Morphine treatment restricts response to immunotherapy in oral squamous cell carcinoma

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.

Correspondence to Dr Nicole N Scheff; [email protected]

WHAT IS ALREADY KNOWN ON THIS TOPIC

• Immune checkpoint inhibitors are becoming the standard of care for recurrent and metastatic cancer, but response rates remain<20% in head and neck squamous cell carcinoma (HNSCC).

• Cancer-related pain is a common issue at any stage of disease and opioid therapy is the primary and most dependable strategy for pain management.

• Opioids are immunosuppressive, directly inhibiting T cells via opioid receptors.

WHAT THIS STUDY ADDS

• Characterization of morphine-induced immunosuppression specific to the oral cancer tumor microenvironment.

• The detrimental effect of morphine on the therapeutic efficacy of anti-Programmed cell death protein 1 (PD-1) treatment, highlighting the importance of considering opioid usage in cancer immunotherapy protocols.

• Supporting data for peripherally-restricted mu opioid receptor antagonism as a potential adjunctive therapeutic strategy in HNSCC to improve response rates to anti-PD1 therapy.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

• This is the first study to focus on the association between immune opioid-related receptors, cancer immunosuppression and response to immunotherapy in a syngeneic mouse model. It supports research to explore whether adjunctive drugs with immunomodulatory effects can mitigate morphine-induced immunosuppression, thereby enhancing the efficacy of immunotherapy in oncological settings.

Introduction

Immune checkpoint inhibitors (ICIs) targeting the programmed cell death protein 1 (PD-1): programmed death-ligand 1 (PD-L1) pathway have become the standard of care treatment for numerous solid tumors in the advanced setting.¹ Consequently, the potential interactions between ICIs and concomitant medications are gaining attention, although the controlled clinical evaluation and reverse translational preclinical data are scarce.² Cancer-related pain is a common issue at any stage of disease, and opioids remain the current recommended therapeutic regimen according to the WHO analgesic ladder.³ Opioids are well documented to be immunosuppressive⁴ raising concerns about their potential to interfere with the efficacy of ICIs. In a recent systematic review of 13 clinical studies, all reported a negative correlation between opioid use and outcomes such as overall survival, progression-free survival, or time-to-treatment failure.⁵ These correlations appear to persist even when controlled for propensity matching of disease activity and pain status.⁶ Therefore, the relationship between opioid use and response to ICIs requires further investigation.

Extensive research exists on mechanisms driving the immunosuppressive effects of opioids on T cells.^7–13 While initially thought to act indirectly,¹⁴ it is now established that opioids can directly inhibit T cells via opioid receptors, primarily mu opioid receptor 1 (OPRM1).^{15 16} OPRM1 surface expression on T cells is modulated by activation status^{17 18} and influenced by extracellular mediators such as interleukin (IL)-4 and tumor necrosis factor (TNF)-α. OPRM1 agonists can suppress crucial elements of T-cell function, including T-cell receptor complex signaling, interferon (IFN)-ɣ activity, IL-2 transcription, and the transcription factors activator protein 1 (AP-1), nuclear factor of activated T cells (NFAT), and nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB).^{7 8 10 13} Furthermore, endogenous opioids have been shown to increase the expression of inhibitory receptors PD-1 and cytotoxic T-lymphocytes-associated protein 4 (CTLA-4) on CD8⁺ T cells in vitro, an effect that was blocked by OPRM1 antagonism.¹⁹ However, the impact of opioids during the induction and maintenance of T-cell exhaustion by the immunosuppressive tumor microenvironment (TME) has not yet been studied. A recent bioinformatic study in triple-negative breast cancer found that both opioids and anti-PD-L1 ICI regulated the same RNA expression network in CD8⁺ T cells, suggesting a link for opioid–ICI interaction.²⁰ Further studies are essential to fully understand how opioids impact T-cell functionality in the context of ICI treatment efficacy.

Head and neck squamous cell carcinoma (HNSCC) is among the most painful cancers, and patients often rate pain as one of the worst symptoms throughout survivorship.²¹ Opioid therapy is the primary and most dependable strategy for pain management in patients with HNSCC. However, due to the degree of pain, the propensity for symptom chronicity, and tolerance,²² the opioid burden for patients is extremely high. Currently, immunotherapy with anti-PD-1 monoclonal antibody pembrolizumab is the standard front-line systemic therapy for recurrent/metastatic (R/M) HNSCC, however only 20% of patients respond to pembrolizumab monotherapy.²³

A recent retrospective analysis of 66 patients with R/M HNSCC treated with anti-PD1 mAb therapy indicated that the majority (63%) of the patients were prescribed opioids before and on the day of ICI treatment, and higher opioid dose was associated with significantly lower progression-free survival and lower overall survival.²⁴ Building on the observation that opioids impact treatment responses, we aim to identify the mechanism responsible for the failure of first-line ICI in the presence of opioids. We hypothesize that exogenous opioids given for analgesia suppress antitumor immunity via T-cell-mediated OPRM1 signaling. Mechanistic evidence suggests opioids may influence antitumor immunity,^{25 26} and peripherally acting mu opioid receptor antagonists (PAMORAs) have been shown to reduce tumor growth in preclinical cancer models.^{27 28} In this study, we employed in vitro assays and a syngeneic HNSCC orthotopic mouse model to determine the impact of exogenous opioids on tumor immunity, the efficacy of ICI therapy and the adjunctive use of PAMORAs in tumor-bearing mice.

Results

OPRM1 expression linked to CD8⁺ T-cell exhaustion in patients with HNSCC

In silico analyses of single-cell RNA sequencing (scRNA-Seq) of patients with head and neck cancer’s tumor-infiltrating leukocytes²⁹ revealed that OPRM1 is primarily expressed by CD8⁺ cells. Patients were then stratified by opioid use, delimited by an active opioid prescription at the time of resection (table 1, online supplemental figure 1), demonstrating that opioid use diminished immune infiltration and overall expression of OPRM1 (figure 1A–E, online supplemental figure 1). Loss of OPRM1 expression is expected given previous studies showing DNA methylation of the OPRM1 promoter region following short-term and long-term opioid use.³⁰ We found that OPRM1⁺CD8⁺ T cells, particularly in patients who had not been prescribed opioids prior to resection, exhibited significantly increased expression of exhaustion markers (eg, PDCD1, CTLA4, LAG3, TIGIT), and decreased cytokine expression (eg, TGFB1, IFNG, TNF) (figure 1F, online supplemental figure 1). Using The Cancer Genome Atlas (TCGA) database, OPRM1 expression across cancer types was compared with site-matched normal tissues; findings indicated statistically significant expression in various tumors, including head and neck squamous cells (figure 1G, online supplemental figure 2). OPRM1 expression was correlated with worsened survival across tumor types, along with immune exhaustion and impaired immune infiltration (eg, TIMER, xCell, EPIC; online supplemental figure 2). Previous reports have posited a direct interface between opioids and tumor cells.^31–33 We could not detect OPRM1 messenger RNA (mRNA) using quantitative PCR in nine human oral squamous cell carcinoma (oSCC) cell lines and five mouse oSCC cell lines and found no proliferative or migratory changes in response to morphine treatment (online supplemental figure 3). Taken together, these data suggest that OPRM1-expressing CD8+T cells may be the target cell for opioid-induced immunosuppression and may exhibit decreased antitumor capabilities.

Figure 1. OPRM1 expression in tumor-infiltrating leukocytes in patients with HNSCC. (A-C) In-silico analysis of single-cell RNA sequencing profiles of tumor-infiltrating leukocytes from patients with head and neck cancer, comparing those exposed to opioids with those who were not. Opioid usage decreases OPRM1 expression across tumor-infiltrating leukocytes, including exhausted CD8+T cells (bottom, identified as Lag3 + CTLA4 + ). (D-E) Proportions and average expression levels of OPRM1 across various cell clusters compared between non-users and opioid-exposed groups. (F) Intratumoral Oprm1+CD8 + T cells overexpress several immune checkpoint markers (TOX, ENTPD1 (known as CD39)), and receptors (PDCD1, HAVCR2, LAG3, CTLA4) compared with Oprm1-negative CD8 + T cells. (G) Box plots comparing bulk OPRM1 gene expression levels (log2 of TPM) between tumor tissues and normal tissues across various TCGA tumor types. Each pair of box plots represents a specific tumor type, with statistical significance indicated by asterisks (*p<=0.05, **p<=0.01, ***p<=0.001). Head and neck tumors show increased OPRM1 expression when compared with normal tissue. Data are presented as UMAP (A, B, C) or bubble (D), box (F, G) and violin plots (E). The intensity of the color indicates the normalized level of OPRM1 expression (D). Statistical significance was indicated using asterisks: *p<0.05, **p<0.01, ***p<0.001, and ****p<0.0001. Opioid-free (n=10/26 patients). HNSCC, head and neck squamous cell carcinoma; OPRM1, mu opioid receptor 1; TCGA, The Cancer Genome Atlas; TPM, transcripts per kilobase million; UMAP, uniform manifold approximation and projection.

Figure 2. Morphine inhibits antitumor immunity. (A) Representative schematic of morphine treatment paradigm in MOC1 orthoptic mouse model. At post-inoculation day (PID) 25, tumor size was recorded by caliper and mice were treated with 10 mg/kg morphine 2×/day for 4.5 days. On day 6, tumor size was measured, and tongue tissue was processed for flow cytometry. (B) Tumor size was measured as a per cent change from baseline at PID 25 and (C-D) mass spectrometric quantification of morphine in homogenized tumor tissue and circulating blood serum from sham and MOC1 tumor-bearing mice 25 days after inoculation (n=6/group). Independent t-test, ***p<0.005 (E) Spleen weights following vehicle and morphine treatments in MOC1 tumor-bearing mice (n=12/group, t-test, *p<0.05). (F-K) Cytometric analysis of CD45+immune subpopulations in tongue tumor tissue from vehicle and morphine treated mice 24 hours after the last drug treatment (n=12/group); Independent and paired t-test, *p<0.05, **p<0.01. (L) CD3 + TIL in the vehicle and morphine-treated wildtype and Oprm1 global knockout mice. Two-way ANOVA, ***p<0.005. (M) Fold change (2 -[DELTA][DELTA]CT ) in Oprm1 expression CD8+T cells FACSorted from MOC1 tongue tumors in wildtype and Oprm1 global KO mice relative to sham; housekeeper= Actb . ANOVA, analysis of variance; KO, knockout; MOC1, mouse oral cancer 1; MOR, morphine; OPRM1, mu opioid receptor 1; PD1, programmed cell death protein 1; RCTV, relative change in tumor volume; TIL, tumor-infiltrating leukocyte.

Figure 3. Morphine inhibits efficacy of anti-PD1 immunotherapy. Survival curves (A) and tumor growth kinetics (B) in MOC1 tumor-bearing mice that received either vehicle or morphine treatment (2× daily for 4.5 days) followed out to 45 days post inoculation; black arrow indicates the treatment start date. (C) Representative schematic of concomitant morphine and anti-PD1 immunotherapy treatment paradigm. At post-inoculation day (PID) 25, tumor size was recorded by caliper and mice were treated with 10 mg/kg morphine 2×/day for 4.5 days. On day 6, mice were subsequently treated with anti-PD1 (250 [micro]g/day, 2-day interval); tissue was harvested 2 days after the final dose (PID 36), tumor size was measured, and tongue tissue was processed for flow cytometry. (D) Tumor size comparison in tumor-bearing mice across treatment groups. (n=10/group) Two-way ANOVA, ****p<0.0001. (E-H) Cytometric analysis of CD4 + , CD8 + , and CD4 + FoxP3 + T-cell incidence and CD8 + PD1 expression in tongue tumor tissue across treatment groups 48 hours after the last ICI treatment (n=10/group); Two-way ANOVA, *p<0.05, **p<0.01. ANOVA, analysis of variance; ICI, immune checkpoint inhibitor; i.p., intraperitoneal; MOC1, mouse oral cancer 1; MOR, morphine; PD1, programmed cell death protein 1; RCTV, relative change in tumor volume; Veh, vehicle.

Table 1

Patient with HNSCC characteristics

Category	N (%)
Age	60.3±12.3 (SD)
Sex	Male: 20 (70) Female 6 (30)
Race	White: 26 (100) Non-white: 0 (0)
Primary site	Oral cavity: 17 (65) Oropharynx: 7 (27) Larynx/hypopharynx: 2 (8)
Pathologic stage	TI–TII: 10 (38) TIII–TIV: 16 (62)
p16 expression	p16−: 19 (73); p16+: 7 (27)*
Opioid prescription at the time of resection	Yes: 16 (62) Oxycodone: 6 (38) Hydrocodone: 10 (62) No: 10 (38)

*Human papilloma virus by p16 for all oropharynx tumors.

Exogenous morphine stunts antitumor immunity

Given the clinical evidence that opioids suppress antitumor immunity and reduce ICI efficacy,^{5 24} we sought to recapitulate these findings in a mouse model. The median duration of opioid use prior to resection is equal to or less than 2 months in the patients with HNSCC assessed for this study. This is consistent with the HNSCC literature in which primary tumor patients undergo treatment shortly after diagnosis as delayed treatment initiation beyond 60 days was associated with worse survival and greater risk of recurrence.^{34 35} We investigated the immunosuppressive effects of morphine, the archetypal opioid analgesic,³⁶ by intraperitoneal (i.p.) administration in tumor-bearing and sham adult C57Bl/6 male and female mice (n=12/group, 10 mg/kg, two times a day for 4.5 days) using the MOC1 orthotopic transplant model (figure 2A). Though there was no significant difference in tumor growth between groups (p=0.175, figure 2B), the concentration of morphine was significantly higher in the tongue tumor tissue compared with sham tongue tissue (figure 2C). Serum levels, however, remained unchanged (figure 2D), indicating preferential uptake, and signaling of morphine within the TME rather than in non-tumor tissue or circulation.

There was a significant reduction in spleen weight in morphine-treated tumor-bearing mice compared with vehicle-treated mice (figure 2E). Flow cytometry analysis revealed a significant decrease in tumor-infiltrating CD45⁺ cells, which included reductions in CD3⁺CD4⁺Foxp3⁻ conventional T helper cells and CD3⁺CD8⁺ cytotoxic T cells in morphine-treated mice compared with vehicle (figure 2F–K). However, non-T-cell leukocyte populations showed no change, except for a significant reduction in the CD11b⁺CD11c⁺ dendritic cell population in morphine-treated mice. Further analysis of the remaining CD8⁺ T cells in morphine-treated mouse tumors indicated a predominance of effector cells (CD44^hiCD62L^lo) with a high percentage expressing PD1 and an exhausted phenotype (PD1⁺Tim3⁺) compared with vehicle-treated mice, where the majority were naïve T cells (CD44⁻CD62L^hi; figure 2K).

In non-tumor-bearing mice, no significant changes were observed in peripheral blood mononuclear cells (PBMCs), splenocyte subpopulations, or lymph node immune subpopulations between morphine and vehicle-treated mice (online supplemental figure 4), suggesting the morphine-induced immunosuppression is strongly associated with the TME. Morphine primarily signals through the OPRM1; however, some reports also implicate opioid interactions with toll-like receptor 4 (TLR4).²⁶ To confirm the primary receptor involved in the immunosuppressive action, we administered morphine to MOC1 tumor-bearing global Oprm1 knockout (KO) mice using the same experimental design. We found no effect of morphine on tumor-infiltrating leukocyte (TIL) subpopulations in Oprm1 KO mice compared with wildtype (n=6/group, figure 2L). Oprm1 expression was then quantified in CD8⁺ TIL from sham and MOC1-tumor bearing wildtype and Oprm1 global knockout mice using fluorescence-activated cell sorting. This revealed fourfold higher Oprm1 in CD8⁺ cells from tumor-bearing wildtype mice compared with sham (n=6–8/group, p=0.002); Oprm1 was not detected in CD8⁺ TIL from MOC1 tumor-bearing Oprm1 global knockout mice (figure 2M).

Figure 4. OPRM1 expression in tumor and immune cells. (A) Relative expression (2 -[DELTA]CT ) of OPRM1 gene before and after stimulation in cultured CD8 T cells isolated by negative selection from human donor PBMCs (n=1 donor); housekeeper= ACTB . (B) Fold change (2 -[DELTA][DELTA]CT ) in IL2 and IFNY genes in cultured CD8 + donor PBMCs after 24 hours administration of CD3/CD28 plus 0.1-10 [micro]M morphine, 10 [micro]M morphine+10 [micro]M MNTX, or 10 [micro]M morphine+1 [micro]M axelopran, relative to vehicle control, which was established by administration of saline and DMSO at the same volume (n=5 donors). One-way ANOVA within a gene, *p<0.05. (C) Representative images of naloxone-FITC conjugate binding to OPRM1 protein on unstimulated (CD3 only) and stimulated (CD3/CD28) CD8 + T cells from mouse spleen after 48 hours; 20× magnification. (D) Fold change (2 -[DELTA][DELTA]CT ) in Il2 and Ifng genes in cultured mouse CD8 + splenocytes after administration of CD3/CD28 plus 10 [micro]M morphine, 10 [micro]M morphine+10 [micro]M MNTX, or 10 [micro]M morphine+1 [micro]M axelopran, relative to vehicle control, which was established by administration of saline and DMSO at the same volume (n=3-5 mice); One-way ANOVA within a gene, *p<0.05. ANOVA, analysis of variance; DMSO, dimethyl sulphoxide; FITC, fluorescein isothiocyanate; MOR, morphine; MNTX, methylnaltrexone; OPRM1, mu opioid receptor 1; PBMCs, peripheral blood mononuclear cells.

Exogenous morphine inhibits the efficacy of anti-PD1 immunotherapy

Given the robust immunologic effect in response to acute morphine administration, a secondary cohort of mice was used to evaluate the impact of morphine on survival and tumor growth kinetics. We found a significant impact of morphine treatment on overall survival (figure 3A) and greater tumor volume in morphine-treated mice at 29-days and 40-days post inoculation (figure 3B). To confirm that the shift in tumor growth was due to immunosuppression early in the progression, we used the non-immunogenic MOC2 cell line to generate an orthotopic transplant model. Morphine treatment did not impact either survival nor tumor growth rate (n=6/group, online supplemental figure 5). Despite the limited number of tumor-infiltrating immune cells, we still detected a significant reduction in CD3⁺CD4⁺Foxp3⁻ conventional T helper cells and CD3⁺CD8⁺ cytotoxic T cells with those that remained to have a more exhausted phenotype in morphine-treated mice compared with vehicle (online supplemental figure 5).

Figure 5. PAMORA treatment blocks morphine-induced immunosuppression. Cytometric analysis of (A-D) CD4 + , CD8 + , and CD4 + FoxP3 + T-cell incidence and (E-F) CD8 + PD1 expression in tongue tumor tissue across treatment groups 24 hours after the last PAMORA treatment (n=5-6/group); Two-way ANOVA, *p<0.05, **p<0.01. (G) Tumor size comparison in tumor-bearing mice across treatment groups. Two-way ANOVA, *p<0.05, **p<0.01. ANOVA, analysis of variance; Axelo, axelopran; MOR, morphine; MNTX, methylnaltrexone; PAMORA, peripherally acting mu opioid receptor antagonism; PD1, programmed cell death protein 1; RCTV, relative change in tumor volume; Veh, vehicle.

To test the subhypothesis that morphine-induced loss of CD3⁺ T-cell infiltrate impacts the efficacy of monoclonal anti-PD1 treatment in enhancing antitumor immunity and reducing tumor size, we used the immunogenic MOC1 orthotopic transplant model in tandem with treatments of morphine and anti-PD1 monoclonal antibody (figure 3C). A majority of MOC1 tumors are responsive to anti-PD1 treatment.³⁷ We observed a significant interaction between morphine and immunotherapy in terms of tumor growth and tumor-associated immune response. While there was no significant difference in tumor size between morphine and vehicle treated mice that received IgG control antibody treatment, morphine treatment notably decreased the efficacy of anti-PD1 therapy in reducing tumor size in MOC1 tumor-bearing mice compared with vehicle treatment (n=10/group, figure 3D). Similarly, morphine administration also suppressed the anti-PD1 therapy-induced increase in CD4⁺ and CD8⁺ TIL but had no effect on CD4⁺FoxP3⁺ regulatory T cells (n=10/group, figure 3E–G). Among the CD8⁺ TIL present in the tumor, significantly fewer PD1⁺CD8⁺ cells were observed in mice treated with vehicle and anti-PD1 compared with those treated with morphine followed by anti-PD1 (figure 3H). This indicates a potentially detrimental effect of morphine on the therapeutic efficacy of anti-PD1 treatment, highlighting the importance of considering opioid usage in cancer immunotherapy protocols.

Morphine-OPRM1 axis inhibits CD8+ T-cell activity

OPRM1 expression has been identified in tumor-infiltrating immune populations and at lower levels in PBMCs in patients with HNSCC.²⁴ We again found that OPRM1 remained below detection levels in human CD8⁺ cells from healthy donor PBMCs prior to activation. After activation with CD3/CD28, detectable OPRM1 expression emerged by day 3 and was maintained through day 5 of the culture (figure 4A). We posit that intrinsic OPRM1 signaling in CD8⁺ T cells within the tumor restrains antitumor immunity via suppression of cytotoxic cytokine (eg, IL-2, IFN-γ) production. To validate this hypothesis, human CD8⁺ cells from donor PBMCs (n=5) were cultured with 0.1–10 µM morphine during anti-CD3/CD28 stimulation. After 24 and 48 hours, a dose-response reduction in IL2 and IFNG expression was observed in morphine-treated CD8⁺ T cells. OPRM1-specific antagonists reversed this effect after 24 (figure 4B) and 48 hours (online supplemental figure 3). Next, we confirmed these findings using cultured CD8+T cells from C57Bl/6 mouse spleens. OPRM1 protein was detected in mouse CD8⁺ T cells only after 48-hour CD3/CD28 stimulation using fluorescein isothiocyanate (FITC)-conjugated OPRM1 antagonist, naloxone (naloxone-FITC, figure 4C). Co-stimulation with CD3/CD28 and 10 µM morphine yielded results comparable to human donor T cells after 24 hours in culture (n=3 mice, Il2, p=0.017; Ifng, p=0.014, figure 4D), which was also reversed with OPRM1 antagonism.

PAMORA effect on morphine-induced immunosuppression and anti-PD1 therapy

The present data suggest peripheral OPRM1-evoked immunosuppression occurs in tandem with centrally-mediated analgesia.³⁸ To determine if PAMORA can be used to block morphine-induced immunosuppression, two different antagonists were tested: methylnaltrexone (MNTX, 10 mg/kg i.p.) and axelopran³⁹ (1 mg/kg i.p.). MOC1 tumor-bearing mice were treated with vehicle, OPRM1 antagonist (MNTX or axelopran), morphine, or co-injected with morphine with PAMORA (MNTX or axelopran, n=6/group). While PAMORA treatment alone did not affect the subpopulations of TILs in the absence of morphine, OPRM1 antagonism effectively countered the morphine-induced reduction in CD4⁺ and CD8⁺ TILs (figure 5A–D). Additionally, PAMORA treatment increased the population of CD8⁺PD1⁻ TILs; significantly fewer CD8⁺PD1⁺ cells were observed in mice that received combined treatment with morphine and PAMORA compared with those treated with morphine alone (figure 5E–F). Additionally, we found no significant interaction between morphine and PAMORA treatment on tumor size (F(2,30)=1.378, p=0.267); however, within treatment analysis found that PAMORAs significantly reduced tumor growth in morphine-treated mice only, likely due to the trend in a larger tumor size in the morphine treated group (figure 5F).

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, OPRM1 antagonism can effectively block this morphine-induced immunosuppression, indicating a potential therapeutic strategy for mitigating the adverse effects of opioid analgesics in cancer treatment. To test whether PAMORA could enhance the response to anti-PD1 immunotherapy in morphine-exposed mice, we performed an experiment involving eight treatment groups (n=6/group) based on a factorial design involving three study drugs and their respective controls: vehicle (saline) versus morphine (10 mg/kg i.p. 2×/day); vehicle (pH 7 Phosphate buffered saline (PBS)) versus axelopran (1 mg/kg i.p. co-injected with morphine or with aPD-1); IgG versus anti-PD1 (250 µg/injection, three injections every other day) (figure 6A).

Figure 6. Adjunctive efficacy of PAMORA treatment during concomitant opioid and immunotherapy treatment. (A) Representative schematic of experimental setup involving eight treatment groups based on a factorial design involving three study drugs and their respective controls. At post-inoculation day (PID) 25, tumor size was recorded by caliper and mice were treated with either morphine or co-injection of morphine and axelopran for 4.5 days. On the sixth day, mice began either anti-PD1 immunotherapy alone or a co-injection of anti-PD1 with axelopran for three treatments (sixth, eighth and 10th day). Tissue was harvested 48 hours after the final dose. Tumor size was measured, and tongue tissue was processed for flow cytometry (n=6 mice/treatment). (B) Tumor size comparison in tumor-bearing mice across treatment groups. Two-way ANOVA, *p<0.05, **p<0.01. (C-E) Cytometric analysis of CD8 + T-cell incidence and CD8 + PD1 expression in tongue tumor tissue across treatment groups 48 hours after the last ICI treatment; Two-way ANOVA, *p<0.05, **p<0.01. (F) Summary of tumor size as a function of CD8 + T-cell infiltration for the three identified immune response categories which are dependent on opioid status.

Significant interactions were observed between morphine and drug treatment combinations regarding tumor size, CD8⁺ T cell infiltration and PD1⁺ expression. Tumor growth was significantly inhibited by anti-PD1 in the absence of morphine, with a corresponding increase in CD8⁺ T-cell infiltration and a decrease in CD8⁺ PD1 expression. Morphine diminished the antitumor efficacy of anti-PD1 evidenced by a lack of significant difference in tumor response between the vehicle and anti-PD1 treatment within the morphine-treated group on tumor size and CD8⁺ T-cell infiltration (figure 6B–E).

Axelopran reversed the effects of morphine and synergized with anti-PD1 to significantly reduce tumor size, substantially enhance tumor immune infiltration, and decrease exhaustion markers on CD8⁺ effector T cells (figure 6B–E). Although axelopran alone showed no significant effect on tumor growth or CD8⁺ T-cell infiltration, its combination with anti-PD1 resulted in a greater than additive decrease in tumor volume (figure 6B) and a notable increase in CD8+T-cell infiltration (figure 6C–D).

A comparative analysis of tumor growth relative to CD8⁺ T-cell infiltration delineated three scenarios defining anti-PD1 response, denoted as a reduction in tumor size from baseline: opioid-induced immune failure, immune permissive, and immune responsive conditions (figure 6F). In the presence of morphine, tumors exhibited the greatest growth rate, and CD8⁺ T cells either failed to infiltrate the TME or, when present, showed the highest levels of PD1⁺ expression. With PAMORA treatment, either with or without morphine, tumors were of intermediate size, and CD8⁺ T-cell infiltration increased compared with morphine alone. This setting, termed immune permissive, led to a significant antitumor response when combined with anti-PD1, categorizing it as immune responsive.

Discussion

Research on opioid-tumor interactions has recently increased,⁵ however, reverse translation into animal models has been limited^{27 40} and research combining opioids with a PAMORA or ICI in vivo was lacking. In the current study, we used scRNA-Seq data of HNSCC TIL, the TCGA database, and a syngeneic orthotopic HNSCC mouse model to demonstrate that opioid treatment significantly reduced the tumor-associated immune response and impaired the efficacy of anti-PD1 ICI therapy, effects that were reversible with co-administration of PAMORAs. Consistent with previous findings in melanoma and glioblastoma,²⁹ systemic morphine was concentrated in the tumor tissue compared with normal tongue muscle or circulating plasma, suggesting that the TME may provide a unique setting for opioid-tumor interactions.

Previous reports have posited a direct interface between opioids and tumor cells. In HNSCC, OPRM1 expression measured by RNA-seq was shown to be very heterogenous across 70 human-derived cell lines, with OPRM1 highly expressed in hypopharyngeal cell line, FaDu, but very low or undetectable in 60% of other HNSCC cell lines tested.²⁸ Our study did not detect mRNA for OPRM1 in either human or mouse oral squamous cell carcinoma cell lines as measured by quantitative PCR. While others have shown modest increases in tumor cell proliferation in response to OPRM1 ligands,^{28 31 41} we did not measure any biologically meaningful changes in proliferation or wound healing in response to morphine. Additional intracellular assays, such as cyclic adenosine monophosphate (cAMP) measurement, are needed to confirm the absence of the receptor in cancer cells. Morphine can also signal through TLR4, which is expressed in immune cells²⁶; however, the reversal of morphine-induced suppression by selective OPRM1 antagonism and the lack of morphine effects on TIL in Oprm1 global KO mice, strongly suggests OPRM1 as the target receptor.

Our results support the hypothesis that opioid-tumor effects are instead primarily mediated through the immune system, particularly CD8⁺ T cells. OPRM1 mRNA was detected in human and mouse CD8⁺ T cells, and morphine application during stimulation suppressed the transcription of key cytokine genes, IL2 and IFNG. These findings align with previous studies indicating that OPRM1 gene expression depends on immune cell activation, which is necessary to produce functional receptors.⁴² In vivo, our data show a reduction in T-cell recruitment to the TME and an increase in the PD1⁺ exhaustion phenotype, potentially due to morphine-induced inhibition of chemotaxis or apoptosis. Although we did not directly assess the effect of morphine on CD8⁺ T-cell chemotaxis, other studies have shown that morphine treatment reduces migration and recruitment of TILs to conditioned media derived from Lewis lung carcinoma.⁴³ Additionally, others have shown that morphine could induce apoptosis in CD8⁺ T cells with the percentage of apoptosis increasing with higher concentrations and longer culture periods.⁴⁴ While OPRM1 is not specific to T lymphocytes and is documented in other immune subtypes^{14 15} and tumor cells,²⁸ our findings indicate no significant morphine-induced changes in myeloid cells, natural killer or B cell numbers in the TME, except for a minor loss of CD11c⁺ dendritic TIL. Morphine-induced suppression of dendritic cell maturation, antigen-presenting abilities, and activation of antigen-specific CD8⁺ T cells have been demonstrated in vitro.⁴⁵ Further studies should explore the effect of morphine on dendritic cell OPRM1 expression, as well as markers for activation and cytokine production to comprehensively understand the impact of opioids on the immune response in cancer.

We previously made clinical observations linking opioid use with immunotherapy outcomes in patients with R/M HNSCC, implicating OPRM1 signaling on CD8⁺ T cells as a mechanism responsible for the failure of ICIs in the presence of opioids.²⁴ Broader pan-cancer analyses have also demonstrated significantly shorter survival in patients on opioids at ICI initiation.⁴⁶ To extend these findings, our in silico bioinformatic analysis found that HNSCC tumor-infiltrating OPRM1-expressing CD8⁺ T cells exhibited differential transcriptomic phenotype compared with OPRM1-negative cells suggesting dysfunctionality, decreased cytokine expression and increased exhaustion markers. Similarly, a single-cell transcriptomic study of PBMCs from opioid-dependent individuals showed widespread cellular suppression in response to lipopolysaccharides (LPS) stimulation.⁴⁷ We also found a significant reduction in OPRM1 expression in TIL from opioid-prescribed patients with HNSCC compared with patients with opioid-free HNSCC. Exposure to high doses of opioids can cause hypermethylation in the promoter region of the OPRM1 gene, which can affect gene transcription resulting in less mRNA detection. A 2017 report by Viet et al demonstrated that OPRM1 is hypermethylated in peripheral leukocytes of patients taking high-dose opioids for cancer pain.⁴⁸ Also, a recent report by Sandoval-Sierra et al demonstrated that short-term prescription opioids following dental surgery can also result in DNA methylation of the OPRM1 promoter.⁴⁹ Together with the mouse model evidence, our data suggests that opioids decrease T-cell recruitment to the TME, and the small numbers that are present are predominantly exhausted and unable to respond to ICI treatment. PAMORAs are already in use for opioid-induced constipation and still allow for centrally-mediated analgesia, raising the potential translational impact of their use in cancer.

There are three primary limitations to this study. First, we used acute morphine administration to recapitulate the patient with HNSCC population which undergoes treatment quickly following diagnosis³⁵ as well as to avoid tolerance and withdrawal effects in our mouse model. However, this model does not reflect the clinical scenario where patients receive long-term opioid treatment as seen in other cancer populations, including recurrent/metastatic HNSCC.²⁴ Previous research has shown that the immunosuppressive effects observed following morphine pellet implantation are reversible by OPRM1 antagonism.⁵⁰ However, these effects have not been evaluated in the context of cancer or immunotherapy treatment, representing a gap in the applicability of our findings to clinical settings. Second, significant differences exist between human and rodent morphine metabolism; in mice, morphine is metabolized only into M3G, whereas in humans, it is metabolized into M3G and M6G. M3G has little affinity for OPRM1, whereas M6G binds to OPRM1 with high affinity and induces potent analgesia.⁵¹ These metabolic differences may limit the translatability of our results, particularly in determining the morphine dose required to achieve full immunosuppression in humans. Lastly, not all opioids share the same immunosuppressive effects⁹ and opioid prescriptions vary, due to the heterogeneity in semi-synthetic and synthetic opioid drugs used for pain management. Further investigation into the concomitant use of various types of opioids, include partial agonists such as buprenorphine, with checkpoint immunotherapy is necessary to understand the full impact and improve clinical strategies for analgesia during treatment.

Opioid treatment has become standard as recent therapeutic advancements significantly prolong the life expectancy of patients with cancer. The immunosuppressive action of opioids has been well documented in murine models, cell cultures, and human studies for decades.¹⁴ However, consideration of opioid immunosuppression on the tumor immune landscape and the subsequent impact on antitumor immunity in response to immunotherapeutic strategies requires more attention. We found that PAMORAs were able to block exogenous opioid-induced immunosuppression. While the impact of PAMORAs in the absence of opioids is currently unknown, OPRM1 KO mice showed decreased tumor growth and metastasis in non-small cell lung cancer and melanoma suggesting endogenous opioid signaling may impact tumor growth^{52 53} and PAMORAs may be affective in improving antitumor immunity in the absence of exogenous opioids as well. To our knowledge, this is the first study to focus on the association between immune opioid-related receptors, cancer immunosuppression and response to immunotherapy. Our study supports the evaluation of PAMORA with anti-PD-1-based immunotherapy in patients receiving opioids.

Methods

Bioinformatic analyses of publicly available RNA sequencing data

scRNA-Seq data analysis

This is a retrospective analysis of opioid intake and its effect on the immune cell transcriptomics in patients with primary HNSCC who underwent resection at the UPMC Hillman Cancer Center between 2015 and 2020 that had consented to the UPMC Hillman tissue banking protocol (HCC 99–069). Clinical data were obtained via chart review, including baseline characteristics such as age at diagnosis, sex, race, human papilloma virus status for oropharyngeal tumors, tumor pathological stage. scRNA-Seq data from TILs of patients with HNSCC were sourced from GSE139324,²⁹ comprizing 10 opioid-naive and 16 opioid-exposed patients’ scRNA-Seq data which were processed using the 10X Genomics Cell Ranger pipeline.²⁹ Seurat objects were created from available raw gene expression matrices for each patient, normalized, and integrated using R with the Seurat package. Principal component analysis was performed, followed by clustering using 20 principal components and a resolution of 0.5. The immune cell clusters were visualized using uniform manifold approximation and projection (UMAP) method and annotated based on gene markers from PanglaoDB.⁵⁴ Feature and violin plots, visualized with the Plotly package, were used to assess OPRM1 expression across immune cell clusters.

OPRM1 gene expression analysis in tumor and adjacent normal tissues

The differential expression of OPRM1 between tumor and adjacent normal tissues was evaluated using the Gene_DE module of the Tumor Immune Estimation Resource V.2 (TIMER2.0) (http://timer.cistrome.org/), which enables the analysis of gene expression levels across all cancer types included in TCGA. Expression distributions for OPRM1 in tumor and normal tissues were visualized using box plots. The statistical significance of the differential expression was calculated using the Wilcoxon test.⁵⁵

Cell culture

Mouse cancer cell culture

Mouse oSCC line 1 (MOC1, MOC2, Kerafast) was cultured in IMDM/F12 (2:1; Thermo Fisher Scientific) supplemented with 5% Fetal Bovine Serum (FBS, Corning), 1% penicillin-streptomycin solution (Thermo Fisher Scientific), 5 µg/mL insulin (Sigma-Aldrich), 40 ng/mL hydrocortisone (Sigma-Aldrich), and 5 ng/mL epidermal growth factor (EMD Millipore). Cells were cultured in 10 cm diameter cell culture dishes at 37°C with 5% CO₂ and collected for inoculation from a passage number less than 19.

Mouse T-cell culture

Naive CD8⁺ T cells (CD3⁺CD8⁺) were magnetically isolated from wildtype mouse spleens following the protocol of the StemCell negative isolation CD8 kit. T-cell culture media was made using 430 mL Roswell Park Memorial Institute Medium (RPMI 1640), 5 mL sodium pyruvate (Gibco: 11 360–70), 5 mL 1 M HEPES (N-2-hydroxyethylpiperazine-N'-2-ethanesulfonic acid) buffer (Fluka 51558), and 5 mL penicillin/streptomycin stock (Cellgro MT-3001-CI). Isolated CD8⁺ T cells were resuspended in this media (25°C) and diluted to a concentration of 1×10⁷ cells/mL. At least 1×10⁶ cells were added to each well of a 12-well tissue culture plate coated with anti-CD3 (1 µg/mL, Fisher) containing either vehicle or one of the following drug treatments: 10 µM morphine (Covetrus, Stock: 1.38 mM in saline), 10 µM morphine+10 µM MNTX (Sigma Aldrich, Stock: 23 mM in H₂0), or 10 µM morphine+1 µM axelopran (Glycyx MOR, Stock: 21.8 mM in dimethyl sulfoxide (DMSO)). Stimulation with anti-CD28 (5 µg/mL, Fisher) was added to each well following plating and cells were kept in a cell culture incubator (37°C, 5% CO2) for 24 hours post-stimulation. For binding to naloxone-fluoresceine (Tocris), isolated CD8⁺ T cells were resuspended in RPMI 1640 media containing sodium pyruvate, HEPES, and penicillin/streptomycin and plated onto glass bottom (12 mm) culture dishes (Electron Microscopy Sciences) coated with anti-CD3. The stimulation group received anti-CD28 (5 µg/mL) to each dish; cells were kept in a cell culture incubator (37°C, 5% CO2) for 48 hours. Live T cells were incubated in naloxone-fluoresceine (25 µM; stock=632 µM in DMSO) for 30 min followed by three 5 min washes with RPMI media and imaged using a Keyence BZ-X810 microscope with Keyence Imaging software at 20× magnification.

Human T-cell culture

PBMCs were isolated on various days from the blood of five healthy donors by Ficoll density gradient centrifugation. Cells were viably frozen in cryovials at 50×10⁶/ml in freezing media (90% FBS/10% DMSO) in the vapor phase of liquid nitrogen until needed for assay. Frozen PBMCs from each of the five donors were thawed and CD8 T cells were isolated by negative selection using a magnetic bead kit (StemCell, EasySep Human CD8⁺ T Cell Isolation Kit). Isolated CD8 T cells were rested overnight in low-dose IL-2 (20 IU/mL). Stimulation plates were prepared by coating 96-well round bottom plates overnight at 4°C with a cocktail of functional CD3 (Invitrogen, Anti-Human CD3, Functional Grade OKT3) at 1 µg/mL. The following day, the contents of the 96-well plate were removed. CD8 cells were retrieved from overnight culture, washed in fresh RPMI, and counted. CD8 cells were then plated in the stimulation plate at a final concentration of 2.5×10/mL. All test wells received co-stim of functional CD28 (1 µg/mL) (Invitrogen, Anti-Human CD28 Functional Grade CD28.2). Four treatment groups were generated: Morphine at 0.1–10 µM or RPMI vehicle control. Within each of these treatment groups, three subgroups were generated to assess the antagonism of the morphine effect on the CD8 T cells: 10 µM MNTX, 1 µM axelopran or RPMI vehicle control. Additional wells were plated without stimulation or treatments to serve as baseline controls for the resting state of the CD8 T cells. Cells were cultured at 37°C for 24 or 48 hours.

Animals

Adult (6–12 weeks, 20–30 g) male and female C57BL/6 (stock #000664; Jackson Labs, Bar Harbor, Maine, USA) mice were used for all experiments. Adult male and female B6.129S2-Oprm1^tm1Kff/J (OPRM1^−/−, Stain # 007559) were used to confirm the site of action of morphine. All mice were housed in a temperature-controlled room on a 12:12-hour light/dark cycle (07:00–19:00 hours light), with unrestricted access to food and water. Researchers were trained under the Animal Welfare Assurance Program. All procedures were approved by the University of Pittsburgh Institutional Animal Care and Use Committee (Protocol #: 23093881) and performed in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals. The ARRIVE Essential 10 was followed for all preclinical experimental designs.

Orthotopic oral cancer mouse model

To generate the syngeneic orthotopic oral cancer mouse model, adult male and female mice under 3–5% isoflurane anesthesia were inoculated into the anterior lateral portion of the tongue with either 7.5×10⁵ MOC1 cells or 2×10⁴ MOC2 cells in 30 µL of a mixture of Dulbecco's Modified Eagle Medium (DMEM, Gibco) and Matrigel (Corning) at a 1:1 ratio. Injection of DMEM and Matrigel alone was used as a control (ie, sham). Following injection, mice were returned to their home cage. Females were housed 3–5 per cage, males were housed 3–4 per cage; tumor-bearing mice were never housed with sham animals. Survival studies were done using a humane tumor volume cut-off of 250 mm³; tumor volume was measured by caliper and the ellipsoid volume formula. A majority (80%) of MOC1 tumors show complete response to anti-PD1 treatment as measured by reduction in tumor volume as well as a 20% increase in total CD8⁺ TIL compared with control.³⁷ During tumorigenesis mice received a single dose or combination of morphine (10 mg/kg, i.p.), MNTX (10 mg/kg, i.p.), axelopran (1 mg/kg, i.p.), or anti-PD-1 (Bio X Cell, 250 µg/kg, 3×2 day interval). No sex differences were identified in pilot studies related to morphine-induced immunosuppression or anti-PD1 immunotherapy response, so groups were allocated evenly by sex. Morphine was selected as the archetypal opioid based on extensive literature characterizing the analgesic efficacy, withdrawal and tolerance dosing in mice.³⁶ All experiments were repeated at least two times (n=4–6/group). All experiments performed included a vehicle group and a morphine group to confirm the morphine-induced immunosuppression was reproducible within all experiments regardless of the pharmacological manipulations tested. Relative change in tumor volume (RCTV) was calculated between treatment groups as the change in tumor volume (TV) from the start of treatment (TV0) to the TV at the endpoint (TVn) divided by TV0 (RCTV=(TVn−TV0)/TV0) and is expressed as a per cent. Mice were excluded from analysis if they succumbed to cancer prior to the planned euthanasia date or if an open tongue lesion/injury was identified in the tumor, likely resulting from the mouse biting through the tongue. Harvest dates were variable based on experimental design; mice under 3–5% isoflurane anesthesia were transcardially perfused with PBS and processed for subsequent experiments.

Quantitative PCR

Total RNA was isolated from pelleted cells (1–1.5×10⁶) from cell lines, cultured T cells, and trigeminal ganglia tissue using the Qiagen RNeasy Plus Mini Kit (Qiagen). Reverse transcription was performed with Quantitect Reverse Transcription Kit (Qiagen) according to the manufacturer’s instructions. Complementary DNA was diluted with nuclease-free water to a 5 ng/µL concentration. Cells-to-CT 1-step TaqMan Kit was used to isolate RNA and measure gene expression from fluorescently sorted tongue/tumor-infiltrating leukocytes according to the manufacturer’s instructions (Thermo Fisher Scientific). Relative expression levels of genes of interest were assessed using TaqMan Gene Expression Assays and TaqMan Fast Advanced Master Mix (Thermo Fisher Scientific), using a 96-well Quantstudio 3 Real-Time PCR System (Thermo Fisher Scientific). GAPDH/Gapdh or ACTB/Actb were used as internal control genes. All samples were run in duplicate or triplicate to account for pipetting error. Relative fold change of gene expression data was calculated using the 2^−ΔΔCt method (cancer mice vs sham mice; T cell treatment vs vehicle).

Analytical cytometry and fluorescence-activated cell sorting

Analytical cytometry and fluorescence-activated cell sorting (FACS) were used to assess immune infiltrate in mouse tongue tumors and bilateral submandibular and cervical lymph nodes and to collect subpopulations of immune cells from dissociated sham and MOC1-tumor tongue tissue. Isolated immune cells were first incubated for 20 min at room temperature (RT) in the dark with Zombie NIR viability dye (1:1000, Thermo Fisher Scientific) followed by a wash with staining media (PBS^−/− with 3% FBS, 1 mM EDTA). To stain extracellular markers, cells were incubated at 4°C in the dark for 30 min with the following antibodies diluted 1:100 in staining media: CD45 BUV-395 (BD Biosciences), CD11b APC (BioLegend), CD11c BV650 (BioLegend), F4/80 BV785 (BioLegend), Ly6G BUV737 (BD Biosciences), CD3 BV421 (BioLegend), CD4 PE Dazzle (BioLegend), CD19 PE-Cy7 (BioLegend), CD8a PerCy5.5 (BioLegend), NK1.1 FITC (BioLegend) followed by a wash with staining media. For characterization of T-cell activation and exhaustion status, cells were first stained with the following extracellular markers: CD45 BUV-395 (BD Biosciences), CD3 FITC (BioLegend), CD4 PE Dazzle (BioLegend), CD8a BUV 737 (BD Biosciences), CD44 BV650 (BioLegend), CD62L BV785 (BioLegend), PD-1 APC (BioLegend), Tim-3 PerCP-Cy5.5 (BD Biosciences), followed by incubation at 4°C in the dark for 30 min in fix/permeabilization buffer (Miltenyi Biotec) and blocking with 5% normal mouse serum (Invitrogen) in 1× perm buffer (Miltenyi Biotec) for 10 min at RT in the dark. FoxP3 BV421 antibody (1:50, BioLegend) was added to cells for 30 min at 4°C in the dark. Leukocytes from the spleen were used for compensation controls on a 5-laser Becton Dickenson LSR Fortessa II analyzer (BD Biosciences) and data were analyzed with FlowJo (V.10, Tree Star). FACS was performed on a Thermo Fisher Bigfoot Spectral Cell Sorter. Tongue infiltrating immune cells were sorted into lysis buffer containing DNase for RNA purification and quantitative PCR using the cell lysis solution and DNase I from the Cell-to-CT Kit (Thermo Fisher) per manufacturer’s instructions. Immune cells were sorted into sterile-filtered lysis buffer, snap frozen, and stored at −80°C until needed.

Semi-targeted high-resolution LC-HRMS

For serum sample preparation, metabolic quenching and metabolite pool extraction were performed by adding an equal volume of acetonitrile (ACN) to 50 µL serum. Deuterated (13C₁)-creatinine and (D₃)-alanine (Sigma-Aldrich) were added to the sample lysates as an internal standard for a final concentration of 10 µM. After 3 min of vortexing, the supernatant was cleared of protein by centrifugation at 16,000×g. 3 µL of cleared supernatant was subjected to online liquid chromatography (LC)- mass spectrometry (MS) analysis. A calibration curve of morphine sulfate heptahydrate was prepared in 50% ACN with deuterated internal standards from 100 pmol/µL to 0.6 fmol/µL. For tumor tissue sample preparation, metabolic quenching and metabolite pool extraction were performed by adding 50% ACN at 1:15 (wt:vol) in MP Biomedicals Matrix A tubes. Deuterated (13C₁)-creatinine and (D₃)-alanine (Sigma-Aldrich) were added to the sample lysates as an internal standard for a final concentration of 10 µM. Samples were homogenized at 60 Hz for 40 s using a FastPrep 24 (MP Biomedical) homogenizer before the supernatant was cleared of protein by centrifugation at 16,000×g. 3 µL of cleared supernatant was subjected to online LC-MS analysis. A calibration curve of morphine sulfate heptahydrate was prepared in 50% ACN with deuterated internal standards from 100 pmol/µL to 0.6 fmol/µL. LC-HRMS analyses were performed by untargeted liquid chromatography (LC) - high resolution mass spectrometry (HRMS). Briefly, samples were injected via a Thermo Vanquish UHPLC and separated over a reversed-phase Phenomenex Kinetix C18 column (2.1×150 mm, 3 µm particle size) maintained at 55°C. For the 15 min LC gradient, the mobile phase consisted of the following: solvent A (water / 0.1% FA) and solvent B (ACN / 0.1% FA). The gradient was the following: 0–2 min 1%B, increase to 5%B over 4 min, continue increasing to 98%B over 5 min, hold at 98%B for 2 min, re-equilibrate at 1%B for 3 min. The Thermo Exploris 240 mass spectrometer was operated in positive ion mode, scanning in ddMS² mode (2 μscans) from 10 to 1,000 m/z at 120,000 resolutions with an automatic gain control target of 2×10⁵ for full scan, 2×10⁴ for ms² scans using higher energy collisional dissociation fragmentation at stepped 15,35,50 collision energies. The source ionization setting was 3.0 and 2.4 kV spray voltage, respectively, for positive and negative mode. Source gas parameters were 50 sheath gas, 12 auxiliary gas at 320°C, and eight sweep gas. Calibration was performed prior to analysis using the Pierce FlexMix Ion Calibration Solutions (Thermo Fisher Scientific). Integrated peak areas were then extracted manually using Quan Browser (Thermo Fisher Xcalibur V.2.7). Extracted peak areas were then normalized to deuterated internals standards before conversion to concentration using the calibration curve.

Statistics

All statistical analyses were performed using Prism (V.10.0.3) statistical software (GraphPad Software). Statistical significance was set at p<0.05. Box/scatter configurations were used to show the biological variability when illustrative. Gene expression differences between patients with HNSCC OPRM1+CD8 T cells versus patients with HNSCC OPRM1-CD8 T cells did not follow a normal distribution (Kolmogorov-Smirnov test) and thus were analyzed non-parametric Mann-Whitney U test. For preclinical studies, given the previous literature demonstrating a sex difference in oral cancer-evoked nociceptive behavior, all studies were powered to detect a sex difference based on previous studies with similar outcome variables. Two-way analysis of variance (ANOVA) was employed to evaluate an interaction between analgesia and immunotherapy. Pilot studies found no significant effect of sex in morphine-induced immunosuppression; therefore, all groups were sex-split (n≥3/sex). For parametric data, results were presented as mean±SEM. One-way ANOVA or independent student’s t-test was employed to evaluate the difference between groups. To adjust for multiple comparisons, the post-hoc Holm-Sidak test statistic was employed. For non-parametric data, results were presented as a median±95% CI.

We acknowledge the University of Pittsburgh Heath Sciences Mass Spectrometry Core supported by NIHS10OD032141 (PI: Gelhaus). This project also used the UPMC Hillman Cancer Center Flow Cytometry, Animal Facilities and Tissue and Research Pathology/Pitt Biospecimen Core shared resource, which was partially supported by award P30CA047904 (NIH). Some images were derived from Biorender.com.

Data availability statement

Data sharing not applicable as no data sets generated and/or analysed for this study. Not applicable.

Ethics statements

Patient consent for publication

Not applicable.

Ethics approval

This study involves human participants and was approved by UPMC Hillman tissue banking protocol IRB# HCC 99–069. Participants gave informed consent to participate in the study before taking part.

Footnote

X @MarciNilsenPhD, @robertferrismd

Contributors LAM and NNS were responsible for the conceptualization, data analysis and methodology of the project as well as writing of the manuscript. AAMM and MMY contributed to data collection in preclinical mouse models. MAA was responsible for project administration and resources as well as editing the manuscript. FO, MLN, TCB, RLF and DPZ contributed to human subject data collection. ARN and ST performed bioinformatic analyses. DNT and LKJ supplied mu opioid receptor antagonist as well as methodology and analytical support for the preclinical studies. NNS served as the guarantor and funded the study.

Funding This work was supported by the NIH NIDCR R01DE033473 (PI: NNS) award and the Virginia Kaufman Foundation Pain Research Challenge Award.

Competing interests DNT and LKJ are Employees and Shareholders of Glycyx MOR, Inc. and supplied the mu opioid antagonist, axelopran. They did not provide any funding for the study. DPZ declares competing interests with BICARA (steering committee), Seagen (steering committee), Inhibrx (consulting), Macrogenics (consulting), Prelude Therapeutics (advisory board), and Merck (advisory board) and research support (institutional) from clinical trials with Merck, BMS, AstraZeneca, GlaxoSmithKline, Aduro, Macrogenics, Bicara, and Novasenta. RLF reports grants or contracts from AstraZeneca/MedImmune, Bristol Myers Squibb, Merck, Novasenta, Tesaro; consulting fees with Adagene, Aduro Biotech, Bicara Therapeutics, Brooklyn ImmunoTherapeutics, Catenion, EMD Serono, Everest Clinical Research Corporation, F. Hoffman-La Roche Ltd., Federation Bio, Genocea Biosciences, Kowa Research Institute, Mirati Therapeutics, Nanobiotix, Novartis, Novasenta, PPD Development, Sanofi, Zymeworks; participation on a data safety monitoring or advisory board with Coherus BioSciences, Eisai Europe Ltd., Genmab, Hookipa, Instil Bio, Lifescience Dynamics, MacroGenics, MeiraGtx, Merck, Mirror Biologics, Numab Therapeutics AG, OncoCyte, Pfizer, Rakuten Medical, Seagen, SIRPant Immunotherapeutics, Vir Biotechnology, stock or stock options with Novasenta.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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