American Association for Cancer Research
10780432ccr130522-sup-tab2.xlsx (60.5 kB)

Supplementary Table 2 from Predicting Drug Responsiveness in Human Cancers Using Genetically Engineered Mice

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posted on 2023-03-31, 17:06 authored by Jerry Usary, Wei Zhao, David Darr, Patrick J. Roberts, Mei Liu, Lorraine Balletta, Olga Karginova, Jamie Jordan, Austin Combest, Arlene Bridges, Aleix Prat, Maggie C. U. Cheang, Jason I. Herschkowitz, Jeffrey M. Rosen, William Zamboni, Norman E. Sharpless, Charles M. Perou

XLSX file - 60K, Gene lists obtained from a study of carboplatin/paclitaxel treated tumors.



Purpose: To use genetically engineered mouse models (GEMM) and orthotopic syngeneic murine transplants (OST) to develop gene expression-based predictors of response to anticancer 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 4 chemotherapeutic or targeted anticancer drugs, alone and in combination, using mouse models representing 3 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, and 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, whereas 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 pathologic 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 patients with cancer. Clin Cancer Res; 19(17); 4889–99. ©2013 AACR.