Many forms of cancer have multiple subtypes with different causes and clinical outcomes. Somatic tumor genome sequences provide a rich new source of data for uncovering these subtypes but have proven difficult to compare, as two tumors rarely share the same mutations. Here we introduce network-based stratification (NBS), a method to integrate somatic tumor genomes with gene networks. This approach allows for stratification of cancer into informative subtypes by clustering together patients with mutations in similar network regions. We demonstrate NBS in ovarian, uterine and lung cancer cohorts from The Cancer Genome Atlas. For each tissue, NBS identifies subtypes that are predictive of clinical outcomes such as patient survival, response to therapy or tumor histology. We identify network regions characteristic of each subtype and show how mutation-derived subtypes can be used to train an mRNA expression signature, which provides similar information in the absence of DNA sequence.
Bibliographical noteFunding Information:
We would like to thank all members of the Ideker lab, specifically J. Dutkowski, R. Srivas, G. Bean, M. Yu and M. Choueiri, for many fruitful discussions during various stages of this project. We also thank G. Hofree for her input, patience and support. J.P.S. is supported in part by grants from the Marsha Rivkin Center for Ovarian Cancer Research and the Conquer Cancer Foundation of the American Society of Clinical Oncology. This work was supported by US National Institutes of Health grants P41 GM103504 and P50 GM085764.