PT  - JOURNAL ARTICLE
AU  - Swetansu Pattnaik
AU  - Catherine Vacher
AU  - Hong Ching Lee
AU  - Warren Kaplan
AU  - David M. Thomas
AU  - Jianmin Wu
AU  - Mark Pinese
TI  - Network-aware mutation clustering of cancer
AID  - 10.1101/432872
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 432872
4099  - http://biorxiv.org/content/early/2018/10/08/432872.short
4100  - http://biorxiv.org/content/early/2018/10/08/432872.full
AB  - The grouping of cancers across tissue boundaries is central to precision oncology, but remains a difficult problem. Here we present EPICC (Experimental Protein Interaction Clustering of Cancer), a novel technique to cluster cancer patients based on DNA mutation profile, that leverages knowledge of protein-protein interactions to reduce noise and amplify biological signal. We applied EPICC to data from The Cancer Genome Atlas (TCGA), and both recapitulated known cancer clusterings, and identified new cross-tissue cancer groups that may indicate novel cancer molecular subtypes. Investigation of EPICC clusters revealed new protein modules which were recurrently mutated across cancers, and indicate new avenues for research into cancer biology. EPICC leveraged the Vodafone DreamLab citizen science platform, and we provide our results as a resource for researchers to investigate the role of protein modules in cancer.