Transcriptomic Profiling of Mouse Mammary Tumors Enables Prognostic and Predictive Biomarker Discovery for Human Breast Cancer
9 affiliations
- 1Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
- 2Department of Genetics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
- 3Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
- 4Department of Pharmacology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
- 5Department of Biological Sciences, DePaul University, Chicago, Illinois.
- 6Division of Oncology, Department of Clinical Sciences, Lund University, Lund, Sweden.
- 7Skåne University Hospital Comprehensive Cancer Center, Lund, Sweden.
- 8Center for Interdisciplinary Research on Cancer and Equity in Women, Lund University, Lund, Sweden.
- 9Salk Institute for Biological Studies, University of California San Diego (UCSD), La Jolla, California.
Abstract
The development and validation of prognostic and predictive biomarkers in breast cancer are limited by the availability of well-annotated datasets linking tumor molecular features to treatment response and survival outcomes. To address this need, we generated an extensive mouse model dataset comprised of 26 immunocompetent mammary tumor models spanning diverse genetic backgrounds, epithelial–mesenchymal states, the basal–luminal axis, and distinct immune microenvironments. For each model, survival was measured under no treatment, immune checkpoint inhibition (ICI), and carboplatin/paclitaxel chemotherapy, and RNA sequencing was performed on baseline tumors and on 7-day on-treatment samples for both regimens. Baseline murine tumor gene expression features were used to train a machine learning elastic net model that predicted survival outcomes on multiple human breast cancer datasets with performance comparable with that of existing prognostic assays. Models trained for ICI benefit, using either the untreated or 7-day ICI-treated samples, predicted ICI benefit on human ICI-treated datasets, with the 7-day treated tumor model showing better performance. A predictor of carboplatin/paclitaxel response developed from the murine mammary tumor data performed well in mice but did not generalize to human chemotherapy cohorts. Finally, comparison of multiple computational approaches, including XGBoost, random forests, and support vector regression, showed that all methods successfully predicted survival outcomes, with elastic net offering the best performance and interpretability. These results indicate conserved cancer biology between mouse and human tumors for prognosis and ICI response and establish a large preclinical dataset with linked phenotypic and genomic data as a resource for biomarker discovery.
Significance:: The development of a genomically and phenotypically diverse murine tumor dataset with linked treatment outcomes establishes a robust translational resource to develop, test, and benchmark clinically relevant prognostic and therapeutic response biomarkers.
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BibTeX
@article{sutcliffe2026transcriptomic,
title = {{Transcriptomic Profiling of Mouse Mammary Tumors Enables Prognostic and Predictive Biomarker Discovery for Human Breast Cancer}},
author = {Matthew D. Sutcliffe and Kevin R. Mott and Tulay Yilmaz-Swenson and Brooke M. Felsheim and Alexander V. Lobanov and Anna R. Michmerhuizen and Patrick D. Rädler and Denis O. Okumu and Xiaping He and Adam D. Pfefferle and Stephanie Dance-Barnes and Christian Brueffer and Lao H. Saal and Michael P. East and Daniel P. Hollern and Timothy C. Elston and Gary L. Johnson and Charles M. Perou},
journal = {Cancer Research},
year = {2026},
month = aug,
doi = {10.1158/0008-5472.can-26-0855},
url = {https://doi.org/10.1158/0008-5472.can-26-0855},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Transcriptomic Profiling of Mouse Mammary Tumors Enables Prognostic and Predictive Biomarker Discovery for Human Breast Cancer
AU - Matthew D. Sutcliffe
AU - Kevin R. Mott
AU - Tulay Yilmaz-Swenson
AU - Brooke M. Felsheim
AU - Alexander V. Lobanov
AU - Anna R. Michmerhuizen
AU - Patrick D. Rädler
AU - Denis O. Okumu
AU - Xiaping He
AU - Adam D. Pfefferle
AU - Stephanie Dance-Barnes
AU - Christian Brueffer
AU - Lao H. Saal
AU - Michael P. East
AU - Daniel P. Hollern
AU - Timothy C. Elston
AU - Gary L. Johnson
AU - Charles M. Perou
PY - 2026
DA - 2026/08/14
JO - Cancer Research
DO - 10.1158/0008-5472.can-26-0855
UR - https://doi.org/10.1158/0008-5472.can-26-0855
AB - The development and validation of prognostic and predictive biomarkers in breast cancer are limited by the availability of well-annotated datasets linking tumor molecular features to treatment response and survival outcomes. To address this need, we generated an extensive mouse model dataset comprised of 26 immunocompetent mammary tumor models spanning diverse genetic backgrounds, epithelial–mesenchymal states, the basal–luminal axis, and distinct immune microenvironments. For each model, survival was measured under no treatment, immune checkpoint inhibition (ICI), and carboplatin/paclitaxel chemotherapy, and RNA sequencing was performed on baseline tumors and on 7-day on-treatment samples for both regimens. Baseline murine tumor gene expression features were used to train a machine learning elastic net model that predicted survival outcomes on multiple human breast cancer datasets with performance comparable with that of existing prognostic assays. Models trained for ICI benefit, using either the untreated or 7-day ICI-treated samples, predicted ICI benefit on human ICI-treated datasets, with the 7-day treated tumor model showing better performance. A predictor of carboplatin/paclitaxel response developed from the murine mammary tumor data performed well in mice but did not generalize to human chemotherapy cohorts. Finally, comparison of multiple computational approaches, including XGBoost, random forests, and support vector regression, showed that all methods successfully predicted survival outcomes, with elastic net offering the best performance and interpretability. These results indicate conserved cancer biology between mouse and human tumors for prognosis and ICI response and establish a large preclinical dataset with linked phenotypic and genomic data as a resource for biomarker discovery. Significance:: The development of a genomically and phenotypically diverse murine tumor dataset with linked treatment outcomes establishes a robust translational resource to develop, test, and benchmark clinically relevant prognostic and therapeutic response biomarkers.
SN - 0008-5472
SN - 1538-7445
ER -