A novel and efficient algorithm for de novo discovery of mutated driver pathways in cancer
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 non-convex programming and non-convex 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.
Liu B, Wu C, Shen X, Pan W. (2017).
A novel and efficient algorithm for de novo discovery of mutated driver pathways in cancer.
Annals of Applied Statistics, 11: 1481-1512.
After obtaining his PhD in Statistics from UW-Madison in 1997, Wei Pan joined the faculty in the Division of Biostatistics, School of Public Health, University of Minnesota, now as a professor. His research interests are in statistical genetics and genomics, statistical learning and data mining, correlated data analysis and survival analysis. Pan and his group have been developing statistical methods to analyze and integrate high-dimensional microarray data, including gene expression, DNA-protein interactions and protein-protein interactions. His work includes gene network- and pathway-based hypothesis testing and network-based penalized regression and classification for high-dimensional microarray data.