Hereditary Cancer Program, Program in Molecular Mechanisms and Experimental Therapy in Oncology (Oncobell), IDIBELL, Catalan Institute of Oncology, L'Hospitalet de Llobregat 08908, Spain.
Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain.
Bioinformatics. 2023 Mar 1;39(3). doi: 10.1093/bioinformatics/btad128.
Germline variant classification allows accurate genetic diagnosis and risk assessment. However, it is a tedious iterative process integrating information from several sources and types of evidence. It should follow gene-specific (if available) or general updated international guidelines. Thus, it is the main burden of the incorporation of next-generation sequencing into the clinical setting.
We created the vaRiants in HC (vaRHC) R package to assist the process of variant classification in hereditary cancer by: (i) collecting information from diverse databases; (ii) assigning or denying different types of evidence according to updated American College of Molecular Genetics and Genomics/Association of Molecular Pathologist gene-specific criteria for ATM, CDH1, CHEK2, MLH1, MSH2, MSH6, PMS2, PTEN, and TP53 and general criteria for other genes; (iii) providing an automated classification of variants using a Bayesian metastructure and considering CanVIG-UK recommendations; and (iv) optionally printing the output to an .xlsx file. A validation using 659 classified variants demonstrated the robustness of vaRHC, presenting a better criteria assignment than Cancer SIGVAR, an available similar tool.
The source code can be consulted in the GitHub repository (https://github.com/emunte/vaRHC) Additionally, it will be submitted to CRAN soon.
种系变异分类可实现准确的基因诊断和风险评估。然而，这是一个繁琐的迭代过程，需要整合来自多个来源和类型证据的信息。它应遵循特定基因（如适用）或一般更新的国际指南。因此，这是将下一代测序纳入临床环境的主要负担。
我们创建了 vaRiants in HC (vaRHC) R 包，通过以下方式协助遗传性癌症变异分类过程：（i）从各种数据库中收集信息；（ii）根据最新的美国分子遗传学和基因组学学院/分子病理学家协会特定于 ATM、CDH1、CHEK2、MLH1、MSH2、MSH6、PMS2、PTEN 和 TP53 的基因以及其他基因的一般标准，分配或否认不同类型的证据；（iii）使用贝叶斯元结构自动分类变体，并考虑 CanVIG-UK 建议；（iv）根据需要将输出打印到.xlsx 文件中。使用 659 个已分类变体进行验证表明，vaRHC 具有稳健性，其标准分配优于可用的类似工具 Cancer SIGVAR。
源代码可在 GitHub 存储库（https://github.com/emunte/vaRHC）中查阅。此外，它很快将被提交到 CRAN。
