Inference of cancer progression from somatic mutation data

Large-scale cancer genomics projects are providing a wealth of somatic mutation data. Therefore, one of the most challenging problems arising from the data is to infer the temporal order of somatic mutations. In the paper, we present a network-based method (NetInf) to infer cancer progression at the pathway level. We apply it to analyze somatic mutation data from real cancer studies. Experimental results show that these detected pathways overlap with known pathways, including RB, P53 signaling pathways. Our method reduces computational complexity and also provides new insights on the temporalorder of somatic mutations at the pathway level.