Immunotherapy for cancer is complex and varied. Previous immunotherapy for cancer patients has problems such as low response rate and significant individual differences, such as immune checkpoint blockade therapy. Metabolic regulatory network converts nutrients into primary and secondary metabolites, which allow cells generate the energy. However, given the infinite cell proliferation patterns of solid tumors, which requires dysfunctional metabolic pattern to achieve faster energy supply, such as “Warburg effect” or “aerobic glycolysis”. Notably, emerging evidence indicates that the intervention of glycolysis pathway is conducive to control tumor cells competing with the energy sources of immune cells and impair tumor progression. Indeed, T cells could sense signals such as various cytokines in the immune microenvironment, and distinguish non-self and prevent autoimmune reactions, thereby identifying and eradicating tumor cells. The objective of targeting cancer metabolism and remoulding immunosuppressive microenvironment is to better harness human immune response, especially the adaptive immunity to fight cancer. Therefore, metabolic intervention might be a bright avenue for checkpoint blockade immunotherapy to destroy solid tumors.
This research topic aims to provide an open forum that, by understanding the metabolic interventions in tumor immunity of patients, mapping out the cancer immunity atlas for individual patients and propose specific immune therapies or combinations of immune therapies. This series will also delineate the global landscape of metabolic crosstalk in tumor cells and immune cells through sequencing techniques, machine-learning based algorithms and bioinformatics analysis to make cancer immunotherapy more manageable than traditional or targeted therapies. Note that studies lacking clinical in-house cohort or biological validation in vivo and/or in vitro are out of scope for this topic. Meanwhile, comments or review articles which help advance science and cover all aspects of cancer metabolic programming and immune response are welcome, including the advantages and limitations of each methodology and application.
We invite authors to submit original and review articles that will help improve the research of this field.
Potential topics can include, but are not limited to:
•Mechanisms of tumor metabolites on T cell dysfunction and roles of metabolic pathways on immune system exclusion
•Analysis of T cell metabolic pattern (e.g. glycolysis, fatty acid oxidation, pentose phosphate pathway) associated with T cell functions and immune checkpoints
•Exploring the novel molecular subtypes of key molecules and/or cells in metabolic interventions on T cell activation
•Applications of machine learning for predictive and prognostic models related to checkpoint blockade immunotherapy in solid tumors
•Exciting findings and optimizations of clinical strategies based on checkpoint blockade immunotherapy and immune metabolism
Immunotherapy for cancer is complex and varied. Previous immunotherapy for cancer patients has problems such as low response rate and significant individual differences, such as immune checkpoint blockade therapy. Metabolic regulatory network converts nutrients into primary and secondary metabolites, which allow cells generate the energy. However, given the infinite cell proliferation patterns of solid tumors, which requires dysfunctional metabolic pattern to achieve faster energy supply, such as “Warburg effect” or “aerobic glycolysis”. Notably, emerging evidence indicates that the intervention of glycolysis pathway is conducive to control tumor cells competing with the energy sources of immune cells and impair tumor progression. Indeed, T cells could sense signals such as various cytokines in the immune microenvironment, and distinguish non-self and prevent autoimmune reactions, thereby identifying and eradicating tumor cells. The objective of targeting cancer metabolism and remoulding immunosuppressive microenvironment is to better harness human immune response, especially the adaptive immunity to fight cancer. Therefore, metabolic intervention might be a bright avenue for checkpoint blockade immunotherapy to destroy solid tumors.
This research topic aims to provide an open forum that, by understanding the metabolic interventions in tumor immunity of patients, mapping out the cancer immunity atlas for individual patients and propose specific immune therapies or combinations of immune therapies. This series will also delineate the global landscape of metabolic crosstalk in tumor cells and immune cells through sequencing techniques, machine-learning based algorithms and bioinformatics analysis to make cancer immunotherapy more manageable than traditional or targeted therapies. Note that studies lacking clinical in-house cohort or biological validation in vivo and/or in vitro are out of scope for this topic. Meanwhile, comments or review articles which help advance science and cover all aspects of cancer metabolic programming and immune response are welcome, including the advantages and limitations of each methodology and application.
We invite authors to submit original and review articles that will help improve the research of this field.
Potential topics can include, but are not limited to:
•Mechanisms of tumor metabolites on T cell dysfunction and roles of metabolic pathways on immune system exclusion
•Analysis of T cell metabolic pattern (e.g. glycolysis, fatty acid oxidation, pentose phosphate pathway) associated with T cell functions and immune checkpoints
•Exploring the novel molecular subtypes of key molecules and/or cells in metabolic interventions on T cell activation
•Applications of machine learning for predictive and prognostic models related to checkpoint blockade immunotherapy in solid tumors
•Exciting findings and optimizations of clinical strategies based on checkpoint blockade immunotherapy and immune metabolism
