A Collaborative Approach to Improving Missense Mutational Effect Predictions in Oncoproteins

Abstract

Missense mutations in oncoproteins play a key role in cancer, which makes their accurate classification essential for cancer diagnosis and treatment. Although various variant effect prediction algorithms have been developed, their accuracy varies across different proteins. In this study, we introduce the APE score, a novel ensemble metric that integrates three state-of-the-art unsupervised predictors--AlphaMissense, PRESCOTT, and ESM1b. We evaluated APE scores performance using nine deep mutational scanning (DMS) experiments on five oncoproteins and a dataset of 1068 clinically labeled variants from 24 oncoproteins. Our results show that APE score is better than its individual components in six out of nine DMS experiments and achieves superior accuracy in distinguishing pathogenic variants from benign ones. Additionally, we established classification thresholds to categorize 444087 variants into likely pathogenic, variants of unknown significance (VUS), and likely benign categories. The APE score reduces misclassification rates and provides a more reliable tool for assessing the functional impact of missense mutations in cancer-related proteins. All scores and classifications are provided in a publicly available repository to support further research and clinical applications.