Related Experiment Video
Updated: May 10, 2026
Testing Cancer Immunotherapeutics in a Humanized Mouse Model Bearing Human Tumors
Published on: December 16, 2022
Predicting drug responsiveness in human cancers using genetically engineered mice
Jerry Usary1, Wei Zhao, David Darr
1Lineberger Comprehensive Cancer Center, Department of Genetics, School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA.
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.
Insights
Gene expression signatures from mouse models predict anticancer drug response in human breast cancer patients, even outperforming existing biomarkers. This research highlights mice as valuable tools for discovering new cancer biomarkers.
Area of Science:
- Oncology
- Genomics
- Translational Research
Background:
- Developing accurate predictors of anticancer drug response is crucial for personalized cancer therapy.
- Mouse models, including genetically engineered mouse models (GEMM) and orthotopic syngeneic murine transplants (OST), offer valuable platforms for preclinical research due to their precise genetics and intact immune systems.
Purpose of the Study:
- To develop gene expression-based predictors of anticancer drug response in human tumors using GEMM and OST.
- To identify novel biomarkers for predicting treatment efficacy in breast cancer.
Main Methods:
- Examined the efficacy of four chemotherapeutic or targeted anticancer drugs in mouse models of three breast cancer subtypes (Basal-like, Luminal B, Claudin-low).
- Performed RNA expression profiling of murine tumors to develop treatment and response signatures.
- Validated the predictive potential of identified signatures using human patient data.
Main Results:
- Combination therapies demonstrated greater efficacy and life prolongation compared to single agents in most mouse models.
- Identified two gene expression signatures from chemotherapy-treated murine tumors that predicted pathologic complete response to neoadjuvant anthracycline/taxane therapy in human breast cancer patients.
- The murine-derived gene signatures showed predictive value independent of common clinical variables and other genomic signatures.
Conclusions:
- Murine-derived gene signatures can serve as reliable predictors of anticancer drug response in human patients.
- These findings underscore the utility of mouse models in discovering novel biomarkers for human cancer treatment.
- The study supports the translation of findings from mouse models to clinical applications in oncology.
