Abstract
Breast tumor heterogeneity has been well documented through the use of multiplatform -omic studies in human tumors. However, there is no integrative database to capture the heterogeneity within mouse models of breast cancer. This project identifies genomic copy number alterations (CNAs) in 600 tumors across 27 major mouse models of breast cancer through the application of a predictive algorithm to publicly available gene expression data. It was found that despite the presence of strong oncogenic drivers in most mouse models, CNAs are extremely common but heterogeneous both between models and within models. Many mouse CNA events are largely conserved in human tumors and in the mouse we show that they are associated with secondary tumor characteristics such as tumor histology, metastasis, as well as enhanced oncogenic signaling. These data serve as an important resource in guiding investigators when choosing a mouse model to understand the gene copy number changes relevant to human breast cancer.