Stratifying patients with autoimmune diseases: Using multi-omic analysis to predict optimal treatment outcomes
Lead Research Organisation:
University College London
Department Name: Medicine
Abstract
The clinical outcome for people affected with complex autoimmune diseases such as multiple sclerosis (MS) and systemic lupus erythematosus (SLE) is unpredictable, highlighting the need for better prognostic tools. Identifying early in the disease course who will experience more severe disease progression and have a poor clinical outcome over time and/or who will respond to specific therapies will aid treatment decision-making, reduce disease severity and minimise long-term treatment costs. Furthermore, this is an important research priority for people affected by autoimmunity.
The hypothesis to be explored is that combining multiple omic signatures with detailed clinical outcome measures and patient features will help to stratify patients for optimal treatment and identify novel treatment targets. The specific aims are to i) Analsyse existing and newly collected omic datasets (metabolomics, transcriptomic, proteomic) to identify signatures associated with disease features; ii) develop advanced computational tools to integrate these complex datasets and iii) test the functional importance of the markers identified in known disease pathogenesis systems.
The student, supported by a cross-disciplinary team including molecular cell biologists, immunologists, clinicians and data scientists, will contribute to the development of novel biomarker signatures able to identify those patients destined to have a poor response to treatment and provide data to support a future studies to test integrated multi-omic (metabolites, gene expression, cell surface markers) predictive signatures both unique and common for autoimmune diseases.
The hypothesis to be explored is that combining multiple omic signatures with detailed clinical outcome measures and patient features will help to stratify patients for optimal treatment and identify novel treatment targets. The specific aims are to i) Analsyse existing and newly collected omic datasets (metabolomics, transcriptomic, proteomic) to identify signatures associated with disease features; ii) develop advanced computational tools to integrate these complex datasets and iii) test the functional importance of the markers identified in known disease pathogenesis systems.
The student, supported by a cross-disciplinary team including molecular cell biologists, immunologists, clinicians and data scientists, will contribute to the development of novel biomarker signatures able to identify those patients destined to have a poor response to treatment and provide data to support a future studies to test integrated multi-omic (metabolites, gene expression, cell surface markers) predictive signatures both unique and common for autoimmune diseases.