Towards In Silico Prediction of the Immune-Checkpoint Blockade Response

Cancer immunotherapy with immune-checkpoint blockade (ICB) is considered a promising strategy for cancer treatment. Identifying predictive biomarkers and developing efficient computational models to predict the ICB response are important issues for successful immunotherapy. Here, we present a concise and intuitive survey of the computational issues for ICB response prediction, providing a summary of the available predictive biomarkers and building of one-stop machine-learning models that integrate biomarkers calculable from high-throughput sequencing (HTS) data. Several points for discussion are highlighted to inspire further research for improving ICB treatment. Continuing efforts are required to improve ICB response prediction and to identify novel predictive biomarkers by taking advantage of the rapid development of computational models and HTS techniques for effective and personalized cancer immunotherapy.