Spatial biomarker-driven deep learning model via digital pathology predicts response to PI3K inhibitor buparlisib in head and neck squamous cell carcinoma

Buparlisib, a pan-class I PI3K inhibitor, combined with paclitaxel, demonstrated improved survival in the BERIL-1 trial for patients with recurrent/metastatic (R/M) head and neck squamous cell carcinoma (HNSCC). However, predictive biomarkers of benefit remain undefined. We evaluated spatial biomarkers derived from hematoxylin and eosin (H&E) images using artificial intelligence (AI)–based digital pathology. Whole-slide H&E images (n=144) from BERIL-1 were analyzed using a deep learning model trained to segment tissue compartments and classify individual cell phenotypes. Three prospectively defined spatial features were evaluated: (1) tumor-infiltrating lymphocyte (TIL) density in the tumor area; (2) tumor microenvironment (TME) heterogeneity; and (3) granulocyte fraction in the tumor invasive margin (TIM). Cox proportional hazards model was used to evaluate biomarker–treatment interactions, with patients stratified by biomarker status. High TIL density (>10%) defined by deep learning–derived analysis of H&E was associated with a significantly improved overall survival with buparlisib versus placebo (HR□=□0.25; 95% CI, 0.01–0.64; p = 0.002), as were high TME heterogeneity (HR□=□0.47; 95% CI, 0.27–0.80; p = 0.005) and granulocyte enrichment in the TIM (HR = 0.51; p = 0.014); in a within-arm proximity analysis, higher granulocyte–tumor cell proximity correlated with improved OS on buparlisib (HR = 0.32; p < 0.001). AI-derived spatial metrics outperformed CD3 immunohistochemistry staining in stratifying survival outcomes. In patients with oropharyngeal tumors, human papillomavirus-positive cases were more frequent among those with high TILs. Spatial features extracted from standard H&E slides using AI-driven digital pathology can predict OS benefit from buparlisib in R/M HNSCC. These cost-effective and scalable biomarkers support image-based patient selection strategies and are being prospectively evaluated in the ongoing BURAN phase 3 trial.