Conclusion
This 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.
Methods
Multiple 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. Result: The 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.
Purpose
Interaction 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.