Abstract 471: AIM PD-L1-NSCLC: Artificial intelligence-powered PD-L1 quantification for accurate prediction of tumor proportion score in diverse, multi-stain clinical tissue samples

Abstract

Abstract Introduction: Important immunotherapy drugs targeting PD-L1 are approved for first and second line treatment for various stages of NSCLC. Reproducible and precise evaluation of PD-L1 expression is essential to accurately evaluate patients’ eligibility for treatment and for enrollment in clinical trials. Current guidelines rely on pathologists to interpret tumor samples, which is challenging in part because different PD-L1 assays have distinct scoring criteria. As a result, determining eligibility by manual assessment can be inconsistent and inaccurate, leading to untreated patients. To support pathologist quantification of PD-L1 in clinical trials, PathAI has developed scanner-and antibody-agnostic machine learning (ML) models, AI-based histologist measurement of PD-L1 in NSCLC (AIM-PD-L1-NSCLC), for the quantification of PD-L1 expression in NSCLC using four PD-L1 immunohistochemistry (IHC) clones. Methods: AIM-PD-L1-NSCLC was trained using convolutional neural networks to identify and quantify PD-L1-positive cells in digitized whole slide images (WSI) of tissue samples. Models were developed using over 5,000 diverse clinical biopsies and resections, including primary and metastatic adenocarcinoma and squamous cell carcinoma samples collected from 10 clinical trials and from two clinical laboratories, each stained for PD-L1 with one of four IHC clones: SP263 (N=1,320), SP142 (N=1,829) (both Ventana Medical Systems Inc., Tucson, AZ), 28-8 (N=1,331), or 22C3 (N=843) (both Agilent Technologies, Santa Clara, USA). Slides were digitized using Aperio, Philips, and Ventana scanners, and WSI were split into training (N=3,818) and test (N=1,505) datasets. The training dataset was annotated by board certified pathologists (313,770 annotations) to label tissue regions and cells. Human Interpretable features representing the number of tumor cells were automatically extracted from the model and a slide level Tumor Proportion Score (TPS) calculated as the proportion of PD-L1+ cancer cells divided by total cancer cells in tumor regions. Model predicted slide level TPS were compared with the median TPS of five pathologists’ scores using intraclass correlation coefficient (ICC) statistics. Results: There was high concordance between ML model-predicted and median pathologists’ slide level TPS for all PD-L1 clones (ICC 0.93 (95% CI 0.90-0.94), and for each individual clone: 22C3 ICC 0.93 (95% CI 0.89-0.96); SP142 ICC 0.88 (95% CI 0.79-0.93); SP263 ICC 0.96 (95% CI 0.93-0.97; 28-8 ICC 0.90 (95% CI 0.85-0.93). Conclusions: AIM PD-L1 NSCLC is highly concordant with the gold standard pathologist consensus score across four PD-L1 clones in a large diverse dataset. This model could support patient enrollment and stratification in prospective clinical trials, as well as quality control of staining and pathology drift. Citation Format: Michael Griffin, Mevlana Gemici, Ashar Javed, Nishant Agrawal, Murray Resnick, Limin Yu, Sara Hoffman, Victoria Mountain, Jamie Harisiades, Megan Rothney, Benjamin Glass, Ilan Wapinski, Andrew Beck, Eric Walk. AIM PD-L1-NSCLC: Artificial intelligence-powered PD-L1 quantification for accurate prediction of tumor proportion score in diverse, multi-stain clinical tissue samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 471.