Inference of Cancer Progression With Probabilistic Graphical Model From Cross-Sectional Mutation Data

Abstract

With the advance of high-throughput sequencing technologies, a great amount of somatic mutation data in cancer have been produced, allowing deep analyzing tumor pathogenesis. However, the majority of these data are cross-sectional rather than temporal, and it is difficulty to infer the temporal order of gene mutations from them. In this paper, we first show a probabilistic graphical model (PGM) to infer the temporal order constrains and selectivity relation among the mutation of cancer driver genes which are presented by a directed acyclic graph. We then apply an exponential function based on the mutation probability of these driver genes to obtain their mutation waiting time which can be used to induce mutually exclusive driver pathways. Finally, we evaluate the performance of the PGM both on simulated data and real-cancer somatic mutation data. The experimental results and comparative analysis reveal that the PGM can capture most of the selectivity relation of mutated driver genes which have been validated by previous works. Furthermore, the PGM can provide new insights on simultaneously inferring driver pathways and the temporal order of their mutations from cross-sectional data.

Keywords

• Probabilistic graphical model
• somatic mutation
• cancer progression
• driver pathway
• waiting time