Gene pathways associated with prognosis and chemotherapy sensitivity in different molecular subtypes of breast cancer
- L Pusztai1
© Pusztai. 2011
Published: 16 November 2011
Breast cancer consists of multiple different molecular subtypes and different biological processes, and consequently different molecular markers are associated with prognosis and chemotherapy sensitivity in the distinct disease subsets . A large number of biological processes including cell cycle regulation, DNA replication, mitotic spindle checkpoint, and p53 function are strongly prognostic in ER+ cancers but not among ER- cancers [2, 3]. Interestingly, the number of biological pathways, and therefore genes, that are associated with prognosis or treatment sensitivity are substantially larger and more consistent in ER+ cancers than among ER- tumors [1, 4]. This implies that it is easier to discover prognostic and predictive markers for ER+ than for ER- cancers. In ER- cancers, the single most consistent, but still modestly accurate, good prognostic predictor is the presence of immune cell infiltration . Immune cell signatures are also associated with more favorable prognosis in highly proliferative ER+ cancers but not in ER+ cancers with low proliferation .
It is also increasingly clear that the same molecular marker can be associated with several different outcome endpoints in various and often opposing manners. For example, high Ki67 expression is predictive of worse prognosis in the absence of any systemic therapy in ER+ cancers, but at the same time it is also predictive of higher sensitivity to chemotherapy. Similar opposing bidirectional associations with treatment response and prognosis exist for many other markers including histologic grade, Tau protein expression and almost all prognostic gene signatures .
It is important to be aware of these complex multi-directional interactions between molecular markers and various clinical endpoints that may also vary from breast cancer subtype to subtype. Ignoring these potential marker-disease subset-outcome interactions can lead to contradictory and confusing results across studies (due to differences in patient composition and heterogeneity of therapy between studies) and may also lead to the discovery of biomarkers that are clinically less useful (because of unrecognized subtype-restricted performance) [8, 9].
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