Optimally discriminative subnetwork markers predict response to chemotherapy

Motivation: Molecular profiles of tumour samples have been widely and successfully used for classification problems. A number of algorithms have been proposed to predict classes of tumor samples based on expression profiles with relatively high performance. However, prediction of response to cancer treatment has proved to be more challenging and novel approaches with improved generalizability are still highly needed. Recent studies have clearly demonstrated the advantages of integrating protein–protein interaction (PPI) data with gene expression profiles for the development of subnetwork markers in classification problems. Results: We describe a novel network-based classification algorithm (OptDis) using color coding technique to identify optimally discriminative subnetwork markers. Focusing on PPI networks, we apply our algorithm to drug response studies: we evaluate our algorithm using published cohorts of breast cancer patients treated with combination chemotherapy. We show that our OptDis method improves over previously published subnetwork methods and provides better and more stable performance compared with other subnetwork and single gene methods. We also show that our subnetwork method produces predictive markers that are more reproducible across independent cohorts and offer valuable insight into biological processes underlying response to therapy. Availability: The implementation is available at: http://www.cs.sfu.ca/~pdao/personal/OptDis.html Contact: cenk@cs.sfu.ca; alapuk@prostatecentre.com; ccollins@prostatecentre.com