A robust platform-independent gene signature for single-sample breast cancer subtyping

The first step in personalizing therapy for a breast cancer patient is to assign the patient to the appropriate subtype. At present this is done through immunohistochemistry (IHC) which is both expensive as well as wasteful of tumor material. Moreover, error rates can be as high as 20%. In this paper, we propose a new approach to the subtyping of breast cancer patients by using data-driven “reference genes” so that the classification can be carried out for one patient at a time, and across multiple platforms. The validity of the proposed approach is established by training a binary classifier on the TCGA breast cancer data set measured on an Agilent platform, and then applying this classifier to five independent test data sets, on both the Agilent as well as the Affymetrix platforms. In all cases, the results are excellent.