The gene expression landscape of breast cancer is shaped by tumor protein p53 status and epithelial-mesenchymal transition

IntroductionGene expression data derived from clinical cancer specimens provide an opportunity to characterize cancer-specific transcriptional programs. Here, we present an analysis delineating a correlation-based gene expression landscape of breast cancer that identifies modules with strong associations to breast cancer-specific and general tumor biology.MethodsModules of highly connected genes were extracted from a gene co-expression network that was constructed based on Pearson correlation, and module activities were then calculated using a pathway activity score. Functional annotations of modules were experimentally validated with an siRNA cell spot microarray system using the KPL-4 breast cancer cell line, and by using gene expression data from functional studies. Modules were derived using gene expression data representing 1,608 breast cancer samples and validated in data sets representing 971 independent breast cancer samples as well as 1,231 samples from other cancer forms.ResultsThe initial co-expression network analysis resulted in the characterization of eight tightly regulated gene modules. Cell cycle genes were divided into two transcriptional programs, and experimental validation using an siRNA screen showed different functional roles for these programs during proliferation. The division of the two programs was found to act as a marker for tumor protein p53 (TP53) gene status in luminal breast cancer, with the two programs being separated only in luminal tumors with functional p53 (encoded by TP53). Moreover, a module containing fibroblast and stroma-related genes was highly expressed in fibroblasts, but was also up-regulated by overexpression of epithelial-mesenchymal transition factors such as transforming growth factor beta 1 (TGF-beta1) and Snail in immortalized human mammary epithelial cells. Strikingly, the stroma transcriptional program related to less malignant tumors for luminal disease and aggressive lymph node positive disease among basal-like tumors.ConclusionsWe have derived a robust gene expression landscape of breast cancer that reflects known subtypes as well as heterogeneity within these subtypes. By applying the modules to TP53-mutated samples we shed light on the biological consequences of non-functional p53 in otherwise low-proliferating luminal breast cancer. Furthermore, as in the case of the stroma module, we show that the biological and clinical interpretation of a set of co-regulated genes is subtype-dependent.

[1]  V. Sukhatme,et al.  Early transcriptional events in cell growth: the Egr family. , 1990, Journal of the American Society of Nephrology : JASN.

[2]  H. Sul,et al.  Understanding adipocyte differentiation. , 1998, Physiological reviews.

[3]  Christian A. Rees,et al.  Molecular portraits of human breast tumours , 2000, Nature.

[4]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[5]  R. Tibshirani,et al.  Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Carsten O. Peterson,et al.  Estrogen receptor status in breast cancer is associated with remarkably distinct gene expression patterns. , 2001, Cancer research.

[7]  Yudong D. He,et al.  Gene expression profiling predicts clinical outcome of breast cancer , 2002, Nature.

[8]  Carsten Peterson,et al.  Expression profiling to predict outcome in breast cancer: the influence of sample selection , 2002, Breast Cancer Research.

[9]  J. Thiery.  Epithelial–mesenchymal transitions in tumour progression , 2002, Nature Reviews Cancer.

[10]  C. Perou,et al.  Molecular portraits and the family tree of cancer , 2002, Nature Genetics.

[11]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[12]  A. Barabasi,et al.  Network biology: understanding the cell's functional organization , 2004, Nature Reviews Genetics.

[13]  S. Boorjian,et al.  Androgen receptor expression is inversely correlated with pathologic tumor stage in bladder cancer. , 2004, Urology.

[14]  David Cameron,et al.  Identification of molecular apocrine breast tumours by microarray analysis , 2005, Oncogene.

[15]  Martin Kuiper,et al.  BiNGO: a Cytoscape plugin to assess overrepresentation of Gene Ontology categories in Biological Networks , 2005, Bioinform..

[16]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[17]  D. Ross,et al.  Basal cytokeratins and their relationship to the cellular origin and functional classification of breast cancer , 2005, Breast Cancer Research.

[18]  P. Hall,et al.  An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[19]  Ju-Hyung Woo,et al.  Increased Expression of Mitotic Checkpoint Genes in Breast Cancer Cells with Chromosomal Instability , 2006, Clinical Cancer Research.

[20]  A. Nobel,et al.  The molecular portraits of breast tumors are conserved across microarray platforms , 2006, BMC Genomics.

[21]  M. J. van de Vijver,et al.  Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. , 2006, Journal of the National Cancer Institute.

[22]  Z. Szallasi,et al.  A signature of chromosomal instability inferred from gene expression profiles predicts clinical outcome in multiple human cancers , 2006, Nature Genetics.

[23]  Robert Tibshirani,et al.  Distinct patterns of DNA copy number alteration are associated with different clinicopathological features and gene‐expression subtypes of breast cancer , 2006, Genes, chromosomes & cancer.

[24]  Marc Buyse,et al.  Gene signature evaluation as a prognostic tool: challenges in the design of the MINDACT trial , 2006, Nature Clinical Practice Oncology.

[25]  Wen-Lin Kuo,et al.  A collection of breast cancer cell lines for the study of functionally distinct cancer subtypes. , 2006, Cancer cell.

[26]  S. Paik.  Development and clinical utility of a 21-gene recurrence score prognostic assay in patients with early breast cancer treated with tamoxifen. , 2007, The oncologist.

[27]  I. Ellis,et al.  An immune response gene expression module identifies a good prognosis subtype in estrogen receptor negative breast cancer , 2007, Genome Biology.

[28]  Gianluca Bontempi,et al.  Biological Processes Associated with Breast Cancer Clinical Outcome Depend on the Molecular Subtypes , 2008, Clinical Cancer Research.

[29]  Carlos Caldas,et al.  A robust classifier of high predictive value to identify good prognosis patients in ER-negative breast cancer , 2008, Breast Cancer Research.

[30]  John M. Maris,et al.  High Myc pathway activity and low stage of neuronal differentiation associate with poor outcome in neuroblastoma , 2008, Proceedings of the National Academy of Sciences.

[31]  Micheline Kirsch-Volders,et al.  Mitotic checkpoints and the maintenance of the chromosome karyotype. , 2008, Mutation research.

[32]  Hideaki Mizuno,et al.  A signature-based method for indexing cell cycle phase distribution from microarray profiles , 2009, BMC Genomics.

[33]  Gema Moreno-Bueno,et al.  Epithelial-mesenchymal transition in breast cancer relates to the basal-like phenotype. , 2008, Cancer research.

[34]  Carlos Caldas,et al.  A comprehensive analysis of prognostic signatures reveals the high predictive capacity of the Proliferation, Immune response and RNA splicing modules in breast cancer , 2008, Breast Cancer Research.

[35]  F. Pépin,et al.  Stromal gene expression predicts clinical outcome in breast cancer , 2008, Nature Medicine.

[36]  A. Nobel,et al.  Supervised risk predictor of breast cancer based on intrinsic subtypes. , 2009, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[37]  Charles M. Perou,et al.  Ki67 Index, HER2 Status, and Prognosis of Patients With Luminal B Breast Cancer , 2009, Journal of the National Cancer Institute.

[38]  Jeffrey M. Rosen,et al.  Residual breast cancers after conventional therapy display mesenchymal as well as tumor-initiating features , 2009, Proceedings of the National Academy of Sciences.

[39]  M. Ringnér,et al.  Genomic subtypes of breast cancer identified by array-comparative genomic hybridization display distinct molecular and clinical characteristics , 2010, Breast Cancer Research.

[40]  A. Balmain,et al.  Systems genetics analysis of cancer susceptibility: from mouse models to humans , 2009, Nature Reviews Genetics.

[41]  Johan Staaf,et al.  Molecular subtypes of breast cancer are associated with characteristic DNA methylation patterns , 2010, Breast Cancer Research.

[42]  Jason I. Herschkowitz,et al.  Phenotypic and molecular characterization of the claudin-low intrinsic subtype of breast cancer , 2010, Breast Cancer Research.

[43]  W. V. van IJcken,et al.  Gene Expression-Based Classification of Non-Small Cell Lung Carcinomas and Survival Prediction , 2010, PloS one.

[44]  David M. Simcha,et al.  Tackling the widespread and critical impact of batch effects in high-throughput data , 2010, Nature Reviews Genetics.

[45]  Johan Staaf,et al.  Identification of subtypes in human epidermal growth factor receptor 2--positive breast cancer reveals a gene signature prognostic of outcome. , 2010, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[46]  J. Pollack,et al.  LYN is a mediator of epithelial-mesenchymal transition and a target of dasatinib in breast cancer. , 2010, Cancer research.

[47]  Kakajan Komurov,et al.  Core epithelial-to-mesenchymal transition interactome gene-expression signature is associated with claudin-low and metaplastic breast cancer subtypes , 2010, Proceedings of the National Academy of Sciences.

[48]  D. Pe’er,et al.  An Integrated Approach to Uncover Drivers of Cancer , 2010, Cell.

[49]  Duane A. Compton,et al.  Proliferation of aneuploid human cells is limited by a p53-dependent mechanism , 2010, The Journal of cell biology.

[50]  A. Ashworth,et al.  Breast cancer molecular profiling with single sample predictors: a retrospective analysis. , 2010, The Lancet. Oncology.

[51]  M. Ringnér,et al.  Gene Expression Profiling–Based Identification of Molecular Subtypes in Stage IV Melanomas with Different Clinical Outcome , 2010, Clinical Cancer Research.

[52]  A. Børresen-Dale,et al.  Identification of fusion genes in breast cancer by paired-end RNA-sequencing , 2011, Genome Biology.

[53]  R. Benezra,et al.  Mad2 is a critical mediator of the chromosome instability observed upon Rb and p53 pathway inhibition. , 2011, Cancer cell.

[54]  C. Perou,et al.  TP53 genomics predict higher clinical and pathologic tumor response in operable early-stage breast cancer treated with docetaxel-capecitabine ± trastuzumab , 2012, Breast Cancer Research and Treatment.

[55]  E. Gabrielson,et al.  High levels of the Mps1 checkpoint protein are protective of aneuploidy in breast cancer cells , 2011, Proceedings of the National Academy of Sciences of the United States of America.

[56]  Vessela Kristensen,et al.  Methylation profiling with a panel of cancer related genes: Association with estrogen receptor, TP53 mutation status and expression subtypes in sporadic breast cancer , 2011, Molecular oncology.

[57]  Johan Staaf,et al.  GOBO: Gene Expression-Based Outcome for Breast Cancer Online , 2011, PloS one.

[58]  Y. Qi,et al.  Distinct p53 Gene Signatures Are Needed to Predict Prognosis and Response to Chemotherapy in ER-Positive and ER-Negative Breast Cancers , 2011, Clinical Cancer Research.

[59]  Zoltan Szallasi,et al.  Paradoxical relationship between chromosomal instability and survival outcome in cancer. , 2011, Cancer research.

[60]  J. Mpindi,et al.  A cell spot microarray method for production of high density siRNA transfection microarrays , 2011, BMC Genomics.

[61]  F. Bertucci,et al.  A refined molecular taxonomy of breast cancer , 2011, Oncogene.

[62]  F. Markowetz,et al.  The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups , 2012, Nature.