Mathematical model of a personalized neoantigen cancer vaccine and the human immune system

Abstract

Cancer vaccines are an important component of the cancer immunotherapy toolkit enhancing immune response to malignant cells by activating CD4+ and CD8+ T cells. Multiple successful clinical applications of cancer vaccines have shown good safety and efficacy. Despite the notable progress, significant challenges remain in obtaining consistent immune responses across heterogeneous patient populations, as well as various cancers. We present a mechanistic mathematical model describing key interactions of a personalized neoantigen cancer vaccine with an individual patient’s immune system. Specifically, the model considers the vaccine concentration of tumor-specific antigen peptides and adjuvant, the patient’s major histocompatibility complexes I and II copy numbers, tumor size, T cells, and antigen presenting cells. We parametrized the model using patient-specific data from a clinical study in which individualized cancer vaccines were used to treat six melanoma patients. Model simulations predicted both immune responses, represented by T cell counts, to the vaccine as well as clinical outcome (determined as change of tumor size). This model, although complex, can be used to describe, simulate, and predict the behavior of the human immune system to a personalized cancer vaccine.

An Immune-Related Long Noncoding RNA Pair as a New Biomarker to Predict the Prognosis of Patients in Breast Cancer

“…After performing the Wilcoxon signed-rank test, we found that the highrisk group was negatively correlated with tumor-infiltrating immune cells, such as neutrophils, M1 macrophages, plasmacytoid dendritic cells, CD4 + T cells, and CD8 + T cells, whereas it was positively correlated with cancer-associated fibroblasts and M2 macrophages. Similarly, it has been reported that the abundance of infiltration of CD4 + T and CD8 + T cells plays an important role in immunotherapy by enhancing the immune response to tumor cells (Rodriguez Messan et al, 2021). Moreover, the high infiltration of M1 macrophages and the low infiltration of M2 macrophages predict satisfactory outcomes for tumor patients, because M1 macrophages have the ability to engulf cancer cells and M2 macrophages may inhibit inflammatory response and repair tissue (Al Haq et al, 2022).…”

Dose optimization of an adjuvanted peptide-based personalized neoantigen melanoma vaccine

“…We use our compartmental model published previously [ 33 ], which captures the interactions among the human immune system, tumor burden, and a personalized neoantigen peptide cancer vaccine. In this paper, we refer to this model as MRM.…”

“…The MRM model [ 33 ] was calibrated using patient-specific data from six patients with melanoma at different disease stages [ 34 ] to estimate key model parameters (see Table A and B in S1 Appendix for a summary of model parameters and estimated values). In our previous study [ 33 ], in silico experiments (virtual simulations) were performed to investigate the response of each patient to cancer immunotherapy and assess changes in tumor sizes, and global sensitivity analysis was performed to identify and study the behavior of parameters that majorly contribute to the uncertainty of outcomes of interest, such as immune response and tumor cell count.…”

“…In this study, we formulate two optimization problems associated with the MRM model [ 33 ]. We will refer to the MRM model as the state system for the purpose of the optimization setup.…”

“…The goal of this study is to propose a novel approach to quantitatively determining the optimal composition, including peptide and adjuvant, of a personalized cancer vaccine. To achieve this goal, we propose two optimization problems using the immunological model we developed previously [ 33 ]. The first optimization problem focuses on minimizing the overall number of tumor cells and total vaccine exposure throughout the treatment.…”