COSMIC 2005

The Catalogue Of Somatic Mutations In Cancer (COSMIC) database and web site was developed to preserve somatic mutation data and share it with the community. Over the past 25 years, approximately 350 cancer genes have been identified, of which 311 are somatically mutated. COSMIC has been expanded and now holds data previously reported in the scientific literature for 28 known cancer genes. In addition, there is data from the systematic sequencing of 518 protein kinase genes. The total gene count in COSMIC stands at 538; 25 have a mutation frequency above 5% in one or more tumour type, no mutations were found in 333 genes and 180 are rarely mutated with frequencies <5% in any tumour set. The COSMIC web site has been expanded to give more views and summaries of the data and provide faster query routes and downloads. In addition, there is a new section describing mutations found through a screen of known cancer genes in 728 cancer cell lines including the NCI-60 set of cancer cell lines

A preexisting rare PIK3CA e545k subpopulation confers clinical resistance to MEK plus CDK4/6 inhibition in NRAS melanoma and is dependent on S6K1 signaling

Combined MEK and CDK4/6 inhibition (MEKi + CDK4i) has shown promising clinical outcomes in patients with NRAS- mutant melanoma. Here, we interrogated longitudinal biopsies from a patient who initially responded to MEKi + CDK4i therapy but subsequently developed resistance. Whole-exome sequencing and functional validation identified an acquired PIK3CA E545K mutation as conferring drug resistance. We demonstrate that PIK3CA E545K preexisted in a rare subpopulation that was missed by both clinical and research testing, but was revealed upon multiregion sampling due to PIK3CA E545K being nonuniformly distributed. This resistant population rapidly expanded after the initiation of MEKi + CDK4i therapy and persisted in all successive samples even after immune checkpoint therapy and distant metastasis. Functional studies identified activated S6K1 as both a key marker and specific therapeutic vulnerability downstream of PIK3CA E545K -induced resistance. These results demonstrate that difficult-to-detect preexisting resistance mutations may exist more often than previously appreciated and also posit S6K1 as a common downstream therapeutic nexus for the MAPK, CDK4/6, and PI3K pathways. SIGNIFICANCE: We report the first characterization of clinical acquired resistance to MEKi + CDK4i, identifying a rare preexisting PIK3CA E545K subpopulation that expands upon therapy and exhibits drug resistance. We suggest that single-region pretreatment biopsy is insufficient to detect rare, spatially segregated drug-resistant subclones. Inhibition of S6K1 is able to resensitize PIK3CA E545K -expressing NRAS-mutant melanoma cells to MEKi + CDK4i. © 2018 AAC

Annotating Whole Genome Sequencing in COSMIC (The Catalogue of Somatic Mutations in Cancer)

&#x22;COSMIC, the Catalogue Of Somatic Mutations In Cancer&#x22;:http://www.sanger.ac.uk/cosmic is designed to store and display somatic mutation information relating to human cancers, combining detailed information on publications, samples and mutation types. The information is curated both from the primary literature and the laboratories at the Cancer Genome Project, Sanger Institute, UK, and then semi-automatically entered into the COSMIC database. The v47 release (May 2010) contained the curation of 9202 papers describing 116,977 mutations across 466,851 samples. In order to provide consistent annotation of the data, COSMIC has developed a classification system for cancer histology and tissue ontology, and adapted HGVS mutation nomenclature recommendations to describe the multiple mutation types involved in cancer. &#xd;&#xa;&#xd;&#xa;Cancer genetics is moving from systematic screens of candidate gene sets to whole genome sequencing analyses, and COSMIC displays and navigates this new data; we have recently included systematic gene screens and whole genome sequencing studies. COSMIC will annotate and display somatic mutation data that will be emerging from the &#x22;International Cancer Genome Consortium (ICGC)&#x22;:http://www.icgc.org/ and &#x22;The Cancer Genome Atlas (TCGA)&#x22;:http://cancergenome.nih.gov/ projects. New tools are being developed to interpret this genomic data with coding mutation annotations. In addition COSMIC will be expanded to curate and display data from mouse insertional mutagenesis screening and mouse cancer model exome/genome sequencing in the future. The data within COSMIC is freely available without restriction via a website, in datasheets on the &#x22;FTP site&#x22;:ftp://ftp.sanger.ac.uk/pub/CGP/cosmic and through the &#x22;COSMIC Biomart&#x22;:http://www.sanger.ac.uk/genetics/CGP/cosmic/biomart/martview/, available from the &#x22;COSMIC homepage&#x22;:http://www.sanger.ac.uk/cosmic &#xd;&#xa

Mutations of the BRAF gene in human cancer

Cancers arise owing to the accumulation of mutations in critical genes that alter normal programmes of cell proliferation, differentiation and death. As the first stage of a systematic genome-wide screen for these genes, we have prioritized for analysis signalling pathways in which at least one gene is mutated in human cancer. The RAS RAF MEK ERK MAP kinase pathway mediates cellular responses to growth signals. RAS is mutated to an oncogenic form in about 15% of human cancer. The three RAF genes code for cytoplasmic serine/threonine kinases that are regulated by binding RAS. Here we report BRAF somatic missense mutations in 66% of malignant melanomas and at lower frequency in a wide range of human cancers. All mutations are within the kinase domain, with a single substitution (V599E) accounting for 80%. Mutated BRAF proteins have elevated kinase activity and are transforming in NIH3T3 cells. Furthermore, RAS function is not required for the growth of cancer cell lines with the V599E mutation. As BRAF is a serine/threonine kinase that is commonly activated by somatic point mutation in human cancer, it may provide new therapeutic opportunities in malignant melanoma

COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer

COSMIC (http://www.sanger.ac.uk/cosmic) curates comprehensive information on somatic mutations in human cancer. Release v48 (July 2010) describes over 136 000 coding mutations in almost 542 000 tumour samples; of the 18 490 genes documented, 4803 (26%) have one or more mutations. Full scientific literature curations are available on 83 major cancer genes and 49 fusion gene pairs (19 new cancer genes and 30 new fusion pairs this year) and this number is continually increasing. Key amongst these is TP53, now available through a collaboration with the IARC p53 database. In addition to data from the Cancer Genome Project (CGP) at the Sanger Institute, UK, and The Cancer Genome Atlas project (TCGA), large systematic screens are also now curated. Major website upgrades now make these data much more mineable, with many new selection filters and graphics. A Biomart is now available allowing more automated data mining and integration with other biological databases. Annotation of genomic features has become a significant focus; COSMIC has begun curating full-genome resequencing experiments, developing new web pages, export formats and graphics styles. With all genomic information recently updated to GRCh37, COSMIC integrates many diverse types of mutation information and is making much closer links with Ensembl and other data resources

Data mining using the Catalogue of Somatic Mutations in Cancer BioMart

Catalogue of Somatic Mutations in Cancer (COSMIC) (http://www.sanger.ac.uk/cosmic) is a publicly available resource providing information on somatic mutations implicated in human cancer. Release v51 (January 2011) includes data from just over 19 000 genes, 161 787 coding mutations and 5573 gene fusions, described in more than 577 000 tumour samples. COSMICMart (COSMIC BioMart) provides a flexible way to mine these data and combine somatic mutations with other biological relevant data sets. This article describes the data available in COSMIC along with examples of how to successfully mine and integrate data sets using COSMICMart

Breast cancer genome heterogeneity: a challenge to personalised medicine?

Implementation of high-throughput genomics sequencing approaches into routine laboratory practice has raised the potential for the identification of multiple breast cancer targets suitable for future therapeutic intervention in order to improve cancer outcomes. Results from these studies have revealed bewildering breast cancer genome complexity with very few aberrations occurring in common between breast cancers. In addition, such complexity is compounded by evidence of genomic heterogeneity occurring within individual breast cancers. Such inter-tumoural and intratumoural heterogeneity is likely to present a challenge to personalised therapeutic approaches that might be circumvented through the definition of genome instability mechanisms governing such diversity and their exploitation using synthetic lethal approaches

Deriving a mutation index of carcinogenicity using protein structure and protein interfaces

With the advent of Next Generation Sequencing the identification of mutations in the genomes of healthy and diseased tissues has become commonplace. While much progress has been made to elucidate the aetiology of disease processes in cancer, the contributions to disease that many individual mutations make remain to be characterised and their downstream consequences on cancer phenotypes remain to be understood. Missense mutations commonly occur in cancers and their consequences remain challenging to predict. However, this knowledge is becoming more vital, for both assessing disease progression and for stratifying drug treatment regimes. Coupled with structural data, comprehensive genomic databases of mutations such as the 1000 Genomes project and COSMIC give an opportunity to investigate general principles of how cancer mutations disrupt proteins and their interactions at the molecular and network level. We describe a comprehensive comparison of cancer and neutral missense mutations; by combining features derived from structural and interface properties we have developed a carcinogenicity predictor, InCa (Index of Carcinogenicity). Upon comparison with other methods, we observe that InCa can predict mutations that might not be detected by other methods. We also discuss general limitations shared by all predictors that attempt to predict driver mutations and discuss how this could impact high-throughput predictions. A web interface to a server implementation is publicly available at http://inca.icr.ac.uk/