- Research article
- Open Access
A genomic analysis of mouse models of breast cancer reveals molecular features ofmouse models and relationships to human breast cancer
Breast Cancer Researchvolume 16, Article number: R59 (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 .
We assembled a database containing 1,172 samples from mouse mammary tumor models,cell types and normal mammary gland. The major mouse models and descriptions arelisted in Table 1. Within a number of these models,variants exist with different alleles, promoters, and genetic backgrounds. Inassembling the database, we measured the non-biological variance between geneexpression studies and batch correction with principle components analysis (PCA)(Additional file 5A-D). PCA demonstrated that normalizationsuccessfully removed artificial variance between datasets (Additional file 5B,D). As a control, we confirmed batch correction utilizingNeu-initiated tumors spanning the Affymetrix and Agilent platforms from severalstudies. Prior to normalization (Additional file 5E) PCAdemonstrated that Neu tumors varied by platform. After correction, Neu tumorsclustered together in PCA, demonstrating that artifactual variance has been removed(Additional file 5F). With platform and batch effectseliminated, we began to explore relationships in the mouse model database.
Gene expression heterogeneity in mouse models
Using unsupervised hierarchical clustering, we examined mouse mammary tumorsinitiated by various oncogenes. Unsupervised hierarchical clustering generated fourmajor clusters (Figure 1A). We observed remarkablevariability in gene expression profiles, including within model heterogeneity. Forexample, Myc initiated tumors span each of the major clusters in the dendrogram. Incontrast, some models show uniformity in gene expression from tumor to tumor,including Ras initiated tumors that ordered into a single cluster. Interestingly,there was significant interrelatedness between tumor models initiated with differentoncogenes. Annotations for individual tumors revealed that similarities in tumorhistology correlated with relationships in gene expression profiles. For example,MMTV-Myc, MMTV-Met and a subset of 7,12-dimethylbenz[a]anthracene (DMBA) inducedtumors of the adenosquamous histology shared gene expression profiles. These datareveal mouse models with various levels of heterogeneity and illustrate some of thetumor phenotypes that drive relationships between different 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
Given that unique initiating events in the tumor models should cause characteristicresponses associated with the tumor initiating event, we used SAM to identify genessignificantly altered within each model compared to all other models (Additional file2). Fold change differences were also calculated betweenthe tumors within a model and normal mammary glands in the corresponding geneticbackground (Additional file 3). As an example, wedetermined fold change gene expression differences for Neu initiated tumors(Figure 2A). Collectively, SAM analysis provided acollection of genes that are differentially expressed in each model.
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.
We also annotated fold change differences between each model using gene ontologies(Additional files 2, 3). As anexample of the utility of the method, we examined the similarities and differences ingene ontologies in the Neu and TAG models (Figure 2C).Both Neu and TAG tumors featured biological processes, metabolism and nucleicacid-related metabolism as major ontological categories. Key differences included Neutumors with genes related to transport, ion transport and biosynthesis, categoriesnot found with TAG gene expression changes. TAG tumors had major ontologiesrepresenting genes involved in cell cycle, cell organization, cytoskeletonorganization and biogenesis, and cell organization and biogenesis. To expand upongene ontology results we compared each model to all other models and separately tonormal mammary gland using GSEA (Additional file 11). Thisanalysis predicted unique features for all models including specific information onmetabolism, microenvironment, metastasis and possible pathway activation(Figure 3). For example, TAG tumors had down regulationof genes significantly enriched for the citric acid cycle TCA) (Figure 3A). Wnt tumors were predicted to have upregulation of tumorangiogenesis (Figure 3B). Not surprisingly, polyoma middleT (PyMT) tumors show enrichment for gene sets that predict metastasis(Figure 3C). Finally, GSEA results predicted that p53mutant tumors may have increased TNF signaling activity (Figure 3D). Together, these results provide a catalogue of possible importantfeatures corresponding to the transcriptional outcomes of an initiating oncogeneevent.
To expand the predictive analysis, we utilized a gene signature approach to predictpathway activation across mouse mammary tumors. The pathway prediction relationshipsbetween the various models were organized with unsupervised hierarchical clustering(Figure 4). Using this approach, we noted a largedegree of heterogeneity within models. Myc tumors showed extensive variation inpathway activation profiles, spanning the spectrum of clusters. To understand betterthe heterogeneity and pathway activity within each model, we viewed the pathwaypredictions on a model-by-model basis (Additional file 12). For example, in PyMT induced tumors, there is a significant differencein predicted pathway activity between tumors from a FVB and AKXD genetic background(Additional file 13). Myc induced tumors with an EMT orsquamous histology had distinct predicted pathway activities relative to tumors witha papillary or microacinar histology (Additional file 14).In Neu-induced tumors, we observed a major difference in predicted pathway activitybetween Neu tumors using the MMTV promoter and a Tet-on system to drive oncogeneexpression (Additional file 15). Taken together, thesedata demonstrate that tumor type, genetic background, and promoter result in keydifferences in pathway activity.
To validate and illustrate the utility of pathway activation predictions fordeveloping hypotheses about pathways that function in tumor progression, weidentified models with clear pathway activity predictions. Previous genetic studiesthat correlate with these predictions are noted (Table 2).Demonstrating the validity of the gene signatures, we observe a large degree ofagreement between pathways with predicted activity and results from previousinvestigations.
Comparisons to human breast cancer
With identification of pathways that function in tumor progression in mouse models,it is important to understand whether the given model is reflective of human breastcancer. To this end, we combined datasets for human breast cancer and the mousemammary tumors in our database, removing both batch and platform effects (Additionalfile 16). To investigate the relationships between themouse mammary tumors and human breast tumors, we used unsupervised hierarchicalclustering. We identified a large number of mouse mammary tumor models that hadsimilarities in gene expression profiles to human breast cancer (Figure 5). Importantly, Myc and Met induced tumors both recapitulate theheterogeneity observed in human breast cancer. Using histological annotations,specific relationships between Myc tumor types and human breast cancer subtypes wereobserved (Additional file 17). For example, Myc tumorswith an EMT histology clustered together with human claudin low breast cancer.Extending this to the cluster of tumors predicted to have mesenchymal gene expressionfeatures (Figure 1C), we observed that a large majority ofthese tumors also clustered with claudin low breast cancer. Importantly, furtherinvestigation of these tumors matched marker expression for claudin low tumors(Additional file 18A-K). Together these data demonstratedthat there are mouse models that share human breast cancer heterogeneity withindividual tumor types that are closely related to subsets of human breast cancer atthe level of gene expression.
In addition to comparing mouse mammary tumors and human breast cancer with geneexpression, we tested relationships using pathway activation predictions. Using amixture modeling approach, we clustered human breast cancer into ten different groupsbased on pathway activation profiles (Figure 6). The piechart above each heatmap shows the spectrum of the intrinsically annotated samples ineach group. No single group was made up of one intrinsic subtype, illustrating theheterogeneity of pathway activation within and between intrinsic subtypes of breastcancer. After groups of human tumors were identified, the probability that anindividual mouse mammary tumor belonged to a group of human breast cancer wascalculated using the pathway activation profile of the mouse mammary tumor sample.Observing these probabilities with a heatmap, we noted that no single group of humanbreast cancer was modeled by a single mouse mammary tumor type at the pathway level.Instead, for each group of human breast cancer, multiple mouse models showed similarpredicted pathway activation profiles. Further, these results demonstrated that mousemodel relationships to human breast cancer extended beyond the initiating oncogene.For example, mouse tumors initiated by Myc overexpression contained several differenttumor types, each modeling a different group of human breast cancer including thosegroups that have lower predicted Myc activity. Moreover, Neu initiated tumors usingan inducible promoter frequently model a single group of human breast cancer(Additional file 19), while other Neu models have diversepathway activation profiles leading to relationships with several different groups ofhuman breast cancer. These results considered together highlight the similarity anddifferences between mouse models and human breast cancers.
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.
Prat A, Parker JS, Karginova O, Fan C, Livasy C, Herschkowitz JI, He X, Perou CM: Phenotypic and molecular characterization of the claudin-low intrinsic subtype ofbreast cancer. Breast Cancer Res. 2010, 12: R68-10.1186/bcr2635.
Gatza ML, Lucas JE, Barry WT, Kim JW, Wang Q, Crawford MD, Datto MB, Kelley M, Mathey-Prevot B, Potti A, Nevins JR: A pathway-based classification of human breast cancer. Proc Natl Acad Sci U S A. 2010, 107: 6994-6999. 10.1073/pnas.0912708107.
Callahan R, Smith GH: MMTV-induced mammary tumorigenesis: gene discovery, progression to malignancy andcellular pathways. Oncogene. 2000, 19: 992-1001. 10.1038/sj.onc.1203276.
Nusse R, Varmus HE: Many tumors induced by the mouse mammary tumor virus contain a provirus integratedin the same region of the host genome. Cell. 1982, 31: 99-109. 10.1016/0092-8674(82)90409-3.
Slamon DJ, Clark GM, Wong SG, Levin WJ, Ullrich A, McGuire WL: Human breast cancer: correlation of relapse and survival with amplification of theHER-2/neu oncogene. Science. 1987, 235: 177-182. 10.1126/science.3798106.
Slamon DJ, Godolphin W, Jones LA, Holt JA, Wong SG, Keith DE, Levin WJ, Stuart SG, Udove J, Ullrich A, Press MF: Studies of the HER-2/neu proto-oncogene in human breast and ovarian cancer. Science. 1989, 244: 707-712. 10.1126/science.2470152.
Muller WJ, Sinn E, Pattengale PK, Wallace R, Leder P: Single-step induction of mammary adenocarcinoma in transgenic mice bearing theactivated c-neu oncogene. Cell. 1988, 54: 105-115. 10.1016/0092-8674(88)90184-5.
Andrechek ER, Hardy WR, Siegel PM, Rudnicki MA, Cardiff RD, Muller WJ: Amplification of the neu/erbB-2 oncogene in a mouse model of mammarytumorigenesis. Proc Natl Acad Sci U S A. 2000, 97: 3444-3449. 10.1073/pnas.97.7.3444.
D'Cruz CM, Gunther EJ, Boxer RB, Hartman JL, Sintasath L, Moody SE, Cox JD, Ha SI, Belka GK, Golant A, Cardiff RD, Chodosh LA: c-MYC induces mammary tumorigenesis by means of a preferred pathway involvingspontaneous Kras2 mutations. Nat Med. 2001, 7: 235-239. 10.1038/84691.
Fluck MM, Schaffhausen BS: Lessons in signaling and tumorigenesis from polyomavirus middle T antigen. Microbiol Mol Biol Rev . 2009, 73: 542-563. 10.1128/MMBR.00009-09. Table of Contents
Herschkowitz JI, Zhao W, Zhang M, Usary J, Murrow G, Edwards D, Knezevic J, Greene SB, Darr D, Troester MA, Hilsenbeck SG, Medina D, Perou CM, Rosen JM: Comparative oncogenomics identifies breast tumors enriched in functionaltumor-initiating cells. Proc Natl Acad Sci U S A. 2011, 109: 2778-2783.
Hunter KW, Broman KW, Voyer TL, Lukes L, Cozma D, Debies MT, Rouse J, Welch DR: Predisposition to efficient mammary tumor metastatic progression is linked to thebreast cancer metastasis suppressor gene Brms1. Cancer Res. 2001, 61: 8866-8872.
Andrechek ER, Laing MA, Girgis-Gabardo AA, Siegel PM, Cardiff RD, Muller WJ: Gene expression profiling of neu-induced mammary tumors from transgenic micereveals genetic and morphological similarities to ErbB2-expressing human breastcancers. Cancer Res. 2003, 63: 4920-4926.
Rosner A, Miyoshi K, Landesman-Bollag E, Xu X, Seldin DC, Moser AR, MacLeod CL, Shyamala G, Gillgrass AE, Cardiff RD: Pathway pathology: histological differences between ErbB/Ras and Wnt pathwaytransgenic mammary tumors. Am J Pathol. 2002, 161: 1087-1097. 10.1016/S0002-9440(10)64269-1.
Andrechek ER, Cardiff RD, Chang JT, Gatza ML, Acharya CR, Potti A, Nevins JR: Genetic heterogeneity of Myc-induced mammary tumors reflecting diverse phenotypesincluding metastatic potential. Proc Natl Acad Sci U S A. 2009, 106: 16387-16392. 10.1073/pnas.0901250106.
Ponzo MG, Lesurf R, Petkiewicz S, O'Malley FP, Pinnaduwage D, Andrulis IL, Bull SB, Chughtai N, Zuo D, Souleimanova M, Germain D, Omeroglu A, Cardiff RD, Hallett M, Park M: Met induces mammary tumors with diverse histologies and is associated with pooroutcome and human basal breast cancer. Proc Natl Acad Sci U S A. 2009, 106: 12903-12908. 10.1073/pnas.0810402106.
Knight JF, Lesurf R, Zhao H, Pinnaduwage D, Davis RR, Saleh SM, Zuo D, Naujokas MA, Chughtai N, Herschkowitz JI, Prat A, Mulligan AM, Muller WJ, Cardiff RD, Gregg JP, Andrulis IL, Hallett MT, Park M: Met synergizes with p53 loss to induce mammary tumors that possess features ofclaudin-low breast cancer. Proc Natl Acad Sci U S A. 2013, 110: E1301-E1310. 10.1073/pnas.1210353110.
Herschkowitz JI, Simin K, Weigman VJ, Mikaelian I, Usary J, Hu Z, Rasmussen KE, Jones LP, Assefnia S, Chandrasekharan S, Backlund MG, Yin Y, Khramtsov AI, Bastein R, Quackenbush J, Glazer RI, Brown PH, Green JE, Kopelovich L, Furth PA, Palazzo JP, Olopade OI, Bernard PS, Churchill GA, Van Dyke T, Perou CM: Identification of conserved gene expression features between murine mammarycarcinoma models and human breast tumors. Genome Biol. 2007, 8: R76-10.1186/gb-2007-8-5-r76.
Hollern DP, Yuwanita I, Andrechek ER: A mouse model with T58A mutations in Myc reduces the dependence on KRas mutationsand has similarities to claudin-low human breast cancer. Oncogene. 2012, 32: 1296-1304.
Fujiwara K, Yuwanita I, Hollern DP, Andrechek ER: Prediction and genetic demonstration of a role for activator E2Fs in myc-inducedtumors. Cancer Res. 2011, 71: 1924-1932. 10.1158/0008-5472.CAN-10-2386.
Schade B, Lesurf R, Sanguin-Gendreau V, Bui T, Deblois G, O'Toole SA, Millar EK, Zardawi SJ, Lopez-Knowles E, Sutherland RL, Giguère V, Kahn M, Hallett M, Muller WJ: beta-catenin signaling is a critical event in ErbB2-mediated mammary tumorprogression. Cancer Res. 2013, 73: 4474-4487. 10.1158/0008-5472.CAN-12-3925.
Dourdin N, Schade B, Lesurf R, Hallett M, Munn RJ, Cardiff RD, Muller WJ: Phosphatase and tensin homologue deleted on chromosome 10 deficiency acceleratestumor induction in a mouse model of ErbB-2 mammary tumorigenesis. Cancer Res. 2008, 68: 2122-2131. 10.1158/0008-5472.CAN-07-5727.
Carvalho CM, Chang J, Lucas JE, Nevins JR, Wang Q, West M: High-dimensional sparse factor modeling: applications in gene expressiongenomics. J Am Stat Assoc. 2008, 103: 1438-1456. 10.1198/016214508000000869.
Johnson WE, Li C, Rabinovic A: Adjusting batch effects in microarray expression data using empirical Bayesmethods. Biostatistics. 2007, 8: 118-127. 10.1093/biostatistics/kxj037.
Tusher VG, Tibshirani R, Chu G: Significance analysis of microarrays applied to the ionizing radiationresponse. Proc Natl Acad Sci U S A. 2001, 98: 5116-5121. 10.1073/pnas.091062498.
Lim E, Wu D, Pal B, Bouras T, Asselin-Labat ML, Vaillant F, Yagita H, Lindeman GJ, Smyth GK, Visvader JE: Transcriptome analyses of mouse and human mammary cell subpopulations revealmultiple conserved genes and pathways. Breast Cancer Res. 2010, 12: R21-10.1186/bcr2560.
Bild AH, Yao G, Chang JT, Wang Q, Potti A, Chasse D, Joshi MB, Harpole D, Lancaster JM, Berchuck A, Olson JA, Marks JR, Dressman HK, West M, Nevins JR: Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature. 2006, 439: 353-357. 10.1038/nature04296.
Wertheim GB, Yang TW, Pan TC, Ramne A, Liu Z, Gardner HP, Dugan KD, Kristel P, Kreike B, van de Vijver MJ, Cardiff RD, Reynolds C, Chodosh LA: The Snf1-related kinase, Hunk, is essential for mammary tumor metastasis. Proc Natl Acad Sci U S A. 2009, 106: 15855-15860. 10.1073/pnas.0906993106.
Sandgren EP, Schroeder JA, Qui TH, Palmiter RD, Brinster RL, Lee DC: Inhibition of mammary gland involution is associated with transforming growthfactor alpha but not c-myc-induced tumorigenesis in transgenic mice. Cancer Res. 1995, 55: 3915-3927.
Leung JY, Andrechek ER, Cardiff RD, Nevins JR: Heterogeneity in MYC-induced mammary tumors contributes to escape from oncogenedependence. Oncogene. 2012, 31: 2545-2554. 10.1038/onc.2011.433.
Zhu M, Yi M, Kim CH, Deng C, Li Y, Medina D, Stephens RM, Green JE: Integrated miRNA and mRNA expression profiling of mouse mammary tumor modelsidentifies miRNA signatures associated with mammary tumor lineage. Genome Biol. 2011, 12: R77-10.1186/gb-2011-12-8-r77.
Ranger JJ, Levy DE, Shahalizadeh S, Hallett M, Muller WJ: Identification of a Stat3-dependent transcription regulatory network involved inmetastatic progression. Cancer Res. 2009, 69: 6823-6830. 10.1158/0008-5472.CAN-09-1684.
Guy CT, Webster MA, Schaller M, Parsons TJ, Cardiff RD, Muller WJ: Expression of the neu protooncogene in the mammary epithelium of transgenic miceinduces metastatic disease. Proc Natl Acad Sci U S A. 1992, 89: 10578-10582. 10.1073/pnas.89.22.10578.
Schade B, Rao T, Dourdin N, Lesurf R, Hallett M, Cardiff RD, Muller WJ: PTEN deficiency in a luminal ErbB-2 mouse model results in dramatic accelerationof mammary tumorigenesis and metastasis. J Biol Chem. 2009, 284: 19018-19026. 10.1074/jbc.M109.018937.
Schade B, Lam SH, Cernea D, Sanguin-Gendreau V, Cardiff RD, Jung BL, Hallett M, Muller WJ: Distinct ErbB-2 coupled signaling pathways promote mammary tumors with uniquepathologic and transcriptional profiles. Cancer Res. 2007, 67: 7579-7588. 10.1158/0008-5472.CAN-06-4724.
Ursini-Siegel J, Cory S, Zuo D, Hardy WR, Rexhepaj E, Lam S, Schade B, Jirstrom K, Bjur E, Piccirillo CA, Denardo D, Coussens LM, Brennan DJ, Gallagher WM, Park M, Pawson T, Hallett M, Muller WJ: Receptor tyrosine kinase signaling favors a protumorigenic state in breast cancercells by inhibiting the adaptive immune response. Cancer Res. 2010, 70: 7776-7787. 10.1158/0008-5472.CAN-10-2229.
Schoenherr RM, Kelly-Spratt KS, Lin C, Whiteaker JR, Liu T, Holzman T, Coleman I, Feng LC, Lorentzen TD, Krasnoselsky AL, Wang P, Liu Y, Gurley KE, Amon LM, Schepmoes AA, Moore RJ, Camp DG, Chodosh LA, Smith RD, Nelson PS, McIntosh MW, Kemp CJ, Paulovich AG: Proteome and transcriptome profiles of a Her2/Neu-driven mouse model of breastcancer. Proteomics Clin Appl. 2011, 5: 179-188. 10.1002/prca.201000037.
Guy CT, Cardiff RD, Muller WJ: Induction of mammary tumors by expression of polyomavirus middle T oncogene: atransgenic mouse model for metastatic disease. Mol Cell Biol. 1992, 12: 954-961.
Hu Y, Wu G, Rusch M, Lukes L, Buetow KH, Zhang J, Hunter KW: Integrated cross-species transcriptional network analysis of metastaticsusceptibility. Proc Natl Acad Sci U S A. 2012, 109: 3184-3189. 10.1073/pnas.1117872109.
Bu W, Chen J, Morrison GD, Huang S, Creighton CJ, Huang J, Chamness GC, Hilsenbeck SG, Roop DR, Leavitt AD, Li Y: Keratin 6a marks mammary bipotential progenitor cells that can give rise to aunique tumor model resembling human normal-like breast cancer. Oncogene. 2011, 30: 4399-4409. 10.1038/onc.2011.147.
Flowers M, Schroeder JA, Borowsky AD, Besselsen DG, Thomson CA, Pandey R, Thompson PA: Pilot study on the effects of dietary conjugated linoleic acid on tumorigenesisand gene expression in PyMT transgenic mice. Carcinogenesis. 2010, 31: 1642-1649. 10.1093/carcin/bgq148.
Klein A, Wessel R, Graessmann M, Jurgens M, Petersen I, Schmutzler R, Niederacher D, Arnold N, Meindl A, Scherneck S, Seitz S, Graessmann A: Comparison of gene expression data from human and mouse breast cancers:identification of a conserved breast tumor gene set. Int J Cancer. 2007, 121: 683-688. 10.1002/ijc.22630.
Maroulakou IG, Anver M, Garrett L, Green JE: Prostate and mammary adenocarcinoma in transgenic mice carrying a rat C3(1) simianvirus 40 large tumor antigen fusion gene. Proc Natl Acad Sci U S A. 1994, 91: 11236-11240. 10.1073/pnas.91.23.11236.
Kretschmer C, Sterner-Kock A, Siedentopf F, Schoenegg W, Schlag PM, Kemmner W: Identification of early molecular markers for breast cancer. Mol Cancer. 2011, 10: 15-10.1186/1476-4598-10-15.
Zhang M, Behbod F, Atkinson RL, Landis MD, Kittrell F, Edwards D, Medina D, Tsimelzon A, Hilsenbeck S, Green JE, Michalowska AM, Rosen JM: Identification of tumor-initiating cells in a p53-null mouse model of breastcancer. Cancer Res. 2008, 68: 4674-4682. 10.1158/0008-5472.CAN-07-6353.
Backlund MG, Trasti SL, Backlund DC, Cressman VL, Godfrey V, Koller BH: Impact of ionizing radiation and genetic background on mammary tumorigenesis inp53-deficient mice. Cancer Res. 2001, 61: 6577-6582.
Li Z, Tognon CE, Godinho FJ, Yasaitis L, Hock H, Herschkowitz JI, Lannon CL, Cho E, Kim SJ, Bronson RT, Perou CM, Sorensen PH, Orkin SH: ETV6-NTRK3 fusion oncogene initiates breast cancer from committed mammaryprogenitors via activation of AP1 complex. Cancer Cell. 2007, 12: 542-558. 10.1016/j.ccr.2007.11.012.
Xu X, Wagner KU, Larson D, Weaver Z, Li C, Ried T, Hennighausen L, Wynshaw-Boris A, Deng CX: Conditional mutation of Brca1 in mammary epithelial cells results in bluntedductal morphogenesis and tumour formation. Nat Genet. 1999, 22: 37-43. 10.1038/8743.
Cho RW, Wang X, Diehn M, Shedden K, Chen GY, Sherlock G, Gurney A, Lewicki J, Clarke MF: Isolation and molecular characterization of cancer stem cells in MMTV-Wnt-1 murinebreast tumors. Stem Cells. 2008, 26: 364-371. 10.1634/stemcells.2007-0440.
Tsukamoto AS, Grosschedl R, Guzman RC, Parslow T, Varmus HE: Expression of the int-1 gene in transgenic mice is associated with mammary glandhyperplasia and adenocarcinomas in male and female mice. Cell. 1988, 55: 619-625. 10.1016/0092-8674(88)90220-6.
Pond AC, Herschkowitz JI, Schwertfeger KL, Welm B, Zhang Y, York B, Cardiff RD, Hilsenbeck S, Perou CM, Creighton CJ, Lloyd RE, Rosen JM: Fibroblast growth factor receptor signaling dramatically accelerates tumorigenesisand enhances oncoprotein translation in the mouse mammary tumor virus-Wnt-1 mousemodel of breast cancer. Cancer Res. 2010, 70: 4868-4879. 10.1158/0008-5472.CAN-09-4404.
Franks SE, Campbell CI, Barnett EF, Siwicky MD, Livingstone J, Cory S, Moorehead RA: Transgenic IGF-IR overexpression induces mammary tumors with basal-likecharacteristics, whereas IGF-IR-independent mammary tumors express a claudin-lowgene signature. Oncogene. 2011, 31: 3298-3309.
Liu S, Umezu-Goto M, Murph M, Lu Y, Liu W, Zhang F, Yu S, Stephens LC, Cui X, Murrow G, Coombes K, Muller W, Hung MC, Perou CM, Lee AV, Fang X, Mills GB: Expression of autotaxin and lysophosphatidic acid receptors increases mammarytumorigenesis, invasion, and metastases. Cancer Cell. 2009, 15: 539-550. 10.1016/j.ccr.2009.03.027.
Bultman SJ, Herschkowitz JI, Godfrey V, Gebuhr TC, Yaniv M, Perou CM, Magnuson T: Characterization of mammary tumors from Brg1 heterozygous mice. Oncogene. 2008, 27: 460-468. 10.1038/sj.onc.1210664.
Yin Y, Bai R, Russell RG, Beildeck ME, Xie Z, Kopelovich L, Glazer RI: Characterization of medroxyprogesterone and DMBA-induced multilineage mammarytumors by gene expression profiling. Mol Carcinog. 2005, 44: 42-50. 10.1002/mc.20119.
Gallahan D, Jhappan C, Robinson G, Hennighausen L, Sharp R, Kordon E, Callahan R, Merlino G, Smith GH: Expression of a truncated Int3 gene in developing secretory mammary epitheliumspecifically retards lobular differentiation resulting in tumorigenesis. Cancer Res. 1996, 56: 1775-1785.
Jiang Z, Deng T, Jones R, Li H, Herschkowitz JI, Liu JC, Weigman VJ, Tsao MS, Lane TF, Perou CM, Zacksenhaus E: Rb deletion in mouse mammary progenitors induces luminal-B or basal-like/EMT tumorsubtypes depending on p53 status. J Clin Invest. 2010, 120: 3296-3309. 10.1172/JCI41490.
Kuraguchi M, Ohene-Baah NY, Sonkin D, Bronson RT, Kucherlapati R: Genetic mechanisms in Apc-mediated mammary tumorigenesis. PLoS Genet. 2009, 5: e1000367-10.1371/journal.pgen.1000367.
Chan SR, Vermi W, Luo J, Lucini L, Rickert C, Fowler AM, Lonardi S, Arthur C, Young LJ, Levy DE, Welch MJ, Cardiff RD, Schreiber RD: STAT1-deficient mice spontaneously develop estrogen receptor alpha-positiveluminal mammary carcinomas. Breast Cancer Res. 2012, 14: R16-10.1186/bcr3100.
Pollock CB, Yin Y, Yuan H, Zeng X, King S, Li X, Kopelovich L, Albanese C, Glazer RI: PPARdelta activation acts cooperatively with 3-phosphoinositide-dependent proteinkinase-1 to enhance mammary tumorigenesis. PLoS One. 2011, 6: e16215-10.1371/journal.pone.0016215.
McCarthy A, Savage K, Gabriel A, Naceur C, Reis-Filho JS, Ashworth A: A mouse model of basal-like breast carcinoma with metaplastic elements. J Pathol. 2007, 211: 389-398. 10.1002/path.2124.
Kumar R, Medina D, Sukumar S: Activation of H-ras oncogenes in preneoplastic mouse mammary tissues. Oncogene. 1990, 5: 1271-1277.
Kim S, Roopra A, Alexander CM: A phenotypic mouse model of basaloid breast tumors. PLoS One. 2012, 7: e30979-10.1371/journal.pone.0030979.
Jin W, Yun C, Hobbie A, Martin MJ, Sorensen PH, Kim SJ: Cellular transformation and activation of the phosphoinositide-3-kinase-Aktcascade by the ETV6-NTRK3 chimeric tyrosine kinase requires c-Src. Cancer Res. 2007, 67: 3192-3200. 10.1158/0008-5472.CAN-06-3526.
Maroulakou IG, Oemler W, Naber SP, Tsichlis PN: Akt1 ablation inhibits, whereas Akt2 ablation accelerates, the development ofmammary adenocarcinomas in mouse mammary tumor virus (MMTV)-ErbB2/neu andMMTV-polyoma middle T transgenic mice. Cancer Res. 2007, 67: 167-177. 10.1158/0008-5472.CAN-06-3782.
Khalil S, Tan GA, Giri DD, Zhou XK, Howe LR: Activation status of Wnt/ss-catenin signaling in normal and neoplastic breasttissues: relationship to HER2/neu expression in human and mouse. PLoS One. 2012, 7: e33421-10.1371/journal.pone.0033421.
Wang J, Fu L, Gu F, Ma Y: Notch1 is involved in migration and invasion of human breast cancer cells. Oncol Rep. 2011, 26: 1295-1303.
Muraoka RS, Dumont N, Ritter CA, Dugger TC, Brantley DM, Chen J, Easterly E, Roebuck LR, Ryan S, Gotwals PJ, Koteliansky V, Arteaga CL: Blockade of TGF-beta inhibits mammary tumor cell viability, migration, andmetastases. J Clin Invest. 2002, 109: 1551-1559. 10.1172/JCI0215234.
Guy CT, Muthuswamy SK, Cardiff RD, Soriano P, Muller WJ: Activation of the c-Src tyrosine kinase is required for the induction of mammarytumors in transgenic mice. Genes Dev. 1994, 8: 23-32. 10.1101/gad.8.1.23.
Liu ML, Von Lintig FC, Liyanage M, Shibata MA, Jorcyk CL, Ried T, Boss GR, Green JE: Amplification of Ki-ras and elevation of MAP kinase activity during mammary tumorprogression in C3(1)/SV40 Tag transgenic mice. Oncogene. 1998, 17: 2403-2411. 10.1038/sj.onc.1202456.
Taneja P, Zhu S, Maglic D, Fry EA, Kendig RD, Inoue K: Transgenic and knockout mice models to reveal the functions of tumor suppressorgenes. Clin Med Insights Oncol. 2011, 5: 235-257.
Jackson JG, Pant V, Li Q, Chang LL, Quintas-Cardama A, Garza D, Tavana O, Yang P, Manshouri T, Li Y, El-Naggar AK, Lozano G: p53-mediated senescence impairs the apoptotic response to chemotherapy andclinical outcome in breast cancer. Cancer Cell. 2012, 21: 793-806. 10.1016/j.ccr.2012.04.027.
Perou CM, Jeffrey SS, van de Rijn M, Rees CA, Eisen MB, Ross DT, Pergamenschikov A, Williams CF, Zhu SX, Lee JC, Lashkari D, Shalon D, Brown PO, Botstein D: Distinctive gene expression patterns in human mammary epithelial cells and breastcancers. Proc Natl Acad Sci U S A. 1999, 96: 9212-9217. 10.1073/pnas.96.16.9212.
Cardiff RD, Anver MR, Gusterson BA, Hennighausen L, Jensen RA, Merino MJ, Rehm S, Russo J, Tavassoli FA, Wakefield LM, Ward JM, Green JE: The mammary pathology of genetically engineered mice: the consensus report andrecommendations from the Annapolis meeting. Oncogene. 2000, 19: 968-988. 10.1038/sj.onc.1203277.
Dillon RL, Brown ST, Ling C, Shioda T, Muller WJ: An EGR2/CITED1 transcription factor complex and the 14-3-3sigma tumor suppressorare involved in regulating ErbB2 expression in a transgenic-mouse model of humanbreast cancer. Mol Cell Biol. 2007, 27: 8648-8657. 10.1128/MCB.00866-07.
DeRose YS, Wang G, Lin YC, Bernard PS, Buys SS, Ebbert MT, Factor R, Matsen C, Milash BA, Nelson E, Neumayer L, Randall RL, Stijleman IJ, Welm BE, Welm AL: Tumor grafts derived from women with breast cancer authentically reflect tumorpathology, growth, metastasis and disease outcomes. Nat Med. 2011, 17: 1514-1520. 10.1038/nm.2454.
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).
The authors declare that they have no competing interests.
DH and EA conceived of the study, and participated in its design and coordination andhelped to draft the manuscript. DH performed the experiments in this study. DH and EAinterpreted the data. Both authors read and approved the final manuscript.
Electronic supplementary material
Additional file 5: Figure S1: Removal of Batch Effects from Affymetrix Datasets. (A) Affymetrix datasetscolor coded according to the study of origin in a principle components analysisplot prior to BFRM batch effect correction. (B) Affymetrix datasets color codedaccording to the study of origin in a principle components analysis plot afterBFRM batch effect correction. (C) Affymetrix datasets are color coded togetherin blue after BFRM batch effect correction. The various Agilent gene expressiondatasets are color-coded and plotted along with Affymetrix data on the threeprinciple components to illustrate platform and batch variance. (D) Agilent andAffymetrix color-coded data plotted after COMBAT removed batch and platformtechnical variance. (E) Neu-induced tumors are color coded in blue and allother tumors are in green, illustrating variance between similar tumor types onthe basis of platform and batch artifacts. (F) Neu-induced tumors are colorcoded in blue and all other tumors are in green illustrating mediation of batchand platform effects. (TIFF 712 KB)
Additional file 8: Figure S2: Gene set enrichment analysis for mouse mammary tumors in the black color-codedcluster. (A) A gene set for down regulated genes in mesenchymal breast canceris significantly enriched (P <.0001) and down regulated inthe black cluster (cluster4) of tumors. (B) A gene set for Zeb1 target genes issignificantly enriched (P = .005) for low expression forthe tumors in the black cluster. (C) A gene set for genes highly expressed inmammary stem cells is significantly enriched (P = .016)and upregulated in tumors from cluster 4 (black). (D) A gene set for genes thatare down regulated in mammary stem cells is significantly enriched(P <.0001) and also down regulated in the cluster 4 (black)tumors. (TIFF 1 MB)
Additional file 9: Figure S3: Tumors that were classified for mesenchymal histology cluster into the blackcluster. Highlighting prior histological annotations for mesenchymal orEMT-like tumors across the Myc, IGF-IR, DMBA, and p53 mutant models show that alarge majority of these tumors cluster together in the black cluster. (TIFF 3 MB)
Additional file 10: Figure S4: Gene set enrichment analysis for mammary cell types across major clusters ofmouse mammary tumors. GSEA for tumors in blue cluster compared to all otherclusters show significant enrichment for a mammary luminal progenitor cell geneexpression signature (P = .006). Similarly, tumors fromthe green cluster associate with a mixture of luminal cell gene expressionfeatures, while tumors in the orange cluster are significantly enriched forgene expression features of mature luminal cells(P = .04). Lastly, tumors in the black cluster aresignificantly enriched for gene expression features of mammary stem cells(P = .01). (TIFF 3 MB)
Additional file 13: Figure S5: Unsupervised hierarchical clustering of pathway probabilities for PyMT inducedtumors. The dendrogram across the top illustrates the relationship between PyMTtumor types on the basis of pathway activation profiles. Below the dendrogramblack bars correspond to sample details on the same line, annotating thegenetic background and sample type for each sample. The heatmap shows thepredicted pathway activity according to the probabilities listed on the colorbar below the heatmap. Directly beside the heatmap, a vertical dendrogramillustrates the degree of correlation between pathways across the samples. (TIFF 2 MB)
Additional file 14: Figure S6: Unsupervised hierarchical clustering of pathway probabilities for Myc inducedtumors. The dendrogram across the top illustrates the relationship between Myctumor types on the basis of pathway activation profiles. Below the dendrogramblack bars correspond to sample details on the same line, annotating the tumorhistology (if known), specific form of Myc expression, recurrence status, andadditional modifications. The heatmap shows the predicted pathway activityaccording to the probabilities listed on the color bar below the heatmap.Directly beside the heatmap, a vertical dendrogram illustrates the degree ofcorrelation between pathways across the samples. (TIFF 3 MB)
Additional file 15: Figure S7: Unsupervised hierarchical clustering of pathway probabilities for Neu inducedtumors. The dendrogram across the top illustrates the relationship between Neutumor types on the basis of pathway activation profiles. Below the dendrogramblack bars correspond to sample details on the same line, annotating thespecific form of Neu, and additional modifications. The heatmap shows thepredicted pathway activity according to the probabilities listed on the colorbar below the heatmap. Directly beside the heatmap, a vertical dendrogramillustrates the degree of correlation between pathways across the samples. (TIFF 2 MB)
Additional file 16: Figure S8: Removal of batch effects between mouse and human breast cancer datasets. (A)Mouse (blue) and human (green) Affymetrix data gene expression variance plottedonto three principle components prior to BFRM. (B) Mouse (blue) and human(green) Affymetrix data gene expression variance plotted onto three principlecomponents after BFRM. (C) Human(green) and mouse (blue) Affymetrix data afterBFRM correction put together with mouse Agilent data (red) prior to COMBAT. (D)Human(green) and mouse (blue) Affymetrix data after BFRM correction puttogether with mouse Agilent data (red) after COMBAT artifact correction. (TIFF 464 KB)
Additional file 17: Figure S9: Unsupervised hierarchical clustering of Myc mouse mammary tumors and humanbreast cancer gene expression data. Across the top, the dendrogram illustratesthe relationship between human and mouse tumor samples on the basis of geneexpression profiles. The red bars mark the intrinsic subtype of each humantumor sample according to the annotation on the same line. The blue barscorrespond to the Myc mouse mammary tumor type. Below this, a heatmap shows thegene expression patterns for each sample, with expression values illustratedaccording to the color bar on the right. The dendrogram beside the heatmapshows the correlation between genes based on expression patterns across thesamples in the dataset. (TIFF 2 MB)
Additional file 18: Figure S10: Claudin low marker expression in the black cluster mouse mammary tumors.Claudin low marker expression comparisons for cluster 4 (black) tumors comparedto tumors in all other clusters as defined by Figure 1A. (A-C) Cell adhesion markers that have low expression in claudinlow human tumors are also down regulated in cluster 4 (black tumors),P <.0001. (D-E) Genes that are involved with the immunesystem that are found to be highly expressed in claudin low human tumors arehighly expressed in mouse cluster 4 tumors (black), P <.01for CD79B and P <.0001 for VAV1. (F) Chemokine (C-X-C motif)ligand 12, involved in cell communication and previously shown to be highlyexpressed in claudin low tumors, is upregulated in cluster 4 (black) mousemammary tumors, P <.0001. (G) Fibroblast growth factor 7, anextracellular matrix related factor and previously shown to be highly expressedin claudin low tumors, is upregulated in cluster 4 (black) mouse mammarytumors, P <.0001. (H-J) Cell migration markers previouslyshown to be highly expressed in human claudin low tumors are upregulated inmouse cluster 4(black) tumors, P <.02 for moesin andP <.0001 for integrin α5. (K) Angiogenesis marker,VEGFC, was previously shown to be upregulated in human claudin low tumors andis highly expressed in mouse cluster 4(black) tumors. (TIFF 750 KB)
Additional file 19: Figure S11: Mixture modeling highlighting pathway relationships between human breast cancerand specific models of Neu mediated tumorigenesis. Pie charts above eachheatmap illustrate the distribution of the intrinsic subtype of samples in eachgroup, according to the color-coded legend. The heatmap for groups 1 to 10 showpredicted pathway activity with probabilities corresponding to the color bar atthe bottom of the figure. Below this, blue bars mark the samples correspondingto annotations on the same line. Following the samples down to the heatmapbelow the blue bars, the probability that a specific type of Neu model hassimilar pathway activation profiles is shown for each group. Probabilities forthis heatmap are shown according to the color bar at the bottom of thefigure. (TIFF 3 MB)
Authors’ original submitted files for images
Below are the links to the authors’ original submitted files for images.