Accuracy of renovo predictions on variants reclassified over time

Abstract

Abstract Background 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. Methods 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. Results 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. Conclusions 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.

Keywords

• Variant classification
• Machine learning
• Precision medicine
• Genomics
• Random forest