Predicting Drug Response in Human Prostate Cancer from Preclinical Analysis of In Vivo Mouse Models
Autor: | Min Zou, Andrea Califano, Michael Michael M Shen, Antonina Mitrofanova, Cory Abate-Shen, Alvaro Aytes |
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Jazyk: | angličtina |
Rok vydání: | 2015 |
Předmět: |
Male
Drug endocrine system diseases Carcinogenesis Chromosomal Proteins Non-Histone media_common.quotation_subject Drug Evaluation Preclinical Antineoplastic Agents Mice Transgenic Pharmacology medicine.disease_cause Biomarkers Pharmacological Article General Biochemistry Genetics and Molecular Biology Mice Prostate cancer Predictive Value of Tests In vivo Cell Line Tumor medicine Animals Humans lcsh:QH301-705.5 media_common biology business.industry Gene Expression Profiling Forkhead Box Protein M1 Microfilament Proteins CENPF Prostatic Neoplasms Drug Synergism Forkhead Transcription Factors medicine.disease Survival Analysis 3. Good health Gene Expression Regulation Neoplastic Gene expression profiling Disease Models Animal lcsh:Biology (General) Genetically Engineered Mouse biology.protein FOXM1 Cancer research business Signal Transduction |
Zdroj: | Cell Reports, Vol 12, Iss 12, Pp 2060-2071 (2015) |
ISSN: | 2211-1247 |
Popis: | SummaryAlthough 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. |
Databáze: | OpenAIRE |
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