Network-aware mutation clustering of cancer

Abstract

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.

Geographic citizen science design:

“…These seven activities that are associated with citizen science show the breadth of the forms via which people can participate in scientific research today. The projects range from a complex effort to engineer bacteria to installing the app Vodafone DreamLab which supports cancer research while participating mobile phones are being charged at night (Pattnaik et al 2018). This variety is causing frequent discussions about the boundaries of citizen science and the need to clarify its terminology (for a detailed discussion, see Eitzel et al 2017).…”