CX3CL1 release during immunogenic apoptosis is associated with enhanced anti-tumour immunity.

Introduction: Immunogenic cell death (ICD) has emerged as a novel option for cancer immunotherapy. The key determinants of ICD encompass antigenicity (the presence of antigens) and adjuvanticity, which involves the release of damage-associated molecular patterns (DAMPs) and various cytokines and chemokines. CX3CL1, also known as neurotactin or fractalkine, is a chemokine involved in cellular signalling and immune cell interactions. CX3CL1 has been denoted as a "find me" signal that stimulates chemotaxis of immune cells towards dying cells, facilitating efferocytosis and antigen presentation. However, in the context of ICD, it is uncertain whether CX3CL1 is an important mediator of the effects of ICD. Methods: In this study, we investigated the intricate role of CX3CL1 in immunogenic apoptosis induced by mitoxantrone (MTX) in cancer cells. The Luminex xMAP technology was used to quantify murine cytokines, chemokines and growth factors to identify pivotal regulatory cytokines released by murine fibrosarcoma MCA205 and melanoma B16-F10 cells undergoing ICD. Moreover, a murine tumour prophylactic vaccination model was employed to analyse the effect of CX3CL1 on the activation of an adaptive immune response against MCA205 cells undergoing ICD. Furthermore, thorough analysis of the TCGA-SKCM public dataset from 98 melanoma patients revealed the role of CX3CL1 and its receptor CX3CR1 in melanoma patients. Results: Our findings demonstrate enhanced CX3CL1 release from apoptotic MCA205 and B16-F10 cells (regardless of the cell type) but not if they are undergoing ferroptosis or accidental necrosis. Moreover, the addition of recombinant CX3CL1 to non-immunogenic doses of MTX-treated, apoptotically dying cancer cells in the murine prophylactic tumour vaccination model induced a robust immunogenic response, effectively increasing the survival of the mice. Furthermore, analysis of melanoma patient data revealed enhanced survival rates in individuals exhibiting elevated levels of CD8+ T cells expressing CX3CR1. Conclusion: These data collectively underscore the importance of the release of CX3CL1 in eliciting an immunogenic response against dying cancer cells and suggest that CX3CL1 may serve as a key switch in conferring immunogenicity to apoptosis.

DC vaccines loaded with glioma cells killed by photodynamic therapy induce Th17 anti-tumor immunity and provide a four-gene signature for glioma prognosis.

Gliomas, the most frequent type of primary tumor of the central nervous system in adults, results in significant morbidity and mortality. Despite the development of novel, complex, multidisciplinary, and targeted therapies, glioma therapy has not progressed much over the last decades. Therefore, there is an urgent need to develop novel patient-adjusted immunotherapies that actively stimulate antitumor T cells, generate long-term memory, and result in significant clinical benefits. This work aimed to investigate the efficacy and molecular mechanism of dendritic cell (DC) vaccines loaded with glioma cells undergoing immunogenic cell death (ICD) induced by photosens-based photodynamic therapy (PS-PDT) and to identify reliable prognostic gene signatures for predicting the overall survival of patients. Analysis of the transcriptional program of the ICD-based DC vaccine led to the identification of robust induction of Th17 signature when used as a vaccine. These DCs demonstrate retinoic acid receptor-related orphan receptor-γt dependent efficacy in an orthotopic mouse model. Moreover, comparative analysis of the transcriptome program of the ICD-based DC vaccine with transcriptome data from the TCGA-LGG dataset identified a four-gene signature (CFH, GALNT3, SMC4, VAV3) associated with overall survival of glioma patients. This model was validated on overall survival of CGGA-LGG, TCGA-GBM, and CGGA-GBM datasets to determine whether it has a similar prognostic value. To that end, the sensitivity and specificity of the prognostic model for predicting overall survival were evaluated by calculating the area under the curve of the time-dependent receiver operating characteristic curve. The values of area under the curve for TCGA-LGG, CGGA-LGG, TCGA-GBM, and CGGA-GBM for predicting five-year survival rates were, respectively, 0.75, 0.73, 0.9, and 0.69. These data open attractive prospects for improving glioma therapy by employing ICD and PS-PDT-based DC vaccines to induce Th17 immunity and to use this prognostic model to predict the overall survival of glioma patients.

Artificial Intelligence Predicts Severity of COVID-19 Based on Correlation of Exaggerated Monocyte Activation, Excessive Organ Damage and Hyperinflammatory Syndrome: A Prospective Clinical Study.

Background: Prediction of the severity of COVID-19 at its onset is important for providing adequate and timely management to reduce mortality. Objective: To study the prognostic value of damage parameters and cytokines as predictors of severity of COVID-19 using an extensive immunologic profiling and unbiased artificial intelligence methods. Methods: Sixty hospitalized COVID-19 patients (30 moderate and 30 severe) and 17 healthy controls were included in the study. The damage indicators high mobility group box 1 (HMGB1), lactate dehydrogenase (LDH), aspartate aminotransferase (AST), alanine aminotransferase (ALT), extensive biochemical analyses, a panel of 47 cytokines and chemokines were analyzed at weeks 1, 2 and 7 along with clinical complaints and CT scans of the lungs. Unbiased artificial intelligence (AI) methods (logistic regression and Support Vector Machine and Random Forest algorithms) were applied to investigate the contribution of each parameter to prediction of the severity of the disease. Results: On admission, the severely ill patients had significantly higher levels of LDH, IL-6, monokine induced by gamma interferon (MIG), D-dimer, fibrinogen, glucose than the patients with moderate disease. The levels of macrophage derived cytokine (MDC) were lower in severely ill patients. Based on artificial intelligence analysis, eight parameters (creatinine, glucose, monocyte number, fibrinogen, MDC, MIG, C-reactive protein (CRP) and IL-6 have been identified that could predict with an accuracy of 83-87% whether the patient will develop severe disease. Conclusion: This study identifies the prognostic factors and provides a methodology for making prediction for COVID-19 patients based on widely accepted biomarkers that can be measured in most conventional clinical laboratories worldwide.

Quasi-stationary states of game-driven systems: A dynamical approach.

Evolutionary game theory is a framework to formalize the evolution of collectives ("populations") of competing agents that are playing a game and, after every round, update their strategies to maximize individual payoffs. There are two complementary approaches to modeling evolution of player populations. The first addresses essentially finite populations by implementing the apparatus of Markov chains. The second assumes that the populations are infinite and operates with a system of mean-field deterministic differential equations. By using a model of two antagonistic populations, which are playing a game with stationary or periodically varying payoffs, we demonstrate that it exhibits metastable dynamics that is reducible neither to an immediate transition to a fixation (extinction of all but one strategy in a finite-size population) nor to the mean-field picture. In the case of stationary payoffs, this dynamics can be captured with a system of stochastic differential equations and interpreted as a stochastic Hopf bifurcation. In the case of varying payoffs, the metastable dynamics is much more complex than the dynamics of the means.