Artificial Intelligence for Precision Gut Microbiome Modulation in Cancer Therapies

The intestinal microbiome has emerged as a pivotal modulator of cancer‑therapy outcomes across diverse modalities such as tyrosine‑kinase inhibitors, immune checkpoint inhibitors, PARP inhibitors, and hormonal agents. Microbiome‑targeted interventions, including fecal microbiota transplantation, high‑fiber or Mediterranean diets, and live bacterial therapeutics have shown promise in restoring responsiveness and mitigating toxicity. At the same time, microbiome-targeted interventions, such as probiotics, prebiotics, postbiotics, fecal microbiota transplantation, and metabolite-based strategies are emerging as potential adjuncts to improve response and tolerability. Because the microbiome is highly dynamic and patient‑specific, integrating longitudinal microbial profiling with tumor genomics, host immunity, and clinical variables demands advanced analytical approaches. Artificial‑intelligence and machine‑learning pipelines are increasingly employed to translate these multi‑omics data into actionable clinical biomarkers and personalized therapeutic recommendations.This Research Topic aims to accelerate the development of predictive, adaptive, and personalized cancer treatment strategies by integrating AI/ML with microbiome science. A central challenge is that microbiome-associated effects on therapy response and toxicity are heterogeneous, context-dependent, and often confounded by diet, drugs (including antibiotics), tumor type, and host factors. We seek contributions that improve the robustness, interpretability, and clinical utility of microbiome-informed models, moving beyond simple associations, and toward validated prediction as well as, where possible, causal understanding. A second priority is the rational design of microbiome interventions as adjuncts to targeted cancer therapies. Submissions should address how to select the optimal intervention (e.g., probiotic, prebiotic, postbiotic, fecal microbiota transplantation, metabolite supplementation), determine appropriate dosing, and time administration relative to therapy initiation and disease progression. We invite original contributions that apply AI/ML approaches to link gut‑microbiome profiles with outcomes of targeted cancer treatments. Topics of interest include, but are not limited to: prediction of response/resistance and toxicity; multimodal modeling integrating microbiome, tumor, clinical, and multi-omics data; AI-guided design and personalization of microbiome interventions (probiotics, prebiotics, postbiotics, FMT, microbial metabolites); and algorithms optimizing the timing of microbiome modulation during treatment. Clinical studies using AI-based microbiome stratification to guide therapeutic decisions are particularly encouraged, as are benchmark datasets, external validations, and interpretable/causal modeling approaches. Manuscript types of interest include Original Research, Methods, Brief Research Reports, Clinical/Translational Studies, Systematic Reviews, and Perspectives that outline best practices, regulatory considerations, and pathways to clinical implementation. Topic editors: Cátia Pacífico, Payton Tung On Yau Source: https://www.frontiersin.org/research-topics/78448/artificial-intelligence-for-precision-gut-microbiome-modulation-in-cancer-therapies