Cancer driver genes: a guilty by resemblance doctrine

A major benefit of expansive cancer genome projects is the discovery of new targets for drug treatment and development. To date, cancer driver genes have been primarily identified by methods based on gene mutation frequency. This approach fails to identify culpable genes that are not mutated, rarely mutated, or contribute to the development of rare forms of cancer. Due to the complexity of the disease and the sheer volume of data, computational methods may encounter a NP-complete problem. We have developed a novel pathway and reach (PAR) method that employs a guilty by resemblance approach to identify cancer driver genes that avoids the above problems. Essentially PAR sifts through a list of genes of biological pathways to find those that are common to the same pathways and possess a similar 2-reach topology metric as a reference set of recognized driver genes. This approach leads to faster processing times and eliminates any dependency on gene mutation frequency. Out of the three pathways, signal transduction, immune system, and gene expression, a set of 50 candidate driver genes were identified, 30 of which were new. The top five were HGF, E2F1, C6, MIF, and CDK2.

Download Full-text

Adjusting for Background Mutation Frequency Biases Improves the Identification of Cancer Driver Genes

IEEE Transactions on NanoBioscience ◽

10.1109/tnb.2013.2263391 ◽

2013 ◽

Vol 12 (3) ◽

pp. 150-157 ◽

Cited By ~ 6

Author(s):

Perry Evans ◽

Stefan Avey ◽

Yong Kong ◽

Michael Krauthammer

Keyword(s):

Mutation Frequency ◽

Driver Genes ◽

Cancer Driver ◽

Cancer Driver Genes

Download Full-text

DriverRWH: Discovering Cancer Driver Genes By Random Walk On a Gene Mutation Hypergraph

10.21203/rs.3.rs-1192205/v1 ◽

2021 ◽

Author(s):

Chenye Wang ◽

Junhan Shi ◽

Jiansheng Cai ◽

Yusen Zhang ◽

Xiaoqi Zheng ◽

...

Keyword(s):

Random Walk ◽

Candidate Genes ◽

Gene Mutation ◽

Network Data ◽

Cumulative Number ◽

Driver Genes ◽

Cancer Driver ◽

Cancer Types ◽

Mutation Data ◽

Cancer Driver Genes

Abstract Background: Recent advances in next-generation sequencing technologies have helped investigators generate massive amounts of cancer genomic data. A critical challenge in cancer genomics is identification of a few driver mutation genes from a much larger number of passenger mutation genes. However, majority of existing computational approaches underuse the co-occurrence information of the individuals, which deems to be important in tumorigenesis and tumor progression. Driver gene list predicted from these tools are prone to be false positive, recent research is far from achieving the ultimate goal of discovering a complete catalog of driver genes. Results: To make full use of co-mutation information, we present a random walk algorithm referred to as DriverRWH on a weighted gene mutation hypergraph model, using somatic mutation data and molecular interaction network data to prioritize candidate driver genes. Applied to tumor samples of different cancer types from The Cancer Genome Atlas (TCGA), DriverRWH shows significantly better performance than state-of-art prioritization methods in terms of the area under the curve (AUC) scores and the cumulative number of known driver genes recovered in top-ranked candidate genes. DriverRWH recovers approximately 50% known driver genes in the top 30 ranked candidate genes for more than half of the cancer types. In addition, DriverRWH is also highly robust to perturbations in the mutation data and gene functional network data. Conclusion: DriverRWH is effective among various cancer types in prioritizes cancer driver genes and provides considerable improvement over other tools with a better balance of precision and sensitivity. It can be a useful tool for detecting potential driver genes and facilitate targeted cancer therapies.

Download Full-text

QuaDMutNetEx: a method for detecting cancer driver genes with low mutation frequency

BMC Bioinformatics ◽

10.1186/s12859-020-3449-2 ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Yahya Bokhari ◽

Areej Alhareeri ◽

Tomasz Arodz

Keyword(s):

Mutation Frequency ◽

Driver Genes ◽

Cancer Driver ◽

Cancer Driver Genes

Download Full-text

Faculty Opinions recommendation of Evaluating the evaluation of cancer driver genes.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.727060594.793535346 ◽

2017 ◽

Author(s):

Ron Shamir

Keyword(s):

Driver Genes ◽

Cancer Driver ◽

Cancer Driver Genes

Download Full-text

driveR: a novel method for prioritizing cancer driver genes using somatic genomics data

BMC Bioinformatics ◽

10.1186/s12859-021-04203-7 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Ege Ülgen ◽

O. Uğur Sezerman

Keyword(s):

Biological Knowledge ◽

Driver Gene ◽

Driver Genes ◽

Cancer Driver ◽

Prior Biological Knowledge ◽

Wilcoxon Rank Sum Test ◽

Cancer Genomes ◽

Novel Method ◽

Cancer Driver Genes ◽

Batch Analysis

Abstract Background Cancer develops due to “driver” alterations. Numerous approaches exist for predicting cancer drivers from cohort-scale genomics data. However, methods for personalized analysis of driver genes are underdeveloped. In this study, we developed a novel personalized/batch analysis approach for driver gene prioritization utilizing somatic genomics data, called driveR. Results Combining genomics information and prior biological knowledge, driveR accurately prioritizes cancer driver genes via a multi-task learning model. Testing on 28 different datasets, this study demonstrates that driveR performs adequately, achieving a median AUC of 0.684 (range 0.651–0.861) on the 28 batch analysis test datasets, and a median AUC of 0.773 (range 0–1) on the 5157 personalized analysis test samples. Moreover, it outperforms existing approaches, achieving a significantly higher median AUC than all of MutSigCV (Wilcoxon rank-sum test p < 0.001), DriverNet (p < 0.001), OncodriveFML (p < 0.001) and MutPanning (p < 0.001) on batch analysis test datasets, and a significantly higher median AUC than DawnRank (p < 0.001) and PRODIGY (p < 0.001) on personalized analysis datasets. Conclusions This study demonstrates that the proposed method is an accurate and easy-to-utilize approach for prioritizing driver genes in cancer genomes in personalized or batch analyses. driveR is available on CRAN: https://cran.r-project.org/package=driveR.

Download Full-text