Background: Databases of perturbation gene expression signatures and drug sensitivity provide a powerful framework to develop personalized medicine approaches, by helping to identify actionable genomic markers and subgroups of patients who may benefit from targeted treatments.
Results: Here we use a perturbation expression signature database encompassing perturbations of over 90 cancer genes, in combination with a large breast cancer expression dataset and a novel statistical denoising algorithm, to help discern cancer perturbations driving most of the variation in breast cancer gene expression. Clustering estrogen receptor positive cancers over the perturbation activity scores recapitulates known luminal subtypes. Analysis of individual activity scores enables identification of a novel cancer subtype, defined by a 31-gene AKT-signaling module. Specifically, we show that activation of this module correlates with a poor prognosis in over 900 endocrine-treated breast cancers, a result we validate in two independent cohorts. Importantly, breast cancer cell lines with high activity of the module respond preferentially to PI3K/AKT/mTOR inhibitors, a result we also validate in two independent datasets. We find that at least 34 % of the downregulated AKT module genes are either mediators of apoptosis or have tumor suppressor functions.
Conclusions: The statistical framework advocated here could be used to identify gene modules that correlate with prognosis and sensitivity to alternative treatments. We propose a randomized clinical trial to test whether the 31-gene AKT module could be used to identify estrogen receptor positive breast cancer patients who may benefit from therapy targeting the PI3K/AKT/mTOR signaling axis.