Journal
CANCER RESEARCH
Volume 79, Issue 17, Pages 4539-4550Publisher
AMER ASSOC CANCER RESEARCH
DOI: 10.1158/0008-5472.CAN-19-0349
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Funding
- SU2C Canada-Canadian Cancer Society Breast Cancer Dream Team Research Funding [SU2C-AACR-DT-18-15]
- Government of Ontario
- Gattuso Slaight Personalized Cancer Medicine Fund at Princess Margaret Cancer Centre
- Canadian Institute of Health Research
- Natural Sciences and Engineering Research Council
- Terry Fox Research Institute fund
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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.
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