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 High vs. DL Low ). In the MSK cohort, DL High patients had significantly longer PFS than DL Low (median 5.7 vs. 2.5 months; HR = 0.63, 95% CI 0.44–0.89; p=0.01). This prognostic value was independently confirmed in the CGFL cohort (median PFS 15.2 vs. 6.2 months; HR = 0.59, 95% CI 0.36–0.96; p=0.03). OS was numerically higher in DL High patients but did not reach significance. DL classification correlated with higher PD-L1 tumor proportion score (TPS). Discordance between DL and TPS was observed, and the DL model further stratified outcomes among patients with TPS ≥50%. A combined model integrating DL groups with clinical variables improved prediction of PFS compared to clinical features alone (HR = 0.50, 95% CI 0.33–0.75; p0.001 in MSK; HR = 0.54, 95% CI 0.31–0.91; p=0.02 in CGFL). 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.