Gene-expression molecular subtyping of triple-negative breast cancer tumours: importance of immune response

IntroductionTriple-negative breast cancers need to be refined in order to identify therapeutic subgroups of patients.MethodsWe conducted an unsupervised analysis of microarray gene-expression profiles of 107 triple-negative breast cancer patients and undertook robust functional annotation of the molecular entities found by means of numerous approaches including immunohistochemistry and gene-expression signatures. A triple-negative external cohort (n = 87) was used for validation.ResultsFuzzy clustering separated triple-negative tumours into three clusters: C1 (22.4%), C2 (44.9%) and C3 (32.7%). C1 patients were older (mean = 64.6 years) than C2 (mean = 56.8 years; P = 0.03) and C3 patients (mean = 51.9 years; P = 0.0004). Histological grade and Nottingham prognostic index were higher in C2 and C3 than in C1 (P < 0.0001 for both comparisons). Significant event-free survival (P = 0.03) was found according to cluster membership: patients belonging to C3 had a better outcome than patients in C1 (P = 0.01) and C2 (P = 0.02). Event-free survival analysis results were confirmed when our cohort was pooled with the external cohort (n = 194; P = 0.01). Functional annotation showed that 22% of triple-negative patients were not basal-like (C1). C1 was enriched in luminal subtypes and positive androgen receptor (luminal androgen receptor). C2 could be considered as an almost pure basal-like cluster. C3, enriched in basal-like subtypes but to a lesser extent, included 26% of claudin-low subtypes. Dissection of immune response showed that high immune response and low M2-like macrophages were a hallmark of C3, and that these patients had a better event-free survival than C2 patients, characterized by low immune response and high M2-like macrophages: P = 0.02 for our cohort, and P = 0.03 for pooled cohorts.ConclusionsWe identified three subtypes of triple-negative patients: luminal androgen receptor (22%), basal-like with low immune response and high M2-like macrophages (45%), and basal-enriched with high immune response and low M2-like macrophages (33%). We noted out that macrophages and other immune effectors offer a variety of therapeutic targets in breast cancer, and particularly in triple-negative basal-like tumours. Furthermore, we showed that CK5 antibody was better suited than CK5/6 antibody to subtype triple-negative patients.

[1]  R. Tibshirani,et al.  Repeated observation of breast tumor subtypes in independent gene expression data sets , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[2]  Israel Steinfeld,et al.  BMC Bioinformatics BioMed Central , 2008 .

[3]  Karin Jirström,et al.  The presence of tumor associated macrophages in tumor stroma as a prognostic marker for breast cancer patients , 2012, BMC Cancer.

[4]  J. Joyce,et al.  IL-4 induces cathepsin protease activity in tumor-associated macrophages to promote cancer growth and invasion. , 2010, Genes & development.

[5]  Charles M. Perou,et al.  Deconstructing the molecular portraits of breast cancer , 2010, Molecular oncology.

[6]  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.

[7]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

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

[9]  M. Mallmann,et al.  High-Resolution Transcriptome of Human Macrophages , 2012, PloS one.

[10]  S. Goerdt,et al.  Macrophage activation and polarization: nomenclature and experimental guidelines. , 2014, Immunity.

[11]  Lajos Pusztai,et al.  Homogeneous Datasets of Triple Negative Breast Cancers Enable the Identification of Novel Prognostic and Predictive Signatures , 2011, PloS one.

[12]  Brad T. Sherman,et al.  Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources , 2008, Nature Protocols.

[13]  Gordon B Mills,et al.  Comprehensive Genomic Analysis Identifies Novel Subtypes and Targets of Triple-Negative Breast Cancer , 2014, Clinical Cancer Research.

[14]  Daniel Birnbaum,et al.  A gene expression signature identifies two prognostic subgroups of basal breast cancer , 2011, Breast Cancer Research and Treatment.

[15]  S. H. van der Burg,et al.  Identification and manipulation of tumor associated macrophages in human cancers , 2011, Journal of Translational Medicine.

[16]  Andrew H. Beck,et al.  The Macrophage Colony-Stimulating Factor 1 Response Signature in Breast Carcinoma , 2009, Clinical Cancer Research.

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

[18]  C. Perou,et al.  Molecular characterization of basal-like and non-basal-like triple-negative breast cancer. , 2013, The oncologist.

[19]  A. Gown,et al.  Immunohistochemical and Clinical Characterization of the Basal-Like Subtype of Invasive Breast Carcinoma , 2004, Clinical Cancer Research.

[20]  G. Natoli,et al.  Transcriptional regulation of macrophage polarization: enabling diversity with identity , 2011, Nature Reviews Immunology.

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

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

[23]  Scott J. Tebbutt,et al.  Two-Stage, In Silico Deconvolution of the Lymphocyte Compartment of the Peripheral Whole Blood Transcriptome in the Context of Acute Kidney Allograft Rejection , 2014, PloS one.

[24]  X. Chen,et al.  Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. , 2011, The Journal of clinical investigation.

[25]  O. Olopade,et al.  The role of tumor-associated macrophages in breast cancer , 2015 .

[26]  Xi Chen,et al.  TNBCtype: A Subtyping Tool for Triple-Negative Breast Cancer , 2012, Cancer informatics.

[27]  Carsten Denkert,et al.  Molecular Pathways Molecular Pathways : Involvement of ImmunePathways in the Therapeutic Response and Outcome in Breast Cancer , 2012 .

[28]  S Michiels,et al.  Tumor infiltrating lymphocytes are prognostic in triple negative breast cancer and predictive for trastuzumab benefit in early breast cancer: results from the FinHER trial. , 2014, Annals of oncology : official journal of the European Society for Medical Oncology.

[29]  Funda Meric-Bernstam,et al.  Differential Response to Neoadjuvant Chemotherapy Among 7 Triple-Negative Breast Cancer Molecular Subtypes , 2013, Clinical Cancer Research.

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

[31]  Brad T. Sherman,et al.  Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists , 2008, Nucleic acids research.

[32]  G. Semenza,et al.  Hypoxia-inducible factor-dependent signaling between triple-negative breast cancer cells and mesenchymal stem cells promotes macrophage recruitment , 2014, Proceedings of the National Academy of Sciences.

[33]  Molin Wang,et al.  Prognostic value of tumor-infiltrating lymphocytes in triple-negative breast cancers from two phase III randomized adjuvant breast cancer trials: ECOG 2197 and ECOG 1199. , 2014, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[34]  C. Liu,et al.  Targeting tumor-associated macrophages as a novel strategy against breast cancer. , 2006, The Journal of clinical investigation.

[35]  Debra L Winkeljohn Triple-negative breast cancer. , 2008, Clinical journal of oncology nursing.

[36]  Lydie Lane,et al.  Down-Regulation of ECRG4, a Candidate Tumor Suppressor Gene, in Human Breast Cancer , 2011, PloS one.

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

[38]  J. Lötsch,et al.  Inhibition of GTP cyclohydrolase attenuates tumor growth by reducing angiogenesis and M2‐like polarization of tumor associated macrophages , 2013, International journal of cancer.

[39]  Zhiyuan Hu,et al.  A compact VEGF signature associated with distant metastases and poor outcomes , 2009, BMC medicine.

[40]  Zhiyuan Hu,et al.  Identification of conserved gene expression features between murine mammary carcinoma models and human breast tumors , 2007, Genome Biology.

[41]  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.

[42]  David Sims,et al.  Genomic distance entrained clustering and regression modelling highlights interacting genomic regions contributing to proliferation in breast cancer , 2010, BMC Systems Biology.

[43]  W. Anderson,et al.  Adenoid cystic carcinoma of the breast in the United States (1977 to 2006): a population-based cohort study , 2010, Breast Cancer Research.

[44]  S. Gordon,et al.  Alternative activation of macrophages: mechanism and functions. , 2010, Immunity.

[45]  Achim Rody,et al.  T cell marker metagene predicts a favourable prognosis in estrogen receptor negative and Her2 positive breast cancers. , 2009 .

[46]  D. Dabbs,et al.  CK5 is more sensitive than CK5/6 in identifying the "basal-like" phenotype of breast carcinoma. , 2008, American journal of clinical pathology.

[47]  Jeffrey W. Pollard,et al.  Macrophage Diversity Enhances Tumor Progression and Metastasis , 2010, Cell.

[48]  T. Nielsen,et al.  The evaluation of tumor-infiltrating lymphocytes (TILs) in breast cancer: recommendations by an International TILs Working Group 2014. , 2015, Annals of oncology : official journal of the European Society for Medical Oncology.

[49]  I. Ellis,et al.  Expression of luminal and basal cytokeratins in human breast carcinoma , 2004, The Journal of pathology.

[50]  Achim Rody,et al.  T-cell metagene predicts a favorable prognosis in estrogen receptor-negative and HER2-positive breast cancers , 2009, Breast Cancer Research.

[51]  Jing Chen,et al.  ToppGene Suite for gene list enrichment analysis and candidate gene prioritization , 2009, Nucleic Acids Res..

[52]  M. Campone,et al.  bc-GenExMiner: an easy-to-use online platform for gene prognostic analyses in breast cancer , 2012, Breast Cancer Research and Treatment.

[53]  D. Quail,et al.  Microenvironmental regulation of tumor progression and metastasis , 2014 .

[54]  L. Esserman,et al.  Proliferating macrophages associated with high grade, hormone receptor negative breast cancer and poor clinical outcome , 2011, Breast Cancer Research and Treatment.

[55]  I. Ellis,et al.  Characteristics of basal cytokeratin expression in breast cancer , 2013, Breast Cancer Research and Treatment.

[56]  Z. Modrušan,et al.  Deconvolution of Blood Microarray Data Identifies Cellular Activation Patterns in Systemic Lupus Erythematosus , 2009, PloS one.