Interaction-Based Feature Selection for Uncovering Cancer Driver Genes Through Copy Number-Driven Expression Level

2017 ◽  
Vol 24 (2) ◽  
pp. 138-152 ◽  
Author(s):  
Heewon Park ◽  
Atsushi Niida ◽  
Seiya Imoto ◽  
Satoru Miyano
Keyword(s):  
Feature Selection ◽  
Copy Number ◽  
Expression Level ◽  
Driver Genes ◽  
Cancer Driver ◽  
Selection For ◽  
Cancer Driver Genes

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

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.

scholarly journals Module Analysis Captures Pancancer Genetically and Epigenetically Deregulated Cancer Driver Genes for Smoking and Antiviral Response

EBioMedicine ◽  
2018 ◽  
Vol 27 ◽  
pp. 156-166 ◽  
Author(s):  
Magali Champion ◽  
Kevin Brennan ◽  
Tom Croonenborghs ◽  
Andrew J. Gentles ◽  
Nathalie Pochet ◽  
...  
Keyword(s):  
Antiviral Response ◽  
Driver Genes ◽  
Cancer Driver ◽  
Module Analysis ◽  
Cancer Driver Genes

scholarly journals Erratum: Comprehensive identification of mutational cancer driver genes across 12 tumor types

2013 ◽  
Vol 3 (1) ◽  
Author(s):  
David Tamborero ◽  
Abel Gonzalez-Perez ◽  
Christian Perez-Llamas ◽  
Jordi Deu-Pons ◽  
Cyriac Kandoth ◽  
...  
Keyword(s):  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Driver Genes ◽  
Tumor Types

Sex disparities in head & neck cancer driver genes: An analysis of the TCGA dataset

Oral Oncology ◽  
2020 ◽  
Vol 104 ◽  
pp. 104614 ◽  
Author(s):  
Neil Mundi ◽  
Farhad Ghasemi ◽  
Peter Y.F. Zeng ◽  
Stephenie D. Prokopec ◽  
Krupal Patel ◽  
...  
Keyword(s):  
Neck Cancer ◽  
Head Neck Cancer ◽  
Driver Genes ◽  
Cancer Driver ◽  
Sex Disparities ◽  
Cancer Driver Genes ◽  
Tcga Dataset ◽  
Head Neck

scholarly journals Systematic identification of mutations and copy number alterations associated with cancer patient prognosis

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Joan C Smith ◽  
Jason M Sheltzer
Keyword(s):  
Copy Number ◽  
Meta Analysis ◽  
Gene Copy Number ◽  
Gene Copy ◽  
Driver Genes ◽  
Cancer Driver ◽  
Molecular Alterations ◽  
Patient Prognosis ◽  
Almost All ◽  
Systematic Identification

Successful treatment decisions in cancer depend on the accurate assessment of patient risk. To improve our understanding of the molecular alterations that underlie deadly malignancies, we analyzed the genomic profiles of 17,879 tumors from patients with known outcomes. We find that mutations in almost all cancer driver genes contain remarkably little information on patient prognosis. However, CNAs in these same driver genes harbor significant prognostic power. Focal CNAs are associated with worse outcomes than broad alterations, and CNAs in many driver genes remain prognostic when controlling for stage, grade, TP53 status, and total aneuploidy. By performing a meta-analysis across independent patient cohorts, we identify robust prognostic biomarkers in specific cancer types, and we demonstrate that a subset of these alterations also confer specific therapeutic vulnerabilities. In total, our analysis establishes a comprehensive resource for cancer biomarker identification and underscores the importance of gene copy number profiling in assessing clinical risk.

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