Abstract PO-076: Adrenergic regulation of the anti-tumor immune response in <i>p53</i>-deficient tumors
Bibliographic record
Abstract
Abstract Several previous studies have shown that β2-adrenergic signaling pathway promotes tumor progression, whereas others have shown the opposite effect. The role of β2-adrenergic activation in oral squamous cell carcinoma (OSCC) remains largely unknown. Our previous study showed that the loss of p53 leads to increased adrenergic tumor innervation in OSCC, resulting in transcriptional programs that establish new neuronal identity. We hypothesized that tumor immunity in OSCC is regulated by adrenergic signaling. The aim of the present study was to determine the influences of adrenergic signaling in immune microenvironment of OSCC. We first performed multiplex immunofluorescence to analyze the immune cell infiltration and adrenergic nerve density in OSCC; and analyzed the Cancer Genome Atlas (TCGA) datasets to establish correlations between immune-related and adrenergic pathway genes. Subsequently, we performed T cell killing assay with p53 knockout (KO) and p53 wild-type (WT) using antigen-specific HLA-matched patient-derived T-cells with adrenergic agonists or antagonists. To confirm p53-dependent adrenergic signaling in OSCC, a genetically engineered p53MDM2 overexpression (OE) cell line was generated and treated with adrenergic agonists or antagonists. To test our hypothesis that p53 regulates adrenergic-dependent paracrine signaling between cancer and T cells we performed a Luminex assay on the conditioned medium from p53 KO and p53 WT coculture system. Finally, in vivo, adrenergic ablation (TH flox/flox ) and novel murine models of OSCC were used to investigate the effect of adrenergic nerve signaling on immune infiltration and responses to immunotherapy. Our data showed that the increase in adrenergic nerve density was inversely proportional to the infiltration of exhausted T cells. Analysis of the TCGA data revealed that ADRB2 strongly correlated with tumor immune infiltrate genes. Adrenergic agonists treatment induced significantly more apoptosis in the p53 KO but not in the p53 WT cells; and overexpression of MDM2 rescued the resistance to apoptosis. Conditioned media experiments showed that the coculture of tumor cells with T cells acts through paracrine signaling and that the secreted factor is CXCL10. We then knocked out CXCL10 and ADRB2 in p53KO cells and confirmed that apoptosis was significantly reduced in the absence of CXCL10 or ADRB2. Genetic ablation of adrenergic signaling in vivo undermined response to anti-programmed cell death protein 1 (anti-PD1) compared to controls. Finally, our mouse model of OSCC showed that isoprenaline (β-adrenergic agonist) treatment in combination with PD-1 therapy significantly enhanced response to immunotherapy and improved survival. Our study provides new evidence that adrenergic signaling induces immune-mediated apoptosis and response to immunotherapy. Further studies are needed to assess the role of isoprenaline combined with anti-PD-1 therapy as a novel therapeutic regimen for patients with p53-deficient OSCC tumors. Citation Format: Deborah A. Silverman, Shamima Akhter, Tongxin Xie, Shajedul Islam, Simone Anfossi, Jennifer D. Covello, Frederico O. Gleber-Netto, Elien Doorduijn, Yunfei Wang, Emily L. Ashkin, Jeffrey N. Myers, George A. Calin, Patrick Hwu, Moran Amit. Adrenergic regulation of the anti-tumor immune response in p53-deficient tumors [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Innovating through Basic, Clinical, and Translational Research; 2023 Jul 7-8; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2023;29(18_Suppl):Abstract nr PO-076.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".