The new generation of antibodies emerges as an alternative to cancer treatment. The therapy is based on modulation and activation of immune cells, increasing the survival of many patients and, leading to long-term remission. The mechanism consists of blocking molecules that halt T cell activation, such as PD-1, PD-L1, and CTL-4, making the immune system more effective, and working against cancer.
The immune-checkpoint blockade therapy (ICB) has been introduced in many anti-cancer treatments, even associated with other drug schedules, which is a new promising therapy option. However, the benefits of ICB are not efficient for all patients. Due to overactivation of the immune system, it can induce a high number of adverse effects such as colitis, hepatitis, adrenocorticotropic hormone insufficiency, which therefore increase morbidity and mortality.
Why is the ICB not effective for some patients? What factors are involved in the success of the therapy? How could we predict which patients would over-activate the immune system? How to predict and prevent drastic side effects?
Recently, some studies indicate that the main factors that limit the success of the therapy are the heterogeneity of the components that make up the tumor microenvironment (TEM) and the immune tumor microenvironment (TIME). TEM orchestrates the behavior of tumorigenic cells, and vice-versa. The intense network among different resident cells it is not that trivial. The heterogeneous components like tumor cells, cancer stem cells, cancer-associated fibroblasts, endothelial and perivascular cells, and various residents and infiltered immune cells such as tumor-associated macrophages, lymphocytes, and neutrophils are in intense crosstalk, which is mediated by extracellular vesicles, secreted factors and components of the extracellular matrix that, collectively, enable tumor growth and progression, and contribute to the immunosuppressive tumor microenvironment.
The current challenge is to understand the chaotic system. The study of the phenotypic profile of cells that compounds the TME and TIME, and how groups of molecules are involved in tumor progression, drug resistance, immune evasion, and immune tumor-control, can open new perspectives in cancer treatment. Thus, varied omics analyses, such as proteomics, epigenomics, metabolomics, single cell analyzes, transcriptomics, exomics and artificial intelligence in bioinformatics, emerge as new generation tools that may solve complex systems, and discover new targets for drug design, predict clinical outcomes and decrease side effects.
This Research Topic encourages scientists to bring different approaches to the understanding and discovery of molecules that distinguish different TME and TIME components and their consequences for cancer therapy and investigating the complex crosstalk between cell signaling. Original Research, Reviews, Mini-Reviews and Methods describing new technologies approaches are welcome.
Please note: The findings based on data integration should be also validated by observational/experimental data. Descriptive studies and studies consisting solely of bioinformatic investigation of publicly available genomic/transcriptomic data do not fall within the scope of the journal.
The new generation of antibodies emerges as an alternative to cancer treatment. The therapy is based on modulation and activation of immune cells, increasing the survival of many patients and, leading to long-term remission. The mechanism consists of blocking molecules that halt T cell activation, such as PD-1, PD-L1, and CTL-4, making the immune system more effective, and working against cancer.
The immune-checkpoint blockade therapy (ICB) has been introduced in many anti-cancer treatments, even associated with other drug schedules, which is a new promising therapy option. However, the benefits of ICB are not efficient for all patients. Due to overactivation of the immune system, it can induce a high number of adverse effects such as colitis, hepatitis, adrenocorticotropic hormone insufficiency, which therefore increase morbidity and mortality.
Why is the ICB not effective for some patients? What factors are involved in the success of the therapy? How could we predict which patients would over-activate the immune system? How to predict and prevent drastic side effects?
Recently, some studies indicate that the main factors that limit the success of the therapy are the heterogeneity of the components that make up the tumor microenvironment (TEM) and the immune tumor microenvironment (TIME). TEM orchestrates the behavior of tumorigenic cells, and vice-versa. The intense network among different resident cells it is not that trivial. The heterogeneous components like tumor cells, cancer stem cells, cancer-associated fibroblasts, endothelial and perivascular cells, and various residents and infiltered immune cells such as tumor-associated macrophages, lymphocytes, and neutrophils are in intense crosstalk, which is mediated by extracellular vesicles, secreted factors and components of the extracellular matrix that, collectively, enable tumor growth and progression, and contribute to the immunosuppressive tumor microenvironment.
The current challenge is to understand the chaotic system. The study of the phenotypic profile of cells that compounds the TME and TIME, and how groups of molecules are involved in tumor progression, drug resistance, immune evasion, and immune tumor-control, can open new perspectives in cancer treatment. Thus, varied omics analyses, such as proteomics, epigenomics, metabolomics, single cell analyzes, transcriptomics, exomics and artificial intelligence in bioinformatics, emerge as new generation tools that may solve complex systems, and discover new targets for drug design, predict clinical outcomes and decrease side effects.
This Research Topic encourages scientists to bring different approaches to the understanding and discovery of molecules that distinguish different TME and TIME components and their consequences for cancer therapy and investigating the complex crosstalk between cell signaling. Original Research, Reviews, Mini-Reviews and Methods describing new technologies approaches are welcome.
Please note: The findings based on data integration should be also validated by observational/experimental data. Descriptive studies and studies consisting solely of bioinformatic investigation of publicly available genomic/transcriptomic data do not fall within the scope of the journal.