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. PDL1 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 tumorinfiltrating immune cells across a full sample, retaining PD-L1 expression information for true tumor cells only.

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. This method may allow for increased accuracy and timeefficiency in providing a TPS over traditional methods alone, which may result in improved patient care.

Bhavika Patel, PhD¹, Stephanie Allen¹, Sameer Talwalkar, MD¹, Navi Mehra, PhD¹, Jeppe Thagaard², Thomas W. Ramsing², Agnete Overgaard, PhD², Jenifer Caldara²

1. 1. Lanterne Dx, Boulder CO.
   2. Visiopharm Corporation