Machine learning-based reclassification of germline variants of unknown significance: The RENOVO algorithm
Machine learning-based reclassification of germline variants of unknown significance: The RENOVO algorithm
复制标题
DOI：
10.1016/j.ajhg.2021.03.010
复制
发表时间：
2021-04-01
影响因子：
9.8
通讯作者：
Mazzarella, Luca
中科院分区：
文献类型：
--
作者：
Favalli, Valentina;Tini, Giulia;Mazzarella, Luca
下一代测序（NGS）允许的基因检测范围不断扩大，这大大增加了被解释为致病性或良性的遗传变异的数量，以便对患者进行适当的管理。尽管如此，解释过程往往无法提供明确的分类，导致未知意义的变体（VUS）或致病性解释（CIP）冲突的变体;这些代表了一个主要的临床问题，因为它们不能为决策提供有用的信息，导致很大一部分遗传决定的疾病仍然治疗不足。我们开发了一种基于机器学习（随机森林）的工具雷诺沃，它根据公开的信息将变异分类为致病性或良性，并提供致病性可能性得分（PLS）。使用指南推荐的相同特征类，我们在ClinVar中对已确定的致病性/良性变异进行了雷诺沃训练（训练集准确度= 99%），并测试了其在解释随时间变化的变异上的性能（测试集准确度= 95%）。我们进一步在其他数据集上验证了该算法，包括通过专家共识（ENIGMA）或基于实验室的功能技术（BRCA 1/2和SCN 5A）验证的未报告变体。在所有数据集上，雷诺沃都优于现有的自动解释工具。基于上述验证指标，我们为所有现有ClinVar VUS分配了定义的PLS，建议重新分类67%，估计精度>90%。雷诺沃提供了一种经过验证的工具，可以减少未解释或误解的变异，解决现代临床遗传学中未满足的需求。
The increasing scope of genetic testing allowed by next-generation sequencing (NGS) dramatically increased the number of genetic variants to be interpreted as pathogenic or benign for adequate patient management. Still, the interpretation process often fails to deliver a clear classification, resulting in either variants of unknown significance (VUSs) or variants with conflicting interpretation of pathogenicity (CIP); these represent a major clinical problem because they do not provide useful information for decision-making, causing a large fraction of genetically determined disease to remain undertreated. We developed a machine learning (random forest)-based tool, RENOVO, that classifies variants as pathogenic or benign on the basis of publicly available information and provides a pathogenicity likelihood score (PLS). Using the same feature classes recommended by guidelines, we trained RENOVO on established pathogenic/benign variants in ClinVar (training set accuracy = 99%) and tested its performance on variants whose interpretation has changed over time (test set accuracy = 95%). We further validated the algorithm on additional datasets including unreported variants validated either through expert consensus (ENIGMA) or laboratory-based functional techniques (on BRCA1/2 and SCN5A). On all datasets, RENOVO outperformed existing automated interpretation tools. On the basis of the above validation metrics, we assigned a defined PLS to all existing ClinVar VUSs, proposing a reclassification for 67% with >90% estimated precision. RENOVO provides a validated tool to reduce the fraction of uninterpreted or misinterpreted variants, tackling an area of unmet need in modern clinical genetics.