About this Research Topic
The National Cancer Act was signed in 1971, beginning the official war on cancer, with the goal of eliminating cancer as a major cause of death. Fifty years later, the war is still raging. It is estimated that there will be nearly 20 million new cancer cases worldwide this year and roughly 10 million cancer patients will succumb to their disease. Despite decades of research, cancer remains amongst the leading causes of death worldwide.
As early as the 19th century, when Dr. William Coley treated patients with the “Coley toxin”, it was recognized that host immunity had the powerful ability, if stimulated correctly, to completely eradicate tumors. Since then, immunotherapy has evolved to include antibodies, antibody-targeted chemotherapy, cancer vaccines, cytokines, cell therapies, engineered viruses, and checkpoint inhibitors, adding to the cancer-fighting armamentarium alongside the mainstays of surgery, radiation therapy, and cytotoxic drugs. As immunotherapy evolves, scientists focus a tremendous effort on understanding the interaction between tumor tissue and host immunity. As a result of this effort, there is an ever-increasing appreciation for the fact that tumor tissues actively regulate (suppresses) the immune system by producing cell surface (e.g. PDL1) or soluble factors (e.g. TGFb) that counter an effective antitumor immune response.
With technological advances including multiparameter flow cytometry, large panel cytokine arrays, next-generation sequencing, and multiplex immunofluorescence, scientists continue to deepen the query not only of circulating immune cells and soluble factors, but also tumor tissues themselves. Application of these technologies across patients and treatments generates large immune data sets that can potentially reveal why some cancer patients dramatically respond to therapy while others are resistant.
To date, the majority of these types of analyses are limited by the number of immune cell subsets and cytokines; yet to fully understand immune homeostasis in cancer patients, a systems-based approach to recognize variations in the vast network of immune interactions is essential. Properly analyzing these data sets to gain a comprehensive understanding of existing immune homeostasis and the changes that occur throughout the course of treatment remains a significant challenge.
The aim of this Research Topic is to focus on mathematical/computational/statistical approaches to glean novel insights into the tumor-immune interactions of cancer patients to better understand how immunity changes as a result of treatment and to apply new knowledge to make informed decisions about treatments on an individual basis in the future. We welcome Original Research articles, Clinical Trials, Reviews, and Methods papers that discuss the potential role of modelling to address various topics related to cancer immune responses, including:
• Insights into the changes in immune homeostasis of cancer patients in response to therapy in clinical and pre-clinical settings
• Characterization of the immune/cancer microenvironment of primary and metastatic cancer tissue
• Applying spatial/temporal data analytics statistics to immunobiology
Note: Manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (clinical cohort or biological validation in vitro or in vivo) are out of scope for this section.
As early as the 19th century, when Dr. William Coley treated patients with the “Coley toxin”, it was recognized that host immunity had the powerful ability, if stimulated correctly, to completely eradicate tumors. Since then, immunotherapy has evolved to include antibodies, antibody-targeted chemotherapy, cancer vaccines, cytokines, cell therapies, engineered viruses, and checkpoint inhibitors, adding to the cancer-fighting armamentarium alongside the mainstays of surgery, radiation therapy, and cytotoxic drugs. As immunotherapy evolves, scientists focus a tremendous effort on understanding the interaction between tumor tissue and host immunity. As a result of this effort, there is an ever-increasing appreciation for the fact that tumor tissues actively regulate (suppresses) the immune system by producing cell surface (e.g. PDL1) or soluble factors (e.g. TGFb) that counter an effective antitumor immune response.
With technological advances including multiparameter flow cytometry, large panel cytokine arrays, next-generation sequencing, and multiplex immunofluorescence, scientists continue to deepen the query not only of circulating immune cells and soluble factors, but also tumor tissues themselves. Application of these technologies across patients and treatments generates large immune data sets that can potentially reveal why some cancer patients dramatically respond to therapy while others are resistant.
To date, the majority of these types of analyses are limited by the number of immune cell subsets and cytokines; yet to fully understand immune homeostasis in cancer patients, a systems-based approach to recognize variations in the vast network of immune interactions is essential. Properly analyzing these data sets to gain a comprehensive understanding of existing immune homeostasis and the changes that occur throughout the course of treatment remains a significant challenge.
The aim of this Research Topic is to focus on mathematical/computational/statistical approaches to glean novel insights into the tumor-immune interactions of cancer patients to better understand how immunity changes as a result of treatment and to apply new knowledge to make informed decisions about treatments on an individual basis in the future. We welcome Original Research articles, Clinical Trials, Reviews, and Methods papers that discuss the potential role of modelling to address various topics related to cancer immune responses, including:
• Insights into the changes in immune homeostasis of cancer patients in response to therapy in clinical and pre-clinical settings
• Characterization of the immune/cancer microenvironment of primary and metastatic cancer tissue
• Applying spatial/temporal data analytics statistics to immunobiology
Note: Manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (clinical cohort or biological validation in vitro or in vivo) are out of scope for this section.
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
