Ontology-based Prediction of Cancer Driver Genes

Overview

Journal Sci Rep

Specialty Science

Date 2019 Nov 24

PMID 31757986

Citations 14

Authors

Sara Althubaiti

Andreas Karwath

Ashraf Dallol

Adeeb Noor

Shadi Salem Alkhayyat

Rolina Alwassia

Katsuhiko Mineta

Takashi Gojobori

Andrew D Beggs

Paul N Schofield

Georgios V Gkoutos

Robert Hoehndorf

Affiliations

Soon will be listed here.

Abstract

Identifying and distinguishing cancer driver genes among thousands of candidate mutations remains a major challenge. Accurate identification of driver genes and driver mutations is critical for advancing cancer research and personalizing treatment based on accurate stratification of patients. Due to inter-tumor genetic heterogeneity many driver mutations within a gene occur at low frequencies, which make it challenging to distinguish them from non-driver mutations. We have developed a novel method for identifying cancer driver genes. Our approach utilizes multiple complementary types of information, specifically cellular phenotypes, cellular locations, functions, and whole body physiological phenotypes as features. We demonstrate that our method can accurately identify known cancer driver genes and distinguish between their role in different types of cancer. In addition to confirming known driver genes, we identify several novel candidate driver genes. We demonstrate the utility of our method by validating its predictions in nasopharyngeal cancer and colorectal cancer using whole exome and whole genome sequencing.

Citing Articles

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Unraveling the Drivers of Tumorigenesis in the Context of Evolution: Theoretical Models and Bioinformatics Tools.

Zhu X, Zhao W, Zhou Z, Gu X J Mol Evol. 2023; 91(4):405-423.

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OncoRTT: Predicting novel oncology-related therapeutic targets using BERT embeddings and omics features.

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