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本文引用的文献

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Leveraging Clinical Tumor-Profiling Programs to Achieve Comprehensive Germline-Inclusive Precision Cancer Medicine.利用临床肿瘤分析项目实现全面的包含种系信息的精准癌症医学。
JCO Precis Oncol. 2019 Dec;3:1-3. doi: 10.1200/PO.19.00108.
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The mutational constraint spectrum quantified from variation in 141,456 humans.从 141456 名人类个体的变异中量化的突变约束谱。
Nature. 2020 May;581(7809):434-443. doi: 10.1038/s41586-020-2308-7. Epub 2020 May 27.
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Calling Variants in the Clinic: Informed Variant Calling Decisions Based on Biological, Clinical, and Laboratory Variables.临床中的变异检测:基于生物学、临床和实验室变量做出明智的变异检测决策
Comput Struct Biotechnol J. 2019 Apr 8;17:561-569. doi: 10.1016/j.csbj.2019.04.002. eCollection 2019.
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Association of Inherited Pathogenic Variants in Checkpoint Kinase 2 (CHEK2) With Susceptibility to Testicular Germ Cell Tumors.遗传型细胞周期检查点激酶 2(CHEK2)致病性变异与睾丸生殖细胞肿瘤易感性的关联。
JAMA Oncol. 2019 Apr 1;5(4):514-522. doi: 10.1001/jamaoncol.2018.6477.
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A universal SNP and small-indel variant caller using deep neural networks.使用深度神经网络的通用 SNP 和小插入缺失变体调用器。
Nat Biotechnol. 2018 Nov;36(10):983-987. doi: 10.1038/nbt.4235. Epub 2018 Sep 24.
6
Variant Review with the Integrative Genomics Viewer.使用综合基因组浏览器进行变异审查。
Cancer Res. 2017 Nov 1;77(21):e31-e34. doi: 10.1158/0008-5472.CAN-17-0337.
7
Genomic evolution and chemoresistance in germ-cell tumours.生殖细胞肿瘤中的基因组进化与化疗耐药性
Nature. 2016 Nov 30;540(7631):114-118. doi: 10.1038/nature20596.
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Analysis of protein-coding genetic variation in 60,706 humans.对60706名人类的蛋白质编码基因变异进行分析。
Nature. 2016 Aug 18;536(7616):285-91. doi: 10.1038/nature19057.
9
A global reference for human genetic variation.人类遗传变异的全球参考。
Nature. 2015 Oct 1;526(7571):68-74. doi: 10.1038/nature15393.
10
Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology.序列变异解读的标准与指南:美国医学遗传学与基因组学学会和分子病理学协会的联合共识推荐
Genet Med. 2015 May;17(5):405-24. doi: 10.1038/gim.2015.30. Epub 2015 Mar 5.

评估基于联合基因分型的分子诊断方法对检测罕见胚系致病性和疑似功能丧失变异体的分子诊断产量。

Evaluating the molecular diagnostic yield of joint genotyping-based approach for detecting rare germline pathogenic and putative loss-of-function variants.

机构信息

Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.

Cancer Program, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.

出版信息

Genet Med. 2021 May;23(5):918-926. doi: 10.1038/s41436-020-01074-w. Epub 2021 Feb 2.

DOI:10.1038/s41436-020-01074-w
PMID:33531667
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8720416/
Abstract

PURPOSE

Cohort-based germline variant characterization is the standard approach for pathogenic variant discovery in clinical and research samples. However, the impact of cohort size on the molecular diagnostic yield of joint genotyping is largely unknown.

METHODS

Head-to-head comparison of the molecular diagnostic yield of joint genotyping in two cohorts of 239 cancer patients in the absence and then in the presence of 100 additional germline exomes.

RESULTS

In 239 testicular cancer patients, 4 (7.4%, 95% confidence interval [CI]: 2.1-17.9) of 54 pathogenic variants in the cancer predisposition and American College of Medical Genetics and Genomics (ACMG) genes were missed by one or both computational runs of joint genotyping. Similarly, 8 (12.1%, 95% CI: 5.4-22.5) of 66 pathogenic variants in these genes were undetected by joint genotyping in another independent cohort of 239 breast cancer patients. An exome-wide analysis of putative loss-of-function (pLOF) variants in the testicular cancer cohort showed that 162 (8.2%, 95% CI: 7.1-9.6) pLOF variants were only detected in one analysis run but not the other, while 433 (22.0%, 95% CI: 20.2-23.9%) pLOF variants were filtered out by both analyses despite having sufficient sequencing coverage.

CONCLUSION

Our analysis of the standard germline variant detection method highlighted a substantial impact of concurrently analyzing additional genomic data sets on the ability to detect clinically relevant germline pathogenic variants.

摘要

目的

基于队列的种系变异特征分析是临床和研究样本中致病性变异发现的标准方法。然而,队列规模对联合基因分型的分子诊断率的影响在很大程度上尚不清楚。

方法

在不存在和存在 100 个额外种系外显子的情况下,对头对头比较 239 例癌症患者的两个队列的联合基因分型的分子诊断率。

结果

在 239 例睾丸癌患者中,联合基因分型在一个或两个计算运行中漏检了 54 个癌症易感性和美国医学遗传学与基因组学学院(ACMG)基因中的 4 个(7.4%,95%置信区间[CI]:2.1-17.9)致病性变异。同样,在另一个独立的 239 例乳腺癌患者队列中,联合基因分型未能检测到这些基因中的 66 个致病性变异中的 8 个(12.1%,95% CI:5.4-22.5)。对睾丸癌队列中假定的功能丧失(pLOF)变异的全外显子分析表明,162 个(8.2%,95% CI:7.1-9.6)pLOF 变异仅在一个分析运行中检测到,但在另一个分析运行中未检测到,而 433 个(22.0%,95% CI:20.2-23.9%)pLOF 变异尽管具有足够的测序覆盖度,但被两种分析过滤掉。

结论

我们对标准种系变异检测方法的分析突出表明,同时分析额外的基因组数据集对检测临床相关种系致病性变异的能力有重大影响。

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