Clinical performance of artificial intelligence-powered annotation of tumor cell PD-L1 expression for treatment of immune-checkpoint inhibitor (ICI) in advanced non-small cell lung cancer (NSCLC).

Abstract

9026 Background: Programmed death ligand 1 (PD-L1) expression is the standard biomarker for first line ICI in advanced NSCLC. However, manual evaluation of tumor proportion score (TPS) by pathologists has practical limitations including intra/inter-observer bias, variation in subjectivity on area of interest and intensive labor. We developed an artificial intelligence (AI)-powered TPS analyzer, namely Lunit SCOPE PD-L1, for objective annotation of tumor cell PD-L1 expression for prediction of ICI response in advanced NSCLC. Methods: Lunit SCOPE PD-L1 was developed by a total of 393,565 tumor cells annotated by board-certified pathologists for PD-L1 expression in 802 whole-slide images (WSI) stained by 22C3 pharmDx immunohistochemistry. A After excluding the in-house control tissue regions, the WSI were divided into patches, from which a deep learning-based model detected the location and PD-L1 positivity of tumor cells. The patch-level cell predictions were aggregated for TPS estimation. Clinical performance of the model was validated in an external cohort of 430 NSCLC tumor slides from patients treated with ≥ ICI at Seoul National University Bundang Hospital and Samsung Medical Center. Independent control TPS annotation of this external validation cohort was performed by three pathologists, and their consensus TPS was calculated by mean value of such. Results: AI-model (Lunit SCOPE PD-L1) predicts PD-L1-positive tumor cell with the area under the curves of 0.889 and PD-L1-negative tumor cells with that of 0.809 at cell-level analysis. At WSI-level, significant positive correlation was observed between TPS by AI model and control TPS by pathologists (Spearman coefficient = 0.9247, P < 0.001). Concordance rate between AI-model and control TPS by pathologists according to expression level of PD-L1 ≥ 50%, 1-49%, and < 1% status was 85.7%, 89.3%, and 52.4%, respectively. Median progression-free survival (mPFS) according to TPS by AI model ≥ 1% vs. < 1% were 2.8 vs. 1.7 months (hazard ratio, HR, 0.52, 95% confidence interval, CI, 0.38-0.71, P < 0.001). In contrast, mPFS according to control TPS was 2.8 vs. 2.1 months (HR 0.70, 95% CI 0.55-0.91, P < 0.001). Forty out of 84 patients (47.6%) annotated as control TPS < 1% by pathologists were considered as TPS ≥ 1% by AI-model and mPFS of this subgroup was 2.7 months. Conclusions: PD-L1 expression by AI-model correlates with PD-L1 expression by pathologists. Clinical performance of AI-model in WSI-level is comparable with assessment by pathologists. The AI-model can accurately predict tumor response and progression-free survival of ICI in advanced NSCLC.