Predicting drug responsiveness in human cancers using genetically engineered mice
Menée sur des modèles murins de cancer du sein, puis validée sur des données cliniques, cette étude met en évidence l'intérêt de ces modèles pour identifier des signatures d'expression de gènes en association avec la réponse à une chimiothérapie ou une thérapie ciblée
Purpose: To use genetically engineered mouse models (GEMMs) and orthotopic syngeneic murine transplants (OSTs) to develop gene-expression based predictors of response to anti-cancer drugs in human tumors. These mouse models offer advantages including precise genetics and an intact microenvironment/immune system.
Experimental Design: We examined the efficacy of four chemotherapeutic or targeted anti-cancer drugs, alone and in combination, using mouse models representing three distinct breast cancer subtypes: Basal-like (C3(1)-T-antigen GEMM), Luminal B (MMTV-Neu GEMM), and Claudin-low (T11/TP53-/- OST). We expression-profiled tumors to develop signatures that corresponded to treatment and response, then tested their predictive potential using human patient data.
Results: Although a single agent exhibited exceptional efficacy (i.e. lapatinib in the Neu-driven model), generally single-agent activity was modest, while some combination therapies were more active and life-prolonging. Through analysis of RNA expression in this large set of chemotherapy-treated murine tumors, we identified a pair of gene expression signatures that predicted pathological complete response to neoadjuvant anthracycline/taxane therapy in human patients with breast cancer.
Conclusions: These results show that murine-derived gene signatures can predict response even after accounting for common clinical variables and other predictive genomic signatures, suggesting that mice can be used to identify new biomarkers for human cancer patients.
Clinical Cancer Research , résumé, 2013