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
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
Externí odkaz: