No retraction, correction or concern found when last checked on Oct 15, 2026.
The COSMIC (Catalogue of Somatic Mutations in Cancer) database and website
S Bamford, E Dawson, S Forbes and 8 more
British Journal of Cancer · 2004
Abstract
The discovery of mutations in cancer genes has advanced our understanding of cancer. These results are dispersed across the scientific literature and with the availability of the human genome sequence will continue to accrue. The COSMIC (Catalogue of Somatic Mutations in Cancer) database and website have been developed to store somatic mutation data in a single location and display the data and other information related to human cancer. To populate this resource, data has currently been extracted from reports in the scientific literature for somatic mutations in four genes, BRAF, HRAS, KRAS2 and NRAS. At present, the database holds information on 66 634 samples and reports a total of 10 647 mutations. Through the web pages, these data can be queried, displayed as figures or tables and exported in a number of formats. COSMIC is an ongoing project that will continue to curate somatic mutation data and release it through the website.
Shown for reading. Its publisher allows quoting it, not republishing it.
Full text
From PubMed Central, U.S. National Library of Medicine, as current on Oct 17, 2026. Paragraph numbers are ours, so an answer can point at one.
Approximately one in three individuals in Europe and North America develops one of the approximately 200 different classes of cancer and it is the cause of death of one in five. All cancers arise as a result of the acquisition of a series of fixed DNA sequence abnormalities, each of which ultimately confers growth advantage upon the clone of cells in which it has occurred. These abnormalities include base substitutions, deletions, amplifications and rearrangements. The extent to which each of these mechanisms contributes to cancer varies markedly between different genes, and probably also between different cancer types. Identification of the genes that are mutated in cancer is a central aim of cancer research. Over the past 25 years, approximately 300 genes have been shown to be somatically mutated in cancer. This work forms the foundation for understanding the biological abnormalities within neoplastic cells, provides information on the function of gene products and sheds light on more complex questions such as the relationships between genes and biochemical pathways. Current strategies for the development of new therapeutic and preventive agents in cancer are increasingly dependent upon modulation of these critical molecular targets.
The scientific literature is a rich source of mutation data that, in general, is published in a piecemeal fashion. More comprehensive data sources do exist, such as Online Mendelian Inheritance in Man (OMIM, ), HGVbase and the Human Gene Mutation Database (HGMD, ). These databases give overviews of the genetics and biology of many genes and associated diseases (OMIM), genome variants and associated genotype–phenotype relationships (HGVbase) or germline mutation data (HGMD). For somatic mutations in cancer, there are many locus-specific web resources, such as those for p53, that cover a single gene in depth. The value of these various databases should not be underestimated; however, none of them offer a comprehensive view of all previously reported somatic mutations in cancer. Looking to the future, the volume of somatic mutation data will continue to expand and the scientific community will be better served if this data is provided in a coherent fashion. A public, comprehensive, intuitive, accessible and integrated database is required to maximise the benefit from this rich data set. The Catalogue of Somatic Mutations in Cancer (COSMIC), (http://www.sanger.ac.uk/cosmic) is a database that holds somatic mutation data and associated information, and can be interrogated through a series of web pages to provide a graphical or tabular view of the data along with various export options. To date, the database has been populated with data from four genes: HRAS, KRAS2, NRAS and BRAF.
Gene selection
The genes that have been selected for curation are taken from the list of cancer genes assembled in the Cancer Gene Census. In the first instance, data was obtained for four genes that are known to be somatically mutated in cancer: HRAS, KRAS2, NRAS and BRAF.
Data extraction from the literature
PubMed is broadly searched for references containing relevant somatic mutation data in cancer (example search: (ras OR genes, ras) AND human AND mutation). In the first instance, the abstract is read to identify, and select for inclusion in the database, papers that are likely to include somatic mutation information relating to cancer or precancerous conditions. Primary research papers are read and information about the samples, mutations and experimental methods (see Table 1 ) is extracted and entered into the database. Reviews are also selected if thought to be specific to a gene of interest. In order to avoid duplication of data, this source is used to identify the relevant primary literature and not as the source of the mutation data. Any references containing incomplete data (e.g. mutations reported but not fully described) or data of insufficient quality (e.g. errors identified in the data) are not fully curated but are added to a list of additional references containing somatic mutation information. Simple mutations are fed through Mutation Checker before being imported to COSMIC, while more complex alterations are manually annotated.
COSMIC DATABASE
The COSMIC database is implemented in an Oracle relational database and has five sections each containing multiple tables.
Gene information
A static version of each gene is maintained in COSMIC. The genomic structure of each gene and chromosome location is derived from Ensembl and cDNA sequence and protein sequence from the RefSeq project. Other information is held to provide links to web resources such as Ensembl, Pfam, InterPro and OMIM.
Paper information
The details of the papers that have been curated are maintained in the paper section and include title, journal, author lists and links to PubMed. There are currently 1483 papers in COSMIC, 865 of these have been curated for mutations, while 618 either have no relevant data or incomplete data that could not accurately be extracted. By gene 30, 249, 718 and 303 papers report BRAF, HRAS, KRAS2 and NRAS mutations, respectively. Of the 865 papers reporting mutations, 615 report data on only one gene, while 72, 174 and four contain data on two, three or all four genes, respectively.
Mutation information
COSMIC can accommodate information on base substitutions, insertions and deletions, translocations and changes in copy number. For the four genes presently in COSMIC, there are 147 unique mutations (36 for BRAF, 27 for HRAS, 52 for KRAS2 and 32 for NRAS). In the tumours that have been analysed, there are a total of 10 647 mutations, 736 in BRAF, 477 in HRAS, 8302 in KRAS2 and 1132 in NRAS.
Tumour classification system
The tissue site and histology data is taken from the curated papers and entered into COSMIC (this forms the ‘paper definition’). Tumour classification is a continually evolving field and there is no standard nomenclature adhered to for the purposes of publication in the various journals. Identical tissues and histologies can have different labels depending on the origin and age of the study. To overcome difficulties caused by these alternate nomenclatures, a standardised system of definitions has been developed (the ‘COSMIC definitions’) through consultation with experts in the field. This groups data from the same tissue types and histologies and can be used to translate the ‘paper definitions’ to ‘COSMIC definitions’. Every sample has up to eight definitions; primary tissue, tissue subtype 1, 2 and 3, primary histology and histology subtypes 1, 2 and 3. If there is no data for any of these definitions, COSMIC records an entry of NS, not specified. A total of 513 tissue definitions have been noted in the papers in COSMIC and have been translated to 372 COSMIC tissue definitions. Likewise, a total of 1150 histology definitions were found in the papers in COSMIC that were translated to 425 COSMIC histology definitions. This unified classification system is presented through the web pages to present a normalised browsing tool.
Individual/tumour/sample data
The sample data is taken from the curated papers and linked to the appropriate gene, paper, classification and when present a mutation. This forms the core of the COSMIC database. An individual can have many tumours and each tumour can have many samples. However in the COSMIC scheme, each sample is unique and could be considered as a single experiment. There are 66 634 sample records in COSMIC (5158, 11 876, 35 716 and 13 884 for BRAF, HRAS, KRAS2 and NRAS, respectively). These samples are derived from 57 444 tumours of which 51 988 were analysed in one gene, 2353 in two genes, 2930 in three genes and 173 in all four genes.
COSMIC WEBSITE
A series of web pages provides query tools to interrogate COSMIC and produces graphical (Figure 1) and tabular (Table 2 ) displays of the data. Currently the output is provided at the amino-acid level based on the protein structure of each gene.
Browse by gene
Immediate access to the data is provided through the Browse by Gene link. This gives an instant overview of the mutation data for one or more genes and gives links to display data for individual tissues.
Browse by tissue
More complex queries can be constructed using the Browse by Tissue link. The user has the option to select one or more tissues, then one or more histologies, and finally one or more genes. If only one tissue or histology is selected, it is possible to select one or more tissue or histology subtypes before making a gene selection. All of the tissues present in the COSMIC classification scheme are available from the first page; however, subsequent pages only show the relevant options and not the entire list of options, for example having selected eye, the tissue subtype options are retina and uveal tract.
Data display
After querying the database, the results are displayed as a figure (Figure 1) and as a series of tables (Table 2) for each gene that was selected. The figure shows the linear amino-acid sequence derived from the gene with the mutations positioned along its length. Further information and links are provided as appropriate to the protein sequence. The table gives a summary of the mutations stratified by tissue and histology. The depth of the stratification relates to the depth of the original query. If only tissue was selected, the data will be stratified by tissue; however, if tissue, subtissue, histology and subhistology are selected, the data will be broken down further. Links from this table reload the figure to display a subset of the data and provide more details of the specific mutations. Two other tables provide a summary of the statistics in COSMIC for the selected gene and a summary of the mutations shown in the figure.
Exports and downloads
Having displayed the results from a query, the data can be formatted in simple text, Excel or HTML that can be downloaded from the COSMIC site. The cDNA and protein sequences are available through the Additional Info. link on the COSMIC home page as is the Classification Scheme.
FUTURE DIRECTIONS
There is a continuing effort to enter additional somatic mutation data in to COSMIC. In order to keep the data in COSMIC up-to-date, we regularly monitor the literature for new reports of mutations in the genes that exist in COSMIC. In addition, further cancer genes will be taken from the Cancer Gene Census and curated. The COSMIC website will be developed further to make use of the underlying data. This will include a DNA view of the mutations and methods to display insertions and deletions. In addition, we will display other data that has already been captured such as the patient sex and age for the samples and the experimental methods used to screen for the mutations. There are however limitations to this data as we can only collect data that is described in the original work. Even with this caveat the data provides a direct summary of the somatic mutation literature. Considering the data set as a whole it will be possible to analyse, in greater detail, the wider aspects of the biology underlying the genetic changes that take place in cancer.
References 16
- 1.
Bateman A, Coin L, Durbin R, Finn RD, Hollich V, Griffiths-Jones S, Khanna A, Marshall M, Moxon S, Sonnhammer EL, Studholme DJ, Yeats C, Eddy SR (2004 ) The Pfam protein families database. Nucleic Acids Res 32: D138–D141
Not in our library10.1093/nar/gkh121
- 2.
Béroud C, Soussi T (2003 ) The UMD-p53 database: new mutations and analysis tools. Hum Mutat 21: 176–181
Not in our library10.1002/humu.10187
- 3.
Birney E, Andrews D, Bevan P, Caccamo M, Cameron G, Chen Y, Clarke L, Coates G, Cox T, Cuff J, Curwen V, Cutts T, Down T, Durbin R, Eyras E, Fernandez-Suarez XM, Gane P, Gibbins B, Gilbert J, Hammond M, Hotz H, Iyer V, Kahari A, Jekosch K, Kasprzyk A, Keefe D, Keenan S, Lehvaslaiho H, McVicker G, Melsopp C, Meidl P, Mongin E, Pettett R, Potter S, Proctor G, Rae M, Searle S, Slater G, Smedley D, Smith J, Spooner W, Stabenau A, Stalker J, Storey R, Ureta-Vidal A, Woodwark C, Clamp M, Hubbard T (2004 ) Ensembl 2004. Nucleic Acids Res 32: D468–D470
Not in our library10.1093/nar/gkh038
- 4.
Davies H, Bignell GR, Cox C, Stephens P, Edkins S, Clegg S, Teague J, Woffendin H, Garnett MJ, Bottomley W, Davis N, Dicks E, Ewing R, Floyd Y, Gray K, Hall S, Hawes R, Hughes J, Kosmidou V, Menzies A, Mould C, Parker A, Stevens C, Watt S, Hooper S, Wilson R, Jayatilake H, Gusterson BA, Cooper C, Shipley J, Hargrave D, Pritchard-Jones K, Maitland N, Chenevix-Trench G, Riggins GJ, Bigner DD, Palmieri G, Cossu A, Flanagan A, Nicholson A, Ho JW, Leung SY, Yuen ST, Weber BL, Seigler HF, Darrow TL, Paterson H, Marais R, Marshall CJ, Wooster R, Stratton MR, Futreal PA (2002 ) Mutations of the BRAF gene in human cancer. Nature 417: 949–954
Not in our library10.1038/nature00766
- 5.
Fredman D, Siegfried M, Yuan YP, Bork P, Lehväslaiho H, Brookes AJ (2002 ) HGVbase: a human sequence variation database emphasizing data quality and a broad spectrum of data sources. Nucleic Acids Res 30: 387–391
Not in our library10.1093/nar/30.1.387
- 6.
Futreal PA, Down T, Coin L, Marshall M, Rahman N, Wooster R, Timothy Hubbard T, Bateman A, Stratton MR (2004 ) A census of human cancer genes. Nat Rev Cancer 4: 177–183
Not in our library10.1038/nrc1299
- 7.
Hall A, Marshall CJ, Spurr NK, Weiss RA (1983 ) Identification of transforming gene in two human sarcoma cell lines as a new member of the ras gene family located on chromosome 1. Nature 303: 396–400
Not in our library10.1038/303396a0
- 8.
Higginson J (1992 ) Human cancer: epidemiology and environmental causes. In: Higginson, Muis, Munoz (eds). Cambridge Monographs on Cancer Research. Cambridge, UK: Cambridge University Press
Not in our library
- 9.
McCoy MS, Toole JJ, Cunningham JM, Chang EH, Lowy DR, Weinberg RA (1983 ) Characterization of a human colon/lung carcinoma oncogene. Nature 302: 79–81
Not in our library10.1038/302079a0
- 10.
Mulder NJ, Apweiler R, Attwood TK, Bairoch A, Barrell D, Bateman A, Binns D, Biswas M, Bradley P, Bork P, Bucher P, Copley RR, Courcelle E, Das U, Durbin R, Falquet L, Fleischmann W, Griffiths-Jones S, Haft D, Harte N, Hulo N, Kahn D, Kanapin A, Krestyaninova M, Lopez R, Letunic I, Lonsdale D, Silventoinen V, Orchard SE, Pagni M, Peyruc D, Ponting CP, Selengut JD, Servant F, Sigrist CJA, Vaughan R, Zdobnov EM (2003 ) The InterPro Database, 2003 brings increased coverage and new features. Nucleic Acids Res 31: 315–318
Not in our library10.1093/nar/gkg046
- 11.
Olivier M, Eeles R, Hollstein M, Khan MA, Harris C.C, Hainaut P (2002 ) The IARC TP53 Database: new online mutation analysis and recommendations to users. Hum Mutat 19: 607–614
Not in our library10.1002/humu.10081
- 12.
Reddy EP, Reynolds RK, Santos E, Barbacid M (1982 ) A point mutation is responsible for the acquisition of transforming properties by the T24 human bladder carcinoma oncogene. Nature 300: 149–152
Not in our library10.1038/300149a0
- 13.
Stajich JE, Block D, Boulez K, Brenner SE, Chervitz SA, Dagdigian C, Fuellen G, Gilbert JG, Korf I, Lapp H, Lehvaslaiho H, Matsalla C, Mungall CJ, Osborne BI, Pocock MR, Schattner P, Senger M, Stein LD, Stupka E, Wilkinson MD, Birney E (2002 ) The Bioperl toolkit: Perl modules for the life sciences. Genome Res 12: 1611–1618
Not in our library10.1101/gr.361602
- 14.
Stenson PD, Ball EV, Mort M, Phillips AD, Shiel JA, Thomas NS, Abeysinghe S, Krawczak M, Cooper DN (2003 ) Human Gene Mutation Database (HGMD(R)): 2003 update. Hum Mutat 21: 577–581
Not in our library10.1002/humu.10212
- 15.
Vogelstein B, Kinzler K (1998 ) The Genetic Basis of Human Cancer. New York: McGraw Hill
Not in our library
- 16.
Wheeler DL, Church DM, Edgar R, Federhen S, Helmberg W, Madden TL, Pontius JU, Schuler GD, Schriml LM, Sequeira E, Suzek TO, Tatusova TA, Wagner L (2004 ) Database resources of the National Center for Biotechnology Information: update. Nucleic Acids Res 32: D35–D40
Not in our library10.1093/nar/gkh073
Cited by papers we hold 35
- COSMIC: a curated database of somatic variants and clinical data for cancer · 2023No notice
- In-silico probing of AML related RUNX1 cancer-associated missense mutations: Predicted relationships to DNA binding and drug interactions · 2022Not checked
- Discovery of putative tumor suppressors from CRISPR screens reveals rewired lipid metabolism in acute myeloid leukemia cells · 2021Not checked
- Debutant iOS app and gene-disease complexities in clinical genomics and precision medicine · 2019Not checked
- A pan-cancer analysis of synonymous mutations · 2019No notice
- Inherent flexibility of CLIC6 revealed by crystallographic and solution studies · 2018Not checked
- COSMIC: the Catalogue Of Somatic Mutations In Cancer · 2018No notice
- The Role of the Core Non-Homologous End Joining Factors in Carcinogenesis and Cancer · 2017No notice
- KRAS, TP53, CDKN2A, SMAD4, BRCA1, and BRCA2 Mutations in Pancreatic Cancer · 2017No notice
- Deconstruction of the Ras switching cycle through saturation mutagenesis · 2017No notice
- Prevalence and detection of low-allele-fraction variants in clinical cancer samples · 2017No notice
- Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden · 2017No notice
- COSMIC: somatic cancer genetics at high-resolution · 2016No notice
- Discovering and understanding oncogenic gene fusions through data intensive computational approaches · 2016No notice
- Structural basis for oxygen degradation domain selectivity of the HIF prolyl hydroxylases · 2016No notice
- Rare disruptive mutations and their contribution to the heritable risk of colorectal cancer · 2016No notice
- Comparative analysis of KRAS codon 12, 13, 18, 61 and 117 mutations using human MCF10A isogenic cell lines · 2015No notice
- Fast and scalable inference of multi-sample cancer lineages · 2015No notice
- Sporadic Early-Onset Colorectal Cancer Is a Specific Sub-Type of Cancer: A Morphological, Molecular and Genetics Study · 2014No notice
- COSMIC: exploring the world's knowledge of somatic mutations in human cancer · 2014No notice
and 15 more
Reads
Read by people on this page
None yet.
Checked or read by AI through naulon
None yet.
A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.
Are you an author?
Is this your paper?
Claim it to add your note and be paid when agents cite it. Free.