Optimized Dose of Dendritic Cell-based Vaccination in Experimental Model of Tumor Using Artificial Neural Network.

Previous studies have demonstrated that maturation of dendritic cells (DCs) by pathogenic components through pathogen-associated molecular patterns (PAMPs) such as Listeria monocytogenes lysate (LML) or CpG DNA can improve cancer vaccination in experimental models. In this study, a mathematical model based on an artificial neural network (ANN) was used to predict several patterns and dosage of matured DC administration for improved vaccination. The ANN model predicted that repeated co-injection of tumor antigen (TA)-loaded DCs matured with CpG (CpG-DC) and LML (List-DC) results in improved antitumor immune response as well as a reduction of immunosuppression in the tumor microenvironment.

Increased efficacy of a dendritic cell-based therapeutic cancer vaccine with adenosine receptor antagonist and CD73 inhibitor.

Dendritic cells are important in initiating immune responses; therefore, a range of dendritic cell-based approaches have been established to induce immune response against cancer cells. However, the presence of immunosuppressive mediators such as adenosine in the tumor microenvironment reduces the efficacy of dendritic cell-based cancer immunotherapy. In this study, we investigated whether blockade of the A2A adenosine receptor with a selective antagonist and a CD73 inhibitor may increase the efficacy of a dendritic cell-based cancer vaccine.