Deep learning-based assessment of PD-L1 expression in NSCLC predicts outcome for patients treated with anti-PD-1 immunotherapy.

Background: PD-L1 expression is widely used as a predictive biomarker for anti-PD-1 therapies in non-small cell lung cancer (NSCLC). However, its prognostic value remains controversial. Here, we investigated whether deep learning (DL) applied to PD-L1 immunohistochemistry (IHC) slides could identify histological patterns predictive of outcome in patients treated with anti-PD-1 therapy. Methods: We analyzed two independent NSCLC cohorts: MSK (n=182, training) and CGFL (n=108, validation). Tumor regions were manually annotated, tiled, stain-normalized, and processed through the UNI foundation model to extract deep features. Clustering of tiles from 10 extreme-outcome MSK cases identified histology-based subgroups. These were then applied to the remaining patients by projection and majority voting. Associations with progression-free survival (PFS) and overall survival (OS) were assessed. DL groups were integrated with clinical covariates in a multivariate model. Results: Clustering revealed two distinct DL-defined groups (DL Conclusions: Deep learning applied to PD-L1 IHC slides identifies reproducible histomorphological patterns associated with outcomes in anti-PD-1-treated NSCLC patients. This approach provides prognostic information beyond conventional PD-L1 scoring and enhances predictive accuracy when combined with clinical factors.