Rangarajan Amith, Sviezhentseva Ilona, Gunderson Emma, Pikman Yana, Jacobson Matthew P, Apsel Winger Beth
Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, United States.
Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, United States.
Front Oncol. 2025 Aug 4;15:1599389. doi: 10.3389/fonc.2025.1599389. eCollection 2025.
Clinical workflows to analyze variants of unknown significance (VUSs) found in clinical next generation sequencing (NGS) are labor intensive, requiring manual analysis of published data for each variant. There is a strong need for tools and resources that provide a consistent way to analyze variants. With the explosion of clinical NGS data and the concurrent availability of protein structures through the Protein Data Bank and protein models through programs such as AlphaFold, there exists an unprecedented opportunity to use structural information to help standardize NGS analysis with the overall goal of advancing personalized cancer therapy.
Using the Catalogue of Somatic Mutations in Cancer (COSMIC), the largest curated database of clinical cancer mutations, we mapped thousands of missense mutations in the kinase and juxtamembrane (JM) domains of 48 receptor tyrosine kinases (RTKs) onto structurally aligned kinase structures, then clustered known activating mutations along with VUSs based on proximity in three-dimensional structure. Using cell-based models we demonstrate that our resource can be used to aid in identification of activating mutations while providing insight into mechanisms of kinase activation and regulation.
We provide a database of structurally aligned and functionally annotated mutations that can be used as a tool to evaluate kinase VUSs based on their structural alignment with known activating mutations. The tool can be accessed through a user-friendly website in which one can input a kinase mutation of interest, and the system will output a list of structurally analogous mutations in other kinases, as well as their functional annotations.
Though our tool is not expected to be used as an isolated source for variant functional prediction, we expect our database will be a valuable addition to the current tools and resources used to analyze clinical NGS, with important clinical implications to guide recommendations for personalized cancer therapy.
分析临床下一代测序（NGS）中发现的意义未明变异（VUS）的临床工作流程劳动强度大，需要对每个变异的已发表数据进行人工分析。迫切需要能够提供一致变异分析方法的工具和资源。随着临床NGS数据的爆炸式增长，以及通过蛋白质数据库（Protein Data Bank）可同时获取蛋白质结构，通过诸如AlphaFold等程序可获取蛋白质模型，利用结构信息来帮助标准化NGS分析，以推进个性化癌症治疗为总体目标，这存在前所未有的机遇。
我们使用癌症体细胞突变目录（COSMIC），这是最大的临床癌症突变 curated 数据库，将48种受体酪氨酸激酶（RTK）的激酶和近膜（JM）结构域中的数千个错义突变映射到结构对齐的激酶结构上，然后根据三维结构中的接近程度，将已知的激活突变与VUS聚类。使用基于细胞的模型，我们证明我们的资源可用于辅助识别激活突变，同时深入了解激酶激活和调控机制。
我们提供了一个结构对齐且功能注释的突变数据库，可作为一种工具，根据激酶VUS与已知激活突变的结构对齐情况来评估它们。该工具可通过一个用户友好的网站访问，在该网站中，用户可以输入感兴趣的激酶突变，系统将输出其他激酶中结构类似的突变列表及其功能注释。
虽然我们的工具预计不会被用作变异功能预测的唯一来源，但我们预计我们的数据库将是当前用于分析临床NGS的工具和资源的有价值补充，对指导个性化癌症治疗的建议具有重要临床意义。
