medRxivGenetic & Genomic MedicinePreprint
HECTOR: A Web-Based Tool for Automated BRCA1/BRCA2 Variant Classification Under the ClinGen ENIGMA Specifications
Tarik DuzenliiD, Ali BabazadeiD, Ozan VuraliD, Yusuf BahapiD, Mehmet Ali ErgüniD
Abstract
BackgroundThe ClinGen ENIGMA BRCA1/BRCA2 Variant Curation Expert Panel (VCEP) has adapted the American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) framework into gene-specific specifications. However, applying these specifications manually remains labor-intensive and prone to inconsistency, requiring integration of population, computational, functional, and clinical evidence through gene-specific decision trees and a points-based classification system. MethodsWe developed HECTOR (HEreditary Cancer varianT Online Reclassifier), a free web-based tool that implements the complete ENIGMA VCEP v1.2 specifications for BRCA1 and BRCA2. HECTOR automatically populates all evidence codes derivable from public data, routes curator-dependent evidence to a manual input layer and returns a transparent five-tier classification with code-level evidence. We validated HECTOR against two independent reference datasets: the 143-variant ENIGMA Evidence Repository, used as a clinical-grade reference standard, and 134 manually curated in-house variants of uncertain significance (132 unique variants). HECTOR was then applied to the complete ClinVar BRCA1/BRCA2 catalog (n = 34,077). ResultsAt the criterion level, HECTOR exactly reproduced 326 of 413 VCEP-assigned criteria (78.9%), with discordance arising predominantly from curator-dependent evidence rather than implementation errors. In an independent cohort of 134 manually curated in-house variants, HECTOR was fully concordant with expert consensus at the classification level and, even without auto-populated likelihood-ratio evidence, resolved more variants to a definitive classification than two generic ACMG/AMP classifiers. Across ClinVar, HECTOR classified 33,913 variants. Agreement with definitive ClinVar classifications was 96.7% for pathogenic variants and %72.7 for benign variants overall. Among variants for which HECTOR generated a definitive classification, directional concordance reached 99.7% for pathogenic and 99.9% for benign variants. HECTOR also resolved a substantial proportion of variants classified as uncertain (67.3%) or conflicting (88.7%), predominantly toward benign classifications. ConclusionsHECTOR provides a faithful, transparent implementation of the ENIGMA VCEP v1.2 specifications for BRCA1 and BRCA2, enabling rapid, standardized, and reproducible application of gene-specific variant classification guidelines while reducing the burden of manual curation.
- organism: human
- assay: whole-exome sequencing
- research focus: variant effect prediction
- approach: statistical model
- Citations
- 1
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Evidence-based recommendations for gene-specific ACMG/AMP variant classification from the ClinGen ENIGMA BRCA1 and BRCA2 Variant Curation Expert Panel
Michael T. Parsons, Miguel de la Hoya, … Amanda B. Spurdle·The American Journal of Human GeneticsPeer-reviewed·Aug 13, 2024
- Analysis of more than 400,000 women provides case-control evidence for BRCA1 and BRCA2 variant classification
Maria Zanti, Denise G O'Mahony, … Gary Zirpoli·Nature CommunicationsPeer-reviewed·May 24, 2025
- Cancer genomic profiling predicts pathogenicity of BRCA1 and BRCA2 variants
Olga Kondrashova, Rebecca L. Johnston, … Amanda B. Spurdle·medRxiv·Mar 6, 2026
- A Comprehensive and Accessible Model for Co-Segregation Analysis in BRCA1, BRCA2, and PALB2 Variant Classification
Setareh Moghadasi, Ramin Monajemi, … Mar Rodríguez‐Girondo·bioRxiv·Sep 23, 2024
- Automating ACMG Variant Classifications With BIAS-2015 v2.0.0: Algorithm Analysis and Benchmark Against the FDA-Approved eRepo Dataset
Christopher Edward Eisenhart, Rachel Brickey, … Vafa Bayat·medRxiv·Feb 23, 2025
- Towards automation of germline variant curation in clinical cancer genetics
Vignesh Ravichandran, Zarina Shameer, … Joseph Vijai·Genetics in MedicinePeer-reviewed·Feb 20, 2019