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Cancer Immunol Immunother. 2016 Dec;65(12):1511-1522. Epub 2016 Sep 29.

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

Author information

1
Department of Oral Pathology, Radiology and Medicine, College of Dentistry, University of Iowa, Iowa City, IA, USA.
2
Iowa Institute for Oral Health Research, N423 DSB, College of Dentistry, The University of Iowa, 801 Newton Road, Iowa City, IA, 52242, USA.
3
Cellworks Research India Ltd, Whitefield, Bangalore, India.
4
College of Dentistry, University of Nebraska Medical Center, 40th and Holdrege, Lincoln, NE, USA.
5
Department of Periodontics, College of Dentistry, The University of Iowa, Iowa City, IA, USA.
6
Cellworks Group Inc, 2033 Gateway Place Suite 500, San Jose, CA, USA.
7
Iowa Institute for Oral Health Research, N423 DSB, College of Dentistry, The University of Iowa, 801 Newton Road, Iowa City, IA, 52242, USA. kim-brogden@uiowa.edu.
8
Department of Periodontics, College of Dentistry, The University of Iowa, Iowa City, IA, USA. kim-brogden@uiowa.edu.

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.

KEYWORDS:

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

PMID:
27688163
PMCID:
PMC5394567
DOI:
10.1007/s00262-016-1907-5
[Indexed for MEDLINE]
Free PMC Article

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