Biomarkers for predicting multiple sclerosis disease activity

Abstract

To provide a method, a kit, a computer system, and a software for determining multiple sclerosis activity in a human subject.SOLUTION: Provided is a method comprising: obtaining or having obtained a dataset comprising expression levels of a plurality of biomarkers: and generating a prediction of multiple sclerosis disease activity by applying a predictive model to the expression levels of the plurality of biomarkers.SELECTED DRAWING: Figure 1A

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of and priority to U.S. Provisional Patent Application No. 62 / 896,430, filed September 5, 2019, the entire disclosure of which is incorporated herein by reference in its entirety for all purposes. Summary of the Invention

[0002] Disclosed herein is a method for predicting multiple sclerosis disease activity using a multivariate biomarker panel.Disclosed herein is a non-transitory computer readable medium for predicting multiple sclerosis disease activity using a multivariate biomarker panel.Disclosed herein is a kit comprising a set of reagents for determining the expression level of a multivariate biomarker that is useful for predicting multiple sclerosis disease activity.Disclosed herein is a system for predicting multiple sclerosis disease activity using a multivariate biomarker panel.

[0003] The advantages of multivariate biomarker panels for detecting disease activity include: ● Improved sensitivity: Multi-biomarker tests improve performance (AUC, precision), especially by eliminating false negatives that cannot be detected by any individual biomarker. Subtle disease activity: Multivariate models may be more useful in distinguishing samples with single gadolinium (Gd) lesions than individual biomarkers alone. Specificity: Individual biomarkers are often differentially expressed in other neurological conditions. Multi-biomarker testing will help differentiate disease activity / progression specific to multiple sclerosis. ● Predictive power: Multivariate models incorporating shifts in biomarker levels identify patients progressing toward incremental or incremental decline in active disease (w / showing stronger performance than individual biomarkers alone).

[0004] Disclosed herein is a method for determining multiple sclerosis activity in a subject, the method comprising obtaining, or having obtained, a dataset comprising expression levels of a plurality of biomarkers, the plurality of biomarkers comprising each biomarker of at least one group selected from group 1, group 2, and group 3, wherein group 1 comprises biomarker 1, biomarker 2, biomarker 3, biomarker 4, biomarker 5, biomarker 6, biomarker 7, and biomarker 8, and wherein biomarker 1 comprises NEFL, MOG, C ADM3, or GFAP; biomarker 2 is MOG, CADM3, KLK6, BCAN, OMG, or GFAP; biomarker 3 is CD6, CD5, CRTAM, CD244, or TNFRSF9; biomarker 4 is CXCL9, CXCL10, IL-12B, CXCL11, or GFAP; biomarker 5 is OPG, TFF3, or ENPP2; biomarker 6 is OPN, OMD, MEPE, or GFAP; and biomarker 7 is CXCL13, NOS3, or MMP-2. and biomarker 8 is GFAP, NEFL, OPN, CXCL9, MOG, or CHI3L1; group 2 includes biomarker 9, biomarker 10, biomarker 11, biomarker 12, biomarker 13, biomarker 14, biomarker 15, biomarker 16, and biomarker 17; and biomarker 9 is CDCP1, IL-18BP, IL-18, GFAP, or MSR1; biomarker 10 is CCL20, CCL3, or TWEAK; and biomarker 11 is IL-12. B, IL12A, or CXCL9; biomarker 12 is APLP1, SEZ6L, BCAN, DPP6, NCAN, or KLK6; biomarker 13 is TNFRSF10A, TNFRSF11A, SPON2, CHI3L1, or IFI30; biomarker 14 is COL4A1, IL6, NOTCH3, or PCDH17; biomarker 15 is SERPINA9, TNFRSF9, or CNTN4; and biomarker 16 is FLRT2, DDR1, NTRK2, CDH6, MMP-2;and biomarker 17 is TNFSF13B, CXCL16, ALCAM, or IL-18; group 3 includes biomarker 18, biomarker 19, biomarker 20, and biomarker 21, where biomarker 18 is GH, GH2, or IGFBP-1, biomarker 19 is VCAN, TINAGL1, CANT1, NECTIN2, MMP-9, or NPDC1, biomarker 20 is PRTG, NTRK2, NTRK3, or CNTN4, and biomarker 21 is CNTN2, DPP6, GDNFR-α3, or SCARF2), and applying a predictive model to the expression levels of the plurality of biomarkers to generate a prediction of multiple sclerosis disease activity.

[0005] In various embodiments, the plurality of biomarkers includes each biomarker in Group 1, where biomarker 1 is NEFL, biomarker 2 is MOG, biomarker 3 is CD6, biomarker 4 is CXCL9, biomarker 5 is OPG, biomarker 6 is OPN, biomarker 7 is CXCL13, and biomarker 8 is GFAP. In various embodiments, the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.667 to 0.869. In various embodiments, the performance of the predictive model is characterized by a positive predictive value ranging from 0.617 to 0.861. In various embodiments, the plurality of biomarkers includes each biomarker of group 2, where biomarker 9 is CDCP1, biomarker 10 is CCL20, biomarker 11 is IL-12B, biomarker 12 is APLP1, biomarker 13 is TNFRSF10A, biomarker 14 is COL4A1, biomarker 15 is SERPINA9, biomarker 16 is FLRT2, and biomarker 17 is TNFSF13B. In various embodiments, the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.595 to 0.761. In various embodiments, the performance of the predictive model is characterized by a positive predictive value ranging from 0.523 to 0.769. In various embodiments, the plurality of biomarkers further comprises each biomarker of group 2, where biomarker 9 is CDCP1, biomarker 10 is CCL20, biomarker 11 is IL-12B, biomarker 12 is APLP1, biomarker 13 is TNFRSF10A, biomarker 14 is COL4A1, biomarker 15 is SERPINA9, biomarker 16 is FLRT2, and biomarker 17 is TNFSF13B. In various embodiments, the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.693 to 0.892. In various embodiments, the performance of the predictive model is characterized by a positive predictive value ranging from 0.613 to 0.843.

[0006] In various embodiments, the plurality of biomarkers includes each biomarker in group 3, where biomarker 18 is GH, biomarker 19 is VCAN, biomarker 20 is PRTG, and biomarker 21 is CNTN2. In various embodiments, the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.566 to 0.644. In various embodiments, the performance of the predictive model is characterized by a positive predictive value ranging from 0.370 to 0.742. In various embodiments, the plurality of biomarkers further includes each biomarker in group 3, where biomarker 18 is GH, biomarker 19 is VCAN, biomarker 20 is PRTG, and biomarker 21 is CNTN2. In various embodiments, the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.686 to 0.889. In various embodiments, the performance of the predictive model is characterized by a positive predictive value ranging from 0.648 to 0.835.

[0007] Further disclosed herein is a method for determining multiple sclerosis activity in a subject, the method comprising: obtaining or having obtained a dataset comprising expression levels of a plurality of biomarkers, including NEFL, MOG, CD6, CXCL9, OPG, OPN, CXCL13, and GFAP; and applying a predictive model to the expression levels of the plurality of biomarkers to generate a prediction of multiple sclerosis disease activity. In various embodiments, the plurality of biomarkers further comprises CDCP1, CCL20, IL-12B, APLP1, TNFRSF10A, COL4A1, SERPINA9, FLRT2, and TNFSF13B. In various embodiments, the plurality of biomarkers further comprises GH, VCAN, PRTG, and CNTN2.

[0008] Further disclosed herein is a method for generating quantitative data for a subject, the method comprising: performing at least one immunoassay on a sample obtained from a subject to generate a dataset comprising quantitative data, the quantitative data representing at least eight protein biomarkers including NEFL, MOG, CD6, CXCL9, OPG, OPN, CXCL13, and GFAP, and the subject having or suspected of having multiple sclerosis. In various embodiments, the quantitative data further represents at least nine additional protein biomarkers including CDCP1, CCL20, IL-12B, APLP1, TNFRSF10A, COL4A1, SERPINA9, FLRT2, and TNFSF13B. In various embodiments, the quantitative data further represents at least four additional protein biomarkers including GH, VCAN, PRTG, and CNTN2. In various embodiments, the method further comprises determining multiple sclerosis activity in the subject, the determining comprising applying a predictive model to the quantitative data. In various embodiments, applying the decision further comprises comparing the score output by the predictive model to a reference score, in various embodiments, the reference score corresponding to either A) a patient at baseline where the patient exhibited no disease activity, B) a patient with no clinically diagnosed disease activity, or C) a healthy patient.

[0009] Further disclosed herein is a method for determining multiple sclerosis activity in a subject, the method comprising obtaining or having obtained a dataset comprising expression levels of a plurality of biomarkers comprising one or more neurodegenerative biomarkers selected from the group consisting of NEFL, APLP1, OPG, SERPINA9, PRTG, GFAP, CNTN2, and FLRT2; one or more inflammatory biomarkers selected from the group consisting of CCL20, GH, CXCL13, IL-12B, VCAN, TNFRSF10A, TNFSF13B, CD6, and CXCL9; one or more immune-modulatory biomarkers selected from the group consisting of CDCP1 and OPN; one or more myelin integrity biomarkers selected from the group consisting of COL4A1 and MOG; and applying a predictive model to the expression levels of the plurality of biomarkers to generate a prediction of multiple sclerosis disease activity.

[0010] In various embodiments, the one or more neurodegeneration biomarkers include NEFL, OPG, and GFAP, the one or more inflammation biomarkers include CXCL13, CD6, and CXCL9, the one or more immune-modulating biomarkers include OPN, and the one or more myelin integrity biomarkers include MOG. In various embodiments, the one or more neurodegeneration biomarkers further include APLP1, SERPINA9, and FLRT2, the one or more inflammation biomarkers further include CCL20, IL-12B, TNFRSF10A, and TNFSF13B, the one or more immune-modulating biomarkers further include CDCP1, and the one or more myelin integrity biomarkers further include COL4A1. In various embodiments, the one or more neurodegeneration biomarkers further include PRTG and CNTN2, and the one or more inflammation biomarkers include GH and VCAN. In various embodiments, the plurality of biomarkers are protein biomarkers.

[0011] In various embodiments, the multiple sclerosis disease activity is any one of the following: the presence of normal disease activity, the presence of minimal disease activity, a shift (increase or decrease) in disease activity, MS severity, relapse or relapse events associated with MS, relapse rate, MS status, response to MS therapy, degree of MS disability, or risk of developing MS. In various embodiments, normal disease activity is the presence or absence of one or more gadolinium-enhancing MRI lesions, and minimal disease activity is the presence of one gadolinium-enhancing MRI lesion. In various embodiments, the severity of MS corresponds to the number of gadolinium-enhancing MRI lesions. In various embodiments, the MS status is a worsening or quiescent state of multiple sclerosis.

[0012] Further disclosed herein is a method for determining multiple sclerosis activity in a subject, the method comprising obtaining or having obtained a dataset comprising expression levels of a plurality of biomarkers, the plurality of biomarkers comprising two or more of NEFL, MOG, CD6, CXCL9, OPG, OPN, CXCL13, GFAP, CDCP1, CCL20, IL-12B, APLP1, TNFRSF10A, COL4A1, SERPINA9, FLRT2, TNFSF13B, GH, VCAN, PRTG, and CNTN2, and applying a predictive model to the expression levels of the plurality of biomarkers to generate a prediction of multiple sclerosis disease activity.

[0013] Further disclosed herein are methods for preparing a biomarker fraction from a sample obtained from a subject, the methods comprising: (a) extracting biomarkers from the sample; (b) generating a fraction of the extracted biomarkers after (a), the fraction of the extracted biomarkers after (b) comprising a plurality of biomarkers, the plurality of biomarkers comprising two or more of NEFL, MOG, CD6, CXCL9, OPG, OPN, CXCL13, GFAP, CDCP1, CCL20, IL-12B, APLP1, TNFRSF10A, COL4A1, SERPINA9, FLRT2, TNFSF13B, GH, VCAN, PRTG, and CNTN2; and (c) analyzing the plurality of biomarkers of the fraction of extracted biomarkers generated in (b). In various embodiments, the two or more biomarkers comprise NEFL and at least one other biomarker. In various embodiments, the two or more biomarkers are a biomarker pair selected from any one of A) NEFL and CD6, B) NEFL and MOG, C) NEFL and CXCL9, and D) NEFL and TNFRSF10A. In various embodiments, the two or more biomarkers are a biomarker triad selected from any one of A) NEFL, CD6, and CXCL9, and B) NEFL, TNFRSF10A, and COL4A1. In various embodiments, the two or more biomarkers are a biomarker quad selected from any one of A) NEFL, MOG, CD6, and CXCL9, B) NEFL, CXCL9, TNFRSF10A, and COL4A1, C) NEFL, CD6, CXCL9, and CXCL13, and D) NEFL, TNFRSF10A, COL4A1, and CCL20. In various embodiments, the two or more biomarkers do not include NEFL. In various embodiments, the two or more biomarkers are a biomarker pair selected from any one of A) MOG and IL-12B, B) CXCL9 and CD6, C) MOG and CXCL9, D) MOG and CD6, E) CXCL9 and COL4A1, and F) CD6 and VCAN.In various embodiments, the two or more biomarkers are a biomarker triad selected from any one of A) MOG, IL-12B, and APLP1, B) MOG, CD6, and CXCL9, C) CXCL9, COL4A1, and VCAN, D) MOG, IL-12B, and CNTN2, and E) CD6, CCL20, and VCAN. In various embodiments, the two or more biomarkers are a biomarker quad selected from any one of A) MOG, CXCL9, IL-12B, APLP1, B) CXCL9, COL4A1, OPG, and VCAN, C) CXCL9, OPG, APLP1, and OPN, D) MOG, IL-12B, OPN, and CNTN2, and E) CD6, COL4A1, CCL20, and VCAN. In various embodiments, the multiple sclerosis activity is a shift in disease activity.

[0014] In various embodiments, the two or more biomarkers are a biomarker pair selected from any one of A) NEFL and TNFSF13B, B) NEFL and CNTN2, and C) NEFL and CXCL9. In various embodiments, the two or more biomarkers are a biomarker triad selected from any one of A) NEFL, CNTN2, and TNFSF13B, B) NEFL, APLP1, and TNFSF13B, and C) NEFL, TNFRSF10A, and TNFSF13B. In various embodiments, the two or more biomarkers are a biomarker quad selected from any one of A) NEFL, TNFRSF10A, CNTN2, and TNFSF13B, B) NEFL, COL4A1, CNTN2, and TNFSF13B, and C) NEFL, TNFRSF10A, APLP1, and TNFSF13B. In various embodiments, the two or more biomarkers are a biomarker pair selected from any one of A) MOG and CDCP1, B) MOG and TNFSF13B, and C) MOG and CXCL9. In various embodiments, the two or more biomarkers are a biomarker triad, and one biomarker in the biomarker triad is MOG. In various embodiments, the biomarker triad is selected from any one of A) MOG, CXCL9, and TNFSF13B, B) MOG, OPG, and TNFSF13B, and C) MOG, CCL20, and TNFSF13B. In various embodiments, the two or more biomarkers are a biomarker quartet, and one biomarker in the biomarker quartet is MOG. In various embodiments, the biomarker quartet is selected from any one of A) MOG, CXCL9, APLP1, and TNFSF13B, B) MOG, CXCL9, OPG, and TNFSF13B, and C) MOG, CXCL9, OPG, and CNTN2. In various embodiments, the multiple sclerosis activity is the presence or absence of multiple sclerosis.

[0015] In various embodiments, the two or more biomarkers are a biomarker pair selected from any one of A) NEFL and TNFSF13B, B) NEFL and SERPINA9, and C) NEFL and GH. In various embodiments, the two or more biomarkers are a biomarker triad selected from any one of A) NEFL, SERPINA9, and TNFSF13B, B) NEFL, CNTN2, and TNFSF13B, and C) NEFL, APLP1, and TNFSF13B. In various embodiments, the two or more biomarkers are a biomarker quartet selected from any one of A) NEFL, CCL20, SERPINA9, and TNFSF13B, B) NEFL, APLP1, SERPINA9, and TNFSF13B, and C) NEFL, CCL20, APLP1, and TNFSF13B. In various embodiments, the two or more biomarkers are a biomarker pair selected from any one of A) MOG and TNFSF13B, B) MOG and CXCL9, and C) MOG and IL-12B. In various embodiments, the two or more biomarkers are a biomarker triad selected from any one of A) MOG, CXCL9, and TNFSF13B, B) MOG, SERPINA9, and TNFSF13B, and C) MOG, OPG, and TNFSF13B. In various embodiments, the two or more biomarkers are a biomarker quad selected from any one of A) MOG, CXCL9, OPG, and TNFSF13B, B) MOG, CXCL9, OPG, and TNFSF13B, and C) MOG, CXCL9, SERPINA9, and TNFSF13B. In various embodiments, the multiple sclerosis activity is disease severity based on a predicted number of gadolinium-enhancing lesions.

[0016] In various embodiments, the two or more biomarkers are a biomarker pair selected from any one of A) GFAP and MOG, B) GFAP and NEFL, C) APLP1 and GFAP, D) NEFL and MOG, or E) CXCL9 and OPG. In various embodiments, the two or more biomarkers are a biomarker triad selected from any one of A) GFAP, NEFL, and MOG, B) NEFL, MOG, and GH, C) NEFL, MOG, and SERPINA9, D) CXCL9, OPG, and SERPINA9, or E) CXCL9, OPG, and TNFRSF10A. In various embodiments, the two or more biomarkers are a biomarker triad selected from any one of: A) GFAP, NEFL, MOG, and IL12B, B) GFAP, NEFL, MOG, and PRTG, C) GFAP, NEFL, MOG, and APLP1, D) NEFL, MOG, GH, and SERPINA9, E) NEFL, MOG, GH, and TNFRSF10A, F) MOG, CXCL9, OPG, and SERPINA9, or G) CD6, IL12B, APLP1, and CCL20. In various embodiments, the multiple sclerosis activity is disease progression.

[0017] In various embodiments, the method further comprises classifying the subject based on the prediction. In various embodiments, generating a prediction of multiple sclerosis disease activity comprises comparing the score output by the predictive model to a reference score. In various embodiments, the reference score corresponds to either A) a patient at baseline where the patient exhibited no disease activity, B) a patient with clinically diagnosed no disease activity, or C) a healthy patient. In various embodiments, the expression level of the plurality of biomarkers is determined from a test sample obtained from the subject. In various embodiments, the test sample is a blood or serum sample. In various embodiments, the subject has multiple sclerosis, is suspected of having multiple sclerosis, or has previously been diagnosed with multiple sclerosis. In various embodiments, obtaining or having obtained the dataset comprises performing an immunoassay to determine the expression level of the plurality of biomarkers. In various embodiments, the immunoassay is a proximity extension assay (PEA) or a LUMINEX xMAP multiplex assay. In various embodiments, performing the immunoassay comprises contacting the test sample with a plurality of reagents including an antibody. In various embodiments, the antibody comprises one of a monoclonal antibody and a polyclonal antibody. In various embodiments, the antibody comprises both a monoclonal antibody and a polyclonal antibody.

[0018] In various embodiments, the method further comprises selecting a therapy for administration to the subject based on the prediction of multiple sclerosis disease activity. In various embodiments, the method further comprises determining the efficacy of a therapy previously administered to the subject based on the prediction of multiple sclerosis disease activity. In various embodiments, determining the therapeutic efficacy of a therapy comprises comparing the prediction to a previous prediction determined for the subject at a previous time point. In various embodiments, determining the therapeutic efficacy of a therapy comprises determining that the therapy exhibits efficacy according to a difference between the prediction and the previous prediction. In various embodiments, determining the therapeutic efficacy of a therapy comprises determining that the therapy does not exhibit efficacy according to a lack of a difference between the prediction and said previous prediction. In various embodiments, the method further comprises determining a differential diagnosis of multiple sclerosis based on the prediction of multiple sclerosis disease activity. In various embodiments, the differential diagnosis of multiple sclerosis includes at least one of relapsing remitting multiple sclerosis (RRMS), secondary progressive multiple sclerosis (SPMS), primary progressive multiple sclerosis (PPMS), progressive relapsing multiple sclerosis (PRMS), and clinically isolated syndrome (CIS).

[0019] Further disclosed herein is a non-transitory computer readable medium for determining multiple sclerosis activity in a subject, the non-transitory computer readable medium, when executed by a processor, causing the processor to obtain a dataset comprising expression levels of a plurality of biomarkers, wherein the plurality of biomarkers comprises each biomarker of at least one group selected from group 1, group 2, and group 3, wherein group 1 comprises biomarker 1, biomarker 2, biomarker 3, biomarker 4, biomarker 5, biomarker 6, biomarker 7, biomarker 8, biomarker 9, biomarker 10, biomarker 11, biomarker 12, biomarker 13, biomarker 14, biomarker 15, biomarker 16, biomarker 17, biomarker 18, biomarker 19, biomarker 20, biomarker 21, biomarker 22, biomarker 23, biomarker 24, biomarker 25, biomarker 26, biomarker 27, biomarker 28, biomarker 29, biomarker 30, biomarker 31, biomarker 32, biomarker 33, biomarker 34, biomarker 35, biomarker 36, biomarker 37, biomarker 38, biomarker 39, biomarker 40, biomarker 41, biomarker 42, biomarker 43, biomarker 44, biomarker 45, biomarker 46, biomarker 47, biomarker 48, biomarker 49, biomarker 50, biomarker 51, biomarker 52, biomarker 53, biomarker 54, biomarker 55, biomarker 56, biomarker 57, biomarker 58, biomarker 59, biomarker 60, biomarker 61, biomarker 62, biomarker 63, biomarker 64, biomarker 65, biomarker 66, biomarker 67, biomarker 68, biomarker 69, biomarker 70, biomarker 7 biomarker 7, and biomarker 8, wherein biomarker 1 is NEFL, MOG, CADM3, or GFAP, biomarker 2 is MOG, CADM3, KLK6, BCAN, OMG, or GFAP, biomarker 3 is CD6, CD5, CRTAM, CD244, or TNFRSF9, biomarker 4 is CXCL9, CXCL10, IL-12B, CXCL11, or GFAP, biomarker 5 is OPG, TFF3, or ENPP2, and biomarker 6 is OPN, OMD, MEPE or GFAP; biomarker 7 is CXCL13, NOS3, or MMP-2; biomarker 8 is GFAP, NEFL, OPN, CXCL9, MOG, or CHI3L1; group 2 includes biomarker 9, biomarker 10, biomarker 11, biomarker 12, biomarker 13, biomarker 14, biomarker 15, biomarker 16, and biomarker 17; biomarker 9 is CDCP1, IL-18BP, IL-18, GFAP, or MSR1; and biomarker 1 Biomarker 0 is CCL20, CCL3, or TWEAK; biomarker 11 is IL-12B, IL12A, or CXCL9; biomarker 12 is APLP1, SEZ6L, BCAN, DPP6, NCAN, or KLK6; biomarker 13 is TNFRSF10A, TNFRSF11A, SPON2, CHI3L1, or IFI30; biomarker 14 is COL4A1, IL6, NOTCH3, or PCDH17; and biomarker 15 is SERPINA9, TNFRSF9, or CNTN4;and generating a prediction of multiple sclerosis disease activity by applying a predictive model to the expression levels of the plurality of biomarkers, wherein biomarker 16 is FLRT2, DDR1, NTRK2, CDH6, MMP-2, biomarker 17 is TNFSF13B, CXCL16, ALCAM, or IL-18, and group 3 includes biomarker 18, biomarker 19, biomarker 20, and biomarker 21, wherein biomarker 18 is GH, GH2, or IGFBP-1, biomarker 19 is VCAN, TINAGL1, CANT1, NECTIN2, MMP-9, or NPDC1, biomarker 20 is PRTG, NTRK2, NTRK3, or CNTN4, and biomarker 21 is CNTN2, DPP6, GDNFR-α3, or SCARF2.

[0020] In various embodiments, the plurality of biomarkers includes each biomarker in Group 1, where biomarker 1 is NEFL, biomarker 2 is MOG, biomarker 3 is CD6, biomarker 4 is CXCL9, biomarker 5 is OPG, biomarker 6 is OPN, biomarker 7 is CXCL13, and biomarker 8 is GFAP. In various embodiments, the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.667 to 0.869. In various embodiments, the performance of the predictive model is characterized by a positive predictive value ranging from 0.617 to 0.861.

[0021] In various embodiments, the plurality of biomarkers includes each biomarker of group 2, where biomarker 9 is CDCP1, biomarker 10 is CCL20, biomarker 11 is IL-12B, biomarker 12 is APLP1, biomarker 13 is TNFRSF10A, biomarker 14 is COL4A1, biomarker 15 is SERPINA9, biomarker 16 is FLRT2, and biomarker 17 is TNFSF13B. In various embodiments, the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.595 to 0.761. In various embodiments, the performance of the predictive model is characterized by a positive predictive value ranging from 0.523 to 0.769.

[0022] In various embodiments, the plurality of biomarkers further comprises each biomarker of group 2, where biomarker 9 is CDCP1, biomarker 10 is CCL20, biomarker 11 is IL-12B, biomarker 12 is APLP1, biomarker 13 is TNFRSF10A, biomarker 14 is COL4A1, biomarker 15 is SERPINA9, biomarker 16 is FLRT2, and biomarker 17 is TNFSF13B. In various embodiments, the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.693 to 0.892. In various embodiments, the performance of the predictive model is characterized by a positive predictive value ranging from 0.613 to 0.843.

[0023] In various embodiments, the plurality of biomarkers includes each biomarker in group 3, where biomarker 18 is GH, biomarker 19 is VCAN, biomarker 20 is PRTG, and biomarker 21 is CNTN2. In various embodiments, the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.566 to 0.644. In various embodiments, the performance of the predictive model is characterized by a positive predictive value ranging from 0.370 to 0.742. In various embodiments, the plurality of biomarkers further includes each biomarker in group 3, where biomarker 18 is GH, biomarker 19 is VCAN, biomarker 20 is PRTG, and biomarker 21 is CNTN2. In various embodiments, the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.686 to 0.889. In various embodiments, the performance of the predictive model is characterized by a positive predictive value ranging from 0.648 to 0.835.

[0024] Further disclosed herein is a non-transitory computer readable medium for determining multiple sclerosis activity in a subject, the non-transitory computer readable medium comprising instructions, when executed by a processor, that cause the processor to perform the steps of obtaining or having obtained a dataset comprising expression levels of a plurality of biomarkers, including NEFL, MOG, CD6, CXCL9, OPG, OPN, CXCL13, and GFAP, and applying a predictive model to the expression levels of the plurality of biomarkers to generate a prediction of multiple sclerosis disease activity. In various embodiments, the plurality of biomarkers further comprises CDCP1, CCL20, IL-12B, APLP1, TNFRSF10A, COL4A1, SERPINA9, FLRT2, and TNFSF13B. In various embodiments, the plurality of biomarkers further comprises GH, VCAN, PRTG, and CNTN2.

[0025] Further disclosed herein is a non-transitory computer readable medium for determining multiple sclerosis activity in a subject, the non-transitory computer readable medium comprising instructions, when executed by a processor, to cause the processor to obtain a dataset comprising expression levels of a plurality of biomarkers comprising one or more neurodegenerative biomarkers selected from the group consisting of NEFL, APLP1, OPG, SERPINA9, PRTG, GFAP, CNTN2, and FLRT2; one or more inflammatory biomarkers selected from the group consisting of CCL20, GH, CXCL13, IL-12B, VCAN, TNFRSF10A, TNFSF13B, CD6, and CXCL9; one or more immune-modulatory biomarkers selected from the group consisting of CDCP1 and OPN; and one or more myelin integrity biomarkers selected from the group consisting of COL4A1 and MOG, and to generate a prediction of multiple sclerosis disease activity by applying a predictive model to the expression levels of the plurality of biomarkers.

[0026] In various embodiments, the one or more neurodegeneration biomarkers include NEFL, OPG, and GFAP, the one or more inflammation biomarkers include CXCL13, CD6, and CXCL9, the one or more immune-modulating biomarkers include OPN, and the one or more myelin integrity biomarkers include MOG. In various embodiments, the one or more neurodegeneration biomarkers further include APLP1, SERPINA9, and FLRT2, the one or more inflammation biomarkers further include CCL20, IL-12B, TNFRSF10A, and TNFSF13B, the one or more immune-modulating biomarkers further include CDCP1, and the one or more myelin integrity biomarkers further include COL4A1. In various embodiments, the one or more neurodegeneration biomarkers further include PRTG and CNTN2, and the one or more inflammation biomarkers include GH and VCAN. In various embodiments, the plurality of biomarkers are protein biomarkers. In various embodiments, the multiple sclerosis disease activity is any one of the following: the presence of normal disease activity, the presence of minimal disease activity, a shift (increase or decrease) in disease activity, MS severity, relapse or relapse events associated with MS, relapse rate, MS status, response to MS therapy, degree of MS disability, or risk of developing MS. In various embodiments, normal disease activity is the presence or absence of one or more gadolinium-enhancing MRI lesions, and minimal disease activity is the presence of one gadolinium-enhancing MRI lesion. In various embodiments, the severity of MS corresponds to the number of gadolinium-enhancing MRI lesions. In various embodiments, the MS status is a worsening or quiescent state of multiple sclerosis.

[0027] Further disclosed herein is a non-transitory computer readable medium for determining multiple sclerosis activity 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, the plurality of biomarkers comprising two or more of NEFL, MOG, CD6, CXCL9, OPG, OPN, CXCL13, GFAP, CDCP1, CCL20, IL-12B, APLP1, TNFRSF10A, COL4A1, SERPINA9, FLRT2, TNFSF13B, GH, VCAN, PRTG, and CNTN2, and generate a prediction of multiple sclerosis disease activity by applying a predictive model to the expression levels of the plurality of biomarkers. In various embodiments, the two or more biomarkers comprise NEFL and at least one other biomarker.

[0028] In various embodiments, the two or more biomarkers are a biomarker pair selected from any one of A) NEFL and CD6, B) NEFL and MOG, C) NEFL and CXCL9, and D) NEFL and TNFRSF10A. In various embodiments, the two or more biomarkers are a biomarker triad selected from any one of A) NEFL, CD6, and CXCL9, and B) NEFL, TNFRSF10A, and COL4A1. In various embodiments, the two or more biomarkers are a biomarker quad selected from any one of A) NEFL, MOG, CD6, and CXCL9, B) NEFL, CXCL9, TNFRSF10A, and COL4A1, C) NEFL, CD6, CXCL9, and CXCL13, and D) NEFL, TNFRSF10A, COL4A1, and CCL20. In various embodiments, the two or more biomarkers do not include NEFL. In various embodiments, the two or more biomarkers are a biomarker pair selected from any one of A) MOG and IL-12B, B) CXCL9 and CD6, C) MOG and CXCL9, D) MOG and CD6, E) CXCL9 and COL4A1, and F) CD6 and VCAN. In various embodiments, the two or more biomarkers are a biomarker triad selected from any one of A) MOG, IL-12B, and APLP1, B) MOG, CD6, and CXCL9, C) CXCL9, COL4A1, and VCAN, D) MOG, IL-12B, and CNTN2, and E) CD6, CCL20, and VCAN. In various embodiments, the two or more biomarkers are a biomarker quartet selected from any one of: A) MOG, CXCL9, IL-12B, APLP1, B) CXCL9, COL4A1, OPG, and VCAN, C) CXCL9, OPG, APLP1, and OPN, D) MOG, IL-12B, OPN, and CNTN2, and E) CD6, COL4A1, CCL20, and VCAN. In various embodiments, the multiple sclerosis activity is a shift in disease activity.

[0029] In various embodiments, the two or more biomarkers are a biomarker pair selected from any one of A) NEFL and TNFSF13B, B) NEFL and CNTN2, and C) NEFL and CXCL9. In various embodiments, the two or more biomarkers are a biomarker triad selected from any one of A) NEFL, CNTN2, and TNFSF13B, B) NEFL, APLP1, and TNFSF13B, and C) NEFL, TNFRSF10A, and TNFSF13B. In various embodiments, the two or more biomarkers are a biomarker quad selected from any one of A) NEFL, TNFRSF10A, CNTN2, and TNFSF13B, B) NEFL, COL4A1, CNTN2, and TNFSF13B, and C) NEFL, TNFRSF10A, APLP1, and TNFSF13B. In various embodiments, the two or more biomarkers are a biomarker pair selected from any one of A) MOG and CDCP1, B) MOG and TNFSF13B, and C) MOG and CXCL9. In various embodiments, the two or more biomarkers are a biomarker triad, and one biomarker in the biomarker triad is MOG. In various embodiments, the biomarker triad is selected from any one of A) MOG, CXCL9, and TNFSF13B, B) MOG, OPG, and TNFSF13B, and C) MOG, CCL20, and TNFSF13B. In various embodiments, the two or more biomarkers are a biomarker quartet, and one biomarker in the biomarker quartet is MOG. In various embodiments, the biomarker quartet is selected from any one of A) MOG, CXCL9, APLP1, and TNFSF13B, B) MOG, CXCL9, OPG, and TNFSF13B, and C) MOG, CXCL9, OPG, and CNTN2. In various embodiments, the multiple sclerosis activity is the presence or absence of multiple sclerosis.

[0030] In various embodiments, the two or more biomarkers are a biomarker pair selected from any one of A) NEFL and TNFSF13B, B) NEFL and SERPINA9, and C) NEFL and GH. In various embodiments, the two or more biomarkers are a biomarker triad selected from any one of A) NEFL, SERPINA9, and TNFSF13B, B) NEFL, CNTN2, and TNFSF13B, and C) NEFL, APLP1, and TNFSF13B. In various embodiments, the two or more biomarkers are a biomarker quartet selected from any one of A) NEFL, CCL20, SERPINA9, and TNFSF13B, B) NEFL, APLP1, SERPINA9, and TNFSF13B, and C) NEFL, CCL20, APLP1, and TNFSF13B. In various embodiments, the two or more biomarkers are a biomarker pair selected from any one of A) MOG and TNFSF13B, B) MOG and CXCL9, and C) MOG and IL-12B. In various embodiments, the biomarker triad is selected from any one of A) MOG, CXCL9, and TNFSF13B, B) MOG, SERPINA9, and TNFSF13B, and C) MOG, OPG, and TNFSF13B. In various embodiments, the biomarker quartet is selected from any one of A) MOG, CXCL9, OPG, and TNFSF13B, B) MOG, CXCL9, OPG, and TNFSF13B, and C) MOG, CXCL9, SERPINA9, and TNFSF13B. In various embodiments, the multiple sclerosis activity is disease severity based on a predicted number of gadolinium-enhancing lesions.

[0031] In various embodiments, the two or more biomarkers are a biomarker pair selected from any one of A) GFAP and MOG, B) GFAP and NEFL, C) APLP1 and GFAP, D) NEFL and MOG, or E) CXCL9 and OPG. In various embodiments, the two or more biomarkers are a biomarker triad selected from any one of A) GFAP, NEFL, and MOG, B) NEFL, MOG, and GH, C) NEFL, MOG, and SERPINA9, D) CXCL9, OPG, and SERPINA9, or E) CXCL9, OPG, and TNFRSF10A. In various embodiments, the two or more biomarkers are a biomarker triad selected from any one of: A) GFAP, NEFL, MOG, and IL12B, B) GFAP, NEFL, MOG, and PRTG, C) GFAP, NEFL, MOG, and APLP1, D) NEFL, MOG, GH, and SERPINA9, E) NEFL, MOG, GH, and TNFRSF10A, F) MOG, CXCL9, OPG, and SERPINA9, or G) CD6, IL12B, APLP1, and CCL20. In various embodiments, the multiple sclerosis activity is disease progression.

[0032] In various embodiments, the step further comprises classifying the subject based on the prediction. In various embodiments, generating a prediction of multiple sclerosis disease activity comprises comparing the score output by the predictive model to a reference score. In various embodiments, the reference score corresponds to either A) a patient at baseline where the patient showed no disease activity, B) a patient with clinically diagnosed no disease activity, or C) a healthy patient.

[0033] In various embodiments, the expression levels of the plurality of biomarkers are determined from a test sample obtained from the subject. In various embodiments, the test sample is a blood or serum sample. In various embodiments, the subject has, is suspected of having, or has previously been diagnosed with multiple sclerosis. In various embodiments, the dataset is obtained from an immunoassay performed. In various embodiments, the immunoassay is a proximity extension assay (PEA) or a LUMINEX xMAP multiplex assay. In various embodiments, performing the immunoassay includes contacting the test sample with a plurality of reagents including an antibody. In various embodiments, the antibody includes one of a monoclonal antibody and a polyclonal antibody. In various embodiments, the antibody includes both a monoclonal antibody and a polyclonal antibody.

[0034] In various embodiments, the steps further include selecting a therapy for administration to the subject based on the prediction of multiple sclerosis disease activity. In various embodiments, the steps further include determining a therapeutic efficacy previously administered to the subject based on the prediction of multiple sclerosis disease activity. In various embodiments, determining the therapeutic efficacy of the therapy includes comparing the prediction to a previous prediction determined for the subject at a previous time point. In various embodiments, determining the therapeutic efficacy of the therapy includes determining that the treatment exhibits efficacy according to a difference between the prediction and the previous prediction. In various embodiments, determining the therapeutic efficacy of the therapy includes determining that the treatment is ineffective according to a lack of a difference between the prediction and said previous prediction. In various embodiments, the steps further include determining a differential diagnosis of multiple sclerosis based on the prediction of multiple sclerosis disease activity. In various embodiments, the differential diagnosis of multiple sclerosis includes any one of relapsing remitting multiple sclerosis (RRMS), secondary progressive multiple sclerosis (SPMS), primary progressive multiple sclerosis (PPMS), progressive relapsing multiple sclerosis (PRMS), and CIS.

[0035] Further disclosed herein is a kit for assessing multiple sclerosis disease activity in a subject, the kit comprising a set of reagents for determining expression levels of a plurality of biomarkers in a test sample from the subject, the plurality of biomarkers including each biomarker of at least one group selected from Group 1, Group 2, and Group 3, wherein Group 1 includes biomarker 1, biomarker 2, biomarker 3, biomarker 4, biomarker 5, biomarker 6, biomarker 7, and biomarker 8, and wherein biomarker 1 is selected from the group consisting of NEFL, ... , MOG, CADM3, or GFAP; biomarker 1 is MOG, CADM3, KLK6, BCAN, OMG, or GFAP; biomarker 2 is CD6, CD5, CRTAM, CD244, or TNFRSF9; biomarker 4 is CXCL9, CXCL10, IL-12B, CXCL11, or GFAP; biomarker 5 is OPG, TFF3, or ENPP2; biomarker 6 is OPN, OMD, MEPE, or GFAP; and biomarker 7 is CXCL13, NOS3, or M Group 1 includes biomarker 9, biomarker 10, biomarker 11, biomarker 12, biomarker 13, biomarker 14, biomarker 15, biomarker 16, and biomarker 17, wherein biomarker 9 is CDCP1, IL-18BP, IL-18, GFAP, or MSR1, biomarker 10 is CCL20, CCL3, or TWEAK, and biomarker 11 is IL- 12B, IL12A, or CXCL9; biomarker 12 is APLP1, SEZ6L, BCAN, DPP6, NCAN, or KLK6; biomarker 13 is TNFRSF10A, TNFRSF11A, SPON2, CHI3L1, or IFI30; biomarker 14 is COL4A1, IL6, NOTCH3, or PCDH17; biomarker 15 is SERPINA9, TNFRSF9, or CNTN4; and biomarker 16 is FLRT2, DDR1, NTRK2, CDH6, MMP-2;and biomarker 17 is TNFSF13B, CXCL16, ALCAM, or IL-18; group 3 includes biomarker 18, biomarker 19, biomarker 20, and biomarker 21, where biomarker 18 is GH, GH2, or IGFBP-1, biomarker 19 is VCAN, TINAGL1, CANT1, NECTIN2, MMP-9, or NPDC1, biomarker 20 is PRTG, NTRK2, NTRK3, or CNTN4, and biomarker 21 is CNTN2, DPP6, GDNFR-α3, or SCARF2, and instructions for using the set of reagents to determine the expression levels of the biomarkers in a test sample.

[0036] In various embodiments, the plurality of biomarkers includes each biomarker in Group 1, where biomarker 1 is NEFL, biomarker 2 is MOG, biomarker 3 is CD6, biomarker 4 is CXCL9, biomarker 5 is OPG, biomarker 6 is OPN, biomarker 7 is CXCL13, and biomarker 8 is GFAP. In various embodiments, the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.667 to 0.869. In various embodiments, the performance of the predictive model is characterized by a positive predictive value ranging from 0.617 to 0.861.

[0037] In various embodiments, the plurality of biomarkers includes each biomarker in group 2, where biomarker 9 is CDCP1, biomarker 10 is CCL20, biomarker 11 is IL-12B, biomarker 12 is APLP1, biomarker 13 is TNFRSF10A, biomarker 14 is COL4A1, biomarker 15 is SERPINA9, biomarker 16 is FLRT2, and biomarker 17 is TNFSF13B. In various embodiments, the predictive model is characterized by an area under the curve (AUC) ranging from 0.595 to 0.761. In various embodiments, the performance of the predictive model is characterized by a positive predictive value ranging from 0.523 to 0.769.

[0038] In various embodiments, the plurality of biomarkers further comprises each biomarker of Group 2, where biomarker 9 is CDCP1, biomarker 10 is CCL20, biomarker 11 is IL-12B, biomarker 12 is APLP1, biomarker 13 is TNFRSF10A, biomarker 14 is COL4A1, biomarker 15 is SERPINA9, biomarker 16 is FLRT2, and biomarker 17 is TNFSF13B. In various embodiments, the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.693 to 0.892. In various embodiments, the performance of the predictive model is characterized by a positive predictive value ranging from 0.613 to 0.843. In various embodiments, the plurality of biomarkers further comprises each biomarker of Group 3, where biomarker 18 is GH, biomarker 19 is VCAN, biomarker 20 is PRTG, and biomarker 21 is CNTN2. In various embodiments, the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.566 to 0.644. In various embodiments, the performance of the predictive model is characterized by a positive predictive value ranging from 0.370 to 0.742. In various embodiments, the plurality of biomarkers further comprises each biomarker of group 3, where biomarker 18 is GH, biomarker 19 is VCAN, biomarker 20 is PRTG, and biomarker 21 is CNTN2. In various embodiments, the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.686 to 0.889. In various embodiments, the performance of the predictive model is characterized by a positive predictive value ranging from 0.648 to 0.835.

[0039] Further disclosed herein is a kit for assessing multiple sclerosis disease activity in a subject, the kit comprising a set of reagents for determining the expression level of a plurality of biomarkers in a test sample from a subject, the plurality of biomarkers comprising NEFL, MOG, CD6, CXCL9, OPG, OPN, CXCL13, and GFAP, and instructions for using the set of reagents to determine the expression level of the biomarkers in the test sample. In various embodiments, the plurality of biomarkers further comprises CDCP1, CCL20, IL-12B, APLP1, TNFRSF10A, COL4A1, SERPINA9, FLRT2, and TNFSF13B. In various embodiments, the plurality of biomarkers further comprises GH, VCAN, PRTG, and CNTN2.

[0040] Further disclosed herein is a kit for assessing multiple sclerosis disease activity in a subject, the kit comprising a set of reagents for determining expression levels of a plurality of biomarkers in a test sample from a subject, the plurality of biomarkers comprising one or more neurodegenerative biomarkers selected from the group consisting of NEFL, APLP1, OPG, SERPINA9, PRTG, GFAP, CNTN2, and FLRT2; one or more inflammatory biomarkers selected from the group consisting of CCL20, GH, CXCL13, IL-12B, VCAN, TNFRSF10A, TNFSF13B, CD6, and CXCL9; one or more immune-modulating biomarkers selected from the group consisting of CDCP1 and OPN; and one or more myelin integrity biomarkers selected from the group consisting of COL4A1 and MOG, as well as instructions for using the set of reagents to determine expression levels of the biomarkers in the test sample. In various embodiments, the one or more neurodegeneration biomarkers include NEFL, OPG, and GFAP, the one or more inflammation biomarkers include CXCL13, CD6, and CXCL9, the one or more immune-modulating biomarkers include OPN, and the one or more myelin integrity biomarkers include MOG. In various embodiments, the one or more neurodegeneration biomarkers further include APLP1, SERPINA9, and FLRT2, the one or more inflammation biomarkers further include CCL20, IL-12B, TNFRSF10A, and TNFSF13B, the one or more immune-modulating biomarkers further include CDCP1, and the one or more myelin integrity biomarkers further include COL4A1. In various embodiments, the one or more neurodegeneration biomarkers further include PRTG and CNTN2, and the one or more inflammation biomarkers include GH and VCAN. In various embodiments, the plurality of biomarkers are protein biomarkers. In various embodiments, the multiple sclerosis disease activity is any one of the presence of usual disease activity, the presence of little disease activity, a shift (increase or decrease) in disease activity, MS severity, relapse or relapse events associated with MS, relapse rate, MS status, response to MS therapy, extent of MS disability, or risk of developing MS.In various embodiments, normal disease activity is the presence or absence of one or more gadolinium-enhancing MRI lesions, and minimal disease activity is the presence of one gadolinium-enhancing MRI lesion. In various embodiments, the severity of MS corresponds to the number of gadolinium-enhancing MRI lesions. In various embodiments, the MS condition is a worsening or quiescent state of multiple sclerosis.

[0041] Further disclosed herein is a kit for determining multiple sclerosis activity in a subject, the kit comprising a set of reagents for determining the expression level of a plurality of biomarkers in a test sample from a subject, the plurality of biomarkers comprising two or more of NEFL, MOG, CD6, CXCL9, OPG, OPN, CXCL13, GFAP, CDCP1, CCL20, IL-12B, APLP1, TNFRSF10A, COL4A1, SERPINA9, FLRT2, TNFSF13B, GH, VCAN, PRTG, and CNTN2, and instructions for using the set of reagents for determining the expression level of the biomarkers in the test sample. In various embodiments, the two or more biomarkers comprise NEFL and at least one other biomarker. In various embodiments, the two or more biomarkers are a biomarker pair selected from any one of A) NEFL and CD6, B) NEFL and MOG, C) NEFL and CXCL9, and D) NEFL and TNFRSF10A. In various embodiments, the two or more biomarkers are a biomarker triad selected from any one of A) NEFL, CD6, and CXCL9, and B) NEFL, TNFRSF10A, and COL4A1. In various embodiments, the two or more biomarkers are a biomarker quad selected from any one of A) NEFL, MOG, CD6, and CXCL9, B) NEFL, CXCL9, TNFRSF10A, and COL4A1, C) NEFL, CD6, CXCL9, and CXCL13, and D) NEFL, TNFRSF10A, COL4A1, and CCL20. In various embodiments, the two or more biomarkers do not include NEFL. In various embodiments, the two or more biomarkers are a biomarker pair selected from any one of A) MOG and IL-12B, B) CXCL9 and CD6, C) MOG and CXCL9, D) MOG and CD6, E) CXCL9 and COL4A1, and F) CD6 and VCAN.In various embodiments, the two or more biomarkers are a biomarker triad selected from any one of A) MOG, IL-12B, and APLP1, B) MOG, CD6, and CXCL9, C) CXCL9, COL4A1, and VCAN, D) MOG, IL-12B, and CNTN2, and E) CD6, CCL20, and VCAN. In various embodiments, the two or more biomarkers are a biomarker quad selected from any one of A) MOG, CXCL9, IL-12B, APLP1, B) CXCL9, COL4A1, OPG, and VCAN, C) CXCL9, OPG, APLP1, and OPN, D) MOG, IL-12B, OPN, and CNTN2, and E) CD6, COL4A1, CCL20, and VCAN. In various embodiments, the multiple sclerosis activity is a shift in disease activity.

[0042] In various embodiments, the two or more biomarkers are a biomarker pair selected from any one of A) NEFL and TNFSF13B, B) NEFL and CNTN2, and C) NEFL and CXCL9. In various embodiments, the two or more biomarkers are a biomarker triad selected from any one of A) NEFL, CNTN2, and TNFSF13B, B) NEFL, APLP1, and TNFSF13B, and C) NEFL, TNFRSF10A, and TNFSF13B. In various embodiments, the two or more biomarkers are a biomarker quad selected from any one of A) NEFL, TNFRSF10A, CNTN2, and TNFSF13B, B) NEFL, COL4A1, CNTN2, and TNFSF13B, and C) NEFL, TNFRSF10A, APLP1, and TNFSF13B. In various embodiments, the two or more biomarkers are a biomarker pair selected from any one of A) MOG and CDCP1, B) MOG and TNFSF13B, and C) MOG and CXCL9. In various embodiments, the two or more biomarkers are a biomarker triad, and one biomarker in the biomarker triad is MOG. In various embodiments, the biomarker triad is selected from any one of A) MOG, CXCL9, and TNFSF13B, B) MOG, OPG, and TNFSF13B, and C) MOG, CCL20, and TNFSF13B. In various embodiments, the two or more biomarkers are a biomarker quartet, and one biomarker in the biomarker quartet is MOG. In various embodiments, the biomarker quartet is selected from any one of A) MOG, CXCL9, APLP1, and TNFSF13B, B) MOG, CXCL9, OPG, and TNFSF13B, and C) MOG, CXCL9, OPG, and CNTN2. In various embodiments, the multiple sclerosis activity is the presence or absence of multiple sclerosis.

[0043] In various embodiments, the two or more biomarkers are a biomarker pair selected from any one of A) NEFL and TNFSF13B, B) NEFL and SERPINA9, and C) NEFL and GH. In various embodiments, the two or more biomarkers are a biomarker triad selected from any one of A) NEFL, SERPINA9, and TNFSF13B, B) NEFL, CNTN2, and TNFSF13B, and C) NEFL, APLP1, and TNFSF13B. In various embodiments, the two or more biomarkers are a biomarker quartet selected from any one of A) NEFL, CCL20, SERPINA9, and TNFSF13B, B) NEFL, APLP1, SERPINA9, and TNFSF13B, and C) NEFL, CCL20, APLP1, and TNFSF13B. In various embodiments, the two or more biomarkers are a biomarker pair selected from any one of A) MOG and TNFSF13B, B) MOG and CXCL9, and C) MOG and IL-12B. In various embodiments, the two or more biomarkers are a biomarker triad selected from any one of A) MOG, CXCL9, and TNFSF13B, B) MOG, SERPINA9, and TNFSF13B, and C) MOG, OPG, and TNFSF13B. In various embodiments, the two or more biomarkers are a biomarker quad selected from any one of A) MOG, CXCL9, OPG, and TNFSF13B, B) MOG, CXCL9, OPG, and TNFSF13B, and C) MOG, CXCL9, SERPINA9, and TNFSF13B. In various embodiments, the multiple sclerosis activity is disease severity based on a predicted number of gadolinium-enhancing lesions.

[0044] In various embodiments, the two or more biomarkers are a biomarker pair selected from any one of A) GFAP and MOG, B) GFAP and NEFL, C) APLP1 and GFAP, D) NEFL and MOG, or E) CXCL9 and OPG. In various embodiments, the two or more biomarkers are a biomarker triad selected from any one of A) GFAP, NEFL, and MOG, B) NEFL, MOG, and GH, C) NEFL, MOG, and SERPINA9, D) CXCL9, OPG, and SERPINA9, or E) CXCL9, OPG, and TNFRSF10A. In various embodiments, the two or more biomarkers are a biomarker triad selected from any one of: A) GFAP, NEFL, MOG, and IL12B, B) GFAP, NEFL, MOG, and PRTG, C) GFAP, NEFL, MOG, and APLP1, D) NEFL, MOG, GH, and SERPINA9, E) NEFL, MOG, GH, and TNFRSF10A, F) MOG, CXCL9, OPG, and SERPINA9, or G) CD6, IL12B, APLP1, and CCL20. In various embodiments, the multiple sclerosis activity is disease progression.

[0045] In various embodiments, the instructions further comprise instructions for generating a prediction of multiple sclerosis disease activity by applying a predictive model to the expression levels of the plurality of biomarkers. In various embodiments, generating a prediction of multiple sclerosis disease activity comprises comparing a score output by the predictive model to a reference score. In various embodiments, the reference score corresponds to either A) a patient at baseline where the patient exhibited no disease activity, B) a patient with clinically diagnosed no disease activity, or C) a healthy patient. In various embodiments, the test sample is a blood or serum sample. In various embodiments, the subject has multiple sclerosis, is suspected of having multiple sclerosis, or has previously been diagnosed with multiple sclerosis. In various embodiments, the instructions for using the set of reagents comprise instructions for performing an immunoassay to determine the expression levels of the plurality of biomarkers. In various embodiments, the immunoassay is a proximity extension assay (PEA) or a LUMINEX xMAP multiplex assay. In various embodiments, performing the immunoassay comprises contacting the test sample with a plurality of reagents including an antibody. In various embodiments, the antibody comprises one of a monoclonal antibody and a polyclonal antibody. In various embodiments, the antibody comprises both a monoclonal antibody and a polyclonal antibody.

[0046] In various embodiments, the kit further comprises instructions for selecting a therapy for administration to the subject based on the prediction of multiple sclerosis disease activity. In various embodiments, the kit further comprises instructions for determining the efficacy of a therapy previously administered to the subject based on the prediction of multiple sclerosis disease activity. In various embodiments, determining the therapeutic efficacy of the therapy comprises comparing the prediction to a previous prediction determined for the subject at a previous time point. In various embodiments, determining the therapeutic efficacy of the therapy comprises determining that the treatment exhibits efficacy according to a difference between the prediction and the previous prediction. In various embodiments, determining the therapeutic efficacy of the therapy comprises determining that the treatment is ineffective according to a lack of a difference between the prediction and said previous prediction. In various embodiments, the kit further comprises instructions for determining a differential diagnosis of multiple sclerosis based on the prediction of multiple sclerosis disease activity. In various embodiments, the differential diagnosis of multiple sclerosis includes any one of relapsing remitting multiple sclerosis (RRMS), secondary progressive multiple sclerosis (SPMS), primary progressive multiple sclerosis (PPMS), progressive relapsing multiple sclerosis (PRMS), and CIS.

[0047] Further disclosed herein is a system for assessing multiple sclerosis disease activity in a subject, the system comprising a set of reagents used to determine expression levels of a plurality of biomarkers in a test sample from the subject, the plurality of biomarkers including each biomarker of at least one group selected from group 1, group 2, and group 3, where group 1 includes biomarker 1, biomarker 2, biomarker 3, biomarker 4, biomarker 5, biomarker 6, biomarker 7, and biomarker 8, and Biomarker 1 is NEFL, MOG, CADM3, or GFAP; biomarker 2 is MOG, CADM3, KLK6, BCAN, OMG, or GFAP; biomarker 3 is CD6, CD5, CRTAM, CD244, or TNFRSF9; biomarker 4 is CXCL9, CXCL10, IL-12B, CXCL11, or GFAP; biomarker 5 is OPG, TFF3, or ENPP2; biomarker 6 is OPN, OMD, MEPE, or GFAP; and biomarker 7 is CXCL13, N Group 1 includes biomarker 1, biomarker 2, biomarker 3, biomarker 4, biomarker 5, biomarker 6, biomarker 7, biomarker 8, biomarker 9, biomarker 10, biomarker 11, biomarker 12, biomarker 13, biomarker 14, biomarker 15, biomarker 16, and biomarker 17, wherein biomarker 9 is CDCP1, IL-18BP, IL-18, GFAP, or MSR1, and biomarker 10 is CCL20, CCL3, or TWEAK; Biomarker 11 is IL-12B, IL12A, or CXCL9; biomarker 12 is APLP1, SEZ6L, BCAN, DPP6, NCAN, or KLK6; biomarker 13 is TNFRSF10A, TNFRSF11A, SPON2, CHI3L1, or IFI30; biomarker 14 is COL4A1, IL6, NOTCH3, or PCDH17; biomarker 15 is SERPINA9, TNFRSF9, or CNTN4; and biomarker 16 is FLRT2, DDR1, NTRK2, CDH6,MMP-2, biomarker 17 is TNFSF13B, CXCL16, ALCAM, or IL-18, group 3 includes biomarker 18, biomarker 19, biomarker 20, and biomarker 21, where biomarker 18 is GH, GH2, or IGFBP-1, biomarker 19 is VCAN, TINAGL1, CANT1, NECTIN2, MMP-9, or NPDC1, biomarker 20 is PRTG, NTRK2, NTRK3, or CNTN4, and biomarker 21 is CNTN2, DPP6, GDNFR-α3, or SCARF2), and a device configured to receive a mixture of one or more of the set of reagents and a test sample and to measure expression levels of the biomarkers in the test sample, and a computer system communicatively connected to the device for obtaining a dataset including expression levels of the plurality of biomarkers in the test sample, and for generating a prediction of multiple sclerosis disease activity by applying a predictive model to the expression levels of the plurality of biomarkers.

[0048] In various embodiments, the plurality of biomarkers includes each biomarker in Group 1, where biomarker 1 is NEFL, biomarker 2 is MOG, biomarker 3 is CD6, biomarker 4 is CXCL9, biomarker 5 is OPG, biomarker 6 is OPN, biomarker 7 is CXCL13, and biomarker 8 is GFAP. In various embodiments, the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.667 to 0.869. In various embodiments, the performance of the predictive model is characterized by a positive predictive value ranging from 0.617 to 0.861.

[0049] In various embodiments, the plurality of biomarkers includes each biomarker of group 2, where biomarker 9 is CDCP1, biomarker 10 is CCL20, biomarker 11 is IL-12B, biomarker 12 is APLP1, biomarker 13 is TNFRSF10A, biomarker 14 is COL4A1, biomarker 15 is SERPINA9, biomarker 16 is FLRT2, and biomarker 17 is TNFSF13B. In various embodiments, the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.595 to 0.761. In various embodiments, the performance of the predictive model is characterized by a positive predictive value ranging from 0.523 to 0.769.

[0050] In various embodiments, the plurality of biomarkers further comprises each biomarker of group 2, where biomarker 9 is CDCP1, biomarker 10 is CCL20, biomarker 11 is IL-12B, biomarker 12 is APLP1, biomarker 13 is TNFRSF10A, biomarker 14 is COL4A1, biomarker 15 is SERPINA9, biomarker 16 is FLRT2, and biomarker 17 is TNFSF13B. In various embodiments, the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.693 to 0.892. In various embodiments, the performance of the predictive model is characterized by a positive predictive value ranging from 0.613 to 0.843.

[0051] In various embodiments, the plurality of biomarkers includes each biomarker in group 3, where biomarker 18 is GH, biomarker 19 is VCAN, biomarker 20 is PRTG, and biomarker 21 is CNTN2. In various embodiments, the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.566 to 0.644. In various embodiments, the performance of the predictive model is characterized by a positive predictive value ranging from 0.370 to 0.742.

[0052] In various embodiments, the plurality of biomarkers further includes each biomarker in group 3, where biomarker 18 is GH, biomarker 19 is VCAN, biomarker 20 is PRTG, and biomarker 21 is CNTN2. In various embodiments, the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.686 to 0.889. In various embodiments, the performance of the predictive model is characterized by a positive predictive value ranging from 0.648 to 0.835.

[0053] Further disclosed herein is a system for assessing multiple sclerosis disease activity in a subject, the system comprising: a set of reagents used to determine expression levels of a plurality of biomarkers in a test sample from a subject, the plurality of biomarkers comprising NEFL, MOG, CD6, CXCL9, OPG, OPN, CXCL13, and GFAP; an apparatus configured to receive a mixture of one or more of the set of reagents and the test sample, and to measure the expression levels of the biomarkers in the test sample; and a computer system communicatively connected to the apparatus for obtaining a dataset comprising the expression levels of the plurality of biomarkers in the test sample, and for generating a prediction of multiple sclerosis disease activity by applying a predictive model to the expression levels of the plurality of biomarkers. In various embodiments, the plurality of biomarkers further comprises CDCP1, CCL20, IL-12B, APLP1, TNFRSF10A, COL4A1, SERPINA9, FLRT2, and TNFSF13B. In various embodiments, the plurality of biomarkers further comprises GH, VCAN, PRTG, and CNTN2.

[0054] Further disclosed herein is a system for assessing multiple sclerosis activity in a subject, the system comprising a set of reagents used to determine expression levels of a plurality of biomarkers in a test sample from a subject, the plurality of biomarkers being one or more neurodegenerative biomarkers selected from the group consisting of NEFL, APLP1, OPG, SERPINA9, PRTG, GFAP, CNTN2, and FLRT2; one or more inflammatory biomarkers selected from the group consisting of CCL20, GH, CXCL13, IL-12B, VCAN, TNFRSF10A, TNFSF13B, CD6, and CXCL9; one or more immune-modulating biomarkers selected from the group consisting of CDCP1 and OPN. and one or more myelin integrity biomarkers selected from the group consisting of COL4A1, COL4A2, and MOG; and an apparatus configured to receive a mixture of one or more reagents of the set and a test sample and to measure the expression levels of the biomarkers in the test sample, and a computer system communicatively connected to the apparatus for obtaining the dataset including the expression levels of the plurality of biomarkers in the test sample and for generating a prediction of multiple sclerosis disease activity by applying a predictive model to the expression levels of the plurality of biomarkers.

[0055] In various embodiments, the one or more neurodegeneration biomarkers include NEFL, OPG, and GFAP, the one or more inflammation biomarkers include CXCL13, CD6, and CXCL9, the one or more immune-modulating biomarkers include OPN, and the one or more myelin integrity biomarkers include MOG. In various embodiments, the one or more neurodegeneration biomarkers further include APLP1, SERPINA9, and FLRT2, the one or more inflammation biomarkers further include CCL20, IL-12B, TNFRSF10A, and TNFSF13B, the one or more immune-modulating biomarkers further include CDCP1, and the one or more myelin integrity biomarkers further include COL4A1. In various embodiments, the one or more neurodegeneration biomarkers further include PRTG and CNTN2, and the one or more inflammation biomarkers include GH and VCAN. In various embodiments, the plurality of biomarkers are protein biomarkers. In various embodiments, the multiple sclerosis disease activity is any one of the following: the presence of normal disease activity, the presence of minimal disease activity, a shift (increase or decrease) in disease activity, MS severity, relapse or relapse events associated with MS, relapse rate, MS status, response to MS therapy, degree of MS disability, or risk of developing MS. In various embodiments, normal disease activity is the presence or absence of one or more gadolinium-enhancing MRI lesions, and minimal disease activity is the presence of one gadolinium-enhancing MRI lesion. In various embodiments, the severity of MS corresponds to the number of gadolinium-enhancing MRI lesions. In various embodiments, the MS status is a worsening or quiescent state of multiple sclerosis.

[0056] Further disclosed herein is a system for assessing multiple sclerosis activity in a subject, the system comprising: a set of reagents used to determine expression levels of a plurality of biomarkers in a test sample from a subject, the plurality of biomarkers comprising two or more of NEFL, MOG, CD6, CXCL9, OPG, OPN, CXCL13, GFAP, CDCP1, CCL20, IL-12B, APLP1, TNFRSF10A, COL4A1, SERPINA9, FLRT2, TNFSF13B, GH, VCAN, PRTG, and CNTN2; and an apparatus configured to receive a mixture of one or more of the set of reagents and the test sample, and to measure the expression levels of the biomarkers in the test sample; and a computer system communicatively connected to the apparatus for obtaining a dataset comprising the expression levels of the plurality of biomarkers in the test sample, and for generating a prediction of multiple sclerosis disease activity by applying a predictive model to the expression levels of the plurality of biomarkers. In various embodiments, the two or more biomarkers comprise NEFL and at least one other biomarker. In various embodiments, the two or more biomarkers are a biomarker pair selected from any one of A) NEFL and CD6, B) NEFL and MOG, C) NEFL and CXCL9, and D) NEFL and TNFRSF10A. In various embodiments, the two or more biomarkers are a biomarker triad selected from any one of A) NEFL, CD6, and CXCL9, and B) NEFL, TNFRSF10A, and COL4A1. In various embodiments, the two or more biomarkers are a biomarker quad selected from any one of A) NEFL, MOG, CD6, and CXCL9, B) NEFL, CXCL9, TNFRSF10A, and COL4A1, C) NEFL, CD6, CXCL9, and CXCL13, and D) NEFL, TNFRSF10A, COL4A1, and CCL20. In various embodiments, the two or more biomarkers do not include NEFL.In various embodiments, the two or more biomarkers are a biomarker pair selected from any one of A) MOG and IL-12B, B) CXCL9 and CD6, C) MOG and CXCL9, D) MOG and CD6, E) CXCL9 and COL4A1, and F) CD6 and VCAN. In various embodiments, the two or more biomarkers are a biomarker triad selected from any one of A) MOG, IL-12B, and APLP1, B) MOG, CD6, and CXCL9, C) CXCL9, COL4A1, and VCAN, D) MOG, IL-12B, and CNTN2, and E) CD6, CCL20, and VCAN. In various embodiments, the two or more biomarkers are a biomarker quartet selected from any one of: A) MOG, CXCL9, IL-12B, APLP1, B) CXCL9, COL4A1, OPG, and VCAN, C) CXCL9, OPG, APLP1, and OPN, D) MOG, IL-12B, OPN, and CNTN2, and E) CD6, COL4A1, CCL20, and VCAN. In various embodiments, the multiple sclerosis activity is a shift in disease activity.

[0057] In various embodiments, the two or more biomarkers are a biomarker pair selected from any one of A) NEFL and TNFSF13B, B) NEFL and CNTN2, and C) NEFL and CXCL9. In various embodiments, the two or more biomarkers are a biomarker triad selected from any one of A) NEFL, CNTN2, and TNFSF13B, B) NEFL, APLP1, and TNFSF13B, and C) NEFL, TNFRSF10A, and TNFSF13B. In various embodiments, the two or more biomarkers are a biomarker quad selected from any one of A) NEFL, TNFRSF10A, CNTN2, and TNFSF13B, B) NEFL, COL4A1, CNTN2, and TNFSF13B, and C) NEFL, TNFRSF10A, APLP1, and TNFSF13B. In various embodiments, the two or more biomarkers are a biomarker pair selected from any one of A) MOG and CDCP1, B) MOG and TNFSF13B, and C) MOG and CXCL9. In various embodiments, the two or more biomarkers are a biomarker triad, and one biomarker in the biomarker triad is MOG. In various embodiments, the biomarker triad is selected from any one of A) MOG, CXCL9, and TNFSF13B, B) MOG, OPG, and TNFSF13B, and C) MOG, CCL20, and TNFSF13B. In various embodiments, the two or more biomarkers are a biomarker quartet, and one biomarker in the biomarker quartet is MOG. In various embodiments, the biomarker quartet is selected from any one of A) MOG, CXCL9, APLP1, and TNFSF13B, B) MOG, CXCL9, OPG, and TNFSF13B, and C) MOG, CXCL9, OPG, and CNTN2. In various embodiments, the multiple sclerosis activity is the presence or absence of multiple sclerosis.

[0058] In various embodiments, the two or more biomarkers are a biomarker pair selected from any one of A) NEFL and TNFSF13B, B) NEFL and SERPINA9, and C) NEFL and GH. In various embodiments, the two or more biomarkers are a biomarker triad selected from any one of A) NEFL, SERPINA9, and TNFSF13B, B) NEFL, CNTN2, and TNFSF13B, and C) NEFL, APLP1, and TNFSF13B. In various embodiments, the two or more biomarkers are a biomarker quartet selected from any one of A) NEFL, CCL20, SERPINA9, and TNFSF13B, B) NEFL, APLP1, SERPINA9, and TNFSF13B, and C) NEFL, CCL20, APLP1, and TNFSF13B. In various embodiments, the two or more biomarkers are a biomarker pair selected from any one of A) MOG and TNFSF13B, B) MOG and CXCL9, and C) MOG and IL-12B. In various embodiments, the two or more biomarkers are a biomarker triad selected from any one of A) MOG, CXCL9, and TNFSF13B, B) MOG, SERPINA9, and TNFSF13B, and C) MOG, OPG, and TNFSF13B. In various embodiments, the two or more biomarkers are a biomarker quad selected from any one of A) MOG, CXCL9, OPG, and TNFSF13B, B) MOG, CXCL9, OPG, and TNFSF13B, and C) MOG, CXCL9, SERPINA9, and TNFSF13B. In various embodiments, the multiple sclerosis activity is disease severity based on a predicted number of gadolinium-enhancing lesions.

[0059] In various embodiments, the two or more biomarkers are a biomarker pair selected from any one of A) GFAP and MOG, B) GFAP and NEFL, C) APLP1 and GFAP, D) NEFL and MOG, or E) CXCL9 and OPG. In various embodiments, the two or more biomarkers are a biomarker triad selected from any one of A) GFAP, NEFL, and MOG, B) NEFL, MOG, and GH, C) NEFL, MOG, and SERPINA9, D) CXCL9, OPG, and SERPINA9, or E) CXCL9, OPG, and TNFRSF10A. In various embodiments, the two or more biomarkers are a biomarker triad selected from any one of: A) GFAP, NEFL, MOG, and IL12B, B) GFAP, NEFL, MOG, and PRTG, C) GFAP, NEFL, MOG, and APLP1, D) NEFL, MOG, GH, and SERPINA9, E) NEFL, MOG, GH, and TNFRSF10A, F) MOG, CXCL9, OPG, and SERPINA9, or G) CD6, IL12B, APLP1, and CCL20. In various embodiments, the multiple sclerosis activity is disease progression.

[0060] In various embodiments, generating a prediction of multiple sclerosis disease activity includes comparing the score output by the predictive model to a reference score, which in various embodiments corresponds to either A) a patient at baseline when the patient exhibited no disease activity, B) a patient with clinically diagnosed no disease activity, or C) a healthy patient.

[0061] In various embodiments, the test sample is a blood or serum sample. In various embodiments, the subject has, is suspected of having, or has previously been diagnosed with multiple sclerosis. In various embodiments, the device is configured to perform an immunoassay to determine expression levels of the plurality of biomarkers. In various embodiments, the immunoassay is a proximity extension assay (PEA) or a LUMINEX xMAP multiplex assay. In various embodiments, performing the immunoassay includes contacting the test sample with a plurality of reagents including an antibody. In various embodiments, the antibody includes one of a monoclonal antibody and a polyclonal antibody. In various embodiments, the antibody includes both a monoclonal antibody and a polyclonal antibody.

[0062] In various embodiments, the computer system is further configured to select a therapy for administration to the subject based on the prediction of multiple sclerosis disease activity. In various embodiments, the computer system is further configured to determine a therapeutic efficacy of a therapy previously administered to the subject based on the prediction of multiple sclerosis disease activity. In various embodiments, determining the therapeutic efficacy of a therapy comprises comparing the prediction to a previous prediction determined for the subject at a previous time point. In various embodiments, determining the therapeutic efficacy of a therapy comprises determining that the treatment exhibits efficacy according to a difference between the prediction and the previous prediction. In various embodiments, determining the therapeutic efficacy of a therapy comprises determining that the treatment is ineffective according to a lack of a difference between the prediction and said previous prediction. In various embodiments, the computer system is further configured to determine a differential diagnosis of multiple sclerosis based on the prediction of multiple sclerosis disease activity. In various embodiments, the differential diagnosis of multiple sclerosis includes any one of relapsing remitting multiple sclerosis (RRMS), secondary progressive multiple sclerosis (SPMS), primary progressive multiple sclerosis (PPMS), progressive relapsing multiple sclerosis (PRMS), and CIS.

[0063] [The present invention 1001] 1. A method for determining multiple sclerosis activity in a subject, the method comprising: obtaining or having obtained a dataset comprising expression levels of a plurality of biomarkers, the plurality of biomarkers comprising each biomarker of at least one group selected from group 1, group 2, and group 3; Group 1 includes biomarker 1, biomarker 2, biomarker 3, biomarker 4, biomarker 5, biomarker 6, biomarker 7, and biomarker 8; Biomarker 1 is NEFL, MOG, CADM3, or GFAP; Biomarker 2 is MOG, CADM3, KLK6, BCAN, OMG, or GFAP; Biomarker 3 is CD6, CD5, CRTAM, CD244, or TNFRSF9; Biomarker 4 is CXCL9, CXCL10, IL-12B, CXCL11, or GFAP; Biomarker 5 is OPG, TFF3, or ENPP2; Biomarker 6 is OPN, OMD, MEPE, or GFAP; Biomarker 7 is CXCL13, NOS3, or MMP-2; Biomarker 8 is GFAP, NEFL, OPN, CXCL9, MOG, or CHI3L1; Group 2 includes biomarker 9, biomarker 10, biomarker 11, biomarker 12, biomarker 13, biomarker 14, biomarker 15, biomarker 16, and biomarker 17; Biomarker 9 is CDCP1, IL-18BP, IL-18, GFAP, or MSR1; Biomarker 10 is CCL20, CCL3, or TWEAK; Biomarker 11 is IL-12B, IL12A, or CXCL9; Biomarker 12 is APLP1, SEZ6L, BCAN, DPP6, NCAN, or KLK6; Biomarker 13 is TNFRSF10A, TNFRSF11A, SPON2, CHI3L1, or IFI30; Biomarker 14 is COL4A1, IL6, NOTCH3, or PCDH17; Biomarker 15 is SERPINA9, TNFRSF9, or CNTN4; Biomarkers 16 are FLRT2, DDR1, NTRK2, CDH6, and MMP-2; Biomarker 17 is TNFSF13B, CXCL16, ALCAM, or IL-18; Group 3 includes biomarker 18, biomarker 19, biomarker 20, and biomarker 21; Biomarker 18 is GH, GH2, or IGFBP-1; Biomarker 19 is VCAN, TINAGL1, CANT1, NECTIN2, MMP-9, or NPDC1; Biomarker 20 is PRTG, NTRK2, NTRK3, or CNTN4; Biomarker 21 is CNTN2, DPP6, GDNFR-α3, or SCARF2; said acquiring or having acquired; and Applying a predictive model to the expression levels of said plurality of biomarkers to generate a prediction of multiple sclerosis disease activity. [The present invention 1002] The method of the present invention 1001, wherein the plurality of biomarkers includes each biomarker of group 1, biomarker 1 is NEFL, biomarker 2 is MOG, biomarker 3 is CD6, biomarker 4 is CXCL9, biomarker 5 is OPG, biomarker 6 is OPN, biomarker 7 is CXCL13, and biomarker 8 is GFAP. [The present invention 1003] The method of any of claims 1001 to 1002, wherein the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.667 to 0.869. [The present invention 1004] The method according to any one of claims 1001 to 1002, wherein the performance of the predictive model is characterized by a positive predictive value ranging from 0.617 to 0.861. [The present invention 1005] The method of the present invention 1001, wherein the plurality of biomarkers includes each biomarker of group 2, wherein biomarker 9 is CDCP1, biomarker 10 is CCL20, biomarker 11 is IL-12B, biomarker 12 is APLP1, biomarker 13 is TNFRSF10A, biomarker 14 is COL4A1, biomarker 15 is SERPINA9, biomarker 16 is FLRT2, and biomarker 17 is TNFSF13B. [The present invention 1006] 1005. The method of claim 10, wherein the performance of said predictive model is characterized by an area under the curve (AUC) ranging from 0.595 to 0.761. [The present invention 1007] 1005. The method of claim 10, wherein the performance of said predictive model is characterized by a positive predictive value ranging from 0.523 to 0.769. [The present invention 1008] The method of the present invention 1002, wherein the plurality of biomarkers further includes each biomarker of group 2, wherein biomarker 9 is CDCP1, biomarker 10 is CCL20, biomarker 11 is IL-12B, biomarker 12 is APLP1, biomarker 13 is TNFRSF10A, biomarker 14 is COL4A1, biomarker 15 is SERPINA9, biomarker 16 is FLRT2, and biomarker 17 is TNFSF13B. [The present invention 1009] 1008. The method of claim 10, wherein the performance of said predictive model is characterized by an area under the curve (AUC) ranging from 0.693 to 0.892. [The present invention 1010] 1008. The method of claim 10, wherein the performance of said predictive model is characterized by a positive predictive value ranging from 0.613 to 0.843. [The present invention 1011] The method of the present invention 1001, wherein the plurality of biomarkers includes each biomarker of group 3, biomarker 18 is GH, biomarker 19 is VCAN, biomarker 20 is PRTG, and biomarker 21 is CNTN2. [The present invention 1012] 1011. The method of claim 10, wherein the performance of said predictive model is characterized by an area under the curve (AUC) ranging from 0.566 to 0.644. [The present invention 1013] The method of the present invention, wherein the performance of the predictive model is characterized by a positive predictive value ranging from 0.370 to 0.742. [The present invention 1014] The method of claim 1008, wherein the plurality of biomarkers further comprises each biomarker of group 3, wherein biomarker 18 is GH, biomarker 19 is VCAN, biomarker 20 is PRTG, and biomarker 21 is CNTN2. [The present invention 1015] The method of claim 10, wherein the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.686 to 0.889. [The present invention 1016] The method of the present invention, wherein the performance of the predictive model is characterized by a positive predictive value ranging from 0.648 to 0.835. [The present invention 1017] 1. A method for determining multiple sclerosis activity in a subject, the method comprising: obtaining or having obtained a dataset comprising expression levels of a plurality of biomarkers including NEFL, MOG, CD6, CXCL9, OPG, OPN, CXCL13, and GFAP; and Applying a predictive model to the expression levels of said plurality of biomarkers to generate a prediction of multiple sclerosis disease activity. [The present invention 1018] The method of claim 1017, wherein the plurality of biomarkers further comprises CDCP1, CCL20, IL-12B, APLP1, TNFRSF10A, COL4A1, SERPINA9, FLRT2, and TNFSF13B. [The present invention 1019] The method of claim 1018, wherein the plurality of biomarkers further comprises GH, VCAN, PRTG, and CNTN2. [The present invention 1020] 1. A method for generating quantitative data of a subject, comprising: performing at least one immunoassay on a sample obtained from said subject to generate a dataset comprising said quantitative data. Including, the quantitative data represents at least eight protein biomarkers, including NEFL, MOG, CD6, CXCL9, OPG, OPN, CXCL13, and GFAP; The subject has or is suspected of having multiple sclerosis. The method. [The present invention 1021] The method of claim 1020, wherein the quantitative data further represents at least nine additional protein biomarkers, including CDCP1, CCL20, IL-12B, APLP1, TNFRSF10A, COL4A1, SERPINA9, FLRT2, and TNFSF13B. [The present invention 1022] The method of claim 1021, wherein the quantitative data further represents at least four additional protein biomarkers, including GH, VCAN, PRTG, and CNTN2. [The present invention 1023] determining multiple sclerosis activity in said subject; and wherein the determining comprises applying a predictive model to the quantitative data. Any of the methods according to the present invention 1020 to 1022. [The present invention 1024] The method of claim 1023, wherein applying the decision further comprises comparing a score output by the predictive model to a reference score. [The present invention 1025] The reference score is A) The patient at baseline when the patient showed no disease activity; B) Patients with clinically diagnosed disease without active disease, or C) Healthy patients; The method of the present invention 1024 corresponds to any one of the above. [The present invention 1026] 1. A method for determining multiple sclerosis activity in a subject, the method comprising: one or more neurodegeneration biomarkers selected from the group consisting of NEFL, APLP1, OPG, SERPINA9, PRTG, GFAP, CNTN2, and FLRT2; one or more inflammatory biomarkers selected from the group consisting of CCL20, GH, CXCL13, IL-12B, VCAN, TNFRSF10A, TNFSF13B, CD6, and CXCL9; one or more immune modulating biomarkers selected from the group consisting of CDCP1 and OPN; One or more myelin integrity biomarkers selected from the group consisting of COL4A1 and MOG. obtaining or having obtained a dataset comprising expression levels of a plurality of biomarkers, including Applying a predictive model to the expression levels of said plurality of biomarkers to generate a prediction of multiple sclerosis disease activity. [The present invention 1027] The method of claim 1026, wherein the one or more neurodegeneration biomarkers include NEFL, OPG, and GFAP, the one or more inflammation biomarkers include CXCL13, CD6, and CXCL9, the one or more immune regulatory biomarkers include OPN, and the one or more myelin integrity biomarkers include MOG. [The present invention 1028] The method of claim 1027, wherein the one or more neurodegeneration biomarkers further include APLP1, SERPINA9, and FLRT2, the one or more inflammation biomarkers further include CCL20, IL-12B, TNFRSF10A, and TNFSF13B, the one or more immune regulatory biomarkers further include CDCP1, and the one or more myelin integrity biomarkers further include COL4A1. [The present invention 1029] The method of claim 1028, wherein the one or more neurodegeneration biomarkers further include PRTG and CNTN2, and the one or more inflammation biomarkers include GH and VCAN. [The present invention 1030] The method of any one of claims 1001 to 1029, wherein said plurality of biomarkers are protein biomarkers. [The present invention 1031] The multiple sclerosis disease activity is the presence of normal disease activity, the presence of minimal disease activity, a shift in disease activity (increase or decrease), the severity of MS, relapse or relapse events associated with MS, relapse rate, MS status, response to MS therapy, degree of MS disability, or risk of developing MS The method according to any one of claims 1001 to 1029, [The present invention 1032] The method of claim 1031, wherein said normal disease activity is the presence or absence of one or more gadolinium-enhancing MRI lesions and said insignificant disease activity is the presence of one gadolinium-enhancing MRI lesion. [The present invention 1033] The method of claim 1031, wherein said MS severity corresponds to the number of gadolinium-enhancing MRI lesions. [The present invention 1034] The method of claim 1031, wherein said MS condition is an exacerbation or quiescence of multiple sclerosis. [The present invention 1035] 1. A method for determining multiple sclerosis activity in a subject, the method comprising: obtaining or having obtained a dataset comprising expression levels of a plurality of biomarkers, the plurality of biomarkers comprising two or more of NEFL, MOG, CD6, CXCL9, OPG, OPN, CXCL13, GFAP, CDCP1, CCL20, IL-12B, APLP1, TNFRSF10A, COL4A1, SERPINA9, FLRT2, TNFSF13B, GH, VCAN, PRTG, and CNTN2; and Applying a predictive model to the expression levels of said plurality of biomarkers to generate a prediction of multiple sclerosis disease activity. [The present invention 1036] 1. A method for preparing a biomarker fraction of a sample obtained from a subject, the method comprising: (a) extracting biomarkers from the sample; (b) generating a fraction of the extracted biomarkers after (a), wherein the fraction of the extracted biomarkers after (b) comprises a plurality of biomarkers, the plurality of biomarkers comprising two or more of NEFL, MOG, CD6, CXCL9, OPG, OPN, CXCL13, GFAP, CDCP1, CCL20, IL-12B, APLP1, TNFRSF10A, COL4A1, SERPINA9, FLRT2, TNFSF13B, GH, VCAN, PRTG, and CNTN2; (c) analyzing the plurality of biomarkers in the fraction of the extracted biomarkers produced in (b). [The present invention 1037] The method of any one of claims 1035 to 1036, wherein said two or more biomarkers include NEFL and at least one other biomarker. [The present invention 1038] The method of the present invention 1037, wherein the two or more biomarkers are a biomarker pair selected from any one of A) NEFL and CD6, B) NEFL and MOG, C) NEFL and CXCL9, and D) NEFL and TNFRSF10A. [The present invention 1039] The method of claim 1037, wherein the two or more biomarkers are a biomarker triplet selected from any one of A) NEFL, CD6, and CXCL9, and B) NEFL, TNFRSF10A, and COL4A1. [The present invention 1040] The method of the present invention 1037, wherein the two or more biomarkers are a biomarker quadruple selected from any one of A) NEFL, MOG, CD6, and CXCL9, B) NEFL, CXCL9, TNFRSF10A, and COL4A1, C) NEFL, CD6, CXCL9, and CXCL13, and D) NEFL, TNFRSF10A, COL4A1, and CCL20. [The present invention 1041] The method of any one of claims 1035 to 1036, wherein said two or more biomarkers do not include NEFL. [The present invention 1042] The method of claim 1041, wherein the two or more biomarkers are a biomarker pair selected from any one of A) MOG and IL-12B, B) CXCL9 and CD6, C) MOG and CXCL9, D) MOG and CD6, E) CXCL9 and COL4A1, and F) CD6 and VCAN. [The present invention 1043] The method of claim 1041, wherein the two or more biomarkers are a biomarker triplet selected from any one of A) MOG, IL-12B, and APLP1, B) MOG, CD6, and CXCL9, C) CXCL9, COL4A1, and VCAN, D) MOG, IL-12B, and CNTN2, and E) CD6, CCL20, and VCAN. [The present invention 1044] The method of claim 1041, wherein the two or more biomarkers are a biomarker quadruple selected from any one of A) MOG, CXCL9, IL-12B, APLP1, B) CXCL9, COL4A1, OPG, and VCAN, C) CXCL9, OPG, APLP1, and OPN, D) MOG, IL-12B, OPN, and CNTN2, and E) CD6, COL4A1, CCL20, and VCAN. [The present invention 1045] The method according to any one of claims 1037 to 1044, wherein the multiple sclerosis activity is a shift in disease activity. [The present invention 1046] The method of the present invention 1037, wherein the two or more biomarkers are a biomarker pair selected from any one of A) NEFL and TNFSF13B, B) NEFL and CNTN2, and C) NEFL and CXCL9. [The present invention 1047] The method of the present invention 1037, wherein the two or more biomarkers are a biomarker triplet selected from any one of A) NEFL, CNTN2, and TNFSF13B, B) NEFL, APLP1, and TNFSF13B, and C) NEFL, TNFRSF10A, and TNFSF13B. [The present invention 1048] The method of the present invention 1037, wherein the two or more biomarkers are a biomarker quadruple selected from any one of A) NEFL, TNFRSF10A, CNTN2, and TNFSF13B, B) NEFL, COL4A1, CNTN2, and TNFSF13B, and C) NEFL, TNFRSF10A, APLP1, and TNFSF13B. [The present invention 1049] The method of claim 1041, wherein the two or more biomarkers are a biomarker pair selected from any one of A) MOG and CDCP1, B) MOG and TNFSF13B, and C) MOG and CXCL9. [The present invention 1050] The method of claim 1041, wherein said two or more biomarkers are a biomarker triad and one biomarker of said biomarker triad is MOG. [The present invention 1051] 1050. The method of claim 1050, wherein the biomarker triad is selected from any one of A) MOG, CXCL9, and TNFSF13B, B) MOG, OPG, and TNFSF13B, and C) MOG, CCL20, and TNFSF13B. [The present invention 1052] The method of claim 1041, wherein said two or more biomarkers are a biomarker quartet, and one biomarker in said biomarker quartet is MOG. [The present invention 1053] 1052. The method of claim 1052, wherein the biomarker quartet is selected from any one of A) MOG, CXCL9, APLP1, and TNFSF13B, B) MOG, CXCL9, OPG, and TNFSF13B, and C) MOG, CXCL9, OPG, and CNTN2. [The present invention 1054] The method of any of claims 1046 to 1053, wherein the multiple sclerosis activity is the presence or absence of multiple sclerosis. [The present invention 1055] The method of the present invention 1037, wherein the two or more biomarkers are a biomarker pair selected from any one of A) NEFL and TNFSF13B, B) NEFL and SERPINA9, and C) NEFL and GH. [The present invention 1056] The method of the present invention 1037, wherein the two or more biomarkers are a biomarker triplet selected from any one of A) NEFL, SERPINA9, and TNFSF13B, B) NEFL, CNTN2, and TNFSF13B, and C) NEFL, APLP1, and TNFSF13B. [The present invention 1057] The method of claim 1037, wherein the two or more biomarkers are a biomarker quadruple selected from any one of A) NEFL, CCL20, SERPINA9, and TNFSF13B; B) NEFL, APLP1, SERPINA9, and TNFSF13B; and C) NEFL, CCL20, APLP1, and TNFSF13B. [The present invention 1058] The method of claim 1041, wherein the two or more biomarkers are a biomarker pair selected from any one of A) MOG and TNFSF13B, B) MOG and CXCL9, and C) MOG and IL-12B. [The present invention 1059] The method of claim 1041, wherein the two or more biomarkers are a biomarker triplet selected from any one of A) MOG, CXCL9, and TNFSF13B, B) MOG, SERPINA9, and TNFSF13B, and C) MOG, OPG, and TNFSF13B. [The present invention 1060] The method of claim 1041, wherein the two or more biomarkers are a biomarker quadruple selected from any one of A) MOG, CXCL9, OPG, and TNFSF13B; B) MOG, CXCL9, OPG, SERPINA9, and TNFSF13B; and C) MOG, CXCL9, SERPINA9, and TNFSF13B. [The present invention 1061] The method according to any one of claims 1055 to 1060, wherein the multiple sclerosis activity is a disease severity based on a predicted number of gadolinium-enhancing lesions. [The present invention 1062] The method of any one of claims 1035 and 1036, wherein the two or more biomarkers are a biomarker pair selected from any one of A) GFAP and MOG, B) GFAP and NEFL, C) APLP1 and GFAP, D) NEFL and MOG, or E) CXCL9 and OPG. [The present invention 1063] The method of any one of claims 1035 or 1036, wherein the two or more biomarkers are a biomarker triplet selected from any one of A) GFAP, NEFL, and MOG, B) NEFL, MOG, and GH, C) NEFL, MOG, and SERPINA9, D) CXCL9, OPG, and SERPINA9, or E) CXCL9, OPG, and TNFRSF10A. [The present invention 1064] The method of any one of claims 1035 and 1036, wherein the two or more biomarkers are a biomarker triplet selected from any one of A) GFAP, NEFL, MOG, and IL12B, B) GFAP, NEFL, MOG, and PRTG, C) GFAP, NEFL, MOG, and APLP1, D) NEFL, MOG, GH, and SERPINA9, E) NEFL, MOG, GH, and TNFRSF10A, F) MOG, CXCL9, OPG, and SERPINA9, or G) CD6, IL12B, APLP1, and CCL20. [The present invention 1065] The method according to any one of claims 1062 to 1064, wherein the multiple sclerosis activity is disease progression. [The present invention 1066] Any of the methods of claims 1001 to 1019 and 1026 to 1065, further comprising classifying the subject based on the prediction. [The present invention 1067] Any of the methods of claims 1001 to 1019 and 1026 to 1065, wherein generating the prediction of multiple sclerosis disease activity comprises comparing the score output by the predictive model with a reference score. [The present invention 1068] The reference score is A) The patient at baseline when the patient showed no disease activity; B) Patients with clinically diagnosed disease without active disease, or C) Healthy patients The method of the present invention 1067, which corresponds to any one of the following: [The present invention 1069] The method of any one of claims 1001 to 1019 and 1026 to 1068, wherein the expression levels of said plurality of biomarkers are determined from a test sample obtained from said subject. [The present invention 1070] The method of claim 1069, wherein the test sample is a blood or serum sample. [The present invention 1071] The method of any one of claims 1069 to 1070, wherein said subject has, is suspected of having, or has previously been diagnosed with multiple sclerosis. [The present invention 1072] Any of the methods of claims 1001 to 1019 and 1026 to 1071, wherein obtaining or having obtained the dataset comprises performing an immunoassay to determine an expression level of the plurality of biomarkers. [The present invention 1073] The method of claim 1072, wherein said immunoassay is a proximity extension assay (PEA) or a LUMINEX xMAP multiplex assay. [The present invention 1074] The method of any one of claims 1072 to 1073, wherein performing said immunoassay comprises contacting the test sample with a plurality of reagents comprising antibodies. [The present invention 1075] The method of any one of claims 1074 to 1075, wherein the antibody comprises one of a monoclonal antibody and a polyclonal antibody. [The present invention 1076] The method of any one of claims 10 to 15, wherein said antibodies include both monoclonal and polyclonal antibodies. [The present invention 1077] Any of the methods of claims 1001 to 1076, further comprising selecting a therapy for administering to said subject based on said prediction of multiple sclerosis disease activity. [The present invention 1078] Any of the methods of claims 1001 to 1076, further comprising determining the therapeutic effectiveness of a therapy previously administered to the subject based on the prediction of multiple sclerosis disease activity. [The present invention 1079] The method of claim 1078, wherein determining the therapeutic efficacy of said treatment method comprises comparing said prediction with a previous prediction determined for said subject at a previous time point. [The present invention 1080] The method of claim 1079, wherein determining the therapeutic efficacy of said treatment comprises determining that said treatment exhibits efficacy according to the difference between said prediction and said prior prediction. [The present invention 1081] The method of claim 1079, wherein determining the therapeutic efficacy of said treatment comprises determining that said treatment is ineffective according to the absence of a difference between said prediction and said previous prediction. [The present invention 1082] The method of any of claims 1001 to 1076, further comprising determining a differential diagnosis of multiple sclerosis based on said prediction of multiple sclerosis disease activity. [The present invention 1083] The method of claim 1082, wherein the differential diagnosis of multiple sclerosis includes at least one of relapsing-remitting multiple sclerosis (RRMS), secondary progressive multiple sclerosis (SPMS), primary progressive multiple sclerosis (PPMS), progressive relapsing multiple sclerosis (PRMS), and clinically isolated syndrome (CIS). [The present invention 1084] 1. A non-transitory computer readable medium for determining multiple sclerosis activity in a subject, the non-transitory computer readable medium comprising instructions that, when executed by a processor, cause the processor to: obtaining a dataset comprising expression levels of a plurality of biomarkers, the plurality of biomarkers comprising each biomarker of at least one group selected from group 1, group 2, and group 3; Group 1 includes biomarker 1, biomarker 2, biomarker 3, biomarker 4, biomarker 5, biomarker 6, biomarker 7, and biomarker 8; Biomarker 1 is NEFL, MOG, CADM3, or GFAP; Biomarker 2 is MOG, CADM3, KLK6, BCAN, OMG, or GFAP; Biomarker 3 is CD6, CD5, CRTAM, CD244, or TNFRSF9; Biomarker 4 is CXCL9, CXCL10, IL-12B, CXCL11, or GFAP; Biomarker 5 is OPG, TFF3, or ENPP2; Biomarker 6 is OPN, OMD, MEPE, or GFAP; Biomarker 7 is CXCL13, NOS3, or MMP-2; Biomarker 8 is GFAP, NEFL, OPN, CXCL9, MOG, or CHI3L1; Group 2 includes biomarker 9, biomarker 10, biomarker 11, biomarker 12, biomarker 13, biomarker 14, biomarker 15, biomarker 16, and biomarker 17; Biomarker 9 is CDCP1, IL-18BP, IL-18, GFAP, or MSR1; Biomarker 10 is CCL20, CCL3, or TWEAK; Biomarker 11 is IL-12B, IL12A, or CXCL9; Biomarker 12 is APLP1, SEZ6L, BCAN, DPP6, NCAN, or KLK6; Biomarker 13 is TNFRSF10A, TNFRSF11A, SPON2, CHI3L1, or IFI30; Biomarker 14 is COL4A1, IL6, NOTCH3, or PCDH17; Biomarker 15 is SERPINA9, TNFRSF9, or CNTN4; Biomarkers 16 are FLRT2, DDR1, NTRK2, CDH6, and MMP-2; Biomarker 17 is TNFSF13B, CXCL16, ALCAM, or IL-18; Group 3 includes biomarker 18, biomarker 19, biomarker 20, and biomarker 21; Biomarker 18 is GH, GH2, or IGFBP-1; Biomarker 19 is VCAN, TINAGL1, CANT1, NECTIN2, MMP-9, or NPDC1; Biomarker 20 is PRTG, NTRK2, NTRK3, or CNTN4; Biomarker 21 is CNTN2, DPP6, GDNFR-α3, or SCARF2; The said obtaining, and Applying a predictive model to the expression levels of said plurality of biomarkers to generate a prediction of multiple sclerosis disease activity. [The present invention 1085] The non-transitory computer readable medium of the present invention 1084, wherein the plurality of biomarkers includes each biomarker of Group 1, wherein biomarker 1 is NEFL, biomarker 2 is MOG, biomarker 3 is CD6, biomarker 4 is CXCL9, biomarker 5 is OPG, biomarker 6 is OPN, biomarker 7 is CXCL13, and biomarker 8 is GFAP. [The present invention 1086] The non-transitory computer readable medium of any one of claims 1084 to 1085, wherein the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.667 to 0.869. [The present invention 1087] The non-transitory computer-readable medium of claim 1084 or 1085, wherein the performance of the predictive model is characterized by a positive predictive value ranging from 0.617 to 0.861. [The present invention 1088] The non-transitory computer readable medium of the present invention 1084, wherein the plurality of biomarkers includes each biomarker of Group 2, wherein biomarker 9 is CDCP1, biomarker 10 is CCL20, biomarker 11 is IL-12B, biomarker 12 is APLP1, biomarker 13 is TNFRSF10A, biomarker 14 is COL4A1, biomarker 15 is SERPINA9, biomarker 16 is FLRT2, and biomarker 17 is TNFSF13B. [The present invention 1089] The non-transitory computer readable medium of the present invention 1088, wherein the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.595 to 0.761. [The present invention 1090] The non-transitory computer readable medium of the present invention 1088, wherein the performance of the predictive model is characterized by a positive predictive value ranging from 0.523 to 0.769. [The present invention 1091] The non-transitory computer readable medium of the present invention 1085, wherein the plurality of biomarkers further comprises each biomarker of Group 2, wherein biomarker 9 is CDCP1, biomarker 10 is CCL20, biomarker 11 is IL-12B, biomarker 12 is APLP1, biomarker 13 is TNFRSF10A, biomarker 14 is COL4A1, biomarker 15 is SERPINA9, biomarker 16 is FLRT2, and biomarker 17 is TNFSF13B. [The present invention 1092] The non-transitory computer-readable medium of the present invention 1091, wherein the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.693 to 0.892. [The present invention 1093] The non-transitory computer readable medium of the present invention 1091, wherein the performance of the predictive model is characterized by a positive predictive value ranging from 0.613 to 0.843. [The present invention 1094] The non-transitory computer readable medium of the present invention 1084, wherein the plurality of biomarkers includes each biomarker of group 3, biomarker 18 is GH, biomarker 19 is VCAN, biomarker 20 is PRTG, and biomarker 21 is CNTN2. [The present invention 1095] The non-transitory computer-readable medium of the present invention 1094, wherein the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.566 to 0.644. [The present invention 1096] The non-transitory computer readable medium of the present invention 1094, wherein the performance of the predictive model is characterized by a positive predictive value ranging from 0.370 to 0.742. [The present invention 1097] The non-transitory computer readable medium of the present invention 1091, wherein the plurality of biomarkers further includes each biomarker of group 3, wherein biomarker 18 is GH, biomarker 19 is VCAN, biomarker 20 is PRTG, and biomarker 21 is CNTN2. [The present invention 1098] The non-transitory computer-readable medium of the present invention 1097, wherein the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.686 to 0.889. [This invention 1099] The non-transitory computer readable medium of the present invention 1097, wherein the performance of the predictive model is characterized by a positive predictive value ranging from 0.648 to 0.835. [The present invention 1100] 1. A non-transitory computer readable medium for determining multiple sclerosis activity in a subject, the non-transitory computer readable medium comprising instructions that, when executed by a processor, cause the processor to perform the steps of: obtaining or having obtained a dataset comprising expression levels of a plurality of biomarkers including NEFL, MOG, CD6, CXCL9, OPG, OPN, CXCL13, and GFAP; Applying a predictive model to the expression levels of said plurality of biomarkers to generate a prediction of multiple sclerosis disease activity. [The present invention 1101] The non-transitory computer readable medium of the present invention 1100, wherein the plurality of biomarkers further comprises CDCP1, CCL20, IL-12B, APLP1, TNFRSF10A, COL4A1, SERPINA9, FLRT2, and TNFSF13B. [The present invention 1102] The non-transitory computer readable medium of any one of claims 1100 to 1101, wherein the plurality of biomarkers further comprises GH, VCAN, PRTG, and CNTN2. [The present invention 1103] 1. A non-transitory computer readable medium for determining multiple sclerosis activity in a subject, the non-transitory computer readable medium comprising instructions that, when executed by a processor, cause the processor to: one or more neurodegeneration biomarkers selected from the group consisting of NEFL, APLP1, OPG, SERPINA9, PRTG, GFAP, CNTN2, and FLRT2; one or more inflammatory biomarkers selected from the group consisting of CCL20, GH, CXCL13, IL-12B, VCAN, TNFRSF10A, TNFSF13B, CD6, and CXCL9; one or more immune modulating biomarkers selected from the group consisting of CDCP1 and OPN; One or more myelin integrity biomarkers selected from the group consisting of COL4A1 and MOG. obtaining a dataset comprising expression levels of a plurality of biomarkers, Applying a predictive model to the expression levels of said plurality of biomarkers to generate a prediction of multiple sclerosis disease activity. [The present invention 1104] The non-transitory computer readable medium of the present invention 1103, wherein the one or more neurodegeneration biomarkers include NEFL, OPG, and GFAP, the one or more inflammation biomarkers include CXCL13, CD6, and CXCL9, the one or more immune regulatory biomarkers include OPN, and the one or more myelin integrity biomarkers include MOG. [The present invention 1105] The non-transitory computer readable medium of the present invention 1104, wherein the one or more neurodegeneration biomarkers further include APLP1, SERPINA9, and FLRT2, the one or more inflammation biomarkers further include CCL20, IL-12B, TNFRSF10A, and TNFSF13B, the one or more immune modulation biomarkers further include CDCP1, and the one or more myelin integrity biomarkers further include COL4A1. [The present invention 1106] The non-transitory computer readable medium of the present invention 1104, wherein the one or more neurodegeneration biomarkers further include PRTG and CNTN2, and the one or more inflammation biomarkers include GH and VCAN. [The present invention 1107] The method according to any one of claims 1084 to 1106, wherein said plurality of biomarkers are protein biomarkers. [The present invention 1108] The non-transitory computer readable medium of any of claims 1084 to 1107, wherein the multiple sclerosis disease activity is any one of the presence of normal disease activity, the presence of little disease activity, a shift (increase or decrease) in disease activity, MS severity, relapse or relapse events associated with MS, relapse rate, MS status, response to MS therapy, degree of MS disability, or risk of developing MS. [The present invention 1109] The non-transitory computer readable medium of the present invention 1108, wherein the normal disease activity is the presence or absence of one or more gadolinium-enhancing MRI lesions and the insignificant disease activity is the presence of one gadolinium-enhancing MRI lesion. [The present invention 1110] The non-transitory computer readable medium of the present invention 1108, wherein the severity of the MS corresponds to the number of gadolinium-enhancing MRI lesions. [The present invention 1111] The non-transitory computer readable medium of the present invention 1108, wherein the MS condition is an exacerbation or quiescence of multiple sclerosis. [The present invention 1112] 1. A non-transitory computer readable medium for determining multiple sclerosis activity in a subject, the non-transitory computer readable medium comprising instructions that, when executed by a processor, cause the processor to: obtaining a dataset comprising expression levels of a plurality of biomarkers, the plurality of biomarkers comprising two or more of NEFL, MOG, CD6, CXCL9, OPG, OPN, CXCL13, GFAP, CDCP1, CCL20, IL-12B, APLP1, TNFRSF10A, COL4A1, SERPINA9, FLRT2, TNFSF13B, GH, VCAN, PRTG, and CNTN2; and Applying a predictive model to the expression levels of said plurality of biomarkers to generate a prediction of multiple sclerosis disease activity. [The present invention 1113] The non-transitory computer readable medium of the present invention 1112, wherein the two or more biomarkers include NEFL and at least one other biomarker. [The present invention 1114] The non-transitory computer readable medium of the present invention 1113, wherein the two or more biomarkers are a biomarker pair selected from any one of A) NEFL and CD6, B) NEFL and MOG, C) NEFL and CXCL9, and D) NEFL and TNFRSF10A. [The present invention 1115] The non-transitory computer readable medium of the present invention 1113, wherein the two or more biomarkers are a biomarker triplet selected from any one of A) NEFL, CD6, and CXCL9, and B) NEFL, TNFRSF10A, and COL4A1. [The present invention 1116] The non-transitory computer readable medium of the present invention 1113, wherein the two or more biomarkers are a biomarker quadruple selected from any one of A) NEFL, MOG, CD6, and CXCL9, B) NEFL, CXCL9, TNFRSF10A, and COL4A1, C) NEFL, CD6, CXCL9, and CXCL13, and D) NEFL, TNFRSF10A, COL4A1, and CCL20. [The present invention 1117] The non-transitory computer readable medium of the present invention 1112, wherein the two or more biomarkers do not include NEFL. [The present invention 1118] The non-transitory computer readable medium of the present invention 1117, wherein the two or more biomarkers are a biomarker pair selected from any one of A) MOG and IL-12B, B) CXCL9 and CD6, C) MOG and CXCL9, D) MOG and CD6, E) CXCL9 and COL4A1, and F) CD6 and VCAN. [The present invention 1119] The non-transitory computer readable medium of the present invention 1117, wherein the two or more biomarkers are a biomarker triplet selected from any one of A) MOG, IL-12B, and APLP1; B) MOG, CD6, and CXCL9; C) CXCL9, COL4A1, and VCAN; D) MOG, IL-12B, and CNTN2; and E) CD6, CCL20, and VCAN. [The present invention 1120] The non-transitory computer readable medium of the present invention 1117, wherein the two or more biomarkers are a biomarker quadruple selected from any one of A) MOG, CXCL9, IL-12B, APLP1; B) CXCL9, COL4A1, OPG, and VCAN; C) CXCL9, OPG, APLP1, and OPN; D) MOG, IL-12B, OPN, and CNTN2; and E) CD6, COL4A1, CCL20, and VCAN. [The present invention 1121] The non-transitory computer-readable medium of any of claims 1113 to 1120, wherein the multiple sclerosis activity is a shift in disease activity. [The present invention 1122] The non-transitory computer readable medium of the present invention 1113, wherein the two or more biomarkers are a biomarker pair selected from any one of A) NEFL and TNFSF13B, B) NEFL and CNTN2, and C) NEFL and CXCL9. [The present invention 1123] The non-transitory computer readable medium of the present invention 1113, wherein the two or more biomarkers are a biomarker triplet selected from any one of A) NEFL, CNTN2, and TNFSF13B, B) NEFL, APLP1, and TNFSF13B, and C) NEFL, TNFRSF10A, and TNFSF13B. [The present invention 1124] The non-transitory computer readable medium of the present invention 1113, wherein the two or more biomarkers are a biomarker quadruple selected from any one of A) NEFL, TNFRSF10A, CNTN2, and TNFSF13B, B) NEFL, COL4A1, CNTN2, and TNFSF13B, and C) NEFL, TNFRSF10A, APLP1, and TNFSF13B. [The present invention 1125] The non-transitory computer readable medium of the present invention 1117, wherein the two or more biomarkers are a biomarker pair selected from any one of A) MOG and CDCP1, B) MOG and TNFSF13B, and C) MOG and CXCL9. [The present invention 1126] The non-transitory computer readable medium of the present invention 1117, wherein the two or more biomarkers are a biomarker triad, and one biomarker in the biomarker triad is MOG. [The present invention 1127] The non-transitory computer readable medium of the present invention 1126, wherein the biomarker triad is selected from any one of A) MOG, CXCL9, and TNFSF13B, B) MOG, OPG, and TNFSF13B, and C) MOG, CCL20, and TNFSF13B. [The present invention 1128] The non-transitory computer readable medium of the present invention 1117, wherein the two or more biomarkers are a biomarker quartet, and one biomarker in the biomarker quartet is MOG. [The present invention 1129] The non-transitory computer readable medium of the present invention 1128, wherein the biomarker quartet is selected from any one of A) MOG, CXCL9, APLP1, and TNFSF13B, B) MOG, CXCL9, OPG, and TNFSF13B, and C) MOG, CXCL9, OPG, and CNTN2. [The present invention 1130] The non-transitory computer-readable medium of any of 1122 to 1129, wherein the multiple sclerosis activity is the presence or absence of multiple sclerosis. [The present invention 1131] The non-transitory computer readable medium of the present invention 1113, wherein the two or more biomarkers are a biomarker pair selected from any one of A) NEFL and TNFSF13B, B) NEFL and SERPINA9, and C) NEFL and GH. [The present invention 1132] The non-transitory computer readable medium of the present invention 1113, wherein the two or more biomarkers are a biomarker triplet selected from any one of A) NEFL, SERPINA9, and TNFSF13B, B) NEFL, CNTN2, and TNFSF13B, and C) NEFL, APLP1, and TNFSF13B. [The present invention 1133] The non-transitory computer readable medium of the present invention 1113, wherein the two or more biomarkers are a biomarker quadruple selected from any one of A) NEFL, CCL20, SERPINA9, and TNFSF13B; B) NEFL, APLP1, SERPINA9, and TNFSF13B; and C) NEFL, CCL20, APLP1, and TNFSF13B. [The present invention 1134] The non-transitory computer readable medium of the present invention 1117, wherein the two or more biomarkers are a biomarker pair selected from any one of A) MOG and TNFSF13B, B) MOG and CXCL9, and C) MOG and IL-12B. [This invention 1135] The non-transitory computer readable medium of the present invention 1117, wherein the biomarker triad is selected from any one of A) MOG, CXCL9, and TNFSF13B, B) MOG, SERPINA9, and TNFSF13B, and C) MOG, OPG, and TNFSF13B. [The present invention 1136] The non-transitory computer readable medium of the present invention 1117, wherein the biomarker quartet is selected from any one of A) MOG, CXCL9, OPG, and TNFSF13B; B) MOG, CXCL9, OPG, SERPINA9, and TNFSF13B; and C) MOG, CXCL9, SERPINA9, and TNFSF13B. [This invention 1137] The non-transitory computer-readable medium of any one of claims 1131 to 1136, wherein the multiple sclerosis activity is disease severity based on a predicted number of gadolinium-enhancing lesions. [The present invention 1138] The non-transitory computer readable medium of the present invention 1112, wherein the two or more biomarkers are a biomarker pair selected from any one of A) GFAP and MOG, B) GFAP and NEFL, C) APLP1 and GFAP, D) NEFL and MOG, or E) CXCL9 and OPG. [The present invention 1139] The non-transitory computer readable medium of the present invention 1112, wherein the two or more biomarkers are a biomarker triplet selected from any one of A) GFAP, NEFL, and MOG, B) NEFL, MOG, and GH, C) NEFL, MOG, and SERPINA9, D) CXCL9, OPG, and SERPINA9, or E) CXCL9, OPG, and TNFRSF10A. [The present invention 1140] The non-transitory computer readable medium of the present invention 1112, wherein the two or more biomarkers are a biomarker triplet selected from any one of A) GFAP, NEFL, MOG, and IL12B; B) GFAP, NEFL, MOG, and PRTG; C) GFAP, NEFL, MOG, and APLP1; D) NEFL, MOG, GH, and SERPINA9; E) NEFL, MOG, GH, and TNFRSF10A; F) MOG, CXCL9, OPG, and SERPINA9; or G) CD6, IL12B, APLP1, and CCL20. [This invention 1141] The non-transitory computer-readable medium of any of claims 1138 to 1140, wherein the multiple sclerosis activity is disease progression. [This invention 1142] The non-transitory computer-readable medium of any of claims 1084 to 1141, further comprising classifying the subject based on the prediction. [This invention 1143] The non-transitory computer-readable medium of any of claims 1084 to 1142, wherein generating the prediction of multiple sclerosis disease activity comprises comparing the score output by the predictive model with a reference score. [This invention 1144] The reference score is A) The patient at baseline when the patient showed no disease activity; B) Patients with clinically diagnosed disease without active disease, or C) Healthy patients A non-transitory computer-readable medium according to the present invention 1143, which corresponds to any one of the above. [This invention 1145] The non-transitory computer readable medium of any of claims 1084 to 1144, wherein the expression levels of the plurality of biomarkers are determined from a test sample obtained from the subject. [This invention 1146] The non-transitory computer readable medium of the present invention 1145, wherein the test sample is a blood or serum sample. [This invention 1147] The non-transitory computer readable medium of any one of claims 1145 to 1146, wherein the subject has, is suspected of having, or has previously been diagnosed with multiple sclerosis. [This invention 1148] The non-transitory computer-readable medium of any of claims 1084 to 1147, wherein the dataset is obtained from a performed immunoassay. [This invention 1149] The non-transitory computer readable medium of the present invention 1148, wherein the immunoassay is a proximity extension assay (PEA) or a LUMINEX xMAP multiplex assay. [The present invention 1150] The non-transitory computer readable medium of the present invention 1148, wherein performing the immunoassay comprises contacting the test sample with a plurality of reagents including antibodies. [This invention 1151] The non-transitory computer readable medium of the present invention 1150, wherein the antibody comprises one of a monoclonal antibody and a polyclonal antibody. [This invention 1152] The non-transitory computer readable medium of the present invention 1150, wherein the antibodies include both monoclonal and polyclonal antibodies. [This invention 1153] The non-transitory computer-readable medium of any of claims 1084 to 1152, further comprising selecting a therapy for administration to the subject based on the prediction of multiple sclerosis disease activity. [This invention 1154] The non-transitory computer-readable medium of any of claims 1084 to 1152, further comprising determining the therapeutic effectiveness of a therapy previously administered to the subject based on the prediction of multiple sclerosis disease activity. [This invention 1155] The non-transitory computer readable medium of the present invention 1154, wherein determining the therapeutic effectiveness of said treatment method comprises comparing said prediction with a previous prediction determined for said subject at a previous time point. [This invention 1156] A non-transitory computer readable medium of the present invention 1155, wherein determining the therapeutic effectiveness of the treatment comprises determining that the treatment exhibits effectiveness according to a difference between the prediction and the previous prediction. [This invention 1157] A non-transitory computer readable medium of the present invention 1155, wherein determining the therapeutic effectiveness of the treatment comprises determining that the treatment is ineffective according to no difference between the prediction and the previous prediction. [This invention 1158] The non-transitory computer-readable medium of any of claims 1084 to 1152, further comprising determining a differential diagnosis for multiple sclerosis based on the prediction of multiple sclerosis disease activity. [This invention 1159] The non-transitory computer readable medium of the present invention 1158, wherein the differential diagnosis of multiple sclerosis includes any one of relapsing remitting multiple sclerosis (RRMS), secondary progressive multiple sclerosis (SPMS), primary progressive multiple sclerosis (PPMS), progressive relapsing multiple sclerosis (PRMS), and CIS. [The present invention 1160] 1. A kit for assessing multiple sclerosis disease activity in a subject, the kit comprising: A set of reagents for determining the expression level of a plurality of biomarkers in a test sample from the subject, comprising: the plurality of biomarkers comprises biomarkers from at least one group selected from group 1, group 2, and group 3; Group 1 includes biomarker 1, biomarker 2, biomarker 3, biomarker 4, biomarker 5, biomarker 6, biomarker 7, and biomarker 8; Biomarker 1 is NEFL, MOG, CADM3, or GFAP; Biomarker 2 is MOG, CADM3, KLK6, BCAN, OMG, or GFAP; Biomarker 3 is CD6, CD5, CRTAM, CD244, or TNFRSF9; Biomarker 4 is CXCL9, CXCL10, IL-12B, CXCL11, or GFAP; Biomarker 5 is OPG, TFF3, or ENPP2; Biomarker 6 is OPN, OMD, MEPE, or GFAP; Biomarker 7 is CXCL13, NOS3, or MMP-2; Biomarker 8 is GFAP, NEFL, OPN, CXCL9, MOG, or CHI3L1; Group 2 includes biomarker 9, biomarker 10, biomarker 11, biomarker 12, biomarker 13, biomarker 14, biomarker 15, biomarker 16, and biomarker 17; Biomarker 9 is CDCP1, IL-18BP, IL-18, GFAP, or MSR1; Biomarker 10 is CCL20, CCL3, or TWEAK; Biomarker 11 is IL-12B, IL12A, or CXCL9; Biomarker 12 is APLP1, SEZ6L, BCAN, DPP6, NCAN, or KLK6; Biomarker 13 is TNFRSF10A, TNFRSF11A, SPON2, CHI3L1, or IFI30; Biomarker 14 is COL4A1, IL6, NOTCH3, or PCDH17; Biomarker 15 is SERPINA9, TNFRSF9, or CNTN4; Biomarkers 16 are FLRT2, DDR1, NTRK2, CDH6, and MMP-2; Biomarker 17 is TNFSF13B, CXCL16, ALCAM, or IL-18; Group 3 includes biomarker 18, biomarker 19, biomarker 20, and biomarker 21; Biomarker 18 is GH, GH2, or IGFBP-1; Biomarker 19 is VCAN, TINAGL1, CANT1, NECTIN2, MMP-9, or NPDC1; Biomarker 20 is PRTG, NTRK2, NTRK3, or CNTN4; Biomarker 21 is CNTN2, DPP6, GDNFR-α3, or SCARF2; The set of reagents, and Instructions for using said set of reagents to determine the expression level of biomarkers in said test sample. [The present invention 1161] The kit of the present invention 1160, wherein the plurality of biomarkers includes each biomarker of group 1, biomarker 1 is NEFL, biomarker 2 is MOG, biomarker 3 is CD6, biomarker 4 is CXCL9, biomarker 5 is OPG, biomarker 6 is OPN, biomarker 7 is CXCL13, and biomarker 8 is GFAP. [The present invention 1162] The kit of the present invention 1160 or 1161, wherein the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.667 to 0.869. [The present invention 1163] The kit of the present invention 1160 or 1161, wherein the performance of the predictive model is characterized by a positive predictive value ranging from 0.617 to 0.861. [The present invention 1164] The kit of the present invention 1160, wherein the plurality of biomarkers includes each biomarker of group 2, wherein biomarker 9 is CDCP1, biomarker 10 is CCL20, biomarker 11 is IL-12B, biomarker 12 is APLP1, biomarker 13 is TNFRSF10A, biomarker 14 is COL4A1, biomarker 15 is SERPINA9, biomarker 16 is FLRT2, and biomarker 17 is TNFSF13B. [The present invention 1165] The kit of the present invention 1164, wherein the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.595 to 0.761. [The present invention 1166] The kit of the present invention 1164, wherein the performance of the predictive model is characterized by a positive predictive value ranging from 0.523 to 0.769. [The present invention 1167] The kit of the present invention 1160, wherein the plurality of biomarkers further comprises each biomarker of Group 2, wherein biomarker 9 is CDCP1, biomarker 10 is CCL20, biomarker 11 is IL-12B, biomarker 12 is APLP1, biomarker 13 is TNFRSF10A, biomarker 14 is COL4A1, biomarker 15 is SERPINA9, biomarker 16 is FLRT2, and biomarker 17 is TNFSF13B. [The present invention 1168] The kit of the present invention 1167, wherein the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.693 to 0.892. [The present invention 1169] The kit of the present invention 1167, in which the performance of the predictive model is characterized by a positive predictive value ranging from 0.613 to 0.843. [The present invention 1170] The kit of the present invention 1160, wherein the plurality of biomarkers includes each biomarker of group 3, biomarker 18 is GH, biomarker 19 is VCAN, biomarker 20 is PRTG, and biomarker 21 is CNTN2. [This invention 1171] The kit of the present invention 1170, wherein the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.566 to 0.644. [This invention 1172] The kit of the present invention 1170, wherein the performance of the predictive model is characterized by a positive predictive value ranging from 0.370 to 0.742. [This invention 1173] The kit of the present invention 1167, wherein the plurality of biomarkers further comprises each biomarker of group 3, wherein biomarker 18 is GH, biomarker 19 is VCAN, biomarker 20 is PRTG, and biomarker 21 is CNTN2. [This invention 1174] The kit of the present invention 1173, wherein the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.686 to 0.889. [This invention 1175] The kit of the present invention 1173, wherein the performance of the predictive model is characterized by a positive predictive value ranging from 0.648 to 0.835. [The present invention 1176] 1. A kit for assessing multiple sclerosis disease activity in a subject, the kit comprising: a set of reagents for determining an expression level of a plurality of biomarkers in a test sample from the subject, the plurality of biomarkers comprising NEFL, MOG, CD6, CXCL9, OPG, OPN, CXCL13, and GFAP; and Instructions for using said set of reagents to determine the expression level of biomarkers in said test sample. [This invention 1177] The kit of the present invention 1176, wherein the plurality of biomarkers further comprises CDCP1, CCL20, IL-12B, APLP1, TNFRSF10A, COL4A1, SERPINA9, FLRT2, and TNFSF13B. [This invention 1178] The kit of the present invention, wherein the plurality of biomarkers further comprises GH, VCAN, PRTG, and CNTN2. [This invention 1179] 1. A kit for assessing multiple sclerosis disease activity in a subject, the kit comprising: A set of reagents for determining an expression level of a plurality of biomarkers in a test sample from the subject, the plurality of biomarkers comprising: one or more neurodegeneration biomarkers selected from the group consisting of NEFL, APLP1, OPG, SERPINA9, PRTG, GFAP, CNTN2, and FLRT2; one or more inflammatory biomarkers selected from the group consisting of CCL20, GH, CXCL13, IL-12B, VCAN, TNFRSF10A, TNFSF13B, CD6, and CXCL9; one or more immune modulating biomarkers selected from the group consisting of CDCP1 and OPN; One or more myelin integrity biomarkers selected from the group consisting of COL4A1 and MOG. The set of reagents comprising: Instructions for using said set of reagents to determine the expression level of biomarkers in said test sample. [The present invention 1180] The kit of the present invention 1179, wherein the one or more neurodegeneration biomarkers include NEFL, OPG, and GFAP, the one or more inflammation biomarkers include CXCL13, CD6, and CXCL9, the one or more immune regulatory biomarkers include OPN, and the one or more myelin integrity biomarkers include MOG. [This invention 1181] The kit of the present invention 1180, wherein the one or more neurodegeneration biomarkers further comprise APLP1, SERPINA9, and FLRT2, the one or more inflammation biomarkers further comprise CCL20, IL-12B, TNFRSF10A, and TNFSF13B, the one or more immune regulatory biomarkers further comprise CDCP1, and the one or more myelin integrity biomarkers further comprise COL4A1. [The present invention 1182] The kit of the present invention 1181, wherein the one or more neurodegeneration biomarkers further comprise PRTG and CNTN2, and the one or more inflammation biomarkers comprise GH and VCAN. [The present invention 1183] The kit according to any one of claims 1160 to 1181, wherein the plurality of biomarkers are protein biomarkers. [This invention 1184] The multiple sclerosis disease activity is the presence of normal disease activity, the presence of minimal disease activity, a shift in disease activity (increase or decrease), the severity of MS, relapse or relapse events associated with MS, relapse rate, MS status, response to MS therapy, degree of MS disability, or risk of developing MS The kit according to any one of claims 1160 to 1183, [This invention 1185] The kit of the present invention 1184, wherein said normal disease activity is the presence or absence of one or more gadolinium-enhancing MRI lesions and said insignificant disease activity is the presence of one gadolinium-enhancing MRI lesion. [The present invention 1186] The kit of the present invention 1184, wherein the severity of MS corresponds to the number of gadolinium-enhancing MRI lesions. [This invention 1187] The kit of the present invention 1184, wherein said MS condition is an exacerbation or quiescence of multiple sclerosis. [The present invention 1188] 1. A kit for determining multiple sclerosis activity in a subject, the kit comprising: a set of reagents for determining an expression level of a plurality of biomarkers in a test sample from the subject, the plurality of biomarkers comprising two or more of NEFL, MOG, CD6, CXCL9, OPG, OPN, CXCL13, GFAP, CDCP1, CCL20, IL-12B, APLP1, TNFRSF10A, COL4A1, SERPINA9, FLRT2, TNFSF13B, GH, VCAN, PRTG, and CNTN2; and Instructions for using said set of reagents to determine the expression level of biomarkers in said test sample. [The present invention 1189] The kit of the present invention 1188, wherein the two or more biomarkers include NEFL and at least one other biomarker. [The present invention 1190] The kit of the present invention 1189, wherein the two or more biomarkers are a biomarker pair selected from any one of A) NEFL and CD6, B) NEFL and MOG, C) NEFL and CXCL9, and D) NEFL and TNFRSF10A. [The present invention 1191] The kit of the present invention 1189, wherein the two or more biomarkers are a biomarker triplet selected from any one of A) NEFL, CD6, and CXCL9, and B) NEFL, TNFRSF10A, and COL4A1. [This invention 1192] The kit of the present invention 1189, wherein the two or more biomarkers are a biomarker quadruple selected from any one of A) NEFL, MOG, CD6, and CXCL9, B) NEFL, CXCL9, TNFRSF10A, and COL4A1, C) NEFL, CD6, CXCL9, and CXCL13, and D) NEFL, TNFRSF10A, COL4A1, and CCL20. [The present invention 1193] The kit of the present invention, wherein the two or more biomarkers do not include NEFL. [This invention 1194] The kit of the present invention 1193, wherein the two or more biomarkers are a biomarker pair selected from any one of A) MOG and IL-12B, B) CXCL9 and CD6, C) MOG and CXCL9, D) MOG and CD6, E) CXCL9 and COL4A1, and F) CD6 and VCAN. [The present invention 1195] The kit of the present invention 1193, wherein the two or more biomarkers are a biomarker triplet selected from any one of A) MOG, IL-12B, and APLP1; B) MOG, CD6, and CXCL9; C) CXCL9, COL4A1, and VCAN; D) MOG, IL-12B, and CNTN2; and E) CD6, CCL20, and VCAN. [The present invention 1196] The kit of the present invention 1193, wherein the two or more biomarkers are a biomarker quadruple selected from any one of A) MOG, CXCL9, IL-12B, APLP1, B) CXCL9, COL4A1, OPG, and VCAN, C) CXCL9, OPG, APLP1, and OPN, D) MOG, IL-12B, OPN, and CNTN2, and E) CD6, COL4A1, CCL20, and VCAN. [The present invention 1197] The kit according to any one of claims 1189 to 1196, wherein the multiple sclerosis activity is a shift in disease activity. [The present invention 1198] The kit of the present invention 1189, wherein the two or more biomarkers are a biomarker pair selected from any one of A) NEFL and TNFSF13B, B) NEFL and CNTN2, and C) NEFL and CXCL9. [The present invention 1199] The kit of the present invention 1189, wherein the two or more biomarkers are a biomarker triplet selected from any one of A) NEFL, CNTN2, and TNFSF13B, B) NEFL, APLP1, and TNFSF13B, and C) NEFL, TNFRSF10A, and TNFSF13B. [The present invention 1200] The kit of the present invention 1189, wherein the two or more biomarkers are a biomarker quadruple selected from any one of A) NEFL, TNFRSF10A, CNTN2, and TNFSF13B, B) NEFL, COL4A1, CNTN2, and TNFSF13B, and C) NEFL, TNFRSF10A, APLP1, and TNFSF13B. [The present invention 1201] The kit of the present invention 1193, wherein the two or more biomarkers are a biomarker pair selected from any one of A) MOG and CDCP1, B) MOG and TNFSF13B, and C) MOG and CXCL9. [The present invention 1202] The kit of the present invention 1193, wherein the two or more biomarkers are a biomarker triad, and one biomarker in the biomarker triad is MOG. [The present invention 1203] The kit of the present invention 1202, wherein the biomarker triad is selected from any one of A) MOG, CXCL9, and TNFSF13B, B) MOG, OPG, and TNFSF13B, and C) MOG, CCL20, and TNFSF13B. [The present invention 1204] The kit of the present invention 1193, wherein the two or more biomarkers are a biomarker quartet, and one biomarker in the biomarker quartet is MOG. [The present invention 1205] The kit of the present invention 1204, wherein the biomarker quadruple is selected from any one of A) MOG, CXCL9, APLP1, and TNFSF13B, B) MOG, CXCL9, OPG, and TNFSF13B, and C) MOG, CXCL9, OPG, and CNTN2. [The present invention 1206] The kit of any of claims 1198 to 1205, wherein the multiple sclerosis activity is the presence or absence of multiple sclerosis. [The present invention 1207] The kit of the present invention 1189, wherein the two or more biomarkers are a biomarker pair selected from any one of A) NEFL and TNFSF13B, B) NEFL and SERPINA9, and C) NEFL and GH. [The present invention 1208] The kit of the present invention 1189, wherein the two or more biomarkers are a biomarker triplet selected from any one of A) NEFL, SERPINA9, and TNFSF13B, B) NEFL, CNTN2, and TNFSF13B, and C) NEFL, APLP1, and TNFSF13B. [This invention 1209] The kit of the present invention 1189, wherein the two or more biomarkers are a biomarker quadruple selected from any one of A) NEFL, CCL20, SERPINA9, and TNFSF13B, B) NEFL, APLP1, SERPINA9, and TNFSF13B, and C) NEFL, CCL20, APLP1, and TNFSF13B. [The present invention 1210] The kit of the present invention 1193, wherein the two or more biomarkers are a biomarker pair selected from any one of A) MOG and TNFSF13B, B) MOG and CXCL9, and C) MOG and IL-12B. [The present invention 1211] The kit of the present invention 1193, wherein the two or more biomarkers are a biomarker triplet selected from any one of A) MOG, CXCL9, and TNFSF13B, B) MOG, SERPINA9, and TNFSF13B, and C) MOG, OPG, and TNFSF13B. [The present invention 1212] The kit of the present invention 1193, wherein the two or more biomarkers are a biomarker quadruple selected from any one of A) MOG, CXCL9, OPG, and TNFSF13B; B) MOG, CXCL9, OPG, SERPINA9, and TNFSF13B; and C) MOG, CXCL9, SERPINA9, and TNFSF13B. [The present invention 1213] The kit according to any one of claims 1207 to 1212, wherein the multiple sclerosis activity is a disease severity based on a predicted number of gadolinium-enhancing lesions. [The present invention 1214] The kit of the present invention 1188, wherein the two or more biomarkers are a biomarker pair selected from any one of A) GFAP and MOG, B) GFAP and NEFL, C) APLP1 and GFAP, D) NEFL and MOG, or E) CXCL9 and OPG. [The present invention 1215] The kit of the present invention 1188, wherein the two or more biomarkers are a biomarker triplet selected from any one of A) GFAP, NEFL, and MOG, B) NEFL, MOG, and GH, C) NEFL, MOG, and SERPINA9, D) CXCL9, OPG, and SERPINA9, or E) CXCL9, OPG, and TNFRSF10A. [The present invention 1216] The kit of the present invention 1188, wherein the two or more biomarkers are a biomarker triplet selected from any one of A) GFAP, NEFL, MOG, and IL12B, B) GFAP, NEFL, MOG, and PRTG, C) GFAP, NEFL, MOG, and APLP1, D) NEFL, MOG, GH, and SERPINA9, E) NEFL, MOG, GH, and TNFRSF10A, F) MOG, CXCL9, OPG, and SERPINA9, or G) CD6, IL12B, APLP1, and CCL20. [The present invention 1217] The kit according to any one of claims 1214 to 1216, wherein the multiple sclerosis activity is disease progression. [The present invention 1218] The kit of any of claims 1160 to 1217, wherein the instructions for use further comprise instructions for generating a prediction of multiple sclerosis disease activity by applying a predictive model to the expression levels of the plurality of biomarkers. [The present invention 1219] The kit of the present invention, wherein generating the prediction of multiple sclerosis disease activity comprises comparing a score output by the predictive model with a reference score. [The present invention 1220] The reference score is A) The patient at baseline when the patient showed no disease activity; B) Patients with clinically diagnosed disease without active disease, or C) Healthy patients A kit according to the present invention, which corresponds to any one of the above. [The present invention 1221] The kit of any one of claims 1160 to 1220, wherein the test sample is a blood or serum sample. [The present invention 1222] The kit of any of claims 1160 to 1221, wherein the subject has multiple sclerosis, is suspected of having multiple sclerosis, or has previously been diagnosed with multiple sclerosis. [The present invention 1223] The kit of any of claims 1160 to 1222, wherein the instructions for using the set of reagents include instructions for performing an immunoassay to determine the expression level of the plurality of biomarkers. [The present invention 1224] The kit of the present invention, wherein the immunoassay is a proximity extension assay (PEA) or a LUMINEXxMAP multiplex assay. [The present invention 1225] The kit of claim 1223 or 1224, wherein performing the immunoassay comprises contacting the test sample with a plurality of reagents comprising antibodies. [The present invention 1226] The kit of the present invention 1225, wherein the antibody comprises one of a monoclonal antibody and a polyclonal antibody. [The present invention 1227] The kit of the present invention, wherein the antibodies include both monoclonal and polyclonal antibodies. [The present invention 1228] The kit of any of claims 1218 to 1227, further comprising instructions for selecting a therapy to administer to the subject based on the prediction of multiple sclerosis disease activity. [The present invention 1229] The kit of any of claims 1218 to 1227, further comprising instructions for use in determining the therapeutic effectiveness of a therapy previously administered to the subject based on the prediction of multiple sclerosis disease activity. [The present invention 1230] The kit of claim 1229, wherein determining the therapeutic efficacy of said treatment method comprises comparing said prediction with a previous prediction determined for said subject at a previous time point. [This invention 1231] The kit of claim 1230, wherein determining the therapeutic efficacy of said treatment comprises determining that said treatment exhibits efficacy according to a difference between said prediction and said previous prediction. [The present invention 1232] The kit of claim 1230, wherein determining the therapeutic efficacy of said treatment comprises determining that said treatment is ineffective according to no difference between said prediction and said previous prediction. [The present invention 1233] The kit of any of claims 1218 to 1227, further comprising instructions for use in determining a differential diagnosis of multiple sclerosis based on said prediction of multiple sclerosis disease activity. [The present invention 1234] The kit of the present invention 1233, wherein the differential diagnosis of multiple sclerosis includes any one of relapsing-remitting multiple sclerosis (RRMS), secondary progressive multiple sclerosis (SPMS), primary progressive multiple sclerosis (PPMS), progressive relapsing multiple sclerosis (PRMS), and CIS. [This invention 1235] 1. A system for assessing multiple sclerosis disease activity in a subject, the system comprising: A set of reagents used to determine an expression level of a plurality of biomarkers in a test sample from the subject, the plurality of biomarkers including each biomarker of at least one group selected from Group 1, Group 2, and Group 3; Group 1 includes biomarker 1, biomarker 2, biomarker 3, biomarker 4, biomarker 5, biomarker 6, biomarker 7, and biomarker 8; Biomarker 1 is NEFL, MOG, CADM3, or GFAP; Biomarker 2 is MOG, CADM3, KLK6, BCAN, OMG, or GFAP; Biomarker 3 is CD6, CD5, CRTAM, CD244, or TNFRSF9; Biomarker 4 is CXCL9, CXCL10, IL-12B, CXCL11, or GFAP; Biomarker 5 is OPG, TFF3, or ENPP2; Biomarker 6 is OPN, OMD, MEPE, or GFAP; Biomarker 7 is CXCL13, NOS3, or MMP-2; Biomarker 8 is GFAP, NEFL, OPN, CXCL9, MOG, or CHI3L1; Group 2 includes biomarker 9, biomarker 10, biomarker 11, biomarker 12, biomarker 13, biomarker 14, biomarker 15, biomarker 16, and biomarker 17; Biomarker 9 is CDCP1, IL-18BP, IL-18, GFAP, or MSR1; Biomarker 10 is CCL20, CCL3, or TWEAK; Biomarker 11 is IL-12B, IL12A, or CXCL9; Biomarker 12 is APLP1, SEZ6L, BCAN, DPP6, NCAN, or KLK6; Biomarker 13 is TNFRSF10A, TNFRSF11A, SPON2, CHI3L1, or IFI30; Biomarker 14 is COL4A1, IL6, NOTCH3, or PCDH17; Biomarker 15 is SERPINA9, TNFRSF9, or CNTN4; Biomarkers 16 are FLRT2, DDR1, NTRK2, CDH6, and MMP-2; Biomarker 17 is TNFSF13B, CXCL16, ALCAM, or IL-18; Group 3 includes biomarker 18, biomarker 19, biomarker 20, and biomarker 21; Biomarker 18 is GH, GH2, or IGFBP-1; Biomarker 19 is VCAN, TINAGL1, CANT1, NECTIN2, MMP-9, or NPDC1; Biomarker 20 is PRTG, NTRK2, NTRK3, or CNTN4; Biomarker 21 is CNTN2, DPP6, GDNFR-α3, or SCARF2; The set of reagents, and a device configured to receive a mixture of one or more reagents of the set and the test sample, and to measure the expression level of the biomarker in the test sample; and A computer system communicatively connected to the device for obtaining a dataset comprising expression levels of the plurality of biomarkers in the test sample, and for generating a prediction of multiple sclerosis disease activity by applying a predictive model to the expression levels of the plurality of biomarkers. [The present invention 1236] The system of the present invention 1227, wherein the plurality of biomarkers includes each biomarker of group 1, biomarker 1 is NEFL, biomarker 2 is MOG, biomarker 3 is CD6, biomarker 4 is CXCL9, biomarker 5 is OPG, biomarker 6 is OPN, biomarker 7 is CXCL13, and biomarker 8 is GFAP. [This invention 1237] The system of claim 1227 or 1236, wherein the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.667 to 0.869. [This invention 1238] The system of claim 1227 or 1236, wherein the performance of the predictive model is characterized by a positive predictive value ranging from 0.617 to 0.861. [This invention 1239] The system of the present invention 1227, wherein the plurality of biomarkers includes each biomarker of group 2, wherein biomarker 9 is CDCP1, biomarker 10 is CCL20, biomarker 11 is IL-12B, biomarker 12 is APLP1, biomarker 13 is TNFRSF10A, biomarker 14 is COL4A1, biomarker 15 is SERPINA9, biomarker 16 is FLRT2, and biomarker 17 is TNFSF13B. [The present invention 1240] The system of the present invention 1239, wherein the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.595 to 0.761. [This invention 1241] The system of the present invention 1239, wherein the performance of the predictive model is characterized by a positive predictive value ranging from 0.523 to 0.769. [This invention 1242] The system of the present invention 1236, wherein the plurality of biomarkers further includes each biomarker of group 2, wherein biomarker 9 is CDCP1, biomarker 10 is CCL20, biomarker 11 is IL-12B, biomarker 12 is APLP1, biomarker 13 is TNFRSF10A, biomarker 14 is COL4A1, biomarker 15 is SERPINA9, biomarker 16 is FLRT2, and biomarker 17 is TNFSF13B. [This invention 1243] The system of the present invention 1242, wherein the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.693 to 0.892. [This invention 1244] The system of the present invention 1242, wherein the performance of the predictive model is characterized by a positive predictive value ranging from 0.613 to 0.843. [This invention 1245] The system of the present invention 1227, wherein the plurality of biomarkers includes each biomarker of group 3, biomarker 18 is GH, biomarker 19 is VCAN, biomarker 20 is PRTG, and biomarker 21 is CNTN2. [This invention 1246] The system of the present invention 1245, wherein the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.566 to 0.644. [This invention 1247] The system of the present invention 1245, wherein the performance of the predictive model is characterized by a positive predictive value ranging from 0.370 to 0.742. [This invention 1248] The system of the present invention 1242, wherein the plurality of biomarkers further includes each biomarker of group 3, wherein biomarker 18 is GH, biomarker 19 is VCAN, biomarker 20 is PRTG, and biomarker 21 is CNTN2. [This invention 1249] The system of the present invention 1248, wherein the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.686 to 0.889. [This invention 1250] The system of the present invention 1248, wherein the performance of the predictive model is characterized by a positive predictive value ranging from 0.648 to 0.835. [This invention 1251] 1. A system for assessing multiple sclerosis disease activity in a subject, the system comprising: a set of reagents used to determine an expression level of a plurality of biomarkers in a test sample from the subject, the plurality of biomarkers comprising NEFL, MOG, CD6, CXCL9, OPG, OPN, CXCL13, and GFAP; and a device configured to receive a mixture of one or more reagents of the set and the test sample, and to measure the expression level of the biomarker in the test sample; and A computer system communicatively connected to the device for obtaining a dataset comprising expression levels of the plurality of biomarkers in the test sample, and for generating a prediction of multiple sclerosis disease activity by applying a predictive model to the expression levels of the plurality of biomarkers. [This invention 1252] The system of the present invention 1251, wherein the plurality of biomarkers further includes CDCP1, CCL20, IL-12B, APLP1, TNFRSF10A, COL4A1, SERPINA9, FLRT2, and TNFSF13B. [This invention 1253] The system of the present invention 1252, wherein the plurality of biomarkers further includes GH, VCAN, PRTG, and CNTN2. [This invention 1254] 1. A system for determining multiple sclerosis activity in a subject, the system comprising: A set of reagents used to determine the expression level of a plurality of biomarkers in a test sample from the subject, the plurality of biomarkers comprising: one or more neurodegeneration biomarkers selected from the group consisting of NEFL, APLP1, OPG, SERPINA9, PRTG, GFAP, CNTN2, and FLRT2; one or more inflammatory biomarkers selected from the group consisting of CCL20, GH, CXCL13, IL-12B, VCAN, TNFRSF10A, TNFSF13B, CD6, and CXCL9; one or more immune modulating biomarkers selected from the group consisting of CDCP1 and OPN; One or more myelin integrity biomarkers selected from the group consisting of COL4A1 and MOG. a) obtaining or having obtained a dataset comprising expression levels of a plurality of biomarkers, The set of reagents comprising: a device configured to receive a mixture of one or more reagents of the set and the test sample, and to measure the expression level of the biomarker in the test sample; and A computer system communicatively connected to the device for obtaining a dataset comprising expression levels of the plurality of biomarkers in the test sample, and for generating a prediction of multiple sclerosis disease activity by applying a predictive model to the expression levels of the plurality of biomarkers. [This invention 1255] The system of the present invention 1254, wherein the one or more neurodegeneration biomarkers include NEFL, OPG, and GFAP, the one or more inflammation biomarkers include CXCL13, CD6, and CXCL9, the one or more immune regulatory biomarkers include OPN, and the one or more myelin integrity biomarkers include MOG. [This invention 1256] The system of the present invention 1255, wherein the one or more neurodegeneration biomarkers further include APLP1, SERPINA9, and FLRT2, the one or more inflammation biomarkers further include CCL20, IL-12B, TNFRSF10A, and TNFSF13B, the one or more immune regulatory biomarkers further include CDCP1, and the one or more myelin integrity biomarkers further include COL4A1. [This invention 1257] The system of the present invention 1256, wherein the one or more neurodegeneration biomarkers further include PRTG and CNTN2, and the one or more inflammation biomarkers include GH and VCAN. [This invention 1258] The system according to any one of claims 1227 to 1257, wherein the plurality of biomarkers are protein biomarkers. [This invention 1259] The multiple sclerosis disease activity is the presence of normal disease activity, the presence of minimal disease activity, a shift in disease activity (increase or decrease), the severity of MS, relapse or relapse events associated with MS, relapse rate, MS status, response to MS therapy, degree of MS disability, or risk of developing MS The system of any one of claims 1227 to 1258. [The present invention 1260] The system of the present invention 1259, wherein the normal disease activity is the presence or absence of one or more gadolinium-enhancing MRI lesions and the minimal disease activity is the presence of one gadolinium-enhancing MRI lesion. [This invention 1261] The system of the present invention, wherein the severity of MS corresponds to the number of gadolinium-enhancing MRI lesions. [This invention 1262] The system of the present invention 1259, wherein the MS condition is an exacerbation or quiescence of multiple sclerosis. [This invention 1263] 1. A system for determining multiple sclerosis activity in a subject, the system comprising: a set of reagents used to determine an expression level of a plurality of biomarkers in a test sample from the subject, the plurality of biomarkers comprising two or more of NEFL, MOG, CD6, CXCL9, OPG, OPN, CXCL13, GFAP, CDCP1, CCL20, IL-12B, APLP1, TNFRSF10A, COL4A1, SERPINA9, FLRT2, TNFSF13B, GH, VCAN, PRTG, and CNTN2; a device configured to receive a mixture of one or more reagents of the set and the test sample, and to measure the expression level of the biomarker in the test sample; and A computer system communicatively connected to the device for obtaining a dataset comprising expression levels of the plurality of biomarkers in the test sample, and for generating a prediction of multiple sclerosis disease activity by applying a predictive model to the expression levels of the plurality of biomarkers. [This invention 1264] The system of the present invention 1259, wherein the two or more biomarkers include NEFL and at least one other biomarker. [This invention 1265] The system of the present invention 1264, wherein the two or more biomarkers are a biomarker pair selected from any one of A) NEFL and CD6, B) NEFL and MOG, C) NEFL and CXCL9, and D) NEFL and TNFRSF10A. [This invention 1266] The system of the present invention 1264, wherein the two or more biomarkers are a biomarker triplet selected from any one of A) NEFL, CD6, and CXCL9, and B) NEFL, TNFRSF10A, and COL4A1. [The present invention 1267] The system of the present invention 1264, wherein the two or more biomarkers are a biomarker quadruple selected from any one of A) NEFL, MOG, CD6, and CXCL9, B) NEFL, CXCL9, TNFRSF10A, and COL4A1, C) NEFL, CD6, CXCL9, and CXCL13, and D) NEFL, TNFRSF10A, COL4A1, and CCL20. [This invention 1268] The system of the present invention 1259, wherein the two or more biomarkers do not include NEFL. [The present invention 1269] The system of the present invention 1268, wherein the two or more biomarkers are a biomarker pair selected from any one of A) MOG and IL-12B, B) CXCL9 and CD6, C) MOG and CXCL9, D) MOG and CD6, E) CXCL9 and COL4A1, and F) CD6 and VCAN. [This invention 1270] The system of the present invention 1268, wherein the two or more biomarkers are a biomarker triplet selected from any one of A) MOG, IL-12B, and APLP1, B) MOG, CD6, and CXCL9, C) CXCL9, COL4A1, and VCAN, D) MOG, IL-12B, and CNTN2, and E) CD6, CCL20, and VCAN. [This invention 1271] The system of the present invention 1268, wherein the two or more biomarkers are a biomarker quadruple selected from any one of A) MOG, CXCL9, IL-12B, APLP1; B) CXCL9, COL4A1, OPG, and VCAN; C) CXCL9, OPG, APLP1, and OPN; D) MOG, IL-12B, OPN, and CNTN2; and E) CD6, COL4A1, CCL20, and VCAN. [This invention 1272] The system of any one of claims 1264 to 1271, wherein the multiple sclerosis activity is a shift in disease activity. [This invention 1273] The system of the present invention 1264, wherein the two or more biomarkers are a biomarker pair selected from any one of A) NEFL and TNFSF13B, B) NEFL and CNTN2, and C) NEFL and CXCL9. [This invention 1274] The system of the present invention 1264, wherein the two or more biomarkers are a biomarker triplet selected from any one of A) NEFL, CNTN2, and TNFSF13B, B) NEFL, APLP1, and TNFSF13B, and C) NEFL, TNFRSF10A, and TNFSF13B. [This invention 1275] The system of the present invention 1264, wherein the two or more biomarkers are a biomarker quadruple selected from any one of A) NEFL, TNFRSF10A, CNTN2, and TNFSF13B, B) NEFL, COL4A1, CNTN2, and TNFSF13B, and C) NEFL, TNFRSF10A, APLP1, and TNFSF13B. [This invention 1276] The system of the present invention 1268, wherein the two or more biomarkers are a biomarker pair selected from any one of A) MOG and CDCP1, B) MOG and TNFSF13B, and C) MOG and CXCL9. [This invention 1277] The system of the present invention 1268, wherein the two or more biomarkers are a biomarker triad and one biomarker in the biomarker triad is MOG. [This invention 1278] The system of the present invention 1277, wherein the biomarker triad is selected from any one of A) MOG, CXCL9, and TNFSF13B, B) MOG, OPG, and TNFSF13B, and C) MOG, CCL20, and TNFSF13B. [This invention 1279] The system of claim 1268, wherein the two or more biomarkers are a biomarker quartet, and one biomarker in the biomarker quartet is MOG. [This invention 1280] The system of the present invention 1279, wherein the biomarker quadruple is selected from any one of A) MOG, CXCL9, APLP1, and TNFSF13B, B) MOG, CXCL9, OPG, and TNFSF13B, and C) MOG, CXCL9, OPG, and CNTN2. [This invention 1281] Any of the systems of 1273 to 1280, wherein the multiple sclerosis activity is the presence or absence of multiple sclerosis. [This invention 1282] The system of the present invention 1264, wherein the two or more biomarkers are a biomarker pair selected from any one of A) NEFL and TNFSF13B, B) NEFL and SERPINA9, and C) NEFL and GH. [This invention 1283] The system of the present invention 1264, wherein the two or more biomarkers are a biomarker triplet selected from any one of A) NEFL, SERPINA9, and TNFSF13B, B) NEFL, CNTN2, and TNFSF13B, and C) NEFL, APLP1, and TNFSF13B. [This invention 1284] The system of the present invention 1264, wherein the two or more biomarkers are a biomarker quadruple selected from any one of A) NEFL, CCL20, SERPINA9, and TNFSF13B, B) NEFL, APLP1, SERPINA9, and TNFSF13B, and C) NEFL, CCL20, APLP1, and TNFSF13B. [This invention 1285] The system of the present invention 1268, wherein the two or more biomarkers are a biomarker pair selected from any one of A) MOG and TNFSF13B, B) MOG and CXCL9, and C) MOG and IL-12B. [This invention 1286] The system of the present invention 1268, wherein the two or more biomarkers are a biomarker triplet selected from any one of A) MOG, CXCL9, and TNFSF13B, B) MOG, SERPINA9, and TNFSF13B, and C) MOG, OPG, and TNFSF13B. [This invention 1287] The system of the present invention 1268, wherein the two or more biomarkers are a biomarker quadruple selected from any one of A) MOG, CXCL9, OPG, and TNFSF13B; B) MOG, CXCL9, OPG, SERPINA9, and TNFSF13B; and C) MOG, CXCL9, SERPINA9, and TNFSF13B. [This invention 1288] Any of the systems of claims 1282 to 1287, wherein the multiple sclerosis activity is a disease severity based on a predicted number of gadolinium-enhancing lesions. [This invention 1289] The system of the present invention 1263, wherein the two or more biomarkers are a biomarker pair selected from any one of A) GFAP and MOG, B) GFAP and NEFL, C) APLP1 and GFAP, D) NEFL and MOG, or E) CXCL9 and OPG. [This invention 1290] The system of the present invention 1263, wherein the two or more biomarkers are a biomarker triplet selected from any one of A) GFAP, NEFL, and MOG, B) NEFL, MOG, and GH, C) NEFL, MOG, and SERPINA9, D) CXCL9, OPG, and SERPINA9, or E) CXCL9, OPG, and TNFRSF10A. [This invention 1291] The system of the present invention 1263, wherein the two or more biomarkers are a biomarker triplet selected from any one of A) GFAP, NEFL, MOG, and IL12B, B) GFAP, NEFL, MOG, and PRTG, C) GFAP, NEFL, MOG, and APLP1, D) NEFL, MOG, GH, and SERPINA9, E) NEFL, MOG, GH, and TNFRSF10A, F) MOG, CXCL9, OPG, and SERPINA9, or G) CD6, IL12B, APLP1, and CCL20. [This invention 1292] The system of any of claims 1289 to 1291, wherein the multiple sclerosis activity is disease progression. [This invention 1293] Any of the systems of claims 1227 to 1292, wherein generating the prediction of multiple sclerosis disease activity comprises comparing the score output by the predictive model with a reference score. [This invention 1294] The reference score is A) The patient at baseline when the patient showed no disease activity; B) Patients with clinically diagnosed disease without active disease, or C) Healthy patients The system of the present invention 1293 corresponds to any one of the above. [This invention 1295] The system of any one of claims 1227 to 1294, wherein the test sample is a blood or serum sample. [This invention 1296] The system of any of claims 1227 to 1294, wherein the subject has multiple sclerosis, is suspected of having multiple sclerosis, or has previously been diagnosed with multiple sclerosis. [This invention 1297] The system of any one of claims 1227 to 1296, wherein the device is configured to perform an immunoassay to determine the expression level of the plurality of biomarkers. [This invention 1298] The system of the present invention, wherein the immunoassay is a proximity extension assay (PEA) or a LUMINEXxMAP multiplex assay. [This invention 1299] The system of any one of claims 1297 to 1298, wherein performing the immunoassay comprises contacting the test sample with a plurality of reagents including antibodies. [The present invention 1300] The system of the present invention, wherein the antibody comprises one of a monoclonal antibody and a polyclonal antibody. [The present invention 1301] The system of the present invention, wherein the antibodies include both monoclonal and polyclonal antibodies. [The present invention 1302] Any of the systems of claims 1235 to 1301, wherein the computer system is further configured to select a therapy for administration to the subject based on the prediction of multiple sclerosis disease activity. [The present invention 1303] Any of the systems of claims 1235 to 1301, wherein the computer system is further configured to determine the therapeutic effectiveness of a therapy previously administered to the subject based on the prediction of multiple sclerosis disease activity. [The present invention 1304] The system of the present invention 1303, wherein determining the therapeutic effectiveness of said treatment method comprises comparing said prediction with a previous prediction determined for said subject at a previous time point. [The present invention 1305] The system of claim 1304, wherein determining the therapeutic efficacy of said treatment comprises determining that said treatment exhibits efficacy according to a difference between said prediction and said previous prediction. [The present invention 1306] The system of claim 1304, wherein determining the therapeutic efficacy of said treatment comprises determining that said treatment is ineffective according to no difference between said prediction and said previous prediction. [The present invention 1307] Any of the systems of claims 1235 to 1301, wherein the computer system is further configured to determine a differential diagnosis of multiple sclerosis based on the prediction of multiple sclerosis disease activity. [The present invention 1308] The system of the present invention 1307, wherein the differential diagnosis of multiple sclerosis includes any one of relapsing-remitting multiple sclerosis (RRMS), secondary progressive multiple sclerosis (SPMS), primary progressive multiple sclerosis (PPMS), progressive relapsing multiple sclerosis (PRMS), and CIS. These and other features, aspects, and advantages of the present invention will become better understood with regard to the following description and accompanying drawings. [Brief description of the drawings]

[0064] [Figure 1A] 1 shows an overview of an environment for assessing a subject's disease activity via an activity prediction system, according to one embodiment. [Figure 1B] 1 is an exemplary block diagram of an activity prediction system, according to one embodiment. [Figure 1C] 1 illustrates an exemplary set of training data, according to one embodiment. [Figure 2A] 1 shows sequential forward selection of features using a sample from the F6 study. [Figure 2B] 1 shows sequential forward selection of features using the F4 study sample. [Figure 3A] 4 shows the ROC curves of the multivariate model (training and testing) compared to the univariate model using neurofilament light as the single feature. [Figure 3B] The confusion matrix for the multivariate model is shown. [Figure 4A] FIG. 1 shows the sequential forward selection of biomarkers for cross-sectional classification of the presence / absence of radiologically defined disease activity. [Figure 4B] 4 shows the ROC curves of the trained models for predicting disease activity (slight, normal, and extreme disease activity). [Figure 4C] Confusion matrices for the "slight" disease model, the "normal" disease model, and the "extreme" disease model are shown. [Figure 4D] 1 shows the progressive selection of biomarkers to predict disease severity according to predicted lesion counts. [Figure 5A] 1 illustrates sequential forward selection of features to build a model for predicting annual relapse rate (ARR). [Figure 5B] 4 shows the ROC curve of the trained model for predicting annual recurrence rate as high (≧0.8) or low (<0.3). [Figure 6A] 1 shows the sequential forward selection of features to build a model for classifying worsening versus quiescent disease state of two separate patient cohorts. [Figure 6B] 4 shows the ROC curve of a trained model for predicting clinically defined disease as worsening versus quiescent. [Figure 7] We present sequential forward selection of features on absolute quantification data using the Expanded Disability Status Scale (EDSS). [Figure 8] 1 illustrates an exemplary computer for implementing the entities illustrated in FIGS. 1A, 1B, and 1C. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0065] Detailed Description I. Definition Terms used in the claims and specification are defined as set forth below, unless otherwise specified.

[0066] The term "subject" includes human or non-human cells, tissues, or organisms, whether male or female, in vivo, ex vivo, or in vitro.

[0067] The term "mammal" encompasses both humans and non-humans, and includes, but is not limited to, humans, non-human primates, canines, felines, murines, bovines, equines, and porcines.

[0068] The term "sample" may include an aliquot of a bodily fluid, such as a single cell or multiple cells or cell fragments or a blood sample, taken from a subject by means including venipuncture, excretion, ejaculation, massage, biopsy, needle aspiration, lavage sample, scraping, surgical incision, or intervention, or other means known in the art. Examples of aliquots of bodily fluids include amniotic fluid, aqueous humor, bile, lymphatic fluid, milk, interstitial fluid, blood, plasma, cerumen, Cowper's fluid, chyle, chyme, female semen, menses, mucus, saliva, urine, vomit, tears, vaginal fluid, sweat, serum, semen, serum, sebum, pus, pleural fluid, cerebrospinal fluid, synovial fluid, intracellular fluid, and vitreous fluid.

[0069] The term "disease activity" encompasses disease activity of any neurodegenerative disease, including multiple sclerosis, Parkinson's disease, Lewy body disease, Alzheimer's disease, amyotrophic lateral sclerosis (ALS), motor neuron disease, Huntington's disease, spinal muscular atrophy, Friedreich's ataxia, and Batten disease.

[0070] The term "multiple sclerosis" or "MS" encompasses all forms of multiple sclerosis, including relapsing-remitting multiple sclerosis (RRMS), secondary progressive multiple sclerosis (SPMS), primary progressive multiple sclerosis (PPMS), progressive relapsing multiple sclerosis (PRMS), and CIS.

[0071] As used herein, the term "multiple sclerosis disease activity" or "disease activity of multiple sclerosis" refers to a diagnosis of multiple sclerosis (MS), the presence or absence of MS (e.g., usual disease, minimal disease), a shift in disease activity (e.g., increase or decrease), disease progression, MS severity, relapses or exacerbation events associated with MS, future or impending relapses or exacerbation events, relapse rate (e.g., annual relapse rate), MS status (e.g., worsening or quiescent), confirmation of no evidence of disease, a diagnosis of multiple sclerosis in response to a treatment, It refers to any measurable that gives information about a subject's response measured, the degree of disability from multiple sclerosis, the risk (e.g., likelihood) that a subject will subsequently develop multiple sclerosis, the change in multiple sclerosis compared to previous measurements (e.g., a patient's longitudinal change relative to a baseline measurement), disease activity, or the differential diagnosis of types of multiple sclerosis, including relapsing-remitting multiple sclerosis (RRMS), secondary progressive multiple sclerosis (SPMS), primary progressive multiple sclerosis (PPMS), progressive relapsing multiple sclerosis (PRMS), and CIS.

[0072] In one embodiment, the term "multiple sclerosis disease activity" or "disease activity of multiple sclerosis" refers to a diagnosis of multiple sclerosis (MS). In one embodiment, the term "multiple sclerosis disease activity" or "disease activity of multiple sclerosis" refers to the presence or absence of MS (e.g., regular disease, slight disease). In one embodiment, the term "multiple sclerosis disease activity" or "disease activity of multiple sclerosis" refers to a shift in disease activity (e.g., an increase or decrease). In one embodiment, the term "multiple sclerosis disease activity" or "disease activity of multiple sclerosis" refers to the severity of MS. In one embodiment, the term "multiple sclerosis disease activity" or "disease activity of multiple sclerosis" refers to relapse or relapse events associated with MS. In one embodiment, the term "multiple sclerosis disease activity" or "disease activity of multiple sclerosis" refers to future or imminent relapse or relapse events. In one embodiment, the term "multiple sclerosis disease activity" or "disease activity of multiple sclerosis" refers to the relapse rate (e.g., annual relapse rate). In one embodiment, the term "multiple sclerosis disease activity" or "disease activity of multiple sclerosis" refers to the MS state (e.g., worsening or quiescence). In one embodiment, the term "multiple sclerosis disease activity" or "disease activity of multiple sclerosis" refers to confirmation of the absence of evidence of the disease state. In one embodiment, the term "multiple sclerosis disease activity" or "disease activity of multiple sclerosis" refers to the response of a subject diagnosed with multiple sclerosis to a therapy. In one embodiment, the term "multiple sclerosis disease activity" or "disease activity of multiple sclerosis" refers to the degree of disability from multiple sclerosis. In one embodiment, the term "multiple sclerosis disease activity" or "disease activity of multiple sclerosis" refers to the risk (e.g., likelihood) of a subject to subsequently develop multiple sclerosis. In one embodiment, the term "multiple sclerosis disease activity" or "disease activity of multiple sclerosis" refers to changes in multiple sclerosis compared to previous measurements (e.g., longitudinal changes in a patient relative to a baseline measurement). In one embodiment, the term "multiple sclerosis disease activity" or "disease activity of multiple sclerosis" refers to measurables that inform disease activity.

[0073] In various embodiments, measurables informative of MS disease activity include measures of marginal disease activity (e.g., the presence or absence of a certain number of gadolinium-enhancing lesions, e.g., just one lesion), normal disease activity (e.g., the presence or absence of one or more gadolinium-enhancing lesions), a shift in disease activity (e.g., the appearance or disappearance of active gadolinium-enhancing lesions), and disease activity severity (e.g., the number of gadolinium-enhancing lesions, where more gadolinium-enhancing lesions indicate increased disease severity). In one embodiment, measures informative of MS disease activity include measures of marginal disease activity (e.g., the presence or absence of a certain number of gadolinium-enhancing lesions, e.g., just one lesion). In one embodiment, measures informative of MS disease activity include measures of normal disease activity (e.g., the presence or absence of one or more gadolinium-enhancing lesions). In one embodiment, measures informative of MS disease activity include measures of shift in disease activity (e.g., the appearance or disappearance of active gadolinium-enhancing lesions). In one embodiment, the metrics informative of MS disease activity include metrics of disease activity severity (e.g., number of gadolinium-enhancing lesions, with more gadolinium-enhancing lesions indicating greater disease severity).

[0074] In one embodiment, the term "multiple sclerosis disease activity" or "disease activity of multiple sclerosis" refers to the progression of MS (e.g., MS disease progression). In one embodiment, the measures that inform MS disease activity include measures of disease progression (e.g., disease progression using the Expanded Disability Status Scale (EDSS)). In some embodiments, the term "multiple sclerosis disease activity" or "disease activity of multiple sclerosis" does not include the progression of MS (e.g., MS disease progression). Specifically, in such embodiments disclosed herein, the biomarker panel used to predict "multiple sclerosis disease activity" is different from the biomarker panel used to predict "multiple sclerosis disease progression".

[0075] The terms "marker(s)" and "biomarker(s)" include, but are not limited to, lipids, lipoproteins, proteins, cytokines, chemokines, growth factors, peptides, nucleic acids, genes, and oligonucleotides, as well as associated complexes, metabolites, mutations, variants, polymorphisms, variants, fragments, subunits, degradation products, elements, and other analyte- or sample-derived metrics. Markers can also include mutant proteins, mutant nucleic acids, copy number variations, and / or transcript variants in situations where such mutations, copy number variations, and / or transcript variants are useful in generating predictive models or are useful in predictive models developed using the associated markers (e.g., non-mutated versions of proteins or nucleic acids, alternative transcripts, etc.).

[0076] The term "antibody" is used in the broadest sense and specifically covers monoclonal antibodies (including full-length monoclonal antibodies), polyclonal antibodies, multispecific antibodies (e.g., bispecific antibodies), and antigen-binding antibody fragments, e.g., antibodies or antigen-binding fragments thereof, so long as they exhibit the desired biological activity.

[0077] As used herein, an "antibody fragment," and all grammatical variants thereof, is defined as a portion of an intact antibody that contains the antigen-binding site or the variable region of the intact antibody, and does not include the constant heavy chain domains of the Fc region of the intact antibody (i.e., CH2, CH3, and CH4, depending on the antibody isotype). Examples of antibody fragments include Fab, Fab', Fab'-SH, F(ab')2, and Fv fragments; diabodies; any antibody fragment that is a polypeptide having a primary structure consisting of one continuous sequence of consecutive amino acid residues (referred to herein as a "single-chain antibody fragment" or "single-chain polypeptide").

[0078] The term "biomarker panel" refers to a set of biomarkers that are informative for predicting multiple sclerosis disease activity. For example, the expression level of a set of biomarkers in a biomarker panel can be informative for predicting multiple sclerosis disease activity, for example, predicting MS relapse. In various embodiments, a biomarker panel can include 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, or 25 biomarkers.

[0079] The term "obtaining a dataset associated with a sample" encompasses obtaining a set of data determined from at least one sample. Obtaining a dataset encompasses obtaining a sample and processing the sample to experimentally determine the data. The phrase also encompasses receiving a set of data from a third party, for example, who is processing the sample to experimentally determine the dataset. Additionally, the phrase encompasses mining data from at least one database, or at least one publication, or a combination of a database and publication. A dataset can be obtained by one of skill in the art through a variety of known means, including being stored in a storage memory.

[0080] It must be noted that, as used herein, the singular forms "a," "an," and "the" include plural referents unless the context clearly indicates otherwise.

[0081] II. System Environment Overview 1A shows an overview of a system environment 100 for assessing disease activity in a subject, according to one embodiment. The system environment 100 provides a context for implementing a marker quantification assay 120 and an activity prediction system 130.

[0082] In various embodiments, a test sample is obtained from the subject 110. The sample may be obtained by an individual or a third party, such as a medical professional. Examples of medical professionals include doctors, emergency medical technicians, nurses, emergency responders, psychologists, phlebotomists, medical physicists, nurse practitioners, surgeons, dentists, and any other obvious medical professionals known to those of skill in the art.

[0083] The test sample is tested to determine the value of one or more markers by performing a marker quantification assay 120. The marker quantification assay 120 determines quantitative expression values ​​of one or more biomarkers in the test sample. The marker quantification assay 120 may be an immunoassay, more specifically a multiplex immunoassay, examples of which are described in more detail below. Expression levels of various biomarkers may be obtained in a single run using a single test sample obtained from the subject 110. The quantified expression values ​​of the biomarkers are provided to an activity prediction system 130.

[0084] Typically, the activity prediction system 130 includes one or more computers implemented as a computer system 700, as described below with respect to Figure 8. Thus, in various embodiments, the steps described with respect to the activity prediction system 130 are performed in silico. The activity prediction system 130 analyzes biomarker expression values ​​received from the marker quantification assays 120 to generate an assessment 140 of disease activity in the subject 110.

[0085] In various embodiments, the marker quantification assay 120 and activity prediction system 130 can be used by a variety of parties. For example, a first party performs the marker quantification assay 120 and then provides the results to a second party that implements the activity prediction system 130. For example, the first party may be a clinical laboratory that obtains a test sample from a subject 110 and performs the assay 120 on the test sample. The second party receives biomarker expression values ​​resulting from the performed assay 120 and analyzes the expression values ​​using the activity prediction system 130.

[0086] Reference is now made to Figure 1B, which illustrates a block diagram illustrating computer logic components of activity prediction system 130, according to one embodiment. Specifically, activity prediction system 130 may include model training module 150, model deployment module 160, and training data store 170.

[0087] Hereinafter, each of the components of the activity prediction system 130 will be described in terms of two phases: 1) a training phase, and 2) a deployment phase. More specifically, the training phase refers to the construction and training of one or more predictive models based on training data including quantitative expression values ​​of biomarkers obtained from individuals known to be in a state of health, quiescence, or remission, or individuals known to be in a state of disease activity, worsening, or relapse. Thus, the predictive models are trained to predict a subject's disease activity based on the quantitative biomarker expression values. In the deployment phase, the predictive models are applied to quantitative biomarker expression values ​​of test samples obtained from the subjects of interest to generate a prediction of the disease activity of the subjects of interest.

[0088] In some embodiments, components of the activity prediction system 130 are applied in one of a training phase and a deployment phase. For example, the model training module 150 and the training data store 170 (shown in dotted lines in FIG. 1B ) are applied in the training phase, while the model deployment module 160 is applied in the deployment phase. In various embodiments, the training phase and the deployment phase can be performed to allow for continuously trained models. For example, the model training module 150 can train a model that the model deployment module 160 can then deploy. The same model can be subjected to additional training by the model training module 150 (e.g., trained continuously, e.g., using new training data that is acquired). Thus, the model can show improved predictive capabilities when analyzing samples during deployment, as it is continuously trained.

[0089] In various embodiments, components of activity prediction system 130 may be implemented by different parties depending on whether the components are applied during a training phase or a deployment phase. In such a scenario, training and deployment of the predictive model may be implemented by different parties. For example, model training module 150 and training data store 170 applied during the training phase may be used by a first party (e.g., to train the predictive model), and model deployment module 160 applied during the deployment phase may be implemented by a second party (e.g., to deploy the predictive model).

[0090] III. Predictive Model IIIA. Training the predictive model In the training phase, the model training module 150 trains one or more predictive models using training data including expression values ​​of biomarkers. With reference to FIG. 1B, the training data may be stored in a training data store 170. In various embodiments, the activity prediction system 130 generates the training data including expression values ​​of biomarkers by analyzing biomarker expression values ​​in a test sample. In various embodiments, the activity prediction system 130 obtains the training data including expression values ​​of biomarkers from a third party. The third party may have analyzed the test sample to determine the expression values ​​of the biomarkers.

[0091] In various embodiments, the training data comprising the expression values ​​of biomarkers are obtained from clinical subjects. For example, the training data can be the expression values ​​of biomarkers measured from test samples obtained from clinical subjects. Examples of the expression values ​​of biomarkers derived from clinical subjects include the biomarker expression values ​​obtained through clinical studies, such as the Multiple Sclerosis CLIMB study (e.g., Brigham and Women's Hospital Comprehensive Longitudinal Investigation of Multiple Sclerosis), the Accelerated Treatment Project (ACP) for Multiple Sclerosis, and the Expression, Proteomics, Imaging, and Clinical (EPIC) study at UCSF and University Hospital Basel Cohort (UHBC).

[0092] In various embodiments, the training data further includes reference ground truths indicative of disease activity, such as multiple sclerosis disease activity. As an example, the training data includes reference ground truths that identify the presence or absence of multiple sclerosis (MS), relapse or relapse events associated with MS, relapse rate (e.g., annual relapse rate), MS status (e.g., worsening or quiescent), the response of a subject diagnosed with multiple sclerosis to a therapy, the degree of disability from multiple sclerosis, the risk (e.g., likelihood) of a subject subsequently developing multiple sclerosis, or a criterion for marginal disease activity (e.g., the presence or absence of a certain number of gadolinium-enhancing lesions, e.g., 1, 2, 3, or 4 lesions), or a criterion for normal disease activity (e.g., the presence or absence of one or more gadolinium-enhancing lesions).

[0093] Reference is made to FIG. 1C, which illustrates an exemplary set of training data 190, according to one embodiment. As illustrated in FIG. 1, the training data 190 includes data corresponding to a number of individuals (e.g., column 1 showing individuals 1, 2, 3, 4...). For each individual, the training data 190 includes quantitative expression values ​​(e.g., A1, B1, A2, B2, etc.) of various biomarkers obtained from the corresponding individual. In some embodiments, the quantitative expression values ​​are determined by the marker quantification assay 120 illustrated in FIG. 1. While FIG. 1C illustrates four individuals and two different markers (marker A and marker B), the training data 190 may include tens, hundreds, or thousands of individuals and tens, hundreds, or thousands of markers.

[0094] As shown in Figure 1C, the first training example of the training data (e.g., row 1) refers to individual 1 and the corresponding quantitative expression value of marker A (e.g., A1) and the quantitative expression value of marker B (e.g., B1). Similarly, the second training example of the training data (e.g., row 2) refers to individual 2 and the corresponding quantitative expression value of marker A (e.g., A2) and the quantitative expression value of marker B (e.g., B2). Individuals 3 and 4 have corresponding marker values ​​as shown in Figure 1C.

[0095] As shown in FIG. 1C, the training data 190 further includes a reference ground truth (the “Indication” column) that identifies whether the corresponding individual has a positive or negative indication for disease activity. As an example, each indication may be an indication of disease activity for a patient. For example, referring to the first training example (e.g., row 1), a “positive” indication may reflect the presence of disease activity for individual 1. For example, an MRI scan of individual 1 may reveal the presence of one or more gadolinium-enhancing lesions. Similarly, an indication of a negative result (e.g., individual 3 or individual 4) reflects a negative indication of disease activity for the corresponding individual. For example, an MRI scan of individuals 3 and 4 may reveal the absence of gadolinium-enhancing lesions.

[0096] In some embodiments, model training module 150 retrieves training data from training data store 170 and randomly splits the training data into a training set and a test set. By way of example, 80% of the training data may be split into the training set and the remaining 20% ​​may be split into the test set. Other ratios of training sets and test sets may be implemented. Thus, the training set is used to train the predictive model, while the test set is used to validate the predictive model.

[0097] In various embodiments, the predictive model is any one of a regression model (e.g., linear regression, logistic regression, or polynomial regression), a decision tree, a random forest, a support vector machine, a naive Bayes model, a k-means model, or a neural network (e.g., a feed-forward network, a convolutional neural network (CNN), a deep neural network (DNN), an autoencoder neural network, a generative adversarial network, or a recurrent network (e.g., a long short-term memory network (LSTM), a bidirectional recurrent network, a deep bidirectional recurrent network)), or any combination thereof. For example, the predictive model can be a stacked classifier that includes both linear regression and a decision tree.

[0098] The predictive model can be trained using any one of machine learning implementation methods, such as linear regression algorithms, logistic regression algorithms, decision tree algorithms, support vector machine classification, naive Bayes classification, k-nearest neighbor classification, random forest algorithms, deep learning algorithms, gradient boosting algorithms, and dimensionality reduction techniques, such as manifold learning, principal component analysis, factor analysis, autoencoder regularization, and independent component analysis, or combinations thereof. In various embodiments, the cellular disease model is trained using a supervised learning algorithm, an unsupervised learning algorithm, a semi-supervised learning algorithm (e.g., partially supervised), weakly supervised, transfer, multi-task learning, or any combination thereof.

[0099] In various embodiments, the predictive model has one or more parameters, such as hyperparameters or model parameters. Hyperparameters are typically established prior to training. Examples of hyperparameters include learning rate, depth or leaves of decision trees, number of hidden layers in deep neural networks, number of clusters in k-means, penalty in regression models, and regularization parameters associated with cost functions. Model parameters are typically adjusted during training. Examples of model parameters include weights associated with nodes in layers of neural networks, support vectors in support vector machines, and coefficients in regression models. Model parameters of the cellular disease model are trained (e.g., adjusted) using training data to improve the predictive ability of the cellular disease model.

[0100] The model training module 150 trains one or more predictive models, each of which receives one or more biomarkers as input. In various embodiments, the model training module 150 builds a predictive model that receives expression values ​​of two biomarkers as input. In various embodiments, the model training module 150 builds a predictive model that receives expression values ​​of three biomarkers as input. In various embodiments, the model training module 150 builds a predictive model that receives expression values ​​of four biomarkers as input. In some embodiments, the model training module 150 builds a predictive model for more than four biomarkers. For example, a predictive model receives expression values ​​of eight biomarkers as input (e.g., any of the eight biomarkers classified as Tier 1 in Table 2 or the corresponding surrogate biomarkers in Table 10). As another example, a predictive model receives expression values ​​of 17 biomarkers as input (e.g., any of the 17 biomarkers classified as Tier 1 or Tier 2 in Table 2 or the corresponding surrogate biomarkers in Table 10). As another example, the predictive model receives as input the expression values ​​of 21 biomarkers (e.g., any of the 21 biomarkers classified as Tier 1, Tier 2, or Tier 3 in Table 2, or the corresponding surrogate biomarkers in Table 10).

[0101] In various embodiments, the model training module 150 identifies a set of biomarkers to be used to train the predictive model. The model training module 150 may start with a list of candidate biomarkers that are promising for predicting disease activity. In one embodiment, the candidate biomarkers may be biomarkers identified through a literature curation process. In some embodiments, the candidate biomarkers may be biomarkers whose expression values ​​in test samples obtained from individuals positive for disease activity (e.g., presence of MS, worsening state, etc.) are statistically significant compared to the expression values ​​of the biomarkers in test samples obtained from individuals negative for disease activity.

[0102] In one embodiment, the model training module 150 performs a feature selection process to identify a set of biomarkers to be included in the biomarker panel. For example, the model training module 150 performs a sequential forward feature selection based on the expression values ​​of the biomarkers and their importance in predicting a particular endpoint (e.g., disease activity). For example, candidate biomarkers determined to be highly correlated with a particular disease activity endpoint may be considered highly important and therefore may be included in the biomarker panel compared to other biomarkers that are not highly correlated with the disease activity endpoint.

[0103] In some embodiments, the importance of each biomarker to the disease activity endpoint is determined by using a method including one of the following algorithms: random forest (RF), gradient boosting (GBM), extreme gradient boosting (XGB), or LASSO. For example, when using the random forest algorithm, the model training module 150 may generate a variable importance plot showing the importance of each candidate biomarker. Specifically, the random forest algorithm may provide for each candidate biomarker: 1) the average decrease in model accuracy, and 2) the average decrease in Gini coefficient, which is a measure of how much each candidate biomarker contributes to the uniformity of the nodes and leaves of the random forest. In one scenario, the importance of each candidate biomarker depends on one or both of the average decrease in model accuracy and the average decrease in Gini coefficient. Each of the GBM, XGB, and LASSO can also be used to rank the importance of each candidate biomarker based on the impact value. Thus, the model training module 150 may generate a ranking of each of the candidate biomarkers using one of the methods including RF, GBM, XGB, or LASSO.

[0104] Each predictive model is trained iteratively using the quantitative expression values ​​of the markers for each individual as input. For example, referring again to FIG. 1C, one iteration includes providing training examples (e.g., rows of training data) that include quantitative expression values ​​of the biomarkers (e.g., "A1" and "B1") for a particular individual (e.g., individual 1). Each predictive model is trained with reference ground truth data that includes indicators (e.g., positive or negative outcomes). In various embodiments, over the training iterations, each predictive model is trained (e.g., parameters are adjusted) to minimize the prediction error between the prediction of MS activity output by the predictive model and the ground truth data. In various embodiments, the prediction error is calculated based on a loss function, examples of which include an L1 regularized (lasso regression) loss function, an L2 regularized (ridge regression) loss function, or a combination of L1 and L2 regularization (ElasticNet).

[0105] III.B. Deploying Predictive Models During the deployment phase, the model deployment module 160 (as shown in FIG. 1B) analyzes quantitative biomarker expression values ​​from test samples obtained from subjects of interest by applying the trained predictive model. In some embodiments, the subject has not been previously diagnosed with the disease, and thus deployment of the predictive model enables in silico diagnosis of disease based on quantitative biomarker expression values ​​from the subject. In some embodiments, the subject has previously been diagnosed with the disease. As used herein, deployment of the predictive model enables in silico prediction of disease activity based on quantitative biomarker expression values ​​from the subject.

[0106] In various embodiments, the quantitative biomarker expression values ​​are provided as input to a predictive model that analyzes the quantitative biomarker expression values ​​and outputs an assessment of disease activity.

[0107] In various embodiments, the assessment of disease activity is a predictive score that can inform the subject's disease activity. In various embodiments, the predictive score output by the predictive model is compared to one or more reference scores to determine a measure of disease activity. The reference score refers to a previously determined score, and is further described below as a "healthy score" or a "disease score" corresponding to a diseased or non-diseased patient. For example, the one or more scores may be a "healthy score" corresponding to a healthy patient, the patient's own baseline at a previous time point where the patient did not show disease activity (e.g., longitudinal analysis), a patient who has been clinically diagnosed with disease but does not show disease activity or a threshold score (e.g., cutoff). As another example, the one or more scores may be a "disease score" corresponding to a diseased patient, the patient's own score showing disease activity at a previous time point, or a threshold score (e.g., cutoff). As an example, the threshold score may correspond to a healthy patient and may be generated by training a predictive model using the expression values ​​of biomarkers of the healthy patient. As another example, the threshold score can correspond to a diseased patient and can be generated by training a predictive model using the expression values ​​of the biomarkers of the diseased patient.

[0108] In various embodiments, the assessment of disease activity corresponds to the presence or absence of disease. In one embodiment, the predicted score output by the predictive model can be compared to a healthy score. If the predicted score of the subject is significantly different (e.g., p-value<0.05) compared to the healthy score, the subject can be classified as having disease. In one embodiment, the diseased score output by the predictive model can be compared to a healthy score. If the predicted score of the subject is significantly different (e.g., p-value<0.05) compared to the diseased score, the subject can be classified as not having disease. In some embodiments, the predicted score output by the predictive model is compared to both the healthy score and the diseased score. For example, if the predicted score of the subject is significantly different (e.g., p-value<0.05) compared to the healthy score and is not significantly different (e.g., p-value>0.05) compared to the diseased score of a patient who has been diagnosed with the disease, the subject can be classified as having disease. In various embodiments, depending on the classification of the subject, the subject can receive a treatment. In other words, the assessment can guide the treatment of the subject. For example, if a subject is classified as having a disease, the subject can be administered a therapeutic intervention to treat the disease.

[0109] In various embodiments, the assessment of disease activity corresponds to the presence or absence of minimal disease. In one embodiment, the predicted score output by the prediction model can be compared to a score corresponding to an individual previously determined to have minimal disease (e.g., a certain number of gadolinium-enhancing lesions on an MRI scan, e.g., just one lesion). If the subject's predicted score is not significantly different (e.g., p-value<0.05) compared to a score corresponding to an individual previously determined to have minimal disease, the subject can be classified as having minimal disease. If the subject's predicted score is significantly different (e.g., p-value<0.05) compared to a score corresponding to an individual previously determined to have no minimal disease, the subject can be classified as having minimal disease. In one embodiment, the predicted score output by the prediction score is compared to a score corresponding to an individual without minimal disease (e.g., zero gadolinium-enhancing lesions on an MRI scan). If the subject's predicted score is not significantly different (e.g., p-value>0.05) compared to a score corresponding to an individual without minimal disease (e.g., zero gadolinium-enhancing lesions on an MRI scan), the subject can be classified as not having minimal disease. Alternatively, a subject can be classified as having minimal disease if the subject's predicted score is significantly different (e.g., p-value <0.05) from a score corresponding to an individual without minimal disease (e.g., zero gadolinium-enhancing lesions on an MRI scan).

[0110] In some embodiments, the predicted score output by the predictive model is compared to both scores corresponding to individuals previously determined to have minimal disease (e.g., a certain number of gadolinium-enhancing lesions on an MRI scan) and scores corresponding to individuals without minimal disease (e.g., zero gadolinium-enhancing lesions on an MRI scan). For example, if the subject's predicted score is significantly different (e.g., p-value<0.05) compared to a score with no minimal disease (e.g., zero gadolinium-enhancing lesions on an MRI scan) and is not significantly different (e.g., p-value>0.05) compared to a score corresponding to an individual previously determined to have minimal disease (e.g., a certain number of gadolinium-enhancing lesions on an MRI scan, e.g., just one gadolinium-enhancing lesion on an MRI scan), the subject can be classified as having minimal disease.

[0111] In various embodiments, the assessment of disease activity corresponds to the presence or absence of a normal disease. In one embodiment, the prediction score output by the prediction model can be compared to a score corresponding to an individual previously determined to have the presence of a normal disease (e.g., one or more gadolinium-enhancing lesions on an MRI scan). If the subject's prediction score is significantly different (e.g., p-value<0.05) compared to a score corresponding to an individual previously determined to have no normal disease, the subject can be classified as having a normal disease. If the subject's prediction score is not significantly different (e.g., p-value>0.05) compared to a score corresponding to an individual previously determined to have no normal disease, the subject can be classified as not having a normal disease. In one embodiment, the prediction score output by the prediction score is compared to a score corresponding to an individual without a normal disease (e.g., zero gadolinium-enhancing lesions on an MRI scan). If the subject's prediction score is not significantly different (e.g., p-value>0.05) compared to a score corresponding to an individual without a normal disease (e.g., zero gadolinium-enhancing lesions on an MRI scan), the subject can be classified as not having a normal disease. If the subject's predicted score is significantly different (e.g., p-value<0.05) from the score corresponding to an individual without normal disease (e.g., zero gadolinium-enhancing lesions on MRI scan), the subject can be classified as having normal disease. In some embodiments, the predicted score output by the prediction model is compared to both the score corresponding to an individual previously determined to have normal disease (e.g., one or more gadolinium-enhancing lesions on MRI scan) and the score corresponding to an individual without normal disease (e.g., zero gadolinium-enhancing lesions on MRI scan). For example, if the subject's predicted score is significantly different (e.g., p-value<0.05) from the score corresponding to an individual previously determined to have normal disease (e.g., one or more gadolinium-enhancing lesions on MRI scan), and is not significantly different (e.g., p-value>0.05) from the score corresponding to an individual previously determined to have normal disease (e.g., one or more gadolinium-enhancing lesions on MRI scan), the subject can be classified as having normal disease.

[0112] In various embodiments, the assessment of disease activity corresponds to a directional shift in disease activity based on a predicted increase or decrease in the number of gadolinium-enhancing lesions. In one embodiment, the prediction score output by the prediction model can be compared to a score corresponding to an individual previously determined to be experiencing increased disease activity (e.g., an increase in the number of gadolinium-enhancing lesions on an MRI scan). If the subject's prediction score is significantly different (e.g., p-value<0.05) compared to a score corresponding to an individual previously determined to be not experiencing increased disease activity, the subject can be classified as likely to experience increased disease activity. If the subject's prediction score is not significantly different (e.g., p-value>0.05) compared to a score corresponding to an individual previously determined to be not experiencing increased disease activity, the subject can be classified as unlikely to experience increased disease activity. In one embodiment, the prediction score output by the prediction score is compared to a score corresponding to an individual previously determined to be experiencing decreased disease activity (e.g., a gradual decrease in the number of gadolinium-enhancing lesions on an MRI scan). If the subject's predicted score is not significantly different (e.g., p-value>0.05) from scores corresponding to individuals experiencing reduced disease activity, the subject can be classified as likely to experience reduced disease activity. If the subject's predicted score is significantly different (e.g., p-value>0.05) from scores corresponding to individuals experiencing reduced disease activity, the subject can be classified as likely to experience reduced disease activity. In some embodiments, the predicted scores output by the predictive model are compared to both scores corresponding to individuals previously determined to be experiencing increased disease activity (e.g., a gradual increase in the number of gadolinium-enhancing lesions on MRI scans) and scores corresponding to individuals experiencing reduced disease activity (e.g., a gradual decrease in the number of gadolinium-enhancing lesions on MRI scans).For example, a subject can be classified as likely to experience increased disease activity if the subject's predicted score is significantly different (e.g., p-value < 0.05) compared to scores corresponding to individuals experiencing decreased disease activity (e.g., a gradual decrease in the number of gadolinium-enhancing lesions on MRI scans) and is not significantly different (e.g., p-value > 0.05) compared to scores corresponding to individuals experiencing increased disease activity (e.g., an increase in the number of gadolinium-enhancing lesions on MRI scans). In various embodiments, a subject can be classified as unlikely to experience either increased or decreased disease activity (e.g., the subject's disease activity is stable) if the subject's predicted score is not significantly different (e.g., p-value > 0.05) compared to scores corresponding to both individuals experiencing increased disease activity and individuals experiencing decreased disease activity.

[0113] In various embodiments, the assessment of disease activity corresponds to the state of the disease in the subject. For example, if the disease is MS, the state of the disease in the subject is quiescent versus worsening. In one embodiment, the predicted score output by the prediction model can be compared to scores corresponding to individuals previously determined to be in quiescent state (e.g., previously determined to be clinically quiescent). If the predicted score of the subject is significantly different (e.g., p-value<0.05) compared to scores corresponding to individuals previously determined to be not in quiescent state, the subject can be classified as being in quiescent state. If the predicted score of the subject is not significantly different (e.g., p-value>0.05) compared to scores corresponding to individuals previously determined to be in quiescent state, the subject can be classified as not being in quiescent state. In one embodiment, the predicted score output by the prediction score is compared to scores corresponding to individuals previously determined to be in a worsening state. If the predicted score of the subject is not significantly different (e.g., p-value>0.05) compared to scores corresponding to individuals previously determined to be in a worsening state, the subject can be classified as being in a worsening state. If the subject's predicted score is significantly different (e.g., p-value<0.05) from the score corresponding to an individual previously determined to be in a deteriorated state, the subject can be classified as not in a deteriorated state. In some embodiments, the predicted score output by the predictive model is compared to both the score corresponding to an individual previously determined to be in a quiescent state and the score corresponding to an individual in a deteriorated state. For example, if the subject's predicted score is significantly different (e.g., p-value<0.05) from the score corresponding to an individual previously determined to be in a deteriorated state, and is not significantly different (e.g., p-value>0.05) from the score corresponding to an individual previously determined to be in a deteriorated state, the subject can be classified as in a deteriorated state.

[0114] In various embodiments, the assessment of disease activity corresponds to a possible response to a therapy provided to the subject. In one embodiment, the predicted score output by the predictive model can be compared to a score corresponding to an individual previously determined to be responsive to the therapy (e.g., previously determined clinically to be responsive to the therapy). If the subject's predicted score is significantly different (e.g., p-value<0.05) compared to a score corresponding to an individual previously determined to be unresponsive to the therapy, the subject can be classified as a responder. If the subject's predicted score is not significantly different (e.g., p-value>0.05) compared to a score corresponding to an individual previously determined to be responsive to the therapy, the subject can be classified as a responder. In one embodiment, the predicted score output by the predictive model is compared to a score corresponding to an individual previously determined to be a non-responder. If the subject's predicted score is not significantly different (e.g., p-value>0.05) compared to a score corresponding to an individual previously determined to be a non-responder, the subject can be classified as a non-responder. If the subject's predicted score is significantly different (e.g., p-value<0.05) from scores corresponding to individuals previously determined to be responders, the subject can be classified as a non-responder. In some embodiments, the predicted scores output by the predictive model are compared to both scores corresponding to individuals previously determined to be responders and scores corresponding to individuals previously determined to be non-responders. For example, if the subject's predicted score is significantly different (e.g., p-value<0.05) from scores corresponding to individuals previously determined to be responders, and is significantly different (e.g., p-value>0.05) from scores corresponding to individuals previously determined to be responders, the subject can be classified as a responder.

[0115] In one embodiment, the assessment of disease activity is an assessment of disease progression and may correspond to the degree of MS disability in a subject diagnosed with multiple sclerosis. In one embodiment, the degree of MS disability corresponds to EDSS. In various embodiments, the assessment (e.g., predicted score) corresponding to the subject is compared to a plurality of scores. Each score may correspond to a group of individuals who have been clinically classified with a degree of disability. For example, a first score may correspond to individuals who have been clinically classified with an EDSS score of 1. Additional scores may correspond to groups of individuals who have been clinically classified with scores of 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0, 8.5, 9.0, 9.5, and 10.0. In one scenario, if the predicted score of the subject output by the predictive model is not significantly different from one group (e.g., p-value>0.05) and is significantly different from all other groups (e.g., p-value<0.05), the subject may be classified as having an EDSS score. The subject may be treated according to a clinical protocol based on the classification.

[0116] In one embodiment, the assessment of disease activity corresponds to the risk (e.g., likelihood) of the subject to subsequently develop the disease. In various embodiments, the assessment (e.g., predictive score) corresponding to the subject is compared to a plurality of scores. Each score may correspond to a group of individuals within a risk group that has been clinically classified at a particular risk of developing MS. As an example, the risk groups may be divided into a high-risk group, a medium-risk group, and a low-risk group. In one scenario, a subject may be classified into a risk group if the subject's predictive score is not significantly different from one group (e.g., p-value>0.05) and is significantly different from the other group (e.g., p-value<0.05). Thus, the subject may make lifestyle and / or treatment changes based on the prediction of the risk / likelihood of developing MS.

[0117] In various embodiments, the disease activity measures predicted by the predictive models provide additional utility for managing a patient's disease activity. By way of example, the disease activity measures predicted by the predictive models are useful for selecting candidate therapeutics or for determining the efficacy of previously administered therapeutics.

[0118] In various embodiments, the disease activity metric predicted by the predictive model for a patient can be compared to a previous metric of disease activity to determine whether the therapeutic agent administered to the patient is showing efficacy. As an example, the previous metric of disease activity can be a prediction (e.g., a baseline metric of disease activity) determined for the same patient. Thus, in this example, the comparison of the disease activity metric and the previous metric of disease activity is a longitudinal analysis of patients undergoing treatment with a therapy. As such, a difference or lack of difference between the disease activity metric and the previous metric of disease activity can indicate that the therapeutic agent is having or is not having an effect. As another example, the previous metric of disease activity can be a metric determined for a patient population (e.g., a reference set of patients). In this example, the comparison of the disease activity metric and the previous metric of disease activity can reveal whether the patient is experiencing a benefit from the therapeutic agent (as evidenced by the disease activity metric) compared to the previous metric of disease activity for the patient population.

[0119] In various embodiments, if a comparison between the disease activity assessment and the previous assessment of disease activity indicates that the currently administered therapeutic agent is ineffective or not as effective as desired, a change in the patient's treatment can be made. In one embodiment, the treatment administration of the currently administered therapeutic agent can be changed to effect a patient response. For example, the currently administered therapeutic agent can be increased in dosage. In one embodiment, a candidate therapeutic agent can be selected for administration to the patient. In various embodiments, the candidate therapeutic agent can be administered to the patient in place of the currently administered therapeutic agent, or the candidate therapeutic agent can be administered to the patient in addition to the currently administered therapeutic agent.

[0120] As another example, disease activity metrics are useful to support symptom and medication tracking, nursing interventions, laboratory monitoring, and controlled longitudinal MRI reporting. In such scenarios, disease activity metrics can reduce unplanned healthcare utilization (e.g., unplanned clinic visits), thereby improving patient and physician satisfaction.

[0121] IV. Biomarker Panels In various embodiments, the assessment of disease activity includes implementing a univariate biomarker panel. Thus, the univariate biomarker panel includes one biomarker. In other embodiments, the assessment of disease activity includes implementing a multivariate biomarker panel. In such embodiments, the multivariate biomarker panel includes a plurality of biomarkers. In various embodiments, the multivariate biomarker panel includes two biomarkers. In various embodiments, the multivariate biomarker panel includes 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, or 50 biomarkers. In certain embodiments, the multivariate biomarker panel comprises two biomarkers. In certain embodiments, the multivariate biomarker panel comprises three biomarkers. In certain embodiments, the multivariate biomarker panel comprises four biomarkers. In certain embodiments, the multivariate biomarker panel comprises seven biomarkers. In certain embodiments, the multivariate biomarker panel comprises eight biomarkers. In certain embodiments, the multivariate biomarker panel comprises seventeen biomarkers. In certain embodiments, the multivariate biomarker panel comprises twenty-one biomarkers.

[0122] In various embodiments described herein, a biomarker panel is implemented for the assessment or prediction of disease progression, such as MS disease progression. In various embodiments, the assessment of disease progression comprises implementing a univariate biomarker panel. Thus, the univariate biomarker panel comprises one biomarker. In other embodiments, the assessment of disease progression comprises implementing a multivariate biomarker panel. In such embodiments, the multivariate biomarker panel for assessing disease progression comprises multiple biomarkers. In various embodiments, the multivariate biomarker panel for assessing disease progression comprises two biomarkers. In various embodiments, the multivariate biomarker panel for assessing disease progression comprises 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, or 50 biomarkers. In certain embodiments, the multivariate biomarker panel comprises two biomarkers. In certain embodiments, the multivariate biomarker panel comprises three biomarkers. In certain embodiments, the multivariate biomarker panel comprises four biomarkers. In certain embodiments, the multivariate biomarker panel comprises seven biomarkers. In certain embodiments, the multivariate biomarker panel comprises 8 biomarkers. In certain embodiments, the multivariate biomarker panel comprises 17 biomarkers. In certain embodiments, the multivariate biomarker panel comprises 21 biomarkers.

[0123] In one embodiment, the biomarkers in the biomarker panel may include one or more of the following: 6Ckine, adiponectin, adrenomedullin (ADM), alpha 1 antitrypsin (AAT), alpha 1 microglobulin (A1Micro), alpha 2 macroglobulin (A2Macro), alpha fetoprotein (AFP), amphiregulin (AR), angiogenin, angiopoietin 1 (ANG-1), angiopoietin 2 (ANG-2), angiotensin converting enzyme (ACE), anti-leukoproteinase (ALP), antithrombin III (AT III), apolipoprotein A (Apo-A), apolipoprotein D (Apo-D), apolipoprotein E (Apo-E), AXL receptor tyrosine kinase (AXL), B cell activating factor (BAFF), B lymphocyte chemoattractant (BLC), beta amyloid (1-40) (AB-40), beta amyloid (1-42) (AB-42), beta 2 microglobulin (B2M), beta cellulin (BTC), brain-derived neurotrophic factor (BDNF), C-reactive protein (CRP), cadherin 1 (E-Cad), calbindin, cancer antigen 125 (CA-125), cancer antigen 15-3 (CA15-3), cancer antigen 19-9 (CA19-9), carbonic anhydrase 9 (CA-9), carcinoembryonic antigen (CEA), carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1), cathepsin D, CD40 ligand (CD40-L), CD163, ceruloplasmin, chemokine CC-4 (HCC-4), chromogranin A (CgA), ciliary neurotrophic factor (CNTF), clusterin (CLU), complement C3 (C3), complement factor H (CFH), complement factor H-related protein 1 (CFHR1), cystatin B, cystatin C, decorin, Dickkop f-related protein 1 (DKK-1), dopamine β-hydroxylase (DBH), E-selectin, EN-RAGE, eotaxin 1, eotaxin 2, eotaxin 3, epidermal growth factor (EGF), epidermal growth factor receptor (EGFR), epiregulin (EPR), epithelial-derived neutrophil-activating protein 78 (ENA-78), erythropoietin (EPO), factor VII, Fas ligand (FasL), FASLG receptor (FAS), ferritin (FRTN), fibrinogen, fibrin 1C (Fib1C), ficolin 3, follicle-stimulating hormone (FSH),Gastric inhibitory polypeptide (GIP), gelsolin, glucagon-like peptide-1 (GLP-1), glycogen phosphorylase isoenzyme BB (GPBB), granulocyte colony-stimulating factor (GCSF), granulocyte-macrophage colony-stimulating factor (GM-CSF), growth differentiation factor 15 (GDF-15), growth hormone (GH), growth regulatory alpha protein (GRO-α), haptoglobin, heat shock protein 70 (HSP-70), heparin-binding EGF-like growth factor (HB-EGF), hepatocyte growth factor (HGF), human chorionic gonadotropin beta (hCG), CG), immunoglobulin A (IgA), immunoglobulin E (IgE), immunoglobulin M (IgM), insulin, insulin-like growth factor binding protein 2 (IGFBP2), intercellular adhesion molecule 1 (ICAM-1), interferon alpha (IFN-α), interferon gamma (IFN-γ), interferon gamma-inducible protein 10 (IP-10), interferon-inducible T cell alpha chemoattractant (ITAC), interleukin 1 alpha (IL-1α), interleukin 1 beta (IL-1β), interleukin 1 receptor antagonist (IL1 ra), interleukin 2 (IL-2), interleukin 2 receptor α (IL2 receptor α), interleukin 3 (IL-3), interleukin 4 (IL-4), interleukin 5 (IL-5), interleukin 6 (IL-6), interleukin 6 receptor (IL6r), interleukin 6 receptor subunit β (IL6Rβ), interleukin 7 (IL-7), interleukin 8 (IL-8), interleukin 10 (IL-10), interleukin 12 subunit p40 (IL12p40), interleukin 12 subunit p70 (IL12p70), interleukin 13 (IL13), interleukin 15 (IL15), interleukin 16 (IL16), interleukin 17 (IL17), interleukin 18 (IL18), interleukin 18 binding protein (IL18bp), interleukin 22 (IL22), interleukin 23 (IL23), interleukin 31 (IL31), kidney injury molecule 1 (KIM-1), lactoferrin (LTF), latency-associated peptide of transforming growth factor-β1 (LAP TGF b1), leptin, leptin receptor (leptin R),Leucine-rich α2 glycoprotein (LRG1), luteinizing hormone (LH), macrophage colony-stimulating factor 1 (M-CSF), macrophage-derived chemokine (MDC), macrophage inflammatory protein 1α (MIP-1α), macrophage inflammatory protein 1β (MIP-1β), macrophage inflammatory protein 3α (MIP-3α), macrophage inflammatory protein 3β (MIP-3β), macrophage migration inhibitory factor (MIF), macrophage stimulating protein (MSP), mast stem cell growth factor receptor (SCFR), matrix Metalloproteinase 1 (MMP-1), Matrix Metalloproteinase 2 (MMP-2), Matrix Metalloproteinase 3 (MMP-3), Matrix Metalloproteinase 7 (MMP-7), Matrix Metalloproteinase 9 (MMP-9), Matrix Metalloproteinase 9 Total (MMP-9 Total), Matrix Metalloproteinase 10 (MMP-10), Microalbumin, Monocyte Chemoattractant Protein 1 (MCP-1), Monocyte Chemoattractant Protein 2 (MCP-2), Monocyte Chemoattractant Protein 3 (MCP-3), Monocyte Chemoattractant Protein 4 (MCP-4), Monocyte Chemoattractant Protein 5 (MCP-5), Monocyte Chemoattractant Protein 6 (MCP-6), Monocyte Chemoattractant Protein 7 (MMP-7), Matrix Metalloproteinase 9 (MMP-9), Matrix Metalloproteinase 9 Total (MMP-9 Total), Matrix Metalloproteinase 10 (MMP-10), Microalbumin, Monocyte Chemoattractant Protein 1 (MCP-1), Monocyte Chemoattractant Protein 2 (MCP-2), Monocyte Chemoattractant Protein 3 (MCP-3), Monocyte Chemoattractant Protein 8 (MCP-8), Monocyte Chemoattractant Protein 9 (MCP-8), Monocyte Chemoattractant Protein 10 (MMP ... mitogenic protein 4 (MCP-4), monokine induced by gamma interferon (MIG), myeloid progenitor inhibitory factor 1 (MPIF-1), myeloperoxidase (MPO), myoglobin, nerve growth factor beta (NGF-β), neurofilament heavy polypeptide (NF-H), neuron-specific enolase (NSE), neuronal cell adhesion molecule (NrCAM), neuropilin-1, neutrophil-activating peptide 2 (NAP-2), omentin, osteocalcin, osteopontin, osteoprotegerin (OPG), P-selectin, pancreatic polypeptide PPP, pancreatic secretory trypsin inhibitor (TATI), paraoxonase-1 (PON1), pepsinogen-I (PGI), periostin, pigment epithelium-derived factor (PEDF), placental growth factor (PLGF), plasminogen activator inhibitor 1 (PAI-1), platelet endothelial cell adhesion molecule (PECAM-1), platelet-derived growth factor BB (PDGF-BB), prolactin (PRL), prostate-specific antigen-free (PSA-f), protein DJ-1 (DJ-1), lung and activation-regulated chemokine (PARC), pulmonary surfactant protein D (SP-D),Receptor for advanced glycation end products (RAGE), resistin, S100 calcium-binding protein B (S100B), serum amyloid A protein (SAA), serum amyloid P component (SAP), sex hormone-binding globulin (SHBG), sortilin, ST2, stem cell factor (SCF), stromal cell-derived factor 1 (SDF-1), superoxide dismutase 1 soluble (SOD-1), T cell-specific protein RANTES (RANTES), T lymphocyte-secreted protein I309 (I309), Tamm-Horsfall glycoprotein (THP), tenascin-C (TN-C), tetranectin, thrombin-activated fibronectin (TAFI), thrombospondin 1, thymus and activation-regulated chemokine (TARC), thyrotropin (TSH), thyroxine-binding globulin (TBG), tissue inhibitor of metalloproteinases 1 (TIM P-1), tissue inhibitor of metalloproteinases 2 (TIMP-2), TNF-related apoptosis-inducing ligand receptor 3 (TRAIL-R3), transferrin receptor protein 1 (TFR1), transforming growth factor beta 3 (TGF-β3), tumor necrosis factor alpha (TNF-α), tumor necrosis factor beta (TNF-β), tumor necrosis factor ligand superfamily member 12 (Tweak), tumor necrosis factor ligand superfamily member 13 (APRIL), tumor necrosis factor receptor I (TNF-RI), tumor necrosis factor receptor 2 (TNFR2), vascular cell adhesion molecule 1 (VCAM-1), vascular endothelial growth factor (VEGF), visceral adipose tissue-derived serpin A12 (Vaspin), visfatin, vitamin D binding protein (VDBP), vitronectin, von Willebrand factor (vWF), or YKL-40.

[0124] In some embodiments, the biomarkers of the biomarker panel include the biomarkers shown in Tables 8-10. In some embodiments, the biomarkers include neurofilament light polypeptide chain (NEFL), myelin oligodendrocyte glycoprotein (MOG), cluster of differentiation 6 (CD6), chemokine (C-X-C motif) ligand 9 (CXCL9), osteoprotegerin (OPG), osteopontin (OPN), matrix metallopeptidase 9 (MMP-9), glial fibrillary acidic protein (GFAP), CUB domain-containing protein 1 (CDCP1), CC motif chemokine ligand 20 (CCL20 / MIP-3α), interleukin-12 subunit 1 (IL-12), and / or IL-12. The biomarkers may include one or more of: interleukin 12B, amyloid beta precursor-like protein 1 (APLP1), tumor necrosis factor receptor superfamily member 10A (TNFRSF10A), collagen, type IV, alpha 1 (COL4A1), serpin family A member 9 (SERPINA9), fibronectin leucine-rich transmembrane protein 2 (FLRT2), chemokine (CXC motif) ligand 13 (CXCL13), growth hormone (GH), versican core protein (VCAN), protogenin (PRTG), contactin 2 (CNTN2). In some embodiments, the biomarkers further include growth hormone (GH2), interleukin 18 (IL18), matrix metalloproteinase 2 (MMP-2), gamma-interferon-inducible lysosomal thiol reductase (IFI30), and chitinase 3-like protein 1 (CHI3L1 / YkL40).

[0125] In some embodiments, the biomarkers may include one or more of the following: cell adhesion molecule 3 (CADM3), kallikrein-related peptidase 6 (KLK6), brevican (BCAN), oligodendrocyte myelin glycoprotein (OMG), CD5 molecule (CD5), cytotoxic and regulatory T cell (CRTAM), CD244 molecule (CD244), tumor necrosis factor receptor superfamily member 9 (TNFRSF9), proteinase 3 (PRTN3), follistatin-like 3 (FSTL3), C-X-C motif chemokine ligand 10 (CXCL1), and / or CXC-related chemokine ligand 10 (CXCL2). 0), C-X-C motif chemokine ligand 11 (CXCL11), interleukin-18 binding protein (IL-18BP), macrophage scavenger receptor 1 (MSR1), C-C motif chemokine ligand 3 (CCL3), tumor necrosis factor ligand superfamily member 12 (TWEAK), trefoil factor 3 (TFF3), ectonucleotide pyrophosphatase / phosphodiesterase 2 (ENPP2), insulin-like growth factor binding protein 1 (IGFBP-1), interleukin 12A (IL12A), and stroke-related 6 homolog-like (SEZ6L), dipeptidyl peptidase-like 6 (DPP6), neurocan (NCAN), tubulointerstitial nephritis antigen-like 1 (TINAGL1), calcium-activated nucleotidase 1 (CANT1), nectin cell adhesion molecule 2 (NECTIN2), neural proliferation, differentiation, and regulation protein 1 (NPDC1), tumor necrosis factor receptor superfamily member 11A (TNFRSF11A), contactin 4 (CNTN4), neurotrophic receptor tyrosine kinase 2 (NTRK2), neurotrophic receptor tyrosine kinase 3 (NTRK3), cadherin 6 (CD H6), carcinoembryonic antigen-related cell adhesion molecule 8 (CEACAM8), mitotic arrest deficient 1-like 1 (MAD1L1), Fc fragment of IgA receptor (FCAR), myeloperoxidase (MPO), osteomodulin (OMD), matrix extracellular phosphoglycoprotein (MEPE), GDNF family receptor α3 (GDNFR-α3), scavenger receptor class F member 2 (SCARF2), CD40 ligand (IgM), tumor necrosis factor receptor superfamily member 1B (TNF-R2), programmed cell death 1 ligand (PD-L1),Notch3 (NOTCH3), contactin 1 (CNTN1), oncostatin M (OSM), transforming growth factor α (TGF-α), peptidoglycan recognition protein 1 (PGLYRP1), nitric oxide synthase 3 (NOS3).

[0126] In certain embodiments, a biomarker panel useful for generating a prediction (e.g., a prediction of MS disease activity or a prediction of MS disease progression) includes biomarkers identified as either Tier A of Table 1, Tier 1 of Table 2, or the corresponding surrogate biomarkers of Table 10. For example, the biomarker panel includes NEFL, MOG, CD6, CXCL9, OPG, OPN, MMP-9, and GFAP (Tier A of Table 1). As another example, the biomarker panel includes NEFL, MOG, CD6, CXCL9, OPG, OPN, CXCL13, and GFAP (Tier 1 of Table 2).

[0127] In certain embodiments, a biomarker panel useful for generating a prediction (e.g., a prediction of MS disease activity or a prediction of MS disease progression) includes biomarkers identified as either Tier B of Table 1, Tier 2 of Table 2, or the corresponding surrogate biomarkers of Table 10. For example, the biomarker panel includes CDCP1, CCL20 / MIP-3α, IL-12B, APLP1, TNFRSF10A, COL4A1, SERPINA9, FLRT2, and CXCL13 (Tier B of Table 1). As another example, the biomarker panel includes CDCP1, CCL20 / MIP-3α, IL-12B, APLP1, TNFRSF10A, COL4A1, SERPINA9, FLRT2, and TNFSF13B (Tier 2 of Table 2).

[0128] In certain embodiments, a biomarker panel useful for generating a prediction (e.g., a prediction of MS disease activity or a prediction of MS disease progression) includes biomarkers identified as either Tier C of Table 1, Tier 2 of Table 3, or the corresponding surrogate biomarkers of Table 10. For example, the biomarker panel includes GH, VCAN, PRTG, and CNTN2 (Tier C of Table 1 and Tier 3 of Table 2).

[0129] In certain embodiments, a biomarker panel useful for generating a prediction (e.g., a prediction of MS disease activity or a prediction of MS disease progression) includes biomarkers identified as either Tier A and Tier B of Table 1, Tier 1 and Tier 2 of Table 2, or the corresponding surrogate biomarkers of Table 10. For example, the biomarker panel includes NEFL, MOG, CD6, CXCL9, OPG, OPN, MMP-9, GFAP, CDCP1, CCL20 / MIP-3α, IL-12B, APLP1, TNFRSF10A, COL4A1, SERPINA9, FLRT2, and CXCL13 (Tiers A and B of Table 1). As another example, the biomarker panel includes NEFL, MOG, CD6, CXCL9, OPG, OPN, CXCL13, GFAP, CDCP1, CCL20 / MIP-3α, IL-12B, APLP1, TNFRSF10A, COL4A1, SERPINA9, FLRT2, and TNFSF13B (Tier 1 and 2 in Table 2).

[0130] In certain embodiments, a biomarker panel useful for generating a prediction (e.g., a prediction of MS disease activity or a prediction of MS disease progression) includes biomarkers identified as any of Tier A, Tier B, or Tier C of Table 1, Tier 1, Tier 2, or Tier 3 of Table 2, or the corresponding surrogate biomarkers of Table 10. For example, the biomarker panel includes NEFL, MOG, CD6, CXCL9, OPG, OPN, MMP-9, GFAP, CDCP1, CCL20 / MIP-3α, IL-12B, APLP1, TNFRSF10A, COL4A1, SERPINA9, FLRT2, CXCL13, GH, VCAN, PRTG, and CNTN2 (Tiers A, B, and C of Table 1). As another example, the biomarker panel includes NEFL, MOG, CD6, CXCL9, OPG, OPN, CXCL13, GFAP, CDCP1, CCL20 / MIP-3α, IL-12B, APLP1, TNFRSF10A, COL4A1, SERPINA9, FLRT2, TNFSF13B, GH, VCAN, PRTG, CNTN2, GH2, IL18, MMP-2, IFI30, and CHI3L1 / YkL40 (Tiers 1, 2, and 3 of Table 2).

[0131] In various embodiments, a biomarker panel for generating a prediction (e.g., a prediction of disease activity or a prediction of disease progression) comprises a minimum set of predictive biomarkers, such as a biomarker pair, a biomarker triad, or a biomarker quad. In various embodiments, at least one of the biomarkers of the biomarker pair, biomarker triad, or biomarker quad is NEFL. In various embodiments, at least one of the biomarkers of the biomarker pair, biomarker triad, or biomarker quad is MOG. In various embodiments, the biomarker pair, biomarker triad, or biomarker quad does not include NEFL. In such embodiments, the biomarker pair, biomarker triad, or biomarker quad does not include NEFL includes MOG.

[0132] Examples of biomarker pairs useful for generating predictions (e.g., predictions of MS disease activity or predictions of MS disease progression) include: 1) NEFL and MOG, 2) NEFL and CD6, 3) NEFL and CXCL9, 4) NEFL and TNFRSF10A, 5) MOG and IL-12B, 6) CXCL9 and CD6, 7) MOG and CXCL9, 8) MOG and CD6, 9) CXCL9 and COL4A1, and 10) CD6 and VCAN. Additional examples of biomarker pairs predicting multiple sclerosis disease activity include: 1) NEFL and TNFSF13B, 2) NEFL and CNTN2, 3) NEFL and CXCL9, 4) MOG and CDCP1, 5) MOG and TNFSF13B, and 6) MOG and CXCL9. Additional examples of biomarker pairs that predict multiple sclerosis disease activity include 1) NEFL and TNFSF13B, 2) NEFL and SERPINA9, 3) NEFL and GH, 4) MOG and TNFSF13B, 5) MOG and CXCL9, and 6) MOG and IL-12B.

[0133] Examples of biomarker triads useful for generating predictions (e.g., predictions of MS disease activity or predictions of MS disease progression) include: 1) MOG, IL-12B, and APLP1; 2) MOG, CD6, and CXCL9; 3) CXCL9, COL4A1, and VCAN; 4) NEFL, CD6, and CXCL9; 5) NEFL, TNFRSF10A, and COL4A1; 6) MOG, IL-12B, and CNTN2; and 7) CD6, CCL20, and VCAN. Additional examples of biomarker triads that predict multiple sclerosis disease activity include: 1) NEFL, CNTN2, and TNFSF13B, 2) NEFL, APLP1, and TNFSF13B, 3) NEFL, TNFRSF10A, and TNFSF13B, 4) MOG, CXCL9, and TNFSF13B, 5) MOG, OPG, and TNFSF13B, and 6) MOG, CCL20, and TNFSF13B. Additional examples of biomarker triads that predict multiple sclerosis disease activity include: 1) NEFL, SERPINA9, and TNFSF13B, 2) NEFL, CNTN2, and TNFSF13B, 3) NEFL, APLP1, and TNFSF13B, 4) MOB, CXCL9, and TNFSF13B, 5) MOG, SERPINA9, and TNFSF13B, and 6) MOG, OPG, and TNFSF13B.

[0134] Examples of biomarker quartets useful for generating predictions (e.g., predictions of MS disease activity or predictions of MS disease progression) include: 1) NEFL, MOG, CD6, and CXCL9; 2) NEFL, CXCL9, TNFRSF10A, and COL4A1; 3) MOG, CXCL9, IL-12B, and APLP1; 4) CXCL9, COL4A1, OPG, and VCAN; 5) CXCL9, OPG, APLP1, and OPN; 6) NEFL, CD6, CXCL9, and CXCL13; 7) NEFL, MOG, CD6, and CXCL9; 8) NEFL, TNFRSF10A, COL4A1, and CCL20; 9) MOG, IL-12B, OPN, and CNTN2; and 10) CD6, COL4A1, CCL20, and VCA. Additional examples of biomarker tetrads predictive of multiple sclerosis disease activity include: 1) NEFL, TNFRSF10A, CNTN2, and TNFSF13B; 2) NEFL, COL4A1, CNTN2, and TNFSF13B; 3) NEFL, TNFRSF10A, APLP1, and TNFSF13B; 4) MOG, CXCL9, APLP1, and TNFSF13B; 5) MOG, CXCL9, OPG, and TNFSF13B; and 6) MOG, CXCL9, OPG, and CNTN2. Additional examples of biomarker tetrads predictive of multiple sclerosis disease activity include: 1) NEFL, CCL20, SERPINA9, and TNFSF13B; 2) NEFL, APLP1, SERPINA9, and TNFSF13B; 3) NEFL, CCL20, APLP1, and TNFSF13B; 4) MOG, CXCL9, OPG, and TNFSF13B; 5) MOG, OPG, SERPINA9, and TNFSF13B; and 6) MOG, CXCL9, SERPINA9, and TNFSF13B.

[0135] V. Biomarkers Dysregulation of the biomarkers disclosed herein may contribute to the development and / or progression of disease activity, for example, disease activity of neurodegenerative diseases including multiple sclerosis, Parkinson's disease, Lewy body disease, Alzheimer's disease, amyotrophic lateral sclerosis (ALS), motor neuron disease, Huntington's disease, spinal muscular atrophy, Friedreich's ataxia, Batten disease, etc. Biomarkers and corresponding classifications of biomarkers are shown below in Table 11. Exemplary categories include neurodegeneration, myelin integrity, neurite outgrowth and neurogenesis, inflammation, immunoregulation, cell regulation, cell adhesion, brain-gut axis, metabolism, and neuroregulation categories. Additionally, biomarkers and their involvement in specific locations (e.g., brain, brain barrier, or blood) and cell types are shown in Tables 12, 13A, and 13B.

[0136] NEFL is a 68kDa biomarker that reflects axonal damage in the microenvironment. In other words, NEFL often serves as a surrogate for axonal degeneration. In addition, NEFL interacts with other biomarkers such as MAP2, protein kinase N1, and tuberous sclerosis complex (TSC1).

[0137] COL4A1 is a 26 kDa biomarker involved in cell proliferation, migration, extracellular matrix formation, and inhibition of endothelial cell proliferation, migration, and tube formation. COL4A1 is involved in the outgrowth of hippocampal embryonic neurons and is further involved in myelin integrity. Type IV collagen is a major component of the glomerular basement membrane (GBM) and forms a wire meshwork together with laminin, proteoglycans, and entactin / nidogen. It contains a C-terminal NC1 domain that inhibits angiogenesis and tumorigenesis. The C-terminal half has been found to have antiangiogenic activity. Type IV collagen also inhibits endothelial cell proliferation, migration, and tube formation, and inhibits the expression of hypoxia-inducible factor 1α and ERK1 / 2 and p38 MAPK activation. COL4A1 mutations are associated with a wide range of phenotypes including both ischemic and hemorrhagic stroke, migraine, leukomalacia, nephropathy, hematuria, chronic muscle spasms, and anterior segment disease including congenital cataract, glaucoma, and Axenfeld-Rieger syndrome. Case Rep Neurol.2015 May-Aug;7(2):142-147.Published online 2015 Jun 2.doi:10.1159 / 000431309.

[0138] APLP1 is a 72 kDa biomarker involved in the regulation of synapse maturation and neurite outgrowth during cortical development. APLP1 is one of two homologs, amyloid-like proteins 1 and 2, or APLP1 and APLP2. The coding gene for APLP1 is a member of the highly conserved amyloid precursor protein gene family. The encoded protein is a membrane-bound glycoprotein that is cleaved by secretases in a manner similar to the cleavage of the amyloid beta A4 precursor protein. This cleavage liberates an intracellular cytoplasmic fragment that can act as a transcriptional activator. APLP1 may also be involved in synapse maturation during cortical development. It may regulate neurite outgrowth by binding to components of the extracellular matrix, such as heparin and collagen I. APLP1 is widely expressed in humans. Functions attributed to APLP1 include neurite outgrowth and synapse formation, protein transport along axons, cell adhesion, calcium metabolism, neuronal injury, synaptic dysfunction, and signal transduction.

[0139] MMP2 (72 kDa) and MMP9 (78-92 kDa) are gelatinases, a type of proteolytic enzyme involved in the degradation of the extracellular matrix. MMP2 and MMP9 are involved in physiological processes such as embryonic development, reproduction, and tissue remodeling. Serum MMP-2 and MMP-9 are elevated in different multiple sclerosis subtypes. Avolio, C., et al. Serum MMP-2 and MMP-9 are elevated in different multiple sclerosis subtypes, J. Neuroimmunol. Mar; 136 (1-2): 46-53. The integrity of the blood-brain barrier, as the main structural interface between the periphery and the brain, appears to play a key role in MS. Reducing the secretion of proteolytic matrix metalloproteinases (MMPs), such as MMP2 and / or MMP9, as disruptors of blood-brain barrier integrity, may have a significant impact on MS. Proschinger et al. “Influence of combined functional resistance and exercise endurance over 12 weeks on matrix metalloproteinase-2 serum concentration in persons with relapsing-remitting multiple sclerosis-a community-based randomized controlled trial.” BMC Neurol 19,314 (2019).

[0140] FLRT2 is a 74 kDa biomarker and a member of the fibronectin leucine-rich transmembrane protein family that functions in cell adhesion and / or receptor signaling. FLRT2 is also expressed in the brain as well as in the heart and several other organs, where it is involved in fibroblast growth factor-mediated signaling cascades. In the heart, it is required for normal organization of the cardiac basement membrane during embryogenesis, as well as for normal embryonic epicardial and cardiac morphogenesis. In the context of neurology, FLRT2 functions in cell-cell adhesion, cell migration, and axon guidance. It may be involved in cortical neuron migration during brain development through its interaction with UNC5D. FLRT2 is also involved in glutamate excitotoxicity, neuronal cell death, and synapse formation and plasticity.

[0141] VCAN (a biomarker of >200 kDa) is involved in cell motility, cell growth and differentiation, cell adhesion, cell proliferation, cell migration, and angiogenesis. VCAN is also involved in myelin protection, astrocytic excitotoxicity, and is an inflammatory mediator secretion. VCAN is a key factor in inflammation through interaction with adhesion molecules on the surface of inflammatory leukocytes and with chemokines involved in the recruitment of inflammatory cells. In the adult central nervous system, versican is found in the perineural network, where it may stabilize synaptic connections. Versican may also inhibit nervous system regeneration and axonal growth after central nervous system injury.

[0142] TNFSF13B, also referred to herein as B cell activating factor (BAFF), is a biomarker involved in T cell-independent B cell activation and ectopic lymphoid follicle formation.

[0143] CHI3L1 is a 40 kDa biomarker involved in inflammation, the innate immune system, tissue remodeling, and the ability of cells to respond and cope with environmental changes. In addition, CHIIL3L1 is involved in T helper cell type 2 (Th2) inflammatory responses and IL-13-induced inflammation, regulating allergen sensitization, inflammatory cell apoptosis, dendritic cell accumulation, and M2 macrophage differentiation. CHI3L1 promotes the invasion of pathogenic enterobacteria into colonic mucosa and lymphoid organs, activation of the AKT1 signaling pathway in colonic epithelial cells with subsequent IL8 production, and antibacterial responses in the lungs by contributing to macrophage bacterial killing, controlling bacterial dissemination, and enhancing host resistance. CHI3L1 also regulates hyperoxia-induced injury, inflammation, and epithelial apoptosis in the lungs.

[0144] IL-12B is a 40 kDa biomarker that represents one subunit of the IL-12 heterodimer. IL-12A (35 kDa) represents the other subunit of the IL-12 heterodimer. IL-12B is involved in innate and adaptive immunity, as well as regulating memory / effector Th1 cells. IL-12B is a growth factor for activated T cells and NK cells. IL-12B associates with IL23A to form the IL-23 interleukin, a heterodimeric cytokine that functions in innate and adaptive immunity. Polymorphisms in the genes encoding the interleukin 23 receptor (IL23R) and the p40 subunit of IL-12 / 23 (IL12B) are associated with risk of multiple sclerosis (MS). Huang et al., “Meta-analysis of the IL23R and IL12B polymorphisms in multiple sclerosis.” Int.J.of Neuroscience, 126:3, 205-212 (2016).

[0145] IFI30 is a 30-35 kDa biomarker involved in antigen processing by promoting complete unfolding of proteins destined for lysosomal degradation. IFI30 promotes the generation of MHC class II restricted epitopes from disulfide bond-containing antigens by endocytic reduction of disulfide bonds. IFI30 also promotes MHC class I restricted recognition of exogenous antigens containing disulfide bonds by CD8+ T cells or cross-presentation. IFI30 is constitutively expressed in antigen presenting cells and is induced by inflammatory cytokines.

[0146] SERPINA9 is a 42 kDa biomarker that is a member of the serpin family of serine protease inhibitors. SERPINA9 has been implicated in neuronal injury. SERPINA9 expression may be restricted to germinal center B cell and lymphoid malignancies. SERPINA9 may function in vivo in germinal centers as an efficient inhibitor of trypsin-like proteases.

[0147] IL18 is involved in immune responses and inflammatory processes. IL18 is a proinflammatory cytokine that is primarily involved in polarized T helper 1 (Th1) and natural killer (NK) cell immune responses. It serves as an inhibitor of early Th1 cytokine responses. Furthermore, it is involved in Th-1 responses through its ability to induce IFN-γ production in T and NK cells. IL-18 in CSF and serum was significantly higher compared to the levels found in patients without enhanced lesions. The results suggest the involvement of IL-18 in the immunopathogenesis of MS, especially during the active phase of the disease. Losy, J., et al. IL-18 in patients with multiple sclerosis. Acta Neurologica Scandinavica, 104: 171-173 (2001). Furthermore, higher IL-18 serum levels and significantly different frequencies of two polymorphisms of IL-18 were found in MS patients. Jahanbani-Ardakani, H. et al., Interleukin 18 polymorphisms and its serum level in patients with multiple sclerosis, Ann Indian Acad Neurol;22:474-76(2019).

[0148] CDCP1 is a 90-140 kDa biomarker involved in T cell migration, cell adhesion, and cell-matrix association. CDCP1 may be involved in regulating anchorage vs. migration or proliferation vs. differentiation through phosphorylation. CDCP1 is expressed in cells with a phenotype reminiscent of mesenchymal stem cells and neural stem cells. In addition, CDCP1 is a ligand for CD6, a receptor molecule expressed on certain T cells, and may be involved in migration and chemotaxis.

[0149] CNTN2 is a 113 kDa biomarker involved in cell adhesion, proliferation, migration, neuronal axon guidance, neuronal injury, and axo-dendritic rearrangement. CNTN2 is a member of the contactin family of proteins, which is part of the immunoglobulin superfamily of cell adhesion molecules. CNTN2 is a glycosylphosphatidylinositol (GPI)-anchored neuronal membrane protein that is involved in proliferation, migration, and axon guidance of neurons in the developing cerebellum. Mutations in the CNTN2 gene may be associated with adult myoclonic epilepsy. Together with another transmembrane protein, CNTNAP2 contributes to the organization of the axonal domain of the node of Ranvier by maintaining voltage-gated potassium channels in the paranodal region.

[0150] GFAP is a 50kDa biomarker involved in demyelination, degeneration, and axonal injury. Astroglial activation is associated with activation of the immune cascade and is thought to contribute to the demyelination and axonal injury observed in MS. Glial fibrillary acidic protein (GFAP) is a major component of the glial scar. GFAP is used as a marker to distinguish astrocytes from other glial cells during development. Higher serum concentrations of both GFAP and NEFL were associated with higher EDSS, older age, longer disease duration, progressive disease course, and MRI pathology. Hogel,H.,et al,.Serum glial fibrillary acidic protein correlates with multiple sclerosis disease severity.Multiple Sclerosis Journal,26(13)2018.

[0151] MOG is a 28 kDa membrane protein expressed on the oligodendrocyte cell surface and the outermost surface of the myelin sheath. Due to this localization, it is a major target antigen involved in immune-mediated demyelination, serving as a cell surface receptor or cell adhesion molecule. The protein may be involved in the completion and maintenance of the myelin sheath, as well as cell-cell communication. Diseases associated with MOG include narcolepsy and rubella. Related pathways include neural stem cell differentiation pathways and lineage-specific markers. A paralog of the MOG gene is BTN1A1.

[0152] CD6 is a 90-130 kDa biomarker involved in the development of the central nervous system. CD6 is a cell adhesion molecule involved in blood-brain barrier breach and T cell-mediated acute inflammatory responses. Recent studies have identified CD6 as a risk gene for multiple sclerosis (MS), a disease in which autoreactive T cells are integrally involved. De Jager, PL., et al., Meta-analysis of genome scans and replication identify CD6, IRF8 and TNFRSF1A as new multiple sclerosis susceptibility loci. Nat Genet. 2009; 41(7): 776-782. CD6 is found on the outer membrane of T lymphocytes and is involved in the migration of leukocytes across the blood-brain barrier.

[0153] CXCL9 is a 12 kDa biomarker involved in immune responses and inflammatory processes. CXCL9 is a cytokine that affects the growth, migration, or activation state of cells involved in immune and inflammatory responses. CXCL9 (MIG) is a chemokine that, when binding to the receptor, CXCR3, induces chemotactic activity for T cells and is involved in inflammatory responses. CXCL9 is not constitutively expressed, but is induced by IFN-γ. CXCL9 has been described to be involved in several inflammation-related diseases, such as hepatitis C, skin inflammation, rheumatoid arthritis, and pharyngitis. Consistent with this observation is the upregulation of ELR-CXC chemokines, CXCL9, CXCL10, and CXCL11, which are upregulated in the CNS of EAE-affected mice induced by migration of Th1 cells. Lovett-Racke, A. et al. Th1 versus Th17:Are T cell cytokines relevant in multiple sclerosis? Biochimca et Biophysica Acta(BBA)-Molecular Basis of Disease.1812(2):246-251(2011).

[0154] CXCL13 is a biomarker involved in cell proliferation, cell renewal, regeneration, and inflammatory responses. CXCL13 belongs to the CXC chemokine family and is selectively chemotactic for B cells. It interacts with the chemokine receptor CXCR5, which regulates B cell organization. Serum levels of CXCL13 are involved in multiple sclerosis. Festa, E. et al. Serum levels of CXCL13 are elevated in active multiple sclerosis. Multiple Sclerosis Journal, 15(11):1271-1279(2009).

[0155] CCL20 is an 11 kDa biomarker involved in axon guidance and chemotaxis of dendritic cells. CCL20 is a chemokine involved in immune regulation and inflammatory processes (e.g., acute inflammatory responses) and is expressed in epithelial cells of the choroid plexus of the human brain. It serves as the cognate ligand for CCR6.

[0156] OPG is a 55-60 kDa biomarker involved in inflammation, cell apoptosis, and T cell activation processes. OPG is a decoy receptor for the cytokines TNFSF11 (RANKL) and possibly TNFSF10 (TRAIL) and belongs to the TNF receptor superfamily. OPG is upregulated by increasing estrogen and calcium concentrations, and it is involved in inflammation, innate immunity, and transcriptional regulation in cell survival and differentiation; for example, OPG binding to TNFSF11 inhibits the differentiation of osteoclast precursor cells into mature osteoclasts, and OPG has been used experimentally to treat osteoporosis. OPG has been described to be involved in several inflammation-related diseases, such as rheumatoid arthritis, inflammatory bowel disease, and periodontitis.

[0157] OPN is a 33-44 kDa biomarker involved in inflammation and immune regulation. OPN is a pleiotropic integrin-binding protein that functions in cell-mediated immunity, inflammation, tissue repair, and cell survival. OPN is also involved in biomineralization.

[0158] PRTG is a 180 kDa biomarker involved in neurogenesis, neurotrophin binding, neuronal survival, and demyelination. It may be involved in anterior-posterior axis elongation. PRTG is a membrane protein and a member of the immunoglobulin superfamily. It is primarily thought to be a developmental protein with some relevance to neuralgia, demyelinating diseases, and dyslexia.

[0159] TNFRSF10A is a 50 kDa biomarker that is a member of the TNF receptor superfamily. TNFRSF10A is involved in inflammatory and neurodegenerative processes. This receptor is activated by tumor necrosis factor-related apoptosis-inducing ligand (TNFSF10 / TRAIL), thereby transmitting cell death signals and inducing cell apoptosis.

[0160] GH, also known as somatotropin or somatropin, is a neuroendocrine marker that stimulates growth, cell proliferation, and reproduction in humans and other animals. It regulates energy homeostasis and metabolism. It is a type of mitogen that is specific for only certain types of cells. Previous studies have shown that it is decreased in the serum of patients with severe MS. Gironi, M., et al. Growth hormone and Disease Severity in Early Stage of Multiple Sclerosis. Multiple Sclerosis International 2013: (2013).

[0161] GH2, also known as growth hormone 2, placenta-specific growth hormone, and growth hormone variant, is a biomarker involved in the control of myoblast growth, differentiation, and proliferation. It regulates energy homeostasis and metabolism. It is produced and secreted by the placenta during pregnancy and is the major form of growth hormone during pregnancy.

[0162] VCAM-1 is an 80 kDa transmembrane biomarker that is normally expressed in blood vessels and mediates cell adhesion to the vascular endothelium. VCAM-1 is characterized by multiple immunoglobulin domains. VCAM-1 is involved in multiple sclerosis. Peterson, J. et al., VCAM-1-Positive Microglia Target Oligodendrocytes at the Border of Multiple Sclerosis Lesions, Journal of Neuropathology & Experimental Neurology, Volume 61, Issue 6, June 2002, Pages 539-546. Matsuda, M. et al. Increased levels of soluble vascular cell adhesion molecule-1 (VCAM-1) in the cerebrospinal fluid and sera of patients with multiple sclerosis and human T lymphotropic virus type-1-associated myelopathy. J. Neuroimmunology, 59 (1-2): 35-40 (1995).

[0163] In various embodiments, the biomarker panel may further comprise additional biomarkers as described herein. In various embodiments, these additional biomarkers serve as alternative biomarkers for the biomarkers described above. In various embodiments, the additional biomarkers include: cell adhesion molecule 3 (CADM3), kallikrein-related peptidase 6 (KLK6), brevican (BCAN), oligodendrocyte myelin glycoprotein (OMG), CD5 molecule (CD5), cytotoxic and regulatory T cell molecule (CRTAM), CD244 molecule (CD244), tumor necrosis factor receptor superfamily member 9 (TNFRSF9), proteinase 3 (PRTN3), follistatin-like 3 (FSTL3), and / or IL-16. L3), C-X-C motif chemokine ligand 10 (CXCL10), C-X-C motif chemokine ligand 11 (CXCL11), interleukin-18 binding protein (IL-18BP), macrophage scavenger receptor 1 (MSR1), C-C motif chemokine ligand 3 (CCL3), tumor necrosis factor ligand superfamily member 12 (TWEAK), trefoil factor 3 (TFF3), ectonucleotide pyrophosphatase / phosphodiesterase 2 (ENPP2), ), insulin-like growth factor binding protein 1 (IGFBP-1), interleukin 12A (IL12A), seizure-associated 6 homolog-like (SEZ6L), dipeptidyl peptidase-like 6 (DPP6), neurocan (NCAN), tubulointerstitial nephritis antigen-like 1 (TINAGL1), calcium-activated nucleotidase 1 (CANT1), nectin cell adhesion molecule 2 (NECTIN2), neural proliferation, differentiation and control protein 1 (NPDC1), tumor necrosis factor receptor superfamily member 1 1A (TNFRSF11A), contactin 4 (CNTN4), neurotrophic receptor tyrosine kinase 2 (NTRK2), neurotrophic receptor tyrosine kinase 3 (NTRK3), cadherin 6 (CDH6), carcinoembryonic antigen-related cell adhesion molecule 8 (CEACAM8), mitotic arrest deficient 1-like 1 (MAD1L1), Fc fragment of IgA receptor (FCAR), myeloperoxidase (MPO), osteomodulin (OMD), matrix extracellular phosphoglycoprotein (MEPE),GDNF family receptor alpha 3 (GDNFR-α3), scavenger receptor class F member 2 (SCARF2), CD40 ligand (IgM), tumor necrosis factor receptor superfamily member 1B (TNF-R2), programmed cell death 1 ligand (PD-L1), Notch3 (NOTCH3), contactin 1 (CNTN1), oncostatin M (OSM), transforming growth factor alpha (TGF-α), peptidoglycan recognition protein 1 (PGLYRP1), nitric oxide synthase 3 (NOS3), discoidin domain receptor tyrosine kinase 1 (DDR1), C-X-C motif chemokine ligand 16 (CXCL16), CD166 antigen (ALCAM), spondin 2 (SPON2), and protocadherin 17 (PCDH17).

[0164] CADM3 is involved in cell-cell adhesion and interacts with IGSF4, NECTIN1, NECTIN3, and EPB41L1. CADM3 is involved in the biological processes of adherens junction organization, heterophilic cell-cell adhesion, homophilic cell adhesion, and protein localization.

[0165] KLK6 is a serine protease that exhibits activity against proteins such as α-synuclein, amyloid precursor protein, myelin basic protein, gelatin, casein, and extracellular matrix proteins such as fibronectin, laminin, vitronectin, and collagen. KLK6 is involved in the biological processes of amyloid precursor protein metabolism, CNS development, myelination, protein autoprocessing, regulation of cell differentiation and neuronal development, response to wounds, and tissue regeneration.

[0166] BCAN is a proteoglycan and a member of the lectican protein family that is involved in the biological processes of cell adhesion, CNS development, chondroitin sulfate biosynthesis and catabolic processes, extracellular matrix organization, and axonal synapse maturation.

[0167] OMG is a cell adhesion molecule involved in myelination in the central nervous system. OMG is involved in the biological processes of cell adhesion, regulation of axon formation, and regeneration of neuronal projections.

[0168] CD5 is a signaling molecule that can be expressed on the surface of cells, such as T lymphocytes. CD5 is involved in the biological processes of apoptosis signaling pathways, cell recognition, T cell proliferation, and T cell costimulation.

[0169] CRTAM is an immunoglobulin superfamily transmembrane protein that is involved in the biological processes of adaptive immune response, cell recognition, stimuli or cell detection, and regulation of immune response. CRTAM is further involved in heterophilic cell-cell adhesion, which regulates activation, differentiation, and tissue retention of various T cell subsets.

[0170] CD244 is a member of the signaling lymphocyte activation molecules expressed on natural killer cells. CD244 is involved in the biological processes of immune responses (adaptive and innate), leukocyte migration, regulation of cytokine secretion, and signal transduction. CD244 regulates the activation and differentiation of a wide variety of immune cells, thereby participating in the regulation and interconnection of both innate and adaptive immune responses.

[0171] TNFRSF9 is a tumor necrosis factor receptor family member and is involved in the biological processes of TNFR signaling pathway, cell proliferation, and apoptosis processes. TNFRSF9 is expressed by activated T cells.

[0172] PRTN3 is a serine protease expressed by neutrophil granulocytes. It is involved in the biological processes of antibacterial humoral responses, blood coagulation, neutrophil activity, protein degradation, and cytokine-mediated signaling pathways.

[0173] FSTL3 is a secreted glycoprotein of the follistatin module protein family. FSTL3 is involved in the biological processes of activin / fibronectin binding, organ development, bone formation, ossification, and regulation of cell-cell adhesion.

[0174] CXCL10 and CXCL11 are cytokines of the CXC chemokine family, respectively, that are involved in the biological processes of immune response, inflammatory response, cell signaling, chemotaxis, T cell recruitment, and cell proliferation.

[0175] IL-18BP is a protein that serves as an inhibitor of the proinflammatory cytokine IL18. IL-18BP is involved in the biological processes of cytokine stimulation, IL-18-mediated signaling pathways, and immune responses.

[0176] MSR1 is a membrane glycoprotein expressed by macrophages. It is involved in the biological processes of endocytosis and cholesterol transport and storage, and may be involved in the pathological deposition of cholesterol in the arterial wall during atherogenesis.

[0177] CCL3 is a monokine with inflammatory and chemokinetic properties that binds to CCR1, CCR4, and CCR5. CCL3 is involved in the biological processes of cell migration (e.g., lymphocyte and macrophage chemotaxis), calcium-mediated signaling, intercellular signaling, cytokine secretion, and inflammatory responses.

[0178] TWEAK is a cytokine of the tumor necrosis factor ligand family. TWEAK is involved in the biological processes of angiogenesis, cell differentiation, immune response, signal transduction, apoptosis, and TNF-mediated signal transduction pathways. TWEAK also promotes the proliferation and migration of endothelial cells.

[0179] TFF3 is a 6 kDa glycoprotein normally produced by goblet cells and involved in the gastrointestinal tract. TFF3 is involved in the biological processes of maintenance and healing of the gastrointestinal epithelium, as well as regulation of glucose metabolic processes.

[0180] ENPP2 is a phosphodiesterase involved in the production of lysophosphatidic acid, a lipid signaling molecule that hydrolyzes lysophospholipids and is involved in the biological processes of cell motility, chemotaxis, immune response, and angiogenesis.

[0181] IGFBP-1 is a member of the insulin-like growth factor binding protein family. It binds to insulin-like growth factors (IGF) I and II. IGFBP-1 is involved in the biological processes of aging, cellular metabolic processes, cell growth, signal transduction, and tissue regeneration.

[0182] IL-12A is a subunit that, together with the other IL-12B subunit, forms the IL-12 heterodimer. IL-12A is involved in the biological processes of cell migration, cell proliferation, cell adhesion, cell differentiation, and regulation of immune cell (e.g., T cells, dendritic cells, natural killer cells) activation.

[0183] SEZ6L is a protein that is primarily located in the endoplasmic reticulum membrane, where it regulates endoplasmic reticulum function in neurons. SEZ6L is involved in the biological processes of synapse maturation, adult motor behavior, and regulation of protein kinase C signaling.

[0184] DPP6 is a membrane protein that is a member of the peptidase S9B family of serine proteases. DPP6 is involved in the biological processes of potassium channel regulation and protein localization to the plasma membrane, and may influence susceptibility to amyotrophic lateral sclerosis.

[0185] NCAN is a protein that is a member of the lectican / chondroitin sulfate proteoglycan family. NCAN is involved in the biological processes of cell adhesion, CNS development, ECM organization, and synthesis of chondroitin sulfate and dermatan. NCAN is involved in neural cell adhesion and neurite outgrowth during development by binding to neural cell adhesion molecules.

[0186] TINAGL1 is an extracellular matrix protein involved in the biological processes of cell adhesion, proliferation, migration, and differentiation. TINAGL1 is further involved in endocytosis and endosomal trafficking.

[0187] CANT1 is a calcium-dependent nucleotidase with a preference for uridine diphosphate. CANT1 is involved in the biological processes of calcium ion binding regulation, neutrophil degranulation, NF-κB signaling regulation, and the proteoglycan biosynthesis process. CANT1 regulates metabolic processes such as nucleotide metabolism.

[0188] NECTIN2 is a membrane glycoprotein that serves as a component of adherens junctions. NECTIN2 is involved in the biological processes of cell-cell adhesion (via the adherens junctions), cytoskeleton organization, viral receptor activity, and regulation of NK and T cell activity.

[0189] NPDC1 is a protein that is mainly expressed in the brain. NPDC1 is involved in the biological processes of immune response regulation and neuronal development and proliferation. NPDC1 suppresses oncogenic transformation of neuronal and non-neuronal cells and downregulates neuronal proliferation.

[0190] TNFRSF11A is a member of the TNF receptor superfamily. TNFRSF11A is involved in the biological processes of cell-cell signaling, immune response, monocyte chemotaxis, and TNF-mediated signaling pathways. In addition, TNFSF11A is involved in osteoclastogenesis.

[0191] CNTN4 is a member of the contactin family of immunoglobulins. CNTN4 is involved in the biological processes of axon guidance and development, synaptogenesis, cell-surface interactions during nervous system development, brain development, neuronal cell-cell adhesion, neuronal projections, neuronal differentiation, and regulation of synaptic plasticity.

[0192] NTKR2 is a receptor tyrosine kinase (part of the neurotrophic factor tyrosine receptor kinase family) that binds to brain-derived neurotrophic factors. NTKR2 is involved in the biological processes of neuronal survival, proliferation, migration, differentiation, synaptogenesis, and synaptic plasticity.

[0193] NTKR3 is a receptor tyrosine kinase (part of the neurotrophin tyrosine receptor kinase family) that binds to neurotrophin 3. NTKR3 is involved in the biological processes of regulating GTPase and MAPK activity, astrocyte differentiation, nervous system development, and neuronal migration.

[0194] CDH6 is a member of the cadherin superfamily that mediates cell-cell adhesion. CDH6 is involved in the biological processes of cell-cell adhesion (adherent junctions), cell morphogenesis, and the Notch signaling pathway. CDH6 mediates both heterotypic and homotypic cell-cell contacts through interaction with CD6. CDH6 is also involved in axonal extension and axonal guidance.

[0195] CEACAM8 is a cell surface glycoprotein that belongs to the carcinoembryonic antigen (CEA) superfamily. CEACAM8 is involved in the biological processes of regulating immune responses, leukocyte migration, neutrophil degranulation, and cell-cell adhesion.

[0196] MAD1L1 is a mitotic spindle assembly checkpoint protein that is involved in the biological processes of cell division and mitotic cell cycle checkpoints.

[0197] FCAR is a transmembrane glycoprotein on the surface of immune cells such as neutrophils, monocytes, and macrophages. FCAR is involved in the biological processes of regulating immune responses, neutrophil activation / degranulation, and response to cytokines (e.g., interferons, interleukins, TNF).

[0198] MPO is a hemoprotein (enzyme) expressed in neutrophil granulocytes. MPO is involved in the biological processes of immune response, neutrophil degranulation, and chromatin / heme / heparin binding.

[0199] OMD is suggested to be involved in biomineralization processes and osteoblast attachment. OMD is involved in the biological process of cell adhesion and in regulating bone mineralization.

[0200] MEPE is a calcium-binding phosphoprotein of the small integrin-binding ligand, N-linked glycoprotein (SIBLING) family. MEPE is involved in the biological processes of extracellular matrix binding / regulation, biomineral tissue development, skeletal system development, and bone and cartilage mineralization. MEPE is involved in renal phosphate excretion and inhibits intestinal phosphate absorption. MEPE is further involved in dental pulp stem cell proliferation and differentiation.

[0201] GDNFR-α3 is a glial cell line-derived neurotrophic factor, a member of the GDNF receptor family, that binds to Artemin (ARTN). GDNFR-α3 is involved in the biological processes of axon guidance, nervous system development, neuronal migration, GDNF receptor activity, and signaling receptor activity and binding.

[0202] SCARF2 is a member of the scavenger receptor type F family. SCARF2 is an adhesion protein and is involved in the biological processes of scavenger receptor activity and cell-cell adhesion.

[0203] CD40 ligand is primarily expressed on activated T cells and members of the TNF superfamily of molecules. CD40 ligand is involved in the biological processes of B cell differentiation and proliferation, inflammatory responses, leukocyte intercellular adhesion, platelet activation, T cell costimulation, and TNF-mediated signaling pathways.

[0204] TNF-R2 is a membrane receptor that binds to tumor necrosis factor alpha. TNF-R2 is involved in the TNF-mediated signaling pathway, neutrophil degranulation, regulation of neuroinflammatory responses, and biological processes of cell signaling. TNF-R2 protects neurons from apoptosis by stimulating antioxidant pathways. TNF-R2 protects neurons from apoptosis by stimulating antioxidant pathways.

[0205] PD-L1 is a ligand that binds to PD-1 and is an important target in research into checkpoint inhibitors for cancer immunotherapy. PD-L1 is involved in the biological processes of immune response, interferon regulation, T cell proliferation, T cell costimulation, cell migration, and cytokine production.

[0206] NOTCH3 is a member of the NOTCH receptor family of proteins involved in the Notch signaling pathway. NOTCH3 is involved in the biological processes of gene activation, calcium ion binding, signaling receptor activity, neuronal fate determination, and brain development. Furthermore, NOTCH3 regulates cell fate determination.

[0207] CNTN1 is a neuronal membrane protein involved in cell adhesion. CNTN1 is involved in the biological processes of neuronal projection development, brain development, cell adhesion, and Notch signaling.

[0208] OSM is a cytokine of the interleukin 6 cytokine family. OSM is involved in the biological processes of immune response, cell growth / division, inflammatory responses, and regulation of cytokine activity (e.g., MAPK and STAT pathways).

[0209] TGFA is a mitogenic polypeptide and part of the epidermal growth factor family. TGFA is involved in the biological processes of growth factor activity, signal transduction pathways (e.g., MAPK, EGF), cell division / proliferation, and signal transduction.

[0210] PGLYRP1 is a peptidoglycan-binding protein and is involved in the biological processes of innate immune responses, inflammatory responses, neutrophil degranulation, and peptidoglycan immune receptor activity.

[0211] NOS3 regulates the production of nitric oxide and is involved in the biological processes of angiogenesis, vascular remodeling, endothelial cell migration, vasodilation, vascular smooth muscle relaxation, and promotion of blood clotting through platelet activation.

[0212] DDR1 regulates cell adhesion to the extracellular matrix, remodeling of the extracellular matrix, cell migration, differentiation, and survival and proliferation. In addition, DDR1 promotes smooth muscle cell migration.

[0213] CXCL166 is involved in immune regulation and serves as a scavenger receptor for macrophages.

[0214] IL6 is a cytokine involved in the differentiation of B cells, lymphocytes, and monocytes.

[0215] ALCAM is involved in axon guidance, differentiation pathways of embryonic and induced pluripotent stem cells, and lineage-specific markers, as well as L1CAM interactions.

[0216] NTRK2 is involved in the development and maturation of the central and peripheral nervous systems through the regulation of neuronal survival, proliferation, migration, differentiation, as well as synaptogenesis and plasticity.

[0217] SPON2 is involved in the outgrowth of embryonic hippocampal neurons.

[0218] NTRK3 is involved in nervous system regulation and cardiac development.

[0219] PCDH17 is involved in the establishment and function of specific cell-cell junctions in the brain.

[0220] VI. Assay As shown in FIG. 1A, the system environment 100 includes implementing a marker quantification assay 120 for assessing the expression level of one or more biomarkers. Examples of assays for one or more markers (e.g., marker quantification assays 120) include DNA assays, microarrays, polymerase chain reaction (PCR), RT-PCR, Southern blots, Northern blots, antibody binding assays, enzyme-linked immunosorbent assays (ELISA), flow cytometry, protein assays, Western blots, nephelometry, turbidimetry, chromatography, mass spectrometry, immunoassays including, but not limited to, RIA, immunofluorescence, immunochemiluminescence, immunoelectrochemiluminescence, or competitive immunoassays, immunoprecipitation, and assays described in the Examples section below. The information of the assays is quantitative and can be sent to the computer system of the invention. The information can also be qualitative, such as observed patterns or fluorescence, which can be converted by the user or automatically by a reader or computer system into a quantitative measurement.

[0221] A variety of immunoassays designed to quantify markers can be used in screening, including multiplex assays. Measuring the concentration of target markers in a sample or fractions thereof can be accomplished by a variety of specific assays. For example, traditional sandwich-type assays can be used in formats such as arrays, ELISAs, RIAs, etc. Other immunoassays include Ouchterlony plates, which provide a simple measurement of antibody binding. Additionally, Western blots can be performed on protein gels or protein spots on filters using convenient labeling methods, optionally with detection systems specific to the markers.

[0222] Protein-based analysis using an antibody that specifically binds to a polypeptide (e.g., a marker) can be used to quantify marker levels in a test sample obtained from a subject. In various embodiments, the antibody that binds to the marker can be a monoclonal antibody. In various embodiments, the antibody that binds to the marker can be a polyclonal antibody. In multiplex analysis of markers, an array containing one or more marker affinity reagents, e.g., antibodies, can be generated. Such arrays can be constructed that include antibodies against the markers. Detection can utilize one or a panel of marker affinity reagents, e.g., a panel or cocktail of affinity reagents specific for 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, or more markers.

[0223] In various embodiments, the multiplex assay involves the use of oligonucleotide-labeled antibody probes that bind to the target biomarkers and allow for subsequent quantification of the biomarkers. One example of a multiplex assay involving oligonucleotide-labeled antibody probes is the proximity extension assay (PEA) technology (Olink Proteomics). In summary, pairs of oligonucleotide-labeled antibodies bind to the biomarkers, and the two oligonucleotide sequences are complementary to each other. Thus, the oligonucleotide sequences will hybridize to each other only if both antibodies bind to the target biomarkers. Mismatched oligonucleotide sequences (resulting from non-specific binding of antibodies or cross-reactivity of antibodies) will not hybridize and therefore no readout will be obtained. The hybridized oligonucleotide sequences undergo nucleic acid extension and amplification and are then quantified using microfluidic qPCR. The quantified levels correlate with the quantitative expression values ​​of the respective biomarkers.

[0224] In various embodiments, the multiplex assay involves the use of bead-conjugated antibodies (e.g., capture antibodies) that allow for the binding and detection of biomarkers. One example of a multiplex assay involving bead-conjugated antibodies is Luminex's xMAP® technology. Here, a bead-conjugated antibody is added to the sample along with a biotinylated detection antibody. Both antibodies are specific for the biomarkers of interest, thus forming an antibody-antigen sandwich. In addition, streptavidin is added, which binds to the biotinylated detection antibody and allows for the detection of the complex. A Luminex 200™ or FlexMap® analyzer is used to identify and quantify the amount of biomarkers in the sample. In various embodiments, the multiplex assay represents an improvement over Luminex's xMAP® technology, e.g., the Multi-Analyte Profile (MAP) technology by Myriad Rules Based Medicine (RBM), Inc.

[0225] In various embodiments, a sample obtained from a subject can be treated prior to implementation of a marker quantification assay 120 (e.g., an immunoassay). In various embodiments, processing the sample enables implementation of the marker quantification assay 120 to more accurately assess the expression level of one or more biomarkers in the sample.

[0226] In various embodiments, a sample from a subject can be processed to extract biomarkers from the sample. In one embodiment, the sample can undergo phase separation to separate the biomarkers from other portions of the sample. For example, the sample can undergo centrifugation (e.g., pelleting or density gradient centrifugation) to separate larger and / or denser entities in the sample (e.g., cells and other macromolecules) from the biomarkers. Other examples include filtration (e.g., ultrafiltration) to phase separate the biomarkers from other portions of the sample.

[0227] In various embodiments, a sample from a subject can be processed to generate subsamples that contain a fraction of the biomarkers that were in the sample. In various embodiments, generating a fraction of the biomarkers can include performing a protein fractionation procedure. An example of a protein fractionation procedure includes chromatography (e.g., gel filtration, ion exchange, hydrophobic chromatography, or affinity chromatography). In certain embodiments, the protein fractionation procedure includes affinity purification or immunoprecipitation, in which the biomarkers are bound by specific antibodies. Such antibodies can be immobilized on a support, such as a magnetic particle or nanoparticle or a plate.

[0228] In various embodiments, a sample from a subject is processed to extract biomarkers from the sample and further processed to generate a subsample containing a fraction of the extracted biomarker. Overall, this allows for a purified subsample of a particular biomarker of interest. Thus, implementing an assay (e.g., an immunoassay) to evaluate the expression level of a particular biomarker of interest may be more accurate and of higher quality. In various embodiments, the particular biomarker may be a biomarker of a biomarker panel, the embodiments of which are described herein. By way of example, the biomarkers of the biomarker panel may include two or more of NEFL, MOG, CD6, CXCL9, OPG, OPN, CXCL13, GFAP, CDCP1, CCL20, IL-12B, APLP1, TNFRSF10A, COL4A1, SERPINA9, FLRT2, TNFSF13B, GH, VCAN, PRTG, and CNTN2.

[0229] VII. THERAPEUTIC AGENTS AND COMPOSITIONS FOR THERAPEUTIC AGENTS In various embodiments, a therapeutic agent is provided to an individual before and / or after obtaining a sample from the individual and determining the quantitative expression values ​​of one or more markers in the obtained sample. In one example, a predictive model receiving the quantitative expression values ​​predicts that an individual will be diagnosed with multiple sclerosis and that a therapeutic agent will be provided. In another example, a predictive model predicts that the provided therapeutic agent will demonstrate therapeutic efficacy for multiple sclerosis in a previously diagnosed individual.

[0230] In various embodiments, the therapeutic agent is a biologic, such as a cytokine, an antibody, a soluble cytokine receptor, an antisense oligonucleotide, an siRNA, etc. Such biologic agents include muteins and derivatives of the biologic agents, which may include, for example, fusion proteins, PEGylated derivatives, cholesterol-conjugated derivatives, etc., as known in the art. Also included are antagonists of cytokines and cytokine receptors, such as traps and monoclonal antagonists, such as IL-1Ra, IL-1 trap, sIL-4Ra, etc. Also included are drugs that are biologically similar or biologically equivalent to the active agents described herein.

[0231] Medications for treating multiple sclerosis include corticosteroids, plasma exchange, ocrelizumab (Ocrevus®), IFN-β (Avonex®, Betaseron®, Rebif®, Extavia®, Plegridy®), glatiramer acetate (Copaxone®, Glatopa®), anti-VLA4 (Tysabri, natalizumab), dimethyl fumarate (Tecfidera®, Vumerity®), teriflunomide (Aubagio®), monomethyl fumarate (Bafiertam™), ozanimod (Zeposia®), sirolimus (Sirolimus ... Examples of therapeutic agents include rifabutinib (Rabbitt®), siponimod (Mayzent®), fingolimod (Gilenya®), anti-CD52 antibodies (e.g., alemtuzumab (Lemtrada®), mitoxantrone (Novantrone®), methotrexate, cladribine (Mavenclad®, simvastatin, and cyclophosphamide). In addition to, or as an alternative to, therapeutic agents, other treatments for multiple sclerosis include lifestyle modifications, e.g., physical therapy or dietary changes. Methods also provide for combination therapy of one or more therapeutic agents and / or additional treatments, which combinations may also provide additive or synergistic benefits.

[0232] The pharmaceutical composition administered to an individual includes an active agent, such as a therapeutic agent as described above. The active ingredient is present in a therapeutically effective amount, i.e., an amount sufficient when administered to treat a disease or a condition mediated thereby. The composition may also include various other agents that enhance delivery and effectiveness, e.g., enhance delivery and stability of the active ingredient. Thus, for example, the composition may also include a pharma- ceutically acceptable non-toxic carrier or diluent, depending on the desired formulation, which is defined as a vehicle commonly used to formulate pharmaceutical compositions for animal or human administration. The diluent is selected so as not to affect the biological activity of the combination. Examples of such diluents are distilled water, buffered water, saline, PBS, Ringer's solution, dextrose solution, and Hank's solution. In addition, the pharmaceutical composition or formulation may include other carriers, adjuvants, or non-toxic, non-therapeutic, non-immunogenic stabilizers, excipients, and the like. The composition may also include additional substances that approximate physiological conditions, e.g., pH adjusting and buffering agents, toxicity adjusting agents, wetting agents, and detergents. The compositions may also include any of a variety of stabilizing agents, such as antioxidants.

[0233] The pharmaceutical compositions described herein can be administered in a variety of different ways, including by administering the composition containing a pharma- ceutical acceptable carrier via oral, intranasal, rectal, topical, intraperitoneal, intravenous, intramuscular, subcutaneous, subdermal, transdermal, intrathecal, or intracranial methods.

[0234] Such pharmaceutical compositions may be administered for prevention (e.g., before diagnosis of a multiple sclerosis patient) or for treatment (e.g., after diagnosis of a multiple sclerosis patient). Preventing, prophylaxis, or prevention of a disease or disorder as used in the context of the present invention refers to administration of a composition that prevents the occurrence or development of multiple sclerosis or some or all of the symptoms of multiple sclerosis, or reduces the likelihood of the onset of a disease or disorder. Treating, treatment, or therapy of multiple sclerosis shall mean slowing, stopping, or reversing the progression of the disease by administration of a treatment according to the present invention. In a preferred embodiment, treating multiple sclerosis means reversing the progression of the disease, ideally to the point of eliminating the disease itself.

[0235] VIII. Subject's Disease Activity The methods described herein focus on assessing a subject's disease activity by applying quantitative expression levels of biomarkers as inputs to a predictive model. In various embodiments, the subject is classified into a category based on the predicted assessment of disease activity. To classify the subject, the subject's prediction may be compared to the results of individuals who have been previously classified into a clinically diagnosed category. For example, an individual may be clinically classified into one of the following: a diagnosis of MS (e.g., presence of MS), a classification of static or worsening status, a classification by level of disability according to the Expanded Disability Status Scale (EDSS), an identification of clinical response to therapy, and a clinical identification of risk of developing MS. Clinical categories may also be determined using either the MS Functional Complex (MSFC), the Timed 25-foot walk (T25Fw), the 9-hole peg test (9HPT), or the PDDS / MSSS (Patient-Derived Disability Status Scale). Individuals may be clinically classified based on measurables for MS disease activity, such as a certain number of gadolinium-enhancing lesions (e.g., minimal disease activity) or the presence of at least one gadolinium-enhancing lesion (e.g., normal disease activity). Clinical classification may also be based on radiological measurements including T2 lesions (new or enlarging), slowly enlarging lesions, lesions with expanding rims, brain parenchymal fraction (BPF) and percent change, gray matter fraction, white matter fraction, thalamic volume, cortical gray matter volume, deep gray matter volume, or radiologist's notes of ancillary features (e.g., Dawson's fingers). Prior classification of individuals may occur based on clinical criteria.

[0236] Clinical diagnosis of MS can be made in a variety of ways. As an example, clinical diagnosis of MS can be made via magnetic resonance imaging (MRI) of the brain and spinal cord to identify lesions or plaques that form as a result of MS. The McDonald criteria can be used in making the diagnosis. Clinical diagnosis of MS can also be made by lumbar puncture (spinal tap) to observe abnormalities in antibody concentrations in the cerebrospinal fluid due to the presence of MS. Clinical diagnosis of MS can also be made by evoked potential testing (electrical signals generated by neurons in the nervous system are recorded in response to a stimulus). Conduction disturbances indicate the presence of MS.

[0237] The clinical classification of a patient previously diagnosed with MS in a quiescent state versus a worsening state may depend on various factors. That is, a patient may be clinically classified in a worsening state after presenting a new disease associated with MS (e.g., a comorbidity or symptom such as clinical depression or optic neuritis). As another example, a patient may be clinically classified in a worsening state if they present a significant worsening of symptoms. Examples may include worsening balance and / or mobility, vision, eye pain, fatigue, and / or heart-related problems. A patient previously diagnosed with MS may be clinically classified in a quiescent state if they do not present a new disease or a change or worsening of symptoms.

[0238] Determining that a patient previously diagnosed with MS is responding to therapy may depend on various clinical variables. For example, response to therapy can be determined based on the occurrence or lack of relapse. If no relapse occurs, the patient can be considered to have responded to therapy. Response to therapy can also be determined based on the total number of relapses, the time to first relapse, the patient's EDSS score, the change in the patient's EDSS score (e.g., an increase in score corresponds to no response to therapy), and the change in MRI status (e.g., the occurrence of additional lesions or plaques corresponds to no response to therapy).

[0239] Patients can be clinically classified into levels of disability that can be used as a criterion for assessing disease progression. For example, the EDSS can be used to determine the severity of a patient's MS. Patients are therefore classified into categories that correspond to EDSS scores of 1.0 to 10.0, in 0.5 point intervals. Typically, an EDSS score of 1.0 to 4.5 refers to an MS patient who is able to walk unaided. An EDSS score of 5.0 to 9.5 refers to an MS patient whose walking ability is impaired, with the higher the score, the greater the degree of disability.

[0240] IX. Computer Implementation The methods of the invention, including methods of assessing multiple sclerosis activity in an individual, are in some embodiments implemented on one or more computers.

[0241] For example, the construction and deployment of the predictive model and database storage can be implemented in hardware or software, or a combination of both. In one embodiment of the present invention, a machine-readable storage medium is provided, the medium including a data storage material encoded with machine-readable data, which when used with a machine programmed with instructions for using the data, can display any of the data sets and the execution and results of the predictive model of the present invention. Such data can be used for a variety of purposes, such as patient monitoring, treatment considerations, etc. The present invention can be implemented in a computer program executed on a programmable computer including a processor, a data storage system (including volatile and non-volatile memory and / or storage elements), a graphics adapter, a pointing device, a network adapter, at least one input device, and at least one output device. A display is connected to the graphics adapter. The program code is applied to the input data to perform the functions described above and generate output information. The output information is applied to one or more output devices in a known manner. The computer can be, for example, a personal computer, a microcomputer, or a workstation of conventional design.

[0242] Each program may be implemented in a high level procedural or object-oriented programming language to communicate with a computer system. However, the programs may be implemented in assembly or machine language, if desired. In either case, the language may be a compiled or interpreted language. Each such computer program for configuring and operating a computer is preferably stored on a general-purpose or special-purpose programmable computer-readable storage medium or device (e.g., a ROM or magnetic diskette) when the storage medium or device is read by a computer to perform the procedures described herein. A system configured with a computer program may also be considered to be implemented as a computer-readable storage medium, with the storage medium so configured causing the computer to operate in a specific predefined manner to perform the functions described herein.

[0243] The signature patterns and their databases can be provided in various media for ease of use. "Media" refers to a product containing the signature pattern information of the present invention. The database of the present invention can be recorded on a computer-readable medium, e.g., any medium that can be directly read and accessed by a computer. Such media include, but are not limited to: magnetic storage media, e.g., floppy disks, hard disk storage media, and magnetic tape; optical storage media, e.g., CD-ROM; electrical storage media, e.g., RAM and ROM; and hybrids of these categories, e.g., magnetic / optical storage media. Those skilled in the art can readily understand which currently known computer-readable media can be used to create a product that includes a recording of the present database information. "Recorded" refers to a process for storing information on a computer-readable medium using methods as known in the art. Any convenient data storage structure can be selected based on the means used to access the stored information. A variety of data processor programs and formats can be used for storage, e.g., word processing text files, database formats, etc.

[0244] In some embodiments, the methods of the present invention, including the method of assessing multiple sclerosis activity in an individual, are implemented on one or more computers in a distributed computing system environment (e.g., a cloud computing environment). In this description, "cloud computing" is defined as a model for enabling on-demand network access to a shared set of configurable computing resources. Cloud computing can be used to provide on-demand access to a shared set of configurable computing resources. The shared set of configurable computing resources can be easily provisioned through virtualization, released with little management effort or service provider interaction, and then scaled accordingly. Cloud computing models can be configured with various characteristics, such as, for example, on-demand self-service, broad network access, shared use of resources, rapid scalability, and scalable services. Cloud computing models can also expose various service models, such as, for example, software delivered as a service ("SaaS"), platform delivered as a service ("PaaS"), and infrastructure delivered as a service ("laaS"). Cloud computing models can also be deployed using various deployment models, such as private cloud, community cloud, public cloud, hybrid cloud, etc. In this description and claims, a "cloud computing environment" is an environment in which cloud computing is used.

[0245] VIII.A. Exemplary Computer Figure 8 illustrates an exemplary computer 800 for implementing the entities illustrated in Figures 1 and 3. Computer 800 includes at least one processor 802 coupled to a chipset 804. Chipset 804 includes a memory controller hub 820 and an input / output (I / O) controller hub 822. Memory 806 and a graphics adapter 812 are coupled to memory controller hub 820, and a display 818 is coupled to graphics adapter 812. Storage devices 808, input devices 814, and a network adapter 816 are coupled to I / O controller hub 822. Other embodiments of computer 800 have different architectures.

[0246] The storage device 808 is a non-transitory computer-readable storage medium, such as a hard drive, a compact disc read-only memory (CD-ROM), a DVD, or a solid-state memory device. The memory 806 holds instructions and data used by the processor 802. The input interface 814 is a touch screen interface, a mouse, a trackball, or other type of pointing device, a keyboard, or some combination thereof, used to input data into the computer 800. In some embodiments, the computer 800 may be configured to receive input (e.g., commands) from the input interface 814 via gestures from a user. The graphics adapter 812 displays images and other information on the display 818. The network adapter 816 connects the computer 800 to one or more computer networks.

[0247] The computer 800 is configured to execute computer program modules to provide the functionality described herein. As used herein, the term "module" refers to computer program logic used to provide a particular function. Thus, a module may be implemented in hardware, firmware, and / or software. In one embodiment, the program modules are stored in the storage device 808, loaded into the memory 806, and executed by the processor 802.

[0248] 1 may vary depending on the embodiment and the processing power required by the entities. For example, the activity prediction system 130 may run on a single computer 800 or on multiple computers 800 that communicate with each other over a network, such as a server farm. The computer 800 may lack some of the components described above, such as the graphics adapter 812 and the display 818.

[0249] X. Kit implementation Also disclosed herein are kits for assessing disease activity (e.g., multiple sclerosis activity) in an individual. Such kits may include reagents for detecting expression levels of one or more biomarkers and instructions for assessing disease activity based on the detected expression levels.

[0250] The detection reagents can be provided as part of a kit. Thus, the present invention further provides kits for detecting the presence of a panel of biomarkers of interest in a biological test sample. The kits can include a set of reagents for generating a data set via at least one protein detection assay (e.g., immunoassay) analyzing a test sample from a subject. In various embodiments, the set of reagents allows for detection of a quantitative expression level of a biomarker from any one of Tables 8-10. In certain embodiments, the set of reagents allows for detection of a quantitative expression level of a biomarker classified as a Tier A, Tier B, or Tier C biomarker in Table 1. In certain embodiments, the set of reagents allows for detection of a quantitative expression level of a biomarker classified as a Tier 1, Tier 2, or Tier 3 biomarker in Table 2. In certain aspects, the reagents include one or more antibodies that bind to one or more of the markers. The antibodies can be monoclonal or polyclonal antibodies. In some aspects, the reagents can include reagents for performing an ELISA including a buffer and a detection agent.

[0251] The kit may include instructions for use of the set of reagents. For example, the kit may include instructions for performing at least one biomarker detection assay, such as an immunoassay, a protein-binding assay, an antibody-based assay, an antigen-binding protein-based assay, a protein-based array, an enzyme-linked immunosorbent assay (ELISA), flow cytometry, a protein array, a blot, a Western blot, nephelometry, turbidimetry, chromatography, mass spectrometry, enzyme activity, a proximity extension assay, and an immunoassay selected from RIA, immunofluorescence, immunochemiluminescence, immunoelectrochemiluminescence, immunoelectrophoresis, competitive immunoassay, and immunoprecipitation.

[0252] In various embodiments, the kit includes instructions for practicing the methods disclosed herein (e.g., for training or deploying a predictive model that predicts the assessment of disease activity). These instructions can be present in the kit in various forms, one or more of which can be present in the kit. One form in which these instructions can be present is printed on a suitable medium or substrate, such as one or more sheets of paper on which the information is printed, in the packaging of the kit, in a package insert, etc. Yet another means would be a computer readable medium on which the information is recorded, such as a diskette, CD, hard drive, network data storage, etc. Yet another means that can be present is a website address that can be used via the Internet to access the information of the removed site. Any convenient means can be present in the kit.

[0253] XI. System Further disclosed herein is a system for analyzing quantitative expression levels of biomarkers to assess disease activity. In various embodiments, such a system may include a set of reagents for detecting expression levels of biomarkers in a biomarker panel, an apparatus configured to receive a mixture of the set of reagents and a test sample obtained from a subject to measure the expression level of a soluble mediator, and a computer system communicatively connected to the apparatus to obtain the measured expression levels and to implement a predictive model to assess disease activity.

[0254] The set of reagents allows for the detection of quantitative expression levels of biomarkers in a biomarker panel. In various embodiments, the set of reagents includes reagents used to perform an assay such as an assay or immunoassay as described above. For example, the reagents include one or more antibodies that bind to one or more of the biomarkers. The antibodies can be monoclonal or polyclonal antibodies. As another example, the reagents can include reagents for performing an ELISA, including a buffer and a detection agent.

[0255] The device is configured to detect the expression level of a biomarker in a mixture of reagents and a test sample. For example, the device may determine the quantitative expression level of a biomarker through an immunological assay or an assay for nucleic acid detection. The mixture of reagents and a test sample may be provided to the device through various conduits, examples of which include wells of a well plate (e.g., a 96-well plate), vials, tubes, and integrated fluidic circuits. Thus, the device may have an opening (e.g., a slot, cavity, opening, slide tray) that can receive a container containing the reagent test sample mixture and perform a reading to generate a quantitative expression value of the biomarker. Examples of devices include plate readers (e.g., luminescence plate readers, absorbance plate readers, fluorescence plate readers), spectrometers, and spectrophotometers.

[0256] A computer system, such as the exemplary computer 800 depicted in Figure 8, is in communication with the device to receive the quantitative expression values ​​of the biomarkers. The...

Claims

1. 1. A method for determining multiple sclerosis activity in a subject, the method comprising: obtaining or having obtained a dataset comprising expression levels of a plurality of biomarkers, the plurality of biomarkers comprising each biomarker of at least one group selected from group 1, group 2, and group 3; Group 1 includes biomarker 1, biomarker 2, biomarker 3, biomarker 4, biomarker 5, biomarker 6, biomarker 7, and biomarker 8; Biomarker 1 is NEFL, MOG, CADM3, or GFAP; Biomarker 2 is MOG, CADM3, KLK6, BCAN, OMG, or GFAP; Biomarker 3 is CD6, CD5, CRTAM, CD244, or TNFRSF9; Biomarker 4 is CXCL9, CXCL10, IL-12B, CXCL11, or GFAP; Biomarker 5 is OPG, TFF3, or ENPP2; Biomarker 6 is OPN, OMD, MEPE, or GFAP; Biomarker 7 is CXCL13, NOS3, or MMP-2; Biomarker 8 is GFAP, NEFL, OPN, CXCL9, MOG, or CHI3L1; Group 2 includes biomarker 9, biomarker 10, biomarker 11, biomarker 12, biomarker 13, biomarker 14, biomarker 15, biomarker 16, and biomarker 17; Biomarker 9 is CDCP1, IL-18BP, IL-18, GFAP, or MSR1; Biomarker 10 is CCL20, CCL3, or TWEAK; Biomarker 11 is IL-12B, IL12A, or CXCL9; Biomarker 12 is APLP1, SEZ6L, BCAN, DPP6, NCAN, or KLK6; Biomarker 13 is TNFRSF10A, TNFRSF11A, SPON2, CHI3L1, or IFI30; Biomarker 14 is COL4A1, IL6, NOTCH3, or PCDH17; Biomarker 15 is SERPINA9, TNFRSF9, or CNTN4; Biomarker 16 is FLRT2, DDR1, NTRK2, CDH6, MMP-2; Biomarker 17 is TNFSF13B, CXCL16, ALCAM, or IL-18; Group 3 includes biomarker 18, biomarker 19, biomarker 20, and biomarker 21; Biomarker 18 is GH, GH2, or IGFBP-1; Biomarker 19 is VCAN, TINAGL1, CANT1, NECTIN2, MMP-9, or NPDC1; Biomarker 20 is PRTG, NTRK2, NTRK3, or CNTN4; Biomarker 21 is CNTN2, DPP6, GDNFR-α3, or SCARF2; said acquiring or having acquired; and Applying a predictive model to the expression levels of said plurality of biomarkers to generate a prediction of multiple sclerosis disease activity.

2. 2. The method of claim 1, wherein the plurality of biomarkers comprises each biomarker in Group 1, wherein biomarker 1 is NEFL, biomarker 2 is MOG, biomarker 3 is CD6, biomarker 4 is CXCL9, biomarker 5 is OPG, biomarker 6 is OPN, biomarker 7 is CXCL13, and biomarker 8 is GFAP.

3. The method of any one of claims 1 to 2, wherein the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.667 to 0.

869.

4. The method of any one of claims 1 to 2, wherein the performance of the predictive model is characterized by a positive predictive value ranging from 0.617 to 0.

861.

5. 2. The method of claim 1, wherein the plurality of biomarkers comprises each biomarker of Group 2, wherein biomarker 9 is CDCP1, biomarker 10 is CCL20, biomarker 11 is IL-12B, biomarker 12 is APLP1, biomarker 13 is TNFRSF10A, biomarker 14 is COL4A1, biomarker 15 is SERPINA9, biomarker 16 is FLRT2, and biomarker 17 is TNFSF13B.

6. 6. The method of claim 5, wherein the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.595 to 0.

761.

7. 6. The method of claim 5, wherein the performance of the predictive model is characterized by a positive predictive value ranging from 0.523 to 0.

769.

8. 3. The method of claim 2, wherein the plurality of biomarkers further comprises each biomarker of Group 2, wherein biomarker 9 is CDCP1, biomarker 10 is CCL20, biomarker 11 is IL-12B, biomarker 12 is APLP1, biomarker 13 is TNFRSF10A, biomarker 14 is COL4A1, biomarker 15 is SERPINA9, biomarker 16 is FLRT2, and biomarker 17 is TNFSF13B.

9. 9. The method of claim 8, wherein the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.693 to 0.

892.

10. 9. The method of claim 8, wherein the performance of the predictive model is characterized by a positive predictive value ranging from 0.613 to 0.

843.

11. 2. The method of claim 1, wherein the plurality of biomarkers includes each biomarker in group 3, biomarker 18 is GH, biomarker 19 is VCAN, biomarker 20 is PRTG, and biomarker 21 is CNTN2.

12. 12. The method of claim 11, wherein the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.566 to 0.

644.

13. 12. The method of claim 11, wherein the performance of the predictive model is characterized by a positive predictive value ranging from 0.370 to 0.

742.

14. 9. The method of claim 8, wherein the plurality of biomarkers further comprises each biomarker in group 3, wherein biomarker 18 is GH, biomarker 19 is VCAN, biomarker 20 is PRTG, and biomarker 21 is CNTN2.

15. 12. The method of claim 11, wherein the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.686 to 0.

889.

16. 12. The method of claim 11, wherein the performance of the predictive model is characterized by a positive predictive value ranging from 0.648 to 0.

835.

17. 1. A method for determining multiple sclerosis activity in a subject, the method comprising: Obtaining or having obtained a dataset comprising expression levels of a plurality of biomarkers including NEFL, MOG, CD6, CXCL9, OPG, OPN, CXCL13, and GFAP; and Applying a predictive model to the expression levels of said plurality of biomarkers to generate a prediction of multiple sclerosis disease activity.

18. 18. The method of claim 17, wherein the plurality of biomarkers further comprises CDCP1, CCL20, IL-12B, APLP1, TNFRSF10A, COL4A1, SERPINA9, FLRT2, and TNFSF13B.

19. 20. The method of claim 18, wherein the plurality of biomarkers further comprises GH, VCAN, PRTG, and CNTN2.

20. 1. A method for generating quantitative data of a subject, comprising: performing at least one immunoassay on a sample obtained from said subject to generate a dataset comprising said quantitative data. Including, the quantitative data represents at least eight protein biomarkers, including NEFL, MOG, CD6, CXCL9, OPG, OPN, CXCL13, and GFAP; The subject has or is suspected of having multiple sclerosis. The method.

21. 21. The method of claim 20, wherein the quantitative data further represents at least nine additional protein biomarkers including CDCP1, CCL20, IL-12B, APLP1, TNFRSF10A, COL4A1, SERPINA9, FLRT2, and TNFSF13B.

22. 22. The method of claim 21, wherein the quantitative data further represents at least four additional protein biomarkers including GH, VCAN, PRTG, and CNTN2.

23. determining multiple sclerosis activity in said subject; and wherein the determining comprises applying a predictive model to the quantitative data. The method according to any one of claims 20 to 22.

24. 24. The method of claim 23, wherein applying the decision further comprises comparing a score output by the predictive model to a reference score.

25. The reference score is A) The patient at baseline when the patient showed no disease activity; B) Patients with clinically diagnosed disease without active disease, or C) healthy patients; 25. The method of claim 24, corresponding to any of the following:

26. 1. A method for determining multiple sclerosis activity in a subject, the method comprising: one or more neurodegeneration biomarkers selected from the group consisting of NEFL, APLP1, OPG, SERPINA9, PRTG, GFAP, CNTN2, and FLRT2; one or more inflammatory biomarkers selected from the group consisting of CCL20, GH, CXCL13, IL-12B, VCAN, TNFRSF10A, TNFSF13B, CD6, and CXCL9; one or more immune modulating biomarkers selected from the group consisting of CDCP1 and OPN; One or more myelin integrity biomarkers selected from the group consisting of COL4A1 and MOG. obtaining or having obtained a dataset comprising expression levels of a plurality of biomarkers, including Applying a predictive model to the expression levels of said plurality of biomarkers to generate a prediction of multiple sclerosis disease activity.

27. 27. The method of claim 26, wherein the one or more neurodegeneration biomarkers comprise NEFL, OPG, and GFAP, the one or more inflammation biomarkers comprise CXCL13, CD6, and CXCL9, the one or more immune modulation biomarkers comprise OPN, and the one or more myelin integrity biomarkers comprise MOG.

28. 28. The method of claim 27, wherein the one or more neurodegeneration biomarkers further comprise APLP1, SERPINA9, and FLRT2, the one or more inflammation biomarkers further comprise CCL20, IL-12B, TNFRSF10A, and TNFSF13B, the one or more immune modulation biomarkers further comprise CDCP1, and the one or more myelin integrity biomarkers further comprise COL4A1.

29. 29. The method of claim 28, wherein the one or more neurodegeneration biomarkers further comprise PRTG and CNTN2, and the one or more inflammation biomarkers comprise GH and VCAN.

30. The method of any one of claims 1 to 29, wherein the plurality of biomarkers are protein biomarkers.

31. The multiple sclerosis disease activity is the presence of normal disease activity, the presence of minimal disease activity, a shift in disease activity (increase or decrease), MS severity, relapse or relapse events associated with MS, relapse rate, MS status, response to MS therapy, extent of MS disability, or risk of developing MS The method according to any one of claims 1 to 29, wherein the method is any one of

32. 32. The method of claim 31, wherein the usual disease activity is the presence or absence of one or more gadolinium-enhancing MRI lesions and the insignificant disease activity is the presence of one gadolinium-enhancing MRI lesion.

33. 32. The method of claim 31 , wherein the severity of MS corresponds to the number of gadolinium-enhancing MRI lesions.

34. 32. The method of claim 31, wherein the MS condition is an exacerbation or quiescence of multiple sclerosis.

35. 1. A method for determining multiple sclerosis activity in a subject, the method comprising: obtaining or having obtained a dataset comprising expression levels of a plurality of biomarkers, the plurality of biomarkers comprising two or more of NEFL, MOG, CD6, CXCL9, OPG, OPN, CXCL13, GFAP, CDCP1, CCL20, IL-12B, APLP1, TNFRSF10A, COL4A1, SERPINA9, FLRT2, TNFSF13B, GH, VCAN, PRTG, and CNTN2; and Applying a predictive model to the expression levels of said plurality of biomarkers to generate a prediction of multiple sclerosis disease activity.

36. 1. A method for preparing a biomarker fraction of a sample obtained from a subject, the method comprising: (a) extracting biomarkers from the sample; (b) after (a), generating a fraction of the extracted biomarkers, wherein the fraction of the extracted biomarkers after (b) comprises a plurality of biomarkers, the plurality of biomarkers comprising two or more of NEFL, MOG, CD6, CXCL9, OPG, OPN, CXCL13, GFAP, CDCP1, CCL20, IL-12B, APLP1, TNFRSF10A, COL4A1, SERPINA9, FLRT2, TNFSF13B, GH, VCAN, PRTG, and CNTN2; (c) analyzing the plurality of biomarkers in the fraction of the extracted biomarkers produced in (b).

37. 37. The method of claim 35 or 36, wherein the two or more biomarkers comprise NEFL and at least one other biomarker.

38. 38. The method of claim 37, wherein the two or more biomarkers are a biomarker pair selected from any one of: A) NEFL and CD6, B) NEFL and MOG, C) NEFL and CXCL9, and D) NEFL and TNFRSF10A.

39. 38. The method of claim 37, wherein the two or more biomarkers are a biomarker triad selected from any one of A) NEFL, CD6, and CXCL9, and B) NEFL, TNFRSF10A, and COL4A1.

40. 38. The method of claim 37, wherein the two or more biomarkers are a biomarker quartet selected from any one of: A) NEFL, MOG, CD6, and CXCL9; B) NEFL, CXCL9, TNFRSF10A, and COL4A1; C) NEFL, CD6, CXCL9, and CXCL13; and D) NEFL, TNFRSF10A, COL4A1, and CCL20.

41. 37. The method of claim 35 or 36, wherein the two or more biomarkers do not include NEFL.

42. 42. The method of claim 41, wherein the two or more biomarkers are a biomarker pair selected from any one of A) MOG and IL-12B, B) CXCL9 and CD6, C) MOG and CXCL9, D) MOG and CD6, E) CXCL9 and COL4A1, and F) CD6 and VCAN.

43. 42. The method of claim 41, wherein the two or more biomarkers are a biomarker triad selected from any one of: A) MOG, IL-12B, and APLP1; B) MOG, CD6, and CXCL9; C) CXCL9, COL4A1, and VCAN; D) MOG, IL-12B, and CNTN2; and E) CD6, CCL20, and VCAN.

44. 42. The method of claim 41, wherein the two or more biomarkers are a biomarker quartet selected from any one of: A) MOG, CXCL9, IL-12B, APLP1; B) CXCL9, COL4A1, OPG, and VCAN; C) CXCL9, OPG, APLP1, and OPN; D) MOG, IL-12B, OPN, and CNTN2; and E) CD6, COL4A1, CCL20, and VCAN.

45. 45. The method of any one of claims 37 to 44, wherein the multiple sclerosis activity is a shift in disease activity.

46. 38. The method of claim 37, wherein the two or more biomarkers are a biomarker pair selected from any one of: A) NEFL and TNFSF13B, B) NEFL and CNTN2, and C) NEFL and CXCL9.

47. 38. The method of claim 37, wherein the two or more biomarkers are a biomarker triad selected from any one of: A) NEFL, CNTN2, and TNFSF13B; B) NEFL, APLP1, and TNFSF13B; and C) NEFL, TNFRSF10A, and TNFSF13B.

48. 38. The method of claim 37, wherein the two or more biomarkers are a biomarker quartet selected from any one of: A) NEFL, TNFRSF10A, CNTN2, and TNFSF13B; B) NEFL, COL4A1, CNTN2, and TNFSF13B; and C) NEFL, TNFRSF10A, APLP1, and TNFSF13B.

49. 42. The method of claim 41, wherein the two or more biomarkers are a biomarker pair selected from any one of: A) MOG and CDCP1, B) MOG and TNFSF13B, and C) MOG and CXCL9.

50. 42. The method of claim 41 , wherein the two or more biomarkers are a biomarker triad and one biomarker in the biomarker triad is MOG.

51. 51. The method of claim 50, wherein the biomarker triad is selected from any one of: A) MOG, CXCL9, and TNFSF13B; B) MOG, OPG, and TNFSF13B; and C) MOG, CCL20, and TNFSF13B.

52. 42. The method of claim 41 , wherein the two or more biomarkers are a biomarker quartet, and one biomarker in the biomarker quartet is MOG.

53. 53. The method of claim 52, wherein the biomarker quartet is selected from any one of A) MOG, CXCL9, APLP1, and TNFSF13B; B) MOG, CXCL9, OPG, and TNFSF13B; and C) MOG, CXCL9, OPG, and CNTN2.

54. The method of any one of claims 46 to 53, wherein the multiple sclerosis activity is the presence or absence of multiple sclerosis.

55. 38. The method of claim 37, wherein the two or more biomarkers are a biomarker pair selected from any one of: A) NEFL and TNFSF13B, B) NEFL and SERPINA9, and C) NEFL and GH.

56. 38. The method of claim 37, wherein the two or more biomarkers are a biomarker triad selected from any one of A) NEFL, SERPINA9, and TNFSF13B; B) NEFL, CNTN2, and TNFSF13B; and C) NEFL, APLP1, and TNFSF13B.

57. 38. The method of claim 37, wherein the two or more biomarkers are a biomarker quartet selected from any one of: A) NEFL, CCL20, SERPINA9, and TNFSF13B; B) NEFL, APLP1, SERPINA9, and TNFSF13B; C) NEFL, CCL20, APLP1, and TNFSF13B.

58. 42. The method of claim 41, wherein the two or more biomarkers are a biomarker pair selected from any one of A) MOG and TNFSF13B, B) MOG and CXCL9, and C) MOG and IL-12B.

59. 42. The method of claim 41, wherein the two or more biomarkers are a biomarker triad selected from any one of A) MOG, CXCL9, and TNFSF13B; B) MOG, SERPINA9, and TNFSF13B; and C) MOG, OPG, and TNFSF13B.

60. 42. The method of claim 41, wherein the two or more biomarkers are a biomarker quartet selected from any one of: A) MOG, CXCL9, OPG, and TNFSF13B; B) MOG, CXCL9, OPG, SERPINA9, and TNFSF13B; C) MOG, CXCL9, SERPINA9, and TNFSF13B.

61. 61. The method of any one of claims 55 to 60, wherein the multiple sclerosis activity is disease severity based on a predicted number of gadolinium-enhancing lesions.

62. 37. The method of claim 35 or 36, wherein the two or more biomarkers are a biomarker pair selected from any one of A) GFAP and MOG, B) GFAP and NEFL, C) APLP1 and GFAP, D) NEFL and MOG, or E) CXCL9 and OPG.

63. 37. The method of claim 35 or 36, wherein the two or more biomarkers are a biomarker triad selected from any one of: A) GFAP, NEFL, and MOG; B) NEFL, MOG, and GH; C) NEFL, MOG, and SERPINA9; D) CXCL9, OPG, and SERPINA9; or E) CXCL9, OPG, and TNFRSF10A.

64. 37. The method of claim 35 or 36, wherein the two or more biomarkers are a biomarker triad selected from any one of: A) GFAP, NEFL, MOG, and IL12B; B) GFAP, NEFL, MOG, and PRTG; C) GFAP, NEFL, MOG, and APLP1; D) NEFL, MOG, GH, and SERPINA9; E) NEFL, MOG, GH, and TNFRSF10A; F) MOG, CXCL9, OPG, and SERPINA9; or G) CD6, IL12B, APLP1, and CCL20.

65. The method of any one of claims 62 to 64, wherein the multiple sclerosis activity is disease progression.

66. 66. The method of any one of claims 1-19 and 26-65, further comprising classifying the subject based on the prediction.

67. 66. The method of any one of claims 1 to 19 and 26 to 65, wherein generating the prediction of multiple sclerosis disease activity comprises comparing a score output by the predictive model to a reference score.

68. The reference score is A) The patient at baseline when the patient showed no disease activity; B) Patients with clinically diagnosed disease without active disease, or C) Healthy patients 68. The method of claim 67, corresponding to any one of:

69. The method of any one of claims 1-19 and 26-68, wherein the expression levels of the plurality of biomarkers are determined from a test sample obtained from the subject.

70. 70. The method of claim 69, wherein the test sample is a blood or serum sample.

71. 71. The method of claim 69 or 70, wherein the subject has, is suspected of having, or has previously been diagnosed with multiple sclerosis.

72. 72. The method of any one of claims 1-19 and 26-71, wherein obtaining or having obtained the dataset comprises performing an immunoassay to determine an expression level of the plurality of biomarkers.

73. 73. The method of claim 72, wherein the immunoassay is a proximity extension assay (PEA) or a LUMINEX xMAP multiplex assay.

74. 74. The method of claim 72 or 73, wherein performing the immunoassay comprises contacting the test sample with a plurality of reagents comprising antibodies.

75. 75. The method of claim 74, wherein the antibody comprises one of a monoclonal antibody and a polyclonal antibody.

76. 75. The method of claim 74, wherein the antibodies include both monoclonal and polyclonal antibodies.

77. 77. The method of any one of claims 1 to 76, further comprising selecting a therapy for administration to the subject based on the prediction of multiple sclerosis disease activity.

78. 77. The method of any one of claims 1 to 76, further comprising determining the therapeutic efficacy of a therapy previously administered to the subject based on the prediction of multiple sclerosis disease activity.

79. 79. The method of claim 78, wherein determining the therapeutic efficacy of said therapy comprises comparing said prediction to a previous prediction determined for said subject at a previous time point.

80. 80. The method of claim 79, wherein determining the therapeutic efficacy of the therapy comprises determining that the therapy exhibits efficacy according to a difference between the prediction and the previous prediction.

81. 80. The method of claim 79, wherein determining the therapeutic efficacy of the therapy comprises determining that the therapy is ineffective responsive to no difference between the prediction and the previous prediction.

82. 77. The method of any one of claims 1 to 76, further comprising determining a differential diagnosis of multiple sclerosis based on the prediction of multiple sclerosis disease activity.

83. 83. The method of claim 82, wherein the differential diagnosis of multiple sclerosis includes at least one of relapsing remitting multiple sclerosis (RRMS), secondary progressive multiple sclerosis (SPMS), primary progressive multiple sclerosis (PPMS), progressive relapsing multiple sclerosis (PRMS), and clinically isolated syndrome (CIS).

84. 1. A non-transitory computer readable medium for determining multiple sclerosis activity in a subject, the non-transitory computer readable medium comprising instructions that, when executed by a processor, cause the processor to: obtaining a dataset comprising expression levels of a plurality of biomarkers, the plurality of biomarkers comprising each biomarker of at least one group selected from group 1, group 2, and group 3; Group 1 includes biomarker 1, biomarker 2, biomarker 3, biomarker 4, biomarker 5, biomarker 6, biomarker 7, and biomarker 8; Biomarker 1 is NEFL, MOG, CADM3, or GFAP; Biomarker 2 is MOG, CADM3, KLK6, BCAN, OMG, or GFAP; Biomarker 3 is CD6, CD5, CRTAM, CD244, or TNFRSF9; Biomarker 4 is CXCL9, CXCL10, IL-12B, CXCL11, or GFAP; Biomarker 5 is OPG, TFF3, or ENPP2; Biomarker 6 is OPN, OMD, MEPE, or GFAP; Biomarker 7 is CXCL13, NOS3, or MMP-2; Biomarker 8 is GFAP, NEFL, OPN, CXCL9, MOG, or CHI3L1; Group 2 includes biomarker 9, biomarker 10, biomarker 11, biomarker 12, biomarker 13, biomarker 14, biomarker 15, biomarker 16, and biomarker 17; Biomarker 9 is CDCP1, IL-18BP, IL-18, GFAP, or MSR1; Biomarker 10 is CCL20, CCL3, or TWEAK; Biomarker 11 is IL-12B, IL12A, or CXCL9; Biomarker 12 is APLP1, SEZ6L, BCAN, DPP6, NCAN, or KLK6; Biomarker 13 is TNFRSF10A, TNFRSF11A, SPON2, CHI3L1, or IFI30; Biomarker 14 is COL4A1, IL6, NOTCH3, or PCDH17; Biomarker 15 is SERPINA9, TNFRSF9, or CNTN4; Biomarker 16 is FLRT2, DDR1, NTRK2, CDH6, MMP-2; Biomarker 17 is TNFSF13B, CXCL16, ALCAM, or IL-18; Group 3 includes biomarker 18, biomarker 19, biomarker 20, and biomarker 21; Biomarker 18 is GH, GH2, or IGFBP-1; Biomarker 19 is VCAN, TINAGL1, CANT1, NECTIN2, MMP-9, or NPDC1; Biomarker 20 is PRTG, NTRK2, NTRK3, or CNTN4; Biomarker 21 is CNTN2, DPP6, GDNFR-α3, or SCARF2; The said obtaining, and Applying a predictive model to the expression levels of said plurality of biomarkers to generate a prediction of multiple sclerosis disease activity.

85. 85. The non-transitory computer readable medium of claim 84, wherein the plurality of biomarkers comprises each biomarker of Group 1, wherein biomarker 1 is NEFL, biomarker 2 is MOG, biomarker 3 is CD6, biomarker 4 is CXCL9, biomarker 5 is OPG, biomarker 6 is OPN, biomarker 7 is CXCL13, and biomarker 8 is GFAP.

86. 86. The non-transitory computer readable medium of claim 84 or 85, wherein the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.667 to 0.

869.

87. 86. The non-transitory computer readable medium of claim 84 or 85, wherein the performance of the predictive model is characterized by a positive predictive value ranging from 0.617 to 0.

861.

88. 85. The non-transitory computer readable medium of claim 84, wherein the plurality of biomarkers comprises each biomarker of Group 2, wherein biomarker 9 is CDCP1, biomarker 10 is CCL20, biomarker 11 is IL-12B, biomarker 12 is APLP1, biomarker 13 is TNFRSF10A, biomarker 14 is COL4A1, biomarker 15 is SERPINA9, biomarker 16 is FLRT2, and biomarker 17 is TNFSF13B.

89. 89. The non-transitory computer readable medium of claim 88, wherein the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.595 to 0.

761.

90. 89. The non-transitory computer readable medium of claim 88, wherein the performance of the predictive model is characterized by a positive predictive value ranging from 0.523 to 0.

769.

91. 86. The non-transitory computer readable medium of claim 85, wherein the plurality of biomarkers further comprises each biomarker of Group 2, wherein biomarker 9 is CDCP1, biomarker 10 is CCL20, biomarker 11 is IL-12B, biomarker 12 is APLP1, biomarker 13 is TNFRSF10A, biomarker 14 is COL4A1, biomarker 15 is SERPINA9, biomarker 16 is FLRT2, and biomarker 17 is TNFSF13B.

92. 92. The non-transitory computer readable medium of claim 91, wherein the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.693 to 0.

892.

93. 92. The non-transitory computer readable medium of claim 91, wherein the performance of the predictive model is characterized by a positive predictive value ranging from 0.613 to 0.

843.

94. 85. The non-transitory computer readable medium of claim 84, wherein the plurality of biomarkers comprises each biomarker in group 3, wherein biomarker 18 is GH, biomarker 19 is VCAN, biomarker 20 is PRTG, and biomarker 21 is CNTN2.

95. 95. The non-transitory computer readable medium of claim 94, wherein the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.566 to 0.

644.

96. 95. The non-transitory computer readable medium of claim 94, wherein the performance of the predictive model is characterized by a positive predictive value ranging from 0.370 to 0.

742.

97. 92. The non-transitory computer readable medium of claim 91 , wherein the plurality of biomarkers further comprises each biomarker of Group 3, wherein biomarker 18 is GH, biomarker 19 is VCAN, biomarker 20 is PRTG, and biomarker 21 is CNTN2.

98. 98. The non-transitory computer readable medium of claim 97, wherein the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.686 to 0.

889.

99. 98. The non-transitory computer readable medium of claim 97, wherein the performance of the predictive model is characterized by a positive predictive value ranging from 0.648 to 0.

835.

100. 1. A non-transitory computer readable medium for determining multiple sclerosis activity in a subject, the non-transitory computer readable medium comprising instructions that, when executed by a processor, cause the processor to perform the steps of: obtaining or having obtained a dataset comprising expression levels of a plurality of biomarkers including NEFL, MOG, CD6, CXCL9, OPG, OPN, CXCL13, and GFAP; and Applying a predictive model to the expression levels of said plurality of biomarkers to generate a prediction of multiple sclerosis disease activity.

101. 101. The non-transitory computer readable medium of claim 100, wherein the plurality of biomarkers further comprises CDCP1, CCL20, IL-12B, APLP1, TNFRSF10A, COL4A1, SERPINA9, FLRT2, and TNFSF13B.

102. 102. The non-transitory computer readable medium of claim 100 or 101, wherein the plurality of biomarkers further comprises GH, VCAN, PRTG, and CNTN2.

103. 1. A non-transitory computer readable medium for determining multiple sclerosis activity in a subject, the non-transitory computer readable medium comprising instructions that, when executed by a processor, cause the processor to: one or more neurodegeneration biomarkers selected from the group consisting of NEFL, APLP1, OPG, SERPINA9, PRTG, GFAP, CNTN2, and FLRT2; one or more inflammatory biomarkers selected from the group consisting of CCL20, GH, CXCL13, IL-12B, VCAN, TNFRSF10A, TNFSF13B, CD6, and CXCL9; one or more immune modulating biomarkers selected from the group consisting of CDCP1 and OPN; One or more myelin integrity biomarkers selected from the group consisting of COL4A1 and MOG. obtaining a dataset comprising expression levels of a plurality of biomarkers, Applying a predictive model to the expression levels of said plurality of biomarkers to generate a prediction of multiple sclerosis disease activity.

104. 104. The non-transitory computer readable medium of claim 103, wherein the one or more neurodegeneration biomarkers comprise NEFL, OPG, and GFAP, the one or more inflammation biomarkers comprise CXCL13, CD6, and CXCL9, the one or more immune modulation biomarkers comprise OPN, and the one or more myelin integrity biomarkers comprise MOG.

105. 105. The non-transitory computer readable medium of claim 104, wherein the one or more neurodegeneration biomarkers further comprise APLP1, SERPINA9, and FLRT2, the one or more inflammation biomarkers further comprise CCL20, IL-12B, TNFRSF10A, and TNFSF13B, the one or more immune modulation biomarkers further comprise CDCP1, and the one or more myelin integrity biomarkers further comprise COL4A1.

106. The non-transitory computer readable medium of claim 104, wherein the one or more neurodegeneration biomarkers further comprise PRTG and CNTN2, and the one or more inflammation biomarkers comprise GH and VCAN.

107. 107. The method of any one of claims 84 to 106, wherein the plurality of biomarkers are protein biomarkers.

108. 108. The non-transitory computer readable medium of any one of claims 84-107, wherein the multiple sclerosis disease activity is any one of: presence of normal disease activity, presence of little disease activity, a shift in disease activity (increase or decrease), MS severity, relapse or exacerbation events associated with MS, relapse rate, MS status, response to MS therapy, extent of MS disability, or risk of developing MS.

109. The non-transitory computer readable medium of claim 108, wherein the usual disease activity is the presence or absence of one or more gadolinium-enhancing MRI lesions and the insignificant disease activity is the presence of one gadolinium-enhancing MRI lesion.

110. 109. The non-transitory computer readable medium of claim 108, wherein the severity of the MS corresponds to the number of gadolinium-enhancing MRI lesions.

111. The non-transitory computer readable medium of claim 108, wherein the MS condition is an exacerbation or quiescence of multiple sclerosis.

112. 1. A non-transitory computer readable medium for determining multiple sclerosis activity in a subject, the non-transitory computer readable medium comprising instructions that, when executed by a processor, cause the processor to: obtaining a dataset comprising expression levels of a plurality of biomarkers, the plurality of biomarkers comprising two or more of NEFL, MOG, CD6, CXCL9, OPG, OPN, CXCL13, GFAP, CDCP1, CCL20, IL-12B, APLP1, TNFRSF10A, COL4A1, SERPINA9, FLRT2, TNFSF13B, GH, VCAN, PRTG, and CNTN2; and Applying a predictive model to the expression levels of said plurality of biomarkers to generate a prediction of multiple sclerosis disease activity.

113. The non-transitory computer readable medium of claim 112, wherein the two or more biomarkers include NEFL and at least one other biomarker.

114. The non-transitory computer readable medium of claim 113, wherein the two or more biomarkers are a biomarker pair selected from any one of A) NEFL and CD6, B) NEFL and MOG, C) NEFL and CXCL9, and D) NEFL and TNFRSF10A.

115. 114. The non-transitory computer readable medium of claim 113, wherein the two or more biomarkers are a biomarker triad selected from any one of A) NEFL, CD6, and CXCL9, and B) NEFL, TNFRSF10A, and COL4A1.

116. 114. The non-transitory computer readable medium of claim 113, wherein the two or more biomarkers are a biomarker quartet selected from any one of: A) NEFL, MOG, CD6, and CXCL9; B) NEFL, CXCL9, TNFRSF10A, and COL4A1; C) NEFL, CD6, CXCL9, and CXCL13; and D) NEFL, TNFRSF10A, COL4A1, and CCL20.

117. The non-transitory computer readable medium of claim 112, wherein the two or more biomarkers do not include NEFL.

118. The non-transitory computer readable medium of claim 117, wherein the two or more biomarkers are a biomarker pair selected from any one of A) MOG and IL-12B, B) CXCL9 and CD6, C) MOG and CXCL9, D) MOG and CD6, E) CXCL9 and COL4A1, and F) CD6 and VCAN.

119. 118. The non-transitory computer readable medium of claim 117, wherein the two or more biomarkers are a biomarker triad selected from any one of: A) MOG, IL-12B, and APLP1; B) MOG, CD6, and CXCL9; C) CXCL9, COL4A1, and VCAN; D) MOG, IL-12B, and CNTN2; and E) CD6, CCL20, and VCAN.

120. 118. The non-transitory computer readable medium of claim 117, wherein the two or more biomarkers are a biomarker quartet selected from any one of: A) MOG, CXCL9, IL-12B, APLP1; B) CXCL9, COL4A1, OPG, and VCAN; C) CXCL9, OPG, APLP1, and OPN; D) MOG, IL-12B, OPN, and CNTN2; and E) CD6, COL4A1, CCL20, and VCAN.

121. 121. The non-transitory computer readable medium of any one of claims 113-120, wherein the multiple sclerosis activity is a shift in disease activity.

122. The non-transitory computer readable medium of claim 113, wherein the two or more biomarkers are a biomarker pair selected from any one of A) NEFL and TNFSF13B, B) NEFL and CNTN2, and C) NEFL and CXCL9.

123. The non-transitory computer readable medium of claim 113, wherein the two or more biomarkers are a biomarker triad selected from any one of: A) NEFL, CNTN2, and TNFSF13B; B) NEFL, APLP1, and TNFSF13B; and C) NEFL, TNFRSF10A, and TNFSF13B.

124. 114. The non-transitory computer readable medium of claim 113, wherein the two or more biomarkers are a biomarker quartet selected from any one of: A) NEFL, TNFRSF10A, CNTN2, and TNFSF13B; B) NEFL, COL4A1, CNTN2, and TNFSF13B; and C) NEFL, TNFRSF10A, APLP1, and TNFSF13B.

125. The non-transitory computer readable medium of claim 117, wherein the two or more biomarkers are a biomarker pair selected from any one of A) MOG and CDCP1, B) MOG and TNFSF13B, and C) MOG and CXCL9.

126. The non-transitory computer readable medium of claim 117, wherein the two or more biomarkers are a biomarker triad and one biomarker in the biomarker triad is MOG.

127. 127. The non-transitory computer readable medium of claim 126, wherein the biomarker triad is selected from any one of A) MOG, CXCL9, and TNFSF13B, B) MOG, OPG, and TNFSF13B, and C) MOG, CCL20, and TNFSF13B.

128. The non-transitory computer readable medium of claim 117, wherein the two or more biomarkers are a biomarker quartet and one biomarker in the biomarker quartet is MOG.

129. 129. The non-transitory computer readable medium of claim 128, wherein the biomarker quartet is selected from any one of A) MOG, CXCL9, APLP1, and TNFSF13B; B) MOG, CXCL9, OPG, and TNFSF13B; and C) MOG, CXCL9, OPG, and CNTN2.

130. 130. The non-transitory computer readable medium of any one of claims 122-129, wherein the multiple sclerosis activity is the presence or absence of multiple sclerosis.

131. The non-transitory computer readable medium of claim 113, wherein the two or more biomarkers are a biomarker pair selected from any one of A) NEFL and TNFSF13B, B) NEFL and SERPINA9, and C) NEFL and GH.

132. The non-transitory computer readable medium of claim 113, wherein the two or more biomarkers are a biomarker triad selected from any one of A) NEFL, SERPINA9, and TNFSF13B, B) NEFL, CNTN2, and TNFSF13B, and C) NEFL, APLP1, and TNFSF13B.

133. 114. The non-transitory computer readable medium of claim 113, wherein the two or more biomarkers are a biomarker quartet selected from any one of: A) NEFL, CCL20, SERPINA9, and TNFSF13B; B) NEFL, APLP1, SERPINA9, and TNFSF13B; C) NEFL, CCL20, APLP1, and TNFSF13B.

134. The non-transitory computer readable medium of claim 117, wherein the two or more biomarkers are a biomarker pair selected from any one of A) MOG and TNFSF13B, B) MOG and CXCL9, and C) MOG and IL-12B.

135. 118. The non-transitory computer readable medium of claim 117, wherein the biomarker triad is selected from any one of A) MOG, CXCL9, and TNFSF13B; B) MOG, SERPINA9, and TNFSF13B; and C) MOG, OPG, and TNFSF13B.

136. 118. The non-transitory computer readable medium of claim 117, wherein the biomarker quartet is selected from any one of A) MOG, CXCL9, OPG, and TNFSF13B; B) MOG, CXCL9, OPG, SERPINA9, and TNFSF13B; C) MOG, CXCL9, SERPINA9, and TNFSF13B.

137. 137. The non-transitory computer readable medium of any one of claims 131 to 136, wherein the multiple sclerosis activity is disease severity based on a predicted number of gadolinium-enhancing lesions.

138. The non-transitory computer readable medium of claim 112, wherein the two or more biomarkers are a biomarker pair selected from any one of A) GFAP and MOG, B) GFAP and NEFL, C) APLP1 and GFAP, D) NEFL and MOG, or E) CXCL9 and OPG.

139. 113. The non-transitory computer readable medium of claim 112, wherein the two or more biomarkers are a biomarker triad selected from any one of: A) GFAP, NEFL, and MOG; B) NEFL, MOG, and GH; C) NEFL, MOG, and SERPINA9; D) CXCL9, OPG, and SERPINA9; or E) CXCL9, OPG, and TNFRSF10A.

140. 113. The non-transitory computer readable medium of claim 112, wherein the two or more biomarkers are a biomarker triad selected from any one of: A) GFAP, NEFL, MOG, and IL12B; B) GFAP, NEFL, MOG, and PRTG; C) GFAP, NEFL, MOG, and APLP1; D) NEFL, MOG, GH, and SERPINA9; E) NEFL, MOG, GH, and TNFRSF10A; F) MOG, CXCL9, OPG, and SERPINA9; or G) CD6, IL12B, APLP1, and CCL20.

141. The non-transitory computer readable medium of any one of claims 138 to 140, wherein the multiple sclerosis activity is disease progression.

142. 142. The non-transitory computer readable medium of any one of claims 84 to 141, further comprising classifying the object based on the prediction.

143. 143. The non-transitory computer readable medium of any one of claims 84-142, wherein generating the prediction of multiple sclerosis disease activity comprises comparing a score output by the predictive model to a reference score.

144. The reference score is A) The patient at baseline when the patient showed no disease activity; B) Patients with clinically diagnosed disease without active disease, or C) Healthy patients 144. The non-transitory computer readable medium of claim 143, corresponding to any one of:

145. The non-transitory computer readable medium of any one of claims 84-144, wherein the expression levels of the plurality of biomarkers are determined from a test sample obtained from the subject.

146. The non-transitory computer readable medium of claim 145, wherein the test sample is a blood or serum sample.

147. The non-transitory computer readable medium of claim 145 or 146, wherein the subject has, is suspected of having, or has previously been diagnosed with multiple sclerosis.

148. 148. The non-transitory computer readable medium of any one of claims 84 to 147, wherein the dataset is obtained from a performed immunoassay.

149. The non-transitory computer readable medium of claim 148, wherein the immunoassay is a proximity extension assay (PEA) or a LUMINEX xMAP multiplex assay.

150. 149. The non-transitory computer readable medium of claim 148, wherein performing the immunoassay comprises contacting the test sample with a plurality of reagents including antibodies.

151. 151. The non-transitory computer readable medium of claim 150, wherein the antibody comprises one of a monoclonal antibody and a polyclonal antibody.

152. The non-transitory computer readable medium of claim 150, wherein the antibodies include both monoclonal and polyclonal antibodies.

153. 153. The non-transitory computer readable medium of any one of claims 84-152, further comprising selecting a therapy for administration to the subject based on the prediction of multiple sclerosis disease activity.

154. The non-transitory computer readable medium of any one of claims 84 to 152, further comprising determining a therapeutic efficacy of a therapy previously administered to the subject based on the prediction of multiple sclerosis disease activity.

155. The non-transitory computer readable medium of claim 154, wherein determining the therapeutic effectiveness of the therapy comprises comparing the prediction to a previous prediction determined for the subject at a previous time point.

156. 156. The non-transitory computer readable medium of claim 155, wherein determining the therapeutic efficacy of the therapy comprises determining that the treatment exhibits efficacy according to a difference between the prediction and the prior prediction.

157. 156. The non-transitory computer readable medium of claim 155, wherein determining the therapeutic effectiveness of the therapy comprises determining that the treatment is ineffective responsive to no difference between the prediction and the previous prediction.

158. 153. The non-transitory computer readable medium of any one of claims 84-152, further comprising determining a differential diagnosis of multiple sclerosis based on the prediction of multiple sclerosis disease activity.

159. 159. The non-transitory computer readable medium of claim 158, wherein the differential diagnosis of multiple sclerosis includes any one of relapsing remitting multiple sclerosis (RRMS), secondary progressive multiple sclerosis (SPMS), primary progressive multiple sclerosis (PPMS), progressive relapsing multiple sclerosis (PRMS), and CIS.

160. 1. A kit for assessing multiple sclerosis disease activity in a subject, the kit comprising: A set of reagents for determining the expression level of a plurality of biomarkers in a test sample from the subject, comprising: the plurality of biomarkers includes biomarkers from at least one group selected from group 1, group 2, and group 3; Group 1 includes biomarker 1, biomarker 2, biomarker 3, biomarker 4, biomarker 5, biomarker 6, biomarker 7, and biomarker 8; Biomarker 1 is NEFL, MOG, CADM3, or GFAP; Biomarker 2 is MOG, CADM3, KLK6, BCAN, OMG, or GFAP; Biomarker 3 is CD6, CD5, CRTAM, CD244, or TNFRSF9; Biomarker 4 is CXCL9, CXCL10, IL-12B, CXCL11, or GFAP; Biomarker 5 is OPG, TFF3, or ENPP2; Biomarker 6 is OPN, OMD, MEPE, or GFAP; Biomarker 7 is CXCL13, NOS3, or MMP-2; Biomarker 8 is GFAP, NEFL, OPN, CXCL9, MOG, or CHI3L1; Group 2 includes biomarker 9, biomarker 10, biomarker 11, biomarker 12, biomarker 13, biomarker 14, biomarker 15, biomarker 16, and biomarker 17; Biomarker 9 is CDCP1, IL-18BP, IL-18, GFAP, or MSR1; Biomarker 10 is CCL20, CCL3, or TWEAK; Biomarker 11 is IL-12B, IL12A, or CXCL9; Biomarker 12 is APLP1, SEZ6L, BCAN, DPP6, NCAN, or KLK6; Biomarker 13 is TNFRSF10A, TNFRSF11A, SPON2, CHI3L1, or IFI30; Biomarker 14 is COL4A1, IL6, NOTCH3, or PCDH17; Biomarker 15 is SERPINA9, TNFRSF9, or CNTN4; Biomarker 16 is FLRT2, DDR1, NTRK2, CDH6, MMP-2; Biomarker 17 is TNFSF13B, CXCL16, ALCAM, or IL-18; Group 3 includes biomarker 18, biomarker 19, biomarker 20, and biomarker 21; Biomarker 18 is GH, GH2, or IGFBP-1; Biomarker 19 is VCAN, TINAGL1, CANT1, NECTIN2, MMP-9, or NPDC1; Biomarker 20 is PRTG, NTRK2, NTRK3, or CNTN4; Biomarker 21 is CNTN2, DPP6, GDNFR-α3, or SCARF2; The set of reagents, and Instructions for using said set of reagents to determine the expression level of biomarkers in said test sample.

161. The kit of claim 160, wherein the plurality of biomarkers comprises each biomarker of Group 1, wherein biomarker 1 is NEFL, biomarker 2 is MOG, biomarker 3 is CD6, biomarker 4 is CXCL9, biomarker 5 is OPG, biomarker 6 is OPN, biomarker 7 is CXCL13, and biomarker 8 is GFAP.

162. 162. The kit of claim 160 or 161, wherein the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.667 to 0.

869.

163. 162. The kit of claim 160 or 161, wherein the performance of the predictive model is characterized by a positive predictive value ranging from 0.617 to 0.

861.

164. The kit of claim 160, wherein the plurality of biomarkers comprises each biomarker of Group 2, wherein biomarker 9 is CDCP1, biomarker 10 is CCL20, biomarker 11 is IL-12B, biomarker 12 is APLP1, biomarker 13 is TNFRSF10A, biomarker 14 is COL4A1, biomarker 15 is SERPINA9, biomarker 16 is FLRT2, and biomarker 17 is TNFSF13B.

165. 165. The kit of claim 164, wherein the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.595 to 0.

761.

166. 165. The kit of claim 164, wherein the performance of the predictive model is characterized by a positive predictive value ranging from 0.523 to 0.

769.

167. The kit of claim 160, wherein the plurality of biomarkers further comprises each biomarker of Group 2, wherein biomarker 9 is CDCP1, biomarker 10 is CCL20, biomarker 11 is IL-12B, biomarker 12 is APLP1, biomarker 13 is TNFRSF10A, biomarker 14 is COL4A1, biomarker 15 is SERPINA9, biomarker 16 is FLRT2, and biomarker 17 is TNFSF13B.

168. 168. The kit of claim 167, wherein the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.693 to 0.

892.

169. 168. The kit of claim 167, wherein the performance of the predictive model is characterized by a positive predictive value ranging from 0.613 to 0.

843.

170. The kit of claim 160, wherein the plurality of biomarkers includes each biomarker of group 3, biomarker 18 is GH, biomarker 19 is VCAN, biomarker 20 is PRTG, and biomarker 21 is CNTN2.

171. 171. The kit of claim 170, wherein the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.566 to 0.

644.

172. 171. The kit of claim 170, wherein the performance of the predictive model is characterized by a positive predictive value ranging from 0.370 to 0.

742.

173. The kit of claim 167, wherein the plurality of biomarkers further comprises each biomarker of group 3, wherein biomarker 18 is GH, biomarker 19 is VCAN, biomarker 20 is PRTG, and biomarker 21 is CNTN2.

174. 174. The kit of claim 173, wherein the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.686 to 0.

889.

175. The kit of claim 173, wherein the performance of the predictive model is characterized by a positive predictive value ranging from 0.648 to 0.

835.

176. 1. A kit for assessing multiple sclerosis disease activity in a subject, the kit comprising: a set of reagents for determining an expression level of a plurality of biomarkers in a test sample from the subject, the plurality of biomarkers comprising NEFL, MOG, CD6, CXCL9, OPG, OPN, CXCL13, and GFAP; and Instructions for using said set of reagents to determine the expression level of biomarkers in said test sample.

177. The kit of claim 176, wherein the plurality of biomarkers further comprises CDCP1, CCL20, IL-12B, APLP1, TNFRSF10A, COL4A1, SERPINA9, FLRT2, and TNFSF13B.

178. The kit of claim 177, wherein the plurality of biomarkers further comprises GH, VCAN, PRTG, and CNTN2.

179. 1. A kit for assessing multiple sclerosis disease activity in a subject, the kit comprising: A set of reagents for determining an expression level of a plurality of biomarkers in a test sample from the subject, the plurality of biomarkers comprising: one or more neurodegeneration biomarkers selected from the group consisting of NEFL, APLP1, OPG, SERPINA9, PRTG, GFAP, CNTN2, and FLRT2; one or more inflammatory biomarkers selected from the group consisting of CCL20, GH, CXCL13, IL-12B, VCAN, TNFRSF10A, TNFSF13B, CD6, and CXCL9; one or more immune modulating biomarkers selected from the group consisting of CDCP1 and OPN; One or more myelin integrity biomarkers selected from the group consisting of COL4A1 and MOG. The set of reagents comprising: Instructions for using said set of reagents to determine the expression level of biomarkers in said test sample.

180. The kit of claim 179, wherein the one or more neurodegeneration biomarkers include NEFL, OPG, and GFAP, the one or more inflammation biomarkers include CXCL13, CD6, and CXCL9, the one or more immune modulation biomarkers include OPN, and the one or more myelin integrity biomarkers include MOG.

181. The kit of claim 180, wherein the one or more neurodegeneration biomarkers further comprise APLP1, SERPINA9, and FLRT2, the one or more inflammation biomarkers further comprise CCL20, IL-12B, TNFRSF10A, and TNFSF13B, the one or more immune modulation biomarkers further comprise CDCP1, and the one or more myelin integrity biomarkers further comprise COL4A1.

182. The kit of claim 181, wherein the one or more neurodegeneration biomarkers further comprise PRTG and CNTN2, and the one or more inflammation biomarkers comprise GH and VCAN.

183. The kit of any one of claims 160 to 181, wherein the plurality of biomarkers are protein biomarkers.

184. The multiple sclerosis disease activity is the presence of normal disease activity, the presence of minimal disease activity, a shift in disease activity (increase or decrease), MS severity, relapse or relapse events associated with MS, relapse rate, MS status, response to MS therapy, extent of MS disability, or risk of developing MS The kit according to any one of claims 160 to 183, wherein the kit is any one of the following:

185. The kit of claim 184, wherein the normal disease activity is the presence or absence of one or more gadolinium-enhancing MRI lesions and the insignificant disease activity is the presence of one gadolinium-enhancing MRI lesion.

186. The kit of claim 184, wherein the severity of MS corresponds to the number of gadolinium-enhancing MRI lesions.

187. The kit of claim 184, wherein the MS condition is an exacerbation or quiescence of multiple sclerosis.

188. 1. A kit for determining multiple sclerosis activity in a subject, the kit comprising: a set of reagents for determining an expression level of a plurality of biomarkers in a test sample from the subject, the plurality of biomarkers comprising two or more of NEFL, MOG, CD6, CXCL9, OPG, OPN, CXCL13, GFAP, CDCP1, CCL20, IL-12B, APLP1, TNFRSF10A, COL4A1, SERPINA9, FLRT2, TNFSF13B, GH, VCAN, PRTG, and CNTN2; and Instructions for using said set of reagents to determine the expression level of biomarkers in said test sample.

189. The kit of claim 188, wherein the two or more biomarkers include NEFL and at least one other biomarker.

190. The kit of claim 189, wherein the two or more biomarkers are a biomarker pair selected from any one of A) NEFL and CD6, B) NEFL and MOG, C) NEFL and CXCL9, and D) NEFL and TNFRSF10A.

191. 190. The kit of claim 189, wherein the two or more biomarkers are a biomarker triad selected from any one of A) NEFL, CD6, and CXCL9, and B) NEFL, TNFRSF10A, and COL4A1.

192. 190. The kit of claim 189, wherein the two or more biomarkers are a biomarker quartet selected from any one of A) NEFL, MOG, CD6, and CXCL9; B) NEFL, CXCL9, TNFRSF10A, and COL4A1; C) NEFL, CD6, CXCL9, and CXCL13; and D) NEFL, TNFRSF10A, COL4A1, and CCL20.

193. The kit of claim 188, wherein the two or more biomarkers do not include NEFL.

194. The kit of claim 193, wherein the two or more biomarkers are a biomarker pair selected from any one of A) MOG and IL-12B, B) CXCL9 and CD6, C) MOG and CXCL9, D) MOG and CD6, E) CXCL9 and COL4A1, and F) CD6 and VCAN.

195. 194. The kit of claim 193, wherein the two or more biomarkers are a biomarker triad selected from any one of A) MOG, IL-12B, and APLP1; B) MOG, CD6, and CXCL9; C) CXCL9, COL4A1, and VCAN; D) MOG, IL-12B, and CNTN2; and E) CD6, CCL20, and VCAN.

196. 194. The kit of claim 193, wherein the two or more biomarkers are a biomarker quartet selected from any one of A) MOG, CXCL9, IL-12B, APLP1; B) CXCL9, COL4A1, OPG, and VCAN; C) CXCL9, OPG, APLP1, and OPN; D) MOG, IL-12B, OPN, and CNTN2; and E) CD6, COL4A1, CCL20, and VCAN.

197. The kit of any one of claims 189 to 196, wherein the multiple sclerosis activity is a shift in disease activity.

198. The kit of claim 189, wherein the two or more biomarkers are a biomarker pair selected from any one of A) NEFL and TNFSF13B, B) NEFL and CNTN2, and C) NEFL and CXCL9.

199. The kit of claim 189, wherein the two or more biomarkers are a biomarker triad selected from any one of A) NEFL, CNTN2, and TNFSF13B; B) NEFL, APLP1, and TNFSF13B; and C) NEFL, TNFRSF10A, and TNFSF13B.

200. The kit of claim 189, wherein the two or more biomarkers are a biomarker quartet selected from any one of A) NEFL, TNFRSF10A, CNTN2, and TNFSF13B, B) NEFL, COL4A1, CNTN2, and TNFSF13B, and C) NEFL, TNFRSF10A, APLP1, and TNFSF13B.

201. The kit of claim 193, wherein the two or more biomarkers are a biomarker pair selected from any one of A) MOG and CDCP1, B) MOG and TNFSF13B, and C) MOG and CXCL9.

202. 194. The kit of claim 193, wherein the two or more biomarkers are a biomarker triad and one biomarker in the biomarker triad is MOG.

203. 203. The kit of claim 202, wherein the biomarker triad is selected from any one of A) MOG, CXCL9, and TNFSF13B; B) MOG, OPG, and TNFSF13B; and C) MOG, CCL20, and TNFSF13B.

204. 194. The kit of claim 193, wherein the two or more biomarkers are a biomarker quartet, and one biomarker in the biomarker quartet is MOG.

205. 205. The kit of claim 204, wherein the biomarker quartet is selected from any one of A) MOG, CXCL9, APLP1, and TNFSF13B; B) MOG, CXCL9, OPG, and TNFSF13B; and C) MOG, CXCL9, OPG, and CNTN2.

206. The kit of any one of claims 198 to 205, wherein the multiple sclerosis activity is the presence or absence of multiple sclerosis.

207. The kit of claim 189, wherein the two or more biomarkers are a biomarker pair selected from any one of A) NEFL and TNFSF13B, B) NEFL and SERPINA9, and C) NEFL and GH.

208. The kit of claim 189, wherein the two or more biomarkers are a biomarker triad selected from any one of A) NEFL, SERPINA9, and TNFSF13B; B) NEFL, CNTN2, and TNFSF13B; and C) NEFL, APLP1, and TNFSF13B.

209. The kit of claim 189, wherein the two or more biomarkers are a biomarker quartet selected from any one of A) NEFL, CCL20, SERPINA9, and TNFSF13B; B) NEFL, APLP1, SERPINA9, and TNFSF13B; and C) NEFL, CCL20, APLP1, and TNFSF13B.

210. The kit of claim 193, wherein the two or more biomarkers are a biomarker pair selected from any one of A) MOG and TNFSF13B, B) MOG and CXCL9, and C) MOG and IL-12B.

211. The kit of claim 193, wherein the two or more biomarkers are a biomarker triad selected from any one of A) MOG, CXCL9, and TNFSF13B, B) MOG, SERPINA9, and TNFSF13B, and C) MOG, OPG, and TNFSF13B.

212. The kit of claim 193, wherein the two or more biomarkers are a biomarker quartet selected from any one of A) MOG, CXCL9, OPG, and TNFSF13B; B) MOG, CXCL9, OPG, SERPINA9, and TNFSF13B; and C) MOG, CXCL9, SERPINA9, and TNFSF13B.

213. The kit of any one of claims 207 to 212, wherein the multiple sclerosis activity is disease severity based on a predicted number of gadolinium-enhancing lesions.

214. The kit of claim 188, wherein the two or more biomarkers are a biomarker pair selected from any one of A) GFAP and MOG, B) GFAP and NEFL, C) APLP1 and GFAP, D) NEFL and MOG, or E) CXCL9 and OPG.

215. The kit of claim 188, wherein the two or more biomarkers are a biomarker triad selected from any one of A) GFAP, NEFL, and MOG; B) NEFL, MOG, and GH; C) NEFL, MOG, and SERPINA9; D) CXCL9, OPG, and SERPINA9; or E) CXCL9, OPG, and TNFRSF10A.

216. 189. The kit of claim 188, wherein the two or more biomarkers are a biomarker triad selected from any one of: A) GFAP, NEFL, MOG, and IL12B; B) GFAP, NEFL, MOG, and PRTG; C) GFAP, NEFL, MOG, and APLP1; D) NEFL, MOG, GH, and SERPINA9; E) NEFL, MOG, GH, and TNFRSF10A; F) MOG, CXCL9, OPG, and SERPINA9; or G) CD6, IL12B, APLP1, and CCL20.

217. The kit of any one of claims 214 to 216, wherein the multiple sclerosis activity is disease progression.

218. The kit of any one of claims 160-217, wherein the instructions further comprise instructions for applying a predictive model to expression levels of the plurality of biomarkers to generate a prediction of multiple sclerosis disease activity.

219. The kit of claim 218, wherein generating the prediction of multiple sclerosis disease activity comprises comparing a score output by the predictive model to a reference score.

220. The reference score is A) The patient at baseline when the patient showed no disease activity; B) Patients with clinically diagnosed disease without active disease, or C) Healthy patients The kit of claim 219, which corresponds to any one of the following:

221. The kit of any one of claims 160 to 220, wherein the test sample is a blood or serum sample.

222. The kit of any one of claims 160-221, wherein the subject has, is suspected of having, or has previously been diagnosed with multiple sclerosis.

223. The kit of any one of claims 160-222, wherein the instructions for using the set of reagents comprise instructions for performing an immunoassay to determine the expression level of the plurality of biomarkers.

224. The kit of claim 223, wherein the immunoassay is a proximity extension assay (PEA) or a LUMINEXxMAP multiplex assay.

225. 225. The kit of claim 223 or 224, wherein performing the immunoassay comprises contacting the test sample with a plurality of reagents including antibodies.

226. The kit of claim 225, wherein the antibody comprises one of a monoclonal antibody and a polyclonal antibody.

227. The kit of claim 225, wherein the antibodies include both monoclonal and polyclonal antibodies.

228. The kit of any one of claims 218 to 227, further comprising instructions for selecting a therapy for administration to the subject based on the prediction of multiple sclerosis disease activity.

229. The kit of any one of claims 218 to 227, further comprising instructions for determining the therapeutic efficacy of a therapy previously administered to the subject based on the prediction of multiple sclerosis disease activity.

230. The kit of claim 229, wherein determining the therapeutic efficacy of the therapy comprises comparing the prediction to a previous prediction determined for the subject at a previous time point.

231. 231. The kit of claim 230, wherein determining the therapeutic efficacy of the therapy comprises determining that the treatment exhibits efficacy according to a difference between the prediction and the previous prediction.

232. 231. The kit of claim 230, wherein determining the therapeutic efficacy of the therapy comprises determining that the treatment is ineffective responsive to no difference between the prediction and the previous prediction.

233. The kit of any one of claims 218 to 227, further comprising instructions for determining a differential diagnosis of multiple sclerosis based on the prediction of multiple sclerosis disease activity.

234. The kit of claim 233, wherein the differential diagnosis of multiple sclerosis includes any one of relapsing remitting multiple sclerosis (RRMS), secondary progressive multiple sclerosis (SPMS), primary progressive multiple sclerosis (PPMS), progressive relapsing multiple sclerosis (PRMS), and CIS.

235. 1. A system for assessing multiple sclerosis disease activity in a subject, the system comprising: A set of reagents used to determine an expression level of a plurality of biomarkers in a test sample from the subject, the plurality of biomarkers including each biomarker of at least one group selected from Group 1, Group 2, and Group 3; Group 1 includes biomarker 1, biomarker 2, biomarker 3, biomarker 4, biomarker 5, biomarker 6, biomarker 7, and biomarker 8; Biomarker 1 is NEFL, MOG, CADM3, or GFAP; Biomarker 2 is MOG, CADM3, KLK6, BCAN, OMG, or GFAP; Biomarker 3 is CD6, CD5, CRTAM, CD244, or TNFRSF9; Biomarker 4 is CXCL9, CXCL10, IL-12B, CXCL11, or GFAP; Biomarker 5 is OPG, TFF3, or ENPP2; Biomarker 6 is OPN, OMD, MEPE, or GFAP; Biomarker 7 is CXCL13, NOS3, or MMP-2; Biomarker 8 is GFAP, NEFL, OPN, CXCL9, MOG, or CHI3L1; Group 2 includes biomarker 9, biomarker 10, biomarker 11, biomarker 12, biomarker 13, biomarker 14, biomarker 15, biomarker 16, and biomarker 17; Biomarker 9 is CDCP1, IL-18BP, IL-18, GFAP, or MSR1; Biomarker 10 is CCL20, CCL3, or TWEAK; Biomarker 11 is IL-12B, IL12A, or CXCL9; Biomarker 12 is APLP1, SEZ6L, BCAN, DPP6, NCAN, or KLK6; Biomarker 13 is TNFRSF10A, TNFRSF11A, SPON2, CHI3L1, or IFI30; Biomarker 14 is COL4A1, IL6, NOTCH3, or PCDH17; Biomarker 15 is SERPINA9, TNFRSF9, or CNTN4; Biomarker 16 is FLRT2, DDR1, NTRK2, CDH6, MMP-2; Biomarker 17 is TNFSF13B, CXCL16, ALCAM, or IL-18; Group 3 includes biomarker 18, biomarker 19, biomarker 20, and biomarker 21; Biomarker 18 is GH, GH2, or IGFBP-1; Biomarker 19 is VCAN, TINAGL1, CANT1, NECTIN2, MMP-9, or NPDC1; Biomarker 20 is PRTG, NTRK2, NTRK3, or CNTN4; Biomarker 21 is CNTN2, DPP6, GDNFR-α3, or SCARF2; The set of reagents, and a device configured to receive a mixture of one or more reagents of the set and the test sample, and to measure the expression level of the biomarker in the test sample; and A computer system communicatively connected to the device for obtaining a dataset comprising expression levels of the plurality of biomarkers in the test sample, and for generating a prediction of multiple sclerosis disease activity by applying a predictive model to the expression levels of the plurality of biomarkers.

236. The system of claim 227, wherein the plurality of biomarkers includes each biomarker of group 1, where biomarker 1 is NEFL, biomarker 2 is MOG, biomarker 3 is CD6, biomarker 4 is CXCL9, biomarker 5 is OPG, biomarker 6 is OPN, biomarker 7 is CXCL13, and biomarker 8 is GFAP.

237. 237. The system of claim 227 or 236, wherein the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.667 to 0.

869.

238. The system of claims 227 or 236, wherein the performance of the predictive model is characterized by a positive predictive value ranging from 0.617 to 0.

861.

239. The system of claim 227, wherein the plurality of biomarkers comprises each biomarker of Group 2, wherein biomarker 9 is CDCP1, biomarker 10 is CCL20, biomarker 11 is IL-12B, biomarker 12 is APLP1, biomarker 13 is TNFRSF10A, biomarker 14 is COL4A1, biomarker 15 is SERPINA9, biomarker 16 is FLRT2, and biomarker 17 is TNFSF13B.

240. 240. The system of claim 239, wherein the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.595 to 0.

761.

241. The system of claim 239, wherein the performance of the predictive model is characterized by a positive predictive value ranging from 0.523 to 0.

769.

242. The system of claim 236, wherein the plurality of biomarkers further comprises each biomarker of Group 2, wherein biomarker 9 is CDCP1, biomarker 10 is CCL20, biomarker 11 is IL-12B, biomarker 12 is APLP1, biomarker 13 is TNFRSF10A, biomarker 14 is COL4A1, biomarker 15 is SERPINA9, biomarker 16 is FLRT2, and biomarker 17 is TNFSF13B.

243. 243. The system of claim 242, wherein the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.693 to 0.

892.

244. 243. The system of claim 242, wherein the performance of the predictive model is characterized by a positive predictive value ranging from 0.613 to 0.

843.

245. The system of claim 227, wherein the plurality of biomarkers includes each biomarker in group 3, biomarker 18 is GH, biomarker 19 is VCAN, biomarker 20 is PRTG, and biomarker 21 is CNTN2.

246. 246. The system of claim 245, wherein the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.566 to 0.

644.

247. The system of claim 245, wherein the performance of the predictive model is characterized by a positive predictive value ranging from 0.370 to 0.

742.

248. The system of claim 242, wherein the plurality of biomarkers further comprises each biomarker of group 3, wherein biomarker 18 is GH, biomarker 19 is VCAN, biomarker 20 is PRTG, and biomarker 21 is CNTN2.

249. The system of claim 248, wherein the performance of the predictive model is characterized by an area under the curve (AUC) ranging from 0.686 to 0.

889.

250. The system of claim 248, wherein the performance of the predictive model is characterized by a positive predictive value ranging from 0.648 to 0.

835.

251. 1. A system for assessing multiple sclerosis disease activity in a subject, the system comprising: A set of reagents used to determine the expression level of a plurality of biomarkers in a test sample from the subject, the plurality of biomarkers comprising NEFL, MOG, CD6, CXCL9, OPG, OPN, CXCL13, and GFAP; and a device configured to receive a mixture of one or more reagents of the set and the test sample, and to measure the expression level of the biomarker in the test sample; and A computer system communicatively connected to the device for obtaining a dataset comprising expression levels of the plurality of biomarkers in the test sample, and for generating a prediction of multiple sclerosis disease activity by applying a predictive model to the expression levels of the plurality of biomarkers.

252. The system of claim 251, wherein the plurality of biomarkers further comprises CDCP1, CCL20, IL-12B, APLP1, TNFRSF10A, COL4A1, SERPINA9, FLRT2, and TNFSF13B.

253. The system of claim 252, wherein the plurality of biomarkers further comprises GH, VCAN, PRTG, and CNTN2.

254. 1. A system for determining multiple sclerosis activity in a subject, the system comprising: A set of reagents used to determine the expression level of a plurality of biomarkers in a test sample from the subject, the plurality of biomarkers comprising: one or more neurodegeneration biomarkers selected from the group consisting of NEFL, APLP1, OPG, SERPINA9, PRTG, GFAP, CNTN2, and FLRT2; one or more inflammatory biomarkers selected from the group consisting of CCL20, GH, CXCL13, IL-12B, VCAN, TNFRSF10A, TNFSF13B, CD6, and CXCL9; one or more immune modulating biomarkers selected from the group consisting of CDCP1 and OPN; One or more myelin integrity biomarkers selected from the group consisting of COL4A1 and MOG. a) obtaining or having obtained a dataset comprising expression levels of a plurality of biomarkers, The set of reagents comprising: a device configured to receive a mixture of one or more reagents of the set and the test sample, and to measure the expression level of the biomarker in the test sample; and A computer system communicatively connected to the device for obtaining a dataset comprising expression levels of the plurality of biomarkers in the test sample, and for generating a prediction of multiple sclerosis disease activity by applying a predictive model to the expression levels of the plurality of biomarkers.

255. The system of claim 254, wherein the one or more neurodegeneration biomarkers include NEFL, OPG, and GFAP, the one or more inflammation biomarkers include CXCL13, CD6, and CXCL9, the one or more immune modulation biomarkers include OPN, and the one or more myelin integrity biomarkers include MOG.

256. The system of claim 255, wherein the one or more neurodegeneration biomarkers further comprise APLP1, SERPINA9, and FLRT2, the one or more inflammation biomarkers further comprise CCL20, IL-12B, TNFRSF10A, and TNFSF13B, the one or more immune modulation biomarkers further comprise CDCP1, and the one or more myelin integrity biomarkers further comprise COL4A1.

257. The system of claim 256, wherein the one or more neurodegeneration biomarkers further include PRTG and CNTN2, and the one or more inflammation biomarkers include GH and VCAN.

258. The system of any one of claims 227 to 257, wherein the plurality of biomarkers are protein biomarkers.

259. The multiple sclerosis disease activity is the presence of normal disease activity, the presence of minimal disease activity, a shift in disease activity (increase or decrease), MS severity, relapse or relapse events associated with MS, relapse rate, MS status, response to MS therapy, extent of MS disability, or risk of developing MS The system according to any one of claims 227 to 258,

260. The system of claim 259, wherein the normal disease activity is the presence or absence of one or more gadolinium-enhancing MRI lesions and the insignificant disease activity is the presence of one gadolinium-enhancing MRI lesion.

261. 260. The system of claim 259, wherein the severity of MS corresponds to the number of gadolinium-enhancing MRI lesions.

262. The system of claim 259, wherein the MS condition is an exacerbation or quiescence of multiple sclerosis.

263. 1. A system for determining multiple sclerosis activity in a subject, the system comprising: A set of reagents used to determine an expression level of a plurality of biomarkers in a test sample from the subject, the plurality of biomarkers comprising two or more of NEFL, MOG, CD6, CXCL9, OPG, OPN, CXCL13, GFAP, CDCP1, CCL20, IL-12B, APLP1, TNFRSF10A, COL4A1, SERPINA9, FLRT2, TNFSF13B, GH, VCAN, PRTG, and CNTN2; and a device configured to receive a mixture of one or more reagents of the set and the test sample, and to measure the expression level of the biomarker in the test sample; and A computer system communicatively connected to the device for obtaining a dataset comprising expression levels of the plurality of biomarkers in the test sample, and for generating a prediction of multiple sclerosis disease activity by applying a predictive model to the expression levels of the plurality of biomarkers.

264. The system of claim 259, wherein the two or more biomarkers include NEFL and at least one other biomarker.

265. The system of claim 264, wherein the two or more biomarkers are a biomarker pair selected from any one of A) NEFL and CD6, B) NEFL and MOG, C) NEFL and CXCL9, and D) NEFL and TNFRSF10A.

266. 265. The system of claim 264, wherein the two or more biomarkers are a biomarker triad selected from any one of A) NEFL, CD6, and CXCL9, and B) NEFL, TNFRSF10A, and COL4A1.

267. 265. The system of claim 264, wherein the two or more biomarkers are a biomarker quartet selected from any one of: A) NEFL, MOG, CD6, and CXCL9; B) NEFL, CXCL9, TNFRSF10A, and COL4A1; C) NEFL, CD6, CXCL9, and CXCL13; and D) NEFL, TNFRSF10A, COL4A1, and CCL20.

268. The system of claim 259, wherein the two or more biomarkers do not include NEFL.

269. The system of claim 268, wherein the two or more biomarkers are a biomarker pair selected from any one of A) MOG and IL-12B, B) CXCL9 and CD6, C) MOG and CXCL9, D) MOG and CD6, E) CXCL9 and COL4A1, and F) CD6 and VCAN.

270. The system of claim 268, wherein the two or more biomarkers are a biomarker triad selected from any one of A) MOG, IL-12B, and APLP1; B) MOG, CD6, and CXCL9; C) CXCL9, COL4A1, and VCAN; D) MOG, IL-12B, and CNTN2; and E) CD6, CCL20, and VCAN.

271. 269. The system of claim 268, wherein the two or more biomarkers are a biomarker quartet selected from any one of: A) MOG, CXCL9, IL-12B, APLP1; B) CXCL9, COL4A1, OPG, and VCAN; C) CXCL9, OPG, APLP1, and OPN; D) MOG, IL-12B, OPN, and CNTN2; and E) CD6, COL4A1, CCL20, and VCAN.

272. The system of any one of claims 264 to 271, wherein the multiple sclerosis activity is a shift in disease activity.

273. The system of claim 264, wherein the two or more biomarkers are a biomarker pair selected from any one of A) NEFL and TNFSF13B, B) NEFL and CNTN2, and C) NEFL and CXCL9.

274. The system of claim 264, wherein the two or more biomarkers are a biomarker triad selected from any one of A) NEFL, CNTN2, and TNFSF13B; B) NEFL, APLP1, and TNFSF13B; and C) NEFL, TNFRSF10A, and TNFSF13B.

275. The system of claim 264, wherein the two or more biomarkers are a biomarker quartet selected from any one of A) NEFL, TNFRSF10A, CNTN2, and TNFSF13B; B) NEFL, COL4A1, CNTN2, and TNFSF13B; and C) NEFL, TNFRSF10A, APLP1, and TNFSF13B.

276. The system of claim 268, wherein the two or more biomarkers are a biomarker pair selected from any one of A) MOG and CDCP1, B) MOG and TNFSF13B, and C) MOG and CXCL9.

277. The system of claim 268, wherein the two or more biomarkers are a biomarker triad and one biomarker in the biomarker triad is MOG.

278. The system of claim 277, wherein the biomarker triad is selected from any one of A) MOG, CXCL9, and TNFSF13B, B) MOG, OPG, and TNFSF13B, and C) MOG, CCL20, and TNFSF13B.

279. The system of claim 268, wherein the two or more biomarkers are a biomarker quartet, and one biomarker in the biomarker quartet is MOG.

280. 280. The system of claim 279, wherein the biomarker quartet is selected from any one of A) MOG, CXCL9, APLP1, and TNFSF13B; B) MOG, CXCL9, OPG, and TNFSF13B; and C) MOG, CXCL9, OPG, and CNTN2.

281. The system of any one of claims 273 to 280, wherein the multiple sclerosis activity is the presence or absence of multiple sclerosis.

282. The system of claim 264, wherein the two or more biomarkers are a biomarker pair selected from any one of A) NEFL and TNFSF13B, B) NEFL and SERPINA9, and C) NEFL and GH.

283. The system of claim 264, wherein the two or more biomarkers are a biomarker triad selected from any one of A) NEFL, SERPINA9, and TNFSF13B; B) NEFL, CNTN2, and TNFSF13B; and C) NEFL, APLP1, and TNFSF13B.

284. The system of claim 264, wherein the two or more biomarkers are a biomarker quartet selected from any one of: A) NEFL, CCL20, SERPINA9, and TNFSF13B; B) NEFL, APLP1, SERPINA9, and TNFSF13B; C) NEFL, CCL20, APLP1, and TNFSF13B.

285. The system of claim 268, wherein the two or more biomarkers are a biomarker pair selected from any one of A) MOG and TNFSF13B, B) MOG and CXCL9, and C) MOG and IL-12B.

286. The system of claim 268, wherein the two or more biomarkers are a biomarker triad selected from any one of A) MOG, CXCL9, and TNFSF13B, B) MOG, SERPINA9, and TNFSF13B, and C) MOG, OPG, and TNFSF13B.

287. The system of claim 268, wherein the two or more biomarkers are a biomarker quartet selected from any one of A) MOG, CXCL9, OPG, and TNFSF13B; B) MOG, CXCL9, OPG, SERPINA9, and TNFSF13B; and C) MOG, CXCL9, SERPINA9, and TNFSF13B.

288. The system of any one of claims 282 to 287, wherein the multiple sclerosis activity is disease severity based on a predicted number of gadolinium-enhancing lesions.

289. The system of claim 263, wherein the two or more biomarkers are a biomarker pair selected from any one of A) GFAP and MOG, B) GFAP and NEFL, C) APLP1 and GFAP, D) NEFL and MOG, or E) CXCL9 and OPG.

290. The system of claim 263, wherein the two or more biomarkers are a biomarker triad selected from any one of A) GFAP, NEFL, and MOG; B) NEFL, MOG, and GH; C) NEFL, MOG, and SERPINA9; D) CXCL9, OPG, and SERPINA9; or E) CXCL9, OPG, and TNFRSF10A.

291. 264. The system of claim 263, wherein the two or more biomarkers are a biomarker triad selected from any one of: A) GFAP, NEFL, MOG, and IL12B; B) GFAP, NEFL, MOG, and PRTG; C) GFAP, NEFL, MOG, and APLP1; D) NEFL, MOG, GH, and SERPINA9; E) NEFL, MOG, GH, and TNFRSF10A; F) MOG, CXCL9, OPG, and SERPINA9; or G) CD6, IL12B, APLP1, and CCL20.

292. The system of any one of claims 289 to 291, wherein the multiple sclerosis activity is disease progression.

293. The system of any one of claims 227 to 292, wherein generating the prediction of multiple sclerosis disease activity comprises comparing a score output by the predictive model to a reference score.

294. The reference score is A) The patient at baseline when the patient showed no disease activity; B) Patients with clinically diagnosed disease without active disease, or C) Healthy patients 294. The system of claim 293, which corresponds to any one of the following:

295. The system of any one of claims 227 to 294, wherein the test sample is a blood or serum sample.

296. The system of any one of claims 227 to 294, wherein the subject has, is suspected of having, or has previously been diagnosed with multiple sclerosis.

297. The system of any one of claims 227 to 296, wherein the device is configured to perform an immunoassay to determine the expression level of the plurality of biomarkers.

298. The system of claim 297, wherein the immunoassay is a proximity extension assay (PEA) or a LUMINEXxMAP multiplex assay.

299. 300. The system of claim 297 or 298, wherein performing the immunoassay comprises contacting the test sample with a plurality of reagents including antibodies.

300. The system of claim 299, wherein the antibody comprises one of a monoclonal antibody and a polyclonal antibody.

301. The system of claim 299, wherein the antibodies include both monoclonal and polyclonal antibodies.

302. The system of any one of claims 235 to 301, wherein the computer system is further configured to select a therapy for administration to the subject based on the prediction of multiple sclerosis disease activity.

303. The system of any one of claims 235 to 301, wherein the computer system is further configured to determine the therapeutic effectiveness of a therapy previously administered to the subject based on the prediction of multiple sclerosis disease activity.

304. The system of claim 303, wherein determining the therapeutic effectiveness of the therapy comprises comparing the prediction to a previous prediction determined for the subject at a previous time point.

305. 305. The system of claim 304, wherein determining the therapeutic efficacy of the treatment comprises determining that the treatment exhibits efficacy according to a difference between the prediction and the previous prediction.

306. The system of claim 304, wherein determining the therapeutic effectiveness of the therapy comprises determining that the treatment is ineffective responsive to no difference between the prediction and the previous prediction.

307. The system of any one of claims 235-301, wherein the computer system is further configured to determine a differential diagnosis of multiple sclerosis based on the prediction of multiple sclerosis disease activity.

308. The system of claim 307, wherein the differential diagnosis of multiple sclerosis includes any one of relapsing remitting multiple sclerosis (RRMS), secondary progressive multiple sclerosis (SPMS), primary progressive multiple sclerosis (PPMS), progressive relapsing multiple sclerosis (PRMS), and CIS.

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