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

10 Citations (Scopus)

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)1-12
Number of pages12
JournalCancer Immunology, Immunotherapy
DOIs
StateAccepted/In press - Sep 29 2016

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Ligands
Neoplasms
Immunohistochemistry
Cell Line
Workflow
T-Cell Antigen Receptor
Genomics
Multiple Myeloma
Immunotherapy
Immunosuppression
Squamous Cell Carcinoma
Flow Cytometry
Biomarkers
Enzyme-Linked Immunosorbent Assay
Therapeutics

Keywords

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

ASJC Scopus subject areas

  • Immunology and Allergy
  • Immunology
  • Oncology
  • Cancer Research

Cite this

Lanzel, E. A., Paula Gomez Hernandez, M., Bates, A. M., Treinen, C. N., Starman, E. E., Fischer, C. L., ... Brogden, K. A. (Accepted/In press). Predicting PD-L1 expression on human cancer cells using next-generation sequencing information in computational simulation models. Cancer Immunology, Immunotherapy, 1-12. https://doi.org/10.1007/s00262-016-1907-5

Predicting PD-L1 expression on human cancer cells using next-generation sequencing information in computational simulation models. / Lanzel, Emily A.; Paula Gomez Hernandez, M.; Bates, Amber M.; Treinen, Christopher N.; Starman, Emily E.; Fischer, Carol L.; Parashar, Deepak; Guthmiller, Janet M; Johnson, Georgia K.; Abbasi, Taher; Vali, Shireen; Brogden, Kim A.

In: Cancer Immunology, Immunotherapy, 29.09.2016, p. 1-12.

Research output: Contribution to journalArticle

Lanzel, EA, Paula Gomez Hernandez, M, Bates, AM, Treinen, CN, Starman, EE, Fischer, CL, Parashar, D, Guthmiller, JM, Johnson, GK, Abbasi, T, Vali, S & Brogden, KA 2016, 'Predicting PD-L1 expression on human cancer cells using next-generation sequencing information in computational simulation models', Cancer Immunology, Immunotherapy, pp. 1-12. https://doi.org/10.1007/s00262-016-1907-5
Lanzel, Emily A. ; Paula Gomez Hernandez, M. ; Bates, Amber M. ; Treinen, Christopher N. ; Starman, Emily E. ; Fischer, Carol L. ; Parashar, Deepak ; Guthmiller, Janet M ; Johnson, Georgia K. ; Abbasi, Taher ; Vali, Shireen ; Brogden, Kim A. / Predicting PD-L1 expression on human cancer cells using next-generation sequencing information in computational simulation models. In: Cancer Immunology, Immunotherapy. 2016 ; pp. 1-12.
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AU - Parashar, Deepak

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