COPD SUBTYPES AND EARLY PREDICTION USING INTEGRATIVE PROBABILISTIC GRAPHICAL MODELS

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COPD SUBTYPING AND EARLY PREDICTION USING INTEGRATIVE PROBABILISTIC GRAPHICAL MODELS ABSTRACT One of the main obstacles in developing efficient personalized therapeutic and disease management strategies is that most common diseases are typically defined based on symptoms and clinical measurements, although they are believed to be syndromes, consisting of multiple subtypes with variable etiology. Identifying disease subtypes has thus become very important, but so far it has been met with limited success for most diseases. In asthma, a notable exception, it was the clinical characterization that led to successful subtyping; and this is now incorporated in treatment guidelines. Unsupervised machine learning approaches of single data modalities (e.g., omics, radiographic images) have not produced actionable subtypes due to instability across cohorts. Developing data integrative approaches for multi-scale data, which are becoming available for a number of diseases, is expected to lead to robust subtyping and provide mechanistic insights of disease onset and progression. This proposal focuses on developing new computational methods, based on probabilistic graphical models (PGMs), to address this unmet need; and apply them to investigate three problems of clinical importance in chronic obstructive pulmonary disease (COPD), which is the fourth leading cause of mortality in USA. Our underlying hypothesis is that PGMs can integrate and analyze under the same probabilistic framework heterogeneous biomedical data (omics, chest CT scan, clinical) and identify disease subtypes and their main determinants. The objectives of our proposal is to build a comprehensive computational framework for disease subclassification, identify stable COPD subtypes at the baseline and longitudinally, and build interpretable models of the disease The deliverables of this project are: (1) new integrative computational approaches for clinical subtyping from multi-scale data; (2) new predictors of COPD progression and severity; (3) new discoveries of longitudinally stable COPD subtypes; (4) new predictors of future development of COPD; (5) new omics datasets that will be invaluable to future research in the area (baseline and longitudinal). To ensure the success of the project we follow a team science approach. This multi-PI proposal builds on the ongoing efforts of our group in the area of graphical models and their applications in biomedicine; and the ongoing collaboration of the three PIs that have complementary strengths: Prof. Benos (systems medicine and machine learning), Dr. Hersh (COPD genetics and genomics) and Dr. Sciurba (clinical aspects of COPD). It is powered by the access of the investigators to three major COPD cohorts (COPDGene®, SCCOR, ECLIPSE) that contain multiple parallel deep phenotyping and omics data from thousands of patients and controls. Although in this project we focus on COPD, our methods are generally applicable to any disease, therefore our project will have a positive impact beyond the above deliverables. We believe that due to their robust nature and interpretability, PGMs will soon become the norm for multi-scale biomedical data integration and modeling, when genetic and genomic data collection will become routine prognostic and diagnostic tools in clinical practice.