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.

Atypical cell cycle regulation promotes mammary stem cell expansion during mammary development and tumourigenesis.

BACKGROUND: The cell cycle of mammary stem cells must be tightly regulated to ensure normal homeostasis of the mammary gland to prevent abnormal proliferation and susceptibility to tumorigenesis. The atypical cell cycle regulator, Spy1 can override cell cycle checkpoints, including those activated by the tumour suppressor p53 which mediates mammary stem cell homeostasis. Spy1 has also been shown to promote expansion of select stem cell populations in other developmental systems. Spy1 protein is elevated during proliferative stages of mammary gland development, is found at higher levels in human breast cancers, and promotes susceptibility to mammary tumourigenesis when combined with loss of p53. We hypothesized that Spy1 cooperates with loss of p53 to increase susceptibility to tumour initiation due to changes in susceptible mammary stem cell populations during development and drives the formation of more aggressive stem like tumours. METHODS: Using a transgenic mouse model driving expression of Spy1 within the mammary gland, mammary development and stemness were assessed. These mice were intercrossed with p53 null mice to study the tumourigenic properties of Spy1 driven p53 null tumours, as well as global changes in signaling via RNA sequencing analysis. RESULTS: We show that elevated levels of Spy1 leads to expansion of mammary stem cells, even in the presence of p53, and an increase in mammary tumour formation. Spy1-driven tumours have an increased cancer stem cell population, decreased checkpoint signaling, and demonstrate an increase in therapy resistance. Loss of Spy1 decreases tumor onset and reduces the cancer stem cell population. CONCLUSIONS: This data demonstrates the potential of Spy1 to expand mammary stem cell populations and contribute to the initiation and progression of aggressive, breast cancers with increased cancer stem cell populations.

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.

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.

Pyruvate kinase isoform expression alters nucleotide synthesis to impact cell proliferation.

Metabolic regulation influences cell proliferation. The influence of pyruvate kinase isoforms on tumor cells has been extensively studied, but whether PKM2 is required for normal cell proliferation is unknown. We examine how PKM2 deletion affects proliferation and metabolism in nontransformed, nonimmortalized PKM2-expressing primary cells. We find that deletion of PKM2 in primary cells results in PKM1 expression and proliferation arrest. PKM1 expression, rather than PKM2 loss, is responsible for this effect, and proliferation arrest cannot be explained by cell differentiation, senescence, death, changes in gene expression, or prevention of cell growth. Instead, PKM1 expression impairs nucleotide production and the ability to synthesize DNA and progress through the cell cycle. Nucleotide biosynthesis is limiting, as proliferation arrest is characterized by severe thymidine depletion, and supplying exogenous thymine rescues both nucleotide levels and cell proliferation. Thus, PKM1 expression promotes a metabolic state that is unable to support DNA synthesis.

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.

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.

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.

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.

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.

A genomic analysis of mouse models of breast cancer reveals molecular features of mouse models and relationships to human breast cancer.

INTRODUCTION: Genomic variability limits the efficacy of breast cancer therapy. To simplify the study of the molecular complexity of breast cancer, researchers have used mouse mammary tumor models. However, the degree to which mouse models model human breast cancer and are reflective of the human heterogeneity has yet to be demonstrated with gene expression studies on a large scale. METHODS: To this end, we have built a database consisting of 1,172 mouse mammary tumor samples from 26 different major oncogenic mouse mammary tumor models. RESULTS: In this dataset we identified heterogeneity within mouse models and noted a surprising amount of interrelatedness between models, despite differences in the tumor initiating oncogene. Making comparisons between models, we identified differentially expressed genes with alteration correlating with initiating events in each model. Using annotation tools, we identified transcription factors with a high likelihood of activity within these models. Gene signatures predicted activation of major cell signaling pathways in each model, predictions that correlated with previous genetic studies. Finally, we noted relationships between mouse models and human breast cancer at both the level of gene expression and predicted signal pathway activity. Importantly, we identified individual mouse models that recapitulate human breast cancer heterogeneity at the level of gene expression. CONCLUSIONS: This work underscores the importance of fully characterizing mouse tumor biology at molecular, histological and genomic levels before a valid comparison to human breast cancer may be drawn and provides an important bioinformatic resource.

Genetic heterogeneity of Myc-induced mammary tumors reflecting diverse phenotypes including metastatic potential.

Human cancers result from a complex series of genetic alterations, resulting in heterogeneous disease states. Dissecting this heterogeneity is critical for understanding underlying mechanisms and providing opportunities for therapeutics matching the complexity. Mouse models of cancer have generally been used to reduce this complexity and focus on the role of single genes. Nevertheless, our analysis of tumors arising in the MMTV-Myc model of mammary carcinogenesis reveals substantial heterogeneity, seen in both histological and expression phenotypes. One contribution to this heterogeneity is the substantial frequency of activating Ras mutations. Additionally, we show that these Myc-induced mammary tumors exhibit even greater heterogeneity, revealed by distinct histological subtypes as well as distinct patterns of gene expression, than many other mouse models of tumorigenesis. Two of the major histological subtypes are characterized by differential patterns of cellular signaling pathways, including beta-catenin and Stat3 activities. We also demonstrate that one of the MMTV-Myc mammary tumor subgroups exhibits metastatic capacity and that the signature derived from the subgroup can predict metastatic potential of human breast cancer. Together, these data reveal that a combination of histological and genomic analyses can uncover substantial heterogeneity in mammary tumor formation and therefore highlight aspects of tumor phenotype not evident in the population as a whole.

A role for E2F activities in determining the fate of Myc-induced lymphomagenesis.

The phenotypic heterogeneity that characterizes human cancers reflects the enormous genetic complexity of the oncogenic process. This complexity can also be seen in mouse models where it is frequently observed that in addition to the initiating genetic alteration, the resulting tumor harbors additional, somatically acquired mutations that affect the tumor phenotype. To investigate the role of genetic interactions in the development of tumors, we have made use of the Emu-myc model of pre-B and B cell lymphoma. Since various studies point to a functional interaction between Myc and the Rb/E2F pathway, we have investigated the role of E2F activities in the process of Myc-induced lymphomagenesis. Whereas the absence of E2F1 and E2F3 function has no impact on Myc-mediated tumor development, the absence of E2F2 substantially accelerates the time of tumor onset. Conversely, tumor development is delayed by the absence of E2F4. The enhanced early onset of tumors seen in the absence of E2F2 coincides with an expansion of immature B lineage cells that are likely to be the target for Myc oncogenesis. In contrast, the absence of E2F4 mutes the response of the lineage to Myc and there is no expansion of immature B lineage cells. We also find that distinct types of tumors emerge from the Emu-myc mice, distinguished by different patterns of gene expression, and that the relative proportions of these tumor types are affected by the absence of either E2F2 or E2F4. From these results, we conclude that there are several populations of tumors that arise from the Emu-myc model, reflecting distinct populations of cells that are susceptible to Myc-mediated oncogenesis and that the proportion of these cell populations is affected by the presence or absence of E2F activities.

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.

Cell type-specific delivery of siRNAs with aptamer-siRNA chimeras.

Technologies that mediate targeted delivery of small interfering RNAs (siRNAs) are needed to improve their therapeutic efficacy and safety. Therefore, we have developed aptamer-siRNA chimeric RNAs capable of cell type-specific binding and delivery of functional siRNAs into cells. The aptamer portion of the chimeras mediates binding to PSMA, a cell-surface receptor overexpressed in prostate cancer cells and tumor vascular endothelium, whereas the siRNA portion targets the expression of survival genes. When applied to cells expressing PSMA, these RNAs are internalized and processed by Dicer, resulting in depletion of the siRNA target proteins and cell death. In contrast, the chimeras do not bind to or function in cells that do not express PSMA. These reagents also specifically inhibit tumor growth and mediate tumor regression in a xenograft model of prostate cancer. These studies demonstrate an approach for targeted delivery of siRNAs with numerous potential applications, including cancer therapeutics.

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.

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.

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.

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.