Author/Authors :
Mirsanei, Zahra Department of Immunology - School of Medicine - Tehran University of Medical Sciences, Tehran, Iran , Habibi, Sima Department of Immunology - School of Medicine - Tehran University of Medical Sciences, Tehran, Iran , Kheshtchin, Nasim Department of Immunology - School of Medicine - Tehran University of Medical Sciences, Tehran, Iran , Mirzaei, Reza Department of Immunology - School of Medicine - Tehran University of Medical Sciences, Tehran, Iran , Arab, Samane Nervous System Stem Cells Research Center - Semnan University of Medical Sciences, Semnan, Iran , Zand, Bahareh Department of Immunology - School of Medicine - Tehran University of Medical Sciences, Tehran, Iran , Jadidi- Niaragh, Farhad Immunology Research Center - Tabriz University of Medical Sciences, Tabriz, Iran , Safvati, Aida Department of Immunology - School of Medicine - Tehran University of Medical Sciences, Tehran, Iran , Sharif-Paghaleh, Ehsan Department of Immunology - School of Medicine - Tehran University of Medical Sciences, Tehran, Iran , Arabameri, Abazar Faculty of Electrical Engineering - K.N. Toosi University of Technology, Tehran, Iran , Asemani, Davud Faculty of Electrical Engineering - K.N. Toosi University of Technology, Tehran, Iran , Hadjati, Jamshid Department of Immunology - School of Medicine - Tehran University of Medical Sciences, Tehran, Iran
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.
