Breast cancer presents an enormous public health concern. One out of eight women will experience breast cancer in her lifetime. To understand the root of breast cancer initiation and progression, many multi “-omic” projects have been undertaken. This effort has been extremely fruitful in in the discovery of many new genomic events in breast cancer. However, what the studies lack is the functional impact of the genomic events discovered. To understand this, researchers must use in vitro and in vivo models of the disease. One common in vivo model used is the genetically engineered mouse model. Despite their widespread use, there is no integrative database to capture the similarity and differences between human tumors and genetically engineered mouse models. To begin to address this critical need, we started by identifying 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. Due to the predictive nature of the previous study, we have completed whole genome sequencing and transcriptome profiling of two widely used mouse models of breast cancer, MMTV-Neu and MMTV-PyMT. This genomic information was integrated with phenotypic data and CRISPR/Cas9 studies to understand the impact of key events on tumor biology To functionalize this data, we followed up on one key amplification event that we found on chromosomes 11D. We identified this event to be associated with worse distant metastasis free survival due to the presence of Co11a1 and CHAD within the amplification event. This was identified through the use of a wound healing assay, tail vein injection, and mammary fat pad injection of CRISPR-Cas9 generated knockout cell lines for Col1a1 and CHAD. In all assays the reduction of metastatic potential was seen. Importantly, we are also able to identify the vulnerability of tumors with the 17q21.33 amplicon to AKT targeted therapy. This was predicted through a number of high throughput genomic and drug compound screens in which unique vulnerabilities were identified in those cell lines containing the 17q21.33 amplicon. Here we also identified a conserved mutation in phosphotyrosine receptor phosphatase type H (Ptprh). The mutation is highly conserved in mouse models of breast cancer and is identified to be mutant in 81% of MMTV-PyMT tumors. A key finding is that Ptprh mutations are associated with high EGFR activity, lower latency and more aggressive tumors in a variety of cancer types. Importantly when cell lines with the Ptprh mutation were compared against those without the mutation we identified an increased sensitivity to EGFR targeted therapy such as erlotinib associated with Ptprh mutation. Through these studies we have identified key genomic alterations within mouse models of breast cancer. Both of the explored events serve as biomarkers of treatment response and could change the course of therapy for patients. We believe that these events are just case studies and many other events exist within mouse models. Taken together this shows the critical need to increase the depth and breadth of full characterization of mouse models of breast cancer.
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