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Updated: Mar 15, 2026
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In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila
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Pathogenicity Prediction of Missense Variations in Hereditary Cancer Genes.
Cemaliye B Akyerli1,2, Gizel Gerdan2, Alper Bülbül3
1Department of Medical Biology, School of Medicine, Acibadem Mehmet Ali Aydinlar University, Istanbul 34638, Türkiye.
International Journal of Molecular Sciences
|March 14, 2026
Summary
HerCanPred, a machine-learning tool, improves hereditary cancer variant interpretation by integrating sequence and structural data. It effectively reclassifies variants of uncertain significance, aiding diagnosis and patient management.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Interpreting missense variants in hereditary cancer syndromes is challenging.
- Existing prediction tools have limitations in accuracy and scope.
Purpose of the Study:
- To develop HerCanPred, a machine-learning classifier for hereditary cancer gene variants.
- To leverage structural information from AlphaFold2 (AF2) for improved pathogenicity prediction.
Main Methods:
- HerCanPred integrates sequence conservation and AF2-derived structural features.
- SHAP analysis identified key structural predictors like pLDDT and solvent accessibility.
- Performance was benchmarked against 23 established predictors.
Main Results:
- HerCanPred outperformed existing predictors.
- AF2-derived structural features were crucial for prediction accuracy.
- 166 variants of uncertain significance (VUS) were reclassified as pathogenic and 75 as benign.
Conclusions:
- HerCanPred offers a robust framework for reducing diagnostic uncertainty in hereditary cancer.
- Structure-aware tools are vital for systematic variant reinterpretation and improved patient care.
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