Molecular subtypes identification to refine breast cancer prognosis
Breast cancer is a global
public health issue. It is the most frequently diagnosed malignancy in
women in the western world and the commonest cause of cancer death in
European and American women. In Europe, one out of eight to ten women,
depending on the country, will develop breast cancer during their
During the last two decades, several clinical and pathological
indicators such as histological grade, tumor size and lymph node
involvement have been used for the survival prediction of breast cancer
patients independently of treatment, also known as prognostication.
Examples of clinical guidelines to the selection of patients who should
receive adjuvant therapy are the St Gallen consensus criteria
(Goldhirsh et al., JCO 2003), the NIH guidelines (Eifel et al., JNCI
2001), the Nottingham prognostic index (Galea et al., BCRT 1992) and
Adjuvant! Online Olivotto et al., JCO 2005). Although BC
prognostication has been the object of intense research, a still open
challenge is how to detect patients who need adjuvant systemic therapy.
The advent of array-based technology and the sequencing of the human
genome brought new insights into breast cancer biology and prognosis.
Several research teams conducted comprehensive genome-wide assessments
of gene expression profiling. Perou et al. highlighted the key
molecular differences between breast tumors (Perou et al, Nature 2000).
Several subtypes were identified based mainly on ER and HER2 signaling
pathways and proliferation. These subtypes exhibited different clinical
outcome, making this classification the first prognostic classifier.
Other research groups identified prognostic gene expression signatures.
Examples of gene signatures which were obtained by studying the
relationship between gene expression profiles and clinical outcome, are
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). With
respect to clinical guidelines, these signatures were shown to
correctly identify a larger group of low-risk patients not needing
treatment in the global population of breast cancer patients. This is
particularly relevant for clinicians since reducing treatments means
also reducing potential side effects and cutting costs.
Other research groups have proposed gene expression signatures that are
predictive of the clinical outcome in breast cancer (Sotiriou et al.,
Nature Cancer Reviews 2007). Although several criticisms arose
concerning the overoptimistic results of early publications (Michiels
et al., Lancet 2005; Ein-Dor et al. Bioinformatics 2005), 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
some prognostic signatures (namely the 70-, 76-gene and GGI) in the
global population of breast cancer patients.
Our group recently introduced a simple method to identify molecular
subtypes in breast cancer (Desmedt et al. CCR 2008; Wirapati et al.,
BCR 2008). This model-based clustering was able to identify such
subtypes in numerous public microarray datasets using different
technologies. This a priori biological information might be integrated
to improve 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-) (Desmedt
et al. CCR 2008; Wirapati et al., BCR 2008; Haibe-Kains et al.,
Bioinformatics 2008). A new classifier combining predictions specific
to each molecular subtype might outperform state-of-the-art prognostic
models based on gene expressions.
In conclusion, thanks to the validation of the microarray technology
and 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.