Combining MAVEs and computational predictors improves variant classification across ancestries in hereditary cancer genes
In brief
Combining lab and computational evidence reclassifies over 90% of uncertain cancer-gene variants
Across five hereditary cancer genes, combining experimental assay results with computational predictions reclassified more than 90% of variants of uncertain significance in two ancestry-diverse datasets as at least likely benign or likely pathogenic. The methods had complementary strengths, but disagreements also exposed their limitations; whether these classifications improve clinical decisions and reduce disparities remains to be tested.
- Journal
- Genome medicine (Q1)
- Published
- 18 October 2026
- Study design
- Cohort / observational study
- Evidence level
- Level 3, Low (CEBM 3b)
- Authors
- Nikita Bora, Mihaly Badonyi, Ankit K Pathak, Rutharra Ghayadthri Manisekaran, Joseph A Marsh, Joanne Ngeow
- PMID
- 42989408
- DOI
- 10.1186/s13073-026-01799-4
Why clinicians should know about it
- Picked for Genetics (clinical) (paper of the day, 8 November 2026): Improves hereditary cancer variant classification and VUS reclassification
Abstract
BACKGROUND: Many commonly used computational tools for variant effect prediction exhibit ancestry-related bias because they are trained on clinical or population datasets that under-represent global diversity, leading to uneven and sometimes unfair variant classification across ancestries. Multiplexed assays of variant effect (MAVEs) and population-free variant effect predictors (VEPs) instead offer alternatives that are unbiased with respect to human ancestry, providing classification evidence that generalises across populations. METHODS: Here, we evaluate MAVE- and VEP-based classification across five cancer-associated genes with high-quality MAVE datasets, focusing primarily on three leading population-free VEPs: GEMME, EVE, and CPT-1. RESULTS: We find that MAVEs are more conservative and decisive in classification, assigning fewer variants to the pathogenic category while yielding fewer indeterminate classifications when calibrated to ACMG/AMP guidelines. Although MAVEs show heightened sensitivity in functionally assayed regions, VEPs identify a broader range of pathogenic variants overall. By combining clinical evidence strengths from MAVEs and VEPs, we reclassify over 90% of variants of uncertain significance across the SG10K_Health and Mexico City Prospective Study reference datasets as at least likely benign or likely pathogenic. We further isolate variants where MAVE- and VEP-based classifications are discordant, highlighting method-specific limitations. CONCLUSIONS: Together, these findings clarify the complementary strengths of experimental and computational classification approaches and provide a path to less biased and more equitable variant interpretation in clinical genomics, helping to mitigate disparities in diagnosis across ancestries.
Abstract as published, via PubMed.
For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.