scholarly journals Comprehensive Characterization of Cancer Driver Genes and Mutations

Cell ◽  
2018 ◽  
Vol 174 (4) ◽  
pp. 1034-1035 ◽  
Author(s):  
Matthew H. Bailey ◽  
Collin Tokheim ◽  
Eduard Porta-Pardo ◽  
Sohini Sengupta ◽  
Denis Bertrand ◽  
...  
Keyword(s):  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Driver Genes ◽  
Comprehensive Characterization

scholarly journals Comprehensive Characterization of Cancer Driver Genes and Mutations

Cell ◽  
2018 ◽  
Vol 173 (2) ◽  
pp. 371-385.e18 ◽  
Author(s):  
Matthew H. Bailey ◽  
Collin Tokheim ◽  
Eduard Porta-Pardo ◽  
Sohini Sengupta ◽  
Denis Bertrand ◽  
...  
Keyword(s):  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Driver Genes ◽  
Comprehensive Characterization

Topological Characterization of Cancer Driver Genes Using Reactome Super Pathways Networks

2021 ◽  
pp. 26-37
Author(s):  
Rodrigo Henrique Ramos ◽  
Jorge Francisco Cutigi ◽  
Cynthia de Oliveira Lage Ferreira ◽  
Adenilso Simao
Keyword(s):  
Driver Genes ◽  
Topological Characterization ◽  
Cancer Driver ◽  
Cancer Driver Genes

scholarly journals Best Paper Selection

2019 ◽  
Vol 28 (01) ◽  
pp. 239-239
Keyword(s):  
Symptom Severity ◽  
Exploratory Study ◽  
Cancer Genome ◽  
Research Network ◽  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Genome Atlas ◽  
Comprehensive Characterization ◽  
Genome Atlas

Bailey MH, Tokheim C, Porta-Pardo E, Sengupta S, Bertrand D, Weerasinghe A, Colaprico A, Wendl MC, Kim J, Reardon B, Ng PK, Jeong KJ, Cao S, Wang Z, Gao J, Gao Q, Wang F, Liu EM, Mularoni L, Rubio-Perez C, Nagarajan N, Cortes- Ciriano I, Zhou DC, Liang WW, Hess JM, Yellapantula VD, Tamborero D, Gonzalez- Perez A, Suphavilai C, Ko JY, Khurana E, Park PJ, Van Allen EM, Liang H; MC3 Working Group; Cancer Genome Atlas Research Network, Lawrence MS, Godzik A, Lopez-Bigas N, Stuart J, Wheeler D, Getz G, Chen K, Lazar AJ, Mills GB, Karchin R, Ding L. Comprehensive characterization of cancer driver genes and mutations. Cell 2018 Apr 5;173(2):371-385.el8 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6029450/ Hosny A, Parmar C, Coroller TP, Grossmann P, Zeleznik R, Kumar A, Bussink J, Gillies RJ, Mak RH, Aerts HJWL. Deep learning for lung cancer prognostication: A retrospective multicohort radiomics study. PLoS Med 2018 Nov 30;15(11):e1002711 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6269088/ Low CA, Dey AK, Ferreira D, Kamarck T, Sun W, Bae S, Doryab A. Estimation of symptom severity during chemotherapy from passively sensed data: Exploratory study. J Med Internet Res 2017 Dec 19;19(12):e420 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5750420/ Tamborero D, Rubio-Perez C, Deu-Pons J, Schroeder MP, Vivancos A, Rovira A, Tusquets I, Albanell J, Rodon J, Tabernero J, de Torres C, Dienstmann R, Gonzalez-Perez A, Lopez-Bigas N. Cancer Genome Interpreter annotates the biological and clinical relevance of tumor alterations. Genome Med 2018 Mar 28;10(1):25 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5875005/

scholarly journals Deep Profiling of Genetic Aberrations and Clonal Evolution in Follicular Lymphoma

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 20-20
Author(s):  
Baoyan Bai ◽  
Daniel Vodák ◽  
Sigve Nakken ◽  
Jillian F. Wise ◽  
Yngvild Nuvin Blaker ◽  
...  
Keyword(s):  
Follicular Lymphoma ◽  
Clonal Evolution ◽  
Divergent Evolution ◽  
Mutational Signatures ◽  
Driver Genes ◽  
Cancer Driver ◽  
Mutational Burden ◽  
Cancer Driver Genes ◽  
Pre Treatment

Introduction: Follicular lymphoma (FL) is an indolent malignancy, characterized by multiple relapses during the disease course. Annually around 2-3% of patients experience transformation to aggressive disease (tFL), most commonly to diffuse large B-cell lymphoma (DLBCL). Both transformation and progression of disease within 2 years (POD24) are associated with poor prognosis, yet the molecular events underlying these processes are not well understood. The existence of common progenitor cells (CPCs) has been inferred from genetic analyses of longitudinal biopsies. Improved characterization of genetic alterations associated with CPCs in cases with transformation and POD24 may improve our understanding of disease progression, and reveal molecular markers for high-risk disease. Methods: We performed whole-exome sequencing of 97 serial tumor biopsies and matched normal samples purified from peripheral blood from 44 FL patients. An average sequencing coverage of 700X was achieved for both tumor and normal samples, which ensured high data quality and ability to detect SNVs and InDels using our bench-marked bioinformatics pipeline. SNP6.0 data was available for 93 sequenced tumors and used to infer allele-specific copy number alterations. Several computational tools were applied to identify potential cancer driver genes (MutSig2CV, 2020plus), mutational signatures (MutationalPatterns), and to study clonal evolution (PyClone, ClonEvol). Results: Twenty-two of the 44 FL patients experienced relapses without transformation (referred to as the nFL group), and 22 patients experienced transformation (the tFL group). Nineteen patients (including both groups) experienced POD24. Both transformation and POD24 were associated with inferior overall survival. The median non-synonymous mutational burden was 96 per biopsy (range 10-326). Pre-treatment FL biopsies from the tFL group had significantly higher mutational burden compared to the nFL group (p<0.02). The application of two different approaches for driver gene discovery resulted in a total of 55 as putative drivers. Sixteen of these genes were identified by both tools, including the known FL driver genes CREBBP, KMT2D, TNFRSF14 and BCL2, all being mutated in more than 40 % of cases, and the novel cancer driver genes CTSS and VPS39. By applying MutSig2CV to different categories of biopsies, we identified RRAGC, ATP6V1B2, and HNRNPU as being significantly mutated in pre-treatment tumors of the nFL group, whereas TNFRSF14 and EZH2 were significantly mutated in pre-treatment tumors of the tFL group. When comparing pretreatment and relapsed FL biopsies, we identified CTSS, ZNF493 and HLA-A as significantly mutated in relapsed FL, thus potentially linking these genes to FL relapse. Analysis of mutational signatures demonstrated the presence of the same signatures for longitudinal samples, and no contrasting difference was found between nFL and tFL biopsies. Finally, we constructed clonal phylogenetic trees for 31 patients with serial biopsies and observed that both non-transformed and transformed tumors can evolve directly from CPCs. Contrary to previous findings, we did not find a preference for divergent evolution from FL to tFL, as compared to FL to relapsed FL. There was no significant correlation between evolution pattern and time intervals in the nFL and tFL groups. Based on the mutational history, we confirmed that CREBBP and KMT2D were the most frequently mutated genes seen in CPCs and PCLO, ATP6V1B2 and LRRN3 were also identified as early mutated genes. TNFRSF14, TBL1XR1 and GNAI2 were often mutated later, but before clonal expansion. Conclusions: We show that at diagnosis, the mutational burden in the tFL group is significantly higher than in the nFL group. By systematically applying driver discovery tools we have confirmed known driver genes and discovered novel genes that may be of importance for FL progression and transformation. Divergent evolution is a common feature both during relapse of FL and transformation to DLBCL, regardless of the time interval between the longitudinal biopsies. A better characterization of the CPCs may provide additional therapeutic opportunities towards a cure for FL patients. Disclosures Holte: Novartis: Honoraria, Other: Advisory board.

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
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