Comprehensive Characterization of Cancer Driver Genes and Mutations

By Matthew H. Bailey, Collin J Tokheim, Eduard Porta-Pardo, Sohini Sengupta, D. Bertrand, A. Weerasinghe, Antonio Colaprico, Michael C. Wendl, Jaegil Kim, B. Reardon and 749 more. Cell, 2018

2,007 citations recorded by the source corpus.

Summary Identifying molecular cancer drivers is critical for precision oncology. Multiple advanced algorithms to identify drivers now exist, but systematic attempts to combine and optimize them on large datasets are few. We report a PanCancer and PanSoftware analysis spanning 9,423 tumor exomes (comprising all 33 The Cancer Genome Atlas projects) and using 26 computational tools to catalogue driver genes and mutations. We identify 299 driver genes with implications regarding their anatomical sites and cancer/cell types. Sequence- and structure-based analyses identified >3,400 putative missense driver mutations supported by multiple lines of evidence. Experimental validation confirmed 60–85% of predicted mutations as likely drivers. We found that >300 MSI tumors are associated with high PD-1/PD-L1 and 57% of tumors analyzed harbor putative clinically actionable events. Our study represents the most comprehensive discovery of cancer genes and mutations to date and will serve as a blueprint for future biological and clinical endeavors.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b475a518b292b087a1b2c92ed289f79ebbe50832

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Citations in the corpus

Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines (2018, neutral): “Moredetailedanalysisofthe characteristics of the data and their biological implications will be discussed in other papers, such as ‘‘Comprehensive Characterization of Cancer Driver Genes and Mutations’’ (Bailey et al., 2018).”
TIMER2.0 for analysis of tumor-infiltrating immune cells (2020, background): “Identifying the functional impact of somatic mutations in the gene expression pattern is critical for precision oncology and drug discovery (36).”
Targeting TGF-β signal transduction for fibrosis and cancer therapy (2022, background): “Furthermore, GNAQ or CNA11 mutations demonstrate low sensitivity or resistance to specific treatments [140, 141].”
LIST-S2: taxonomy based sorting of deleterious missense mutations across species (2020, background): “the 299 identified by (30)) that are also expressed in the cancer tissue, since mutations with higher LIST-S2 scores are more likely to alter the functionality of these driver genes which make them candidate driver mutations.”
Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genome Atlas (2018, positive): “Our analysis used data and best practices from the PanCanAtlas Drivers/Essentiality Working Group (Bailey et al., 2018) and identiﬁed 48 DDR genes as potential drivers by at least one driver identiﬁcation algorithm.”
A compendium of mutational cancer driver genes (2020, background): “Primary (23,363;180) Metastasis (4,648;36) Relapse (65;5) Adult (25,277;173) Paediatric (2,799;48) 28,076 samples 66 cancer types 221 cohorts”
Proteogenomic characterization reveals therapeutic vulnerabilities in lung adenocarcinoma (2020, background): “, 2008) (Table S5) influenced expression of either the cognate gene product (cis effects), or other gene products (trans effects), specifically of a defined set of cancer-related genes (Bailey et al., 2018).”
The microbiome and human cancer (2021, background): “For example, a blood-derived TP53 mutation can indicate host cancer status but implicates >25 cancer types (115); conversely, Streptococcus gallolyticus (formerly S.”