Opioid Nonadherence Risk Prediction of Patients with Cancer-Related Pain Based on Five Machine Learning Algorithms.

Objectives: Opioid nonadherence represents a significant barrier to cancer pain treatment efficacy. However, there is currently no effective prediction method for opioid adherence in patients with cancer pain. We aimed to develop and validate a machine learning (ML) model and evaluate its feasibility to predict opioid nonadherence in patients with cancer pain. Methods: This was a secondary analysis from a cross-sectional study that included 1195 patients from March 1, 2018, to October 31, 2019. Five ML algorithms, such as logistic regression (LR), random forest, eXtreme Gradient Boosting, multilayer perceptron, and support vector machine, were used to predict opioid nonadherence in patients with cancer pain using 43 demographic and clinical factors as predictors. The predictive effects of the models were compared by the area under the receiver operating characteristic curve (AUC_ROC), accuracy, precision, sensitivity, specificity, and F1 scores. The value of the best model for clinical application was assessed using decision curve analysis (DCA). Results: The best model obtained in this study, the LR model, had an AUC_ROC of 0.82, accuracy of 0.82, and specificity of 0.71. The DCA showed that clinical interventions for patients at high risk of opioid nonadherence based on the LR model can benefit patients. The strongest predictors for adherence were, in order of importance, beliefs about medicines questionnaire (BMQ)-harm, time since the start of opioid, and BMQ-necessity. Discussion. ML algorithms can be used as an effective means of predicting adherence to opioids in patients with cancer pain, which allows for proactive clinical intervention to optimize cancer pain management. This trial is registered with ChiCTR2000033576.

Myeloid cell-derived creatine in the hypoxic niche promotes glioblastoma growth.

Glioblastoma (GBM) is a malignancy dominated by the infiltration of tumor-associated myeloid cells (TAMCs). Examination of TAMC metabolic phenotypes in mouse models and patients with GBM identified the de novo creatine metabolic pathway as a hallmark of TAMCs. Multi-omics analyses revealed that TAMCs surround the hypoxic peri-necrotic regions of GBM and express the creatine metabolic enzyme glycine amidinotransferase (GATM). Conversely, GBM cells located within these same regions are uniquely specific in expressing the creatine transporter (SLC6A8). We hypothesized that TAMCs provide creatine to tumors, promoting GBM progression. Isotopic tracing demonstrated that TAMC-secreted creatine is taken up by tumor cells. Creatine supplementation protected tumors from hypoxia-induced stress, which was abrogated with genetic ablation or pharmacologic inhibition of SLC6A8. Lastly, inhibition of creatine transport using the clinically relevant compound, RGX-202-01, blunted tumor growth and enhanced radiation therapy in vivo. This work highlights that myeloid-to-tumor transfer of creatine promotes tumor growth in the hypoxic niche.

The CXCL16-CXCR6 axis in glioblastoma modulates T-cell activity in a spatiotemporal context.

Introduction: Glioblastoma multiforme (GBM) pathobiology is characterized by its significant induction of immunosuppression within the tumor microenvironment, predominantly mediated by immunosuppressive tumor-associated myeloid cells (TAMCs). Myeloid cells play a pivotal role in shaping the GBM microenvironment and influencing immune responses, with direct interactions with effector immune cells critically impacting these processes. Methods: Our study investigates the role of the CXCR6/CXCL16 axis in T-cell myeloid interactions within GBM tissues. We examined the surface expression of CXCL16, revealing its limitation to TAMCs, while microglia release CXCL16 as a cytokine. The study explores how these distinct expression patterns affect T-cell engagement, focusing on the consequences for T-cell function within the tumor environment. Additionally, we assessed the significance of CXCR6 expression in T-cell activation and the initial migration to tumor tissues. Results: Our data demonstrates that CXCL16 surface expression on TAMCs results in predominant T-cell engagement with these cells, leading to impaired T-cell function within the tumor environment. Conversely, our findings highlight the essential role of CXCR6 expression in facilitating T-cell activation and initial migration to tumor tissues. The CXCL16-CXCR6 axis exhibits dualistic characteristics, facilitating the early stages of the T-cell immune response and promoting T-cell infiltration into tumors. However, once inside the tumor, this axis contributes to immunosuppression. Discussion: The dual nature of the CXCL16-CXCR6 axis underscores its potential as a therapeutic target in GBM. However, our results emphasize the importance of carefully considering the timing and context of intervention. While targeting this axis holds promise in combating GBM, the complex interplay between TAMCs, microglia, and T cells suggests that intervention strategies need to be tailored to optimize the balance between promoting antitumor immunity and preventing immunosuppression within the dynamic tumor microenvironment.