Discovering novel pharmacogenomic biomarkers by imputing drug response in cancer patients from large genomics studies

Paul Geeleher, Zhenyu Zhang, Fan Wang, Robert F. Gruener, Aritro Nath, Gladys Morrison, Steven Bhutra, Robert L. Grossman, R. Stephanie Huang

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

Abstract

Obtaining accurate drug response data in large cohorts of cancer patients is very challenging; thus, most cancer pharmacogenomics discovery is conducted in preclinical studies, typically using cell lines and mouse models. However, these platforms suffer from serious limitations, including small sample sizes. Here, we have developed a novel computational method that allows us to impute drug response in very large clinical cancer genomics data sets, such as The Cancer Genome Atlas (TCGA). The approach works by creating statistical models relating gene expression to drug response in large panels of cancer cell lines and applying these models to tumor gene expression data in the clinical data sets (e.g., TCGA). This yields an imputed drug response for every drug in each patient. These imputed drug response data are then associated with somatic genetic variants measured in the clinical cohort, such as copy number changes or mutations in protein coding genes. These analyses recapitulated drug associations for known clinically actionable somatic genetic alterations and identified new predictive biomarkers for existing drugs.

Original languageEnglish (US)
Pages (from-to)1743-1751
Number of pages9
JournalGenome Research
Volume27
Issue number10
DOIs
StatePublished - Oct 2017

Bibliographical note

Funding Information:
This work was supported by a research grant from the Avon Foundation for Women. R.S.H. also received support from NIH/ NIGMS grant K08GM089941, NIH/NCI grant R21 CA139278, NIH/NIGMS grant U01GM61393, and a Circle of Service Foundation Early Career Investigator award. P.G. received support from the Chicago Biomedical Consortium grant PDR-020.

Publisher Copyright:
© 2017 Geeleher et al.

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

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