Morphine treatment restricts response to immunotherapy in oral squamous cell carcinoma
Bibliographic record
Abstract
BACKGROUND: Immune checkpoint inhibitors (ICIs) are becoming the standard of care for recurrent and metastatic cancer. Opioids, the primary treatment for cancer-related pain, are immunosuppressive raising concerns about their potential to interfere with the efficacy of ICIs. We hypothesize that exogenous opioids given for analgesia suppress antitumor immunity via T cell-mediated mu opioid receptor 1 (OPRM1) signaling. METHODS: In silico bioinformatics were used to assess OPRM1 receptor expression on tumor-infiltrating immune cells in patients with head and neck squamous cell carcinoma (HNSCC) and across different cancer types. A syngeneic orthotopic mouse model of oral squamous cell carcinoma was used to study the impact of morphine and OPRM1 antagonism on tumor-infiltrating immune cells, tumor growth and antitumor efficacy of anti-Programmed cell death protein 1 (PD-1) monoclonal antibody treatment. RESULTS: In patients with HNSCC, OPRM1 expression was most abundant in CD8+ T cells, particularly in patients who had not been prescribed opioids prior to resection and exhibited increased expression of exhaustion markers. Exogenous morphine treatment in tumor-bearing mice reduced CD4+ and CD8+ T-cell infiltration and subsequently anti-PD1 ICI efficacy. Peripherally acting mu opioid receptor antagonism, when administered in the adjunctive setting, was able to block morphine-induced immunosuppression and recover the antitumor efficacy of anti-PD1. CONCLUSIONS: These findings suggest that morphine acts via a peripheral OPRM1-mediated mechanism to suppress CD8+ T cells, thereby fostering a pro-tumor-impaired immune response. Importantly, peripherally-restricted OPRM1 antagonism can effectively block this morphine-induced immunosuppression while still allowing for centrally-mediated analgesia, indicating a potential therapeutic strategy for mitigating the adverse effects of opioid pain relief in cancer treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".