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

First Author  Mitrofanova A Year  2015
Journal  Cell Rep Volume  12
Issue  12 Pages  2060-71
PubMed ID  26387954 Mgi Jnum  J:314628
Mgi Id  MGI:6822795 Doi  10.1016/j.celrep.2015.08.051
Citation  Mitrofanova A, et al. (2015) Predicting Drug Response in Human Prostate Cancer from Preclinical Analysis of In Vivo Mouse Models. Cell Rep 12(12):2060-71
abstractText  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.
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