COSMIC: somatic cancer genetics at high-resolution

By S. Forbes, D. Beare, Harry Boutselakis, S. Bamford, Nidhi Bindal, John G. Tate, C. Cole, Sari Ward, E. Dawson, Laura Ponting and 9 more. Nucleic Acids Res., 2016

1,840 citations recorded by the source corpus.

COSMIC, the Catalogue of Somatic Mutations in Cancer (http://cancer.sanger.ac.uk) is a high-resolution resource for exploring targets and trends in the genetics of human cancer. Currently the broadest database of mutations in cancer, the information in COSMIC is curated by expert scientists, primarily by scrutinizing large numbers of scientific publications. Over 4 million coding mutations are described in v78 (September 2016), combining genome-wide sequencing results from 28 366 tumours with complete manual curation of 23 489 individual publications focused on 186 key genes and 286 key fusion pairs across all cancers. Molecular profiling of large tumour numbers has also allowed the annotation of more than 13 million non-coding mutations, 18 029 gene fusions, 187 429 genome rearrangements, 1 271 436 abnormal copy number segments, 9 175 462 abnormal expression variants and 7 879 142 differentially methylated CpG dinucleotides. COSMIC now details the genetics of drug resistance, novel somatic gene mutations which allow a tumour to evade therapeutic cancer drugs. Focusing initially on highly characterized drugs and genes, COSMIC v78 contains wide resistance mutation profiles across 20 drugs, detailing the recurrence of 301 unique resistance alleles across 1934 drug-resistant tumours. All information from the COSMIC database is available freely on the COSMIC website.

Subfields: Cancer Research, Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c10a185cecf2428ed8499129574c38bdc4db3619

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Citations in the corpus

COSMIC: a curated database of somatic variants and clinical data for cancer (2023, positive): “This data is then translated into a standardised format, integrated, and made available to the research community through well-structured datasets and user-friendly data exploration web-sites and tools ( 4 ,5 ).”
Project DRIVE: A Compendium of Cancer Dependencies and Synthetic Lethal Relationships Uncovered by Large-Scale, Deep RNAi Screening. (2017, neutral): “Many of these genes are found on the COSMIC gene consensus list (Forbes et al., 2017).”
Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics (2017, background): “Cancers’ acquisition of drug resistance is partly linked to specific somatic mutations of the anticancer targets and the target/drug regulatory genes (45).”
Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods (2019, background): “The cancer-relevant fusions include those reported in the Mitelman Database of Chromosome Aberrations and Gene Fusions in Cancer [90], ChimerDB 2.0 [91], COS-MIC [92, 93], and fusions discovered in cancer cell lines and surveys of tumor samples [1, 94, 95].”
RMBase v2.0: deciphering the map of RNA modifications from epitranscriptome sequencing data (2017, methodology): “To investigate the relationships of disease-related SNVs and SNPs with RNA modifications, we collected cancer somatic mutations from the COSMIC database (34) and previous publications (35,36).”
New approach for understanding genome variations in KEGG (2018, methodology): “The KEGG VARIANT database also contains links to outside databases such as ClinVar (5), dbSNP (6) and COSMIC (7).”
A Patient-Derived Glioblastoma Organoid Model and Biobank Recapitulates Inter- and Intra-tumoral Heterogeneity. (2019, neutral): “, 2011, 2016), COSMIC v88 (Forbes et al., 2017), Clin-Var (Landrum et al.”
Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genome Atlas (2018, neutral): “The consequences of DDR deficiency are becoming better understood through analyses of DDR gene alterations in cancer (Alexandrov et al., 2013; Forbes et al., 2017; Garraway and Lander, 2013; Martincorena and Campbell, 2015).”
Precision Oncology: The Road Ahead (2017, background): “Moreover, large-scale sequencing of human cancer genomes and transcriptomes have identified nearly 200 “consensus” driver genes (of which ~15% were identified primarily using DNA sequencing of cancer genomes [29]) and an additional 300 putative driver genes have been suggested [30, 31].”
Open Targets Platform: new developments and updates two years on (2018, methodology): “We summarise information on cancer hallmarks (24,25), which are curated by COSMIC (26) and integrated in the Cancer Gene Census (14).”