Leveraging Pharmacogenomics in Asthma for Predication, Mechanism and Endotyping

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Abstract Asthma affects over 300 million individuals worldwide. An estimated $81.9 billion dollars were spent on the diagnosis and management of asthma in the U.S. in 2013. Uncontrolled asthma is associated with a doubling of direct costs; it has been estimated that 20% of the subjects with asthma contribute 80% of the economic costs of asthma. For severe asthma, multiple new FDA approved biologic therapies exist, but they remain very expensive and there are a significant proportion of nonresponders. Current biomarkers may not distinguish reliably between responders and non-responders; ~40% of those expected to respond continue to have exacerbations and ~40% of those not expected to respond become symptom free. In this proposal, we will use novel genomics approaches to assess and predict responses using therapy-induced phenotypes across a spectrum of asthma severity and endotypes. We hypothesize that comprehensive characterization using clinical metrics, ‘omics’ approaches, and novel systems biology approaches will generate more precise treatment response biomarkers, further define disease heterogeneity, and uncover novel biologic mechanisms as related to the therapy of moderate to severe asthma. To address this hypothesis, we have specified three specific aims, centered around the combination of a well-characterized, within-person evoked phenotype clinical cohort, including subjects with both type 2 (i.e. those expected to respond based on current biomarkers) and non-type 2 moderate to severe asthma, to anti-IL5 (benralizumab) and anti-IL4/IL13 (dupilumab) with deep genomic interrogation, including single cell and bulk RNA sequencing in both sputum and blood across each of the biologic interventions. The first aim will be to identify, and subsequently validate, pharmacogenomic transcripts that predict response to each therapy, thereby yielding clinically relevant biomarkers for response to asthma biologics. Our second aim takes advantage of the biologic interventions as immunomodulators of specific pathways serving as “human knockdown models” to elicit the underlying mechanistic response at the level of the single cell to the biologic therapies. The final aim will provide novel insights into cohort via the characterization of genomic signals that influence clinical asthma subtypes and via the identification of molecular endotypes, which will be compared to the tradtional clinical subtypes and evaluated for their response to the biologics. Analyses for each aim with include both traditional statistical models as well as novel systems medicine and network biology approaches. The strengths of our study include a melding a unique longitudinal clinical evoked phenotype cohort with state of the art genomics analyses. Successful completion of this study will drive understanding of severe asthma response to biologics to an unprecendented level, provide novel therapeutic biomarkers leading to direct clinical application, and detail previously unknown cellular and genomic pathway mechanisms underlying severe asthma.