A NOVEL AND EFFICIENT ALGORITHM FOR DE NOVO DISCOVERY OF MUTATED DRIVER PATHWAYS IN CANCER

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

Background

Recent cancer genomic studies have generated detailed molecular data on a large number of cancer patients. A key remaining problem in cancer genomics is the identification of driver genes.

Results

We propose BetweenNet, a computational approach that integrates genomic data with a protein-protein interaction network to identify cancer driver genes. BetweenNet utilizes a measure based on betweenness centrality on patient specific networks to identify the so-called outlier genes that correspond to dysregulated genes for each patient. Setting up the relationship between the mutated genes and the outliers through a bipartite graph, it employs a random-walk process on the graph, which provides the final prioritization of the mutated genes. We compare BetweenNet against state-of-the art cancer gene prioritization methods on lung, breast, and pan-cancer datasets.

Conclusions

Our evaluations show that BetweenNet is better at recovering known cancer genes based on multiple reference databases. Additionally, we show that the GO terms and the reference pathways enriched in BetweenNet ranked genes and those that are enriched in known cancer genes overlap significantly when compared to the overlaps achieved by the rankings of the alternative methods.

MEXCOwalk: mutual exclusion and coverage based random walk to identify cancer modules

MOTIVATION

Genomic analyses from large cancer cohorts have revealed the mutational heterogeneity problem which hinders the identification of driver genes based only on mutation profiles. One way to tackle this problem is to incorporate the fact that genes act together in functional modules. The connectivity knowledge present in existing protein-protein interaction (PPI) networks together with mutation frequencies of genes and the mutual exclusivity of cancer mutations can be utilized to increase the accuracy of identifying cancer driver modules.

RESULTS

We present a novel edge-weighted random walk-based approach that incorporates connectivity information in the form of protein-protein interactions (PPIs), mutual exclusivity and coverage to identify cancer driver modules. MEXCOwalk outperforms several state-of-the-art computational methods on TCGA pan-cancer data in terms of recovering known cancer genes, providing modules that are capable of classifying normal and tumor samples and that are enriched for mutations in specific cancer types. Furthermore, the risk scores determined with output modules can stratify patients into low-risk and high-risk groups in multiple cancer types. MEXCOwalk identifies modules containing both well-known cancer genes and putative cancer genes that are rarely mutated in the pan-cancer data. The data, the source code and useful scripts are available at: https://github.com/abu-compbio/MEXCOwalk.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Knowledge-Guided Statistical Learning Methods for Analysis of High-Dimensional -Omics Data in Precision Oncology

High-dimensional -omics data such as genomic, transcriptomic, and metabolomic data offer great promise in advancing precision medicine. In particular, such data have enabled the investigation of complex diseases such as cancer at an unprecedented scale and in multiple dimensions. However, a number of analytical challenges complicate analysis of high-dimensional -omics data. One is the growing recognition that complex diseases such as cancer are multifactorial and may be attributed to harmful changes on multiple -omics levels and on the pathway level. When individual genes in an important pathway have relatively weak signals, it can be challenging to detect them on their own, but the aggregated signal in the pathway can be considerably stronger and hence easier to detect with the same sample size. To address these challenges, there is a growing body of literature on knowledge-guided statistical learning methods for analysis of high-dimensional -omics data that can incorporate biological knowledge such as functional genomics and functional proteomics. These methods have been shown to improve predication and classification accuracy and yield biologically more interpretable results compared with statistical learning methods that do not use biological knowledge. In this review, we survey current knowledge-guided statistical learning methods, including both supervised learning and unsupervised learning, and their applications to precision oncology, and we discuss future research directions.