Immune checkpoint inhibitors (ICIs) such as anti-PD-1 and anti-PD-L1 antibodies are highly effective against many types of cancer, yet durable responses are limited to a subset of patients highlighting the need for the development of effective biomarkers to predict prognosis and efficacy. Currently, PD-L1 expression in tumors, microsatellite instability (MSI) or mismatch repair deficiency (dMMR), and tumor mutation burden (TMB) are known as biomarkers for cancer immunotherapy but are not sufficient. Combination therapy with immune checkpoint inhibitors and chemotherapy or radiation therapy, as well as diverse therapies targeting intra-tumoral regulatory T cells have been described, but there are currently no unifying biomarkers that are applicable to clinically, a simple, fast, non-invasive method that can yield biomarkers of disease with a minimal adverse effect on patients is desirable. Recent findings suggest that the balancing of effector T cells and regulatory cells in the tumor microenvironment is associated with cancer progression and prognosis. Cells and molecules involved in the control of cancer are complex, and a better understanding of the tumor immune environment will lead to the development of truly effective biomarkers. This topic will focus on novel biomarkers that predict efficacy, prognosis, or the development of adverse events in various cancer immunotherapies, and extensive basic research leading to the development of biomarkers. Manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by robust and relevant validation (clinical cohort or biological validation in vitro or in vivo) are out of scope for this topic. We expect a wide range of research, not only in serology, genetics, and immunocytochemistry but also in bacterial flora. Research on the development of novel assays and bioinformatics methods is also welcome: • Non-invasive biomarkers for cancer immunotherapy. • Bulk RNA-seq, scRNA-seq, or Rep-seq methods. • Correlation of tumor immune cells with gut microbiota in tumor immunotherapy. • Impact of Teff and Treg balance in the tumor microenvironment on tumor prognosis. • Inflammatory and immune signatures associated with drug response versus resistance in cancer.