The Annals of Applied Statistics

A novel and efficient algorithm for de novo discovery of mutated driver pathways in cancer

Binghui Liu, Chong Wu, Xiaotong Shen, and Wei Pan

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Next-generation sequencing studies on cancer somatic mutations have discovered that driver mutations tend to appear in most tumor samples, but they barely overlap in any single tumor sample, presumably because a single driver mutation can perturb the whole pathway. Based on the corresponding new concepts of coverage and mutual exclusivity, new methods can be designed for de novo discovery of mutated driver pathways in cancer. Since the computational problem is a combinatorial optimization with an objective function involving a discontinuous indicator function in high dimension, many existing optimization algorithms, such as a brute force enumeration, gradient descent and Newton’s methods, are practically infeasible or directly inapplicable. We develop a new algorithm based on a novel formulation of the problem as nonconvex programming and nonconvex regularization. The method is computationally more efficient, effective and scalable than existing Monte Carlo searching and several other algorithms, which have been applied to The Cancer Genome Atlas (TCGA) project. We also extend the new method for integrative analysis of both mutation and gene expression data. We demonstrate the promising performance of the new methods with applications to three cancer datasets to discover de novo mutated driver pathways.

Article information

Ann. Appl. Stat., Volume 11, Number 3 (2017), 1481-1512.

Received: September 2015
Revised: March 2017
First available in Project Euclid: 5 October 2017

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Zentralblatt MATH identifier

DNA sequencing driver mutations optimization subset selection truncated $L_{1}$ penalty


Liu, Binghui; Wu, Chong; Shen, Xiaotong; Pan, Wei. A novel and efficient algorithm for de novo discovery of mutated driver pathways in cancer. Ann. Appl. Stat. 11 (2017), no. 3, 1481--1512. doi:10.1214/17-AOAS1042.

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