Cell genomics and immunosuppressive biomarker expression influence PD-L1 immunotherapy treatment responses in HNSCC—a computational study

Abstract

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.

HBD3 Induces PD-L1 Expression on Head and Neck Squamous Cell Carcinoma Cell Lines

“…PD-L1 expression in cancers is used to predict a favorable outcome to PD-1 and PD-L1 immunotherapy treatments [49,50]. In recent studies, we predicted that PD-L1 induction stimuli in HNSCC cell lines SCC4, SCC15, and SCC25 were processed via ERK signaling pathways (via EGFR, BRAF-V600E (BRAF), MEK1/2 (MAP2K1, MAP2K2), ERK1/2 (MAPK3, MAPK1), and c-Jun (JUN)) [18,19]. We predicted high levels of PD-L1 expression is processed through STAT3 and ERK signaling pathways [51,52].…”

“…The interaction of PD-L1 with PD-1 regulates the balance between co-stimulatory and co-inhibitory immune signals, maintains the breadth and magnitude of the immune response, maintains self-tolerance, prevents adverse autoimmune inflammatory events, protects the host from uncontrolled immune responses to pathogens, and prevents inflammatory tissue damage. Increases in PD-L1 expression can occur on SCC cells [8] as a result of mutations in tumor cell signaling pathways or exposure of tumor cells to inflammatory cytokines IL-1, IL-6, GM-CSF, IFNγ, TNFα, and VEGF [15][16][17][18][19] and the gamma-chain cytokines IL-2, IL-7, IL-10, IL-15, and IL-21 [20]. The latter group plays a role in peripheral T-cell expansion and survival.…”

Human beta defensin 3 alters matrix metalloproteinase production in human dendritic cells exposed to Porphyromonas gingivalis hemagglutinin B

“…30 It has also been used to predict the PD-L1 expression on oral squamous cell carcinoma cells. 31, 32…”

Matrix metalloproteinase and cytokine profiles from cell co-cultures and their role in oral inflammation and head and neck cancer

“…The technology has been recently used to predict a) elastin triggered transient pro-inflammatory responses by human dermal fibroblasts [30]; b) antiaggregation of alpha-synuclein in M17 dopaminergic cells [31]; ATPase activity of heat shock protein 90 in tumor cells, endothelial cells, and stromal cells [32]; c) effects of progesterone and Cox2 inhibitors on progesterone receptor isoforms A and B in myometrial cells [33]; d) butein inhibition of STAT3 expression in HepG2, SNU-387, and PLC/PRF5 human hepatocellular carcinoma cells [34]; and e) effects of isorhamnetin on the peroxisome proliferator-activated receptor gamma signaling cascade [35]. It has also been used to predict the PD-L1 expression on oral squamous cell carcinoma cells [36,37].…”

“…The computational network was created as recently described using published studies and information from genomic, transcriptomic, proteomic, and metabolomic datasets [37,[55][56][57][58][59]. Specific information was identified, interpreted, and annotated into signal transduction datasets.…”