Protein-Based Classifier to Predict Conversion from Clinically Isolated Syndrome to Multiple Sclerosis

Multiple sclerosis is an inflammatory, demyelinating, and neurodegenerative disease of the central nervous system. In most patients, the disease initiates with an episode of neurological disturbance referred to as clinically isolated syndrome, but not all patients with this syndrome develop multiple sclerosis over time, and currently, there is no clinical test that can conclusively establish whether a patient with a clinically isolated syndrome will eventually develop clinically defined multiple sclerosis. Here, we took advantage of the capabilities of targeted mass spectrometry to establish a diagnostic molecular classifier with high sensitivity and specificity able to differentiate between clinically isolated syndrome patients with a high and a low risk of developing multiple sclerosis. Based on the combination of abundances of proteins chitinase 3-like 1 and ala-β-his-dipeptidase in cerebrospinal fluid, we built a statistical model able to assign to each patient a precise probability of conversion to clinically defined multiple sclerosis. Our results are of special relevance for patients affected by multiple sclerosis as early treatment can prevent brain damage and slow down the disease progression.Multiple sclerosis is an inflammatory, demyelinating, and neurodegenerative disease of the central nervous system, and although the etiology of the disease is not fully understood, it is probably caused by the interaction of a complex genetic architecture and environmental factors. Multiple sclerosis affects over 2 million people worldwide, and it is typically diagnosed between ages 20 and 40, thus making a significant impact on public health and its economy (1).In most patients, the disease initiates with an episode of neurological disturbance referred to as clinically isolated syndrome. However, not all patients with this syndrome develop multiple sclerosis over time (2), and currently, the magnetic resonance imaging (MRI) abnormalities and the presence of IgG oligoclonal bands in cerebrospinal fluid (CSF) are used as predictors for later conversion to clinically definite multiple sclerosis (CDMS)¹ (3–5). Although such abnormalities are considered important factors that influence the likelihood of developing CDMS, there is currently no clinical test that can conclusively establish whether a patient with a clinically isolated syndrome will eventually develop CDMS.The lack of diagnostic and prognostic biomarkers is a common problem for many diseases lacking a complete etiology, which is the case for most neurological disorders related to the central nervous system such as Parkinson''s and Alzheimer''s diseases, schizophrenia, and multiple sclerosis. In the particular case of multiple sclerosis, early treatment of patients with a clinically isolated syndrome can prevent brain damage and slow down the disease progression (6). Therefore, the availability of a diagnostic test in the initial stages of the disease is not only desirable but also of extreme relevance to attenuate the degenerative effects of the disease.Biomarker validation has traditionally been dominated by enzyme linked immuno-sorbent assays (ELISA), but recent advances in proteomics techniques have enabled the measurement of a subset of selected proteins over a large dynamic concentration range in multiple samples. Targeted mass spectrometry has thus become the method of choice when quantifying simultaneously a panel of proteins across many different biological samples (7–9). In particular, selected reaction monitoring (SRM) is the gold standard targeted mass spectrometry method for protein quantification due to its high precision, reliability, and throughput (10–13). This targeted mass spectrometry method is performed on triple quadrupole instruments, in which a predefined peptide precursor ion is first isolated, and then selected fragment ions arising from its collisional dissociation are measured over time. Each pair of precursor and fragment ion is called a transition, and multiple transitions can be coordinately measured and used to conclusively identify and quantify a peptide in a clinical complex sample.In a previous study, we used a screening mass spectrometric approach to discover potential markers for multiple sclerosis conversion in patients that initially presented a clinical isolated syndrome (14). In that discovery phase, quantitative mass spectrometry with iTRAQ labeling was used to measure protein abundances in pooled CSF samples from patients presenting a clinical isolated syndrome that either remained normal (CIS) or had eventually converted to clinically definite multiple sclerosis (CDMS) (n = 60). In the initial screening, several proteins exhibited significant differences in abundance when comparing these two groups of patients. The abundance change in one of the altered proteins, chitinase 3-like 1 (CH3L1), was confirmed by ELISA in CSF of individual patients, whereas for others, such as semaphorin 7A (SEM7A) and ala-β-his-dipeptidase (CNDP1), their abundance changes were confirmed by targeted mass spectrometry in follow-up studies with independent cohorts (15). Moreover, the levels of CH3L1 were associated with brain MRI abnormalities and disability progression during the follow-up period, as well as with shorter time to conversion to clinically definite multiple sclerosis (14).We now set out to establish a diagnostic protein classifier with high sensitivity and specificity able to differentiate between patients with a clinically isolated syndrome that have either a high or a low risk of developing clinically definite multiple sclerosis over time. For this purpose, CSF samples from an independent patient cohort from the one used in the discovery study were collected, and a set of preselected protein biomarker candidates were systematically quantified by targeted mass spectrometry (SRM) and evaluated for their classification power. Out of this study, we established a protein classifier based on the combination of abundances of proteins chitinase 3-like 1 and ala-β-his-dipeptidase, which is able to differentiate with high sensitivity and specificity between patients with a clinically isolated syndrome that have either a high or low risk of developing clinically definite multiple sclerosis. Moreover, the statistical model built around this protein classifier enables clinicians to easily assign to each patient a precise probability of conversion to clinically definite multiple sclerosis (Fig. 1).Open in a separate windowFig. 1.General workflow used in the present study. Initially, protein candidates identified in our previous discovery studies—together with several proteins described by other groups—were selected and quantified by targeted mass spectrometry (SRM) in a relatively large cohort individual patients. Protein quantities were then evaluated by their capability of classifying patients with clinical isolated syndrome, and thus, the best prognostic protein combination was identified.