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]).