# US 2023/0266342 A1 · Biomarkers for Predicting Multiple Sclerosis Disease Progression > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2023/0266342 A1 - Document ID: US20230266342A1 - Status: Published application - Kind: A1 - Application number: 18/024,149 - Filed: 2021-09-03 - Published: 2023-08-24 - Earliest claimed: 2020-09-04 - Applicants: Octave Bioscience, Inc. - Inventors: Michael Justin Becich; Victor Michael Gehman; Ferhan Qureshi; William A. Hagstrom; Fatima Rubio da Costa; Fujun Zhang - Web page: https://patentgrep.com/patent/US20230266342A1 ## CPC - G01N 33/6896: {Neurological disorders, e.g. Alzheimer's disease} - G01N 33/68: involving proteins, peptides or amino acids {(involving lipoproteins G01N33/92)} - G16B 25/10: Gene or protein expression profiling; Expression-ratio estimation or normalisation - G16B 40/20: Supervised data analysis - G16H 50/30: for calculating health indices; for individual health risk assessment - G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients - G01N 2800/285: Demyelinating diseases; Multipel sclerosis - G01N 2800/52: Predicting or monitoring the response to treatment, e.g. for selection of therapy based on assay results in personalised medicine; Prognosis - G01N 2800/60: Complex ways of combining multiple protein biomarkers for diagnosis - G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q) - G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## Abstract Disclosed herein are methods for analyzing quantitative expression values of biomarkers of a biomarker panel for determining multiple sclerosis disease activity (e.g., multiple sclerosis disease progression) in a human subject. Further disclosed herein are kits for measuring quantitative expression values of the markers as well as computer systems and software embodiments of predictive models for determining multiple sclerosis disease activity (e.g., multiple sclerosis disease progression) in human subjects based on the quantitative expression values of the markers. ## Claims ### Claim 1 A method for predicting multiple sclerosis disease progression in a subject, the method comprising: obtaining or having obtained a dataset comprising expression levels of a plurality of biomarkers, wherein the plurality of biomarkers comprise one or more biomarkers in at least one group selected from group 1, group 2, and group 3, wherein group 1 comprises one or more of biomarker 1, biomarker 2, biomarker 3, biomarker 4, biomarker 5, biomarker 6, biomarker 7, and biomarker 8, wherein biomarker 1 is GFAP, NEFL, OPN, CXCL9, MOG, or CHI3L1 wherein biomarker 2 is CDCP1, IL-18BP, IL-18, GFAP, or MSR1, wherein biomarker 3 is MOG, CADM3, KLK6, BCAN, OMG, or GFAP, wherein biomarker 4 is CXCL13, NOS3, or MMP-2, wherein biomarker 5 is OPG, TFF3, or ENPP2, wherein biomarker 6 is APLP1, SEZ6L, BCAN, DPP6, NCAN, or KLK6, wherein biomarker 7 is VCAN, TINAGL1, CANT1, NECTIN2, MMP-9, or NPDC1, wherein biomarker 8 is NEFL, MOG, CADM3, or GFAP, and wherein group 2 comprises one or more of biomarker 9, biomarker 10, biomarker 11, biomarker 12, biomarker 13, biomarker 14, biomarker 15, biomarker 16, and biomarker 17, wherein biomarker 9 is CXCL9, CXCL10, IL-12B, CXCL11, or GFAP, wherein biomarker 10 is TNFRSF10A, TNFRSF11A, SPON2, CHI3L1, or IFI30, wherein biomarker 11 is CCL20, CCL3, or TWEAK, wherein biomarker 12 is TNFSF13B, CXCL16, ALCAM, or IL-18, wherein biomarker 13 is OPN, OMD, MEPE, or GFAP, wherein biomarker 14 is SERPINA9, TNFRSF9, or CNTN4, wherein biomarker 15 is CD6, CD5, CRTAM, CD244, or TNFRSF9, wherein biomarker 16 is FLRT2, DDR1, NTRK2, CDH6, MMP-2, wherein biomarker 17 is CNTN2, DPP6, GDNFR-alpha-3, or SCARF2, and wherein group 3 comprises one or more of biomarker 18, biomarker 19, biomarker 20, and biomarker 21, wherein biomarker 18 is COL4A1, IL-6, Notch 3, or PCDH17, wherein biomarker 19 is GH, GH2, or IGFBP-1, wherein biomarker 20 is IL-12B, IL12A, or CXCL9, and wherein biomarker 21 is PRTG, NTRK2, NTRK3, or CNTN4, and generating a prediction of multiple sclerosis disease progression by applying a predictive model to the expression levels of the plurality of biomarkers. ### Claim 2 (depends on 1) The method of claim 1, wherein the plurality of biomarkers comprise each biomarker in group 1, wherein biomarker 1 is GFAP, wherein biomarker 2 is CDCP1, wherein biomarker 3 is MOG, wherein biomarker 4 is CXCL13, wherein biomarker 5 is OPG, wherein biomarker 6 is APLP1, wherein biomarker 7 is VCAN, and wherein biomarker 8 is NEFL. ### Claim 3 (depends on 2) The method of claim 2, wherein a performance of the predictive model is characterized by a correlation coefficient (R2) of at least 0.31. ### Claim 4 (depends on 2) The method of claim 2, wherein a performance of the predictive model is characterized by an AUROC of at least 0.77. ### Claim 5 (depends on 2) The method of claim 2, wherein a performance of the predictive model is characterized by a PPV of at least 0.19. ### Claim 6 (depends on 2) The method of claim 2, wherein the plurality of biomarkers further comprise each biomarker in group 2, wherein biomarker 9 is CXCL9, wherein biomarker 10 is TNFRSF10A, wherein biomarker 11 is CCL20, wherein biomarker 12 is TNFSF13B, wherein biomarker 13 is OPN, wherein biomarker 14 is SERPINA9, wherein biomarker 15 is CD6, wherein biomarker 16 is FLRTs, and wherein biomarker 17 is CNTN2. ### Claim 7 (depends on 6) The method of claim 6, wherein a performance of the predictive model is characterized by a correlation coefficient (R2) of at least 0.35. ### Claim 8 (depends on 6) The method of claim 6, wherein a performance of the predictive model is characterized by an AUROC of at least 0.76. ### Claim 9 (depends on 6) The method of claim 6, wherein a performance of the predictive model is characterized by a PPV of at least 0.19. ### Claim 10 (depends on 6, 7, 8, 9) The method of any one of claims 6-9, wherein the plurality of biomarkers further comprise each biomarker in group 3, wherein biomarker 18 is COL4A1, wherein biomarker 19 is GH, wherein biomarker 20 is IL-12B, and wherein biomarker 21 is PRTG. ### Claim 11 (depends on 10) The method of claim 10, wherein a performance of the predictive model is characterized by a correlation coefficient (R2) of at least 0.36. ### Claim 12 (depends on 10) The method of claim 10, wherein a performance of the predictive model is characterized by an AUROC of at least 0.74. ### Claim 13 (depends on 10) The method of claim 10, wherein a performance of the predictive model is characterized by a PPV of at least 0.19. ### Claim 14 (depends on 1) The method of claim 1, wherein the plurality of biomarkers comprises one or more biomarkers in group 1, wherein the one or more biomarkers in group 1 comprises GFAP. ### Claim 15 (depends on 14) The method of claim 14, wherein a performance of the predictive model is characterized by an AUROC of at least 0.70. ### Claim 16 (depends on 14, 15) The method of claim 14 or 15, wherein a performance of the predictive model is characterized by an AUROC between 0.72 and 0.78. ### Claim 17 (depends on 14) The method of claim 14, wherein a performance of the predictive model is characterized by a Pearson's R2 coefficient of at least 0.10. ### Claim 18 (depends on 14, 15) The method of claim 14 or 15, wherein a performance of the predictive model is characterized by an Pearson's R2 coefficient between 0.11 and 0.22. ### Claim 19 (depends on 1) The method of claim 1, wherein the plurality of biomarkers does not include GFAP. ### Claim 20 (depends on 19) The method of claim 19, wherein a performance of the predictive model is characterized by an AUROC of at least 0.65. ### Claim 21 (depends on 19, 20) The method of claim 19 or 20, wherein a performance of the predictive model is characterized by an AUROC between 0.66 and 0.70. ### Claim 22 (depends on 14) The method of claim 14, wherein a performance of the predictive model is characterized by a Pearson's R2 coefficient of at least 0.10. ### Claim 23 (depends on 14, 15) The method of claim 14 or 15, wherein a performance of the predictive model is characterized by an Pearson's R2 coefficient between 0.015 and 0.090. ### Claim 24 (depends on 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23) The method of any one of claims 1-23, wherein the prediction of multiple sclerosis disease progression is a measure of brain parenchymal fraction value. ### Claim 25 (depends on 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23) The method of any one of claims 1-23, wherein the prediction of multiple sclerosis disease progression is a measure of expanded disability status scale (EDSS) score. ### Claim 26 (depends on 25) The method of claim 25, wherein an expanded disability status scale (EDSS) score less than 6 indicates a mild/moderate MS disease progression and a EDSS score greater than or equal to 6 indicates a severe MS disease progression. ### Claim 27 (depends on 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23) The method of any one of claims 1-23, wherein the prediction of multiple sclerosis disease progression is a measure of a patient determined disease steps (PDDS) score. ### Claim 28 (depends on 27) The method of claim 27, wherein the prediction of multiple sclerosis disease progression is a patient determined disease steps (PDDS) score differentiating between severe and mild/moderate MS disease progression. ### Claim 29 (depends on 28) The method of claim 28, wherein a patient determined disease steps (PDDS) score less than or equal to 4 indicates a mild/moderate MS disease progression and a PDDS score greater than 4 indicates a severe MS disease progression. ### Claim 30 (depends on 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23) The method of claim 1-23, wherein the prediction of multiple sclerosis disease progression is a PRO measurement information system (PROMIS) score. ### Claim 31 (depends on 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23) The method of claim 1-23, wherein the prediction of multiple sclerosis disease progression is a Multiple Sclerosis Rating Scale, Revised (MSRS-R) score. ### Claim 32 A method for predicting multiple sclerosis disease progression in a subject, the method comprising: obtaining or having obtained a dataset comprising expression levels of a plurality of biomarkers comprising at least one of: one or more neuroaxonal integrity biomarkers selected from a group consisting of CNTN2, FLRT2, NEFL, PRTG, SERPINA9, OPG, GFAP, and TNFRSF10A; one or more neuroinflammation biomarkers selected from a group consisting of CCL20, GH, TNFRSF10A, CXCL9, CXCL13, IL-12B, CD6, and TNFSF13B; one or more immune modulation biomarkers selected from a group consisting of CDCP1, CD6, CXCL9, CXCL13, IL-12B, and TNFSF13B; or one or more myelination biomarkers selected from a group consisting of MOG, APLP1, and OPN; and generating a prediction of multiple sclerosis disease progression by applying a predictive model to the expression levels of the plurality of biomarkers. ### Claim 33 A method for predicting multiple sclerosis disease progression in a subject, the method comprising: obtaining or having obtained a dataset comprising expression levels of a plurality of biomarkers comprising at least one of: one or more neuroaxonal integrity biomarkers selected from a group consisting of CNTN2, FLRT2, NEFL, PRTG, SERPINA9, OPG, GFAP, and TNFRSF10A; one or more neuroinflammation biomarkers selected from a group consisting of CCL20, GH, TNFRSF10A, CXCL9, CXCL13, IL-12B, CD6, and TNFSF13B; one or more immune modulation biomarkers selected from a group consisting of CDCP1, CD6, CXCL9, CXCL13, IL-12B, and TNFSF13B; one or more myelination biomarkers selected from a group consisting of MOG, APLP1, and OPN; or one or more cerebrovascular function biomarkers selected from a group consisting of COL4A1, VCAN, GFAP, and CD6; and generating a prediction of multiple sclerosis disease progression by applying a predictive model to the expression levels of the plurality of biomarkers. ### Claim 34 (depends on 32, 33) The method of claim 32 or 33, wherein the one or more neuroaxonal integrity biomarkers comprise NEFL, OPG, and GFAP, wherein the one or more neuroinflammation biomarkers comprise CXCL13, and CXCL9, wherein the one or more immune modulation biomarkers comprise CDCP1, and wherein the one or more myelination biomarkers comprise MOG and APLP1. ### Claim 35 (depends on 34) The method of claim 34, wherein the one or more neuroaxonal integrity biomarkers further comprise SERPINA9, FLRT2, and CNTN2, wherein the one or more neuroinflammation biomarkers further comprise CCL20, CXCL9, TNFRSF10A, and CD6, wherein the one or more immune modulation biomarkers further comprise TNFSF13B, and wherein the one or more myelination biomarkers further comprise OPN. ### Claim 36 (depends on 35) The method of claim 35, wherein the one or more neuroaxonal integrity biomarkers further comprise PRTG, and wherein the one or more immune modulation biomarkers further comprise IL-12B. ### Claim 37 (depends on 36) The method of claim 36, wherein a performance of the predictive model is characterized by a correlation coefficient (R2) of at least 0.35. ### Claim 38 (depends on 36) The method of claim 36, wherein a performance of the predictive model is characterized by an AUROC of at least 0.74. ### Claim 39 (depends on 36) The method of claim 36, wherein a performance of the predictive model is characterized by a PPV of at least 0.17. ### Claim 40 (depends on 32) The method of claim 32, wherein the plurality of biomarkers comprises one or more neuroaxonal integrity biomarkers, wherein the one or more neuroaxonal integrity biomarkers comprises GFAP. ### Claim 41 (depends on 40) The method of claim 40, wherein a performance of the predictive model is characterized by an AUROC of at least 0.70. ### Claim 42 (depends on 40, 41) The method of claim 40 or 41, wherein a performance of the predictive model is characterized by an AUROC between 0.72 and 0.78. ### Claim 43 (depends on 40) The method of claim 40, wherein a performance of the predictive model is characterized by a Pearson's R2 coefficient of at least 0.10. ### Claim 44 (depends on 40, 41) The method of claim 40 or 41, wherein a performance of the predictive model is characterized by an Pearson's R2 coefficient between 0.11 and 0.22. ### Claim 45 (depends on 32) The method of claim 32, wherein the plurality of biomarkers does not include GFAP. ### Claim 46 (depends on 45) The method of claim 45, wherein a performance of the predictive model is characterized by an AUROC of at least 0.65. ### Claim 47 (depends on 45, 46) The method of claim 45 or 46, wherein a performance of the predictive model is characterized by an AUROC between 0.66 and 0.70. ### Claim 48 (depends on 45) The method of claim 45, wherein a performance of the predictive model is characterized by a Pearson's R2 coefficient of at least 0.10. ### Claim 49 (depends on 45, 46) The method of claim 45 or 46, wherein a performance of the predictive model is characterized by an Pearson's R2 coefficient between 0.015 and 0.090. ### Claim 50 (depends on 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49) The method of any one of claims 32-49, wherein the prediction of multiple sclerosis disease progression is a brain parenchymal fraction value. ### Claim 51 (depends on 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49) The method of any one of claims 32-49, wherein the prediction of multiple sclerosis disease progression is an expanded disability status scale (EDSS) score. ### Claim 52 (depends on 51) The method of claim 51, wherein an expanded disability status scale (EDSS) score less than 6 indicates a mild/moderate MS disease progression and a EDSS score greater than or equal to 6.0 indicates a severe MS disease progression. ### Claim 53 (depends on 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49) The method of any one of claims 32-49, wherein the prediction of multiple sclerosis disease progression is a patient determined disease steps (PDDS) score. ### Claim 54 (depends on 53) The method of claim 53, wherein the prediction of multiple sclerosis disease progression is a patient determined disease steps (PDDS) score differentiating between severe and mild/moderate MS disease progression. ### Claim 55 (depends on 54) The method of claim 54, wherein a patient determined disease steps (PDDS) score less than or equal to 4 indicates a mild/moderate MS disease progression and a PDDS score greater than 4 indicates a severe MS disease progression. ### Claim 56 (depends on 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49) The method of claim 32-49, wherein the prediction of multiple sclerosis disease progression is a PRO measurement information system (PROMIS) score. ### Claim 57 (depends on 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49) The method of claim 32-49, wherein the prediction of multiple sclerosis disease progression is a Multiple Sclerosis Rating Scale, Revised (MSRS-R) score. ### Claim 58 A method for predicting multiple sclerosis disease progression in a subject, the method comprising: obtaining or having obtained a dataset comprising expression levels of a plurality of biomarkers, wherein the plurality of biomarkers comprises two or more of: GFAP, CDCP1, MOG, CXCL13, OPG, APLP1, VCAN, NEFL, CXCL9, TNFRSF10A, CCL20/MIP 3-α, TNFSF13B, CD6, SERPINA9, FLRT2, OPN, CNTN2, COL4A1, GH, IL-12B, and PRTG; and generating a prediction of multiple sclerosis disease progression by applying a predictive model to the expression levels of the plurality of biomarkers. ### Claim 59 (depends on 58) The method of claim 58, wherein the plurality of biomarkers comprise each of GFAP, CDCP1, MOG, CXCL13, OPG, APLP1, VCAN, NEFL, CXCL9, TNFRSF10A, CCL20/MIP 3-α, TNFSF13B, CD6, SERPINA9, FLRT2, OPN, CNTN2, COL4A1, GH, IL-12B, and PRTG. ### Claim 60 (depends on 59) The method of claim 59, wherein a performance of the predictive model is characterized by a correlation coefficient (R2) of at least 0.35. ### Claim 61 (depends on 59) The method of claim 59, wherein a performance of the predictive model is characterized by an AUROC of at least 0.74. ### Claim 62 (depends on 59) The method of claim 59, wherein a performance of the predictive model is characterized by a PPV of at least 0.17. ### Claim 63 (depends on 58) The method of claim 58, wherein the plurality of biomarkers comprises GFAP. ### Claim 64 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises CDCP1. ### Claim 65 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises APLP1. ### Claim 66 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises CXCL13. ### Claim 67 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises MOG. ### Claim 68 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises OPG. ### Claim 69 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises CDCP1 and APLP1. ### Claim 70 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises MOG and CDCP1. ### Claim 71 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises APLP1 and CXCL 13. ### Claim 72 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises CDCP1 and SERPINA9. ### Claim 73 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises MOG and CXCL13. ### Claim 74 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises CDCP1, CCL20, and APLP1. ### Claim 75 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises CDCP1, APLP1 and CXCL13. ### Claim 76 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises CDCP1, CCL20, and MOG. ### Claim 77 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises CDCP1, APLP1, and SERPINA9. ### Claim 78 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises CDCP1, MOG, and APLP1. ### Claim 79 (depends on 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78) The method of any one of claims 63-78, wherein a performance of the predictive model is characterized by an AUROC of at least 0.70. ### Claim 80 (depends on 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79) The method of any one of claims 63-79, wherein a performance of the predictive model is characterized by an AUROC between 0.72 and 0.78. ### Claim 81 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises MOG. ### Claim 82 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises APLP1. ### Claim 83 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises OPG. ### Claim 84 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises TNFRSF10A. ### Claim 85 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises CDCP1. ### Claim 86 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises APLP1. ### Claim 87 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises NEFL. ### Claim 88 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises CNTN2. ### Claim 89 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises GH. ### Claim 90 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises CXCL9. ### Claim 91 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises OPG and MOG. ### Claim 92 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises OPG and APLP1. ### Claim 93 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises TNFRSF10A and MOG. ### Claim 94 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises CXCL9 and OPG. ### Claim 95 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises TNFRSF10A and APLP1. ### Claim 96 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises APLP1 and NEFL. ### Claim 97 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises CXCL13 and APLP1. ### Claim 98 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises FLRT2 and APLP1. ### Claim 99 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises CXCL9 and APLP1. ### Claim 100 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises GH and APLP1. ### Claim 101 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises CXCL9, OPG, and MOG. ### Claim 102 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises CNTN2, OPG, and MOG. ### Claim 103 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises CXCL9, OPG, and APLP1. ### Claim 104 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises OPG, PRTG, and MOG. ### Claim 105 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises OPG, OPN, and MOG. ### Claim 106 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises CXCL13, APLP1, and NEFL. ### Claim 107 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises FLRT2, APLP1, and NEFL. ### Claim 108 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises OPN, APLP1, and NEFL. ### Claim 109 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises CXCL9, APLP1, and NEFL. ### Claim 110 (depends on 63) The method of claim 63, wherein the plurality of biomarkers further comprises CXCL13, FLRT2, and APLP1. ### Claim 111 (depends on 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110) The method of any one of claims 81-110, wherein a performance of the predictive model is characterized by a Pearson's R2 coefficient of at least 0.10. ### Claim 112 (depends on 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111) The method of any one of claims 81-111, wherein a performance of the predictive model is characterized by an Pearson's R2 coefficient between 0.11 and 0.22. ### Claim 113 (depends on 58) The method of claim 58, wherein the plurality of biomarkers does not include GFAP. ### Claim 114 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises CDCP1 and OPG. ### Claim 115 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises CDCP1 and SERPINA9. ### Claim 116 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises OPG and TNFRSF10A. ### Claim 117 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises OPG and MOG. ### Claim 118 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises CDCP1 and MOG. ### Claim 119 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises CDCP1, MOG, and OPG. ### Claim 120 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises CDCP1, SERPIN A9, and OPG. ### Claim 121 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises CDCP1, OPG, and CXCL13. ### Claim 122 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises CDCP1, CXCL9, and OPG. ### Claim 123 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises CDCP1, FLRT2, and OPG. ### Claim 124 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises CDCP1, MOG, OPG, and CXCL13. ### Claim 125 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises CDCP1, MOG, TNFRSF10A, and OPG. ### Claim 126 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises CDCP1, CXCL9, SERPINA9, and OPG, ### Claim 127 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises CDCP1, CNTN2, SERPINA9, and OPG. ### Claim 128 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises CDCP1, SERPINA9, CD6, and OPG. ### Claim 129 (depends on 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128) The method of any one of claims 113-128, wherein a performance of the predictive model is characterized by an AUROC of at least 0.65. ### Claim 130 (depends on 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129) The method of any one of claims 113-129, wherein a performance of the predictive model is characterized by an AUROC between 0.66 and 0.70. ### Claim 131 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises OPG and NEFL. ### Claim 132 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises OPG and OPN. ### Claim 133 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises OPG and FLRT2. ### Claim 134 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises OPG and MOG. ### Claim 135 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises CXCL9 and OPG. ### Claim 136 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises GH and NEFL. ### Claim 137 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises CXCL13 and NEFL. ### Claim 138 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises APLP1 and NEFL. ### Claim 139 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises CCL20 and NEFL. ### Claim 140 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises CXCL9 and NEFL. ### Claim 141 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises OPG, MOG, and NEFL. ### Claim 142 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises OPG, FLRT2, and NEFL. ### Claim 143 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises CXCL9, OPG, and NEFL. ### Claim 144 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises OPG, CDCP1, and NEFL. ### Claim 145 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises OPG, OPN, and NEFL. ### Claim 146 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises GH, APLP1, and NEFL. ### Claim 147 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises GH, CXCL13, and NEFL. ### Claim 148 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises GH, CDCP1, and NEFL. ### Claim 149 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises CXCL13, CCL20, and NEFL. ### Claim 150 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises GH, CCL20, and NEFL. ### Claim 151 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises CDCP1, CXCL13, MOG, and NEFL. ### Claim 152 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises CD6, CXCL9, CXCL13, and NEFL. ### Claim 153 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises CXCL9, CXCL13, MOG, and NEFL. ### Claim 154 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises CD6, CDCP1, CXCL13, and NEFL. ### Claim 155 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises CD6, CXCL9, CXCL13, and MOG. ### Claim 156 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises CDCP1, CXCL13, MOG, and NEFL. ### Claim 157 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises CD6, CDCP1, CXCL13, and NEFL. ### Claim 158 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises CXCL9, CXCL13, MOG, and NEFL. ### Claim 159 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises CD6, CXCL9, CXCL13, and NEFL. ### Claim 160 (depends on 113) The method of claim 113, wherein the plurality of biomarkers comprises CD6, CDCP1, CXCL13, and MOG. ### Claim 161 (depends on 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160) The method of claim 131-160, wherein a performance of the predictive model is characterized by a Pearson's R2 coefficient of at least 0.10. ### Claim 162 (depends on 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161) The method of claim 131-161, wherein a performance of the predictive model is characterized by an Pearson's R2 coefficient between 0.015 and 0.090. ### Claim 163 (depends on 58, 162) The method of any one of claims 58-162, wherein the prediction of multiple sclerosis disease progression is a brain parenchymal fraction value. ### Claim 164 (depends on 58, 162) The method of any one of claims 58-162, wherein the prediction of multiple sclerosis disease progression is an expanded disability status scale (EDSS) score. ### Claim 165 (depends on 164) The method of claim 164, wherein an expanded disability status scale (EDSS) score less than or equal to 6 indicates a mild/moderate MS disease progression and a EDSS score greater than 6.5 indicates a severe MS disease progression. ### Claim 166 (depends on 58, 162) The method of any one of claims 58-162, wherein the prediction of multiple sclerosis disease progression is a patient determined disease steps (PDDS) score. ### Claim 167 (depends on 166) The method of claim 166, wherein the prediction of multiple sclerosis disease progression is a patient determined disease steps (PDDS) score differentiating between severe and mild/moderate MS disease progression. ### Claim 168 (depends on 167) The method of claim 167, wherein a patient determined disease steps (PDDS) score less than or equal to 4 indicates a mild/moderate MS disease progression and a PDDS score greater than 4 indicates a severe MS disease progression. ### Claim 169 (depends on 58, 162) The method of claim 58-162, wherein the prediction of multiple sclerosis disease progression is a PRO measurement information system (PROMIS) score. ### Claim 170 (depends on 58, 162) The method of claim 58-162, wherein the prediction of multiple sclerosis disease progression is a Multiple Sclerosis Rating Scale, Revised (MSRS-R) score. ### Claim 171 (depends on 1, 170) The method of any one of claims 1-170, wherein generating the prediction of multiple sclerosis disease progression by applying the predictive model to the expression levels of the plurality of biomarkers further comprises applying the predictive model to one or more subject attributes of the subject, wherein subject attributes comprise any of age, sex, and disease duration. ### Claim 172 (depends on 1, 171) The method of any one of claims 1-171, wherein generating the prediction of multiple sclerosis disease progression comprises comparing a score outputted by the predictive model to a reference score. ### Claim 173 (depends on 172) The method of claim 172, wherein the reference score corresponds to any of: A) an EDSS score; B) a brain parenchymal fraction value; C) a PDDS score; D) a PROMIS score; or E) a MSRS-R score. ### Claim 174 (depends on 173) The method of claim 173, wherein the reference score further corresponds to a mild/moderate MS disease progression or a severe MS disease progression. ### Claim 175 (depends on 1, 174) The method of any one of claims 1-174, wherein the expression levels of the plurality of biomarkers is determined from a test sample obtained from the subject. ### Claim 176 (depends on 175) The method of claim 175, wherein the test sample is a blood or serum sample. ### Claim 177 (depends on 175, 176) The method of claim 175 or 176, wherein the subject has multiple sclerosis, is suspected of having multiple sclerosis, or was previously diagnosed with multiple sclerosis. ### Claim 178 (depends on 1, 177) The method of any one of claims 1-177, wherein obtaining or having obtained the dataset comprises performing an immunoassay to determine the expression levels of the plurality of biomarkers. ### Claim 179 (depends on 178) The method of claim 178, wherein the immunoassay is a Proximity Extension Assay (PEA) or LUMINEX xMAP Multiplex Assay. ### Claim 180 (depends on 178, 179) The method of claim 178 or 179, wherein performing the immunoassay comprises contacting a test sample with a plurality of reagents comprising antibodies. ### Claim 181 (depends on 180) The method of claim 180, wherein the antibodies comprise one of monoclonal and polyclonal antibodies. ### Claim 182 (depends on 180) The method of claim 180, wherein the antibodies comprise both monoclonal and polyclonal antibodies. ### Claim 183 (depends on 1, 182) The method of any one of claims 1-182, further comprising: selecting a therapy for administering to the subject based on the prediction of multiple sclerosis disease progression. ### Claim 184 (depends on 1, 182) The method of any one of claims 1-182, further comprising: determining a therapeutic efficacy of a therapy previously administered to the subject based on the prediction of multiple sclerosis disease progression. ### Claim 185 (depends on 184) The method of claim 184, wherein determining the therapeutic efficacy of the therapy comprises comparing the prediction to a prior prediction determined for the subject at a prior timepoint ### Claim 186 (depends on 185) The method of claim 185, wherein determining the therapeutic efficacy of the therapy comprises determining that the therapy exhibits efficacy responsive to a difference between the prediction and the prior prediction. ### Claim 187 (depends on 185) The method of claim 185, wherein determining the therapeutic efficacy of the therapy comprises determining that the therapy lacks efficacy responsive to a lack of difference between the prediction and the prior prediction. ### Claim 188 A non-transitory computer readable medium for predicting multiple sclerosis disease progression in a subject, the non-transitory computer readable medium comprising instructions that, when executed by a processor, cause the processor to: obtain a dataset comprising expression levels of a plurality of biomarkers, wherein the plurality of biomarkers comprise one or more biomarkers in at least one group selected from group 1, group 2, and group 3, wherein group 1 comprises one or more of biomarker 1, biomarker 2, biomarker 3, biomarker 4, biomarker 5, biomarker 6, biomarker 7, and biomarker 8, wherein biomarker 1 is GFAP, NEFL, OPN, CXCL9, MOG, or CHI3L1 wherein biomarker 2 is CDCP1, IL-18BP, IL-18, GFAP, or MSR1, wherein biomarker 3 is MOG, CADM3, KLK6, BCAN, OMG, or GFAP, wherein biomarker 4 is CXCL13, NOS3, or MMP-2, wherein biomarker 5 is OPG, TFF3, or ENPP2, wherein biomarker 6 is APLP1, SEZ6L, BCAN, DPP6, NCAN, or KLK6, wherein biomarker 7 is VCAN, TINAGL1, CANT1, NECTIN2, MMP-9, or NPDC1, wherein biomarker 8 is NEFL, MOG, CADM3, or GFAP, and wherein group 2 comprises one or more of biomarker 9, biomarker 10, biomarker 11, biomarker 12, biomarker 13, biomarker 14, biomarker 15, biomarker 16, and biomarker 17, wherein biomarker 9 is CXCL9, CXCL10, IL-12B, CXCL11, or GFAP, wherein biomarker 10 is TNFRSF10A, TNFRSF11A, SPON2, CHI3L1, or IFI30, wherein biomarker 11 is CCL20, CCL3, or TWEAK, wherein biomarker 12 is TNFSF13B, CXCL16, ALCAM, or IL-18, wherein biomarker 13 is OPN, OMD, MEPE, or GFAP, wherein biomarker 14 is SERPINA9, TNFRSF9, or CNTN4, wherein biomarker 15 is CD6, CD5, CRTAM, CD244, or TNFRSF9, wherein biomarker 16 is FLRT2, DDR1, NTRK2, CDH6, MMP-2, wherein biomarker 17 is CNTN2, DPP6, GDNFR-alpha-3, or SCARF2, and wherein group 3 comprises one or more of biomarker 18, biomarker 19, biomarker 20, and biomarker 21, wherein biomarker 18 is COL4A1, IL-6, Notch 3, or PCDH17, wherein biomarker 19 is GH, GH2, or IGFBP-1, wherein biomarker 20 is IL-12B, IL12A, or CXCL9, and wherein biomarker 21 is PRTG, NTRK2, NTRK3, or CNTN4, and generate a prediction of multiple sclerosis disease progression by applying a predictive model to the expression levels of the plurality of biomarkers. ### Claim 189 (depends on 188) The non-transitory computer readable medium of claim 188, wherein the plurality of biomarkers comprise each biomarker in group 1, wherein biomarker 1 is GFAP, wherein biomarker 2 is CDCP1, wherein biomarker 3 is MOG, wherein biomarker 4 is CXCL13, wherein biomarker 5 is OPG, wherein biomarker 6 is APLP1, wherein biomarker 7 is VCAN, and wherein biomarker 8 is NEFL. ### Claim 190 (depends on 189) The non-transitory computer readable medium of claim 189, wherein a performance of the predictive model is characterized by a correlation coefficient (R2) of at least 0.31. ### Claim 191 (depends on 189) The non-transitory computer readable medium of claim 189, wherein a performance of the predictive model is characterized by an AUROC of at least 0.77. ### Claim 192 (depends on 189) The non-transitory computer readable medium of claim 189, wherein a performance of the predictive model is characterized by a PPV of at least 0.19. ### Claim 193 (depends on 189) The non-transitory computer readable medium of claim 189, wherein the plurality of biomarkers further comprise each biomarker in group 2, wherein biomarker 9 is CXCL9, wherein biomarker 10 is TNFRSF10A, wherein biomarker 11 is CCL20, wherein biomarker 12 is TNFSF13B, wherein biomarker 13 is OPN, wherein biomarker 14 is SERPINA9, wherein biomarker 15 is CD6, wherein biomarker 16 is FLRTs, and wherein biomarker 17 is CNTN2. ### Claim 194 (depends on 193) The non-transitory computer readable medium of claim 193, wherein a performance of the predictive model is characterized by a correlation coefficient (R2) of at least 0.35. ### Claim 195 (depends on 193) The non-transitory computer readable medium of claim 193, wherein a performance of the predictive model is characterized by an AUROC of at least 0.76. ### Claim 196 (depends on 193) The non-transitory computer readable medium of claim 193, wherein a performance of the predictive model is characterized by a PPV of at least 0.19. ### Claim 197 (depends on 193, 194, 195, 196) The non-transitory computer readable medium of any one of claims 193-196, wherein the plurality of biomarkers further comprise each biomarker in group 3, wherein biomarker 18 is COL4A1, wherein biomarker 19 is GH, wherein biomarker 20 is IL-12B, and wherein biomarker 21 is PRTG. ### Claim 198 (depends on 197) The non-transitory computer readable medium of claim 197, wherein a performance of the predictive model is characterized by a correlation coefficient (R2) of at least 0.36. ### Claim 199 (depends on 197) The non-transitory computer readable medium of claim 197, wherein a performance of the predictive model is characterized by an AUROC of at least 0.74. ### Claim 200 (depends on 197) The non-transitory computer readable medium of claim 197, wherein a performance of the predictive model is characterized by a PPV of at least 0.19. ### Claim 201 (depends on 188) The non-transitory computer readable medium of claim 188, wherein the plurality of biomarkers comprises one or more biomarkers in group 1, wherein the one or more biomarkers in group 1 comprises GFAP. ### Claim 202 (depends on 201) The non-transitory computer readable medium of claim 201, wherein a performance of the predictive model is characterized by an AUROC of at least 0.70. ### Claim 203 (depends on 201, 202) The non-transitory computer readable medium of claim 201 or 202, wherein a performance of the predictive model is characterized by an AUROC between 0.72 and 0.78. ### Claim 204 (depends on 201) The non-transitory computer readable medium of claim 201, wherein a performance of the predictive model is characterized by a Pearson's R2 coefficient of at least 0.10. ### Claim 205 (depends on 201, 202) The non-transitory computer readable medium of claim 201 or 202, wherein a performance of the predictive model is characterized by an Pearson's R2 coefficient between 0.11 and 0.22. ### Claim 206 (depends on 188) The non-transitory computer readable medium of claim 188, wherein the plurality of biomarkers does not include GFAP. ### Claim 207 (depends on 206) The non-transitory computer readable medium of claim 206, wherein a performance of the predictive model is characterized by an AUROC of at least 0.65. ### Claim 208 (depends on 206, 207) The non-transitory computer readable medium of claim 206 or 207, wherein a performance of the predictive model is characterized by an AUROC between 0.66 and 0.70. ### Claim 209 (depends on 206) The non-transitory computer readable medium of claim 206, wherein a performance of the predictive model is characterized by a Pearson's R2 coefficient of at least 0.10. ### Claim 210 (depends on 206, 209) The non-transitory computer readable medium of claim 206 or 209, wherein a performance of the predictive model is characterized by an Pearson's R2 coefficient between 0.015 and 0.090. ### Claim 211 (depends on 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210) The non-transitory computer readable medium of any one of claims 188-210, wherein the prediction of multiple sclerosis disease progression is a measure of brain parenchymal fraction value. ### Claim 212 (depends on 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210) The non-transitory computer readable medium of any one of claims 188-210, wherein the prediction of multiple sclerosis disease progression is a measure of expanded disability status scale (EDSS) score. ### Claim 213 (depends on 212) The non-transitory computer readable medium of claim 212, wherein an expanded disability status scale (EDSS) score less than 6 indicates a mild/moderate MS disease progression and a EDSS score greater than or equal to 6 indicates a severe MS disease progression. ### Claim 214 (depends on 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210) The non-transitory computer readable medium of any one of claims 188-210, wherein the prediction of multiple sclerosis disease progression is a measure of a patient determined disease steps (PDDS) score. ### Claim 215 (depends on 214) The non-transitory computer readable medium of claim 214, wherein the prediction of multiple sclerosis disease progression is a patient determined disease steps (PDDS) score differentiating between severe and mild/moderate MS disease progression. ### Claim 216 (depends on 215) The non-transitory computer readable medium of claim 215, wherein a patient determined disease steps (PDDS) score less than or equal to 4 indicates a mild/moderate MS disease progression and a PDDS score greater than 4 indicates a severe MS disease progression. ### Claim 217 (depends on 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210) The non-transitory computer readable medium of any one of claims 188-210, wherein the prediction of multiple sclerosis disease progression is a PRO measurement information system (PROMIS) score. ### Claim 218 (depends on 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210) The non-transitory computer readable medium of any one of claims 188-210, wherein the prediction of multiple sclerosis disease progression is a Multiple Sclerosis Rating Scale, Revised (MSRS-R) score. ### Claim 219 A non-transitory computer readable medium for predicting multiple sclerosis disease progression in a subject, the non-transitory computer readable medium comprising instructions that, when executed by a processor, cause the processor to: obtaining or having obtained a dataset comprising expression levels of a plurality of biomarkers comprising at least one of: one or more neuroaxonal integrity biomarkers selected from a group consisting of CNTN2, FLRT2, NEFL, PRTG, SERPINA9, OPG, GFAP, and TNFRSF10A; one or more neuroinflammation biomarkers selected from a group consisting of CCL20, GH, TNFRSF10A, CXCL9, CXCL13, IL-12B, CD6, and TNFSF13B; one or more immune modulation biomarkers selected from a group consisting of CDCP1, CD6, CXCL9, CXCL13, IL-12B, and TNFSF13B; or one or more myelination biomarkers selected from a group consisting of MOG, APLP1, and OPN; and generating a prediction of multiple sclerosis disease progression by applying a predictive model to the expression levels of the plurality of biomarkers. ### Claim 220 A non-transitory computer readable medium for predicting multiple sclerosis disease progression in a subject, the non-transitory computer readable medium comprising instructions that, when executed by a processor, cause the processor to: obtain a dataset comprising expression levels of a plurality of biomarkers comprising at least one of: one or more neuroaxonal integrity biomarkers selected from a group consisting of CNTN2, FLRT2, NEFL, PRTG, SERPINA9, OPG, GFAP, and TNFRSF10A; one or more neuroinflammation biomarkers selected from a group consisting of CCL20, GH, TNFRSF10A, CXCL9, CXCL13, IL-12B, CD6, and TNFSF13B; one or more immune modulation biomarkers selected from a group consisting of CDCP1, CD6, CXCL9, CXCL13, IL-12B, and TNFSF13B; one or more myelination biomarkers selected from a group consisting of MOG, APLP1, and OPN; or one or more cerebrovascular function biomarkers selected from a group consisting of COL4A1, VCAN, GFAP, and CD6; and generate a prediction of multiple sclerosis disease progression by applying a predictive model to the expression levels of the plurality of biomarkers. ### Claim 221 (depends on 219, 220) The non-transitory computer readable medium of claim 219 or 220, wherein the one or more neuroaxonal integrity biomarkers comprise NEFL, OPG, and GFAP, wherein the one or more neuroinflammation biomarkers comprise CXCL13, and CXCL9, wherein the one or more immune modulation biomarkers comprise CDCP1, and wherein the one or more myelination biomarkers comprise MOG and APLP1. ### Claim 222 (depends on 221) The non-transitory computer readable medium of claim 221, wherein the one or more neuroaxonal integrity biomarkers further comprise SERPINA9, FLRT2, and CNTN2, wherein the one or more neuroinflammation biomarkers further comprise CCL20, CXCL9, TNFRSF10A, and CD6, wherein the one or more immune modulation biomarkers further comprise TNFSF13B, and wherein the one or more myelination biomarkers further comprise OPN. ### Claim 223 (depends on 222) The non-transitory computer readable medium of claim 222, wherein the one or more neuroaxonal integrity biomarkers further comprise PRTG, and wherein the one or more immune modulation biomarkers further comprise IL-12B. ### Claim 224 (depends on 223) The non-transitory computer readable medium of claim 223, wherein a performance of the predictive model is characterized by a correlation coefficient (R2) of at least 0.35. ### Claim 225 (depends on 223) The non-transitory computer readable medium of claim 223, wherein a performance of the predictive model is characterized by an AUROC of at least 0.74. ### Claim 226 (depends on 223) The non-transitory computer readable medium of claim 223, wherein a performance of the predictive model is characterized by a PPV of at least 0.17. ### Claim 227 (depends on 219) The non-transitory computer readable medium of claim 219, wherein the plurality of biomarkers comprises one or more neuroaxonal integrity biomarkers, wherein the one or more neuroaxonal integrity biomarkers comprises GFAP. ### Claim 228 (depends on 227) The non-transitory computer readable medium of claim 227, wherein a performance of the predictive model is characterized by an AUROC of at least 0.70. ### Claim 229 (depends on 227, 228) The non-transitory computer readable medium of claim 227 or 228, wherein a performance of the predictive model is characterized by an AUROC between 0.72 and 0.78. ### Claim 230 (depends on 227) The non-transitory computer readable medium of claim 227, wherein a performance of the predictive model is characterized by a Pearson's R2 coefficient of at least 0.10. ### Claim 231 (depends on 227, 230) The non-transitory computer readable medium of claim 227 or 230, wherein a performance of the predictive model is characterized by an Pearson's R2 coefficient between 0.11 and 0.22. ### Claim 232 (depends on 219) The non-transitory computer readable medium of claim 219, wherein the plurality of biomarkers does not include GFAP. ### Claim 233 (depends on 232) The non-transitory computer readable medium of claim 232, wherein a performance of the predictive model is characterized by an AUROC of at least 0.65. ### Claim 234 (depends on 232, 233) The non-transitory computer readable medium of claim 232 or 233, wherein a performance of the predictive model is characterized by an AUROC between 0.66 and 0.70. ### Claim 235 (depends on 232) The non-transitory computer readable medium of claim 232, wherein a performance of the predictive model is characterized by a Pearson's R2 coefficient of at least 0.10. ### Claim 236 (depends on 232, 235) The non-transitory computer readable medium of claim 232 or 235, wherein a performance of the predictive model z is characterized by an Pearson's R2 coefficient between 0.015 and 0.090. ### Claim 237 (depends on 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236) The non-transitory computer readable medium of any one of claims 219-236, wherein the prediction of multiple sclerosis disease progression is a brain parenchymal fraction value. ### Claim 238 (depends on 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236) The non-transitory computer readable medium of any one of claims 219-236, wherein the prediction of multiple sclerosis disease progression is an expanded disability status scale (EDSS) score. ### Claim 239 (depends on 238) The non-transitory computer readable medium of claim 238, wherein an expanded disability status scale (EDSS) score less than 6 indicates a mild/moderate MS disease progression and a EDSS score greater than or equal to 6.0 indicates a severe MS disease progression. ### Claim 240 (depends on 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236) The non-transitory computer readable medium of any one of claims 219-236, wherein the prediction of multiple sclerosis disease progression is a patient determined disease steps (PDDS) score. ### Claim 241 (depends on 240) The non-transitory computer readable medium of claim 240, wherein the prediction of multiple sclerosis disease progression is a patient determined disease steps (PDDS) score differentiating between severe and mild/moderate MS disease progression. ### Claim 242 (depends on 241) The non-transitory computer readable medium of claim 241, wherein a patient determined disease steps (PDDS) score less than or equal to 4 indicates a mild/moderate MS disease progression and a PDDS score greater than 4 indicates a severe MS disease progression. ### Claim 243 (depends on 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236) The non-transitory computer readable medium of claim 219-236, wherein the prediction of multiple sclerosis disease progression is a PRO measurement information system (PROMIS) score. ### Claim 244 (depends on 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236) The non-transitory computer readable medium of claim 219-236, wherein the prediction of multiple sclerosis disease progression is a Multiple Sclerosis Rating Scale, Revised (MSRS-R) score. ### Claim 245 A non-transitory computer readable medium for predicting multiple sclerosis disease progression in a subject, the non-transitory computer readable medium comprising instructions that, when executed by a processor, cause the processor to: obtain a dataset comprising expression levels of a plurality of biomarkers, wherein the plurality of biomarkers comprises two or more of: GFAP, CDCP1, MOG, CXCL13, OPG, APLP1, VCAN, NEFL, CXCL9, TNFRSF10A, CCL20/MIP 3-α, TNFSF13B, CD6, SERPINA9, FLRT2, OPN, CNTN2, COL4A1, GH, IL-12B, and PRTG; and generate a prediction of multiple sclerosis disease progression by applying a predictive model to the expression levels of the plurality of biomarkers. ### Claim 246 (depends on 245) The non-transitory computer readable medium of claim 245, wherein the plurality of biomarkers comprise each of GFAP, CDCP1, MOG, CXCL13, OPG, APLP1, VCAN, NEFL, CXCL9, TNFRSF10A, CCL20/MIP 3-α, TNFSF13B, CD6, SERPINA9, FLRT2, OPN, CNTN2, COL4A1, GH, IL-12B, and PRTG. ### Claim 247 (depends on 246) The non-transitory computer readable medium of claim 246, wherein a performance of the predictive model is characterized by a correlation coefficient (R2) of at least 0.35. ### Claim 248 (depends on 247) The non-transitory computer readable medium of claim 247, wherein a performance of the predictive model is characterized by an AUROC of at least 0.74. ### Claim 249 (depends on 247) The non-transitory computer readable medium of claim 247, wherein a performance of the predictive model is characterized by a PPV of at least 0.17. ### Claim 250 (depends on 245) The non-transitory computer readable medium of claim 245, wherein the plurality of biomarkers comprises GFAP. ### Claim 251 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises CDCP1. ### Claim 252 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises APLP1. ### Claim 253 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises CXCL13. ### Claim 254 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises MOG. ### Claim 255 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises OPG. ### Claim 256 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises CDCP1 and APLP1. ### Claim 257 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises MOG and CDCP1. ### Claim 258 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises APLP1 and CXCL13. ### Claim 259 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises CDCP1 and SERPINA9. ### Claim 260 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises MOG and CXCL13. ### Claim 261 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises CDCP1, CCL20, and APLP1. ### Claim 262 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises CDCP1, APLP1 and CXCL13. ### Claim 263 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises CDCP1, CCL20, and MOG. ### Claim 264 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises CDCP1, APLP1, and SERPINA9. ### Claim 265 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises CDCP1, MOG, and APLP1. ### Claim 266 (depends on 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265) The non-transitory computer readable medium of any one of claims 250-265, wherein a performance of the predictive model is characterized by an AUROC of at least 0.70. ### Claim 267 (depends on 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266) The non-transitory computer readable medium of any one of claims 250-266, wherein a performance of the predictive model is characterized by an AUROC between 0.72 and 0.78. ### Claim 268 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises MOG. ### Claim 269 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises APLP1. ### Claim 270 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises OPG. ### Claim 271 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises TNFRSF10A. ### Claim 272 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises CDCP1. ### Claim 273 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises APLP1. ### Claim 274 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises NEFL. ### Claim 275 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises CNTN2. ### Claim 276 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises GH. ### Claim 277 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises CXCL9. ### Claim 278 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises OPG and MOG. ### Claim 279 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises OPG and APLP1. ### Claim 280 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises TNFRSF10A and MOG. ### Claim 281 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises CXCL9 and OPG. ### Claim 282 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises TNFRSF10A and APLP1. ### Claim 283 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises APLP1 and NEFL. ### Claim 284 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises CXCL13 and APLP1. ### Claim 285 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises FLRT2 and APLP1. ### Claim 286 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises CXCL9 and APLP1. ### Claim 287 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises GH and APLP1. ### Claim 288 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises CXCL9, OPG, and MOG. ### Claim 289 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises CNTN2, OPG, and MOG. ### Claim 290 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises CXCL9, OPG, and APLP1. ### Claim 291 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises OPG, PRTG, and MOG. ### Claim 292 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises OPG, OPN, and MOG. ### Claim 293 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises CXCL13, APLP1, and NEFL. ### Claim 294 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises FLRT2, APLP1, and NEFL. ### Claim 295 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises OPN, APLP1, and NEFL. ### Claim 296 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises CXCL9, APLP1, and NEFL. ### Claim 297 (depends on 250) The non-transitory computer readable medium of claim 250, wherein the plurality of biomarkers further comprises CXCL13, FLRT2, and APLP1. ### Claim 298 (depends on 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297) The non-transitory computer readable medium of any one of claims 268-297, wherein a performance of the predictive model is characterized by a Pearson's R2 coefficient of at least 0.10. ### Claim 299 (depends on 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298) The non-transitory computer readable medium of any one of claims 268-298, wherein a performance of the predictive model is characterized by an Pearson's R2 coefficient between 0.11 and 0.22. ### Claim 300 (depends on 245) The non-transitory computer readable medium of claim 245, wherein the plurality of biomarkers does not include GFAP. ### Claim 301 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises CDCP1 and OPG. ### Claim 302 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises CDCP1 and SERPINA9. ### Claim 303 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises OPG and TNFRSF10A. ### Claim 304 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises OPG and MOG. ### Claim 305 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises CDCP1 and MOG. ### Claim 306 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises CDCP1, MOG, and OPG. ### Claim 307 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises CDCP1, SERPIN A9, and OPG. ### Claim 308 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises CDCP1, OPG, and CXCL13. ### Claim 309 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises CDCP1, CXCL9, and OPG. ### Claim 310 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises CDCP1, FLRT2, and OPG. ### Claim 311 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises CDCP1, MOG, OPG, and CXCL13. ### Claim 312 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises CDCP1, MOG, TNFRSF10A, and OPG. ### Claim 313 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises CDCP1, CXCL9, SERPINA9, and OPG, ### Claim 314 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises CDCP1, CNTN2, SERPINA9, and OPG. ### Claim 315 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises CDCP1, SERPINA9, CD6, and OPG. ### Claim 316 (depends on 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315) The non-transitory computer readable medium of any one of claims 300-315, wherein a performance of the predictive model is characterized by an AUROC of at least 0.65. ### Claim 317 (depends on 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316) The non-transitory computer readable medium of any one of claims 300-316, wherein a performance of the predictive model is characterized by an AUROC between 0.66 and 0.70. ### Claim 318 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises OPG and NEFL. ### Claim 319 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises OPG and OPN. ### Claim 320 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises OPG and FLRT2. ### Claim 321 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises OPG and MOG. ### Claim 322 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises CXCL9 and OPG. ### Claim 323 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises GH and NEFL. ### Claim 324 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises CXCL13 and NEFL. ### Claim 325 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises APLP1 and NEFL. ### Claim 326 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises CCL20 and NEFL. ### Claim 327 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises CXCL9 and NEFL. ### Claim 328 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises OPG, MOG, and NEFL. ### Claim 329 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises OPG, FLRT2, and NEFL. ### Claim 330 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises CXCL9, OPG, and NEFL. ### Claim 331 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises OPG, CDCP1, and NEFL. ### Claim 332 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises OPG, OPN, and NEFL. ### Claim 333 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises GH, APLP1, and NEFL. ### Claim 334 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises GH, CXCL13, and NEFL. ### Claim 335 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises GH, CDCP1, and NEFL. ### Claim 336 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises CXCL13, CCL20, and NEFL. ### Claim 337 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises GH, CCL20, and NEFL. ### Claim 338 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises CDCP1, CXCL13, MOG, and NEFL. ### Claim 339 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises CD6, CXCL9, CXCL13, and NEFL. ### Claim 340 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises CXCL9, CXCL13, MOG, and NEFL. ### Claim 341 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises CD6, CDCP1, CXCL13, and NEFL. ### Claim 342 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises CD6, CXCL9, CXCL13, and MOG. ### Claim 343 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises CDCP1, CXCL13, MOG, and NEFL. ### Claim 344 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises CD6, CDCP1, CXCL13, and NEFL. ### Claim 345 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises CXCL9, CXCL13, MOG, and NEFL. ### Claim 346 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises CD6, CXCL9, CXCL13, and NEFL. ### Claim 347 (depends on 300) The non-transitory computer readable medium of claim 300, wherein the plurality of biomarkers comprises CD6, CDCP1, CXCL13, and MOG. ### Claim 348 (depends on 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347) The non-transitory computer readable medium of claim 318-347, wherein a performance of the predictive model is characterized by a Pearson's R2 coefficient of at least 0.10. ### Claim 349 (depends on 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348) The non-transitory computer readable medium of claim 318-348, wherein a performance of the predictive model is characterized by an Pearson's R2 coefficient between 0.015 and 0.090. ### Claim 350 (depends on 245, 349) The non-transitory computer readable medium of any one of claims 245-349, wherein the prediction of multiple sclerosis disease progression is a brain parenchymal fraction value. ### Claim 351 (depends on 245, 349) The non-transitory computer readable medium of any one of claims 245-349, wherein the prediction of multiple sclerosis disease progression is an expanded disability status scale (EDSS) score. ### Claim 352 (depends on 351) The non-transitory computer readable medium of claim 351, wherein an expanded disability status scale (EDSS) score less than or equal to 6 indicates a mild/moderate MS disease progression and a EDSS score greater than 6.5 indicates a severe MS disease progression. ### Claim 353 (depends on 245, 349) The non-transitory computer readable medium of any one of claims 245-349, wherein the prediction of multiple sclerosis disease progression is a patient determined disease steps (PDDS) score. ### Claim 354 (depends on 353) The non-transitory computer readable medium of claim 353, wherein the prediction of multiple sclerosis disease progression is a patient determined disease steps (PDDS) score differentiating between severe and mild/moderate MS disease progression. ### Claim 355 (depends on 354) The non-transitory computer readable medium of claim 354, wherein a patient determined disease steps (PDDS) score less than or equal to 4 indicates a mild/moderate MS disease progression and a PDDS score greater than 4 indicates a severe MS disease progression. ### Claim 356 (depends on 245, 349) The non-transitory computer readable medium of any one of claims 245-349, wherein the prediction of multiple sclerosis disease progression is a PRO measurement information system (PROMIS) score. ### Claim 357 (depends on 245, 349) The non-transitory computer readable medium of any one of claims 245-349, wherein the prediction of multiple sclerosis disease progression is a Multiple Sclerosis Rating Scale, Revised (MSRS-R) score. ### Claim 358 (depends on 188, 357) The non-transitory computer readable medium of any one of claims 188-357, wherein generating the prediction of multiple sclerosis disease progression by applying the predictive model to the expression levels of the plurality of biomarkers further comprises applying the predictive model to one or more subject attributes of the subject, wherein subject attributes comprise any of age, sex, and disease duration. ### Claim 359 (depends on 188, 358) The non-transitory computer readable medium of any one of claims 188-358, wherein generating the prediction of multiple sclerosis disease progression comprises comparing a score outputted by the predictive model to a reference score. ### Claim 360 (depends on 359) The non-transitory computer readable medium of claim 359, wherein the reference score corresponds to any of: A) an EDSS score; B) a brain parenchymal fraction value; C) a PDDS score; D) a PROMIS score; or E) a MSRS-R score. ### Claim 361 (depends on 360) The non-transitory computer readable medium of claim 360, wherein the reference score further corresponds to a mild/moderate MS disease progression or a severe MS disease progression. ### Claim 362 (depends on 188, 361) The non-transitory computer readable medium of any one of claims 188-361, wherein the expression levels of the plurality of biomarkers is determined from a test sample obtained from the subject. ### Claim 363 (depends on 362) The non-transitory computer readable medium of claim 362, wherein the test sample is a blood or serum sample. ### Claim 364 (depends on 362, 363) The non-transitory computer readable medium of claim 362 or 363, wherein the subject has multiple sclerosis, is suspected of having multiple sclerosis, or was previously diagnosed with multiple sclerosis. ### Claim 365 (depends on 188, 364) The non-transitory computer readable medium of any one of claims 188-364, wherein obtaining or having obtained the dataset comprises performing an immunoassay to determine the expression levels of the plurality of biomarkers. ### Claim 366 (depends on 365) The non-transitory computer readable medium of claim 365, wherein the immunoassay is a Proximity Extension Assay (PEA) or LUMINEX xMAP Multiplex Assay. ### Claim 367 (depends on 365, 366) The non-transitory computer readable medium of claim 365 or 366, wherein performing the immunoassay comprises contacting a test sample with a plurality of reagents comprising antibodies. ### Claim 368 (depends on 367) The non-transitory computer readable medium of claim 367, wherein the antibodies comprise one of monoclonal and polyclonal antibodies. ### Claim 369 (depends on 367) The non-transitory computer readable medium of claim 367, wherein the antibodies comprise both monoclonal and polyclonal antibodies. ### Claim 370 (depends on 188, 369) The non-transitory computer readable medium of any one of claims 188-369, further comprising instructions that, when executed by a processor, cause the processor to: select a therapy for administering to the subject based on the prediction of multiple sclerosis disease progression. ### Claim 371 (depends on 188, 370) The non-transitory computer readable medium of any one of claims 188-370, further comprising instructions that, when executed by a processor, cause the processor to: determine a therapeutic efficacy of a therapy previously administered to the subject based on the prediction of multiple sclerosis disease progression. ### Claim 372 (depends on 371) The non-transitory computer readable medium of claim 371, wherein the instructions that cause the processor to determine the therapeutic efficacy of the therapy further comprises instructions that, when executed by the processor, cause the processor to compare the prediction to a prior prediction determined for the subject at a prior timepoint ### Claim 373 (depends on 372) The non-transitory computer readable medium of claim 372, wherein the instructions that cause the processor to determine the therapeutic efficacy of the therapy further comprises instructions that, when executed by the processor, cause the processor to determine that the therapy exhibits efficacy responsive to a difference between the prediction and the prior prediction. ### Claim 374 (depends on 372) The non-transitory computer readable medium of claim 372, wherein the instructions that cause the processor to determine the therapeutic efficacy of the therapy further comprises instructions that, when executed by the processor, cause the processor to determine that the therapy lacks efficacy responsive to a lack of difference between the prediction and the prior prediction.