Gastric adenocarcinoma are characterized by a dismal prognosis largely driven by recurrence.  Current clinical predictors, such as TNM staging, offer limited precision, hindering personalized treatment strategies. This proposal outlines a transformative research project aimed at developing a novel, AI-driven predictive model for gastric adenocarcinoma recurrence, leveraging the tumor-associated microbiota.  We hypothesize that deep learning analysis of microbial profiles can identify key biomarkers predicting recurrence, develop tailored therapeutic interventions and improved outcomes. This will also prevent side effects of specific chemotherapy medications which may offer no benefit to that patient.

This project will employ integrating 16S rRNA gene sequencing and metagenomic shotgun sequencing of tumor tissue samples collected from a cohort of patients undergoing surgical resection(R0) for localized gastric adenocarcinoma.  Advanced bioinformatics pipelines, incorporating QIIME2 and established databases, will be utilized for microbial community profiling.  Machine learning algorithms, including deep learning architectures, will be trained and validated to predict recurrence probability based on microbial signatures. Feature engineering and model optimization will be performed, with explainable AI-techniques employed to help translate the results to clinical use.  Model performance will be evaluated using metrics such as AUC and sensitivity and compared against established clinical predictors. External validation will be done to ensure generalisability.

This research has the potential to revolutionize gastric cancer management by providing an accurate and personalized recurrence prediction tool.  Identifying key microbial taxa associated with recurrence will offer novel insights into the underlying biological mechanisms driving disease progression.  Critically, this knowledge will raise the foundation for development of targeted therapeutic strategies, including personalized immunotherapy, probiotic use, etc, ultimately improving patient survival and quality of life.  This proposal lays the foundation for moving towards precision oncology guided by AI-driven microbial profiling.  The successful completion of this project will not only significantly impact clinical practice but also advance our fundamental understanding of the complex interplay between the tumor microenvironment and cancer progression.