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Leveraging Whole-Genome Sequencing in Pharmacogenomics

  • Author(s): Standish, Kristopher Andrew
  • Advisor(s): Schork, Nicholas J
  • Yeo, Eugene
  • et al.
Abstract

As the vision of personalized medicine increasingly comes into focus, the approaching years promise to see vast reform in the way medicine is practiced. Whether applied to early detection or improved interventions, data obtained from high-throughput sequencing and molecular profiling technologies will be fundamental to today's discoveries that will impact the care of tomorrow's patients. Data rigorously collected from therapeutic registration trials remains a robust source of clinical data that can be exploited for basic research of disease mechanism, early-stage drug discovery efforts, and the development of companion diagnostics.

In collaboration with Janssen R&D, LLC, we performed a retrospective pharmacogenetic study of a phase III clinical trial leveraging whole-genome sequencing of 436 patients with moderate to severe rheumatoid arthritis. Rheumatoid arthritis is an autoimmune disorder that results in painful inflammation of the joints that, when untreated, results in erosion of the bone, permanent joint damage, and increased mortality. As with many diseases, early and effective treatment is fundamental to slowing disease progression and improving quality of life. Having the ability to reliably predict how a patient will respond to a particular line of therapy prior to initiation would be invaluable to the clinician attempting to determine the proper course of treatment. The goal of this study is to identify novel genetic predictors of differential patient response to anti-TNFalpha agent, golimumab, which could form the basis for a clinically meaningful companion diagnostic. More broadly, we aim to uncover insights into the mechanisms of disease presentation and treatment response. The successful execution of this study relies on reliably characterizing genetic variation across a large clinical cohort, identifying heritable phenotypes that accurately describes a patient's response to treatment, and integrating these data sets in sophisticated ways that account for potentially confounding non-genetic factors.

Ultimately, our experiences expose the nuances of retrospectively leveraging clinical trial cohorts and next-generation sequencing technologies in pharmacogenetic studies. This study could act as a blueprint for future studies aimed at understanding how a drug functions and developing diagnostics that provide actionable information to clinicians trying to determine the best treatment for their patients. By building on our work and the results of related studies, we can drive discovery in order to guide clinical decision-making and deliver on the promise of personalized medicine.

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