The role of opioids in cancer response to immunotherapy

Botticelli, Andrea; Cirillo, Alessio; Pomati, Giulia; Cerbelli, Bruna; Scagnoli, Simone; Roberto, Michela; Mammone, Giulia; Calandrella, Maria Letizia; Cerbelli, Edoardo; Pietro, Francesca Di; Galitiis, Federica De; Lanzetta, Gaetano; Cortesi, Enrico; Mezi, Silvia; Marchetti, Paolo

doi:10.1186/s12967-021-02784-8

Cited by 31 publications

(28 citation statements)

References 61 publications

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“…This study showed that opioid use was negatively associated with thyroid-related complications in patients treated with PD-1/PD-L1 inhibitors. Opioids play a crucial role in increasing the resistance to immunotherapy [29]. Morphine and buprenorphine reduce the levels of interleukin-4 mRNA and protein in T cells [30].…”

Section: Discussionmentioning

confidence: 99%

“…Morphine and buprenorphine reduce the levels of interleukin-4 mRNA and protein in T cells [30]. A retrospective study on 102 cancer patients administered opioids and ICIs showed poor outcomes [31], possibly because of the presence of opioid receptors on immune cells [29]. As opioid receptors are expressed in immune cells, opioids could alter immune responses [32].…”

Section: Discussionmentioning

confidence: 99%

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Factors Associated with Thyroid-Related Adverse Events in Patients Receiving PD-1 or PD-L1 Inhibitors Using Machine Learning Models

Kim

Cho

Kim

et al. 2021

Cancers

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Targets of immune checkpoint inhibitors (ICIs) regulate immune homeostasis and prevent autoimmunity by downregulating immune responses and by inhibiting T cell activation. Although ICIs are widely used in immunotherapy because of their good clinical efficacy, they can also induce autoimmune-related adverse events. Thyroid-related adverse events are frequently associated with anti-programmed cell death 1 (PD-1) or anti-programmed cell death-ligand 1 (PD-L1) agents. The present study aims to investigate the factors associated with thyroid dysfunction in patients receiving PD-1 or PD-L1 inhibitors and to develop various machine learning approaches to predict complications. A total of 187 patients were enrolled in this study. Logistic regression analysis was conducted to investigate the association between such factors and adverse events. Various machine learning methods were used to predict thyroid-related complications. After adjusting for covariates, we found that smoking history and hypertension increase the risk of thyroid dysfunction by approximately 3.7 and 4.1 times, respectively (95% confidence intervals (CIs) 1.338–10.496 and 1.478–11.332, p = 0.012 and 0.007). In contrast, patients taking opioids showed an approximately 4.0-fold lower risk of thyroid-related complications than those not taking them (95% CI 1.464–11.111, p = 0.007). Among the machine learning models, random forest showed the best prediction, with an area under the receiver operating characteristic of 0.770 (95% CI 0.648–0.883) and an area under the precision-recall of 0.510 (95%CI 0.357–0.666). Hence, this study utilized various machine learning models for prediction and showed that factors such as smoking history, hypertension, and opioids are associated with thyroid-related adverse events in cancer patients receiving PD-1/PD-L1 inhibitors.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Factors Associated with Thyroid-Related Adverse Events in Patients Receiving PD-1 or PD-L1 Inhibitors Using Machine Learning Models

Kim

Cho

Kim

et al. 2021

Cancers

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“…We too read with interest the article “The Role of Opioids in Cancer Response to Immunotherapy” by Botticelli et al, which suggests that opioids impair the immune response to cancer in patients receiving immunotherapy [ 1 ].…”

Section: Dear Editormentioning

confidence: 99%

“…In the Results section, the authors state: “ …opioids use was significantly higher in patients affected by NSCLC [Non-Small Cell Lung Cancer] (p < 0.0001), in patients with a worse ECOG PS [Eastern Cooperative Oncology Group Performance Status] (p < 0.0001), in second-line setting subgroup (p = 0.009), in patients taking corticosteroids (p < 0.0001), and in patients with a high tumor burden (p = 0.006) ” [ 1 ].…”

Section: Dear Editormentioning

confidence: 99%

“…Of those five listed variables, the only one that was not included in the multivariate analysis (MA) was NSCLC. Given that the opioid and non-opioid cohorts consisted of 62% and 22% NSCLC patients, respectively [ 1 ], which is an approximately threefold (2.82) difference in proportion, we think that this would have been an important variable to include in the MA. Furthermore, melanoma was overrepresented in the non-opioid group by more than three-fold (3.59; 17% vs. 61% in opioid and non-opioid cohorts, respectively) [ 1 ].…”

Section: Dear Editormentioning

confidence: 99%

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Risk of confounding variables in multivariate analysis

Gagnon

Hanna

2022

J Transl Med

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Development of a predictive model for patients with bone metastases referred to palliative radiotherapy: Secondary analysis of a multicenter study (the PRAIS trial)

Rossi,

Medici,

Habberstad

et al. 2024

Cancer Medicine

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BackgroundThe decision to administer palliative radiotherapy (RT) to patients with bone metastases (BMs), as well as the selection of treatment protocols (dose, fractionation), requires an accurate assessment of survival expectancy. In this study, we aimed to develop three predictive models (PMs) to estimate short‐, intermediate‐, and long‐term overall survival (OS) for patients in this clinical setting.Materials and MethodsThis study constitutes a sub‐analysis of the PRAIS trial, a longitudinal observational study collecting data from patients referred to participating centers to receive palliative RT for cancer‐induced bone pain. Our analysis encompassed 567 patients from the PRAIS trial database. The primary objectives were to ascertain the correlation between clinical and laboratory parameters with the OS rates at three distinct time points (short: 3 weeks; intermediate: 24 weeks; prolonged: 52 weeks) and to construct PMs for prognosis. We employed machine learning techniques, comprising the following steps: (i) identification of reliable prognostic variables and training; (ii) validation and testing of the model using the selected variables. The selection of variables was accomplished using the LASSO method (Least Absolute Shrinkage and Selection Operator). The model performance was assessed using receiver operator characteristic curves (ROC) and the area under the curve (AUC).ResultsOur analysis demonstrated a significant impact of clinical parameters (primary tumor site, presence of non‐bone metastases, steroids and opioid intake, food intake, and body mass index) and laboratory parameters (interleukin 8 [IL‐8], chloride levels, C‐reactive protein, white blood cell count, and lymphocyte count) on OS. Notably, different factors were associated with the different times for OS with only IL‐8 included both in the PMs for short‐ and long‐term OS. The AUC values for ROC curves for 3‐week, 24‐week, and 52‐week OS were 0.901, 0.767, and 0.806, respectively.ConclusionsWe successfully developed three PMs for OS based on easily accessible clinical and laboratory parameters for patients referred to palliative RT for painful BMs. While our findings are promising, it is important to recognize that this was an exploratory trial. The implementation of these tools into clinical practice warrants further investigation and confirmation through subsequent studies with separate databases.

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The role of opioids in cancer response to immunotherapy

Cited by 31 publications

References 61 publications

Factors Associated with Thyroid-Related Adverse Events in Patients Receiving PD-1 or PD-L1 Inhibitors Using Machine Learning Models

Factors Associated with Thyroid-Related Adverse Events in Patients Receiving PD-1 or PD-L1 Inhibitors Using Machine Learning Models

Risk of confounding variables in multivariate analysis

Development of a predictive model for patients with bone metastases referred to palliative radiotherapy: Secondary analysis of a multicenter study (the PRAIS trial)

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