scholarly journals The Candidate Cancer Gene Database: a database of cancer driver genes from forward genetic screens in mice

2014 ◽  
Vol 43 (D1) ◽  
pp. D844-D848 ◽  
Author(s):  
Kenneth L. Abbott ◽  
Erik T. Nyre ◽  
Juan Abrahante ◽  
Yen-Yi Ho ◽  
Rachel Isaksson Vogel ◽  
...  
Keyword(s):  
Cancer Gene ◽  
Genetic Screens ◽  
Driver Genes ◽  
Cancer Driver ◽  
Forward Genetic Screens ◽  
Candidate Cancer Gene ◽  
Gene Database ◽  
Cancer Driver Genes

Profile of somatic mutations in young adults with pancreatic cancer.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e15790-e15790
Author(s):  
Livia Munhoz Rodrigues ◽  
Simone Maistro ◽  
Maria Lucia Hirata Katayama ◽  
Rosimeire Aparecida Roela ◽  
Maria A. A. Koike Folgueira
Keyword(s):  
Pancreatic Cancer ◽  
Young Adults ◽  
Somatic Mutations ◽  
Cancer Gene ◽  
Driver Genes ◽  
Cancer Driver ◽  
Three Samples ◽  
Candidate Cancer Gene ◽  
Gene Database ◽  
Carcinogenic Process

e15790 Background: Most pancreatic carcinomas (PC) occur in older people, however a few cases are detected in young adults. In this age group, the carcinogenic process is less well understood. Our goal was to identify and to characterize cancer driver genes in early age onset PC. Methods: Somatic variants of individuals affected by PC aged ≤45 years were searched in the COSMIC and CBioPortal databases. The variants were annotated using Oncotator, excluding the silent and intronic variants. Implication in cancer causality was evaluated in the Cancer Gene Census (CGC) and the Candidate Cancer Gene Database (CCGD). The most frequently mutated genes were identified and investigated to determine if they configured FrequentLy mutAted GeneS (FLAGs). Results: Whole genome (4) or exome (29) sequencing was available from 33 individuals (14 females and 19 males). A median of 31 (7-102) alterations per tumor, mainly represented by C > T substitutions (median 16, 2-71), was detected. A median of 3 (0-11) truncated alterations, 4 (1-13) genes cataloged as CGC and 8 (1-22) genes cataloged as CCGD rank A or B was identified per tumor. The most frequently affected genes were those characteristic of tumor promotion in pancreatic cancer carcinogenesis, such as KRAS (79%), TP53 (64%), SMAD4 (18%), followed by RYR1 (15%) and TTN (12%) genes, the latter two classified as FLAGs and, finally, HERC2, GREB1 and DMBT1 (9%). Seventeen samples presented variants in both TP53 and KRAS (17/33), 9 and 4 presented only KRAS or TP53 variants, respectively. Three samples with mutations in neither of these genes presented mutations in genes such as BCLAF1, DCC, BRAF, CDH11 and CDKN2A, both CGCs. Three out of 9 samples carrying KRAS but not TP53 mutations presented variants in DNA homologous repair (HHR) genes. Among all the altered genes, the main biological processes were cell adhesion (139 genes involved) and anatomical structure formation involved in morphogenesis (127), while the most enriched pathways were Wnt (45) and Cadherin (30). Conclusions: TP53 and KRAS are the somatic mutations most frequently detected in PC. 10% of the samples showed no change in these genes, but showed changes in other CGCs. HERC2, GREB1 and DMBT1 are potential cancer drivers in young adult PCs.

scholarly journals Somatic Mutational Profile of High-Grade Serous Ovarian Carcinoma and Triple-Negative Breast Carcinoma in Young and Elderly Patients: Similarities and Divergences

Cells ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 3586
Author(s):  
Pedro Adolpho de Menezes Pacheco Serio ◽  
Gláucia Fernanda de Lima Pereira ◽  
Maria Lucia Hirata Katayama ◽  
Rosimeire Aparecida Roela ◽  
Simone Maistro ◽  
...  
Keyword(s):  
Elderly Patients ◽  
Triple Negative ◽  
Median Number ◽  
High Grade ◽  
Cancer Gene ◽  
Driver Genes ◽  
Mutational Signature ◽  
Triple Negative Breast Carcinoma ◽  
Candidate Cancer Gene ◽  
Gene Database

Background: Triple-negative breast cancer (TNBC) and High-Grade Serous Ovarian Cancer (HGSOC) are aggressive malignancies that share similarities; however, different ages of onset may reflect distinct tumor behaviors. Thus, our aim was to compare somatic mutations in potential driver genes in 109 TNBC and 81 HGSOC from young (Y ≤ 40 years) and elderly (E ≥ 75 years) patients. Methods: Open access mutational data (WGS or WES) were collected for TNBC and HGSOC patients. Potential driver genes were those that were present in the Cancer Gene Census—CGC, the Candidate Cancer Gene Database—CCGD, or OncoKB and those that were considered pathogenic in variant effect prediction tools. Results: Mutational signature 3 (homologous repair defects) was the only gene that was represented in all four subgroups. The median number of mutated CGCs per sample was similar in HGSOC (Y:3 vs. E:4), but it was higher in elderly TNBC than it was in young TNBC (Y:3 vs. E:6). At least 90% of the samples from TNBC and HGSOC from Y and E patients presented at least one known affected TSG. Besides TP53, which was mutated in 67–83% of the samples, the affected TSG in TP53 wild-type samples were NF1 (yHGSOC and yTNBC), PHF6 (eHGSOC and yTNBC), PTEN, PIK3R1 and ZHFX3 (yTNBC), KMT2C, ARID1B, TBX3, and ATM (eTNBC). A few samples only presented one affected oncogene (but no TSG): KRAS and TSHR in eHGSOC and RAC1 and PREX2 (a regulator of RAC1) in yTNBC. At least ⅔ of the tumors presented mutated oncogenes associated with tumor suppressor genes; the Ras and/or PIK3CA signaling pathways were altered in 15% HGSOC and 20–35% TNBC (Y vs. E); DNA repair genes were mutated in 19–33% of the HGSOC tumors but were more frequently mutated in E-TNBC (56%). However, in HGSOC, 9.5% and 3.3% of the young and elderly patients, respectively, did not present any tumors with an affected CGC nor did 4.65% and none of the young and elderly TNBC patients. Conclusion: Most HGSOC and TNBC from young and elderly patients present an affected TSG, mainly TP53, as well as mutational signature 3; however, a few tumors only present an affected oncogene or no affected cancer-causing 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
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