Key points are not available for this paper at this time.
Next-generation pathogenicity predictors are designed to identify pathogenic mutations in genetic disorders but are increasingly used to detect driver mutations in cancer. Despite this, their suitability for cancer is not fully established. Here we have assessed the effectiveness of next-generation pathogenicity predictors when applied to cancer by using a comprehensive experimental benchmark of cancer driver and neutral mutations. Our findings indicate that state-of-the-art methods AlphaMissense and VARITY demonstrate commendable performance despite generally underperforming compared to cancer-specific methods. This is notable considering that these methods do not explicitly incorporate cancer-related data in their training and have made concerted efforts to prevent data leakage from the human-curated training and test sets. Nevertheless, it should be mentioned that a significant limitation of using pathogenicity predictors for cancer arises from their inability to detect cancer potential driver mutations specific for a particular cancer type.
Building similarity graph...
Analyzing shared references across papers
Loading...
Daria Ostroverkhova
Yiru Sheng
Anna R. Panchenko
Journal of Molecular Biology
Queen's University
Ontario Institute for Cancer Research
Institute of Cancer Research
Building similarity graph...
Analyzing shared references across papers
Loading...
Ostroverkhova et al. (Wed,) studied this question.
www.synapsesocial.com/papers/68e65f99b6db6435875edaca — DOI: https://doi.org/10.1016/j.jmb.2024.168644