A new molecular breast cancer subclass defined from a large scale real-time quantitative RT-PCR study

Maïa Chanrion,Alain Hénaut,Jean-Marie Darbon,Vincent Negre,Hélène Fontaine,Frédéric Bibeau,Carmen Rodriguez,Charles Theillet

doi:10.1186/1471-2407-7-39

Abstract

BackgroundCurrent histo-pathological prognostic factors are not very helpful in predicting the clinical outcome of breast cancer due to the disease's heterogeneity. Molecular profiling using a large panel of genes could help to classify breast tumours and to define signatures which are predictive of their clinical behaviour.MethodsTo this aim, quantitative RT-PCR amplification was used to study the RNA expression levels of 47 genes in 199 primary breast tumours and 6 normal breast tissues. Genes were selected on the basis of their potential implication in hormonal sensitivity of breast tumours. Normalized RT-PCR data were analysed in an unsupervised manner by pairwise hierarchical clustering, and the statistical relevance of the defined subclasses was assessed by Chi2 analysis. The robustness of the selected subgroups was evaluated by classifying an external and independent set of tumours using these Chi2-defined molecular signatures.ResultsHierarchical clustering of gene expression data allowed us to define a series of tumour subgroups that were either reminiscent of previously reported classifications, or represented putative new subtypes. The Chi2 analysis of these subgroups allowed us to define specific molecular signatures for some of them whose reliability was further demonstrated by using the validation data set. A new breast cancer subclass, called subgroup 7, that we defined in that way, was particularly interesting as it gathered tumours with specific bioclinical features including a low rate of recurrence during a 5 year follow-up.ConclusionThe analysis of the expression of 47 genes in 199 primary breast tumours allowed classifying them into a series of molecular subgroups. The subgroup 7, which has been highlighted by our study, was remarkable as it gathered tumours with specific bioclinical features including a low rate of recurrence. Although this finding should be confirmed by using a larger tumour cohort, it suggests that gene expression profiling using a minimal set of genes may allow the discovery of new subclasses of breast cancer that are characterized by specific molecular signatures and exhibit specific bioclinical features.

Highlights

Current histo-pathological prognostic factors are not very helpful in predicting the clinical outcome of breast cancer due to the disease's heterogeneity
Gene set selection We selected 47 candidate genes from the published litterature and genomic databases. Most of these genes were chosen as likely to be involved in breast tumour sensitivity to steroid hormones. They included ERα target genes, which are either up- or downregulated by oestrogen (Table 2), genes that specify the already reported breast cancer molecular subtypes, and genes that have been previously shown to be involved in sensitivity to the anti-oestrogen tamoxifen
The selected gene set included some putative stem cell markers and genes coding for cell cycle regulators, because these genes are believed to contribute to tumor aggressiveness

Summary

Introduction

Current histo-pathological prognostic factors are not very helpful in predicting the clinical outcome of breast cancer due to the disease's heterogeneity. Breast cancer is the most common female cancer in the Western world and the leading cause of death by cancer among women [1] It is a complex genetic disease characterized by an accumulation of molecular alterations resulting in an important clinical heterogeneity. Hierarchical clustering of gene expression patterns has been successfully used to identify subtypes of breast tumours that exhibit distinct clinical behaviours [2,3,4,5,6]. At least five subtypes (luminal A, luminal B, basal-like, ERBB2, and normal-like) have been identified on the basis of the pattern of expression of a 500-gene set. This molecular classification has been confirmed using extended or different tumour sets [4], as well as partly distinct or reduced gene sets [4,5,6]

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