Abstract 3555: COSMIC: two decades of curating somatic variants in cancer
Authors: Zbyslaw Sondka, Madiha Ahmed, Joanna Argasinska, David Beare, Nidhi Bindal Dhir, Denise Carvalho-Silva, Manpreet Singh Chawla, Stephen Duke, Ilaria Fasanella, Muhammed Fouzan, Avirup Guha Neogi, Susan Haller, Bhavana Harsha, Balazs Hetenyi, Leonie Hodges, Alex Holmes, Steven Jupe, Rachel Lyne, Madhumita Madhumita, Thomas Maurel, Karen McLaren, Sumodh Nair, Helder Pedro, Amaia Sangrador-Vegas, Helen Schuilenburg, Zoe Sheard, Michael Starkey, Rebecca Steele, Sari Ward, Jennifer Wilding, Siew Yit Yong, Jon Teague
Journal: Cancer Research (2024)

Abstract In 2004, COSMIC was one of the first initiatives to integrate global data on somatic mutations in cancer. At the time it was explicit that the fragmentation of genetic datasets was a major obstacle to understand the processes driving cancer. A team of expert curators and bioinformaticians was tasked with identifying and cataloguing data related to somatic variants, as well as relevant demographic, clinical, and patient information from published studies and making these data easily accessible to the research community. Over the last two decades we have witnessed the incredible progress in cancer genomics enabling whole genome studies, resulting in the exponential growth of data generated by cancer research. During its first year, COSMIC integrated data from 1672 scientific papers, cataloguing 1755 unique mutations across 21 genes. At present, twenty years later it is common for a single publication to describe tens of thousands of genome-wide mutations and now COSMIC includes 24 million genomic variants collected from more than 1.5 million patient samples acquired from over 29,000 scientific publications. Today, an important part of COSMIC's mission is to help translate this information for improving cancer treatment and patient care. To achieve this, the main catalogue of somatic mutations is supported by 6 accompanying resources that focus on different aspects of molecular oncology. The Cancer Gene Census and Cancer Mutation Census describe the roles of genes and mutations in oncogenesis, based on literature curation and analysis of the core mutation catalogue. COSMIC 3-D visualises mutation frequency in the context of the protein structure, and Mutational Signatures catalogues the mutagenic processes behind the nature of mutations at the genome level. To help target molecular alterations in clinical practice, Actionability and the catalogue of mutations causing drug resistance inform the availability of therapeutic options for cancer and how their efficacy is influenced by the genetic profile of the cancer. The ongoing development of sequencing techniques and new diagnostic methods as well as computational approaches, including deep learning and use of generative AI, give us hope that the next 20 years will be equally revolutionary for data-driven oncology. However, the abundance of new sources of diverse datasets makes data fragmentation an evolving challenge for the whole research community. Developing and adopting common standards for data formats, management and usage will be critical to assure inclusive, efficient, and effective translation of genomic research into a clinical practice. Citation Format: Zbyslaw Sondka, Madiha Ahmed, Joanna Argasinska, David Beare, Nidhi Bindal Dhir, Denise Carvalho-Silva, Manpreet Singh Chawla, Stephen Duke, Ilaria Fasanella, Muhammed Fouzan, Avirup Guha Neogi, Susan Haller, Bhavana Harsha, Balazs Hetenyi, Leonie Hodges, Alex Holmes, Steven Jupe, Rachel Lyne, Madhumita Madhumita, Thomas Maurel, Karen McLaren, Sumodh Nair, Helder Pedro, Amaia Sangrador-Vegas, Helen Schuilenburg, Zoe Sheard, Michael Starkey, Rebecca Steele, Sari Ward, Jennifer Wilding, Siew Yit Yong, Jon Teague. COSMIC: two decades of curating somatic variants in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3555.