Dept. of Medical Bioinformatics, University Medical Center Göttingen, Germany.
Dept. of Gastroenterology, gastrointestinal Oncology and Endocrinology, University Medical Center Göttingen, Germany.
Stud Health Technol Inform. 2021 Sep 21;283:209-216. doi: 10.3233/SHTI210562.
Precision oncology utilizing molecular biomarkers for targeted therapies is one of the hopes to treat cancer. The availability of patient specific molecular profiling through next-generation sequencing, though, increases the amount of available data per patient to an extent that computational support is required to identify potential driver alterations for targeted therapies and rational decision-making in molecular tumor boards (MTBs). For some genetic variants evidence-based drug recommendations are available in public databases, but for the majority, the variants of unknown significance (VUS), this clinical information is missing. Additionally, for most of these variants no information about the functional impact on the protein is accessible. To acquire maximal functional evidence for VUS, the VUS-Predict pipeline collects estimations about the effect of a VUS by integrating multiple pre-existing tools. Pre-existing tools implement different approaches for their predictions, which are summarized by our newly developed tool with a common score and classification in neutral or deleterious variants. The primary tools are chosen based on their sensitivity and specificity on well-known variants of the transcription factor TP53. Resulting negative and positive predictive values are used to calibrate the VUS-Predict pipeline. Further, the pipeline is evaluated using data from public cancer databases and cases of the MTB in Göttingen, both also in comparison with the ensemble method REVEL. The results show that VUS-Predict has clear advantages in a clinical setting due to clear and traceable predictions. In particular, VUS outperforms REVEL in the real-life setting of a MTB. Likewise, an evaluation on variants of public cancer databases confirms the good results of VUS-Predict and shows the need for a reliable gold standard and unambiguous results of the tools under test.
精准肿瘤学利用分子生物标志物进行靶向治疗是治疗癌症的希望之一。然而，通过下一代测序获得的患者特异性分子谱分析，使得每个患者的可用数据量增加到需要计算支持的程度，以识别潜在的驱动基因改变，为靶向治疗和分子肿瘤委员会（MTB）的合理决策提供依据。对于一些遗传变异，公共数据库中提供了基于证据的药物推荐，但对于大多数变异，即意义不明的变异（VUS），则缺乏临床信息。此外，对于这些变异中的大多数，关于它们对蛋白质功能影响的信息是不可用的。为了获得 VUS 的最大功能证据，VUS-Predict 管道通过整合多个现有的工具来收集关于 VUS 效应的估计。现有的工具采用不同的方法进行预测，我们新开发的工具对这些方法进行了总结，用共同的评分和分类将其分为中性或有害变异。主要工具是根据它们在转录因子 TP53 的知名变异上的灵敏度和特异性来选择的。将得到的阴性和阳性预测值用于校准 VUS-Predict 管道。此外，还使用来自公共癌症数据库的数据和哥廷根 MTB 的病例对该管道进行了评估，并与集合方法 REVEL 进行了比较。结果表明，由于预测结果清晰可追溯，VUS-Predict 在临床环境中具有明显的优势。特别是在 MTB 的实际环境中，VUS-Predict 优于 REVEL。同样，对公共癌症数据库中的变异进行评估也证实了 VUS-Predict 的良好结果，并表明需要一个可靠的金标准和测试工具的明确结果。
