期刊
AMERICAN JOURNAL OF HUMAN GENETICS
卷 108, 期 4, 页码 682-695出版社
CELL PRESS
DOI: 10.1016/j.ajhg.2021.03.010
关键词
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资金
- Italian Ministry of Health [GR-2016-02361272]
- Alliance Against Cancer funds
- Italian Ministry of Health
A machine learning tool called RENOVO has been developed to accurately classify genetic variants as pathogenic or benign, providing a pathogenicity likelihood score. The tool outperformed existing automated interpretation tools and showed great potential in reducing the fraction of uninterpreted or misinterpreted variants.
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.
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