Cancer is a complex adaptive dynamic system that causes both local and systemic failures in the patient. Cancer is caused by a number of gain-of-function and loss-of-function events, that lead to cells proliferating without control by the host organism over time. In cancer, the immune system modulates cancer cell population heterogeneity and plays a crucial role in disease outcomes. The immune system itself also generates multiple clones of different cell types, with some clones proliferating quickly and maturing into effector cells. By creating regulatory signals and their networks, and generating effector cells and molecules, the immune system recognizes and kills abnormal cells. Anti-cancer immune mechanisms are realized as multi-layer, nonlinear cellular and molecular interactions. A number of factors determine the outcome of immune system-tumor interactions, including cancer-associated antigens, immune cells, and host organisms.
In cancer immunology and therapy, theoretical models and their applications are becoming increasingly valuable in understanding and controlling these processes. Data-driven and theory-based mathematical models enable computational predictions and optimize the identification of new targets for combination therapies that overcome resistance to current treatments and promote long-term cancer control. As these models are developed and quantified from experimental models and patient data, they may help
to improve outcomes for certain cancer types and subtypes, such as response rates to targeted and cytotoxic therapies. By analyzing data and identifying critical mechanisms and parameters, mathematical models can be used to stratify patients’ pathological responses, disease-free recurrence, and metastasis risks based on the molecular characteristics of their tumors and immune cells.
In Frontiers in Immunology, this Research Topic is dedicated to displaying and reviewing
original studies in the dynamic fields of mathematical biology and immunology of
cancer.
We welcome manuscripts focusing on, but not limited to, the following sub-topics:
• Analysis of molecular and cellular interactions between the immune system and tumor cells using dynamical systems and stochastic process models
• Modelling and simulation of the targeting of tumor cells by T lymphocytes and NK cells in vitro and in vivo.
• Model-based and data-driven analysis of infiltrating lymphocyte-tumor cell dynamics in space and time.
• Identification of systems and computational prediction in oncoimmunology
• Diagnosing, analyzing, and predicting tumor-immune network interactions
• Modelling, data analysis, optimization, and outcome evaluation of immunotherapy and combined therapies.
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.
Topic editor Vladimir Kuznetsov is employed by AptaMatrix, Inc. All other Topic Editors declare no competing interests with regards to the Research Topic subject.
Cancer is a complex adaptive dynamic system that causes both local and systemic failures in the patient. Cancer is caused by a number of gain-of-function and loss-of-function events, that lead to cells proliferating without control by the host organism over time. In cancer, the immune system modulates cancer cell population heterogeneity and plays a crucial role in disease outcomes. The immune system itself also generates multiple clones of different cell types, with some clones proliferating quickly and maturing into effector cells. By creating regulatory signals and their networks, and generating effector cells and molecules, the immune system recognizes and kills abnormal cells. Anti-cancer immune mechanisms are realized as multi-layer, nonlinear cellular and molecular interactions. A number of factors determine the outcome of immune system-tumor interactions, including cancer-associated antigens, immune cells, and host organisms.
In cancer immunology and therapy, theoretical models and their applications are becoming increasingly valuable in understanding and controlling these processes. Data-driven and theory-based mathematical models enable computational predictions and optimize the identification of new targets for combination therapies that overcome resistance to current treatments and promote long-term cancer control. As these models are developed and quantified from experimental models and patient data, they may help
to improve outcomes for certain cancer types and subtypes, such as response rates to targeted and cytotoxic therapies. By analyzing data and identifying critical mechanisms and parameters, mathematical models can be used to stratify patients’ pathological responses, disease-free recurrence, and metastasis risks based on the molecular characteristics of their tumors and immune cells.
In Frontiers in Immunology, this Research Topic is dedicated to displaying and reviewing
original studies in the dynamic fields of mathematical biology and immunology of
cancer.
We welcome manuscripts focusing on, but not limited to, the following sub-topics:
• Analysis of molecular and cellular interactions between the immune system and tumor cells using dynamical systems and stochastic process models
• Modelling and simulation of the targeting of tumor cells by T lymphocytes and NK cells in vitro and in vivo.
• Model-based and data-driven analysis of infiltrating lymphocyte-tumor cell dynamics in space and time.
• Identification of systems and computational prediction in oncoimmunology
• Diagnosing, analyzing, and predicting tumor-immune network interactions
• Modelling, data analysis, optimization, and outcome evaluation of immunotherapy and combined therapies.
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.
Topic editor Vladimir Kuznetsov is employed by AptaMatrix, Inc. All other Topic Editors declare no competing interests with regards to the Research Topic subject.
