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

Balletta, Lorraine

Bridges, Arlene

Cheang, Maggie C.U.

Combest, Austin

Darr, David

Herschkowitz, Jason I.

Jordan, Jamie

Karginova, Olga

Liu, Mei

Perou, Charles M.

Prat, Aleix

Roberts, Patrick J.

Rosen, Jeffrey M.

Sharpless, Norman E.

Usary, Jerry

Zamboni, William

Zhao, Wei

PubMed

Carolina Digital Repository

Predicting Drug Responsiveness in Human Cancers Using Genetically Engineered Mice

Abstract
               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.</jats:p

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

Crossref

Clinical Cancer Research

http://dx.doi.org/10.1158/1078-0432.CCR-13-0522

Predicting Drug Responsiveness in Human Cancers Using Genetically Engineered Mice

Abstract

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