Integrative multi-omic sequencing reveals the MMTV-Myc mouse model mimics human breast cancer heterogeneity.

BACKGROUND: Breast cancer is a complex and heterogeneous disease with distinct subtypes and molecular profiles corresponding to different clinical outcomes. Mouse models of breast cancer are widely used, but their relevance in capturing the heterogeneity of human disease is unclear. Previous studies have shown the heterogeneity at the gene expression level for the MMTV-Myc model, but have only speculated on the underlying genetics. METHODS: Tumors from the microacinar, squamous, and EMT histological subtypes of the MMTV-Myc mouse model of breast cancer underwent whole genome sequencing. The genomic data obtained were then integrated with previously obtained matched sample gene expression data and extended to additional samples of each histological subtype, totaling 42 gene expression samples. High correlation was observed between genetic copy number events and resulting gene expression by both Spearman's rank correlation coefficient and the Kendall rank correlation coefficient. These same genetic events are conserved in humans and are indicative of poor overall survival by Kaplan-Meier analysis. A supervised machine learning algorithm trained on METABRIC gene expression data was used to predict the analogous human breast cancer intrinsic subtype from mouse gene expression data. RESULTS: Herein, we examine three common histological subtypes of the MMTV-Myc model through whole genome sequencing and have integrated these results with gene expression data. Significantly, key genomic alterations driving cell signaling pathways were well conserved within histological subtypes. Genomic changes included frequent, co-occurring mutations in KIT and RARA in the microacinar histological subtype as well as SCRIB mutations in the EMT subtype. EMT tumors additionally displayed strong KRAS activation signatures downstream of genetic activating events primarily ascribed to KRAS activating mutations, but also FGFR2 amplification. Analogous genetic events in human breast cancer showed stark decreases in overall survival. In further analyzing transcriptional heterogeneity of the MMTV-Myc model, we report a supervised machine learning model that classifies MMTV-Myc histological subtypes and other mouse models as being representative of different human intrinsic breast cancer subtypes. CONCLUSIONS: We conclude the well-established MMTV-Myc mouse model presents further opportunities for investigation of human breast cancer heterogeneity.

Molecular Characterization and Landscape of Breast cancer Models from a multi-omics Perspective.

Breast cancer is well-known to be a highly heterogenous disease. This facet of cancer makes finding a research model that mirrors the disparate intrinsic features challenging. With advances in multi-omics technologies, establishing parallels between the various models and human tumors is increasingly intricate. Here we review the various model systems and their relation to primary breast tumors using available omics data platforms. Among the research models reviewed here, breast cancer cell lines have the least resemblance to human tumors since they have accumulated many mutations and copy number alterations during their long use. Moreover, individual proteomic and metabolomic profiles do not overlap with the molecular landscape of breast cancer. Interestingly, omics analysis revealed that the initial subtype classification of some breast cancer cell lines was inappropriate. In cell lines the major subtypes are all well represented and share some features with primary tumors. In contrast, patient-derived xenografts (PDX) and patient-derived organoids (PDO) are superior in mirroring human breast cancers at many levels, making them suitable models for drug screening and molecular analysis. While patient derived organoids are spread across luminal, basal- and normal-like subtypes, the PDX samples were initially largely basal but other subtypes have been increasingly described. Murine models offer heterogenous tumor landscapes, inter and intra-model heterogeneity, and give rise to tumors of different phenotypes and histology. Murine models have a reduced mutational burden compared to human breast cancer but share some transcriptomic resemblance, and representation of many breast cancer subtypes can be found among the variety subtypes. To date, while mammospheres and three- dimensional cultures lack comprehensive omics data, these are excellent models for the study of stem cells, cell fate decision and differentiation, and have also been used for drug screening. Therefore, this review explores the molecular landscapes and characterization of breast cancer research models by comparing recent published multi-omics data and analysis.

Elevated phosphorylation of EGFR in NSCLC due to mutations in PTPRH.

The role of EGFR in lung cancer is well described with numerous activating mutations that result in phosphorylation and tyrosine kinase inhibitors that target EGFR. While the role of the EGFR kinase in non-small cell lung cancer (NSCLC) is appreciated, control of EGFR signaling pathways through dephosphorylation by phosphatases is not as clear. Through whole genome sequencing we have uncovered conserved V483M Ptprh mutations in PyMT induced tumors. Profiling the downstream events of Ptprh mutant tumors revealed AKT activation, suggesting a key target of PTPRH was EGFR tyrosine 1197. Given the role of EGFR in lung cancer, we explored TCGA data which revealed that a subset of PTPRH mutant tumors shared gene expression profiles with EGFR mutant tumors, but that EGFR mutations and PTPRH mutations were mutually exclusive. Generation of a PTPRH knockout NSCLC cell line resulted in Y1197 phosphorylation of EGFR, and a rescue with expression of wild type PTPRH returned EGFR phosphorylation to parental line values while rescue with catalytically dead PTPRH did not. A dose response curve illustrated that two human NSCLC lines with naturally occurring PTPRH mutations responded to EGFR tyrosine kinase inhibition. Osimertinib treatment of these tumors resulted in a reduction of tumor volume relative to vehicle controls. PTPRH mutation resulted in nuclear pEGFR as seen in immunohistochemistry, suggesting that there may also be a role for EGFR as a transcriptional co-factor. Together these data suggest mutations in PTPRH in NSCLC is inhibitory to PTPRH function, resulting in aberrant EGFR activity and ultimately may result in clinically actionable alterations using existing therapies.

Metastasis is altered through multiple processes regulated by the E2F1 transcription factor.

The E2F family of transcription factors is important for many cellular processes, from their canonical role in cell cycle regulation to other roles in angiogenesis and metastasis. Alteration of the Rb/E2F pathway occurs in various forms of cancer, including breast cancer. E2F1 ablation has been shown to decrease metastasis in MMTV-Neu and MMTV-PyMT transgenic mouse models of breast cancer. Here we take a bioinformatic approach to determine the E2F1 regulated genomic alterations involved in the metastatic cascade, in both Neu and PyMT models. Through gene expression analysis, we reveal few transcriptome changes in non-metastatic E2F1-/- tumors relative to transgenic tumor controls. However investigation of these models through whole genome sequencing found numerous differences between the models, including differences in the proposed tumor etiology between E2F1-/- and E2F1+/+ tumors induced by Neu or PyMT. For example, loss of E2F1 within the Neu model led to an increased contribution of the inefficient double stranded break repair signature to the proposed etiology of the tumors. While the SNV mutation burden was higher in PyMT mouse tumors than Neu mouse tumors, there was no statistically significant differences between E2F WT and E2F1 KO mice. Investigating mutated genes through gene set analysis also found a significant number of genes mutated in the cell adhesion pathway in E2F1-/- tumors, indicating this may be a route for disruption of metastasis in E2F1-/- tumors. Overall, these findings illustrate the complicated nature of uncovering drivers of the metastatic process.

Low E2F2 activity is associated with high genomic instability and PARPi resistance.

The E2F family, classically known for a central role in cell cycle, has a number of emerging roles in cancer including angiogenesis, metabolic reprogramming, metastasis and DNA repair. E2F1 specifically has been shown to be a critical mediator of DNA repair; however, little is known about DNA repair and other E2F family members. Here we present an integrative bioinformatic and high throughput drug screening study to define the role of E2F2 in maintaining genomic integrity in breast cancer. We utilized in vitro E2F2 ChIP-chip and over expression data to identify transcriptional targets of E2F2. This data was integrated with gene expression from E2F2 knockout tumors in an MMTV-Neu background. Finally, this data was compared to human datasets to identify conserved roles of E2F2 in human breast cancer through the TCGA breast cancer, Cancer Cell Line Encyclopedia, and CancerRx datasets. Through these methods we predict that E2F2 transcriptionally regulates mediators of DNA repair. Our gene expression data supports this hypothesis and low E2F2 activity is associated with a highly unstable tumor. In human breast cancer E2F2, status was also correlated with a patient's response to PARP inhibition therapy. Taken together this manuscript defines a novel role of E2F2 in cancer progression beyond cell cycle and could impact patient treatment.

Studying Lymphatic Metastasis in Breast Cancer: Current Models, Strategies, and Clinical Perspectives.

Breast cancer is the most commonly diagnosed cancer in women and the second most common cause of cancer-related deaths in the United States. Although early detection has significantly decreased breast cancer mortality, patients diagnosed with distant metastasis still have a very poor prognosis. The most common site that breast cancer spreads to are local lymph nodes. Therefore, the presence of lymph node metastasis remains one of most important prognostic factors in breast cancer patients. Given its significant clinical implications, increased efforts have been dedicated to better understand the molecular mechanism governing lymph node metastasis in breast cancer. The identification of lymphatic-specific biomarkers, including podoplanin and LYVE-1, has propelled the field of lymphatic metastasis forward. In addition, several animal models such as cell line-derived xenografts, patient-derived xenografts, and spontaneous tumor models have been developed to recreate the process of lymphatic metastasis. Moreover, the incorporation of various -omic platforms have provided further insight into the genetic drivers facilitating lymphatic metastasis, as well as potential biomarkers and therapeutic targets. Here, we highlight various models of lymphatic metastasis, their potential pitfalls, and other tools available to study lymphatic metastasis including imaging modalities and -omic studies.

Reduction of Global H3K27me3 Enhances HER2/ErbB2 Targeted Therapy.

Monoclonal antibodies (mAbs) targeting the oncogenic receptor tyrosine kinase ERBB2/HER2, such as Trastuzumab, are the standard of care therapy for breast cancers driven by ERBB2 overexpression and activation. However, a substantial proportion of patients exhibit de novo resistance. Here, by comparing matched Trastuzumab-naive and post-treatment patient samples from a neoadjuvant trial, we link resistance with elevation of H3K27me3, a repressive histone modification catalyzed by polycomb repressor complex 2 (PRC2). In ErbB2+ breast cancer models, PRC2 silences endogenous retroviruses (ERVs) to suppress anti-tumor type-I interferon (IFN) responses. In patients, elevated H3K27me3 in tumor cells following Trastuzumab treatment correlates with suppression of interferon-driven viral defense gene expression signatures and poor response. Using an immunocompetent model, we provide evidence that EZH2 inhibitors promote interferon-driven immune responses that enhance the efficacy of anti-ErbB2 mAbs, suggesting the potential clinical benefit of epigenomic reprogramming by H3K27me3 depletion in Trastuzumab-resistant disease.

Functional Redundancy between ß1 and ß3 Integrin in Activating the IR/Akt/mTORC1 Signaling Axis to Promote ErbB2-Driven Breast Cancer.

Integrin receptors coordinate cell adhesion to the extracellular matrix (ECM) to facilitate many cellular processes during malignant transformation. Despite their pro-tumorigenic roles, therapies targeting integrins remain limited. Here, we provide genetic evidence supporting a functional redundancy between ß1 and ß3 integrin during breast cancer progression. Although ablation of ß1 or ß3 integrin alone has limited effects on ErbB2-driven mammary tumorigenesis, deletion of both receptors resulted in a significant delay in tumor onset with a corresponding impairment in lung metastasis. Mechanistically, stiff ECM cooperates with integrin receptors to recruit insulin receptors (IRs) to focal adhesion through the formation of integrin/IR complexes, thereby preventing their lysosomal degradation. ß1/ß3 integrin-deficient tumors that eventually emerged exhibit impaired Akt/mTORC1 activity. Murine and human breast cancers exhibiting enhanced integrin-dependent activity also display elevated IR/Akt/mTORC1 signaling activity. Together, these observations argue that integrin/IR crosstalk transduces mechanical cues from the tumor microenvironment to promote ErbB2-dependent breast cancer progression.

An ErbB2/c-Src axis links bioenergetics with PRC2 translation to drive epigenetic reprogramming and mammary tumorigenesis.

Dysregulation of histone modifications promotes carcinogenesis by altering transcription. Breast cancers frequently overexpress the histone methyltransferase EZH2, the catalytic subunit of Polycomb Repressor Complex 2 (PRC2). However, the role of EZH2 in this setting is unclear due to the context-dependent functions of PRC2 and the heterogeneity of breast cancer. Moreover, the mechanisms underlying PRC2 overexpression in cancer are obscure. Here, using multiple models of breast cancer driven by the oncogene ErbB2, we show that the tyrosine kinase c-Src links energy sufficiency with PRC2 overexpression via control of mRNA translation. By stimulating mitochondrial ATP production, c-Src suppresses energy stress, permitting sustained activation of the mammalian/mechanistic target of rapamycin complex 1 (mTORC1), which increases the translation of mRNAs encoding the PRC2 subunits Ezh2 and Suz12. We show that Ezh2 overexpression and activity are pivotal in ErbB2-mediated mammary tumourigenesis. These results reveal the hitherto unknown c-Src/mTORC1/PRC2 axis, which is essential for ErbB2-driven carcinogenesis.

Integrated analyses of murine breast cancer models reveal critical parallels with human disease.

Mouse models have an essential role in cancer research, yet little is known about how various models resemble human cancer at a genomic level. Here, we complete whole genome sequencing and transcriptome profiling of two widely used mouse models of breast cancer, MMTV-Neu and MMTV-PyMT. Through integrative in vitro and in vivo studies, we identify copy number alterations in key extracellular matrix proteins including collagen 1 type 1 alpha 1 (COL1A1) and chondroadherin (CHAD) that drive metastasis in these mouse models. In addition to copy number alterations, we observe a propensity of the tumors to modulate tyrosine kinase-mediated signaling through mutation of phosphatases such as PTPRH in the MMTV-PyMT mouse model. Mutation in PTPRH leads to increased phospho-EGFR levels and decreased latency. These findings underscore the importance of understanding the complete genomic landscape of a mouse model and illustrate the utility this has in understanding human cancers.

E2F1 Drives Breast Cancer Metastasis by Regulating the Target Gene FGF13 and Altering Cell Migration.

In prior work we demonstrated that loss of E2F transcription factors inhibits metastasis. Here we address the mechanisms for this phenotype and identify the E2F regulated genes that coordinate tumor cell metastasis. Transcriptomic profiling of E2F1 knockout tumors identified a role for E2F1 as a master regulator of a suite of pro-metastatic genes, but also uncovered E2F1 target genes with an unknown role in pulmonary metastasis. High expression of one of these genes, Fgf13, is associated with early human breast cancer metastasis in a clinical dataset. Together these data led to the hypothesis that Fgf13 is critical for breast cancer metastasis, and that upregulation of Fgf13 may partially explain how E2F1 promotes breast cancer metastasis. To test this hypothesis we ablated Fgf13 via CRISPR. Deletion of Fgf13 in a MMTV-PyMT breast cancer cell line reduces colonization of the lungs in a tail vein injection. In addition, loss of Fgf13 reduced in vitro cell migration, suggesting that Fgf13 may be critical for tumor cells to escape the primary tumor and to colonize the distal sites. The significance of this work is twofold: we have both uncovered genomic features by which E2F1 regulates metastasis and we have identified new pro-metastatic functions for the E2F1 target gene Fgf13.

How to Choose a Mouse Model of Breast Cancer, a Genomic Perspective.

Human breast cancer is a heterogeneous disease with numerous subtypes that have been defined through immunohistological, histological, and gene expression patterns. The diversity of breast cancer has made the study of its various underlying causes complex. To facilitate the examination of particular facets of breast cancer, mouse models have been generated, ranging from carcinogen induced models to genetically engineered mice. While mouse models have been generated to mimic the initiating event, including p53 loss, BRCA loss, or overexpression of HER2 / Neu / erbB2, other genomic events are often not well characterized. However, these secondary genetic events are often critical to the mouse tumor evolution, subtype, and outcome, just as they are in human breast cancer. As such, these other genomic events are a critical component of what models are chosen to study specific subtypes of human breast cancer. Here we review the genomic analyses that have been completed for various genetically engineered mouse models, how they compare to human breast cancer, and detail how this information can be used in choosing a mouse model for analysis.

Evaluating cell lines as models for metastatic breast cancer through integrative analysis of genomic data.

Cell lines are widely-used models to study metastatic cancer although the extent to which they recapitulate the disease in patients remains unknown. The recent accumulation of genomic data provides an unprecedented opportunity to evaluate the utility of them for metastatic cancer research. Here, we reveal substantial genomic differences between breast cancer cell lines and metastatic breast cancer patient samples. We also identify cell lines that more closely resemble the different subtypes of metastatic breast cancer seen in the clinic and show that surprisingly, MDA-MB-231 cells bear little genomic similarities to basal-like metastatic breast cancer patient samples. Further comparison suggests that organoids more closely resemble the transcriptome of metastatic breast cancer samples compared to cell lines. Our work provides a guide for cell line selection in the context of breast cancer metastasis and highlights the potential of organoids in these studies.

Transcription factor compensation during mammary gland development in E2F knockout mice.

The E2F transcription factors control key elements of development, including mammary gland branching morphogenesis, with several E2Fs playing essential roles. Additional prior data has demonstrated that loss of individual E2Fs can be compensated by other E2F family members, but this has not been tested in a mammary gland developmental context. Here we have explored the role of the E2Fs and their ability to functionally compensate for each other during mammary gland development. Using gene expression from terminal end buds and chromatin immunoprecipitation data for E2F1, E2F2 and E2F3, we noted both overlapping and unique mammary development genes regulated by each of the E2Fs. Based on our computational findings and the fact that E2Fs share a common binding motif, we hypothesized that E2F transcription factors would compensate for each other during mammary development and function. To test this hypothesis, we generated RNA from E2F1-/-, E2F2-/- and E2F3+/- mouse mammary glands. QRT-PCR on mammary glands during pregnancy demonstrated increases in E2F2 and E2F3a in the E2F1-/- mice and an increase in E2F2 levels in E2F3+/- mice. During lactation we noted that E2F3b transcript levels were increased in the E2F2-/- mice. Given that E2Fs have previously been noted to have the most striking effects on development during puberty, we hypothesized that loss of individual E2Fs would be compensated for at that time. Double mutant mice were generated and compared with the single knockouts. Loss of both E2F1 and E2F2 revealed a more striking phenotype than either knockout alone, indicating that E2F2 was compensating for E2F1 loss. Interestingly, while E2F2 was not able to functionally compensate for E2F3+/- during mammary outgrowth, increased E2F2 expression was observed in E2F3+/- mammary glands during pregnancy day 14.5 and lactation day 5. Together, these findings illustrate the specificity of E2F family members to compensate during development of the mammary gland.

Identification of an Unfavorable Immune Signature in Advanced Lung Tumors from Nrf2-Deficient Mice.

AIMS: Activation of the nuclear factor (erythroid-derived 2)-like 2 (Nrf2) pathway in normal cells inhibits carcinogenesis, whereas constitutive activation of Nrf2 in cancer cells promotes tumor growth and chemoresistance. However, the effects of Nrf2 activation in immune cells during lung carcinogenesis are poorly defined and could either promote or inhibit cancer growth. Our studies were designed to evaluate tumor burden and identify immune cell populations in the lungs of Nrf2 knockout (KO) versus wild-type (WT) mice challenged with vinyl carbamate. RESULTS: Nrf2 KO mice developed lung tumors earlier than the WT mice and exhibited more and larger tumors over time, even at late stages. T cell populations were lower in the lungs of Nrf2 KO mice, whereas tumor-promoting macrophages and myeloid-derived suppressor cells were elevated in the lungs and spleen, respectively, of Nrf2 KO mice relative to WT mice. Moreover, 34 immune response genes were significantly upregulated in tumors from Nrf2 KO mice, especially a series of cytokines (Cxcl1, Csf1, Ccl9, Cxcl12, etc.) and major histocompatibility complex antigens that promote tumor growth. INNOVATION: Our studies discovered a novel immune signature, characterized by the infiltration of tumor-promoting immune cells, elevated cytokines, and increased expression of immune response genes in the lungs and tumors of Nrf2 KO mice. A complementary profile was also found in lung cancer patients, supporting the clinical significance of our findings. CONCLUSION: Overall, our results confirmed a protective role for Nrf2 in late-stage carcinogenesis and, unexpectedly, suggest that activation of Nrf2 in immune cells may be advantageous for preventing or treating lung cancer. Antioxid. Redox Signal.

Integrin-uPAR signaling leads to FRA-1 phosphorylation and enhanced breast cancer invasion.

BACKGROUND: The Fos-related antigen 1 (FRA-1) transcription factor promotes tumor cell growth, invasion and metastasis. Phosphorylation of FRA-1 increases protein stability and function. We identify a novel signaling axis that leads to increased phosphorylation of FRA-1, increased extracellular matrix (ECM)-induced breast cancer cell invasion and is prognostic of poor outcome in patients with breast cancer. METHODS: While characterizing five breast cancer cell lines derived from primary human breast tumors, we identified BRC-31 as a novel basal-like cell model that expresses elevated FRA-1 levels. We interrogated the functional contribution of FRA-1 and an upstream signaling axis in breast cancer cell invasion. We extended this analysis to determine the prognostic significance of this signaling axis in samples derived from patients with breast cancer. RESULTS: BRC-31 cells display elevated focal adhesion kinase (FAK), SRC and extracellular signal-regulated (ERK2) phosphorylation relative to luminal breast cancer models. Inhibition of this signaling axis, with pharmacological inhibitors, reduces the phosphorylation and stabilization of FRA-1. Elevated integrin αVß3 and uPAR expression in these cells suggested that integrin receptors might activate this FAK-SRC-ERK2 signaling. Transient knockdown of urokinase/plasminogen activator urokinase receptor (uPAR) in basal-like breast cancer cells grown on vitronectin reduces FRA-1 phosphorylation and stabilization; and uPAR and FRA-1 are required for vitronectin-induced cell invasion. In clinical samples, a molecular component signature consisting of vitronectin-uPAR-uPA-FRA-1 predicts poor overall survival in patients with breast cancer and correlates with an FRA-1 transcriptional signature. CONCLUSIONS: We have identified a novel signaling axis that leads to phosphorylation and enhanced activity of FRA-1, a transcription factor that is emerging as an important modulator of breast cancer progression and metastasis.

Using gene expression data to direct breast cancer therapy: evidence from a preclinical trial.

The heterogeneity both within and between breast cancers presents a significant clinical challenge for both diagnosis and therapy. This heterogeneity is present at all levels of analysis in breast cancer, ranging from genomic to metabolomic. A function of this heterogeneity is that numerous signaling networks are activated, and while treatment of one arm may be initially effective, this allows the tumor to be poised to evolve a resistance mechanism. Here we review the classification of breast cancers and discuss therapy of hormone positive, HER2 positive, and triple negative breast cancers. Model systems for breast cancer are examined allowing for a preclinical trial using a personalized medicine approach to be tested. This preclinical trial was based solely on cell signaling pathway activation and effectively and specifically blocked tumor growth in a preclinical model system.

Histological subtypes of mouse mammary tumors reveal conserved relationships to human cancers.

Human breast cancer has been characterized by extensive transcriptional heterogeneity, with dominant patterns reflected in the intrinsic subtypes. Mouse models of breast cancer also have heterogeneous transcriptomes and we noted that specific histological subtypes were associated with particular subsets. We hypothesized that unique sets of genes define each tumor histological type across mouse models of breast cancer. Using mouse models that contained both gene expression data and expert pathologist classification of tumor histology on a sample by sample basis, we predicted and validated gene expression signatures for Papillary, EMT, Microacinar and other histological subtypes. These signatures predict known histological events across murine breast cancer models and identify counterparts of mouse mammary tumor types in subtypes of human breast cancer. Importantly, the EMT, Adenomyoepithelial, and Solid signatures were predictive of clinical events in human breast cancer. In addition, a pan-cancer comparison revealed that the histological signatures were active in a variety of human cancers such as lung, oral, and esophageal squamous tumors. Finally, the differentiation status and transcriptional activity implicit within these signatures was identified. These data reveal that within tumor histology groups are unique gene expression profiles of differentiation and pathway activity that stretch well beyond the transgenic initiating events and that have clear applicability to human cancers. As a result, our work provides a predictive resource and insights into possible mechanisms that govern tumor heterogeneity.

Triple-negative breast cancer and the potential for targeted therapy.

Breast cancer is composed of several well-recognized subtypes including estrogen receptor, progesterone receptor and HER2 triple-negative breast cancer (TNBC). Without available targeted therapy options, standard of care for TNBC remains chemotherapy. It is of interest to note that TNBC tumors generally have better responses to chemotherapy compared with other subtypes. However, patients without complete response account for approximately 80% of TNBC. Mounting evidence suggests significant heterogeneity within the TNBC subtype, and studies have focused on genetic targets with high rates of altered expression. Recent studies suggest clear possibilities for benefits from targeted therapy in TNBC. In this review, we summarize studies of targeted therapy, including within mouse models, and discuss their applications in the development of combinatorial treatments to treat TNBC.

Mouse Models of Breast Cancer Share Amplification and Deletion Events with Human Breast Cancer.

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.