US20100203569A1

US20100203569A1 - Method for Evaluating Risk in Multiple Sclerosis

Info

Publication number: US20100203569A1
Application number: US12/556,221
Authority: US
Inventors: Nir Dotan; Avinoam Dukler
Original assignee: Glycominds Ltd
Current assignee: Glycominds Ltd
Priority date: 2009-02-12
Filing date: 2009-09-09
Publication date: 2010-08-12
Also published as: WO2010092488A2; EP2396662A2; CN102439457A; WO2010092488A3; BRPI1008364A2; US20100227337A1; US8048639B2; RU2011137462A; CA2751690A1

Abstract

The invention relates to methods and reagents for diagnosing and assessing the prognosis of multiple sclerosis. The present invention is based, in part, upon the discovery that anti-glycan antibodies are useful in evaluating the risk of whether clinically isolated syndrome (CIS) patients suggestive of Multiple sclerosis (MS) will have a clinical relapse within, e.g., 24 months. The invention is also based upon the discovery that anti-glycan antibodies are useful for evaluating the risk of CIS patients suggestive of MS to have a rapid disease progression and accumulate disabilities, e.g., permanent disability, within a certain time frame, e.g., 5 years.

Description

RELATED APPLICATIONS

This application claims the benefit of U.S. Ser. No. 61/152,168, filed Feb. 12, 2009, the contents of which are incorporated by reference herein in their entirety.

FIELD OF THE INVENTION

The invention relates to methods and reagents for diagnosing and assessing the prognosis of multiple sclerosis.

BACKGROUND OF THE INVENTION

Multiple sclerosis (MS) is a chronic autoimmune inflammatory disease of the central nervous system. It is a common cause of persistent disability in young adults. In patients suffering from MS, the immune system destroys the myelin sheath of axons in the brain and the spinal cord, causing a variety of neurological pathologies. In the most common form of MS, Relapsing-Remitting (RRMS), episodes of acute worsening of neurological function (exacerbations, attacks) are followed by partial or complete recovery periods (remissions) that are free of disease progression (stable).
There is a clinical need for a simple serological assay that predicts: i) whether patients at First Presentation (FP), also known as clinically isolated syndrome (CIS) suggestive of MS, will develop MS in a certain timeframe; ii) whether patients at CIS have a risk of rapid disease progression with accumulation of clinical disabilities and therefore might require aggressive disease modifying therapy (DMT); or iii) whether patients at CIS will have a regular or relatively slow MS disease progression with fewer disabilities that would require less aggressive therapy.

SUMMARY OF THE INVENTION

The invention solves a long standing problem in the field of MS—how to predict the disease course for a subject at clinically isolated syndrome (CIS) suggestive of MS. The method involves characterizing an individual as a member of one of two groups: (i) those who are likely to progress to clinically definite multiple sclerosis (CDMS) rapidly, i.e., within 24 months, and (ii) those who are likely to progress to CDMS more slowly, i.e., within greater than 24 months. The method also involves characterizing an individual as a member of one of two other groups: (i) those who are likely to progress to clinical disabilities rapidly, i.e., within 5 years, and (ii) those who are likely to progress to clinical disabilities more slowly, i.e., within greater than 5 years.
The present invention is based, in part, upon the discovery that anti-glycan antibodies are useful in evaluating the risk of whether clinically isolated syndrome (CIS) patients suggestive of Multiple sclerosis (MS) will have a clinical relapse within, e.g., 24 months. The invention is also based upon the discovery that anti-glycan antibodies are useful for evaluating the risk of CIS patients suggestive of MS to have a rapid disease progression and accumulate disabilities, e.g., permanent disability, within a certain time frame, e.g., 5 years.
The anti-glycan antibodies described herein are used to predict a second neurological attack within a defined time period, e.g., within about forty-eight months; within about thirty-six months within about twenty-four months; within about twelve months; or within about six months. The anti-glycan antibodies are also used to predict the progression in MS disease course as measured by the expanded disability status scale (EDSS), a rating system that is used for classifying and standardizing the condition of MS patients. For example, MS disease progression is predicted within a defined time period, e.g., within about twenty years; within about ten years; within about seven years; within about five years; or within about four years. MS disease progression is measured by EDSS units, e.g., progression is greater than one EDSS unit; progression is greater than two EDSS units; progression is greater than three EDSS units; or progression is greater than six EDSS units. The rate (fast vs. slow) of disease progression is determined by the relative amount/level of anti-glycan antibodies in a patient sample.
First, a test sample is provided from a CIS or newly diagnosed subject characterized as having suffered a first neurological event. Suitable test samples include a biological fluid selected from the group consisting of whole blood, serum, and plasma. Various anti-glycan antibodies are detected in the test sample, such as an anti-Glc (α 1,2) Glc (α) antibody (GAGA2), an anti-Glc (α 1,3) Glc (α) antibody (GAGA3), an anti-Glc (α 1,4) Glc (α) antibody (GAGA4), and an anti-Glc (α 1,6) Glc (α) antibody (GAGA6). Preferably, the antibodies are IgM type antibodies. The levels of the antibodies in the test sample are compared to the levels of the antibodies in a control sample, wherein a higher level of the antibodies in the test sample compared to the level of the antibodies in a control sample indicates the subject is at risk of having a second neurological attack within a defined time period, e.g., within about forty-eight months; within about thirty-six months within about twenty-four months; within about twelve months; or within about six months.
The control in relation to the prediction of a next relapse within 24 months is a reference value from one or more MS patients characterized as having suffered a first neurological attack followed by a subsequent (second) neurological attack more than twenty-four months after the first neurological attack. For example, the control reference level is a value associated with late relapse. Late relapse refers to an MS patient that has experienced a first neurological attack or first presentation followed by a second neurological attack twenty-four months or more after the first neurological attack or first presentation.
The invention also provides a method of differentiating between a CIS subject who will progress in Expanded Disability Status Scale (EDSS) score within twenty years of the subject's First Presentation (FP) and a subject who will not progress in EDSS score within twenty years of said subject's FP. First, a test sample is provided from a CIS or newly diagnosed subject characterized as having suffered a first neurological event. Suitable test samples include a biological fluid selected from the group consisting of whole blood, serum, and plasma. Various anti-glycan antibodies are detected in the test sample, such as an anti-Glc (α 1,2) Glc (α) antibody (GAGA2), an anti-Glc (α 1,3) Glc (α) antibody (GAGA3), an anti-Glc (α 1,4) Glc (α) antibody (GAGA4), and an anti-Glc (α 1,6) Glc (α) antibody (GAGA6). Preferably, the antibodies are IgM type antibodies. The levels of the antibodies in the test sample are compared to the levels of the antibodies in a control sample, wherein a higher level of the antibodies in the test sample compared to the level of the antibodies in a control sample indicates the subject is at risk of having rapid MS disease progression (severe disease course) with higher level of disabilities as measured by EDSS score within a defined time period, e.g., within about twenty years; within about ten years; within about seven years; within about five years; or within about four years. The method provides the clinician with valuable information to predict rapid or slow progression to permanent or clinical disability as measured by EDSS, for example, the subject progresses at least one EDSS unit, e.g., at least two EDSS units; at least three EDSS units; greater than three EDSS units; greater than four EDSS units; or greater than six EDSS units, etc., within twenty years. For example, rapid progression is characterized by advancement of 6 or more EDSS units within 20 years. Slow progression is characterized by advancement of 3 or fewer EDSS units within 20 years.
Alternatively, a low level of the antibodies in the test sample equal or similar to the level of the antibodies in a control sample indicates the subject is likely to have a slow MS disease progression within same time period. The control in relation to the prediction of rapid MS disease progression is a reference value from one or more MS patients characterized as having higher or lower EDSS score.
By “reference level” is meant the mean, median, or range level of each antibody in a defined cohort of MS individuals. For example, the cohort is characterized by patients who progressed from CIS to next relapse in 24 months or who experienced rapid progression of MS disability within a defined time period. An exemplary “reference level” of antibody is the mean+/−0.5, 1, 1.5, or 2 standard deviations (SD) of the mean. Preferably, the reference level is the mean antibody level+/−1.5 SD. The population of individuals is at least 5; at least 10, at least 25; at least 50; at least 100 or more individuals. Reference levels are obtained using samples from patients with a known MS progression profile, e.g., archived blood, serum, or plasma samples.
In practice, by examining a statistically-relevant cohort of patients (e.g., 50 patients, 100 patients, etc.) a reference sera sample similar to the chosen cutoff level is determined for each antibody, i.e., the upper 25%, the upper 20%, the upper 15%, the upper 10%; or the lower 25%, the lower 20%, the lower 15%, or the lower 10%. Antibody levels in the upper percentiles indicate a risk of rapid relapse or rapid disease progression, while antibody levels in the lower percentiles indicate a slow disease progression and low risk of relapse. Aliquots of the reference sera samples are prepared and kept frozen for future use. The reference sera samples are calibration samples, having a preset antibody level (cutoff level; reference level; control level). When test samples are analyzed for their antibody levels, the test sample is examined in parallel with the reference/calibration/control sample. If the antibody level in the test sample is higher compared to the antibody level in the reference/calibration sample, the test sample is considered “positive”, e.g., it belongs to the subgroup of CIS patients with an elevated level of the antibody. Similarly, if the antibody level in the test sample is equal/similar or lower compared to the antibody level in the reference/calibration sample, the test sample is considered “negative”, e.g., it belongs to the subgroup of CIS patients without an elevated level of the antibody.
The clinical parameters of sensitivity, specificity, negative predictive value, positive predictive value and efficiency are calculated using true positives, false positives, false negatives and true negatives. A “true positive” sample is a sample positive for MS according to art-recognized methods for diagnosing/prognosing MS, which is also diagnosed positive according to a method of the invention. A “false positive” sample is a sample negative by an art-recognized method, which is diagnosed positive according to a method of the invention. Similarly, a “false negative” is a sample positive for an art-recognized analysis, which is diagnosed negative according to a method of the invention. A “true negative” is a sample negative for the assessed trait by an art-recognized method, and also negative according to a method of the invention. See, for example, Mousy (Ed.), Intuitive Biostatistics New York: Oxford University Press (1995), which is incorporated herein by reference.
As used herein, the term “sensitivity” means the probability that a laboratory method is positive in the presence of the measured trait. Sensitivity is calculated as the number of true positive results divided by the sum of the true positives and false negatives. Sensitivity essentially is a measure of how well a method correctly identifies those with the clinical question. In a method of the invention, the antibody values can be selected such that the sensitivity of diagnosing/prognosing an individual whether with higher risk for next relapse or rapid MS progression is at least about 20%, and can be, for example, at least about 30%, 40%, 50%, 60%, 70%, 80% or 90%.
As used herein, the term “specificity” means the probability that a method is negative in the absence of the measured trait. Specificity is calculated as the number of true negative results divided by the sum of the true negatives and false positives. Specificity essentially is a measure of how well a method excludes those who do not have the measured trait. The antibody cut-off value can be selected such that, when the sensitivity is at least about 30%, the specificity of diagnosing/prognosing an individual is in the range of 60-70%, 80-85%, 90-95%, or 95-99.9%.
By “progression in EDSS score” is meant a progression from about 1.0 to about 10.0 in increments of 0.5 or 1.0, e.g., about 1.0 to about 5.0; about 5.0 to about 10.0; about 2.0 to about 4.0; about 4.0 to about 6.0; about 6.0 to about 8.0; about 8.0 to about 10.0; about 1.5 to about 2.5; about 3.0 to about 3.5, or any other incremental increase between 1.0 and 10.0.
In some instances, MS patients have a poor prognosis despite treatment. The term “breakthrough disease” is used to indicate clinical or imaging evidence for disease activity or progression (e.g., measured by Expanded Disability Status Scale (EDSS score)) despite disease-modifying therapy (DMT). Breakthrough disease does not imply that the patient has not responded to treatment—it implies only that disease activity is present despite therapy. Prior to the invention, it was difficult to differentiate between a rapid progression good responder to DMT and a slow progressor with a poor response to DMT. Accordingly, the invention provides methods of identifying a subject who will develop breakthrough MS. First, a test sample is provided from a subject. Suitable test samples include a biological fluid selected from the group consisting of whole blood, serum, and plasma. An antibody selected from the group consisting of anti-Glc (α 1,2) Glc (α) antibody, an anti-Glc (α 1,3) Glc (α) antibody, an anti-Glc (α 1,4) Glc (α) antibody, and an anti-Glc (α 1,6) Glc (α) antibody is detected in the test sample. In one aspect, the antibody is an IgM antibody. The levels of the antibodies in the test sample are compared to a control level of the antibodies, wherein a higher level of at least one of the antibodies compared to the control level of the antibodies indicates that the subject is likely to develop breakthrough MS.
Alternatively, the levels of the antibody in the blood sample are compared to levels of the antibody in a control sample and, a subject who will not progress to a breakthrough MS is differentiated by detection of an equal/similar or lower level of the antibody in the subject sample compared to levels of the antibody in the control sample.
The invention also provides methods of identifying a subject with a clinically isolated syndrome (CIS) who will progress to clinically definitive multiple sclerosis (CDMS) within twenty-four months by providing a blood sample from the subject and detecting in the blood sample an anti-Glc (α 1,2) Glc (α) antibody, an anti-Glc (α 1,3) Glc (α) antibody, an anti-Glc (α 1,4) Glc (α) antibody, and an anti-Glc (α 1,6) Glc (α) antibody. The levels of the antibodies in the blood sample are compared to levels of the antibodies in CIS individuals who did not progress to CDMS within twenty-four months. A subject with CIS who will progress to CDMS within twenty-four months is identified by detection of higher level of the antibodies in the blood sample compared to levels of the antibodies in CIS individuals who did not progress to CDMS within twenty-four months.
The invention also provides methods of identifying a subject with CIS who is likely to experience rapid progression of MS disease severity within twenty years by providing a blood sample from a subject at the time of a CIS and detecting in the blood sample an anti-Glc(α 1,2) Glc(α) (GAGA2) IgM isotype antibody, an anti-Glc(α1,3)Glc(α) (GAGA3) IgM isotype antibody, an anti-Glc(α1,4)Glc(α) (GAGA4) IgM isotype antibody, and an anti-Glc(α1,6)Glc(α) (GAGA6) IgM isotype antibody. The levels of the antibodies in the blood sample are compared to levels of the antibodies in individuals who did not experience rapid progression of MS disease severity within twenty years. A subject with CIS who is likely to progress greater than one unit in a EDSS score within twenty years is identified by detection of higher level of the antibodies in the blood sample compared to levels of the antibodies in individuals who did not experience rapid progression of MS disease severity within twenty years, the EDSS score being indicative of disease severity.
The invention also provides methods of identifying a subject with CIS who is likely to experience a slow progression of MS disease severity by providing a blood sample from a subject at the time of a CIS and detecting in the blood sample an anti-Glc(α1,2)Glc(α) (GAGA2) IgM isotype antibody, an anti-Glc(α1,3)Glc(α) (GAGA3) IgM isotype antibody, an anti-Glc(α1,4)Glc(α) (GAGA4) IgM isotype antibody, and an anti-Glc(α1,6)Glc(α) (GAGA6) IgM isotype antibody. The levels of the antibodies in the blood sample are compared to levels of the antibodies in individuals who experienced a slow progression of MS disease severity within twenty years. A subject with CIS who is likely to progress slowly in EDSS score within twenty years is identified by detection of an equal/similar or lower level of the antibodies in the blood sample compared to levels of the antibody in individuals who experienced a slow progression of MS disease severity within twenty years, the EDSS score being indicative of disease severity.
The invention also provides methods of identifying a subject who will develop breakthrough MS by providing a blood sample from the subject, and detecting in the blood sample an anti-Glc (α 1,2) Glc (α) antibody, an anti-Glc (α 1,3) Glc (α) antibody, an anti-Glc (a 1,4) Glc (α) antibody, and an anti-Glc (α 1,6) Glc (α) antibody. The levels of the antibodies in the blood sample are compared to levels of the antibodies in individuals that did not develop breakthrough MS, e.g., individuals that do not progress by clinical or image detection with or without DMT treatment. A subject who will develop breakthrough MS is identified by detection of higher level of the antibody in the blood sample compared to levels of the antibody in individuals that did not develop breakthrough MS.
The invention also provides a computer-readable medium having computer-executable instructions for performing a method. First, at least one first variable associated with the level of at least one antibody in a blood sample of a patient is stored. Optionally, the antibody levels are detected via binding to immobilized glycans in a chamber or well, and emitting a fluorescent signal; chemiluminescence signal; or optical density signal by adding a pre-labeled secondary antibody recognizing a common reign in IgM isotype. Second, at least one second variable associated with at least one reference level is stored. The patient's risk factor for the relevant clinical question: i) conversion to clinically definite multiple sclerosis (CDMS); ii) rapid progression in MS as measured by EDSS; or iii) breakthrough MS, is calculated as a function of at least the first and second variables. Finally, the risk factor is outputted. Optionally, the at least one first variable corresponds to levels of an anti-Glc (α 1,2) Glc (α) antibody, an anti-Glc (α 1,3) Glc (α) antibody, an anti-Glc (α 1,4) Glc (α) antibody, and an anti-Glc (α 1,6) Glc (α) antibody in the blood sample. In another aspect, the at least one first variable corresponds to the level of said antibodies in the blood selected from the group consisting of: an anti-Glc(α1,2)Glc(α) (GAGA2) IgM isotype antibody, an anti-Glc(α1,3)Glc(α) (GAGA3) IgM isotype antibody, an anti-Glc(α1,4)Glc(α) (GAGA4) IgM isotype antibody, and an anti-Glc(α1,6)Glc(α) (GAGA6) IgM isotype antibody.
Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, suitable methods and materials are described below. All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety. In the case of conflict, the present specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting.
Other features and advantages of the invention will be apparent from the following detailed description and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a glycan array format: A) glass slide patterned with Teflon mask creating 7 clusters of microwells, 32 wells in each cluster; B) an adhesive silicon superstructure attached to the slide defines wells for manual application of multiple serum samples per slide; and C) antigens and controls lay out in each gasket well.

FIG. 2 shows a time to clinically definite MS (CDMS), Kaplan-Meier, survival plot for cohort-C. FP patients positive for ≧1 (anti-GAGA4, anti-GAGA2, anti-GAGA3, or anti-GAGA6 IgM) versus patients negative for all markers. Cutoff values: 4.0, 4.5, 4.5, and 4.3 for anti-GAGA2, anti-GAGA3, anti-GAGA4, and anti-GAGA6 μM, respectively. P-values determined by log rank test. With Bonferroni correction only P-values below 0.0125 are considered significant.

FIG. 3 shows a time to confirmed EDSS progression, Kaplan-Meier, survival plot. CIS patients positive for ≧1 (anti-GAGA2, anti-GAGA3, anti-GAGA4, or anti-GAGA6 IgM) versus patients negative for all markers. Cutoff values: 148.8, 164.6, 133.6, and 168,1 EU for anti-GAGA2, anti-GAGA3, anti-GAGA4, and anti-GAGA6 IgM, respectively. P-value determined by log rank test was P=0.011664.

FIG. 4 shows a block flow diagram of a process of determining whether a CIS patient is at risk of a relapse within 24 months.

FIG. 5 shows a block flow diagram of a process of determining whether a patient is likely to progress in Expanded Disability Status Scale (EDSS) score within twenty years.

FIG. 6 shows a block flow diagram of a process of determining whether a patient is likely to develop breakthrough MS.

DETAILED DESCRIPTION

Multiple sclerosis (MS) is an inflammatory demyelinating disease of the central nervous system (CNS), though the exact etiology and pathogenesis have not yet been deciphered. Diagnosis of MS requires exclusion of diseases that could better explain the clinical, imaging, and paraclinical findings. The MS diagnosis criteria, also known as the revised McDonald criteria, define the parameters required for a subject having MS (Polman et al., 2005 Ann Neurol, 58: 840-6). To better define suspected MS cases that may or may not meet the McDonald criteria, an International Panel of MS experts recently developed consensus-determined guidelines for differential diagnosis leading to MS (Miller et al., 2008 Multiple Sclerosis, 14:1157-1174).
The course of MS disease follows unpredictable patterns of evolution and widely variable timetables, with disability accumulation adhering to no particular blueprint making it difficult or impossible to accurately determine the prognosis of individual patients (Vollmer T, 2007 J Neurol Sci, 256 Suppl 1:S5-13). Heterogeneity can be related to the timeframe a CIS patient will have the next relapse or the response to disease modifying therapy (DMT). Heterogeneity can also refer to the disease progression rate and accumulation of disabilities. In many instances, MS patients have a poor prognosis despite treatment. Specifically, the term “breakthrough disease” is used to indicate clinical or imaging evidence for disease activity or progression (as may measured by Expanded Disability Status Scale (EDSS score)) despite DMT (Rudick and Polman 2009, Lancet Neurology, 8:545-559). Breakthrough disease does not imply that the patient has not responded to treatment—it implies only that disease activity is present despite therapy.
The invention describes methods regarding the prediction of disease course for a subject at clinically isolated syndrome (CIS) suggestive of MS. The method involves characterizing an individual as a member of one of two groups: (i) those who are likely to progress to clinically definite multiple sclerosis (CDMS) rapidly, i.e., within 24 months, and (ii) those who are likely to progress to CDMS slower, i.e., within greater than 24 months. The method also involves characterizing an individual as a member of one of two other groups: (i) those who are likely to progress in clinical disabilities rapidly, i.e., within 5 years, and (ii) those who are likely to progress in clinical disabilities more slowly, i.e., within greater than 5 years.
The current clinical practice is to treat CIS and newly diagnosed patients with first line DMT. Physicians point out the benefits of starting treatment early, but not all patients comply. A recent survey among United States neurologists revealed that at least 10% of newly diagnosed MS patients delayed their treatment due to their fear of injections (Perrin et al., 2009 JMCP, 15(7): 572). Therefore, the segregation of patients at CIS based on the risk to convert from CIS to CDMS within 24 months can be an important convincing tool for patient compliance with early DMT treatment, because those in group (i) will agree to receive DMT, whereas those in group (ii) can delay treatment until the next clinical or image episode.
Another aspect of current clinical practice is switching to another existing DMT due to the failure of the first DMT. Switching can be for another 1^stline DMT or to a more aggressive 2^ndline (2^ndgeneration) DMT. Therefore, the segregation of patients at CIS based on predicting the risk of rapid progression of MS and the risk of having more disabilities as measured by EDSS within, e.g., 5 years, is a critical factor in the determination of a treatment regimen, because those in group (i) will receive an aggressive DMT regimen, whereas those in group (ii) receive less aggressive DMT or another 1^stline DMT.
During symptomatic attacks, administration of high doses of intravenous corticosteroids, such as methylprednisolone is the routine therapy for acute relapses. The aim of this kind of treatment is to end the attack sooner and leave fewer lasting deficits in the patient. Although generally effective in the short term for relieving symptoms, corticosteroid treatments do not appear to have a significant impact on long-term recovery (Brusaferri and Candelise, 2000 J. Neurol., 247(6): 435-42). Potential side effects include osteoporosis and impaired memory, the latter being reversible (Dovio et al., 2004 Clin. Endocrinol. Metab., 89(10): 4923-8; Uttner et al., 2005 Neurology, 64(11): 1971-3). Severe attacks which do not respond to corticosteroids might be treated by plasmapheresis. The earliest clinical presentation of relapsing-remitting MS (RRMS) is the clinically isolated syndrome (CIS). Several studies have shown that treatment with interferons during an initial attack can decrease the chance that a patient will develop clinical MS (Jacobs et al., 2000 N Engl J Med, 343(13): 898-904; Comi et al., 2001 Lancet, 357(9268): 1576-82; Kappos et al., 2007 Lancet, 370(9585): 389-97). These and other CIS studies that showed the benefit in early treatment with DMT evolved to current clinical practice in which newly diagnosed RRMS and CIS suggestive of MS patients are initiating DMT treatment immediately after first presentation and abnormal MRI scan.
Six disease-modifying treatments have been approved by regulatory agencies of different countries for RRMS (Rudick and Polman 2009, Lancet Neural, 8: 545-559). Three are interferons: two formulations of interferon beta-1a (trade names Avonex®, CinnoVex™, ReciGen and Rebif®) and one of interferon beta-1b (U.S. trade name Betaseron®, in Europe and Japan Betaferon®). A fourth medication is glatiramer acetate (Copaxone®). The fifth medication, mitoxantrone, is an immunosuppressant also used in cancer chemotherapy, approved only in the USA and largely for secondary progressive MS. The sixth is natalizumab (marketed as Tysabri®). All six medications are modestly effective at decreasing the number of attacks and slowing progression to disability, although their efficacy rates differ, and studies of their long-term effects are still lacking (Ruggieri et al., 2007 CNS Drug Rev, 13(2): 178-91; Munari et al., Cochrane Database of Systematic Reviews 2003, Issue 4. Art. No.: CD004678; Rice et al., 2001 Cochrane Database Syst Rev (4): CD002002; Martinelli Boneschi et al., 2005 Cochrane Database Syst Rev (4): CD002127). Comparisons between immunomodulators (all but mitoxantrone) show that the most effective is natalizumab, both in terms of relapse rate reduction and halting disability progression (Johnson K P, 2007 J. Neurol. Sci., 256 Suppl 1: S23-8); it has also been shown to reduce the severity of MS. Mitoxantrone may be the most effective of them all (Gonsette R E, 2007 Expert opinion on pharmacotherapy, 8(8): 1103-16); however, it is generally not considered as a long-term therapy, as its use is limited by severe cardiotoxicity (Murray T J, 2006 Expert opinion on drug safety, 5 (2): 265-74). The interferons and glatiramer acetate are delivered by frequent injections, varying from once-per-day for glatiramer acetate to once-per-week (but intra-muscular) for Avonex®. Natalizumab and mitoxantrone are given by IV infusion at monthly intervals. These DMTs are also known as 1^stline DMT.
Recently, a “true” 2^ndgeneration DMT successfully completed phase III clinical trials. Fingolimod (FTY720) and Mylinax (Cladrabine) have shown higher efficacy as compared to the 1^stline DMT studies in the active arm during these phase III clinical trials. 2nd generation Disease Modifying Therapy (DMT) is currently being introduced to the MS market; however, adverse side affects such as death from Herpes infections and skin cancers were reported during the phase III clinical studies. Due to the severe side effects of the 2^ndgeneration DMTs, they will not be prescribed to all patients. In addition, four other agents with more active efficacy than 1^stline DMTs, but with adverse side effects, are currently in phase III clinical trials.
The heterogeneity of disability progression in MS patients is seen by observing the Expanded Disability Status Scale (EDSS) in populations having an RRMS onset at different time intervals. After 5 years from disease onset, 10-15% of RRMS patients reached an expanded disability status score (EDSS) of 3.0 (corresponding to a patient who is fully ambulatory, but has moderate disability), after 8-12 years, 27%-30% of RRMS patients had an EDSS score of 3, and by 20 years, 39% of RRMS patients had an EDSS score of up to 3, while 39% of RRMS patients had an EDSS score of at least 6 (corresponding to a patient needing assistance of a cane to walk; Simone I L et al., 2002 Neurology, 24; 59(12): 1922-8; Pittock S J et al., 2004 Ann Neurol, 56: 303-306; Tintoré M et al., 2006 Neurology, 67: 968-972).
Recently, clinically isolated syndrome (CIS) was “re-defined” as a monophasic presentation with suspected underlying inflammatory demyelinating disease (Miller D H et al., 2008 Multiple Sclerosis, 14: 1157-1174). “Monophasic presentation” implies a single clinical episode at first presentation that is of relatively rapid onset. Multiple simultaneous clinical/paraclinical presentations (representing dissemination in space) are possible, although dissemination in time should not be evident. Four classes of CIS were defined based on whether the monophasic clinical presentation has mono- or multifocal clinical or MRI features (Miller D H et al., 2008 Multiple Sclerosis, 14:1157-1174). Finally, there should be reasonable grounds for suspecting inflammatory demyelinating disease as the underlying pathology.
In CIS patients suggestive of multiple sclerosis, the EDSS progression is as follows. Twenty-five percent (25%) of CIS patients have an EDSS score of at least 6.0 after 20 years. Approximately 45% of CIS patients reach an EDSS score of 3.0 during the same 20 year period, while the remaining 30% of CIS patients have an EDSS score under 3.0 after 20 years (Fisniku L K et al., 2008 Brain, 131: 808-817). Thus, a certain subpopulation of patients with RRMS onset have a more severe disease, in that these patients have a higher probability of developing a greater disability level, i.e., rapid or “fast” MS patients. Certain clinical (e.g., male gender, late age of onset, and polysymptomatic onset) and paraclinical factors (e.g., number, volume, and site of magnetic resonance Imaging (MRI) lesions, and presence of oligoclonal bands (OBs) in cerebrospinal fluid (CSF)) have shown a moderate ability to predict patients whose disability will progress more rapidly (Tintoré M et al., 2006 Neurology, 67:968-972; and Bergamaschi R 2007 Int Rev Neurobiol, 79:423-447). However, these predictive variables have limitations and only seem to aid in disability prediction of up to an EDSS score of 4 (Confavreux C et al., 2003 Brain, 126:770-782). As all current Disease Modifying Therapy (DMT) can only be utilized in patients with an EDSS score lower than 6.5, and as 2^ndgeneration aggressive DMT are associated with adverse side affect or are recently completed phase III studies, a set of prognostic tools that can improve the ability of the clinician to predict when a CIS patient will have a next relapse or if a CIS patient will progress quickly is needed. Such a prognostic tools will aid in the appropriate counsel of patients, identification of patients for more aggressive DMT, and improve the design and analysis of therapeutic trials (Miller D H, 2004 NeuroRx, 1:284-294; Bergamaschi R 2007 Int Rev Neurobiol, 79:423-447).
Prior to the invention, no known antigenic specificity profile existed to aid in the determination of disease activity (next relapse), disease severity (breakthrough disease), or the rate of disease course progression (EDSS progression). A test such as the one described herein that provides information regarding next relapse or how fast MS is likely to progress informs a physician to appropriately tailor therapeutic intervention. For example, a patient who is incorrectly considered to be unlikely to develop clinically definite MS (CDMS) within 24 months based upon the lack of an abnormal MRI scan at FP is treated if the methods of the invention indicates that the patient is likely to have a relapse or progress within 24 months. A patient who will imminently experience a CDMS neurological attack and who has abnormal MRI, but refuses DMT can be convinced to comply with therapy if the methods of the invention predict the CIS patient has a high likelihood for next relapse within 24 months.
Other examples of the clinical utilities of such a test and its contribution to current clinical practice is related to the ability of these four glycans to predict rapid MS progression and breakthrough disease. About 25% of CIS cases will progress to an EDSS score of 6.0 within 20 years. The remaining 75% will either progress to an EDSS score of 3.0 (45%) or will be below an EDSS score of 3.0 (Fisniku L K et al., 2008 Brain, 131: 808-817). The current 1^stline of DMT (when prescribed at CIS) postpones the next relapse on average for 2 years (Kappos L, et al., 2007 Lancet, 370(9585):389-97). An MS specialist will not know if the newly-diagnosed MS patient or CIS patient is a rapid or slow progressor, responder or poor responder to DMT, or the likelihood for a breakthrough disease. Therefore, most of newly diagnosed MS patients or CIS patients with abnormal MRI will be prescribed with 1^stline DMT. At day 731, a patient with an additional clinical event will still not have sufficient data regarding the course of disease. Such a patient might be a rapid MS progressor that respond well to DMT. Alternatively, the same patient could be a slow progressor patient that will not respond to DMT. The current practice is “wait and see”. As time lapses and more data on MRI lesion, and EDSS progression accumulates, a decision becomes possible. The four glycans which predict at CIS which patients have a high likelihood of progressing disability enables the decision of switching therapy to more aggressive treatment, if necessary. For a “positive” patient, the switching can be for the new 2^ndgeneration DMT, which are more aggressive and associated with adverse side affects. By contrast, a “negative” patient, can be offered less aggressive treatment, e.g., 1^stline DMT.
Currently, self-injectable and infusion therapies are the only approved treatments for relapsing forms of MS in the United States. Oral therapies like Fingolimod and Mylinax are in development and will soon expand the treatment options. Recently, in order to examine physician perspectives of the unmet needs in the treatment of MS related to CIS patients, an in-depth, telephonic survey was conducted among a sample of neurologists in the United States. The survey was fielded between November 2008 and February 2009. This survey, a joint collaboration between the National Multiple Sclerosis Society (NMSS) and EMD Serono, Inc., was conducted by GFK Roper. Among the neurologists participating in this survey (n=250), most were from private practice (77%) and had ≧16 years experience treating MS (57%). Almost all physicians (98%) indicated that they always explain the benefits of starting drug treatment early with their patients. Physicians state that, on average, at least 10% of their newly diagnosed MS patients in the past 12 months delayed treatment for at least 6 months. Physicians believe fear of injections (68%), fear of possible side effects (66%), and denial about having MS (61%) are the top reasons for the delay. When describing desired improvements in MS therapy, 70% specifically mentioned oral administration, followed by better balance of efficacy and safety (49%) and fewer side effects (47%). In ranking overall perceived benefits, physicians believe that oral therapies will help improve patient adherence (53% total mention), improve quality of life (49% total mention), and make it easier for patients to stay on treatment (47% total mention). When asked specifically about adherence, almost all physicians (97%) agree that an oral therapy, if approved, would improve the patient's ability to adhere to MS medication as prescribed. Physicians point out the benefits of starting treatment early, but not all patients comply. The availability of treatments that can be administered orally was the most desired improvement in MS therapy (Perrin et al., 2009 JMCP, 15(7): 572).
Thus, the panel of 4 glycans and the assay that measures blood levels of antibodies to those 4 glycans provide invaluable information upon which the treating physician relies to design the most effective treatment regimen, as well as provide a tool for convincing CIS patients suggestive of MS to be in treatment compliance as prescribed by their physicians.
The search has been ongoing for useful serum derived biomarkers, including antibodies. Serum IgM antibodies to an N-glucosylated peptide were specifically increased in relapsing remitting multiple sclerosis (RRMS) patients (Lolli F, et al. 2005 Proc Natl Acad Sci USA, 102: 10273-78; Lolli F, et al. 2005 J Neuroimmunol, 167: 131-37). High antibody titers to two myelin peptides, myelin oligodendrocyte glycoprotein (MOG) and myelin basic protein (MBP) were reported by some (Berger T, et al. 2003 N Engl J Med, 349: 139-45), but not others (Kuhle J, et al. 2007 N Engl J Med, 356: 371-78) to predict early relapse in CIS patients.
The finding of IgG antibody formation specifically in the cerebrospinal fluid (CSF), but not in a corresponding serum (i.e., oligoclonal banding), has been a useful test for diagnosis and differential diagnosis of MS (Freedman M S, et al. 2005 Arch Neurol, 62:865-70). Moreover, recent results show elevated levels of IgM antibodies to Glc (α1,4) Glc (α) (GAGA4) in RRMS patients in comparison to patients with other neurological diseases (OND) (Schwarz M, et al. 2006 J Neurol Sci, 244: 59-68). The results presented herein identify when during the course of disease (beginning at CIS) higher antibody levels to GAGA2, GAGA3, GAGA4 or GAGA6 predict various clinical episodes associate with disease activity (next relapse), disease severity (breakthrough), or its disease progression within a certain time frame.
Prior to the invention, there was no specific panel of serum based biomarkers known for the diagnosis or prognosis of relapsing-remitting multiple sclerosis (RRMS). As described below, cohort-C (FP (n=100)) IgM antibodies to glucose based glycan panel were measured by immunofluorescence. In cohort C, 58 patients experienced early relapse (<24 months); 31 had late relapse (≧24 months); and 11 did not experience second attack during follow-up. Kaplan-Meier curves demonstrated decrease in time to next relapse for patients positive for the antibody panel (P=0.02, log rank).

Example 1

Anti-α-Glucose Based Glycan IgM Antibodies Predict Relapse Activity in Multiple Sclerosis After the First Presentation

Serum Samples

The results presented herein are from a retrospective study of frozen (−70° C.) and re-thawed serum samples collected from patients at the time of diagnostic work-up for their FP who were later diagnosed as RRMS. The control group included sera samples taken from patients with other neurological diseases (OND) that were stored around the same time from routine samples sent to the respective cerebrospinal fluid (CSF) diagnostic laboratories. Demographic and clinical data were obtained from hospital records. Inclusion criteria for MS samples were as follows: patient age (18-60 years) at time of sampling, follow-up for at least 4 years from blood sampling, and diagnosis of RRMS according to Poser criteria (Poser C M, et al. 1983 Ann Neurol, 13: 227-31). Samples meeting the above criteria were identified from one of two serum repositories located at the Ottawa Hospital-General Campus, Ottawa, Canada or the Cliniques Universitaires Saint-Luc in Brussels, Belgium. Relapse was defined as any new neurological event accompanied by symptoms or signs, or significant worsening of previous symptoms or signs in the absence of fever that lasted at least 48 hours. All samples were encoded at respective institutions before being sent to Glycominds Ltd. laboratories for antibody analysis. Decoding occurred only after all analyses was completed. Three distinct cohorts were analyzed: cohort-A included 88 samples (44 FP n=44, OND n=44), OND patients were matched to the MS patients according to age and gender; cohort-B included 252 samples (FP n=167, OND n=85); and cohort C included 100 FP patients. All samples were assayed in a ‘blinded’ fashion.

Total IgM Measurement

Total IgM levels were measured using a commercial enzyme linked immunosorbent assay (ELISA) kit (Bethyl laboratories, Montgomery, Tex., USA) according to manufacturer instructions and reported in mg/mL.
Immunofluorescence Assay for anti-GAGA2, anti-GAGA3, anti-GAGA4, and Anti-GAGA6 IgM Using Glycan Array
Levels of anti-GAGA2, anti-GAGA3, anti-GAGA4, and anti-GAGA6 IgM antibodies in cohort C samples were measured by immunofluorescence assay (IFA), using glass slides patterned with teflon mask, creating 7 clusters of microwells, 32 wells in each cluster (FIG. 1A). An adhesive silicon superstructure (FIG. 1B) was attached to the slide. This silicon gasket defined wells for manual application of multiple serum samples per slide. Each well was arrayed with glycan antigens and internal controls (FIG. 1C). p-Nitrophenyl derivatives of GAGA2, GAGA3, GAGA4, and GAGA6 (Toronto Research Chemicals, Toronto, Canada) were covalently bound by a linker to the glass slide as previously described (Schwarz M, et al. 2006 J Neurol Sci, 244: 59-68).

Assay Procedures

The slide wells were incubated for 60 minutes at room temperature with blocking solution (400 μL/well). After removal of blocking solution, 300 μL/well of patients' sera, diluted 1:40 in aqueous solution of 1% bovine serum albumin (BSA) in 20 mM Tris-HCl pH 7.2, 0.9% NaCl, 0.05% Tween-20 was added to each well. Each slide included 5 sera samples and a reference sample for one arbitrary unit. Each sample was tested five times on different slides. Samples were incubated for 45 minutes. Sera were removed and slides further washed and processed in an HS4800 system. Briefly, slides were washed in TNTT buffer (20 mM Tris-HCl pH 7.2, 2 M NaCl, 0.05% Tween-20, 0.05% Triton X-100) by the hybridization system. Biotinylated goat anti-human IgM (1:500) and Alexa-633-labeled streptavidin (1:150; Molecular Probes, Oreg., USA) were incubated sequentially with washings in between for 1 h at 32° C. in the light-protected and temperature-controlled environment of the hybridization system.
Following the washing and drying, slides were scanned using laser scanner (GenePix 4000B, Molecular Device, Baltimore, Md., USA); slide image was analyzed using Optiquant™” software and RFU representing relative binding of anti-glycan IgM to each antigen and control micro-well were calculated. The data quality from each well was verified by ensuring signal levels from human IgM, and anti-human IgM spots above cutoff. If wells did not meet criteria, samples were tested again. Levels of anti-GAGA2, anti-GAGA3, anti-GAGA4, and anti-GAGA6 IgM were calculated for each sample by dividing the sample RFU by the reference sample RFU. The coefficient of variation (CV) of anti-α-glucose levels was 8-12% for intra-slides wells, and 15-22% for different hybridization station running cycles (inter-slide). Samples were considered as positive if results were above cutoff levels for at least one of the 4 antibodies. Cutoff values for each antigen were calculated as mean value of the FP population plus 1, 1.5, or 2 SD, and best fit cutoffs (4.0, 4.5, 4.5, and 4.3 for anti-GAGA2, anti-GAGA3, anti-GAGA4, and anti-GAGA6 IgM respectively).

Statistical Methods

Numerical variables were compared across groups by Student's t-test or by the Mann-Whitney U-test, depending on whether or not they followed a normal distribution, and the X²test for rates comparison between groups or the Fisher exact if any cells had an expected count of less than 5. Pearson correlations were calculated between numerical variables. P-values of less than 0.05 were considered to be statistically significant. Uncertainty of results was expressed by 95% confidence intervals. Diagnostic accuracy was calculated by sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). The cumulative risk of the development of clinically definite MS (CDMS) was calculated for each group according to the Kaplan-Meier method, and differences between the groups were evaluated in a univariate analysis by the log-rank test.
For testing the ability of the marker antibodies to differentiate FP patients with a high risk vs. a low risk for early conversion to CDMS (cohort C), several cutoff values were investigated. A Bonferroni correction factor was applied to the analysis.
Cohort-A: Identifying FP Patients Who Will have a Second Attack within 24 Months
Clinical and demographic characteristics as well as anti-GAGA4 levels for cohort-A are described in Table 1. Data regarding the time period between blood extraction and first relapse was available for 41/44 FP patients. Twenty six (26/41) FP patients (63%) had their first relapse within 2 years of blood sampling. Mean age of these patients was significantly lower than those suffering from a second attack later (mean age 34 versus 42 years, p=0.001); however, no association was found between age and anti-GAGA4 IgM levels among the study population. To examine for a relationship between anti-GAGA4 IgM levels and the risk of an earlier (i.e., 2 vs. 4 years) second attack, patients whose anti-GAGA4 IgM levels were above the median antibody levels for the FP group as a whole were examined. Sixteen (16/20) patients (80%) with antibody titers above median had a second clinical attack within two years compared to only 10/21 (47%) patients with titers equal or below the median (odds ratio 4.4 CI 95% 1.1-17.7, Fisher exact test, P=0.05).

TABLE 1

Demographic, clinical characteristics and anti-GAGA4 IgM levels of cohort-A.

	OND
FP	(total n = 44,	Ottawa	Brussels
(n = 44)	OIND 21, ONIND 23)	(n = 66)	(n = 22)

Mean age, years (SD)	37.6	(9.0)	38.5	(9.5)	39.1	(8.8)	35.1	(9.8)
Female, n (%)	38	(86)	33	(75)	55	(83)^a	16	(73)

Center Ottawa, n (%)

37

(84)^b

26

(59)

NA

Total IgM, Mean RFU*10⁶(SD)

2.10

(0.80)

1.94

(0.68)

2.08

(0.79)

1.83

(0.53)

2nd relapse within 24 months,	26	(60)^c	NA	21	(57)	5	(71)
n (% of FP population only)

Anti-GAGA4 IgM,	0.44	(0.30)^d	0.30	(0.11)	0.37	(0.26)	0.35	(0.16)
Mean signal intensity, OD/(Total IgM
RFU*10⁶)^0.5(SD)

All FP patients had CDMS at the end of the follow-up.
Other inflammatory neurological disease (OIND) group included: 2 bacterial meningitis, 4 viral meningitis, 1 IVIg induced meningitis, 2 optic neuritis, 2 cerebral vasculitis, 1 Churg-Strauss vasculitis, 2 myelitis, 1 sarcoidosis, 1 SLE, 1 HIV, 1 sinusitis, 1 brachial plexitis, 1 epidural abscess; Other non-inflammatory neurological disease (ONIND) group included: 2 fibromyalgia, 2 migraine, 5 idiophathic headache, 1 progressive dementia, 1 motor neuron disease, 1 valproate induced encephalophaty, 1 migraine + stroke, 1 dementia NYD, 1 neurosis, 1 brainstem stroke, 1 anisocoria, 1 chronic insomnia, 1 diabetic amyotropy, 1 numbness, 1 motor neuropathy, 1 occipital neuralgia and ataxia, 1 breast cancer: NA, not applicable.
[Note:
all the data in this Table derive from samples collected at or near the time of FP] FP, first presentation; OND, other neurological diseases; SD, Standard deviation; RFU, relative fluorescence units; OD, optical density.
^aχ²test P = 0.025 versus Brussels.
^bχ²test P = 0.01 versus OND
^cVerified data regarding occurrence of second relapse within 24 months and EDSS score at 4 years were available only for 41 of the 44 FP patients.
^dMann-Whitney U-test, P = 0.01 versus OND.

Cohort-C: Levels of Anti-α-Glucose IgM in FP Patients that Will have a Second Early Relapse (up to 24 Months), Versus Late or No Relapse

Demographics, clinical characteristics, and anti-glycan antibodies levels of FP (n=100) patients who had early relapse (≦24 months, n=58) versus late or no relapse (>24 months, n=42) are described in Table 2.
Except for time to first relapse, no significant differences were found among demographics, clinical characteristics, and square root total IgM of the early versus late relapsing group. Therefore, in this cohort, there was no need to correct for total IgM. Levels of all anti-glycan antibodies were higher in the early versus later relapsers; however, this did not reach statistical significance. To evaluate the possible relationship between levels of anti-α glucose based glycans IgM levels and the risk of imminent (i.e., within 24 months) first relapse, positive patients whose antibodies levels were above cutoff levels were analyzed for at least one of the four antibodies versus patients negative for all four antibodies. Twenty-two (22/58 (38%) early relapsing patients were positive for at least one antibody compared to only 5/42 (12%) positive patients that had a late or no relapse at all, (p=0.003 X²test, odds ratio=4.5 (95% CI [1.5-13.2]), 0.0125 (Bonferroni correction) should be considered the significant threshold for this analysis, since four different methods for determining the cutoff values of the antibodies was applied. In the group of patients who did not experience a second attack within the study period, only one was antibody-positive. Kaplan-Meier survival plot comparison (FIG. 2) between cumulative risk of FP patients who were positive for at least one marker (anti-GAGA4, anti-GAGA2, anti-GAGA3, or anti-GAGA6 IgM) versus negative patients revealed significant differences between the groups (P=0.0025 for up to 24 months respectively) indicating that antibody positive patients consistently had their first relapse earlier. A high level in at least one of the anti-α-glucose IgM antibodies, identified 37.9% of FP patients who had an early attack (<24 months) versus those who had a late or no attack within the follow-up period with 88.1% specificity, 81.5% positive predictive value (PPV), and 50.7% negative predictive value (NPV) (Table 3).
The levels of anti-GAGA2, anti-GAGA3, anti-GAGA4, and anti-GAGA6 IgM antibodies in FP patients are broadly distributed. The subgroup of FP patients with extremely elevated levels of either anti-GAGA2, anti-GAGA3, anti-GAGA4, or anti-GAGA6, relative to the entire FP population, are at a higher risk for faster disease progression (e.g., a higher risk for a relapse in the near future, or an early EDSS score progression) relative to patients that do not have elevated levels of anti-GAGA2, anti-GAGA3, anti-GAGA4, or anti-GAGA6.
Based on the cohort of 100 FP patients, optimized cutoff levels were set for determination of FP patients who have elevated levels of the antibodies compared to the entire FP patient population. Patients were considered as having elevated antibody levels if antibody levels were above a certain cutoff level for at least one of the 4 antibodies. For determination of cutoff levels/values for each antigen, the mean and standard deviation (SD) of the FP population (n=100) were calculated. The cutoff levels were calculated as the mean value of the FP population plus 1, 1.5, or 2 SD, or best fit cutoffs (mean+1.4*SD, mean+1.8*SD, mean+0.8*SD, and mean+1.7*SD for anti-GAGA2, anti-GAGA3, anti-GAGA4, and anti-GAGA6 IgM, respectively). As shown in Table 3, the cutoff level used includes a range (mean+one or two SD). The cutoff level for determination of an extremely elevated antibody level is determined as an upper percentile, e.g., the upper 20%, the upper 15% or the upper 10% of a population of FP patients.
In practice, by testing this cohort of 100 FP patient samples, or any other large enough cohort of FP patients, a reference sera sample similar to the chosen cutoff level is determined for each antibody. Aliquots of the reference sera samples are prepared and kept frozen, for future use. The reference/control sera samples are calibration samples, having a preset antibody level (cutoff level; reference level; control level). When test samples are analyzed for antibody levels, the test sample is examined in parallel with the reference/calibration sample. If the antibody level in the test sample is elevated compared to the antibody level in the reference/calibration sample, the test sample is considered “positive”, e.g., it belongs to the subgroup of FP patients with an elevated level of the antibody.

TABLE 2

Demographic characteristics, clinical characteristics, and anti-glycan
antibodies levels of FP (n = 100) patients who had early relapse
(≦24 months) versus late or no relapse (>24 months) in cohort-C.

		Late or no
	Early relapse, ≦24 m	relapse, >24 m
	(n = 58)	(n = 42)^a

Mean age, years (SD)	34.9	(10.9)	36.2	(8.2)
Female, n (%)	43	(74)	29	(69)
Center Ottawa, n (%)	22	(38)	23	(54)
Square root total IgM, Mean mg/mL (SD)	1.18	(0.26)	1.13	(0.20)
Time to relapse, mean months (SD)^b	11.4	(7.0)	44.8	(18.8)^c
Anti-GAGA2 IgM levels, Mean (SD)	2.4	(1.3)	2.2	(0.9)
Anti-GAGA3 IgM levels, Mean (SD)	2.5	(1.3)	2.2	(0.9)
Anti-GAGA4 IgM levels, Mean (SD)	2.4	(1.3)	2.0	(1.1)
Anti-GAGA6 IgM levels, Mean (SD)^d	3.1	(1.9)	2.4	(1.1)

SD—Standard deviation.
^a11 patients remain still FP without drug treatment, 2 patients for 72 months and 9 patients for 48 months.
^bCalculated only for patients who had relapse during the follow-up time.
^cP < 0.0001, T-test.
^dFisher exact test of Anti-GAGA6 status (positive or negative) vs. early/late relapse,
P = 0.015, Odds Ratio 6.4(95% CI 1.4-29.8).

TABLE 3

Diagnostic characteristics for identification of FP patients who had an early
relapse (≦24 months, n = 58) vs. late or no relapse (>24 months,
n = 42) using a panel of anti-α-glucose disaccharide and different cutoffs (Cohort C).

Cutoff used ^a	Mean + SD	Mean + 1.5 SD	Mean + 2 SD	Best fit

Sensitivity, % (95% CI)	37.9 (25.5-51.6)	24.1 (13.9-37.2)	15.5 (7.3-27.4)	37.9 (25.5-51.6)
Specificity, % (95% CI)	83.3 (68.6-93.0)	88.1 (74.4-96.0)	95.2 (83.8-99.4)	88.1 (74.4-96.0)
PPV, %	75.9	73.7	81.8	81.5
NPV, %	49.3	45.7	44.9	50.7
Efficiency, %	57.0	51.0	49.0	59.0
Relative risk ^b	1.5	2.5	3.3	3.2

SD—Standard deviation,
^aCutoffs were calculated for each marker using mean of all 100 FP samples plus 1, 1.5, and 2 SD; PPV, positive predictive value; NPV, negative predictive value.
^bRelative risk of patient positive for at least one marker among the 4 markers for having second relapse in up to 24 months, versus negative patients.

In summary, the results of testing a total of 100 frozen sera samples taken from FP patients at or near the time of their first neurological event demonstrate that FP patients with higher levels of a panel of anti-α-glucose IgM antibodies had a higher probability for having a second attack within 24 months, Patients positive for at least one of the anti-α-glucose IgM antibodies had significantly higher cumulative risk (Kaplan-Meier) for having an earlier relapse, identifying about a third of all the early relapsers. An interesting categorical analysis revealed that anti-GAGA6 status alone (positive or negative) had the strongest predictive value of an early relapse. Finding higher levels of serum anti-GAGA2, anti-GAGA3, anti-GAGA4, or anti-GAGA6 IgM antibodies in FP patients predicts, with high specificity, those who will convert to CDMS within 2 years. Such information is invaluable for physicians having some difficulty in deciding upon a course of early therapy for their FP patients.
In the initial discovery phase (Schwarz M et al., 2006 J Neurol Sci, 244:59-68), fourty different glycans were screened. IgM (not IgG or IgA) antibodies to α-glucose antigens could distinguish MS patients from OND controls. This type of IgM response to carbohydrates is most likely produced by self-replenishing B-1 B cells, which respond poorly to protein, but much better to carbohydrate antigens (Goldsby R A et al., 2000 Kuby Immunology, fourth edition W.H. Freeman and Company). In general, B1 B-cells require a high amount of antigen for induction and play an important role as a first line of defense against invading pathogens, removal of senescent cells, cell debris, and other self-antigens (Binder C J et al., 2005 Lipid Res, 46:1353-63; DeFranco A L et al., 2007 Immunity: The Immune Response to Infectious and Inflammatory Disease. New Science Press). Serum derived human IgM monoclonal antibodies were found to accumulate in areas of CNS damage and promote remyelination in demyelinated mice (Pirko I et al., 2004 FASEB J, 18:1577-9; Warrington A E et al., 2007 J Neurosci Res, 85:967-76). This possibly reflects a type of “house-keeping” role, a recognized property of these antibodies (Binder C J et al., 2005 Lipid Res, 46:1353-63). Also, in agreement with the results presented herein, higher levels of IgM antibodies in CSF were found to predict a more severe MS course (Mandrioli J et al., 2008 J Neurol, 255:1023-31). Thus, a test for the presence of anti-α-glucose IgM antibodies in the CSF of MS patients could be useful, though a simple blood test would still be more preferable, especially if it needed to be repeated.
MRI seems sensitive at predicting ultimate vs. imminent conversion to CDMS. Although this cannot be directly compared to the diagnostic performance of antibody measurement in the retrospective study of FP patients (37% sensitivity, 88% specificity, 81% PPV, and 50% NPV), it suggests that measuring anti-α-glucose IgM levels could provide an independent and specific predictive factor for early conversion to CDMS (within 24 months). Finding higher levels of serum anti-GAGA2, anti-GAGA3, anti-GAGA4, or anti-GAGA6 IgM antibodies in FP patients predicts, with high specificity, those who will convert to CDMS within 2 years. Such information might be invaluable for physicians having some difficulty in deciding upon a course of early therapy for their FP patients.

Example 2

Panel of Anti-α-Glucose Based Glycan IgM Antibodies (Anti-GAGA2, Anti-GAGA3, Anti-GAGA4, and Anti-GAGA6) Predict Expanded Disability Status Scale Progression in Clinically Isolated Syndrome Patients Suggestive of Multiple Sclerosis

Prior to the invention, there was no specific serum based biomarker for the prognosis of Expanded Disability Status Scale (EDSS) progression in clinically isolated syndrome (CIS) patients suggestive of Multiple Sclerosis (MS). For patients with CIS suggestive of MS, there is a need to predict the patients with higher risk for rapid progression in EDSS score. As described in detail below, levels of IgM antibodies to a panel of four glucose-based glycans were analyzed for their ability to predict risk of rapid EDSS progression in CIS patients suggestive of MS within 5 years in patients treated early and those that received delayed treatment.
A prospective analysis was performed on 286 sera samples taken from CIS patients at baseline in the Betaferon®/Betaseon® in Newly Emerging MS for Initial Treatment (BENEFIT) study. The patients' EDSS score was determined and they were followed for at least 5 years. Levels of IgM antibodies to glucose based glycan panel (anti-Glc (α1,2) Glc (α) (GAGA2), anti-Glc (α1,3) Glc (α) (GAGA3), anti-Glc (α1,4) Glc (α) (GAGA4), anti-Glc (α1,6) Glc (α) (GAGA6)) in the sera were measured by enzyme immunoassay (EIA). Patients were classified as positive (above cutoff) in one or more antibodies, or negative (below cutoff) for all antibodies.
The results show that 54/286 patients (including those that were treated early and those that received delayed treatment) were classified as positive and 232/286 were classified as negative at baseline. At day 1800, the Kaplan-Meier estimate, i.e., the estimated percentage of patients without confirmed EDSS progression of the group that was classified as negative, was 76.73% compared to only 58.21% in the group classified as positive (P=0.01, log rank test). Cox proportional hazard regression model analysis including all covariates indicate that patients positive for anti-glucose were are at higher risk for progression in EDSS within 5 years (P=0.009, Hazard Ratio (HR) 2.05, CI95% 1.2-3.5). Thus, higher levels of serum anti-α-glucose IgM in CIS patients predicts a high risk of progression in EDSS within 5 years, while lower levels of serum anti-α-glucose IgM in CIS patients predicts a low risk of progression in EDSS within 5 years.

BENEFIT Study and Retained Samples

The BENEFIT study (Betaferon®/Betaseron® in Newly Emerging MS for Initial Treatment) consisted of a placebo-controlled phase and a follow-up phase (Kappos L, et al., 2007 Lancet, 370(9585):389-97). The 5-year double-blinded, placebo-controlled phase, assessed the safety, tolerability, and efficacy of interferon beta-1b 250 μg (8 MIU) delivered subcutaneously every other day in CIS patients with a first event and abnormal magnetic resonance imaging (MRI) scan suggestive of MS. Eligible patients had experienced a first neurological event suggestive of MS and had at least two clinically silent lesions on a T2-weighted brain MRI scan. Within 60 days of the onset of the first clinical event, and after providing written informed consent, patients were randomly assigned, in a 5:3 ratio, to interferon beta-1b 250 μg or placebo subcutaneously every other day. All placebo CIS patients were transferred to the treatment group within 24 months or if they converted to CDMS, whichever came first. After 5 years, CIS patients that began treatment at baseline were defined as “early treatment”, while placebo CIS patients that were transferred to the treatment group due to conversion to CDMS or the passage of 24 months were defined as “delayed treatment”. Thus, the BENEFIT study described herein evaluates breakthrough MS, i.e., MS patients that have a poor prognosis despite treatment. Breakthrough disease does not imply that the patient has not responded to treatment—it implies only that disease activity is present despite therapy.
The study also assessed the long-term course of disease, including delay of disability. During the study, sera from patients with a first clinical event suggestive of multiple sclerosis and at least two clinically silent lesions on T2-weighted brain MRI were retained. Patients were followed up and regular visits were scheduled for the assessment of their EDSS during a 5 year period. Sera samples collected at baseline of the study, along with clinical data from the BENEFIT study were used to evaluate the prediction value of serum anti-α-glucose based glycan IgM antibodies measured at baseline to predict EDSS progression in clinically isolated syndrome patients suggestive of Relapsing Remitting Multiple Sclerosis.

Expanded Disability Status Scale (EDSS)

The Kurtzke Expanded Disability Status Scale (EDSS) (Kurtzke J F, 1983 Neurology, 33(11):1444-52) is a method of quantifying disability in multiple sclerosis. The EDSS quantifies disability in eight Functional Systems (FS) and allows neurologists to assign a Functional System Score (FSS) in each of these. The Functional Systems are: pyramidal, cerebellar, brainstem, sensory, bowel and bladder, visual, cerebral, and other. EDSS steps 1.0 to 4.5 refer to people with MS who are fully ambulatory. EDSS steps 5.0 to 9.5 are defined by an impairment to ambulation. By “progression in EDSS score” is meant a progression from about 1.0 to about 10.0, e.g., about 1.0 to about 5.0; about 5.0 to about 10.0; about 2.0 to about 4.0; about 4.0 to about 6.0; about 6.0 to about 8.0; about 8.0 to about 10.0, about 0.5 to about 1.0; about 1.5 to about 2.5; about 2.0 to about 3.5; or any other incremental increase between 1.0 and 10.0 in increments of 0.5.

Materials and Methods

The study was performed on sera that were collected from patients during the baseline for the BENEFIT study. Study participants were patients who had a clinically isolated syndrome suggestive of MS, having a First Presentation and at least two clinically silent lesions on a T2-weighted brain MRI. Baseline EDSS was between 0 to 5. Patients who met the study inclusion and exclusion criteria were enrolled in the study.

Inclusion Criteria

Subjects enrolled into the BENEFIT study met all of the following inclusion criteria: 1) age between 18 and 45 years inclusive; 2) first clinical episode suggestive of demyelinating disease within the last 60 days (measured from onset of the first episode to treatment start), based on the appearance of a new neurological abnormality which must be present for at least 24 hours. Objective clinical evidence of at least one neurological abnormality other than vegetative or cerebral dysfunction or paresthesia must be present or documented; 3) score of 0 to 5.0 on the Kurtzke Expanded Disability Status Scale’ (EDSS) for at least one time point during the screening period before start of treatment; 4) at least two clinically silent lesions on the T2-weighted brain MRI scan with a size of at least 3 mm, at least one of which is ovoid or periventricular or infratentorial; 5) females of child-bearing potential must agree to practice adequate contraception methods; 6) written informed consent; 7) shipment time of sera was below 3 days.

Exclusion Criteria

Subjects with the following exclusion criteria were excluded from the BENEFIT study:
1) any disease other than MS that could better explain the patient's signs and symptoms;
2) any previous clinical event attributable to acute demyelination in one or more regions of the central nervous system lasting for at least 24h, regardless of whether medical attention was obtained; 3) complete transverse myelitis or bilateral optic neuritis; 4) clinically significant heart diseases; 5) history of severe depression or attempted suicide or current suicidal ideation; 6) clinically significant liver, renal, or bone marrow dysfunction; 7) history of hypergammaglobulinemia; 8) epilepsy not adequately controlled by treatment; 9) any conditions that could interfere with the MRI or any other evaluation in the study; 10) known allergy to gadolinium-DTPA; 11) known allergy to human proteins including albumin and interferons (IFN); 12) intolerance or any contraindication to paracetamol/acetaminophen or ibuprofen; 13) previous participation in this study; 14) participation in any clinical trial within the past six months; 15) pre-treatment with the following substances prior to study enrollment within the following time frames: —at any time—: any IFN, glatiramer acetate (Copolymer I), total lymphoid irradiation, anti-lymphocyte monoclonal antibody treatment (e.g., anti-CD4, anti-CD52 (alemtuzumab), anti-VLA4 (Very Late Antigen-4; natalizumab)), mitoxantrone, cyclophosphamide, azathioprine, Mg, cyclosporine A, methotrexate, or any other immuno modulating or immunosuppressive drug including other recombinant or non-recombinant cytokines; —3 months prior study entry—: any other treatment known to be used for putative or experimental MS treatment including pentoxifylline; 16) pregnancy or lactation; 17) history of alcohol or drug abuse; 18) inability to administer subcutaneous injections either by self or by caregiver; 19) medical, psychiatric or other conditions that compromise the patient's ability to understand the patient information, to comply with the trial protocol, or to complete the study.
Patients were followed for up to 60 months or until CDMS during the placebo-controlled part of the study and were offered to take part in the follow-up phase afterwards. Blood samples were taken from the enrolled patients at baseline visit and at different points in time during the course of the study. Clinical data and EDSS score were taken at screening, baseline and at each follow up visit of the patients. Patient clinical data and MRI includes the following: EDSS, Relapses, T2-weighted brain MRI, T1-weighted brain MRI, Gd enhancing lesions, MS diagnosis according to the McDonald criteria, and CDMS diagnosis. Serum samples attained at baseline (before treatment was initiated) were used for analysis of the gMS panel of glucose IgM antibodies. Sera samples were shipped under ambient temperature to a central lab and were frozen.

Enzyme Immuno-Assay for Anti-GAGA 2, Anti-GAGA 3, Anti-GAGA 4, and Anti-GAGA 6 IgM Levels

Each sample was tested for levels of anti-GAGA: anti-GAGA2, anti-GAGA 3, anti-GAGA 4, and anti-GAGA 6 μM using the enzyme immuno-assay (EIA) method in a 96 micro-well plate platform. 96 micro-well plates were prepared as follows: 96-well microwell plates with the antigens of GAGA2, GAGA3, GAGA4, and GAGA6 were covalently bound by a linker to the wells walls as previously described (Schwarz, et al., 2003 Glycobiology, 13(11): 749-754). Sera measurements were done in duplicate, each plate included 5 points calibration curve. Results were reported in arbitrary EIA Units (EU).

EIA Procedures

The serum samples were stored frozen and were transported frozen to Glycominds Ltd., Lod, Israel. Samples were stored in −80° C. until use. Prior to use, the samples were thawed by incubation in 37° C. for 2 hours. IgG depletion was performed using a commercial mini Rapi-Sep® units (PanBio, Baltimore, Md., USA). Serum samples were diluted 1:1200 in a sample diluent, dispensed into the wells in duplicates, and incubated for 180 min in 4° C., then washed with wash buffer. Bound antibodies were labeled with horseradish peroxidase (HRP)-conjugated goat anti-human IgM type-specific antibody, washed, and 3,3′,5,5′-tetramethylbenzidine was added for detection. After 30 minutes, the enzymatic reaction was stopped with 1% sulfuric acid solution and optical density (OD) in the wells was read at 450 nm with a microwell plate reader (Wallac, Turku, Finland). Each plate included a 5 point calibration curve with a preset unit value for each point on the curve. The calibration curve used to calculate the EIA units (EU), and to normalize results obtained from different plates. Results were reported in EU.

Statistical Analysis

Numerical variables were compared across groups by Student's t-test or by the Mann-Whitney U-test, depending on whether or not they followed a normal distribution, and the X²test for rates comparison between groups. P-values of less than 0.05 were considered to be statistically significant. Uncertainty of results was expressed by 95% confidence intervals. Diagnostic accuracy was calculated by sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Based on the anti-GAGA2, anti-GAGA3, anti-GAGA4, and anti-GAGA6, EU values measured for each sample, the following classification rule was used (Classifier 1): Samples were considered as positive if results were above cutoff levels for at least one of the 4 antibodies. Cutoff values for each antigen were calculated based on the mean and standard deviation values of the full population. Thus, the following rules, which were determined based on results in Table 4 (best fit column) were used for determining cutoff values: GAGA2: Mean+1.4*SD; GAGA3: Mean+1.8*SD; GAGA4: Mean+1.7*SD; and GAGA6: Mean+0.8*SD. These cutoff values were determined according to the total population, both treatment and placebo group. The cumulative risk of confirmed progression to 1.0 EDSS was calculated for the positive group according to classifier 1, and to the negative group according to classifier 1, according to the Kaplan-Meier method, and differences between the groups were evaluated in a univariate analysis by the log-rank test.
Cox proportional hazards regression models were applied to time to confirmed EDSS progression in order to assess whether classifier 1 groups (≦cut-off in at least one of the 4 antibodies, versus>cut-off) predict the progression of the disease.
It was determined if the distribution of the “time-to-event” times in the classifier 1 groups differ, and how the effects of the different classifier 1 groups act on the hazard. The Cox proportional hazards regression models will include adjustments for the following baseline covariates: age, sex, onset of disease (multi- vs. monofocal), use or nonuse of corticosteroid treatment at the first event, the effect of early versus delayed treatment with IFNB-1b, the presence of gadolinium-enhancing lesions on T1-weighted MRI scans at baseline (no versus at least one lesion), and the number of hyperintense lesions on T2-weighted scans (less than nine versus at least nine T2 lesions).

Results

A total of 286 base line samples from patients after first neurological event suggestive of MS were included in the study. The overall distribution of anti-GAGA 2, anti-GAGA 3, anti-GAGA 4, and anti-GAGA 6 IgM levels in the study population is described in Table 5. The frequency of base line and MRI screening parameters in the total population and by classifier 1 (positive/negative) is described in Table 6. The results of the Kaplan-Meier estimate analysis for day 1800 is described in FIG. 3 and Table 7.
54/286 patients were classified as positive and 232/286 were classified as negative at base line (Table 8). Table 9 describes the detailed data. At day 1800, the Kaplan-Meier estimate, i.e., the estimated percentage of patients without confirmed EDSS progression of the group that was classified as negative was 76.73%, compared to only 58.21% in the group classified as positive (P=0.01, log rank test). Cox proportional hazard regression model analysis, including all covariates, indicates that classifier 1 contributed very significantly (P=0.009, Hazard Ratio (HR) 2.05, C195% 1.2-3.5) to model for prediction of time to confirmed EDSS progression in the evaluated population. In addition, treatment, age and the presence of Gd-enhancing lesions at screening contributed significantly to the model fit. See, Tables 10 and 11.
Both Kaplan-Meier survival and Cox proportional hazard analysis indicate that patients with higher levels of anti-GAGA 2, anti-GAGA 3, anti-GAGA 4, or anti-GAGA 6 have a higher risk for progression in EDSS within 5 years. These results indicate that classifying patients based on anti-α-glucose IgM levels provides an independent and more specific risk predictive factor for early one point progression in EDSS (within 5 years). Finding higher levels of serum anti-GAGA 2, anti-GAGA3, anti-GAGA4, or anti-GAGA6 IgM antibodies in CIS patients predicts those who will progress in their EDSS within 5 years. This information is invaluable for disease management and the prediction of risk for rapid EDSS MS progression and for breakthrough MS.
In one aspect, referring to FIG. 4, a process (100) for determining if a CIS patient is at risk for a conversion to CDMS within 24 months includes the stages shown. In the process (100), a level of at least one antibody in a patient sample, e.g., blood, is compared to a reference level. The process (100), however, is exemplary only and not limiting. The process (100) may be altered, e.g., by having stages added, removed, or rearranged. In an embodiment, the process (100) is implemented on a system including one or more computers or computer networks configured to read and execute computer-readable instructions stored on computer-readable mediums. A computer-readable medium is defined as any kind of computer memory such as floppy disks, conventional hard disks, CD and DVD ROMS, Flash ROMS, nonvolatile ROM, and RAM. The computers can be configured to receive data from a user via a user interface, or automatically via machine interface (e.g., a USB connection to a blood analyzer).
At stage 102, a test sample (e.g., whole blood, serum, or plasma) is obtained from a patient. Optionally, the patient's blood sample is collected by venipuncture. Following blood collection, the blood will clot, and the serum is separated from the blood clot by centrifugation of the collection tube. Sera samples are collected from a patient and stored prior to use, e.g., the sample is stored at −20° C., −70° C. or −80° C. until use. At stage 104, the level of at least one antibody is measured. For example, in an embodiment, levels of an anti-Glc (α 1,2) Glc (α) antibody, an anti-Glc (α 1,3) Glc (α) antibody, an anti-Glc (α 1,4) Glc (α) antibody, and an anti-Glc (α 1,6) Glc (α) antibody are detected. Optionally, levels of an anti-Glc(α1,2)Glc(α) (GAGA2) IgM isotype antibody, an anti-Glc(α1,3)Glc(α) (GAGA3) IgM isotype antibody, an anti-Glc(α1,4)Glc(α) (GAGA4) IgM isotype antibody, or an anti-Glc(α1,6)Glc(α) (GAGA6) IgM isotype antibody are detected.
At stage 106, the level of at least one antibody in the test sample is compared to one or more reference levels. Optionally, a computer or computer network is used to make the comparison. For example, the reference levels are stored on a computer-readable medium associated with computer or computer network, and the patient's at least one antibody level is received via an input device (e.g., keyboard, GUI, COM port).
At stage 108, the patient's risk factor for progressing to CDMS is determined. For example, if the level of at least one of the antibodies in the test sample is higher than a reference level, then the patient is at higher risk of progressing to CDMS within 24 months. In an embodiment, a computer can be used to generate an output (e.g., report, message, display) to indicate the corresponding risk factor.
In another aspect, referring to FIG. 5, a process (110) for determining if a patient is likely to progress in EDSS score within twenty years includes the stages shown. In the process (110), a level of at least one antibody in the patient sample, e.g., blood, is compared to a reference level. The process (110), however, is exemplary only and not limiting. The process (110) may be altered, e.g., by having stages added, removed, or rearranged. In an embodiment, the process (110) is implemented on a system including one or more computers or computer networks configured to read and execute computer-readable instructions stored on computer-readable mediums. A computer-readable medium is defined as any kind of computer memory such as floppy disks, conventional hard disks, CD and DVD ROMS, Flash ROMS, nonvolatile ROM, and RAM. The computers can be configured to receive data from a user via a user interface, or automatically via machine interface (e.g., a USB connection to a blood analyzer).
At stage 112, a test sample (e.g., whole blood, serum, or plasma) is obtained from a patient. At stage 114, the level of at least one antibody is measured. For example, in an embodiment, levels of an anti-Glc (α 1,2) Glc (α) antibody, an anti-Glc (α 1,3) Glc (α) antibody, an anti-Glc (α 1,4) Glc (α) antibody, and an anti-Glc (α 1,6) Glc (α) antibody are detected. Optionally, levels of an anti-Glc(α1,2)Glc(α) (GAGA2) IgM isotype antibody, an anti-Glc(α1,3)Glc(α) (GAGA3) IgM isotype antibody, an anti-Glc(α1,4)Glc(α) (GAGA4) IgM isotype antibody, or an anti-Glc(α1,6)Glc(α) (GAGA6) IgM isotype antibody are detected.
At stage 116, the level of at least one antibody in the test sample is compared to one or more reference levels. Optionally, a computer or computer network is used to make the comparison. For example, the reference levels are stored on a computer-readable medium associated with computer or computer network, and the patient's at least one antibody level is received via an input device (e.g., keyboard, GUI, COM port).
At stage 118, the patient's risk factor for progressing in EDSS score is determined. For example, if the level of at least one of the antibodies in the test sample is higher than a reference level, then the patient is at higher risk of progressing in EDSS score within twenty years. In an embodiment, a computer can be used to generate an output (e.g., report, message, display) to indicate the corresponding risk factor.
In another aspect, referring to FIG. 6, a process (120) for determining if a patient is likely to develop breakthrough MS includes the stages shown. In the process (120), a level of at least one antibody in the patient sample, e.g., blood, is compared to a reference level. The process (120), however, is exemplary only and not limiting. The process (120) may be altered, e.g., by having stages added, removed, or rearranged. In an embodiment, the process (120) is implemented on a system including one or more computers or computer networks configured to read and execute computer-readable instructions stored on computer-readable mediums. A computer-readable medium is defined as any kind of computer memory such as floppy disks, conventional hard disks, CD and DVD ROMS, Flash ROMS, nonvolatile ROM, and RAM. The computers can be configured to receive data from a user via a user interface, or automatically via machine interface (e.g., a USB connection to a blood analyzer).
At stage 122, a test sample (e.g., whole blood, serum, or plasma) is obtained from a patient. At stage 124, the level of at least one antibody is measured. For example, in an embodiment, levels of an anti-Glc (α 1,2) Glc (α) antibody, an anti-Glc (α 1,3) Glc (α) antibody, an anti-Glc (α 1,4) Glc (α) antibody, and an anti-Glc (α 1,6) Glc (α) antibody are detected. Optionally, levels of an anti-Glc(α1,2)Glc(α) (GAGA2) IgM isotype antibody, an anti-Glc(α1,3)Glc(α) (GAGA3) IgM isotype antibody, an anti-Glc(α1,4)Glc(α) (GAGA4) IgM isotype antibody, or an anti-Glc(α1,6)Glc(α) (GAGA6) IgM isotype antibody are detected.
At stage 126, the level of at least one antibody in the test sample is compared to one or more reference levels. Optionally, a computer or computer network is used to make the comparison. For example, the reference levels are stored on a computer-readable medium associated with computer or computer network, and the patient's at least one antibody level is received via an input device (e.g., keyboard, GUI, COM port).
At stage 128, the patient's risk factor for developing breakthrough MS is determined. For example, if the level of at least one of the antibodies in the test sample is higher than a reference level, then the patient is at higher risk of developing breakthrough MS. In an embodiment, a computer can be used to generate an output (e.g., report, message, display) to indicate the corresponding risk factor.

TABLE 4

Anti-GAGA2, anti-GAGA3, anti-GAGA4 and anti-GAGA6 levels in
the placebo and IFNB-1b treatment. arms of BENEFIT study.

	Treatment	n	Nmiss	Mean	SD	Min	Median	Max

Anti-GAGA 2 [EIA(EU)]	Placebo	109	0	68.57140	58.331532	−11.6015	57.93817	274.4357
	IFNB-1b	177	0	65.04004	59.359072	−12.0682	46.65570	352.5607
	Total	286	0	66.38591	58.891945	−12.0682	50.21277	352.5607
Anti-GAGA 3 [EIA(EU)]	Placebo	109	0	71.11539	55.016680	−9.1998	58.15728	336.0155
	IFNB-1b	177	0	66.86592	52.483847	−8.2925	51.51775	315.0962
	Total	286	0	68.48548	53.407335	−9.1998	56.58532	336.0155
Anti-GAGA 4 [EIA(EU)]	Placebo	109	0	78.92899	67.735684	−0.8989	65.95567	402.3137
	IFNB-1b	177	0	72.49212	76.716617	−11.5659	55.49377	667.4310
	Total	286	0	74.94533	73.368802	−11.5659	58.57662	667.4310
Anti-GAGA 6 [EIA(EU)]	Placebo	109	0	66.74723	58.274748	−14.6643	51.26553	327.1028
	IFNB-1b	177	0	65.41337	61.363594	−13.2663	49.00671	407.4335
	Total	286	0	65.92173	60.105421	−14.6643	51.10276	407.4335

TABLE 5

Frequency table of base line and MRI screening parameters in the total
population (n = 286) and by classifier 1 (positive/negative).

	Negative	Positive	Total

Sex
Number of subjects	232	(100.0%)	54	(100.0%)	286	(100.0%)
male	69	(29.7%)	8	(14.8%)	77	(26.9%)
female	163	(70.3%)	46	(85.2%)	209	(73.1%)
Treatment
Number of subjects	232	(100.0%)	54	(100.0%)	286	(100.0%)
Placebo	87	(37.5%)	22	(40.7%)	109	(38.1%)
IFNB-1b	145	(62.5%)	32	(59.3%)	177	(61.9%)
Steroid use during first event
Number of subjects	232	(100.0%)	54	(100.0%)	286	(100.0%)
no	66	(28.4%)	9	(16.7%)	75	(26.2%)
yes	166	(71.6%)	45	(83.3%)	211	(73.8%)
onset of disease in 304747
Number of subjects	232	(100.0%)	54	(100.0%)	286	(100.0%)
monofocal	128	(55.2%)	26	(48.1%)	154	(53.8%)
multifocal	104	(44.8%)	28	(51.9%)	132	(46.2%)
CSF result
Number of subjects	166	(100.0%)	33	(100.0%)	199	(100.0%)
negative	29	(17.5%)	2	(6.1%)	31	(15.6%)
positive	137	(82.5%)	31	(93.9%)	168	(84.4%)
>=1 Gd-enhancing lesion at screening
Number of subjects	230	(100.0%)	54	(100.0%)	284	(100.0%)
no	130	(56.5%)	34	(63.0%)	164	(57.7%)
yes	100	(43.5%)	20	(37.0%)	120	(42.3%)

TABLE 6

Kaplan-Meier product limit estimates at day 1800 and log rank P-
value for comparison between patients positive for classifier 1
versus negative with respect to time to confirmed EDSS progression.

Time-to-event outcome

	#events and Kaplan-Meier
	estimates at day 1800	Test result

Negative	Positive	(p-value) of
(n = 232)	(n = 54)	log-rank test

Time to confirmed EDSS	49/76.73%	20/58.21%	0.011664
progression

TABLE 7

Kaplan-Meier analysis of time to confirmed
EDSS progression - data summary.

					Survival
		Number	Number confirmed		Standard
Day	Classifier 1	at Risk	EDSS progression	Survival	Error

0	<=cut-off 1	232	0	1.0000	0
	>cut-off 1	54	0	1.0000	0
90	<=cut-off 1	225	3	0.9869	0.00750
	>cut-off 1	51	1	0.9815	0.0183
180	<=cut-off 1	218	7	0.9693	0.0114
	>cut-off 1	47	3	0.9422	0.0324
270	<=cut-off 1	208	15	0.9337	0.0165
	>cut-off 1	47	3	0.9422	0.0324
360	<=cut-off 1	204	17	0.9247	0.0176
	>cut-off 1	46	4	0.9221	0.0374
450	<=cut-off 1	200	20	0.9111	0.0190
	>cut-off 1	43	7	0.8620	0.0485
540	<=cut-off 1	197	22	0.9019	0.0199
	>cut-off 1	43	7	0.8620	0.0485
630	<=cut-off 1	191	27	0.8790	0.0219
	>cut-off 1	40	8	0.8420	0.0513
720	<=cut-off 1	177	31	0.8599	0.0234
	>cut-off 1	39	8	0.8420	0.0513
810	<=cut-off 1	169	34	0.8452	0.0245
	>cut-off 1	35	12	0.7556	0.0616
900	<=cut-off 1	167	35	0.8401	0.0248
	>cut-off 1	35	12	0.7556	0.0616
990	<=cut-off 1	163	37	0.8301	0.0255
	>cut-off 1	33	14	0.7124	0.0652
1080	<=cut-off 1	159	38	0.8249	0.0259
	>cut-off 1	32	15	0.6908	0.0667
1170	<=cut-off 1	156	41	0.8093	0.0269
	>cut-off 1	31	16	0.6692	0.0680
1260	<=cut-off 1	156	41	0.8093	0.0269
	>cut-off 1	31	16	0.6692	0.0680
1350	<=cut-off 1	154	43	0.7990	0.0276
	>cut-off 1	30	17	0.6477	0.0692
1440	<=cut-off 1	151	45	0.7886	0.0282
	>cut-off 1	28	19	0.6045	0.0710
1530	<=cut-off 1	149	45	0.7886	0.0282
	>cut-off 1	26	20	0.5821	0.0718
1620	<=cut-off 1	149	45	0.7886	0.0282
	>cut-off 1	26	20	0.5821	0.0718
1710	<=cut-off 1	144	48	0.7673	0.0291
	>cut-off 1	25	20	0.5821	0.0718
1800	<=cut-off 1	96	49	0.7726	0.0293
	>cut-off 1	19	20	0.5821	0.0718
1890	<=cut-off 1	2	49	0.7673	0.0293
	>cut-off 1	1	20	0.5821	0.0718
1980	<=cut-off 1	0	49	.	.
	>cut-off 1	0	20	.	.

TABLE 8

BENEFIT study raw data

		Anti-		Anti-		Anti-		Anti-		Classifier
		GAGA2	GAGA2 > 148.8,	GAGA3	GAGA3 > 164.6,	GAGA4	GAGA4 > 133.6,	GAGA6	GAGA6 > 168.1,	status,
	Patient	IgM	Y = Yes,	IgM	Y = Yes,	IgM	Y = Yes,	IgM	Y = Yes,	P = positive,
#	ID	Units	N = No	Units	N = No	Units	N = No	Units	N = No	N = negative

1	15060	36.0	N	36.1	N	16.5	N	37.8	N	N
2	15062	44.2	N	51.3	N	33.8	N	85.9	N	N
3	15063	57.9	N	91.3	N	73.0	N	143.8	N	N
4	15064	75.9	N	148.7	N	65.5	N	212.4	Y	P
5	15065	15.3	N	47.2	N	8.1	N	21.8	N	N
6	15066	23.5	N	6.2	N	8.6	N	9.2	N	N
7	15067	14.5	N	74.0	N	0.5	N	99.7	N	N
8	15068	13.1	N	15.1	N	5.4	N	14.8	N	N
9	15070	92.1	N	15.8	N	82.9	N	37.0	N	N
10	15071	83.0	N	35.7	N	49.0	N	75.3	N	N
11	15072	25.3	N	79.8	N	35.2	N	88.8	N	N
12	15073	13.3	N	161.8	N	19.5	N	136.7	N	N
13	15075	13.4	N	18.4	N	10.8	N	15.8	N	N
14	15076	10.2	N	27.0	N	3.2	N	79.3	N	N
15	15077	10.6	N	19.7	N	14.4	N	24.5	N	N
16	15078	47.5	N	13.2	N	73.8	N	21.1	N	N
17	15079	22.9	N	28.1	N	28.8	N	49.0	N	N
18	15080	37.4	N	156.0	N	61.1	N	234.0	Y	P
19	15081	31.4	N	58.4	N	53.2	N	31.2	N	N
20	15082	8.5	N	41.5	N	23.8	N	19.2	N	N
21	15083	45.7	N	8.6	N	83.3	N	15.7	N	N
22	15084	34.8	N	22.7	N	59.3	N	45.7	N	N
23	15085	8.5	N	80.9	N	27.4	N	204.5	Y	P
24	15086	29.2	N	4.2	N	28.7	N	14.9	N	N
25	15087	33.4	N	58.2	N	37.8	N	104.6	N	N
26	15088	24.8	N	100.0	N	28.7	N	85.0	N	N
27	15089	5.0	N	98.0	N	4.1	N	44.9	N	N
28	15091	21.5	N	42.9	N	36.7	N	51.3	N	N
29	15092	−12.1	N	80.3	N	−11.6	N	102.3	N	N
30	15094	6.1	N	56.8	N	26.3	N	72.9	N	N
31	15095	91.1	N	22.9	N	134.6	Y	29.4	N	P
32	15097	21.2	N	59.1	N	49.2	N	178.3	Y	P
33	15098	49.2	N	71.4	N	50.4	N	34.8	N	N
34	15099	15.3	N	55.3	N	47.2	N	14.8	N	N
35	15100	11.6	N	41.0	N	28.8	N	10.3	N	N
36	15101	6.5	N	31.0	N	12.9	N	19.3	N	N
37	15102	21.5	N	39.9	N	34.4	N	21.1	N	N
38	15104	67.7	N	35.8	N	141.5	Y	62.7	N	P
39	15105	38.6	N	72.2	N	51.8	N	45.7	N	N
40	15106	6.1	N	25.0	N	33.8	N	2.6	N	N
41	15107	2.3	N	38.8	N	23.1	N	21.7	N	N
42	15108	32.5	N	9.1	N	58.6	N	1.8	N	N
43	15110	105.4	N	22.7	N	162.2	Y	50.9	N	P
44	15111	59.9	N	125.3	N	186.0	Y	112.6	N	P
45	15113	28.7	N	45.0	N	36.9	N	31.3	N	N
46	15114	7.0	N	16.0	N	13.6	N	37.5	N	N
47	15115	15.6	N	9.5	N	58.5	N	29.8	N	N
48	15116	26.3	N	31.2	N	21.8	N	68.4	N	N
49	15117	43.9	N	40.8	N	43.1	N	407.4	Y	P
50	15118	211.4	Y	59.0	N	667.4	Y	89.7	N	P
51	15119	30.6	N	15.5	N	269.1	Y	40.3	N	P
52	15120	0.4	N	86.0	N	77.2	N	84.2	N	N
53	15121	21.4	N	59.6	N	55.5	N	12.0	N	N
54	15123	29.5	N	−8.3	N	112.3	N	−13.3	N	N
55	15124	11.3	N	17.1	N	43.5	N	47.6	N	N
56	15125	24.0	N	36.5	N	49.7	N	16.6	N	N
57	15126	32.8	N	22.0	N	49.1	N	3.9	N	N
58	15127	52.4	N	66.3	N	115.1	N	37.2	N	N
59	15128	72.0	N	127.9	N	88.2	N	56.0	N	N
60	15129	8.0	N	30.5	N	5.7	N	35.1	N	N
61	15130	147.3	N	38.7	N	209.3	Y	20.8	N	P
62	15131	15.5	N	78.1	N	53.3	N	63.5	N	N
63	15132	1.7	N	78.0	N	6.7	N	64.9	N	N
64	15133	19.7	N	23.4	N	24.7	N	17.5	N	N
65	15134	18.6	N	95.8	N	22.9	N	85.2	N	N
66	15135	131.1	N	46.3	N	247.7	Y	44.2	N	P
67	15136	36.8	N	52.8	N	82.4	N	37.0	N	N
68	15137	12.3	N	59.9	N	15.4	N	81.1	N	N
69	15138	3.9	N	36.8	N	34.9	N	32.8	N	N
70	15139	52.0	N	14.5	N	69.2	N	20.9	N	N
71	15141	80.6	N	96.2	N	187.9	Y	122.8	N	P
72	15142	82.2	N	37.0	N	152.0	Y	46.1	N	P
73	15143	61.7	N	70.5	N	136.0	Y	52.0	N	P
74	15144	50.3	N	71.7	N	72.5	N	61.0	N	N
75	15145	24.1	N	61.0	N	15.9	N	37.7	N	N
76	15146	13.6	N	21.6	N	23.7	N	12.2	N	N
77	15147	19.1	N	54.2	N	19.3	N	10.4	N	N
78	15148	75.4	N	48.2	N	35.4	N	100.0	N	N
79	15149	134.7	N	47.7	N	62.6	N	82.7	N	N
80	15150	106.8	N	38.0	N	65.8	N	108.2	N	N
81	15151	25.6	N	22.7	N	36.3	N	48.1	N	N
82	15152	135.7	N	137.8	N	121.5	N	102.8	N	N
83	15153	121.9	N	118.7	N	63.2	N	53.8	N	N
84	15154	144.1	N	114.6	N	74.0	N	53.0	N	N
85	15156	126.5	N	39.4	N	42.1	N	121.5	N	N
86	15157	191.1	Y	141.7	N	248.5	Y	123.9	N	P
87	15158	66.2	N	3.8	N	29.9	N	63.5	N	N
88	15159	33.1	N	18.5	N	35.3	N	26.3	N	N
89	15160	20.4	N	29.0	N	39.6	N	28.5	N	N
90	15161	14.9	N	16.1	N	11.1	N	6.4	N	N
91	15162	52.5	N	68.5	N	32.0	N	56.7	N	N
92	15163	187.8	Y	278.2	Y	224.4	Y	99.9	N	P
93	15164	4.6	N	6.7	N	3.9	N	−0.3	N	N
94	15166	274.4	Y	130.0	N	114.0	N	106.0	N	P
95	15167	18.3	N	42.8	N	21.5	N	16.8	N	N
96	15169	118.1	N	102.4	N	82.4	N	86.1	N	N
97	15170	127.8	N	183.7	Y	57.2	N	80.8	N	P
98	15171	46.1	N	93.6	N	44.3	N	71.2	N	N
99	15172	4.0	N	15.0	N	18.2	N	7.3	N	N
100	15173	66.3	N	58.1	N	58.3	N	69.9	N	N
101	15174	37.3	N	30.3	N	51.1	N	38.7	N	N
102	15175	2.0	N	5.9	N	19.9	N	4.1	N	N
103	15176	40.0	N	41.0	N	80.1	N	43.4	N	N
104	15177	352.6	Y	278.7	Y	417.1	Y	348.3	Y	P
105	15178	150.9	Y	125.9	N	215.6	Y	83.8	N	P
106	15179	143.0	N	113.6	N	98.8	N	124.5	N	N
107	15180	59.2	N	33.4	N	34.9	N	35.5	N	N
108	15181	95.4	N	44.9	N	70.7	N	110.1	N	N
109	15182	72.2	N	59.4	N	59.7	N	78.5	N	N
110	15183	65.2	N	59.9	N	120.3	N	34.5	N	N
111	15184	87.2	N	120.3	N	122.6	N	88.6	N	N
112	15185	67.7	N	14.8	N	34.7	N	45.0	N	N
113	15186	63.0	N	37.1	N	53.7	N	55.5	N	N
114	15187	12.7	N	7.7	N	21.6	N	1.9	N	N
115	15188	58.6	N	33.8	N	62.7	N	53.2	N	N
116	15189	24.8	N	19.9	N	20.6	N	18.1	N	N
117	15191	44.2	N	36.3	N	59.8	N	33.8	N	N
118	15192	127.7	N	107.8	N	228.3	Y	178.6	Y	P
119	15193	52.0	N	43.3	N	73.1	N	66.9	N	N
120	15194	47.6	N	87.7	N	54.4	N	46.7	N	N
121	15195	63.2	N	15.3	N	41.7	N	78.6	N	N
122	15196	114.3	N	82.2	N	165.9	Y	121.3	N	P
123	15197	96.7	N	51.5	N	38.0	N	119.4	N	N
124	15198	22.6	N	6.9	N	7.9	N	11.5	N	N
125	15199	193.1	Y	69.8	N	104.4	N	131.1	N	P
126	15200	8.6	N	13.5	N	21.2	N	6.9	N	N
127	15201	68.0	N	66.2	N	104.2	N	55.0	N	N
128	15202	50.0	N	90.7	N	99.7	N	18.6	N	N
129	15203	249.5	Y	88.0	N	191.6	Y	157.3	N	P
130	15204	47.5	N	45.2	N	33.3	N	20.7	N	N
131	15205	199.0	Y	47.4	N	191.3	Y	62.6	N	P
132	15207	140.9	N	35.8	N	55.0	N	44.1	N	N
133	15208	32.6	N	8.0	N	62.2	N	34.9	N	N
134	15209	167.3	Y	30.6	N	160.0	Y	109.9	N	P
135	15211	29.7	N	31.2	N	43.4	N	11.8	N	N
136	15212	33.2	N	60.0	N	85.7	N	29.5	N	N
137	15213	46.4	N	8.0	N	59.3	N	30.6	N	N
138	15214	30.3	N	17.3	N	22.3	N	10.1	N	N
139	15215	31.8	N	6.4	N	24.0	N	35.5	N	N
140	15216	50.1	N	33.2	N	69.6	N	35.2	N	N
141	15217	60.4	N	52.3	N	38.2	N	67.8	N	N
142	15218	95.3	N	122.0	N	114.4	N	106.5	N	N
143	15219	97.6	N	154.4	N	93.8	N	100.6	N	N
144	15220	52.6	N	46.9	N	38.1	N	73.0	N	N
145	15221	7.9	N	6.7	N	7.6	N	0.4	N	N
146	15222	92.8	N	65.9	N	101.5	N	100.1	N	N
147	15223	38.5	N	56.3	N	64.0	N	44.3	N	N
148	15224	17.7	N	29.2	N	16.2	N	9.7	N	N
149	15225	37.9	N	74.3	N	116.9	N	62.2	N	N
150	15226	10.5	N	28.1	N	107.2	N	75.7	N	N
151	15227	20.6	N	72.7	N	72.2	N	52.4	N	N
152	15229	13.1	N	38.6	N	49.8	N	29.8	N	N
153	15230	6.8	N	24.4	N	34.4	N	8.9	N	N
154	15231	39.4	N	124.0	N	66.6	N	35.8	N	N
155	15232	86.1	N	64.7	N	56.6	N	41.7	N	N
156	15233	16.6	N	15.2	N	16.9	N	0.1	N	N
157	15234	107.1	N	60.4	N	79.6	N	44.8	N	N
158	15236	82.3	N	96.6	N	86.3	N	46.0	N	N
159	15237	66.4	N	86.2	N	51.1	N	27.5	N	N
160	15238	81.7	N	74.1	N	62.9	N	111.4	N	N
161	15239	59.8	N	56.2	N	82.6	N	49.8	N	N
162	15240	24.8	N	9.3	N	8.6	N	−1.0	N	N
163	15241	89.6	N	47.1	N	94.4	N	30.2	N	N
164	15242	77.6	N	219.4	Y	75.4	N	56.6	N	P
165	15243	111.6	N	67.9	N	78.8	N	60.4	N	N
166	15244	203.6	Y	134.1	N	125.8	N	164.1	N	P
167	15245	152.1	Y	151.4	N	120.3	N	120.2	N	P
168	15246	165.3	Y	125.3	N	377.0	Y	122.2	N	P
169	15247	51.5	N	42.5	N	51.1	N	27.3	N	N
170	15248	96.9	N	110.6	N	218.1	Y	119.0	N	P
171	15249	54.5	N	119.5	N	116.7	N	118.9	N	N
172	15250	97.5	N	87.5	N	110.5	N	72.1	N	N
173	15252	221.2	Y	336.0	Y	243.1	Y	172.2	Y	P
174	15253	22.4	N	34.8	N	66.2	N	51.7	N	N
175	15254	57.3	N	68.3	N	129.5	N	107.8	N	N
176	15255	−2.5	N	12.8	N	7.1	N	−5.1	N	N
177	15256	69.7	N	36.5	N	48.1	N	52.0	N	N
178	15257	77.7	N	57.7	N	69.0	N	79.4	N	N
179	15258	44.5	N	39.0	N	54.7	N	70.1	N	N
180	15259	22.0	N	36.4	N	12.6	N	14.9	N	N
181	15260	262.7	Y	224.3	Y	179.0	Y	341.6	Y	P
182	15262	100.9	N	121.6	N	85.6	N	151.2	N	N
183	15263	186.2	Y	154.7	N	149.9	Y	228.2	Y	P
184	15264	30.8	N	21.5	N	40.4	N	22.6	N	N
185	15265	99.2	N	30.7	N	155.4	Y	128.7	N	P
186	15266	79.6	N	139.0	N	86.0	N	95.1	N	N
187	15267	172.4	Y	183.0	Y	161.1	Y	168.1	N	P
188	15268	18.6	N	35.8	N	18.6	N	31.3	N	N
189	15269	60.7	N	36.7	N	30.8	N	37.7	N	N
190	15271	106.4	N	76.4	N	102.6	N	90.9	N	N
191	15272	138.0	N	77.3	N	77.5	N	48.2	N	N
192	15273	33.7	N	32.5	N	26.9	N	9.9	N	N
193	15275	33.0	N	49.6	N	14.1	N	1.8	N	N
194	15277	161.9	Y	129.3	N	166.2	Y	93.9	N	P
195	15278	81.6	N	59.0	N	66.0	N	21.7	N	N
196	15279	76.7	N	51.1	N	92.7	N	46.7	N	N
197	15280	83.3	N	84.2	N	86.3	N	66.6	N	N
198	15281	37.0	N	103.4	N	64.2	N	87.1	N	N
199	15282	−1.2	N	14.6	N	2.6	N	−6.6	N	N
200	15283	44.3	N	105.8	N	58.8	N	141.1	N	N
201	15284	30.6	N	154.9	N	44.0	N	35.7	N	N
202	15285	22.1	N	35.9	N	13.8	N	32.1	N	N
203	15286	13.6	N	78.2	N	36.5	N	27.1	N	N
204	15287	215.5	Y	90.5	N	93.7	N	71.6	N	P
205	15288	28.1	N	62.5	N	42.0	N	60.8	N	N
206	15290	40.5	N	28.1	N	18.4	N	4.5	N	N
207	15291	46.3	N	235.7	Y	75.9	N	81.7	N	P
208	15292	19.7	N	42.7	N	17.4	N	4.0	N	N
209	15293	87.1	N	116.8	N	86.2	N	46.7	N	N
210	15294	48.1	N	68.0	N	52.0	N	30.8	N	N
211	15295	169.3	Y	172.4	Y	166.1	Y	157.6	N	P
212	15296	26.6	N	84.5	N	40.5	N	26.9	N	N
213	15297	−11.6	N	−9.2	N	−0.9	N	−14.7	N	N
214	15298	4.5	N	26.4	N	16.8	N	−4.3	N	N
215	15299	88.0	N	165.7	Y	123.5	N	72.5	N	P
216	15300	104.7	N	101.0	N	73.0	N	48.8	N	N
217	15301	59.0	N	44.1	N	29.3	N	7.5	N	N
218	15302	291.1	Y	91.5	N	137.2	Y	188.9	Y	P
219	15303	46.8	N	48.9	N	58.0	N	26.8	N	N
220	15304	92.1	N	124.4	N	199.2	Y	101.0	N	P
221	15305	103.9	N	147.3	N	140.7	Y	156.4	N	P
222	15306	220.1	Y	126.9	N	212.4	Y	173.9	Y	P
223	15307	94.1	N	97.9	N	101.5	N	56.8	N	N
224	15308	148.1	N	78.9	N	148.4	Y	71.2	N	P
225	15309	20.0	N	10.1	N	20.9	N	5.6	N	N
226	15310	52.4	N	18.3	N	53.7	N	21.4	N	N
227	15311	92.2	N	57.8	N	106.7	N	79.5	N	N
228	15312	83.0	N	140.6	N	84.1	N	116.1	N	N
229	15500	89.1	N	85.5	N	84.5	N	69.3	N	N
230	15501	86.1	N	121.8	N	89.7	N	132.8	N	N
231	15502	49.7	N	43.4	N	60.4	N	38.9	N	N
232	15503	28.1	N	47.9	N	22.5	N	51.3	N	N
233	15504	30.3	N	34.7	N	14.8	N	17.1	N	N
234	15505	18.4	N	22.5	N	19.7	N	28.5	N	N
235	15506	19.1	N	26.0	N	17.6	N	20.9	N	N
236	15507	70.7	N	55.6	N	79.8	N	85.4	N	N
237	15508	86.1	N	115.1	N	131.0	N	112.6	N	N
238	15509	146.2	N	128.0	N	73.9	N	90.8	N	N
239	15511	46.0	N	24.4	N	20.3	N	16.8	N	N
240	15512	248.7	Y	315.1	Y	304.8	Y	258.9	Y	P
241	15513	15.9	N	28.3	N	10.7	N	12.4	N	N
242	15514	57.3	N	97.2	N	110.3	N	63.0	N	N
243	15515	152.5	Y	195.6	Y	214.7	Y	231.7	Y	P
244	15516	45.1	N	80.9	N	64.1	N	47.4	N	N
245	15517	60.9	N	35.7	N	75.7	N	45.3	N	N
246	15518	106.9	N	88.0	N	103.4	N	79.3	N	N
247	15519	40.9	N	22.7	N	83.4	N	75.1	N	N
248	15520	93.2	N	76.0	N	20.9	N	19.1	N	N
249	15521	67.7	N	62.4	N	119.7	N	61.2	N	N
250	15522	14.4	N	42.7	N	37.5	N	30.1	N	N
251	15523	70.5	N	50.5	N	61.5	N	59.2	N	N
252	15524	19.8	N	85.7	N	25.6	N	36.9	N	N
253	15525	71.1	N	89.6	N	52.4	N	73.7	N	N
254	15526	20.1	N	29.3	N	21.4	N	28.3	N	N
255	15527	63.5	N	93.6	N	61.1	N	51.4	N	N
256	15528	97.2	N	93.6	N	46.0	N	119.4	N	N
257	15529	16.5	N	63.4	N	10.6	N	22.3	N	N
258	15530	99.6	N	139.4	N	75.8	N	158.9	N	N
259	15531	43.7	N	47.3	N	22.1	N	30.0	N	N
260	15532	46.7	N	44.1	N	45.2	N	52.4	N	N
261	15533	213.8	Y	98.7	N	77.6	N	120.3	N	P
262	15534	112.6	N	157.8	N	108.8	N	109.2	N	N
263	15535	83.4	N	82.3	N	77.5	N	95.9	N	N
264	15536	191.6	Y	73.6	N	97.0	N	181.0	Y	P
265	15537	71.6	N	90.2	N	48.5	N	54.7	N	N
266	15538	86.6	N	81.1	N	60.3	N	61.0	N	N
267	15539	8.8	N	13.4	N	5.2	N	6.3	N	N
268	15540	79.7	N	82.8	N	74.6	N	110.2	N	N
269	15541	165.9	Y	160.9	N	402.3	Y	327.1	Y	P
270	15542	84.0	N	173.2	Y	67.5	N	57.6	N	P
271	15543	21.4	N	90.1	N	35.2	N	30.1	N	N
272	15544	33.8	N	78.7	N	43.6	N	43.7	N	N
273	15546	52.1	N	105.3	N	69.6	N	92.2	N	N
274	15547	54.3	N	88.6	N	131.4	N	40.7	N	N
275	15548	15.1	N	34.6	N	30.2	N	19.0	N	N
276	15549	57.2	N	19.2	N	31.9	N	21.8	N	N
277	15550	69.1	N	95.3	N	46.7	N	37.9	N	N
278	15551	67.0	N	50.6	N	74.2	N	73.1	N	N
279	15552	31.7	N	62.0	N	59.1	N	115.6	N	N
280	15553	45.1	N	61.2	N	64.5	N	69.7	N	N
281	15554	32.1	N	21.6	N	26.6	N	22.0	N	N
282	15555	68.9	N	29.9	N	79.3	N	35.3	N	N
283	15556	62.7	N	159.2	N	52.5	N	35.9	N	N
284	15557	50.8	N	29.5	N	52.6	N	20.5	N	N
285	15558	126.8	N	73.2	N	85.8	N	105.9	N	N
286	15559	46.0	N	49.0	N	46.1	N	26.0	N	N

TABLE 9

Cox proportional hazard model for time to confirmed EDSS
progression for classifier 1 with set of covariats.

	Parameter	Standard		Two-sided	Hazard	95% Confidence
Parameter	Estimate	Error	Chi-Square	p-value	Ratio	Interval

Classifier

1	0.71694	0.27461	6.8162	0.0090	2.048	1.196-3.508
IFNB-1b	−0.59253	0.25038	5.6004	0.0180	0.553	0.338-0.903
Steroid use 1^stevent	−0.29597	0.26421	1.2549	0.2626	0.744	0.443-1.248
>=9 T2 lesions	0.04083	0.29539	0.0191	0.8901	1.042	0.584-1.859
Multifocal onset	−0.23473	0.24441	0.9224	0.3368	0.791	0.490-1.277
Age (per year)	0.03327	0.01661	4.0139	0.0451	1.034	1.001-1.068
Female	0.49537	0.31100	2.5371	0.1112	1.641	0.892-3.019
>=1 Gd-enh. lesions	0.79179	0.25799	9.4195	0.0021	2.207	1.331-3.660

TABLE 10

Estimated coefficient for the effect of classifier 1 in the proportional
hazard regression models with additional covariates.

Time-to-event

Parameter

Hazard ratio

outcome	Estimate	SE	Estimate	95% CI	p-value

Time to con-	0.7169	0.2746	2.048	1.196-3.508	0.0090
firmed EDSS
progression

CI = confidence interval; SE = standard error

Claims

1. A method of identifying a subject with a clinically isolated syndrome (CIS) who will progress to clinically definitive multiple sclerosis (CDMS) within twenty-four months, the method comprising:

providing a test sample from said subject;

detecting in said test sample an anti-Glc (α 1,2) Glc (α) antibody, an anti-Glc (α 1,3) Glc (α) antibody, an anti-Glc (α 1,4) Glc (α) antibody, and an anti-Glc (α 1,6) Glc (α) antibody; and

comparing the levels of said antibodies in said test sample to a reference level of said antibodies, wherein a higher level of at least one of said antibodies compared to the reference level of said antibodies indicates that said subject is likely to progress to CDMS within twenty-four months.

2. The method of claim 1, wherein a higher level of one of said antibodies in said test sample compared to the reference level of said antibodies indicates said subject is at risk of having a second neurological attack within thirty-six months.

3. The method of claim 1, wherein a higher level of one of said antibodies in said test sample compared to the reference level of said antibodies indicates said subject is at risk of having a second neurological attack within forty-eight months.

4. The method of claim 1, wherein said test sample is a biological fluid is whole blood, serum, or plasma.

5. The method of claim 1, wherein said antibodies are IgM isotype antibodies.

6. A method of identifying a subject with CIS who is likely to experience rapid progression of MS disease severity within twenty years, the method comprising:

providing a test sample from a subject at the time of a CIS;

detecting in said test sample an anti-Glc(α1,2)Glc(α) (GAGA2) antibody, an anti-Glc(α1,3)Glc(α) (GAGA3) antibody, an anti-Glc(α1,4)Glc(α) (GAGA4) antibody, or an anti-Glc(α1,6)Glc(α) (GAGA6) antibody;

comparing the sample level of each antibody in said test sample to a reference level of said antibody; and

wherein a higher level of at least one of said antibodies compared to said reference level indicates that said subject is likely to progress greater than one unit in EDSS score within twenty years, said EDSS score being indicative of disease severity.

7. The method of claim 6, wherein a higher level of at least one of said antibodies indicates that said subject is likely to progress at least two units in EDSS score within twenty years.

8. The method of claim 6, wherein a higher level of at least one of said antibodies indicates that said subject is likely to progress at least three units in EDSS score within twenty years.

9. The method of claim 6, wherein a higher level of at least one of said antibodies indicates that said subject is likely to progress greater than three units in EDSS score within twenty years.

10. The method of claim 6, wherein a higher level of at least one of said antibodies indicates that said subject is likely to progress greater than six units in EDSS score within twenty years.

11. The method of claim 6, wherein a higher level of at least one of said antibodies indicates that said subject is likely to progress greater than one unit in EDSS score within ten years

12. The method of claim 6, wherein a higher level of at least one of said antibodies indicates that said subject is likely to progress greater than one unit in EDSS score within seven years.

13. The method of claim 6, wherein a higher level of at least one of said antibodies indicates that said subject is likely to progress greater than one unit in EDSS score within five years.

14. The method of claim 6, wherein a higher level of at least one of said antibodies indicates that said subject is likely to progress greater than one unit in EDSS score within four years.

15. The method of claim 6, wherein said test sample is a biological fluid is whole blood, serum, or plasma.

16. The method of claim 6, wherein said antibodies are IgM isotype antibodies.

17. A method of identifying a subject with CIS who is likely to experience a slow progression of MS disease severity, the method comprising:

providing a test sample from a subject at the time of a CIS;

detecting in said test sample an anti-Glc(α1,2)Glc(α) (GAGA2) antibody, an anti-Glc(α1,3)Glc(α) (GAGA3) antibody, an anti-Glc(α1,4)Glc(α) (GAGA4) antibody, and an anti-Glc(α1,6)Glc(α) (GAGA6) antibody;

wherein a equal or lower level of each of said antibodies indicates that said subject is likely to progress slowly in EDSS score within twenty years, said EDSS score being indicative of disease severity.

18. The method of claim 17, wherein an equal or lower level of each of said antibodies indicates that said subject is likely to not progress greater than one unit in EDSS score within five years.

19. The method of claim 17, wherein said test sample is a biological fluid is whole blood, serum, or plasma.

20. The method of claim 17, wherein said antibodies are IgM isotype antibodies.

21. A method of identifying a subject who will develop breakthrough MS, the method comprising:

providing a test sample from said subject;

comparing the levels of said antibodies in said test sample to a reference level of said antibodies, wherein a higher level of at least one of said antibodies compared to the reference level of said antibodies indicates that said subject is likely to develop breakthrough MS.

22. The method of claim 21, wherein a higher level of at least one of said antibodies indicates that said subject is likely to develop breakthrough MS within five years.

23. The method of claim 21, wherein said test sample is a biological fluid is whole blood, serum, or plasma.

24. The method of claim 21, wherein said antibodies are IgM isotype antibodies.

25. A computer-readable medium having computer-executable instructions for performing a method comprising:

storing at least one first variable associated with the level of at least one antibody in a test sample of a CIS patient;

storing at least one second variable associated with at least one reference level;

calculating the patient's risk factor for conversion to clinically definite multiple sclerosis (CDMS) as a function of at least the first and second variables; and

outputting the risk factor.

26. The computer-readable medium of claim 25, wherein the at least one first variable corresponds to levels of an anti-Glc (α 1,2) Glc (α) antibody, an anti-Glc (α 1,3) Glc (α) antibody, an anti-Glc (α 1,4) Glc (α) antibody, and an anti-Glc (α 1,6) Glc (α) antibody in the test sample.

27. The computer-readable medium of claim 25, wherein the at least one first variable corresponds to at least one level of antibody in the test sample selected from the group consisting of: an anti-Glc(α1,2)Glc(α) (GAGA2) IgM isotype antibody, an anti-Glc(α1,3)Glc(α) (GAGA3) IgM isotype antibody, an anti-Glc(α1,4)Glc(α) (GAGA4) IgM isotype antibody, and an anti-Glc(α1,6)Glc(α) (GAGA6) IgM isotype antibody.

28. A computer-readable medium having computer-executable instructions for performing a method comprising:

calculating the patient's risk factor for progressing in EDSS score as a function of at least the first and second variables; and

outputting the risk factor.

29. The computer-readable medium of claim 28, wherein the at least one first variable corresponds to levels of an anti-Glc (α 1,2) Glc (α) antibody, an anti-Glc (α 1,3) Glc (α) antibody, an anti-Glc (α 1,4) Glc (α) antibody, and an anti-Glc (α 1,6) Glc (α) antibody in the test sample.

30. The computer-readable medium of claim 28, wherein the at least one first variable corresponds to at least one level of antibody in the test sample selected from the group consisting of an anti-Glc(α1,2)Glc(α) (GAGA2) IgM isotype antibody, an anti-Glc(α1,3)Glc(α) (GAGA3) IgM isotype antibody, an anti-Glc(α1,4)Glc(α) (GAGA4) IgM isotype antibody, and an anti-Glc(α1,6)Glc(α) (GAGA6) IgM isotype antibody.

31. A computer-readable medium having computer-executable instructions for performing a method comprising:

storing at least one first variable associated with the level of at least one antibody in a test sample of a patient;

calculating the patient's risk factor for developing breakthrough MS as a function of at least the first and second variables; and

outputting the risk factor.

32. The computer-readable medium of claim 31, wherein the at least one first variable corresponds to levels of an anti-Glc (α 1,2) Glc (α) antibody, an anti-Glc (α 1,3) Glc (α) antibody, an anti-Glc (α 1,4) Glc (α) antibody, and an anti-Glc (α 1,6) Glc (α) antibody in the test sample.

33. The computer-readable medium of claim 31, wherein the at least one first variable corresponds to at least one level of antibody in the test sample selected from the group consisting of: an anti-Glc(α1,2)Glc(α) (GAGA2) IgM isotype antibody, an anti-Glc(α1,3)Glc(α) (GAGA3) IgM isotype antibody, an anti-Glc(α1,4)Glc(α) (GAGA4) IgM isotype antibody, and an anti-Glc(α1,6)Glc(α) (GAGA6) IgM isotype antibody.