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.