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精准基因组肿瘤学应用中变异解读的资源

Resources for Interpreting Variants in Precision Genomic Oncology Applications.

作者信息

Tsang Hsinyi, Addepalli KanakaDurga, Davis Sean R

机构信息

Center for Biomedical Informatics and Information Technology, National Cancer Institute, National Institutes of Health, Gaithersburg, MD, United States.

Attain, LLC, McLean, VA, United States.

出版信息

Front Oncol. 2017 Sep 19;7:214. doi: 10.3389/fonc.2017.00214. eCollection 2017.

DOI:10.3389/fonc.2017.00214
PMID:28975082
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5610688/
Abstract

Precision genomic oncology-applying high throughput sequencing (HTS) at the point-of-care to inform clinical decisions-is a developing precision medicine paradigm that is seeing increasing adoption. Simultaneously, new developments in targeted agents and immunotherapy, when informed by rich genomic characterization, offer potential benefit to a growing subset of patients. Multiple previous studies have commented on methods for identifying both germline and somatic variants. However, interpreting individual variants remains a significant challenge, relying in large part on the integration of observed variants with biological knowledge. A number of data and software resources have been developed to assist in interpreting observed variants, determining their potential clinical actionability, and augmenting them with ancillary information that can inform clinical decisions and even generate new hypotheses for exploration in the laboratory. Here, we review available variant catalogs, variant and functional annotation software and tools, and databases of clinically actionable variants that can be used in an approach with research samples or incorporated into a data platform for interpreting and formally reporting clinical results.

摘要

精准基因组肿瘤学——在临床护理点应用高通量测序(HTS)以指导临床决策——是一种正在逐渐被广泛采用的精准医学模式。同时,在丰富的基因组特征指导下,靶向药物和免疫疗法的新进展为越来越多的患者带来了潜在益处。此前已有多项研究对识别种系和体细胞变异的方法进行了评论。然而,解释单个变异仍然是一项重大挑战,这在很大程度上依赖于将观察到的变异与生物学知识相结合。已经开发了许多数据和软件资源来协助解释观察到的变异,确定其潜在的临床可操作性,并通过辅助信息对其进行补充,这些辅助信息可为临床决策提供依据,甚至为实验室探索产生新的假设。在此,我们综述了可用的变异目录、变异和功能注释软件及工具,以及可用于研究样本或纳入数据平台以解释和正式报告临床结果的临床可操作变异数据库。

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Resources for Interpreting Variants in Precision Genomic Oncology Applications.精准基因组肿瘤学应用中变异解读的资源
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Available resources and challenges for the clinical annotation of somatic variations.体细胞变异临床注释的可用资源和挑战。
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Findings from precision oncology in the clinic: rare, novel variants are a significant contributor to scaling molecular diagnostics.临床精准肿瘤学的研究结果:罕见的新型变异体是分子诊断扩展的重要贡献因素。
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Brief Bioinform. 2021 Nov 5;22(6). doi: 10.1093/bib/bbab134.
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Clinical Genome Data Model (cGDM) provides Interactive Clinical Decision Support for Precision Medicine.临床基因组数据模型(cGDM)为精准医学提供交互式临床决策支持。
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本文引用的文献

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Inferring the molecular and phenotypic impact of amino acid variants with MutPred2.使用 MutPred2 推断氨基酸变异的分子和表型影响。
Nat Commun. 2020 Nov 20;11(1):5918. doi: 10.1038/s41467-020-19669-x.
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Cancer Genome Interpreter annotates the biological and clinical relevance of tumor alterations.癌症基因组解读器注释肿瘤改变的生物学和临床相关性。
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OncoKB: A Precision Oncology Knowledge Base.OncoKB:一个精准肿瘤知识库。
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Experience with precision genomics and tumor board, indicates frequent target identification, but barriers to delivery.精准基因组学和肿瘤多学科会诊的经验表明,靶点识别很常见,但在治疗实施方面存在障碍。
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Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden.对10万个人类癌症基因组的分析揭示了肿瘤突变负荷的全貌。
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Data resources for the identification and interpretation of actionable mutations by clinicians.临床医生识别和解读可操作突变的数据源。
Ann Oncol. 2017 May 1;28(5):946-957. doi: 10.1093/annonc/mdx023.
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Targeting neoantigens to augment antitumour immunity.靶向新抗原以增强抗肿瘤免疫力。
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