Personalized protein-protein interactomes and precision medicine in pulmonary arterial hypertension

Project Summary/Abstract Pulmonary arterial hypertension (PAH) is a highly morbid cardiopulmonary disease characterized by an obliterative vasculopathy involving distal pulmonary arterials that promotes right heart failure. Complex and integrated pathobiological signaling pathways drive vascular remodeling in PAH: the arteriopathy includes

numerous endophenotypes (i.e., specific features, such as fibrosis, cellular proliferation, others) that occur to differing extent across patients. Wide variability in the proteomic and genetic profile is also observed in PAH, which accounts for phenotypic heterogeneity and inconsistent clinical response to drug therapies reported in

clinical trials and at point-of-care. Overall, these observations suggest that opportunity exists to improve clinical outcome by individualizing the pathobiology-clinical phenotype relationship. Nonetheless, precision medicine in PAH remains unrealized, which we postulate is due to limitations inherent in conventional analytical methods

that average biological data and overlook functionally important signaling pathways. Work from our laboratory and others has demonstrated the importance of studying functionally significant signaling pathways using network medicine to discover novel and modifiable therapeutic targets in PAH. This approach differs from classic reductionist methods that infer functionality based on transcript or protein quantity

alone, which may erroneously implicate molecular bystanders in the pathogenesis of disease. However we have innovated a precision-based network medicine strategy that generates patient-specific protein-protein interaction (PPI) networks (e.g., patient-level molecular wiring map). Our approach unmasks molecular interactions that

distinguish (and group together) individual patients with the same clinical phenotype. We present novel preliminary data in the accompanying application to support the central hypothesis: Developing patient- specific PPI networks will personalize clinical phenotyping and optimize prognosis in PAH. Our findings

will also clarify the relationship between PAH genetic risk and pathobiology on an individual-patient level, and inform rationale and personalized drug selection using the PPI networks. To test our hypothesis, we will leverage a rich dataset from the United Kingdom PAH Phenome Biobank. This dataset includes comprehensive

proteomic, genomic, clinical, and outcome data across two timepoints (1 yr apart) for idiopathic PAH, hereditary PAH, asymptomatic family members of patients with PAH, including three patients that developed PAH during the study, and healthy volunteer controls (N=500 total). The Aims are: (1) Profile patient-specific PPI networks

using proteomic and genetic data, and analyze temporal differences in network features by patient group, (2) Develop, test, and validate a network score that informs phenotype and outcome of individual PAH patients. As an exploratory aim, we will use the PPI networks to predict patient-specific drug therapies. Overall, findings from

this project will advance precision medicine in PAH with direct relevance to the clinical management of patients.