Simultaneous Inference of Cancer Pathways and Tumor Progression from Cross-Sectional Mutation Data

摘要: Recent cancer sequencing studies provide a wealth of somatic mutation data from large number patients. One the most intriguing and challenging questions arising this is to determine whether temporal order mutations in follows any common progression. Since we usually obtain only one sample patient, such inferences are commonly made cross-sectional different This analysis complicated by extensive variation across patients, that reduced examining combinations various pathways. Thus far, methods reconstruction tumor progression at pathway level have restricted attention known, priori defined pathways. In work show how simultaneously infer pathways their data, leveraging on exclusivity property driver within pathway. We define Pathway Linear Progression Model, derive combinatorial formulation for problem finding optimal model data. while NP-hard, with enough samples its solution uniquely identifies correct high probability even when errors present then formulate as an integer linear program ILP, which allows datasets recent samples. use our algorithm analyze three studies, including two The Cancer Genome Atlas TCGA colorectal glioblastoma. models reconstructed method capture current knowledge these types, also providing new insights level.