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

Abstract

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.

MEXCOWalk: Mutual Exclusion and Coverage Based Random Walk to Identify Cancer Modules

“…The module identification approaches as applied to cancer can be viewed in two broad categories based on the types of input data they employ. The de novo methods rely only on genetic data to discover novel genetic interactions, as well as cancer-related functional modules (Miller et al, 2011;Vandin et al, 2011b;Leiserson et al, 2013;Liu et al, 2017). Due to the large solution space such methods usually apply a prefiltering based on alteration frequency to reduce the inherent computational complexity which may reduce sensitivity by overlooking modules involving rare alterations (Deng et al, 2019).…”

Integrative Learning of Structured High-Dimensional Data from Multiple Datasets

“…One popular approach is to incorporate prior knowledge on high-dimensional predictors such as gene regulatory pathways and co-expression networks that are represented by graphs and can be obtained from public or commercial databases such as Kyoto Encyclopedia of Genes and Genomes (KEGG, Kanehisa et al (2017)) and Gene Ontology (Ashburner et al, 2000). The knowledge-guided approach for structured data whose variables lie on a graph (Li and Zhang, 2010) has been adopted in supervised learning such as regression (Li and Li, 2008;Pan et al, 2010;Yu and Liu, 2016;Chang et al, 2018) and in unsupervised learning (Li et al, 2020;Liu et al, 2017), through carefully designed penalty functions in a frequentist framework or prior specifications in a Bayesian framework. The rationale behind incorporating the graphical structure of features into supervised learning is the fact that phenotypic biomarkers are often manifested as a result of interaction between a group of genes (pathway).…”

Ranking cancer drivers via betweenness-based outlier detection and random walks

“…A key current challenge in cancer genomics is to distinguish driver mutations that are causal for cancer progression from passenger mutations that do not confer any selective advantage. Consequently, several computational methods have been proposed for the identification of cancer driver genes or driver modules of genes by integrating mutations data with various other types of genetic data [ 3 – 10 ]; see [ 11 – 14 ] for recent comprehensive evaluations and surveys on the topic.…”

“…genes or driver modules of genes by integrating mutations data with various other types of genetic data [3][4][5][6][7][8][9][10]; see [11][12][13][14] for recent comprehensive evaluations and surveys on the topic.…”