Computational Biology Predicts the Efficacy of Tumor Immune Checkpoint Blockade

Abstract

Tumor immunotherapy is considered as one of the most promising methods in cancer treatment in recent years. Immune checkpoint blockade (ICB) can activate immune cells to destroy tumors by relieving the inhibitory pathway of tumor cells to immune cells. In silico prediction of the ICB response is an important step toward achieving effective and personalized cancer immunotherapy. Although immune checkpoint inhibitors have shown exciting clinical effects in the treatment of many types of tumors, there are still some clinical problems in practical application, such as low response rate and large individualized differences. How to predict the efficacy of effective individualized immune checkpoint inhibitors for tumor patients based on specific biomarkers and computational models is one of the key issues in the immunotherapy of this kind of tumor. In our work, from the five levels of genome level, transcription level, epigenetic level, microbial taxonomy level, and the immune cell infiltration profile level, the biomarkers and in silico calculation methods that affect the efficacy of tumor immune checkpoint inhibitors are comprehensively summarized.