Optimized Dose of Dendritic Cell-Based Vaccination in Experimental Model of Tumor Using Artificial Neural Network Publisher Pubmed



Mirsanei Z¹ ; Habibi S¹ ; Kheshtchin N¹ ; Mirzaei R¹ ; Arab S^{2, 3} ; Zand B¹ ; Niaragh FJ⁴ ; Safvati A¹ ; Sharifpaghaleh E^{1, 5} ; Arabameri A⁶ ; Asemani D^{6, 7} ; Hadjat J^{1, 8} Show Affiliations
Source: Iranian Journal of Allergy# Asthma and Immunology Published:2020

Abstract

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. In the present study, we evaluated the ANN prediction accuracy about DC-based cancer vaccines pattern in the treatment of Wehi164 fibrosarcoma cancer-bearing mice. Our results showed that the administration of the DC vaccine according to ANN predicted pattern, leads to a decrease in the rate of tumor growth and size and augments CTL effector function. Furthermore, gene expression analysis confirmed an augmented immune response in the tumor microenvironment. Experimentations justified the validity of the ANN model forecast in the tumor growth and novel optimal dosage that led to more effective treatment. © 2020 Tehran University of Medical Sciences. All rights reserved.