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Cell Rep. 2015 Sep 29;12(12):2060-71. doi: 10.1016/j.celrep.2015.08.051. Epub 2015 Sep 17.

Predicting Drug Response in Human Prostate Cancer from Preclinical Analysis of In Vivo Mouse Models.

Author information

1
Department of Systems Biology, Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY 10032, USA.
2
Department of Urology, Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY 10032, USA.
3
Department of Systems Biology, Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY 10032, USA; Department of Urology, Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY 10032, USA; Department of Medicine, Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY 10032, USA; Department of Genetics and Development, Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY 10032, USA; Institute of Cancer Genetics, Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY 10032, USA.
4
Department of Systems Biology, Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY 10032, USA; Department of Urology, Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY 10032, USA; Department of Pathology and Cell Biology, Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY 10032, USA; Institute of Cancer Genetics, Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY 10032, USA. Electronic address: cabateshen@columbia.edu.
5
Department of Systems Biology, Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY 10032, USA; Department of Biochemistry and Molecular Biophysics, Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY 10032, USA; Institute of Cancer Genetics, Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY 10032, USA. Electronic address: califano@c2b2.columbia.edu.

Abstract

Although genetically engineered mouse (GEM) models are often used to evaluate cancer therapies, extrapolation of such preclinical data to human cancer can be challenging. Here, we introduce an approach that uses drug perturbation data from GEM models to predict drug efficacy in human cancer. Network-based analysis of expression profiles from in vivo treatment of GEM models identified drugs and drug combinations that inhibit the activity of FOXM1 and CENPF, which are master regulators of prostate cancer malignancy. Validation of mouse and human prostate cancer models confirmed the specificity and synergy of a predicted drug combination to abrogate FOXM1/CENPF activity and inhibit tumorigenicity. Network-based analysis of treatment signatures from GEM models identified treatment-responsive genes in human prostate cancer that are potential biomarkers of patient response. More generally, this approach allows systematic identification of drugs that inhibit tumor dependencies, thereby improving the utility of GEM models for prioritizing drugs for clinical evaluation.

PMID:
26387954
PMCID:
PMC4591242
DOI:
10.1016/j.celrep.2015.08.051
[Indexed for MEDLINE]
Free PMC Article

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