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Cell Syst. 2018 Mar 28;6(3):343-354.e5. doi: 10.1016/j.cels.2018.01.009. Epub 2018 Feb 7.

Genome-Scale Signatures of Gene Interaction from Compound Screens Predict Clinical Efficacy of Targeted Cancer Therapies.

Author information

1
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA.
2
Department of Pathology, Brigham and Women's Hospital, Boston, MA 02115, USA.
3
School of Life Science and Technology, Tongji University, Shanghai 200092, China.
4
Department of Statistics, Harvard University, Cambridge, MA 02138, USA.
5
Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA 02115, USA; Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02115, USA.
6
Department of Pathology, Brigham and Women's Hospital, Boston, MA 02115, USA. Electronic address: jaster@rics.bwh.harvard.edu.
7
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA; School of Life Science and Technology, Tongji University, Shanghai 200092, China; Department of Statistics, Harvard University, Cambridge, MA 02138, USA; Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02115, USA. Electronic address: xsliu@jimmy.harvard.edu.

Abstract

Identifying reliable drug response biomarkers is a significant challenge in cancer research. We present computational analysis of resistance (CARE), a computational method focused on targeted therapies, to infer genome-wide transcriptomic signatures of drug efficacy from cell line compound screens. CARE outputs genome-scale scores to measure how the drug target gene interacts with other genes to affect the inhibitor efficacy in the compound screens. Such statistical interactions between drug targets and other genes were not considered in previous studies but are critical in identifying predictive biomarkers. When evaluated using transcriptome data from clinical studies, CARE can predict the therapy outcome better than signatures from other computational methods and genomics experiments. Moreover, the CARE signatures for the PLX4720 BRAF inhibitor are associated with an anti-programmed death 1 clinical response, suggesting a common efficacy signature between a targeted therapy and immunotherapy. When searching for genes related to lapatinib resistance, CARE identified PRKD3 as the top candidate. PRKD3 inhibition, by both small interfering RNA and compounds, significantly sensitized breast cancer cells to lapatinib. Thus, CARE should enable large-scale inference of response biomarkers and drug combinations for targeted therapies using compound screen data.

KEYWORDS:

compound screen; drug resistance; gene interaction; immunotherapy; response biomarker; targeted therapy

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