An Active Learning Framework Improves Tumor Variant Interpretation

For precision medicine to reach its full potential for treatment of cancer and other diseases, protein variant effect prediction tools are needed to characterize variants of unknown significance (VUS) in a patient's genome with respect to their likelihood to influence treatment response and outcomes. However, the performance of most variant prediction tools is limited by the difficulty of acquiring sufficient training and validation data. To overcome these limitations, we applied an iterative active learning approach starting from available biochemical, evolutionary, and functional annotations. With active learning, VUS that are most challenging to classify by an initial machine learning model are functionally evaluated and then reincorporated with the phenotype information in subsequent iterations of algorithm training. The potential of active learning to improve variant interpretation was first demonstrated by applying it to synthetic and deep mutational scanning datasets for four cancer-relevant

Automated summary

To fully utilize precision medicine in treating diseases like cancer, it is important to develop protein variant effect prediction tools to evaluate variants of unknown significance in a patient's genome. However, current prediction tools are limited by the lack of training and validation data. In this study, researchers applied an iterative active learning approach to overcome this limitation and improve variant interpretation.