This is a K23 award application for Dr. Andrew Sweatt, a pulmonary/critical care physician and young investigator at Stanford University who is establishing a niche in pulmonary arterial hypertension (PAH) precision phenotyping. His work centers on using machine learning to reclassify PAH, where hidden patterns are detected in high-throughput molecular data to uncover new phenotypes. The existing PAH clinical classification does not inform therapy decisions, and outcomes are overall poor with a ?one-size-fits-all? treatment approach. There is a critical need for molecular phenotyping efforts, to develop classification schemes that sit closer to pathobiology and identify therapeutically-targetable patient subsets. Dr. Sweatt?s K23 builds on an innovative foundational study where he used machine learning to cluster PAH patients based on blood immune profiling, without guidance from clinical features. This agnostic approach uncovered 4 immune phenotypes with distinct cytokine profiles that are independent of clinical subtypes and stratify disease risk. These findings indicate that inflammation is a viable platform for PAH reclassification. Extensive research has implicated inflammation in PAH and multiple immune-targeting therapies are under active investigation, but these studies rest on the assumption that a common pathophenotype exists. The objective of Dr. Sweatt?s K23 is to better understand PAH immune phenotypes in terms of their longitudinal evolution, mechanistic underpinnings, and therapeutic implications. First, he will perform serial cytokine profiling in two observational cohorts (Stanford, USA; Sheffield, UK) to reassess immune phenotypes during the disease course (Aim 1). Based on preliminary data, dynamic phenotype switches may occur in some patients and reflect changes in clinical disease severity. Next, he will integrate blood transcriptomic profiling and apply sophisticated computational tools to provide phenotype-specific mechanistic insights (Aim 2). He postulates that distinct transcriptomic profiles will link phenotypes to specific signaling pathways and immune cell subsets. Findings will be validated using multi-cohort data from public repositories. Finally, he will perform post-hoc cytokine profiling in two recent PAH trial cohorts where immune modulators were tested, to assess if therapy responses differ across phenotypes (Aim 3). His research could help identify patients who will respond to specific therapies, inform clinical trial designs, lead to biomarker discovery, and define novel biology in PAH. The K23 will provide Dr. Sweatt with the critical support needed to transition to an independent research career and be a leader in PAH precision phenotyping. His K23 objectives are to gain experience in PAH clinical phenotyping/cohort building, expand expertise in bioinformatics, cultivate collaboration, and translate findings to new hypotheses for R01 development. He will be guided by a committed team of multidisciplinary mentors (Roham Zamanian [expert in PAH clinical trial design/biomarkers], Marlene Rabinovitch [leader in translational PAH research], and Purvesh Khatri [pioneer in bioinformatics]) and scientific advisors (Mark Nicolls [translational PAH immunology], PJ Utz [immunology], and Manisha Desai [biostatistics]).
This proposed research is aimed at classifying novel subtypes of pulmonary arterial hypertension (PAH), by studying inflammation in the blood of patients (complex protein and genetic profiles) and using sophisticated computational methods (machine learning and systems-based network analysis) to find data patterns that would otherwise remain hidden. This research is relevant to public health as the burden of morbidity and mortality is high in PAH, and the current system used to classify patients does not inform treatment decisions. Our project has significant potential to improve public health, as it may yield a PAH classification scheme that sits closer to underlying pathobiology, provide a critical step toward identifying patients who will respond to specific therapies, help improve the design of forthcoming clinical trials, and create a framework for precision medicine.
