IADR Abstract Archives
Determination of Immune Evasion Phenotypes of HNSCC
Objectives: The programmed death (PD-1/PD-L1) pathway is the focus of immunotherapeutic checkpoint inhibition for the control of head and neck squamous cell carcinoma (HNSCC). However, it may be only part of the total immunosuppressive biomarker profile of these cells. The objective of this study was to determine immunosuppressive biomarker profiles to identify immune evasion phenotypes of HNSCC using a computational and experimental approach.
Methods: A computational simulation model was developed and used to predict immunosuppressive biomarker profiles and HNSCC (UM-SCC) cells grown in culture were used to confirm the responses. 1.0 × 106 viable cells/ml of SCC4, SCC15, SCC25, SCC19, SCC84, SCC92, and SCC99 were incubated in culture for 24 hours. RPMI-1640 was removed and concentrations of extracellular biomarkers were determined (Milliplex immunoassays, Millipore, Billerica, MA). Adherent cells were lysed and concentrations of cell associated biomarkers were determined (ELISA, American Research Products, Inc., Waltham, MA). An analogous two-way fixed effect ANOVA was fit to log-transformed concentrations of biomarkers (JMP10, SAS, Cary, NC) and pairwise group comparisons were conducted at the 0.05 level using the method of Tukey’s Honest Significant Differences (HSD).
Results: There was an average of 85% correlation between predictive vs. experimental biomarker trends for SCC4, SCC15, and SCC25 when comparing 2 cell lines at a time. In culture, SCC cell lines produced different concentrations of immunosuppressive biomarkers including IL6, IL10, TGFβ, VEGF, FASL, CD47, IDO, PD-L1 and other chemokines and cytokines. SCC4, SCC15, SCC25, and SCC99 cell lines produced significantly higher concentrations (p<0.05) of immunosuppressive biomarkers than SCC19, SCC84, and SCC92 cell lines.
Conclusions: Expression of immunosuppressive biomarkers by HNSCC cell lines can be determined based on their profile of genomic aberrations using a computational approach. These cells likely represent different immune evasion phenotypes, a finding that will clearly impact the type of immunotherapy used to treat patients with HNSCC.
Methods: A computational simulation model was developed and used to predict immunosuppressive biomarker profiles and HNSCC (UM-SCC) cells grown in culture were used to confirm the responses. 1.0 × 106 viable cells/ml of SCC4, SCC15, SCC25, SCC19, SCC84, SCC92, and SCC99 were incubated in culture for 24 hours. RPMI-1640 was removed and concentrations of extracellular biomarkers were determined (Milliplex immunoassays, Millipore, Billerica, MA). Adherent cells were lysed and concentrations of cell associated biomarkers were determined (ELISA, American Research Products, Inc., Waltham, MA). An analogous two-way fixed effect ANOVA was fit to log-transformed concentrations of biomarkers (JMP10, SAS, Cary, NC) and pairwise group comparisons were conducted at the 0.05 level using the method of Tukey’s Honest Significant Differences (HSD).
Results: There was an average of 85% correlation between predictive vs. experimental biomarker trends for SCC4, SCC15, and SCC25 when comparing 2 cell lines at a time. In culture, SCC cell lines produced different concentrations of immunosuppressive biomarkers including IL6, IL10, TGFβ, VEGF, FASL, CD47, IDO, PD-L1 and other chemokines and cytokines. SCC4, SCC15, SCC25, and SCC99 cell lines produced significantly higher concentrations (p<0.05) of immunosuppressive biomarkers than SCC19, SCC84, and SCC92 cell lines.
Conclusions: Expression of immunosuppressive biomarkers by HNSCC cell lines can be determined based on their profile of genomic aberrations using a computational approach. These cells likely represent different immune evasion phenotypes, a finding that will clearly impact the type of immunotherapy used to treat patients with HNSCC.