期刊
CELL REPORTS
卷 12, 期 12, 页码 2060-2071出版社
CELL PRESS
DOI: 10.1016/j.celrep.2015.08.051
关键词
-
类别
资金
- Marie Curie International Outgoing Fellowship [PIOF-GA-2009-253290]
- Catalan Institute of Oncology-Bellvitge Institute for Biomedical Research, Barcelona, Spain
- Irving Institute for Clinical and Translational Research at Columbia University - National Center for Advancing Translational Sciences, NIH [UL1 TR000040]
- Prostate Cancer Foundation Young Investigator Award
- [CA173481]
- [U01 CA084294]
- [U54 CA121852]
- [P01 CA154293]
- [U01HL111566-02S2]
- [U01CA168426]
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据