Predicting clinical benefit from everolimus in patients with advanced solid tumors, the CPCT-03 study

Background: In this study, our aim was to identify molecular aberrations predictive for response to everolimus, an mTOR inhibitor, regardless of tumor type. Methods: To generate hypotheses about potential markers for sensitivity to mTOR inhibition, drug sensitivity and genomic profiles of 835 cell lines were analyzed. Subsequently, a...

Integration of Clinical and Gene Expression Data Has a Synergetic Effect on Predicting Breast Cancer Outcome

Breast cancer outcome can be predicted using models derived from gene expression data or clinical data. Only a few studies have created a single prediction model using both gene expression and clinical data. These studies often remain inconclusive regarding an obtained improvement in prediction performance. We rigorously compare three different...

A comprehensive analysis of prognostic signatures reveals the high predictive capacity of the Proliferation, Immune response and RNA splicing modules in breast cancer

Pooling breast cancer datasets has a synergetic effect on classification performance and improves signature stability

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SIRAC: Supervised Identification of Regions of Aberration in aCGH datasets