Please note that we have stopped the regular imports of Gene Expression Omnibus (GEO) data into ArrayExpress. This may not be the latest version of this experiment.
E-GEOD-37915 - Predicting drug responsiveness in humans cancers using genetically engineered mice
Released on 1 January 2014, last updated on 2 June 2014
Anti-cancer drug testing is challenging, but genetically engineered mouse models (GEMMs) and orthotopic, syngeneic transplants (OSTs) may offer advantages for pre-clinical testing including an intact microenvironment. We examined the efficacy of six chemotherapeutic or targeted anti-cancer drugs, alone and in combination, using over 500 GEMMs/OSTs representing three distinct breast cancer subtypes: Basal-like (C3(1)-T-antigen GEMM), Luminal B (MMTV-Neu GEMM), and Claudin-low (T11/TP53-/- OST). While a few single agents offered exceptional efficacy like lapatinib in the Neu/ERBB2 driven model, combination therapies tended to be more active and life prolonging. Using expression profiling of chemotherapy treated murine tumors, we identified an expression signature that was able to predict pathological complete response to neoadjuvant anthracycline-taxane treated human breast cancer patients, even after accounting for the common clinical variables and other genomic signatures. These results show that credentialed murine models can predict the efficacy of would-be anti-cancer compounds in humans, and that GEMMs can be used to develop new biomarkers of therapeutic responsiveness in humans. control X treatment
transcription profiling by array
Charles Perou <firstname.lastname@example.org>, Charles M Perou, Jerry Usary