• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Breast Cancer Research >A knowledge-based framework for the discovery of cancer-predisposing variants using large-scale sequencing breast cancer data
【24h】

A knowledge-based framework for the discovery of cancer-predisposing variants using large-scale sequencing breast cancer data

机译：基于知识的框架，可使用大规模测序乳腺癌数据发现易患癌症的变异

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

BackgroundThe landscape of cancer-predisposing genes has been extensively investigated in the last 30?years with various methodologies ranging from candidate gene to genome-wide association studies. However, sequencing data are still poorly exploited in cancer predisposition studies due to the lack of statistical power when comparing millions of variants at once. MethodTo overcome these power limitations, we propose a knowledge-based framework founded on the characteristics of known cancer-predisposing variants and genes. Under our framework, we took advantage of a combination of previously generated datasets of sequencing experiments to identify novel breast cancer-predisposing variants, comparing the normal genomes of 673 breast cancer patients of European origin against 27,173 controls matched by ethnicity. ResultsWe detected several expected variants on known breast cancer-predisposing genes, like BRCA1 and BRCA2 , and 11 variants on genes associated with other cancer types, like RET and AKT1 . Furthermore, we detected 183 variants that overlap with somatic mutations in cancer and 41 variants associated with 38 possible loss-of-function genes, including PIK3CB and KMT2C . Finally, we found a set of 19 variants that are potentially pathogenic, negatively correlate with age at onset, and have never been associated with breast cancer. ConclusionsIn this study, we demonstrate the usefulness of a genomic-driven approach nested in a classic case-control study to prioritize cancer-predisposing variants. In addition, we provide a resource containing variants that may affect susceptibility to breast cancer.
机译：背景技术在过去30年中，从候选基因到全基因组关联研究的各种方法已广泛研究了癌症易感基因的前景。但是，由于在一次比较数百万个变体时缺乏统计能力，因此在癌症易感性研究中仍然无法充分利用测序数据。方法为了克服这些功能限制，我们提出了一个基于知识的框架，该框架基于已知的易患癌症的变体和基因的特征。在我们的框架下，我们利用先前生成的测序实验数据集的组合来识别新型的易患乳腺癌的变体，将欧洲起源的673名乳腺癌患者的正常基因组与种族匹配的27,173名对照进行了比较。结果我们在已知的易患乳腺癌的基因（如BRCA1和BRCA2）上检测到了几个预期的变异，并在与其他癌症类型（如RET和AKT1）相关的基因上检测到11个变异。此外，我们检测到183个与癌症中的体细胞突变重叠的变体以及41个与38个可能的功能丧失基因相关的变体，包括PIK3CB和KMT2C。最后，我们发现了一组19种可能具有致病性，与发病年龄负相关且从未与乳腺癌相关的变体。结论在这项研究中，我们证明了在经典病例对照研究中嵌套使用基因组驱动方法来区分癌症易感性变异的重要性。此外，我们提供了包含可能影响乳腺癌易感性的变异的资源。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号