A "GENUS" Approach for Breast Cancer Prognostication
Haibe-Kains B
Short abstract:
Since the advent of high throughput technologies, scientists attempted
to improve current prognostic model in cancer-related diseases. In
breast cancer, numerous gene expression profiling studies showed that
this disease, in addition to be clinically heterogeneous, is also
heterogeneous at the molecular level, 3 to 5 different subtypes being
identified. From a prognostic point of view, several research groups
identified gene expression signatures (and their corresponding
prediction models) able to improve current prognostic clinical models.
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 and clinical models.
Long abstract:
Since the advent of high throughput 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. 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.