Predicting PD-L1 expression on human cancer cells using next-generation sequencing information in computational simulation models

Emily A. Lanzel, M. Paula Gomez Hernandez, Amber M. Bates, Christopher N. Treinen, Emily E. Starman, Carol L. Fischer, Deepak Parashar, Janet M. Guthmiller, Georgia K. Johnson, Taher Abbasi, Shireen Vali, Kim A. Brogden

Research output: Contribution to journalArticle

11 Scopus citations

Abstract

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. 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. 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.

Original languageEnglish (US)
Pages (from-to)1511-1522
Number of pages12
JournalCancer Immunology, Immunotherapy
Volume65
Issue number12
DOIs
StatePublished - Dec 1 2016

Keywords

  • Computational modeling
  • Multiple myeloma
  • Oral squamous cell carcinoma
  • PD-L1
  • Simulation modeling

ASJC Scopus subject areas

  • Immunology and Allergy
  • Immunology
  • Oncology
  • Cancer Research

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  • Cite this

    Lanzel, E. A., Paula Gomez Hernandez, M., Bates, A. M., Treinen, C. N., Starman, E. E., Fischer, C. L., Parashar, D., Guthmiller, J. M., Johnson, G. K., Abbasi, T., Vali, S., & Brogden, K. A. (2016). Predicting PD-L1 expression on human cancer cells using next-generation sequencing information in computational simulation models. Cancer Immunology, Immunotherapy, 65(12), 1511-1522. https://doi.org/10.1007/s00262-016-1907-5