A genomic analysis of mouse models of breast cancer reveals molecular features ofmouse models and relationships to human breast cancer
© Hollern and Andrechek; licensee BioMed Central Ltd. 2014
Received: 1 October 2013
Accepted: 4 December 2013
Published: 5 June 2014
Genomic variability limits the efficacy of breast cancer therapy. To simplify thestudy of the molecular complexity of breast cancer, researchers have used mousemammary tumor models. However, the degree to which mouse models model human breastcancer and are reflective of the human heterogeneity has yet to be demonstratedwith gene expression studies on a large scale.
To this end, we have built a database consisting of 1,172 mouse mammary tumorsamples from 26 different major oncogenic mouse mammary tumor models.
In this dataset we identified heterogeneity within mouse models and noted asurprising amount of interrelatedness between models, despite differences in thetumor initiating oncogene. Making comparisons between models, we identifieddifferentially expressed genes with alteration correlating with initiating eventsin each model. Using annotation tools, we identified transcription factors with ahigh likelihood of activity within these models. Gene signatures predictedactivation of major cell signaling pathways in each model, predictions thatcorrelated with previous genetic studies. Finally, we noted relationships betweenmouse models and human breast cancer at both the level of gene expression andpredicted signal pathway activity. Importantly, we identified individual mousemodels that recapitulate human breast cancer heterogeneity at the level of geneexpression.
This work underscores the importance of fully characterizing mouse tumor biologyat molecular, histological and genomic levels before a valid comparison to humanbreast cancer may be drawn and provides an important bioinformatic resource.
Breast cancer is a heterogeneous disease with significant mortality associated withmetastatic progression. Classification subdivides human breast cancer into sixcategories including Luminal A, Luminal B, HER2+, Basal, Claudin-low and normal-like. Recent work suggests additionalsubclasses exist within each intrinsic subtype including three basal subtypes withstriking differences in overall survival .Further, The Cancer Genome Atlas (TCGA) and the Encyclopedia of DNA Elements (ENCODE)projects show remarkable variability in genetic alterations beyond gene expression bothacross and within subtypes of human breast cancer. Together these genomic analysesdemonstrate the complex nature of human breast cancer.
To more readily study mechanisms leading to breast cancer, research has turned to themouse as a model. Mouse models of breast cancer have employed various methods ofinitiation, including mouse mammary tumor virus (MMTV) infection, chemical mutagenesisand genetically engineered mice (GEM). This pioneering work identified and tested therole of many oncogenes in breast cancer. With the insertion of MMTV into the genome,numerous key oncogenes were uncovered [3, 4]. The later development of MMTV driven transgenics allowed fordevelopment of spontaneous models. With the identification of human epithelial growthfactor receptor 2 (HER2) amplification in human breast cancer [5, 6], the observation that MMTV drivenexpression of the activated rat form of HER2 (NeuNT) resulted in breast cancerreinforced the importance of HER2 as a driving oncogene . More recently, models have been refined to include tissuespecific activation resulting in gene amplification, analogous to human HER2+ breastcancer , as well as temporal control wheretransgene expression can be activated or inactivated .
Individual mouse models have been used to model aspects of human breast cancer and theselection of the appropriate model to compare to human breast cancer has been directedby phenotype or known genetic events. For instance, the MMTV-PyMT model is widely usedto examine metastasis  while P53 knockoutmammary epithelium transplanted into wild type hosts results in tumors with variousgenetic mutations . Another aspect is thehistological subtype associated with various tumors in GEM models and the metastaticability can be altered with background .Indeed, similarities between mouse models such as Neu and Wnt as well as their humancounterparts have been previously noted [13, 14]. Importantly, in both human breast cancer and inmany GEM models, there is significant histological heterogeneity [15–17].These attributes illustrate the importance and utility of mouse models to examine breastcancer.
With the number and variety of GEM models, it is important to consider how accuratelythese various systems model human breast cancer. Initial studies using intrinsicclustering revealed similarities between mouse models and human breast cancer, albeit ina limited number of samples . Yet, a moredetailed characterization of a larger number of p53 null tumors revealed a variety ofsubtypes with strong similarities to human breast cancer , revealing the importance of examining a large number ofsamples to capture tumor heterogeneity and variability. Further, expanding the number ofMyc induced tumors revealed that a subpopulation of Myc induced tumors had similaritiesto claudin-low human breast cancer . Takentogether, recent comparative studies [11, 17, 19–22] highlighted a clear need for acomprehensive examination of the genomic features of mouse models of breast cancer andtheir relation to human breast cancer. To this end, we assembled an expansive dataset ofmouse models of breast cancer. This dataset reveals the genomic heterogeneity of mousemodels and offers a predictive resource for essential cell signaling pathways.Importantly, all comparisons between all models are made available with our report.These data demonstrate the similarities and differences of the various subtypes of mousemodels to the key subtypes of human breast cancer and underscore the necessity for aninformed choice of the appropriate mouse model for studying specific types of humanbreast cancer.
Combination of datasets
Datasets (GSE10450, GSE11259, GSE13221, GSE13231, GSE13259, GSE13553, GSE13916,GSE14226, GSE14457, GSE14753, GSE15119, GSE15263, GSE15632, GSE15904, GSE16110,GSE17916, GSE18996, GSE20465, GSE20614, GSE21444, GSE22150, GSE22406, GSE23938,GSE24594, GSE25488, GSE27101, GSE30805, GSE30866, GSE3165, GSE31942, GSE32152,GSE34146, GSE34479, GSE6453, GSE6581, GSE6772, GSE7595, GSE8516, GSE8828, GSE8863,GSE9343, GSE9355 GSE37954, GSE2034, GSE2603, GSE4922, GSE6532, AND GSE14020) weredownloaded from Gene Expression Omnibus. E-TABM-683 and E-TABM-684 were downloadedfrom Array Express. For Affymetrix data, Bayesian Factor Regression Methods (BFRM) were used to combine datasets andremove batch effects . Agilent data wasmerged with Affymetrix data using Chip Comparer  and Filemerger .To remove platform effects between Affymetrix and Agilent data and batch effectsbetween individual Agilent studies we used COMBAT [27, 28]. Batch effects and batchcorrection were visualized by principle component analysis in Matlab (for code seeAdditional file 1).
Unsupervised hierarchical clustering was done using Cluster 3.0 and exported usingJava Tree View. The color scheme for the heatmap and sample legends were made usingMatlab. Human breast cancer sample intrinsic subtypes were classified according toprotocol . Prior to clustering mouse modelswith human breast cancer, we clustered the human breast tumor samples on their own,to identify genes that would organize the breast tumors according to their intrinsicsubtype in the combined dataset. We used these genes to filter the mouse and humancombined gene expression dataset for unsupervised hierarchical clustering.
Significance analysis of microarrays  wasused for fold change analysis. Settings for each comparison can be found in the exceldownload for each model (Additional files 2, 3). Gene ontology and TRANSFAC predictions were made usingGATHER . Gene set enrichment analysis wasconducted using Genepattern . Thegene-set describing mammary cell-types was derived from .
Pathway activation was predicted according to previous studies [2, 33]. For mouse samples,specific conditions for each pathway signature can be found in Additional file4. For human breast tumor samples, pathway activationwas predicted using Score Signatures  andconditions can be found . Mixture modelingwas implemented according to .
List of mouse models in the dataset
Myc mammary tumors of various histological types, expression levelsand stability with variable Kras mutations.
Induction of adenocarcinomas with pulmonary metastasis.
MMTV K6/RCAS MMTV/RCAS
Rapid induction of luminal-type mammary tumors with pulmonarymetastasis.
SV40 Large T Antigen
Induction of mammary tumors with similarities to human basal typebreast cancer.
Tumors with similarities to human basal type breast cancer.
Fusion oncoprotein transforms through activation of AP1.
Diverse histologies with similarities to human basal breastcancer.
WAP MMTV BLG
CKO of BRCA1 in a p53 null background. Tumors similar to human basalbreast cancer.
Induction of mammary tumors with diverse gene expression patterns.
Basal-like mammary tumors. Recurrent tumors resemble humanclaudin-low.
ER positive, metastatic tumors.
Induction of mammary tumors
Heterogeneous breast cancers.
Mammary carcinomas with three phenotypes: adenocarcinoma, squamouscell carcinoma, and myoepithelial cell carcinoma.
Induction of mammary tumors with rapid tumor onset.
Adeno and adenosquamous carcinomas similar to luminal B or basal.
CKO results in adenocarcinomas with histological and molecularheterogeneity.
ER + metastatic mammary tumors.
Tumors similar to human basal type breast cancer.
ERa + PR+, hormone dependent like humanERa + luminal.
Induction of mammary adenocarcinomas.
Induction of mammary tumors
Normal Mammary Gland
Normal mammary gland samples from FVB, BalbC, and CD1 geneticbackgrounds.
Gene expression heterogeneity in mouse models
To define the characteristics of each cluster, we used Significance Analysis ofMicroarrays (SAM) to identify differentially regulated genes that define tumorswithin each cluster (Additional file 6). We interrogatedgene lists for gene ontologies (Additional file 6). Forinstance, Figure 1B shows the gene ontologies for theupregulated genes in the blue cluster in Figure 1A.Ontological categories included genes involved in biological processes andmetabolism. To refine these results, tumors from each cluster were examined with GeneSet Enrichment Analysis (GSEA) (Additional file 7).Focusing on tumors in the black cluster, GSEA showed enrichment for gene setsseparating mesenchymal cells from luminal cells (Figure 1C, Additional file 8A), including low expressionof Zeb1 target genes (Additional file 8B). Gene lists thatdefine mammary stem cells demonstrated that this cluster also had a gene expressionprofile enriched for mammary stem cell-like features (Additional file 8C,D). In agreement, the majority of epithelial to mesenchymaltransition (EMT) like tumors were observed in the black cluster (Figure 1A, Additional file 9). GSEA alsodemonstrated that tumors from the other clusters had gene expression profilesconsistent with luminal cells (Additional file 10). Forexample, tumors within the blue cluster correlated with gene signatures for luminalprogenitor cells and the orange cluster had similarities in gene expression to matureluminal cells. Together, these results define the characteristics of the tumorscontained in the major clusters.
Fold change analysis
To identify possible transcription factors that could be active in mediating thesegene expression changes, we annotated fold change results for each model usingTRANSFAC (Additional file 2, 3).For example, for genes regulated by Neu (Figure 2A), wepredicted that a significant number of genes had predicted binding sites for the Kroxfamily of transcription factors (Figure 2B). The completeresults for the transcription factor binding predictions are included in theadditional data for each of the models.
Validation of pathway predictions
Demonstrated high activation of β-catenin signaling in thesetumors.
High levels of Myc demonstrated by IHC in these mammary tumors.
BRCA & P53 mut
Using IHC, EGFR was shown to be overexpressed in this mouse model.
Observation of H-Ras mutations in mammary hyperplastic outgrowthsafter treatment with DMBA.
Using western blot and IHC, EGFR signaling was shown to be active inDMBA induced mammary tumors.
ETV6-Ntrk3 binds to and activates c-Src, and inhibition of c-Srcactivation blocks EN transforming activity using mouse engineeredmouse embryonic fibroblasts.
Activating mutations in K-Ras found in a subset MMTV-Myc inducedtumors with a predicted elevation of Ras signalling.
IHC analysis demonstrates higher expression of B-Catenin in themicroacinar histology of Myc driven tumors.
E2F1 E2F2 E2F3
E2F loss altered tumor latency and Myc proliferative effects on themammary gland.
Akt loss effects tumor development in the MMTV-Neu mouse model.
Using a beta-gal reporter, ß-catenin/TCF-dependent transcriptionwas shown to be elevated in MMTV-Neu mouse mammary glands.
Knocking down Notch in a human breast cancer cell line also impactedlevels of beta-catenin.
Blockade of TGF-beta inhibits mammary tumor metastasis.
Loss of c-Src greatly reduced the occurrence of mammary tumors in theMMTV-PyMT mouse model.
K-ras amplifications observed in large t-antigen mediatedtumorigeneis.
E2F2 E2F3 RB KO
Large T Antigen simulates loss of Rb by leading to deregulatedacitvation of the E2F transcription factors.
MMTV-Wnt1 mammary tumors with mutant p53 exhibited a superior clinicalresponse compared to tumors with wild-type p53.
Comparisons to human breast cancer
Here we have described the genomic analysis of a dataset composed of publicly availablegene expression data for mouse models of breast cancer. These data have been analyzedthrough a variety of mechanisms to ask how mouse models are distinct, what propertiesthey share and how they reflect human breast cancer. These data indicate that great careshould be taken to appropriately choose the mouse model to use and that a genomic andhistological characterization of tumors should be completed followingexperimentation.
In the examination of mouse models in the database, unsupervised hierarchical clusteringrevealed significant heterogeneity both between models and within models and waspronounced in tumor models with a large number of samples. Between model differenceswere fully expected given the unique initiating events causing tumor formation. However,prior studies with relatively few samples for each model did not demonstrate extensivewithin model heterogeneity . In comparison,we have demonstrated extensive heterogeneity within many models. In part this is due todifferences between intrinsic clustering methods  and unsupervised hierarchical clustering. However, given that wehave noted corresponding differences in fold change, GSEA predictions and pathwaysignature probabilities, it is likely that this is a reflection of the number of samplesused in the analysis. As such, this provides an important caution to characterize asufficiently large population of tumors to capture heterogeneity in the analysis.
Given that there is typically a predominant histological pattern associated with a givenGEM tumor type , it is not surprising thatthere is a predominant genomic pattern. Indeed, we noted for many models that histologyis predictive of the genomic subtype. Interestingly, this histological and genomicinteraction is capable of spanning tumor initiating events from different mouse models.Indeed, EMT and spindle-type tumors from diverse models clustered together and weredistinct from the non-EMT samples originating in the same model system. Thus, it is alsocritical for investigators to analyze all tumors from a given model for bothhistological and genomic patterns.
Mouse models were also investigated individually in comparison to the entire datasetusing a variety of methods. This revealed characteristic gene expression patterns at thefold change level, specific GSEA enrichment effects and key pathway signaturedifferences. In many cases, these results correlated with prior studies. For instance,annotation of fold change results predicted that Neu induced tumors upregulated Krox 20which is consistent with previous chromatin immunoprecipitation (ChIP) results. When pathway signatures wereexamined, there were a large number of predictions that could be made for pathways usedin specific GEM tumor models. Importantly, while these pathway signatures havepreviously been validated , the model by modelpathway predictions shown in Table 2 are highly consistentwith previously published tests. For instance, the pathway signatures predicted a highprobability of Src activation in PyMT tumors in the FVB background and recent work hasdemonstrated the necessity for c-Src in PyMT induced tumors . Collectively, for the pathways listed in Table 2, we note agreement between the pathway signature predictions andthe reported genetic crosses. Moreover, the pathway signature predictions are alsoreflective of additional mutations that accumulate in the samples. This was noted in theMyc and TAG induced tumors where the Ras signature was predicted to be elevated,consistent with the large number of Ras activating mutations in these strains[15, 77]. Given thatnumerous published genetic tests are in agreement with the pathway predictions, theremaining cell signaling pathway predictions offer a large number of testablehypotheses. In the future, pathway predictions in the various models should prove to bean important resource for initiating studies into investigating the importance ofvarious signaling pathways in tumor biology.
One of the key aspects of this study was the comparison between mouse models and humanbreast cancer. These data demonstrated similarities and differences between the twogroups and should serve as an important consideration when attempting to extend thecomparison of mouse models to human cancer. Taking into account the clustering data, wereadily noted that the heterogeneity between human breast cancer samples was presentwithin individual mouse models. Despite capturing the genomic diversity of the samples,we noted several samples with no genomic similarity to human breast cancer, includingtumors from strains with other samples that had clear similarity to human breast cancer.This clearly suggests that if conclusions are to be drawn from mouse models of breastcancer, that the mouse samples should be compared and clustered with a variety of humantumors.
In addition to clustering of genomic data, we compared mouse models to human breastcancer through signaling pathway activation predictions. These results showed that forany given group of human breast cancer samples, there was a mouse model with similarpathway activation profiles. Using these results, it is possible to select the mousemodel that most closely represents a group of human breast cancer for the signalingpathways of interest. However, it is critical to consider both clustering and pathwayactivation and to combine these methods to choose the most appropriate model to mimichuman breast cancer. For example, to model HER2+ breast cancer and to study the role ofHER2 in tumor development, research initially used the MMTV-Neu mice . However, the gene expression data reveals that thisstrain does not associate with the HER2+ human samples through genomic clustering.However, mixture modeling indicated that a proportion of HER + human cancersdid group with the MMTV-Neu samples at the level of pathway activation. This indicatesthat in some aspects the mouse model is appropriately related to human HER2+ breastcancer. Further, recent reports demonstrate that a strain of mice with conditionalactivation of Neu under the control of the endogenous promoter which undergoamplification  far more closely recapitulatehuman HER2+ breast cancer . Taken together,these data illustrate the importance of fully characterizing and using all genomicinformation to select the appropriate model for examination.
Recent reports have described the development of serially transplantable human breastcancer samples that are grown in a murine host with clear genomic similarity to theprimary human breast cancer samples  andobviously this is an optimal model for specific studies. However, there is clear utilityfor GEM models, especially with regard to the ability to ask defined genetic questionswith regard to key signaling pathways in tumor biology. As such, the priorcharacterization of mouse and human breast cancer similarities was a criticaldevelopment . The expanded number of samplesand methods of analysis in this report have clearly illustrated additional components ofmouse breast cancer biology that require careful consideration. Indeed, the extent ofgenomic heterogeneity was only appreciated previously for select models [11, 15–17], but our work indicates that this is a generalcharacteristic across the majority of breast cancer model systems. As such, this workunderscores the requirement to fully characterize mouse tumor biology at histologicaland genomic levels before a valid comparison to human breast cancer may be drawn. Thus,we have provided the complete files for all of the comparisons made in this manuscript,from fold change between models to GSEA and pathway predictions, with the intent of thisbeing used as a resource to choose and compare mouse models in breast cancerresearch.
Collectively, our work demonstrates genomic heterogeneity in mouse mammary tumor models.As an additional outcome of this research, we have provided a large scale predictiveresource for each of the mouse models in the database. With heterogeneity driving avariety of relationships between individual mouse mammary tumors and human breastcancer, this work highlights the necessity of fully characterizing mouse tumor biologyat molecular, histological and genomic levels before a valid comparison to human breastcancer may be drawn.
1, 2, 3,6, 7, 11, 12 are available for download at:https://www.msu.edu/~andrech1/BCR_Supplemental/BCR_Supplemental.html. Theresults of each analysis are provided as links to zipped folders as described below andare numbered according to their reference in the manuscript. Clicking on a link willbegin the download of the zipped material.
epithelial to mesenchymal transition
Encyclopedia of DNA Elements
gene annotation tool to help explainrelationships
genetically engineered mice
gene set enrichment analysis
human epidermal growth factor receptor 2
mouse mammary tumor virus
principle components analysis
polyoma middle T.
significance analysis ofmicroarrays
large T antigen
the citric acid cycle
The Cancer GenomeAtlas
tumor necrosis factor
transcription factor database.
NIH/NCI R01 (1R01CA160514) and Susan G. Komen Career Catalyst Award (KG110510).
EA is supported by grants from NIH/NCI (1R01CA160514) and Susan G. Komen CareerCatalyst Award (KG110510).
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