During the past few decades, immunotherapy has become an established pillar of cancer treatment improving the survival of numerous patients with diverse solid and hematologic tumors. The leading causes behind the success are the discovery of immune checkpoint inhibitors (ICIs) and the development of chimeric antigen receptor (CAR) T/M/NK cells. As for ICIs, malignancies take advantage of the inhibitory programmed cell death protein 1/programmed cell death protein ligand 1 (PD-1/PD-L1) or cytotoxic T-lymphocyte-associated protein (CTLA-4) pathways to evade the immune system, and disruption of the axis by immune checkpoint inhibitors can achieve durable disease remissions, which has been proved by basic researches and (pre-) clinical studies among lung cancer, melanoma, renal cell cancer, head, and neck squamous cell carcinoma, urothelial cancer, and Hodgkin’s disease. However, the 5-year survival rate of patients treated with ICIs varies with each individual and also relies on tumor specific pathological or molecular subtypes. Besides, the efficacy of ICIs is still limited in terms of drug resistance and fewer potential responders. Thus, there is a big challenge to identify and develop more novel reliable ICIs, as well as sensibilize existing ICIs for patients with drug resistance or even for non-responders.
In the era of precision medicine, multiple sequencing methods have been rapidly developed, including high throughput RNA/DNA sequencing, RNA/DNA methylome-seq, ATAC-seq, single-cell sequencing, TCR-seq, and so on. All the above approaches help broaden the horizons of the transcriptomic, genetic and epigenetic information that is available for exploring the underlying immunologic mechanisms and selecting the optimal cancer patients who are suitable for receiving immunotherapy. Besides, a variety of new computational strategies and novel integrated bioinformatics methods constantly emerge, providing different perspectives and more evidence for the potential onco-immunological mechanisms and cancer immunotherapy. Combined with the above approaches, to some extent, researchers might find new ways to overcome the limitations of ICIs.
The harsh truth is that tumor heterogeneity leads to heterogeneous and complicated immune escape mechanisms, only a few patients respond to immunotherapy, especially therapies that rely on endogenous immune activation, which means it presents a major challenge in selecting the appropriate candidates. Therefore, the identification of new biomarkers for cancer immunotherapy is meaningful for cancer treatment. The aim of the topic is to provide a collection of multidisciplinary research (purely bioinformatic papers are also permitted only with at least one independent validation cohort or more than 50 samples in single experimental cohort for rare diseases) , review articles, together with clinical studies, addressing cancer immunotherapy that includes, but are not limited to the following:
1. PD-1/PD-L1, CTLA-4 in immunotherapy;
2. Single-cell analysis of immune microenvironment changes before and after ICIs treatment;
3. Novel immune drug delivery methods, such as nanotechnology-assisted immunotherapy;
4. Potential mechanisms of ICIs resistance and its dynamic monitoring;
5. Novel approaches for predicting responses to ICIs based on multiscale biological data;
6. Translational research of ICIs;
7. Epigenetic mechanisms and identification of epigenetic biomarkers in immunotherapy;
8. Identification of new biomarkers associated with ICIs using multi-omic data;
9. Immunotherapy sensitizer for neoadjuvant or adjuvant immunotherapy;
10. Immune microenvironment (TME) and the efficacy of ICIs.
During the past few decades, immunotherapy has become an established pillar of cancer treatment improving the survival of numerous patients with diverse solid and hematologic tumors. The leading causes behind the success are the discovery of immune checkpoint inhibitors (ICIs) and the development of chimeric antigen receptor (CAR) T/M/NK cells. As for ICIs, malignancies take advantage of the inhibitory programmed cell death protein 1/programmed cell death protein ligand 1 (PD-1/PD-L1) or cytotoxic T-lymphocyte-associated protein (CTLA-4) pathways to evade the immune system, and disruption of the axis by immune checkpoint inhibitors can achieve durable disease remissions, which has been proved by basic researches and (pre-) clinical studies among lung cancer, melanoma, renal cell cancer, head, and neck squamous cell carcinoma, urothelial cancer, and Hodgkin’s disease. However, the 5-year survival rate of patients treated with ICIs varies with each individual and also relies on tumor specific pathological or molecular subtypes. Besides, the efficacy of ICIs is still limited in terms of drug resistance and fewer potential responders. Thus, there is a big challenge to identify and develop more novel reliable ICIs, as well as sensibilize existing ICIs for patients with drug resistance or even for non-responders.
In the era of precision medicine, multiple sequencing methods have been rapidly developed, including high throughput RNA/DNA sequencing, RNA/DNA methylome-seq, ATAC-seq, single-cell sequencing, TCR-seq, and so on. All the above approaches help broaden the horizons of the transcriptomic, genetic and epigenetic information that is available for exploring the underlying immunologic mechanisms and selecting the optimal cancer patients who are suitable for receiving immunotherapy. Besides, a variety of new computational strategies and novel integrated bioinformatics methods constantly emerge, providing different perspectives and more evidence for the potential onco-immunological mechanisms and cancer immunotherapy. Combined with the above approaches, to some extent, researchers might find new ways to overcome the limitations of ICIs.
The harsh truth is that tumor heterogeneity leads to heterogeneous and complicated immune escape mechanisms, only a few patients respond to immunotherapy, especially therapies that rely on endogenous immune activation, which means it presents a major challenge in selecting the appropriate candidates. Therefore, the identification of new biomarkers for cancer immunotherapy is meaningful for cancer treatment. The aim of the topic is to provide a collection of multidisciplinary research (purely bioinformatic papers are also permitted only with at least one independent validation cohort or more than 50 samples in single experimental cohort for rare diseases) , review articles, together with clinical studies, addressing cancer immunotherapy that includes, but are not limited to the following:
1. PD-1/PD-L1, CTLA-4 in immunotherapy;
2. Single-cell analysis of immune microenvironment changes before and after ICIs treatment;
3. Novel immune drug delivery methods, such as nanotechnology-assisted immunotherapy;
4. Potential mechanisms of ICIs resistance and its dynamic monitoring;
5. Novel approaches for predicting responses to ICIs based on multiscale biological data;
6. Translational research of ICIs;
7. Epigenetic mechanisms and identification of epigenetic biomarkers in immunotherapy;
8. Identification of new biomarkers associated with ICIs using multi-omic data;
9. Immunotherapy sensitizer for neoadjuvant or adjuvant immunotherapy;
10. Immune microenvironment (TME) and the efficacy of ICIs.
