1. Integrative Pharmacogenomics Analysis of Patient-Derived Xenografts
- Author
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Anna Goldenberg, David Cescon, Wail Ba-alawi, Petr Smirnov, Janosch Ortmann, Ben Brew, Yi Xiao Wang, Arvind Singh Mer, Benjamin Haibe-Kains, and Ming-Sound Tsao
- Subjects
0301 basic medicine ,Cancer Research ,Computational biology ,Biology ,Tumor response ,Precision medicine ,Xenograft Model Antitumor Assays ,03 medical and health sciences ,030104 developmental biology ,0302 clinical medicine ,Oncology ,Pharmacogenetics ,In vivo ,Neoplasms ,030220 oncology & carcinogenesis ,Pharmacogenomics ,Drug response ,Animals ,Heterografts ,Humans ,Precision Medicine ,Biomarker discovery ,Pathway activity - Abstract
Identifying robust biomarkers of drug response constitutes a key challenge in precision medicine. Patient-derived tumor xenografts (PDX) have emerged as reliable preclinical models that more accurately recapitulate tumor response to chemo- and targeted therapies. However, the lack of computational tools makes it difficult to analyze high-throughput molecular and pharmacologic profiles of PDX. We have developed Xenograft Visualization & Analysis (Xeva), an open-source software package for in vivo pharmacogenomic datasets that allows for quantification of variability in gene expression and pathway activity across PDX passages. We found that only a few genes and pathways exhibited passage-specific alterations and were therefore not suitable for biomarker discovery. Using the largest PDX pharmacogenomic dataset to date, we identified 87 pathways that are significantly associated with response to 51 drugs (FDR < 0.05). We found novel biomarkers based on gene expressions, copy number aberrations, and mutations predictive of drug response (concordance index > 0.60; FDR < 0.05). Our study demonstrates that Xeva provides a flexible platform for integrative analysis of preclinical in vivo pharmacogenomics data to identify biomarkers predictive of drug response, representing a major step forward in precision oncology. Significance: A computational platform for PDX data analysis reveals consistent gene and pathway activity across passages and confirms drug response prediction biomarkers in PDX. See related commentary by Meehan, p. 4324
- Published
- 2019