RT Journal Article
SR Electronic
T1 Network-aware mutation clustering of cancer
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 432872
DO 10.1101/432872
A1 Swetansu Pattnaik
A1 Catherine Vacher
A1 Hong Ching Lee
A1 Warren Kaplan
A1 David M. Thomas
A1 Jianmin Wu
A1 Mark Pinese
YR 2018
UL http://biorxiv.org/content/early/2018/10/08/432872.abstract
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.