Mapping and intercepting inflammation-fueled cancer: multi-scale systems immunology approaches

Cancer immunity and immunotherapy research increasingly recognizes that chronic inflammation is a major driver of tumorigenesis, yet the complex and dynamic interplay between immune dysregulation, tissue remodeling, and oncogenic transformation remains incompletely understood. Traditional studies often concentrate on singular pathways or specific cellular components, providing fragmented insights in static temporal contexts. This leaves significant gaps in the ability to identify which patients with chronic inflammatory conditions are at highest risk of malignant transformation and when that risk emerges. Recent advances in single-cell multi-omics, spatial profiling, and computational network modeling are now transforming this landscape, enabling integrative analyses of the evolving tissue ecosystem during the transition from chronic inflammation to cancer. However, translating these systems-level insights into tangible strategies for early intervention and prevention remains a major challenge within the field.

This Research Topic aims to establish and advance a systems immunology paradigm for understanding and intercepting inflammation-driven tumorigenesis. By quantitatively modeling how persistent inflammatory signals orchestrate malignant transformation across cellular, molecular, and tissue scales, we seek to uncover the early and reversible immunological checkpoints that determine patient trajectory. Objectives include integrating multi-omic and spatial data to develop predictive models of cancer risk, identifying dynamic biomarker signatures for real-time disease monitoring, and designing interventions that target network-level susceptibilities rather than isolated pathways. The ultimate goal is to foster translational research that bridges computational discovery with mechanistically informed prevention trials and diagnostics, advancing the field toward proactive oncology in chronic inflammatory diseases.

This Research Topic is defined by a focus on multi-scale, systems-level investigations that treat inflammation-driven oncogenesis as a dynamic, computable process, rather than a collection of discrete events. Submissions should address innovations or novel insights within these boundaries and are encouraged to tackle, but are not limited to, the following areas:
• Integration of multi-omics and spatial immune profiling through network modeling or causal inference approaches
• Characterization of temporal immunological transitions in longitudinal cohorts or experimental systems
• Discovery of druggable network hubs or early biomarker panels for cancer interception in high-risk inflammatory states
• Development of computational platforms or digital twins to simulate immune–tumor co-evolution and optimize therapeutic strategies
• Reports on interception clinical trials and biomarker-guided prevention initiatives in inflammation-associated cancers
• Mechanistic studies of feedback loops among stromal remodeling, microbiome dynamics, and immune dysregulation in the progression to malignancy

Please note: Manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) are out of scope for this section and will not be accepted as part of this Research Topic.