Bonetti Emanuele, Tini Giulia, Mazzarella Luca
Department of Experimental Oncology, European Institute of Oncology, IEO-IRCCS, Milan, 20139, Italy.
J Transl Med. 2024 Jul 31;22(1):713. doi: 10.1186/s12967-024-05508-w.
Interpreting the clinical consequences of genetic variants is the central problem in modern clinical genomics, for both hereditary diseases and oncology. However, clinical validation lags behind the pace of discovery, leading to distressing uncertainty for patients, physicians and researchers. This "interpretation gap" changes over time as evidence accumulates, and variants initially deemed of uncertain (VUS) significance may be subsequently reclassified in pathogenic/benign. We previously developed RENOVO, a random forest-based tool able to predict variant pathogenicity based on publicly available information from GnomAD and dbNFSP, and tested on variants that have changed their classification status over time. Here, we comprehensively evaluated the accuracy of RENOVO predictions on variants that have been reclassified over the last four years.
we retrieved 16 retrospective instances of the ClinVar database, every 3 months since March 2020 to March 2024, and analyzed time trends of variant classifications. We identified variants that changed their status over time and compared RENOVO predictions generated in 2020 with the actual reclassifications.
VUS have become the most represented class in ClinVar (44.97% vs. 9.75% (likely) pathogenic and 40,33% (likely) benign). The rate of VUS reclassification is linear and slow compared to the rate of VUS reporting, exponential and currently ~ 30x faster, creating a growing divide between what can be sequenced vs. what can be interpreted. Out of 10,196 VUS variants in January 2020 that have undergone a clinically meaningful reclassification to march 2024, RENOVO correctly classified 82.6% in 2020. In addition, RENOVO correctly identified the majority of the few variants that switched clinically meaningful classes (e.g., from benign to pathogenic and vice versa). We highlight variant classes and clinically relevant genes for which RENOVO provides particularly accurate estimates. In particularly, genes characterized by large prevalence of high- or low-impact variants (e.g., POLE, NOTCH1, FANCM etc.). Suboptimal RENOVO predictions mostly concern genes validated through dedicated consortia (e.g., BRCA1/2), in which RENOVO would anyway have a limited impact.
Time trend analysis demonstrates that the current model of variant interpretation cannot keep up with variant discovery. Machine learning-based tools like RENOVO confirm high accuracy that can aid in clinical practice and research.
对于遗传性疾病和肿瘤学而言，解读基因变异的临床后果是现代临床基因组学的核心问题。然而，临床验证滞后于发现的速度，给患者、医生和研究人员带来了令人苦恼的不确定性。随着证据的积累，这种“解读差距”会随时间变化，最初被认为意义不确定（VUS）的变异可能随后被重新分类为致病/良性。我们之前开发了RENOVO，这是一种基于随机森林的工具，能够根据来自GnomAD和dbNFSP的公开信息预测变异的致病性，并在随时间改变分类状态的变异上进行了测试。在此，我们全面评估了RENOVO对过去四年中已重新分类的变异预测的准确性。
我们检索了ClinVar数据库从2020年3月到2024年3月每3个月的16个回顾性实例，并分析了变异分类的时间趋势。我们确定了随时间改变状态的变异，并将2020年生成的RENOVO预测与实际重新分类进行比较。
VUS已成为ClinVar中占比最大的类别（44.97%，而致病（可能）为9.75%，良性（可能）为40.33%）。与VUS报告的速度相比，VUS重新分类的速度呈线性且缓慢，而VUS报告的速度呈指数增长，目前快约30倍，这使得可测序的内容与可解读的内容之间的差距越来越大。在2020年1月有临床意义地重新分类到2024年3月的10196个VUS变异中，RENOVO在2020年正确分类了82.6%。此外，RENOVO正确识别了少数转换临床意义类别的变异中的大多数（例如，从良性到致病，反之亦然）。我们突出了RENOVO提供特别准确估计的变异类别和临床相关基因。特别是，以高影响或低影响变异普遍存在为特征的基因（例如，POLE、NOTCH1、FANCM等）。RENOVO预测效果欠佳的大多涉及通过专门联盟验证的基因（例如，BRCA1/2），在这些基因中RENOVO的影响无论如何都有限。
时间趋势分析表明，当前的变异解读模式无法跟上变异发现的步伐。像RENOVO这样基于机器学习的工具证实了其高准确性，可有助于临床实践和研究。
