZNF536 Silent INTRON 4.57E−07 24% 1% 0 0 11 2 12 77
    rs7252241 19 30967836 ZNF536 Silent INTRON 4.57E−07 24% 1% 0 0 11 2 12 77
    rs1978720 19 30968371 ZNF536 Silent INTRON 4.57E−07 24% 1% 0 0 11 2 12 77
    kgp146166 19 30965980 ZNF536 Silent INTRON 1.91E−06 30% 5% 0 0 14 8 9 71
    rs8112863 19 30965063 ZNF536 Silent INTRON 2.19E−06 30% 5% 0 0 14 8 9 70
    rs7565256 2 79227275 ? ? ? 2.25E−06 48% 14%  4 0 14 22 5 56
    kgp6295377 19 30953846 ZNF536 Silent INTRON 3.08E−06 20% 2% 0 1 9 1 14 77
    rs4477500 12 128645821 ? ? ? 3.54E−06 64% 26%  9 2 10 37 3 39
    rs11705401 22 37678096 ? ? ? 3.97E−06 13% 37%  2 4 2 50 19 25
    kgp2536097 6 25181978 ? ? ? 5.58E−06 74% 41%  14 8 6 48 3 23
    rs2109066 19 28835327 ? ? ? 5.93E−06  9% 31%  2 5 0 39 21 35
    kgp2877482 6 1644677 GMDS, GM
    Figure US20180002753A1-20180104-P00899
    		 Silent, Silen
    Figure US20180002753A1-20180104-P00899
    		 INTRON 6.14E−06 24% 3% 0 0 11 4 12 75
    kgp9775757 1 23068465 EPHB2, EP
    Figure US20180002753A1-20180104-P00899
    		 Silent, Silen
    Figure US20180002753A1-20180104-P00899
    		 INTRON 6.34E−06 46% 14%  3 0 15 22 5 57
    kgp6601755 19 28886975 ? ? ? 6.76E−06 11% 33%  2 3 1 45 19 30
    kgp8598661 6 1627678 GMDS, GM
    Figure US20180002753A1-20180104-P00899
    		 Silent, Silen
    Figure US20180002753A1-20180104-P00899
    		 INTRON 8.89E−06 28% 4% 1 0 11 6 11 73
    rs2159327 19 28835571 ? ? ? 9.75E−06  9% 31%  2 5 0 39 20 35
    kgp2151888 2 79295288 ? ? ? 9.91E−06 43% 12%  3 0 13 19 6 60
    rs2289333 15 40617209 ? ? ? 1.01E−05 24% 2% 1 0 9 3 13 76
    kgp2471573 15 40633138 C15orf52 Synonymo
    Figure US20180002753A1-20180104-P00899
    		 EXON 1.01E−05 24% 2% 1 0 9 3 13 76
    kgp6870400 2 79278036 ? ? ? 1.27E−05 46% 14%  4 0 13 22 6 57
    kgp6850713 19 28885593 ? ? ? 1.28E−05  7% 32%  1 3 1 44 20 32
    kgp7851536 15 27960322 ? ? ? 1.33E−05 15% 0% 0 0 7 0 16 79
    kgp9348779 15 101900592 PCSK6, PCS
    Figure US20180002753A1-20180104-P00899
    		 Silent, Silen
    Figure US20180002753A1-20180104-P00899
    		 INTRON 1.33E−05 15% 0% 0 0 7 0 16 79
    rs995834 19 28866596 ? ? ? 1.39E−05 11% 32%  2 4 1 43 20 32
    rs1773631 10 25665449 GPR158 Silent INTRON 1.43E−05 27% 7% 0 2 12 7 10 70
    kgp8034516 8 97282138 PTDSS1 Silent INTRON 1.46E−05 20% 3% 0 1 9 2 14 76
    rs13280716 8 97282560 PTDSS1 Silent INTRON 1.46E−05 20% 3% 0 1 9 2 14 76
    kgp303315 8 97283313 PTDSS1 Silent INTRON 1.46E−05 20% 3% 0 1 9 2 14 76
    kgp5433489 8 97302091 PTDSS1 Silent INTRON 1.46E−05 20% 3% 0 1 9 2 14 76
    rs17707686 8 97312442 PTDSS1 Silent INTRON 1.46E−05 20% 3% 0 1 9 2 14 76
    rs727047 22 37677719 ? ? ? 1.48E−OS 13% 35%  2 4 2 48 19 27
    kgp7521451 8 97297894 PTDSS1 Silent INTRON 1.49E−05 20% 4% 0 2 9 2 13 74
    rs2056136 12 20867893 SLCO1C1,
    Figure US20180002753A1-20180104-P00899
    		 Silent, Silen
    Figure US20180002753A1-20180104-P00899
    		 INTRON 1.50E−05 35% 9% 1 1 14 12 8 66
    rs10746192 12 81942162 PPFIA2, PP
    Figure US20180002753A1-20180104-P00899
    		 Silent, Silen
    Figure US20180002753A1-20180104-P00899
    		 INTRON 1.54E−05 67% 35%  8 5 15 45 0 29
    rs2555629 4 175430288 HPGD, HPG
    Figure US20180002753A1-20180104-P00899
    		 Silent, Silen
    Figure US20180002753A1-20180104-P00899
    		 INTRO, E
    Figure US20180002753A1-20180104-P00899
    		 1.57E−05 61% 25%  11 4 6 32 6 43
    kgp12537012 8 97285429 PTDSS1 Silent INTRON 1.60E−05 20% 3% 0 1 9 2 14 75
    rs9969509 8 97293953 PTDSS1 Silent INTRON 1.60E−05 20% 3% 0 1 9 2 14 75
    kgp6535349 15 40614200 ? ? ? 1.60E−05 20% 1% 0 0 9 2 14 76
    rs16846841 2 197063250 ? ? ? 1.73E−05 17% 1% 0 0 8 1 15 78
    kgp12396787 22 27267611 ? ? ? 1.87E−05 17% 1% 0 0 8 1 15 77
    kgp4985243 7 136556162 CHRM2, C
    Figure US20180002753A1-20180104-P00899
    		 Silent, Silen
    Figure US20180002753A1-20180104-P00899
    		 INTRON 1.91E−05 33% 7% 1 0 13 11 9 68
    rs4935590 10 57059483 ? ? ? 1.98E−05 35% 8% 2 0 12 12 9 67
    rs16907220 10 57059690 ? ? ? 1.98E−05 35% 8% 2 0 12 12 9 67
    rs1073665 10 57061057 ? ? ? 1.98E−05 35% 8% 2 0 12 12 9 67
    rs2292275 1 163292217 NUF2, NU
    Figure US20180002753A1-20180104-P00899
    		 Silent, Silen
    Figure US20180002753A1-20180104-P00899
    		 INTRON, E
    Figure US20180002753A1-20180104-P00899
    		 2.11E−05 57% 27%  4 6 18 30 1 42
    rs7962380 12 128643018 ? ? ? 2.27E−05 67% 34%  11 5 9 44 3 30
    kgp10638512 3 196573166 ? ? ? 2.34E−05  7% 44%  0 17 3 35 20 27
    Figure US20180002753A1-20180104-P00899
    indicates data missing or illegible when filed
    • 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.
    • 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 3.6×10−32, odds ratio 14.5 (95% CI 8.6-24.4)) and “Baseline Expanded Disability Status Scale (EDSS) Score” (combined cohorts p-value 5.9×10−10, 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.
  • 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.
    • 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.
    • 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, ALOX5AP, 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-DRB1*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/DOB, p<0.001 in Gala, p<0.001 in Forte, p-value 5.33E-06, odds ratio 0.53; rs1894408 in HLA-DOB/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 rs10162089 in ALOX5AP, 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 (rs16886004 in MAGI2, p<0.005 in Gala, p<0.00005 in Forte, p-value 0.00000098 combined, odds ratio 2.8; kgp24415534 in the ZAK/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 44b
    Genotypic Model Characteristics of Individual SNPs in Model
    SNP - rs SNP - kgp
    rs759458
    rs139890339 kgp24415534
    rs3135391
    rs28724893 kgp8817856
    rs1894408
    rs16886004
    rs80191572 kgp6214351
    rs10162089
    rs1789054 kgp7747883
    rs117602254 kgp6599438
    rs73166319 kgp8110667
    • 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 (FIG. 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) (FIG. 2), while maximizing the number of predicted responders (predicted responders >60%) (FIG. 3) was selected. This threshold also coincided with the lowest p-value of all the thresholds examined (Chi square p-value 6.1×10−46, odds ratio 19.9) (FIG. 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% (FIG. 5).
  • Compared to the “predicted non-responders”, the “predicted responders” exhibited a 2.7-fold improved response rate (91% vs. 34%) (P<10−40); 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 2.2×10−25).
  • 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 7.70×10−32).
  • The number of T1 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 (40 mg Copaxone TIW, or placebo) and one year FORTE study (20 mg Copaxone or 40 mg 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) (FIG. 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 (FIG. 7). In the high response/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.
  • The signature was associated with Copaxone®-, and not placebo-response since 129 placebo-treated patients were predicted to be high Copaxone®-responders based on the signature. These patients of 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® naïve 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
    Study
    GALA FORTE
    Population ITT PGx ITT PGx
    N 1404 1158 (82%) 1155 604 (52%)
    Age (Ave ± SD) 37.6 ± 9.35 37.71 ± 9.38  36.27 ± 8.99  35.97 ± 8.82 
    Gender (% Female) 67.90% 67.90% 71.70% 72.20%
    Caucasian 97.60% 97.90% 95.20%   100%
    Disease duration (years) 3.76 ± 4.9  3.74 ± 4.94 3.16 ± 4.41 2.86 ± 4.05
    No. of Relapses in the Last 2 Years 1.91 ± 0.91 1.89 ± 0.92 2.01 ± 1.00 1.97 ± 0.89
    Baseline EDSS 2.79 ± 1.23 2.77 ± 1.21 2.12 ± 1.12 2.13 ± 1.12
  • TABLE 46
    Genes of the 11 SNP Signature
    GALA FORTE
    GA-treated GA-treated
    Genes of 11-SNP Signature * OR OR
    HLA-DRB1*15:01 0.7 0.6
    HLA gene region 1.7 1.8
    Myelin basic protein gene 0.7 0.6
    Receptor-tyrosine protein phosphatase gene 0.2 0.3
    Arachidonate 5-lipoxygenase-activating 1.6 1.6
    protein
    Membrane-associated guanylate kinase gene 2.2 5.6
    Solute carrier family 5 (low affinity glucose Inf. Inf.
    co-transporter) gene
    HLA gene region 0.5 0.5
    Mitogen-activated protein kinase gene region  0.05 0.1
    Radiation resistance-associated gene protein 0.2 0.1
    Glutamate/neutral amino acid transporter 3.3 1.9
    * All SNPs met statistical significance
    • Example 19
  • Additional genotyping of the 11 SNPs of the predictive model (rs3135391, rs1894408, kgp7747883, kpg6599438, rs10162089, rs16886004, 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 (FIG. 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” (0.185±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 at least one single nucleotide polymorphisms (SNPs) are selected from the group consisting of kgp24415534, kgp6214351, kgp6599438, kgp7747883, kgp8110667, kgp8817856, rs10162089, rs16886004, rs1894408, 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, rs10162089, rs16886004, rs1894408, 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, rs10162089, rs16886004, rs1894408, 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, rs10162089, rs16886004, rs1894408 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, rs10162089, rs16886004, rs1894408, 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, rs10162089, rs16886004, rs1894408, rs3135391, and rs759458.
  • In some embodiments, one or more SNPs is selected from the group consisting of kgp24415534, kgp6214351, kgp6599438, kgp7747883, kgp8110667, kgp8817856, rs10162089, rs16886004, rs1894408, 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, rs10162089, rs16886004, rs1894408, 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 FIG. 8 or FIG. 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, rs10162089, rs16886004, rs1894408, rs3135391, and rs759458; or
      • wherein the at least one SNP is selected from the group consisting of kgp24415534, kgp6214351, kgp6599438, kgp7747883, kgp8110667, kgp8817856, rs10162089, rs16886004, rs1894408, and rs759458.
  • In some embodiments, the kit further comprising applying the algorithm depicted in FIG. 8 or FIG. 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” (FIG. 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” (0.290±0.03) (p-value=0.0113).
  • In some embodiments, the method further comprising applying the algorithm depicted in FIG. 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 FIG. 8 or FIG. 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 FIG. 8 or FIG. 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, rs1894408, kpg6599438, rs10162089, rs16886004, 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, rs10162089, rs16886004, rs1894408, rs3135391, and rs759458.
  • In some embodiments, one or more SNPs is selected from the group consisting of kgp24415534, kgp6214351, kgp6599438, kgp8110667, kgp8817856, rs10162089, rs16886004, rs1894408, 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, rs10162089, rs16886004, rs1894408, 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, rs10162089, rs16886004, rs1894408, 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, rs10162089, rs16886004, rs1894408, rs3135391, and rs759458; or
      • wherein the at least one SNP is selected from the group consisting of kgp24415534, kgp6214351, kgp6599438, kgp8110667, kgp8817856, rs10162089, rs16886004, rs1894408, and rs759458.
  • In some embodiments, the method further comprising applying the algorithm depicted in FIG. 8 or FIG. 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 FIG. 8 or FIG. 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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Claims (21)

1-96. (canceled)
97. 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 the genotype of the subject at a single nucleotide polymorphism (SNP) kgp7747883;
(ii) identifying the subject as a predicted responder to glatiramer acetate if the genotype of the subject contains one or more G alleles at the location of kgp7747883; 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.
98. The method of claim 97, wherein 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.
99. The method of claim 97, wherein the pharmaceutical composition is
a unit dose of a 1 ml aqueous solution comprising 40 mg of glatiramer acetate; or
a unit dose of a 1 ml aqueous solution comprising 20 mg of glatiramer acetate.
100. The method of claim 97, wherein the human subject is a naïve patient, has been previously administered glatiramer acetate, or has been previously administered a multiple sclerosis drug other than glatiramer acetate.
101. The method of claim 97, which comprises determining the genotype of the subject at 4 SNPs selected from the group consisting of kgp24415534, kgp6214351, kgp6599438, kgp7747883, kgp8110667, kgp8817856, rs10162089, rs16886004, rs1894408, rs3135391, and rs759458.
102. The method of claim 97, further comprising determining the genotype of the subject at SNP kgp6214351, and 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 kgp6214351.
103. The method of claim 102, further comprising determining the genotype of the subject at SNP kgp24415534, and identifying the subject as a predicted responder to glatiramer acetate if the genotype of the subject contains one or more G alleles at the location of kgp24415534.
104. The method of claim 103, further comprising determining the genotype of the subject at SNP kgp8817856, and identifying the subject as a predicted responder to glatiramer acetate if the genotype of the subject contains one or more G alleles at the location of kgp8817856.
105. The method of claim 97, which comprises determining the genotype of the subject at SNPs kgp6214351, kgp8817856, kgp7747883 and kgp24415534.
106. The method of claim 105, wherein the patient is identified as a responder if the genotype of the subject contains one or more G alleles at the locations of kgp7747883, kgp24415534, and kgp8817856 and one or more A alleles at the location of kgp6214351.
107. 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 the steps of:
(i) determining using a probe or primer the genotype of the subject at single nucleotide polymorphism (SNP) kgp7747883 and
(ii) identifying the subject as a predicted responder to glatiramer acetate if the genotype of the subject contains
one or more G alleles at the location of kgp7747883; or
identifying the human subject as a predicted non-responder to glatiramer acetate if the genotype of the subject contains
no G alleles at the location of kgp7747883,
thereby 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.
108. The method of claim 107, which comprises determining the genotype of the subject at 4 SNPs selected from the group consisting of kgp24415534, kgp6214351, kgp6599438, kgp7747883, kgp8110667, kgp8817856, rs10162089, rs16886004, rs1894408, rs3135391, and rs759458.
109. The method of claim 108, further comprising determining using a probe or primer the genotype of the subject at SNP kgp6214351, and
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 kgp6214351 or
identifying the subject as a predicted non-responder to glatiramer acetate if the genotype of the subject contains no A alleles at the location of kgp6214351.
110. The method of claim 109, further comprising determining using a probe or primer the genotype of the subject at SNP kgp24415534, and
identifying the subject as a predicted responder to glatiramer acetate if the genotype of the subject contains one or more G alleles at the location of kgp24415534 or
identifying the subject as a predicted non-responder to glatiramer acetate if the genotype of the subject contains no G alleles at the location of kgp24415534.
111. The method of claim 110, further comprising determining using a probe or primer the genotype of the subject at SNP kgp8817856, and
identifying the subject as a predicted responder to glatiramer acetate if the genotype of the subject contains one or more G alleles at the location of kgp8817856 or
identifying the subject as a predicted non-responder to glatiramer acetate if the genotype of the subject contains no G alleles at the location of kgp8817856.
112. The method of claim 107, which comprises determining using a probe or primer the genotype of the subject at SNPs kgp6214351, kgp8817856, kgp7747883 and kgp24415534.
113. The method of claim 112, wherein the patient is identified as a responder if the genotype of the subject contains one or more G alleles at the locations of kgp7747883, kgp24415534, and kgp8817856 and one or more A alleles at the location of kgp6214351, or wherein the subject is identified as a non-responder if the genotype of the subject contains no G alleles at the location of kgp7747883, no G alleles at the location of kgp24415534, no G alleles at the location of kgp8817856, or no A alleles at the location of kgp6214351.
114. The method of claim 97,
(a) wherein the genotype is determined from a nucleic acid-containing sample that has been obtained from the subject;
(b) wherein 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;
(c) wherein the genotype is determined using at least one pair of PCR primers and at least one probe;
(d) wherein determining the genotype comprises using an array, wherein the array is selected from the group consisting of a gene chip, and a TaqMan Open Array, wherein if the array is a gene chip, then 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;
(e) wherein the genotype of the subject at the location corresponding to the location of said SNP 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 said SNP;
(f) wherein the genotype of the subject at the location corresponding to the location of said SNP is determined by indirect genotyping, and
the indirect genotyping allows identification of the genotype of the subject at the location corresponding to the location of said SNP with a probability of at least 85%, at least 90%, or at least 99%;
(g) further comprising determining the log number of relapses in the last two years for the human subject;
(h) further comprising determining the baseline Expanded Disability Status Scale (EDSS) score for the human subject; or
(i) wherein determining the genotype of the subject at the location corresponding to the location of said SNP comprises:
obtaining DNA from a sample that has been obtained from the subject;
optionally amplifying the DNA; and
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, wherein
i) the array comprises a plurality of probes suitable for determining the identity of the one or more SNPs; or
ii) the array is a gene chip, and the gene chip is a whole genome genotyping array.
115. The method of claim 97, wherein
(a) step (i) further comprises determining a genotype of the subject at one or more single nucleotide polymorphism (SNP):
kgp6214351, kgp24415534, kgp10090631, kgp1009249, kgp10152733, kgp10224254, kgp10305127, kgp10351364, kgp10372946, kgp10404633, kgp10412303, kgp10523170, kgp1054273, kgp10558725, kgp10564659, kgp10591989, kgp10594414, kgp10619195, kgp10620244, kgp10632945, kgp10633631, kgp10679353, kgp10788130, kgp10826273, kgp10910719, kgp10922969, kgp10948564, kgp10967046, kgp10974833, kgp1098237, kgp11002881, kgp11010680, kgp11077373, kgp11141512, kgp11206453, kgp11210903, kgp1124492, kgp11281589, kgp11285862, kgp11328629, kgp11356379, kgp11407560, kgp11453406, kgp11467007, kgp11514107, kgp11543962, kgp11580695, kgp11627530, kgp11633966, kgp11686146, kgp11702474, kgp11711524, kgp11768533, kgp11804835, kgp11843177, kgp12008955, kgp12083934, kgp12182745, kgp12230354, kgp1224440, kgp12371757, kgp124162, kgp12426624, kgp12557319, kgp1285441, kgp13161760, kgp1355977, kgp1371881, kgp15390522, kgp1683448, kgp1688752, kgp1699628, kgp1753445, kgp1779254, kgp1786079, kgp18379774, kgp18432055, kgp18525257, kgp1912531, kgp19568724, kgp20163979, kgp2023214, kgp2045074, kgp20478926, kgp2092817, kgp21171930, kgp2245775, kgp2262166, kgp22778566, kgp22793211, kgp22811918, kgp22823022, kgp2282938, kgp2299675, kgp23298674, kgp2356388, kgp23672937, kgp23737989, kgp2388352, kgp2391411, kgp24131116, 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, 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, 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, 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, rs10049206, rs10124492, rs10125298, rs10162089, rs10201643, rs10203396, rs10251797, rs10278591, rs10489312, rs10492882, rs10498793, rs10501082, rs10510774, rs10512340, rs1079303, rs10815160, rs10816302, rs10841322, rs10841337, rs10954782, rs11002051, rs11022778, rs11029892, rs11029907, rs11029928, rs11083404, rs11085044, rs11136970, rs11147439, rs11192461, rs11192469, rs11559024, rs1157449, rs11648129, rs11691553, rs12013377, rs12494712, rs12943140, rs13002663, rs13394010, rs13415334, rs13419758, rs1380706, rs1387768, rs1410779, rs1478682, rs1508102, rs1532365, rs1544352, rs1545223, rs1579771, rs1604169, rs1621509, rs1644418, rs16886004, rs16895510, rs16901784, rs16927077, rs16930057, rs17029538, rs17224858, rs17238927, rs17329014, rs17400875, rs17449018, rs17577980, rs17638791, rs1858973, rs1886214, rs1894406, rs1894407, rs1894408, rs196295, rs196341, rs196343, rs197523, rs1979992, rs1979993, 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 or rs9931211, 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 kgp6214351, kgp10152733, kgp10224254, kgp10305127, kgp10351364, kgp10372946, kgp10404633, kgp10564659, kgp10591989, kgp10594414, kgp10619195, kgp10620244, kgp10633631, kgp10974833, kgp11002881, kgp11285862, kgp11328629, kgp11407560, kgp11514107, kgp11627530, kgp11702474, kgp11711524, kgp11768533, kgp11804835, kgp12083934, kgp12182745, kgp12230354, kgp1224440, kgp124162, kgp12557319, kgp1371881, kgp1699628, kgp1753445, kgp1779254, kgp1786079, kgp18379774, kgp18525257, 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, 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, rs10049206, rs10124492, rs10125298, rs10162089, rs10203396, rs10251797, rs10278591, rs10489312, rs10492882, rs10498793, rs10501082, rs10510774, rs10512340, rs10815160, rs10816302, rs10841337, rs11029892, rs11029928, rs11192469, rs11559024, rs11648129, rs12013377, rs13394010, rs13415334, rs1478682, rs1544352, rs1545223, rs1604169, rs1621509, rs1644418, rs17029538, rs17400875, rs17449018, rs17577980, rs1858973, rs1894406, rs1894407, rs197523, 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,
one or more C alleles at the location of kgp10910719, kgp11077373, kgp11453406, kgp12426624, kgp2045074, kgp22811918, kgp23298674, kgp2709692, kgp28687699, kgp3496814, kgp3669685, kgp3730395, kgp4056892, kgp4370912, kgp5053636, kgp5216209, kgp5292386, kgp6023196, kgp652534, kgp7059449, kgp7189498, kgp7521990, kgp7792268, kgp8303520, kgp9320791, kgp9795732, rs10201643, rs11022778, rs11136970, rs11147439, rs11691553, rs1579771, rs16901784, rs2136408, rs2325911, rs241443, rs2857104, rs3803277, rs3885907, rs4738738, rs4894701, rs502530, rs6032205, rs6687976, rs6718758, rs6835202, rs714342, rs7524868, rs7844274, rs9393727 or rs9671182,
one or more G alleles at the location of kgp24415534, kgp10090631, kgp1009249, kgp10412303, kgp10523170, kgp1054273, kgp10558725, kgp10632945, kgp10679353, kgp10788130, kgp10826273, kgp10922969, kgp10948564, kgp10967046, kgp1098237, kgp11010680, kgp11141512, kgp11206453, kgp11210903, kgp1124492, kgp11281589, kgp11356379, kgp11467007, kgp11543962, kgp11580695, kgp11633966, kgp11686146, kgp11843177, kgp12008955, kgp12371757, kgp1285441, kgp13161760, kgp1355977, kgp15390522, kgp1683448, kgp1688752, kgp1912531, kgp19568724, kgp2092817, kgp2245775, kgp22793211, kgp22823022, kgp2282938, kgp2299675, kgp2356388, kgp23672937, kgp23737989, kgp2388352, kgp2391411, kgp24131116, 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, kgp8107491, kgp8169636, kgp8174785, kgp8183049, kgp8192546, kgp8335515, kgp8437961, kgp8440036, kgp85534, kgp8599417, kgp8767692, kgp8777935, kgp8869954, kgp9071686, kgp9078300, kgp9354820, kgp9421884, kgp9551947, kgp9601362, kgp9627406, kgp9699754, kgp971582, kgp9854133, rs1079303, rs10841322, rs10954782, rs11002051, rs11029907, rs11083404, rs11085044, rs11192461, rs1157449, rs12494712, rs12943140, rs13002663, rs13419758, rs1380706, rs1387768, rs1410779, rs1508102, rs1532365, rs16886004, rs16895510, rs16927077, rs16930057, rs17224858, rs17238927, rs17329014, rs17638791, rs1886214, rs1894408, rs196295, rs196341, rs196343, rs1979992, rs1979993, rs2043136, rs2071470, rs2074037, rs2175121, rs241435, rs241447, rs241451, 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, or
one or more T alleles at the location of kgp18432055, kgp279772, kgp3991733 or kgp7242489,
thereby 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;
(b) step (i) further comprises determining a genotype of the subject at one or more single nucleotide polymorphism (SNP): rs10988087, rs1573706, rs17575455, rs2487896, rs3135391, rs6097801 or 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 rs10988087, one or more C alleles at the location of rs17575455, or one or more G alleles at the location of rs1573706, rs2487896, rs3135391, rs6097801 or rs947603,
thereby 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
(c) step (i) further comprises determining a genotype of the subject at one or more single nucleotide polymorphism (SNP):
kgp10148554, kgp10215554, kgp10762962, kgp10836214, kgp10989246, kgp11285883, kgp11604017, kgp11755256, kgp1211163, kgp12253568, kgp12562255, kgp1432800, kgp1682126, kgp1758575, 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, rs10038844, rs1026894, rs10495115, rs11562998, rs11563025, rs11750747, rs11947777, rs12043743, rs12233980, rs12341716, rs12472695, rs12881439, rs13168893, rs13386874, rs1357718, rs1393037, rs1393040, rs1397481, rs1474226, rs1508515, rs1534647, rs16846161, rs1715441, rs17187123, rs17245674, rs17419416, rs1793174, rs1883448, rs1905248, 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 or rs9953274, 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 kgp10762962, kgp11285883, kgp11604017, kgp1211163, kgp12253568, kgp12562255, 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, rs10038844, rs10495115, rs11750747, rs12341716, rs12881439, rs13168893, rs1393040, rs1474226, rs1534647, rs1715441, rs17187123, rs17245674, rs17419416, rs1793174, rs1883448, rs1905248, rs263247, rs34647183, rs35615951, rs3847233, rs3858038, rs4740708, rs528065, rs6459418, rs6577395, rs6811337, rs7680970, rs7684006, rs7698655, rs7961005, rs8018807, rs9948620 or rs9953274,
one or more C alleles at the location of kgp10836214, kgp1432800, kgp22839559, kgp6301155, kgp6828277, rs2354380, rs2662, rs3858035, rs3894712, rs4797764 or rs7696391,
one or more G alleles at the location of kgp10148554, kgp10215554, kgp10989246, kgp11755256, kgp1682126, kgp1758575, kgp2920925, kgp3287349, kgp3420309, kgp3488270, kgp3598966, kgp3697615, kgp4137144, kgp5691690, kgp5924341, kgp5949515, kgp6213972, kgp625941, kgp6889327, kgp6990559, kgp7778345, kgp7932108, kgp8847137, kgp956070, rs1026894, rs11562998, rs11563025, rs11947777, rs12233980, rs12472695, rs13386874, rs1357718, rs1393037, rs1397481, rs1508515, rs16846161, rs209568, rs2618065, rs28993969, rs3768769, rs3858034, rs3858036, rs4978567, rs7119480, rs7123506, rs7231366, rs7819949, rs7846783, rs7949751, rs8000689, rs961090 or rs967616, or
one or more T alleles at the location of rs12043743,
thereby identifying a human subject afflicted with multiple sclerosis or a single clinical attack consistent with multiple sclerosis as a predicted responder to glatiramer acetate.
116. The method of claim 107, wherein
(a) step (i) further comprises determining a genotype of the subject at one or more single nucleotide polymorphism (SNP):
kgp10090631, kgp1009249, kgp10152733, kgp10224254, kgp10305127, kgp10351364, kgp10372946, kgp10404633, kgp10412303, kgp10523170, kgp1054273, kgp10558725, kgp10564659, kgp10591989, kgp10594414, kgp10619195, kgp10620244, kgp10632945, kgp10633631, kgp10679353, kgp10788130, kgp10826273, kgp10910719, kgp10922969, kgp10948564, kgp10967046, kgp10974833, kgp1098237, kgp11002881, kgp11010680, kgp11077373, kgp11141512, kgp11206453, kgp11210903, kgp1124492, kgp11281589, kgp11285862, kgp11328629, kgp11356379, kgp11407560, kgp11453406, kgp11467007, kgp11514107, kgp11543962, kgp11580695, kgp11627530, kgp11633966, kgp11686146, kgp11702474, kgp11711524, kgp11768533, kgp11804835, kgp11843177, kgp12008955, kgp12083934, kgp12182745, kgp12230354, kgp1224440, kgp12371757, kgp124162, kgp12426624, kgp12557319, kgp1285441, kgp13161760, kgp1355977, kgp1371881, kgp15390522, kgp1683448, kgp1688752, kgp1699628, kgp1753445, kgp1779254, kgp1786079, kgp18379774, kgp18432055, kgp18525257, kgp1912531, kgp19568724, 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, 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, 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, rs10049206, rs10124492, rs10125298, rs10162089, rs10201643, rs10203396, rs10251797, rs10278591, rs10489312, rs10492882, rs10498793, rs10501082, rs10510774, rs10512340, rs1079303, rs10815160, rs10816302, rs10841322, rs10841337, rs10954782, rs11002051, rs11022778, rs11029892, rs11029907, rs11029928, rs11083404, rs11085044, rs11136970, rs11147439, rs11192461, rs11192469, rs11559024, rs1157449, rs11648129, rs11691553, rs12013377, rs12494712, rs12943140, rs13002663, rs13394010, rs13415334, rs13419758, rs1380706, rs1387768, rs1410779, rs1478682, rs1508102, rs1532365, rs1544352, rs1545223, rs1579771, rs1604169, rs1621509, rs1644418, rs16886004, rs16895510, rs16901784, rs16927077, rs16930057, rs17029538, rs17224858, rs17238927, rs17329014, rs17400875, rs17449018, rs17577980, rs17638791, rs1858973, rs1886214, rs1894406, rs1894407, rs1894408, rs196295, rs196341, rs196343, rs197523, rs1979992, rs1979993, 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, and
ii) 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 kgp10152733, kgp10224254, kgp10305127, kgp10351364, kgp10372946, kgp10404633, kgp10564659, kgp10591989, kgp10594414, kgp10619195, kgp10620244, kgp10633631, kgp10974833, kgp11002881, kgp11285862, kgp11328629, kgp11407560, kgp11514107, kgp11627530, kgp11702474, kgp11711524, kgp11768533, kgp11804835, kgp12083934, kgp12182745, kgp12230354, kgp1224440, kgp124162, kgp12557319, kgp1371881, kgp1699628, kgp1753445, kgp1779254, kgp1786079, kgp18379774, kgp18525257, 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, rs10049206, rs10124492, rs10125298, rs10162089, rs10203396, rs10251797, rs10278591, rs10489312, rs10492882, rs10498793, rs10501082, rs10510774, rs10512340, rs10815160, rs10816302, rs10841337, rs11029892, rs11029928, rs11192469, rs11559024, rs11648129, rs12013377, rs13394010, rs13415334, rs1478682, rs1544352, rs1545223, rs1604169, rs1621509, rs1644418, rs17029538, rs17400875, rs17449018, rs17577980, rs1858973, rs1894406, rs1894407, rs197523, 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,
one or more C alleles at the location of kgp10910719, kgp11077373, kgp11453406, kgp12426624, kgp2045074, kgp22811918, kgp23298674, kgp2709692, kgp28687699, kgp3496814, kgp3669685, kgp3730395, kgp4056892, kgp4370912, kgp5053636, kgp5216209, kgp5292386, kgp6023196, kgp652534, kgp7059449, kgp7189498, kgp7521990, kgp7792268, kgp8303520, kgp9320791, kgp9795732, rs10201643, rs11022778, rs11136970, rs11147439, rs11691553, rs1579771, rs16901784, rs2136408, rs2325911, rs241443, rs2857104, rs3803277, rs3885907, rs4738738, rs4894701, rs502530, rs6032205, rs6687976, rs6718758, rs6835202, rs714342, rs7524868, rs7844274, rs9393727 or rs9671182,
one or more G alleles at the location of kgp10090631, kgp1009249, kgp10412303, kgp10523170, kgp1054273, kgp10558725, kgp10632945, kgp10679353, kgp10788130, kgp10826273, kgp10922969, kgp10948564, kgp10967046, kgp1098237, kgp11010680, kgp11141512, kgp11206453, kgp11210903, kgp1124492, kgp11281589, kgp11356379, kgp11467007, kgp11543962, kgp11580695, kgp11633966, kgp11686146, kgp11843177, kgp12008955, kgp12371757, kgp1285441, kgp13161760, kgp1355977, kgp15390522, kgp1683448, kgp1688752, kgp1912531, kgp19568724, 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, kgp8107491, kgp8169636, kgp8174785, kgp8183049, kgp8192546, kgp8335515, kgp8437961, kgp8440036, kgp85534, kgp8599417, kgp8767692, kgp8777935, kgp8869954, kgp9071686, kgp9078300, kgp9354820, kgp9421884, kgp9551947, kgp9601362, kgp9627406, kgp9699754, kgp971582, kgp9854133, rs1079303, rs10841322, rs10954782, rs11002051, rs11029907, rs11083404, rs11085044, rs11192461, rs1157449, rs12494712, rs12943140, rs13002663, rs13419758, rs1380706, rs1387768, rs1410779, rs1508102, rs1532365, rs16886004, rs16895510, rs16927077, rs16930057, rs17224858, rs17238927, rs17329014, rs17638791, rs1886214, rs1894408, rs196295, rs196341, rs196343, rs1979992, rs1979993, rs2043136, rs2071470, rs2074037, rs2175121, rs241435, rs241447, rs241451, 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, or
one or more T alleles at the location of kgp18432055, 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 kgp10152733, kgp10224254, kgp10305127, kgp10351364, kgp10372946, kgp10404633, kgp10564659, kgp10591989, kgp10594414, kgp10619195, kgp10620244, kgp10633631, kgp10974833, kgp11002881, kgp11285862, kgp11328629, kgp11407560, kgp11514107, kgp11627530, kgp11702474, kgp11711524, kgp11768533, kgp11804835, kgp12083934, kgp12182745, kgp12230354, kgp1224440, kgp124162, kgp12557319, kgp1371881, kgp1699628, kgp1753445, kgp1779254, kgp1786079, kgp18379774, kgp18525257, 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, rs10049206, rs10124492, rs10125298, rs10162089, rs10203396, rs10251797, rs10278591, rs10489312, rs10492882, rs10498793, rs10501082, rs10510774, rs10512340, rs10815160, rs10816302, rs10841337, rs11029892, rs11029928, rs11192469, rs11559024, rs11648129, rs12013377, rs13394010, rs13415334, rs1478682, rs1544352, rs1545223, rs1604169, rs1621509, rs1644418, rs17029538, rs17400875, rs17449018, rs17577980, rs1858973, rs1894406, rs1894407, rs197523, 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,
no C alleles at the location of kgp10910719, kgp11077373, kgp11453406, kgp12426624, kgp2045074, kgp22811918, kgp23298674, kgp2709692, kgp28687699, kgp3496814, kgp3669685, kgp3730395, kgp4056892, kgp4370912, kgp5053636, kgp5216209, kgp5292386, kgp6023196, kgp652534, kgp7059449, kgp7189498, kgp7521990, kgp7792268, kgp8303520, kgp9320791, kgp9795732, rs10201643, rs11022778, rs11136970, rs11147439, rs11691553, rs1579771, rs16901784, rs2136408, rs2325911, rs241443, rs2857104, rs3803277, rs3885907, rs4738738, rs4894701, rs502530, rs6032205, rs6687976, rs6718758, rs6835202, rs714342, rs7524868, rs7844274, rs9393727 or rs9671182,
no G alleles at the location of kgp10090631, kgp1009249, kgp10412303, kgp10523170, kgp1054273, kgp10558725, kgp10632945, kgp10679353, kgp10788130, kgp10826273, kgp10922969, kgp10948564, kgp10967046, kgp1098237, kgp11010680, kgp11141512, kgp11206453, kgp11210903, kgp1124492, kgp11281589, kgp11356379, kgp11467007, kgp11543962, kgp11580695, kgp11633966, kgp11686146, kgp11843177, kgp12008955, kgp12371757, kgp1285441, kgp13161760, kgp1355977, kgp15390522, kgp1683448, kgp1688752, kgp1912531, kgp19568724, 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, kgp8107491, kgp8169636, kgp8174785, kgp8183049, kgp8192546, kgp8335515, kgp8437961, kgp8440036, kgp85534, kgp8599417, kgp8767692, kgp8777935, kgp8869954, kgp9071686, kgp9078300, kgp9354820, kgp9421884, kgp9551947, kgp9601362, kgp9627406, kgp9699754, kgp971582, kgp9854133, rs1079303, rs10841322, rs10954782, rs11002051, rs11029907, rs11083404, rs11085044, rs11192461, rs1157449, rs12494712, rs12943140, rs13002663, rs13419758, rs1380706, rs1387768, rs1410779, rs1508102, rs1532365, rs16886004, rs16895510, rs16927077, rs16930057, rs17224858, rs17238927, rs17329014, rs17638791, rs1886214, rs1894408, rs196295, rs196341, rs196343, rs1979992, rs1979993, rs2043136, rs2071470, rs2074037, rs2175121, rs241435, rs241447, rs241451, 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, or
no T alleles at the location of kgp18432055, kgp279772, kgp3991733 or kgp7242489,
thereby 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;
(b) step (i) further comprises determining a genotype of the subject at one or more single nucleotide polymorphism (SNP): rs10988087, rs1573706, rs17575455, rs2487896, rs3135391, rs6097801 or 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 rs10988087, one or more C alleles at the location of rs17575455, or one or more G alleles at the location of rs1573706, 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 rs10988087, no C alleles at the location of rs17575455, or no G alleles at the location of rs1573706, rs2487896, rs3135391, rs6097801 or rs947603,
thereby 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
(c) step (i) further comprises determining a genotype of the subject at one or more single nucleotide polymorphism (SNP)
kgp10148554, kgp10215554, kgp10762962, kgp10836214, kgp10989246, kgp11285883, kgp11604017, kgp11755256, kgp1211163, kgp12253568, kgp12562255, kgp1432800, kgp1682126, kgp1758575, 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, rs10038844, rs1026894, rs10495115, rs11562998, rs11563025, rs11750747, rs11947777, rs12043743, rs12233980, rs12341716, rs12472695, rs12881439, rs13168893, rs13386874, rs1357718, rs1393037, rs1393040, rs1397481, rs1474226, rs1508515, rs1534647, rs16846161, rs1715441, rs17187123, rs17245674, rs17419416, rs1793174, rs1883448, rs1905248, 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 or rs9953274, 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 kgp10762962, kgp11285883, kgp11604017, kgp1211163, kgp12253568, kgp12562255, 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, rs10038844, rs10495115, rs11750747, rs12341716, rs12881439, rs13168893, rs1393040, rs1474226, rs1534647, rs1715441, rs17187123, rs17245674, rs17419416, rs1793174, rs1883448, rs1905248, rs263247, rs34647183, rs35615951, rs3847233, rs3858038, rs4740708, rs528065, rs6459418, rs6577395, rs6811337, rs7680970, rs7684006, rs7698655, rs7961005, rs8018807, rs9948620 or rs9953274,
one or more C alleles at the location of kgp10836214, kgp1432800, kgp22839559, kgp6301155, kgp6828277, rs2354380, rs2662, rs3858035, rs3894712, rs4797764 or rs7696391,
one or more G alleles at the location of kgp10148554, kgp10215554, kgp10989246, kgp11755256, kgp1682126, kgp1758575, kgp2920925, kgp3287349, kgp3420309, kgp3488270, kgp3598966, kgp3697615, kgp4137144, kgp5691690, kgp5924341, kgp5949515, kgp6213972, kgp625941, kgp6889327, kgp6990559, kgp7778345, kgp7932108, kgp8847137, kgp956070, rs1026894, rs11562998, rs11563025, rs11947777, rs12233980, rs12472695, rs13386874, rs1357718, rs1393037, rs1397481, rs1508515, rs16846161, rs209568, rs2618065, rs28993969, rs3768769, rs3858034, rs3858036, rs4978567, rs7119480, rs7123506, rs7231366, rs7819949, rs7846783, rs7949751, rs8000689, rs961090 or rs967616, or
one or more T alleles at the location of rs12043743,
thereby identifying a human subject afflicted with multiple sclerosis or a single clinical attack consistent with multiple sclerosis as a predicted responder to glatiramer acetate.
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