A consensus prognostic gene expression classifier for ER positive breast cancer

Here we perform a combined analysis of three major breast cancer microarray data sets to hone in on a universally valid prognostic molecular classifier in estrogen receptor (ER) positive tumors. Using a recently developed robust measure of prognostic separation, we further validate the prognostic classifier in three external independent cohorts, confirming the validity of our molecular classifier in a total of 877 ER positive samples. Furthermore, we find that molecular classifiers may not outperform classical prognostic indices but that they can be used in hybrid molecular-pathological classification schemes to improve prognostic separation.The prognostic molecular classifier presented here is the first to be valid in over 877 ER positive breast cancer samples and across three different microarray platforms. Larger multi-institutional studies will be needed to fully determine the added prognostic value of molecular classifiers when combined with standard prognostic factors.The identification of a prognostic gene expression signature in breast cancer that is valid across multiple independent data sets and different microarray platforms is a challenging problem [1]. Recently, there have been reports of molecular prognostic and predictive signatures that were also valid in external independent cohorts [2-7]. One of these studies derived the prognostic signature from genes correlating with histological grade [4], while in [5] it was derived directly from correlations with clinical outcome data and was validated in estrogen receptor positive lymph node negative (ER+LN-) breast cancer. Another study validated a predictive score, based on 21 genes, for ER+LN-tamoxifen treated breast cancer [2]. These results are encouraging, yet, as explained recently in [8,9], much larger cohort sizes may be needed before a consensus prognostic signature emerges. While the intrinsic subtype classification does appear to constitute a set of consensus signatures [7], it is also clear that