Gene Modules, Breast Cancer Subtypes and Prognosis

Haibe-Kains B

Since the advent of array-based technology and the sequencing of the human genome, scientists attempted to bring new insights into breast cancer biology and prognosis. Perou et al. highlighted the key molecular differences between breast tumors by identifying sets of co-expressed genes and tumors sharing similar gene expressions (Perou et al, Nature 2000). Several subtypes were identified based mainly on ER and HER2 phenotypes and proliferation. Although these early results were promising, the hierarchical clustering used in the original publications lacked of robustness and was hardly applicable to new data. In order to alleviate these constraints, our group recently introduced robust methods: (i) to identify gene modules, i.e. sets of genes that are specifically co-expressed with genes of interest; (ii) to identify molecular subtypes in breast cancer (Wirapati et al., BCR 2008; Desmedt et al. CCR 2008).

Additionally to these new biological insights, several research groups identified prognostic gene expression signatures. the first gene signatures were obtained by studying the relationship between gene expression profiles and clinical outcome as the 70-gene (van't Veer et al., Nature 2002) and the 76-gene (Wang et al., Lancet 2005) signatures. Another example of gene  signature, called GGI, was defined to characterize at the molecular level the histological grade, a well-established pathological indicator rooted in the cell biology of breast cancer (Sotiriou et al., JNCI 2006). Recent validation studies (Buyse et al. JNCI 2006; Desmedt et al., CCR 2007; Haibe-Kains et al. BMC Genomics 2008) supported the good performance of these prognostic signatures.

Combining the identification of molecular subtypes and prognostic gene signatures might improve our understanding of biological phenomena involved in breast cancer prognostication. Indeed, our group showed that the prognostic value of most gene signatures, mainly driven by proliferation, is limited to the luminal subtype (ER+/HER2-) (Wirapati et al., BCR 2008; Desmedt et al. CCR 2008; Haibe-Kains et al., Bioinformatics 2008). In the other subtypes (namely ER-/HER2- and HER2+), immune response and tumor invasion might be highly prognostic.

In conclusion, thanks to the recent meta-analyses and reviews which successfully recapitulated the main discoveries made these late decades, we benefit from this strong basis to go a step further to improve breast cancer prognosis using microarrays. A new prognostic model combining the identification of molecular subtypes and gene signatures will be the subject of further research.