COSMIC

• Cited more than 10,000 times
• Curated knowledge from more than 28,000 peer-reviewed publications
• Genome-wide screen data from more than 38,000 genomes
• More than 38 million somatic mutations
• Cancer Mutation Census

COSMIC is the gold standard database for somatic mutation information.

COSMIC, the Catalogue Of Somatic Mutations In Cancer, is the most detailed and comprehensive resource for exploring the effect of somatic mutations in human cancer. In addition to coding mutations, COSMIC covers all the genetic mechanisms by which somatic mutations promote cancer, including non-coding mutations, gene fusions, copy-number variants and drug-resistance mutations.
The core COSMIC database is complemented by additional datasets that allow users to contextualize their detected biomarkers.
1. Cancer Gene Census presents an in-depth catalogue of all 723 genes that are causally implicated in cancer, along with their biological function and the description of their genetic mechanisms driving cancer.
2. Cancer Mutation Census provides information on the mutation significance of all coding mutations based on biological and biochemical information from multiple sources.
3. Mutation Actionability in Precision Oncology (Actionability) provides up-to-date information about drugs that target specific somatic mutations at all stages of development.
4. Cell Lines Project puts forward cell line 'omics data through systematic characterization of the genetics and genomics (alterations and gene expression) of 1020 cancer cell lines.

COSMIC data are carefully selected, collected and standardized through manual curation by scientific experts, ensuring high quality, accuracy and descriptive data capture.

COSMIC data are available as download files, ready for you to integrate into your current pipelines and tools. You also will get access to any updates released during the subscription period of one year, as well as access to our support team.

COSMIC provides curated knowledge about somatic mutations in cancer for a wide range of applications, including the following:
• Discover potential drug targets and biomarkers for cancer.
• Improve cohort selection for clinical trials.
• Identify driver mutations and relevant genes to support patient diagnosis.