About this Research Topic
The exploration of cancer metabolism within the realm of tumor immunology represents a burgeoning field of research, providing fresh insights into how metabolic pathways influence the immune landscape of tumors. This nexus of cancer metabolism and immunology, enriched by computational methods, offers a novel perspective on tumor progression and the response to therapies. Metabolic processes within the tumor microenvironment not only fuel tumor growth but also modulate immune cell function, impacting the efficacy of immunotherapeutic interventions. The application of computational models and machine learning algorithms to dissect these complex interactions holds the promise of unveiling new metabolic targets for cancer therapy, optimizing immunotherapeutic strategies, and enhancing patient outcomes. As we delve deeper into the metabolic underpinnings of cancer, the integration of computational tools emerges as crucial for accelerating discovery and harnessing metabolic pathways for therapeutic benefit.
This Research Topic aims to spotlight the intersection of cancer metabolism and tumor immunology, with a particular focus on the role of computational innovations in advancing our understanding and therapeutic targeting of metabolic processes in cancer. We seek contributions that illuminate the impact of tumor metabolism on the immune microenvironment and how these insights can lead to the development of novel immunometabolic therapies. By harnessing computational methods, from data analytics to machine learning, we aim to decode the metabolic complexities of tumors, identify metabolic vulnerabilities, and predict therapeutic responses. This collection will emphasize the translational potential of metabolic research, showcasing how computational insights can guide the design of new therapeutic strategies, enhance the precision of immunotherapies, and ultimately improve patient care in oncology. Through this focused exploration, we aspire to foster a deeper understanding of the metabolic mechanisms driving cancer immunity and to pave the way for innovative treatments that exploit these pathways.
We welcome original research, reviews, method articles, and perspectives that provide innovative computational insights into cancer metabolism and its impact on the immune system. Contributions should underscore the potential of these approaches to uncover new therapeutic avenues and improve the design and efficacy of cancer treatments. Specific themes include:
• Computational modeling of metabolic pathways in the tumor microenvironment
• The influence of cancer metabolism on immune cell function and immunotherapy outcomes
• Machine learning algorithms for identifying metabolic biomarkers and therapeutic targets
• Integrative analyses of metabolomics, immunomics, and mitochondrial dynamics data to uncover novel insights into tumor immunometabolism
• Development of computational tools for predicting response to immunometabolic 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.
This Research Topic aims to spotlight the intersection of cancer metabolism and tumor immunology, with a particular focus on the role of computational innovations in advancing our understanding and therapeutic targeting of metabolic processes in cancer. We seek contributions that illuminate the impact of tumor metabolism on the immune microenvironment and how these insights can lead to the development of novel immunometabolic therapies. By harnessing computational methods, from data analytics to machine learning, we aim to decode the metabolic complexities of tumors, identify metabolic vulnerabilities, and predict therapeutic responses. This collection will emphasize the translational potential of metabolic research, showcasing how computational insights can guide the design of new therapeutic strategies, enhance the precision of immunotherapies, and ultimately improve patient care in oncology. Through this focused exploration, we aspire to foster a deeper understanding of the metabolic mechanisms driving cancer immunity and to pave the way for innovative treatments that exploit these pathways.
We welcome original research, reviews, method articles, and perspectives that provide innovative computational insights into cancer metabolism and its impact on the immune system. Contributions should underscore the potential of these approaches to uncover new therapeutic avenues and improve the design and efficacy of cancer treatments. Specific themes include:
• Computational modeling of metabolic pathways in the tumor microenvironment
• The influence of cancer metabolism on immune cell function and immunotherapy outcomes
• Machine learning algorithms for identifying metabolic biomarkers and therapeutic targets
• Integrative analyses of metabolomics, immunomics, and mitochondrial dynamics data to uncover novel insights into tumor immunometabolism
• Development of computational tools for predicting response to immunometabolic 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.
Keywords: Cancer Metabolism, Tumor Immunology, Computational Biology, Metabolic Pathways, Immunometabolic Therapy, Machine Learning, Metabolomics, Therapeutic Targeting
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.
