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 as 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. Results 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.

Background: Biomarkers like programmed death ligand-1 (PDL1) have become a focal point for immunotherapeutic checkpoint inhibition in head and neck squamous cell carcinoma (HNSCC). However, it’s only part of the total immunosuppressive biomarker profile of HNSCC cells. Matrix metalloproteinases (MMPs) are enzymes that break down the basement membrane allowing cancer cells to metastasize and play an important role in the tumor microenvironment. MMPs can also activate certain cytokines, growth factors, and chemokines post-translationally. The objective of this study was to determine MMP and biomarker profiles of seven different HNSCC cell lines. Methods: Authenticated cell lines were grown in minimal media at 1×106 viable cells/mL and incubated at 37 °C. After 24 hrs supernatants were collected, and adhering cells were lysed. Multiplex immunoassays were used to determine MMP1, MMP7, MMP9, IL-6, VEGFA, IL-1α, TNF-α, GM-CSF, IL-1RA, and IL-8 concentrations in supernatants. ELISAs were used to determine PDL1, CD47, FASL, and IDO concentrations in cell lysates. A one-way ANOVA was fit to examine log-transformed concentrations of biomarkers between seven HNSCC cell lines, and pairwise group comparisons were conducted using post- hoc Tukey’s honest significance test (α=0.05). Results: Significant differences (P<0.05) in MMP and biomarker concentrations were found between the seven HNSCC cell lines. For example, MMP9 was highest in SCC25 and UM-SCC99, MMP7 was highest in SCC25 and UM-SCC19, and MMP1 was highest in SCC25. Conclusions: These results suggest different patients’ HNSCC cells can express distinct profiles of select biomarkers and MMPs, which could be due to metastatic stage of the cancer, primary tumor site, type of tissue the tumor originated from, or genomic differences between patients. MMP and biomarker expression profiles should be considered when choosing cell lines for future studies. The results support the reason for personalized medicine and the need to further investigate how it can be used to treat HNSCC.

Long-chain bases are present in the oral cavity. Previously we determined that sphingosine, dihydrosphingosine, and phytosphingosine have potent antimicrobial activity against oral pathogens. Here, we determined the cytotoxicities of long-chain bases for oral cells, an important step in considering their potential as antimicrobial agents for oral infections. This information would clearly help in establishing prophylactic or therapeutic doses. To assess this, human oral gingival epithelial (GE) keratinocytes, oral gingival fibroblasts (GF), and dendritic cells (DC) were exposed to 10.0-640.0 µM long-chain bases and glycerol monolaurate (GML). The effects of long-chain bases on cell metabolism (conversion of resazurin to resorufin), membrane permeability (uptake of propridium iodide or SYTOX-Green), release of cellular contents (LDH), and cell morphology (confocal microscopy) were all determined. GE keratinocytes were more resistant to long-chain bases as compared to GF and DC, which were more susceptible. For DC, 0.2 to 10.0 µM long-chain bases and GML were not cytotoxic; 40.0 to 80.0 µM long-chain bases, but not GML, were cytotoxic; and 80.0 µM long-chain bases induced cellular damage and death in less than 20 minutes. The LD50 of long-chain bases for GE keratinocytes, GF, and DC were considerably higher than their minimal inhibitory concentrations for oral pathogens, a finding important to pursuing their future potential in treating periodontal and oral infections.

Objectives Sjögren’s syndrome is an autoimmune disease most commonly diagnosed in adults but can occur in children. Our objective was to assess the presence of chemokines, cytokines, and biomarkers (CCBMs) in saliva from these children that were associated with lymphocyte and mononuclear cell functions. Methods Saliva was collected from 11 children diagnosed with Sjögren’s syndrome prior to age 18 years and 16 normal healthy children. 105 CCBMs were detected in multiplex microparticle-based immunoassays. ANOVA and t test (0.05 level) were used to detect differences. Ingenuity Pathway Analysis (IPA) was used to assess whether elevated CCBMs were in annotations associated with immune system diseases and select leukocyte activities and functions. Machine learning methods were used to evaluate the predictive power of these CCBMs for Sjögren’s syndrome and were measured by receiver operating characteristic (ROC) curve and area under curve (AUC). Results 40.9% (43/105) CCBMs were different (p &lt; 0.05) in children with Sjögren’s syndrome compared to the healthy study controls and could differentiate the two groups (p &lt; 0.05). Elevated CCBMs in IPA annotations were associated with autoimmune diseases and with leukocyte chemotaxis, migration, proliferation, and regulation of T-cell activation. The best AUC value in ROC analysis was 0.93, indicating that there are small numbers of CCBMs that may be useful for diagnosis of Sjögren’s syndrome. Conclusion While 35/43 CCBMs have been previously reported in Sjögren’s syndrome, 8 CCBMs had not. Additional studies focusing on these CCBMs may provide further insight into disease pathogenesis and may contribute to diagnosis of Sjögren’s syndrome in children.

Objectives PD-L1 expression is correlated with objective responses rates (ORR) to PD-1 and D-L1 immunotherapies. However, both immunotherapies have only demonstrated 12.0–24.8% ORR in patients with HNSCC showing a need for a more accurate method to identify those who will respond prior to their therapy. Immunohistochemistry to detect PD-L1 reactivity in tumors can be challenging and additional methods are needed to predict and confirm PD-L1 expression. Here, we hypothesized that HNSCC tumor cell genomics influences cell signaling and downstream effects on immunosuppressive biomarkers and that these profiles can predict patient clinical responses. Study Design We identified deleterious gene mutations in SCC4, SCC15, and SCC25 and created cell line-specific predictive computational simulation models. The expression of 24 immunosuppressive biomarkers were then predicted and used to sort cell lines into those that would or would not respond to PD-L1 immunotherapy. Results SCC15 and SCC25 were identified as cell lines that would respond to PD-L1 immunotherapy treatment and SCC4 was identified as a cell line that would not likely respond to PD-L1 immunotherapy treatment. Conclusions This approach, when applied to patient HNSCC cancer cells, has the ability to predict PD-L1 expression and predict PD-1 or PD-L1 targeted treatment responses in those patients.