A network-based regression approach for identifying subject-specific driver mutations

TL;DR

This paper introduces a network-based regression method to identify subject-specific driver mutations in cancer genomics, leveraging gene networks and mutation effects to prioritize mutations influencing gene expression.

Contribution

It presents a novel random-effect regression model that accounts for individual variability and uses gene networks to estimate mutation effects on gene expression.

Findings

Effective in simulation studies

Successfully applied to breast cancer data

Prioritizes mutations with large downstream effects

Abstract

In cancer genomics, it is of great importance to distinguish driver mutations, which contribute to cancer progression, from causally neutral passenger mutations. We propose a random-effect regression approach to estimate the effects of mutations on the expressions of genes in tumor samples, where the estimation is assisted by a prespecified gene network. The model allows the mutation effects to vary across subjects. We develop a subject-specific mutation score to quantify the effect of a mutation on the expressions of its downstream genes, so mutations with large scores can be prioritized as drivers. We demonstrate the usefulness of the proposed methods by simulation studies and provide an application to a breast cancer genomics study.