Network-Based Method for Inferring Cancer Progression at the Pathway Level from Cross-Sectional Mutation Data.

Area of Science:

• Genomics
• Computational Biology
• Cancer Research

Background:

• Large-scale cancer genomics projects generate vast somatic mutation data.
• Inferring the temporal order of mutations for cancer progression is a significant challenge due to the scarcity of longitudinal patient samples.

Purpose of the Study:

• To develop and validate a novel computational method for inferring cancer progression pathways from cross-sectional genomic data.
• To address the challenge of reconstructing the temporal order of somatic mutations across a cohort of cancer patients.

Main Methods:

• A Network-based method (NetInf) was developed to infer cancer progression at the pathway level.
• The method leverages the exclusive property of driver mutations within pathways and linear progression between pathways.
• NetInf was assessed using simulated data with varying noise levels and applied to real somatic mutation data from three cancer studies.

Main Results:

• NetInf successfully inferred cancer progression pathways from cross-sectional data.
• The identified pathways demonstrated significant biological enrichment.
• The method showed robustness against noise in simulated datasets.

Conclusions:

• NetInf provides an efficient computational approach to infer cancer progression at the pathway level.
• The method offers new insights into the temporal order of somatic mutations, moving beyond gene-level analysis.
• This approach reduces computational complexity by constructing gene networks without pre-defining the number of pathways.