Histopathology-based Spatial Profiling of Immune and Molecular Features Predicts Cancer risk in Barrett's Esophagus.

Background: Improved cancer risk stratification is needed to differentiate high-risk individuals with Barrett's esophagus (BE) from low-risk populations to reduce overtreatment and improve outcome. The evolution of BE towards adenocarcinoma is likely driven by a combination of genomic and microenvironmental factors, yet existing predictive models rarely integrate both using routine specimens. Method: We developed BEACON (Barrett Esophagus DNA content Abnormality and immune ecology for Cancer Outcome), a spatially aware framework predicting DNA content abnormalities and characterizing immune spatial ecology from routine histopathology. First, using 777 BE biopsies with flow cytometry-based DNA content data scanned at two institutions, we trained and tested DACOR (DNA content abnormality recognition), a multi-instance learning model that predicts DNA content abnormalities from histopathology. Next, complementary models for cell classification and tissue segmentation enabled spatial immune ecology metric computations. Lastly, a logistic regression model integrated molecular immune ecological features and epithelial morphology for cancer risk stratification. Results: DACOR achieved 0.825 AUC in the test cohort for DNA content abnormality prediction. DNA content abnormal regions exhibited increased lymphoplasma cellular inflammation versus normal regions (p=0.006). Patients classified as DNA content abnormal by DACOR demonstrated increased cancer progression (p=0.0001). Among patients with DNA content abnormality, cancer progressors exhibited increased plasma cell clustering adjacent to abnormal epithelium compared to non-progressors. The integrated risk classification model stratified DNA content abnormal patients into high- and low-risk groups with 0.817 AUC. Conclusion: BEACON spatially integrates molecular abnormality with immune spatial ecology to stratify BE patients by cancer progression risk using routine pathology images. This scalable, explainable approach could improve clinical decision-making and reduce unnecessary surveillance in low-risk patients.