PO.BCS01.01 · 生物信息与计算

COSMIC:推进体细胞突变的癌症基因组学知识库

COSMIC: Advancing the cancer genomics knowledgebase of somatic mutations

海报缩略图:COSMIC:推进体细胞突变的癌症基因组学知识库
编号 56 展板 18 时间 4/19 02:00–05:00 区域 Section 3 主讲 Madhumita Madhumita, PhD
分会场 Application of Bioinformatics to Cancer Biology 1
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作者与单位 Authors & Affiliations

Madhumita Madhumita, Madiha Ahmed, Joanna Argasinska, David Armstrong, Nidhi Bindal Dhir, Denise Carvalho-Silva, Lucie Chadelle, Patrick Dao, Stephen Duke, Giovanna Fasanella, Muhammad Fouzan, Abishekraj Gnanasambandam, Avirup Guha Neogi, Susan Haller, Bhavana Harsha, Balazs Hetenyi, Leonie Hodges, Steven Jupe, Rachel Lyne, Thomas Maurel, Karen McLaren, Thomas Mutimer, Sumodh Nair, Hanna Najgebauer, Helder Pedro, Sophie Poole, Amaia Sangrador-Vegas, Zoe Sheard, Manpreet Singh Chawla, Michael Starkey, Rebecca Steele, Sari Ward, Ellen Wiedemann, Jennifer Wilding, Siew Yit Yong, Jon Teague

COSMIC, Wellcome Sanger Institute, Cambridge, United Kingdom

摘要 Abstract

中文摘要
COSMIC(癌症体细胞突变目录)已从最初的目录发展为世界上最全面的癌症体细胞变异知识库,建立在持续的专家人工审编基础之上。COSMIC目前汇集了超过2900万个从150万个样本中精心审编的独特体细胞变异,成为研究癌症基因组的重要资源。这一丰富的数据集是广泛而专注工作的成果,借鉴了来自30,000余篇科学论文和重大研究的见解。 COSMIC由一套专门的模块构成,这些模块共同将原始基因组变异转化为具有生物学和临床意义的洞见。这些模块包括:癌症基因普查(CGC),系统地对因果性癌症基因进行分类;癌症突变普查(CMC),通过计算和基于证据的注释来区分驱动突变和搭车突变;突变特征(Mutational Signatures),捕捉全基因组范围的致突变过程;COSMIC 3D,将变异置于蛋白质结构的背景中;以及可操作性与耐药性资源,将基因组改变映射到治疗反应和耐药机制。这些模块共同提供了在精准肿瘤学中解读体细胞变异全貌所必需的框架。 我们重点介绍关于下一代癌症突变普查(CMC v2)的正在进行的研究,其设计目的是增强COSMIC从大规模体细胞数据集中提取具有生物学意义信号的能力。CMC v2应用精细化的背景模型,在泛癌背景下于氨基酸水平识别癌症基因中的突变热点,聚焦于呈现出体细胞变异统计学显著富集的位置。该方法分离出非随机的、空间上连贯的突变簇,这些突变簇是驱动活性的强有力候选。尽管目前仍在开发中,这些分析展示了CMC v2提供更高分辨率的癌症基因失调洞见、并支持对肿瘤演化进行更细致解读的潜力。 CMC v2的开发标志着COSMIC朝着更加数据驱动、更具生物学基础地解读癌症变异迈出的关键一步。通过将统计建模与专家见解相结合,CMC v2完善了我们在不同肿瘤背景下将有意义的突变模式与背景噪声区分开来的能力。这些进展体现了COSMIC持续致力于将大规模基因组学转化为可操作生物学知识的承诺。随着该知识库不断扩展并与全球癌症研究界互动,COSMIC仍是理解癌症基因功能、完善生物标志物发现以及支持精准肿瘤学不可或缺的基石。
查看英文原文 English abstract
COSMIC (Catalogue Of Somatic Mutations in Cancer) has evolved from an initial catalogue to the world's most comprehensive knowledgebase of somatic variants in cancer, built upon a foundation of continuous, expert curation. COSMIC currently aggregates over 29 million unique somatic variants carefully curated from 1.5 million samples, establishing an essential resource for studying the cancer genome. This rich dataset is the result of extensive, dedicated work, drawing on insights from more than 30,000 scientific publications and major studies. COSMIC is structured into a suite of specialized modules that collectively transform raw genomic variants into biologically and clinically meaningful insight. These include the Cancer Gene Census (CGC), which systematically classifies causal cancer genes; the Cancer Mutation Census (CMC), which distinguishes driver from passenger mutations through computational and evidence-based annotation; Mutational Signatures, which captures genome-wide mutagenic processes; COSMIC 3D, which contextualizes variants within protein structures; and the Actionability and Resistance resources, which map genomic alterations to therapeutic response and resistance mechanisms. Together, these modules provide a framework essential for interpreting somatic variant landscapes in precision oncology. We highlight ongoing research on the next iteration of the Cancer Mutation Census (CMC v2), designed to enhance COSMIC's ability to extract biologically meaningful signals from large-scale somatic datasets. CMC v2 applies refined background models to identify mutation hotspots at the amino acid level across cancer genes in a pan-cancer context, focusing on positions exhibiting statistically significant enrichment of somatic variants. This approach isolates non-random, spatially coherent clusters of mutations that represent strong candidates for driver activity. Although currently under development, these analyses demonstrate the potential of CMC v2 to provide higher-resolution insights into cancer gene dysregulation and support more nuanced interpretation of tumor evolution. The development of CMC v2 marks a key step in COSMIC's evolution toward more data-driven, biologically grounded interpretation of cancer variants. By integrating statistical modeling with expert insight, CMC v2 refines our capacity to distinguish meaningful mutational patterns from background noise across diverse tumor contexts. These advances exemplify COSMIC's ongoing commitment to translating large-scale genomics into actionable biological knowledge. As the knowledgebase continues to expand and engage with the global cancer research community, COSMIC remains an indispensable foundation for understanding cancer gene function, refining biomarker discovery, and supporting precision oncology.
利益披露 Disclosure
M. Madhumita, None.. M. Ahmed, None.. J. Argasinska, None.. D. Armstrong, None.. N. B. Dhir, None.. D. Carvalho-Silva, None.. L. Chadelle, None.. P. Dao, None.. S. Duke, None.. G. Fasanella, None.. M. Fouzan, None.. A. Gnanasambandam, None.. A. G. Neogi, None.. S. Haller, None.. B. Harsha, None.. B. Hetenyi, None.. L. Hodges, None.. S. Jupe, None.. R. Lyne, None.. T. Maurel, None.. K. McLaren, None.. T. Mutimer, None.. S. Nair, None.. H. Najgebauer, None.. H. Pedro, None.. S. Poole, None.. A. Sangrador-Vegas, None.. Z. Sheard, None.. M. Singh Chawla, None.. M. Starkey, None.. R. Steele, None.. S. Ward, None.. E. Wiedemann, None.. J. Wilding, None.. S. Yit Yong, None.. J. Teague, None.

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