Pan-Cancer Pharmacogenomic Analysis of Patient-Derived Tumor Cells Using Clinically Relevant Drug Exposures.

As a result of tumor heterogeneity and solid cancers harboring multiple molecular defects, precision medicine platforms in oncology are most effective when both genetic and pharmacologic determinants of a tumor are evaluated. Expandable patient-derived xenograft (PDX) mouse tumor and corresponding PDX culture (PDXC) models recapitulate many of the biological and genetic characteristics of the original patient tumor, allowing for a comprehensive pharmacogenomic analysis. Here, the somatic mutations of 23 matched patient tumor and PDX samples encompassing four cancers were first evaluated using next-generation sequencing (NGS). 19 antitumor agents were evaluated across 78 patient-derived tumor cultures using clinically relevant drug exposures. A binarization threshold sensitivity classification determined in culture (PDXC) was used to identify tumors that best respond to drug in vivo (PDX). Using this sensitivity classification, logic models of DNA mutations were developed for 19 antitumor agents to predict drug response. We determined that the concordance of somatic mutations across patient and corresponding PDX samples increased as variant allele frequency increased. Notable individual PDXC responses to specific drugs, as well as lineage-specific drug responses were identified. Robust responses identified in PDXC were recapitulated in vivo in PDX-bearing mice and logic modeling determined somatic gene mutation(s) defining response to specific antitumor agents. In conclusion, combining NGS of primary patient tumors, high-throughput drug screen using clinically relevant doses, and logic modeling, can provide a platform for understanding response to therapeutic drugs targeting cancer.

Allopurinol pharmacogenetics: assessment of potential clinical usefulness.

Use of pharmacogenetics to inform treatment decisions remains a priority for clinicians, patients and public health agencies. We previously developed a framework for systematically assessing whether pharmacogenetic test information would likely bring value to clinical decision-making and enjoy practical uptake. We applied this tool to allopurinol to determine potential usefulness of HLA genetic information in assessing risk for allopurinol-induced severe cutaneous adverse reactions. We quantified allopurinol use data and the magnitude of adverse event signals using US FDA databases, reviewed reported cases of allopurinol-associated severe cutaneous adverse reactions to assess whether clinical subtypes of patients could be identified, performed pooled analyses of associations between HLA variation and allopurinol-induced severe cutaneous adverse reactions and described considerations in clinical implementation of allopurinol pharmacogenetics.