Long non-coding RNA signatures to distinguish relapsing-remitting multiple sclerosis from primary progressive and secondary progressive multiple sclerosis

Decode Health, Inc., Nashville TN

Investigators

Abstract

PROJECT SUMMARY Diagnosis and monitoring of multiple sclerosis (MS) rests on clinical symptoms and examinations as outlined in the revised McDonald criteria. These criteria are supported by appropriate magnetic resonance imaging (MRI) findings or other laboratory tests such as detection of oligoclonal bands in cerebrospinal fluid and evoked potential testing.(1-7) Approximately 10,000-15,000 new diagnoses of MS are made in the United States each year.(8) MS is classified into phenotypes depending on the patterns of demyelination of the central nervous system [CNS], inflammation and disability progression.(9) The vast majority of patients, approximately 80%- 90%, will develop a relapsing-remitting course of disease (RRMS) where symptoms develop over the course of a few days or a few months and then greatly improve or remit entirely. Up to 50% of patients with RRMS advance to secondary progressive MS (SPMS) within 10-15 years of the initial relapsing-remitting course and up to 90% of RRMS patients will transition to SPMS within 20-25 years.(10, 11) In contrast to the variations in RRMS symptoms, SPMS patients typically experience a steady progression of disease with or without relapses. Should relapses occur in SPMS, they typically do not fully remit. Early treatment with disease-modifying therapies has been shown to slow or prevent the transition of RRMS to SPMS. In addition to RRMS and SPMS, approximately 15% of patients will develop a primary progressive course of disease (PPMS) where disability progression continuously accumulates without evidence of remission. Disability in MS accrues predominantly in the progressive forms of the disease, creating a substantial health-care burden at individual, family and community levels.(10) There are more than a dozen approved therapies for RRMS.(12, 13) In contrast, only one treatment is approved to treat PPMS (ocrelizumab). Furthermore, with the exception of siponimod, approved in 2019 and investigated in the largest randomized clinical trial to date in SPMS, clinical data collected in SPMS patients treated with approved RRMS disease-modifying therapies remains an area of active investigation and debate.(10) Most clinicians commonly prescribe ocrelizumab, rituximab, or siponimod based on emerging evidence showing decreased disability.(14-16) The ability to quickly and accurately distinguish each type of MS in patients is important, as each MS subtype requires specific approaches to ensure effective treatments are prescribed and optimal clinical outcomes are achieved.(9, 17) Mischaracterization of MS can produce a significant cost burden on the healthcare system since certain approved therapies for RRMS lack evidence showing efficacy slowing SPMS or PPMS. Difficulties in identifying the correct MS phenotype can lead to patients receiving/remaining on therapies that are ineffective resulting in unnecessary costs and potential for adverse effects. As new therapies are introduced, especially those with potential neuroprotective effects for treatment progressive forms of MS, early classification of disease phenotype may represent a window of opportunity for therapeutic intervention.(9, 10, 16, 17) The cost of managing MS patients is rising and can exceed $50,000 per year. Identification of actionable biomarkers would provide clinicians with additional information for the purposes of diagnosis, prognosis, clinical subtyping and therapy selection. The question of whether or not disease classifiers capable of providing clinically useful information could be built based upon disease-specific expression levels of mRNAs in whole blood has been a subject of research for greater than ten years. Many disease-specific gene expression signatures have been identified in the research setting. Long non-coding RNAs (lncRNA) are recently discovered regulatory RNA molecules that do not code for proteins but influence a vast array of biological processes. In vertebrates, the number of lncRNA genes is thought to greatly exceed the number of protein-coding genes. It is also thought that lncRNAs drive biologic complexity observed in vertebrates compared to invertebrates. These lncRNAs also appear to show much greater cell-type specific expression patterns than mRNAs. Humans also develop many more complex diseases than other organisms. As such, our data presented in preliminary studies, support the notion that disease-associated lncRNAs exhibit far greater differences in expression than disease-associated mRNAs. In this application, we propose to explore the hypothesis that lncRNAs are better biomarkers of human disease than mRNAs. Here, we will focus on RRMS, SPMS, PPMS, and disease controls as disease categories. We will identify and validate differentially expressed lncRNAs found in each MS subtype that are capable of distinguishing among each subtype versus healthy controls as well as disease controls.