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 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".