The utilization of artificial intelligence to produce clinically relevant scores on PD-L1 immunostained non-small cell lung cancer biopsies.

Abstract

e14576 Background: Immunotherapies targeting complex programmed death-ligand 1 (PD-L1) interactions have been groundbreaking in the treatment of non-small cell lung cancer (NSCLC), offering new strategies for patients who are likely to respond. PD-L1 binds to the checkpoint PD-1 to regulate T cells, which has an important role in boosting the immune response. Identifying patients who may benefit from PD-L1 focused care is dependent on the interpretation of the drug’s associated companion diagnostic (CDx) immunohistochemistry (IHC) assay. PD-L1 IHC assays stratify NSCLC patients using a Tumor Proportion Score (TPS), a manual scoring paradigm which must be applied by a clinical pathologist. Application of the score is complex as it requires the reader to visually exclude stromal and tumor-infiltrating immune cells across a full sample, retaining PD-L1 expression information for true tumor cells only. Methods: In this study, we utilized the PD-L1 [28-8] immunohistochemistry (IHC) assay to stain 10 NCSLC specimens. Stained slides were then imaged using the AT2 scanner (Leica Biosystems, Buffalo Grove, IL) scanner and analyzed using the Visiopharm Image Analysis platform. Results: The intricacy of TPS application is time consuming for the pathologist and introduces opportunity for inter- and intra-reader variability. Our novel image analysis approach utilizes artificial intelligence (AI) to automatically denote tumor nests and exclude tumor-infiltrating immune cells. The remaining true tumor cells may then be quantified for PD-L1 expression across the full biopsy, producing a TPS for each sample. Conclusions: This method allows for great accuracy and time-efficiency in providing a TPS than traditional methods, which may result in improved patient care.