Predicting PD L1 expression on human cancer cells using next generation sequencing information in computational simulation models
PurposeInteraction of the programmed death 1 (PD 1) co receptor on T cells with the programmed death ligand 1 (PD L1) on tumor cells can lead to immunosuppression, a key event in the pathogenesis of many tumors. Thus, determining the amount of PD L1 in tumors by immunohistochemistry (IHC) is important as both a diagnostic aid and a clinical predictor of immunotherapy treatment success. Because IHC reactivity can vary, we developed computational simulation models to accurately predict PD L1 expression as a complementary assay to affirm IHC reactivity.MethodsMultiple myeloma (MM) and oral squamous cell carcinoma (SCC) cell lines were modeled as examples of our approach. Non transformed cell models were first simulated to establish non tumorigenic control baselines. Cell line genomic aberration profiles, from next generation sequencing (NGS) information for MM.1S, U266B1, SCC4, SCC15, and SCC25 cell lines, were introduced into the workflow to create cancer cell line specific simulation models. Percentage changes of PD L1 expression with respect to control baselines were determined and verified against observed PD L1 expression by ELISA, IHC, and flow cytometry on the same cells grown in culture.ResultThe observed PD L1 expression matched the predicted PD L1 expression for MM.1S, U266B1, SCC4, SCC15, and SCC25 cell lines and clearly demonstrated that cell genomics play an integral role by influencing cell signaling and downstream effects on PD L1 expression.ConclusionThis concept can easily be extended to cancer patient cells where an accurate method to predict PD L1 expression would affirm IHC results and improve its potential as a biomarker and a clinical predictor of treatment success.
Cancer Immunology, Immunotherapy