The intratumoral microbiota is a key modulator of the tumor immune microenvironment. This study aims to clinically validate a novel, AI-driven digital pathology biomarker-the Microbial-Sensory Coupling (MSC) index-which measures the spatial proximity between the intratumoral bacterium Campylobacter gracilis(C. gracilis) and host Trace Amine-Associated Receptor 1 (TAAR1)+ tumor cells. The study will evaluate whether the MSC index, measured in pre-treatment tumor biopsy tissues, can accurately predict clinical responsiveness and survival outcomes in ESCC patients undergoing anti-PD-1-based immunotherapy.
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This retrospective cohort includes 120 ESCC patients who received anti-PD-1-based therapy. Archived pre-treatment FFPE biopsy specimens are retrieved and used to establish the MSC index mathematical model via multiplexed FISH and mIF combined with deep-learning image segmentation. ROC analysis is applied to determine the optimal diagnostic cut-off value for predicting anti-PD-1 responsiveness.
This independent retrospective cohort includes 150 ESCC patients who received anti-PD-1-based therapy, identified from a non-overlapping case series. Archived pre-treatment FFPE biopsy specimens are used to validate the pre-determined MSC index cut-off value for its predictive specificity, sensitivity, and clinical utility in distinguishing immunotherapy responders from non-responders, as well as its association with PFS and OS.
Beijing, China
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