Guest Editors
Dr. Yutian Zou, Sun Yat-Sen University Cancer Center, China. E-mail: zouyt@sysucc.org.cn
Dr. Zui Pan, University of Texas at Arlington, United States. E-mail: zui.pan@uta.edu
Dr. Zhi Tian, University of South Florida, United States. E-mail: ztian@usf.edu
Dr. Jindong Xie, Sun Yat-Sen University Cancer Center, China. E-mail: xiejd1@sysucc.org.cn
Summary
According to statistics from the World Health Organization, cancer is regarded as the leading cause of death around the world and continues to remain an issue in the 21st century. Recently, immune checkpoint therapy has been proven an effective strategy in various advanced solid tumors and has rapidly become a hotspot in the research of antitumor drugs. Immune checkpoint therapy targeting programmed death ligand-1 (PD-L1) and programmed cell death protein-1 (PD-1) has emerged as an effective strategy for various cancers, yielding significant improvement in disease-free and overall survival of patients with cancer. However, some kinds of cancer were regarded as immune-quiescent tumors, which means only a small proportion of patients would benefit from immunotherapy in addition to a high rate of severe adverse events. With the development of multi-omics analyses, exploring a new strategy to target the tumor microenvironment factors might be possible for cancer immunotherapy.
The scope covers all aspects of the immunotherapies targeting the tumor microenvironment of different cancers, including but not limited to new strategies to enhance immunotherapy effects, immunotherapy resistance in cancer, personalized immunotherapy for cancer, biomarkers for immunotherapy response, bioinformatics and machine learning approaches in efficacy prediction; non-coding RNAs for immunotherapy of cancer, lipid metabolism, exosomes, immunotherapeutic drug delivery system, mechanism of immune escape in cancer, novel immune cells or molecules in the cancer microenvironment.
We invite to this Special Issue the submission of scientific articles or reviews that focus on the application of multi-omics analyses in tumor immunotherapy.
Keywords
Immunotherapy; Multi-omics; Biomarker; Drug response; Molecular mechanism; Tumor microenvironment; Machine-learning method; Bioinformatics
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