A robust classifier of high predictive value to identify good prognosis patients in ER-negative breast cancer

Building on a previously identified seven-gene prognostic immune response module for ER- breast cancer, we developed a novel statistical tool based on Mixture Discriminant Analysis in order to build a classifier that could accurately identify ER- patients with a good prognosis.We report the construction of a seven-gene expression classifier that accurately predicts, across a training cohort of 183 ER- tumours and six independent test cohorts (a total of 469 ER- tumours), ER- patients of good prognosis (in test sets, average predictive value = 94% [range 85 to 100%], average hazard ratio = 0.15 [range 0.07 to 0.36] p < 0.000001) independently of lymph node status and treatment.This seven-gene classifier could be used in a polymerase chain reaction-based clinical assay to identify ER- patients with a good prognosis, who may therefore benefit from less aggressive treatment regimens.Oestrogen receptor (ER) negative (-) breast cancer accounts for about 30% of all breast cancer cases and generally has a worse prognosis compared with ER positive (+)disease [1,2]. Nevertheless, a significant proportion of ER- cases have shown a favourable outcome and could potentially benefit from a less aggressive course of therapy [3]. Reliable identification of such ER- patients with a good prognosis is, however, difficult and at present only possible through examining histopathological factors.Recently, attempts have been made to explain the observed clinical heterogeneity of ER- disease in terms of gene expression signatures [4-7]. However, most of these studies clearly indicated the difficulty of identifying a prognostic gene expression signature for ER- disease [4,6,7], unlike ER+ breast cancer where a multitude of alternative prognostic signatures have been identified [3,8-11]. Nevertheless, using an integrative analysis of gene expression microarray data from three untreated (no chemotherapy) ER- breast cancer cohorts (a total of 186 patients) [3,8,10] and a novel feature selection