Robust identification of breast cancer molecular subtypes to refine prognosis
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.
Additionally to these new biological insights, several research groups
identified prognostic gene expression signatures. 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 the early
breast cancer prognostic signatures, namely GENE70 (van't Veer et al.,
Nature 2002), GENE76 (Wang et al., Lancet 2005) and GGI (Sotiriou et
al., JNCI 2006).
Combining the identification of molecular subtypes and prognostic gene
signatures might improve our understanding of biological phenomena
involved in breast cancer prognostication. Interestingly, our group
showed that the prognostic value of the early gene signatures, mainly
driven by proliferation, is limited to the luminal subtype (ER+/HER2-)
(Wirapati et al., BCR 2008; Desmedt et al. CCR 2008). In the other
subtypes (i.e. ER-/HER2- and HER2+), immune response and angiogenesis
might be highly prognostic.
In this presentation, we will introduce a novel prognostic model which
integrates the identification of the breast cancer molecular subtypes
and uses a local modeling approach to improve state-of-the-art
prognostic gene signatures.