Histopathology-based spatial profiling of immune and molecular features predicts cancer risk in Barrett's esophagus

BackgroundImproved cancer risk stratification is needed to differentiate high-risk individuals with Barretts 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. MethodWe 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. ResultsDACOR 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. ConclusionBEACON 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.