Association of artificial intelligence-powered and manual quantification of programmed death-ligand 1 (PD-L1) expression with outcomes in patients treated with nivolumab ± ipilimumab

• Published in: Modern pathology Vol. 35; no. 11; pp. 1529 - 1539
• Main Authors: Baxi, Vipul, Lee, George, Duan, Chunzhe, Pandya, Dimple, Cohen, Daniel N., Edwards, Robin, Chang, Han, Li, Jun, Elliott, Hunter, Pokkalla, Harsha, Glass, Benjamin, Agrawal, Nishant, Lahiri, Abhik, Wang, Dayong, Khosla, Aditya, Wapinski, Ilan, Beck, Andrew, Montalto, Michael
• Format: Journal Article
• Language: English
• Published: New York Elsevier Inc 01.11.2022; Nature Publishing Group US; Elsevier Limited
• Subjects: 13/51; 631/250/251; 631/67/1059/2325; 631/67/1857; 631/67/2321; 692/53/2421; Algorithms; Antibodies, Monoclonal - therapeutic use; Apoptosis; Artificial Intelligence; B7-H1 Antigen - metabolism; Biomarkers; Biomarkers, Tumor - metabolism; Bladder cancer; Cancer immunotherapy; Carcinoma, Non-Small-Cell Lung - pathology; Carcinoma, Transitional Cell; Clinical trials; Drug development; Head and neck carcinoma; Humans; Immunohistochemistry; Immunotherapy; Ipilimumab; Laboratory Medicine; Ligands; Lung carcinoma; Lung Neoplasms - pathology; Medicine; Medicine & Public Health; Melanoma; Monoclonal antibodies; Nivolumab - therapeutic use; Non-small cell lung carcinoma; Oncology; Pathology; Patients; PD-L1 protein; Quantitation; Retrospective Studies; Small cell lung carcinoma; Squamous cell carcinoma; Targeted cancer therapy; Tumor cells; Urinary Bladder Neoplasms; Urothelial carcinoma
• ISSN: 0893-3952; 1530-0285
• DOI: 10.1038/s41379-022-01119-2

Assessment of programmed death ligand 1 (PD-L1) expression by immunohistochemistry (IHC) has emerged as an important predictive biomarker across multiple tumor types. However, manual quantitation of PD-L1 positivity can be difficult and leads to substantial inter-observer variability. Although the development of artificial intelligence (AI) algorithms may mitigate some of the challenges associated with manual assessment and improve the accuracy of PD-L1 expression scoring, use of AI-based approaches to oncology biomarker scoring and drug development has been sparse, primarily due to the lack of large-scale clinical validation studies across multiple cohorts and tumor types. We developed AI-powered algorithms to evaluate PD-L1 expression on tumor cells by IHC and compared it with manual IHC scoring in urothelial carcinoma, non-small cell lung cancer, melanoma, and squamous cell carcinoma of the head and neck (prospectively determined during the phase II and III CheckMate clinical trials). 1,746 slides were retrospectively analyzed, the largest investigation of digital pathology algorithms on clinical trial datasets performed to date. AI-powered quantification of PD-L1 expression on tumor cells identified more PD-L1–positive samples compared with manual scoring at cutoffs of ≥1% and ≥5% in most tumor types. Additionally, similar improvements in response and survival were observed in patients identified as PD-L1–positive compared with PD-L1–negative using both AI-powered and manual methods, while improved associations with survival were observed in patients with certain tumor types identified as PD-L1–positive using AI-powered scoring only. Our study demonstrates the potential for implementation of digital pathology-based methods in future clinical practice to identify more patients who would benefit from treatment with immuno-oncology therapy compared with current guidelines using manual assessment.