Decoding adaptive immune states and trajectories from infectious diseases to cancer with immune twins

Immunity has long stood as a correlate of health. The immune system is traditionally understood as a central guardian, prepared to protect against infections and eliminate damaged or abnormal cells. Yet immunity is not a simple shield: it is a dynamic interface between the organism and its environment, continuously sensing, interpreting, and adapting its response to internal and external signals. This Research Topic explores how adaptive immunity moves along the continuum from protection to pathology, with cancer as the primary lens, and the infection as an intermediate versus cancer.The proposed theme centers on adaptive immunity in cancer: particularly T-cell mediated immune surveillance and B-cell repertoires. It explores the mechanisms that determine whether an immune response resolves and controls malignant cells, or instead becomes exhausted, dysregulated, or evaded by tumors. Understanding these transitions at the cellular and molecular level may reveal common principles linking cancer immunology to other immune-mediated conditions.By viewing immunity as an interface, the topic aims to move beyond conventional disease categories and investigate the shifting balance between effective anti-tumor immunity and immune escape, and this is influenced by other disease states. A defining strand of this Research Topic is the use of immune twins, alongside artificial intelligence, as tools to model this interface directly — simulating adaptive immune dynamics, predicting T-cell and tumor trajectories, and identifying therapeutically relevant immune states before they are observable in the clinic.Welcomed themes (but not limited to) include:● T-cell mediated immune surveillance in cancer and mechanisms of T-cell exhaustion and dysregulation in the tumor microenvironment● B-cell repertoire dynamics in anti-tumor immunity● Adaptive immunity across the protection-to-pathology continuum, with infection as an intermediate state● Shared mechanisms linking cancer immunology to other immune-mediated conditions● Digital twin modeling of adaptive immune dynamics● AI-driven prediction of T-cell and tumor trajectories● Computational immunology approaches to identifying therapeutically relevant immune states from infectious diseases to cancerThe Research Topic welcomes interdisciplinary approaches spanning adaptive immunity, cancer immunology, T-cell biology, B-cell biology, immune surveillance, computational immunology, and translational medicine. We aim to understand when and why adaptive immunity fails to control cancer — and how digital twin and AI-driven modeling, combined with mechanistic insight, can be translated into strategies that predict disease, restore immune balance, and improve outcomes. Note for transparency and potential COIs: E.M. declares to participate as co-owner of the spin-off aiNET GmbH Topic editors: Enkelejda Miho, Theam Soon Lim, Li Song Source: https://www.frontiersin.org/research-topics/85817/decoding-adaptive-immune-states-and-trajectories-from-infectious-diseases-to-cancer-with-immune-twins