Induction of T-cell memory by a dendritic cell vaccine: a computational model

Pappalardo, Francesco; Pennisi, Marzio; Ricupito, Alessia; Topputo, Francesco; Bellone, Matteo

doi:10.1093/bioinformatics/btu059

Cited by 37 publications

(26 citation statements)

References 47 publications

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“…36). Cancer-immunity models have been constructed to investigate the effects of therapy (37)(38)(39), cancer dormancy (40), and interactions with time delay (41). Other types of modeling methods have also been applied.…”

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confidence: 99%

Modeling putative therapeutic implications of exosome exchange between tumor and immune cells

Huang

Hanash

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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Development of effective strategies to mobilize the immune system as a therapeutic modality in cancer necessitates a better understanding of the contribution of the tumor microenvironment to the complex interplay between cancer cells and the immune response. Recently, effort has been directed at unraveling the functional role of exosomes and their cargo of messengers in this interplay. Exosomes are small vesicles (30-200 nm) that mediate local and long-range communication through the horizontal transfer of information, such as combinations of proteins, mRNAs and microRNAs. Here, we develop a tractable theoretical framework to study the putative role of exosome-mediated cell-cell communication in the cancer-immunity interplay. We reduce the complex interplay into a generic model whose three components are cancer cells, dendritic cells (consisting of precursor, immature, and mature types), and killer cells (consisting of cytotoxic T cells, helper T cells, effector B cells, and natural killer cells). The framework also incorporates the effects of exosome exchange on enhancement/reduction of cell maturation, proliferation, apoptosis, immune recognition, and activation/ inhibition. We reveal tristability-

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mentioning

confidence: 99%

Modeling putative therapeutic implications of exosome exchange between tumor and immune cells

Huang

Hanash

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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“…Issues will need to be addressed, including the synergistic orchestration of both the mechanistic targeting and/or co-uptake of vaccine components and the optimal logistics of vaccination protocols, such as chosen regimen and the size, number and frequency of vaccinations (Moyer et al , 2016). In this regard, computational modelling frameworks have much to offer, allowing us to predict overall immune system response dynamics and pharmacokinetics that should allow us to optimize memory effects and the dosing of vaccine administration (Pappalardo et al , 2014). …”

Section: Resultsmentioning

confidence: 99%

Towards the knowledge-based design of universal influenza epitope ensemble vaccines

et al. 2016

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Motivation: Influenza A viral heterogeneity remains a significant threat due to unpredictable antigenic drift in seasonal influenza and antigenic shifts caused by the emergence of novel subtypes. Annual review of multivalent influenza vaccines targets strains of influenza A and B likely to be predominant in future influenza seasons. This does not induce broad, cross protective immunity against emergent subtypes. Better strategies are needed to prevent future pandemics. Cross-protection can be achieved by activating CD8+ and CD4+ T cells against highly conserved regions of the influenza genome. We combine available experimental data with informatics-based immunological predictions to help design vaccines potentially able to induce cross-protective T-cells against multiple influenza subtypes.Results: To exemplify our approach we designed two epitope ensemble vaccines comprising highly conserved and experimentally verified immunogenic influenza A epitopes as putative non-seasonal influenza vaccines; one specifically targets the US population and the other is a universal vaccine. The USA-specific vaccine comprised 6 CD8+ T cell epitopes (GILGFVFTL, FMYSDFHFI, GMDPRMCSL, SVKEKDMTK, FYIQMCTEL, DTVNRTHQY) and 3 CD4+ epitopes (KGILGFVFTLTVPSE, EYIMKGVYINTALLN, ILGFVFTLTVPSERG). The universal vaccine comprised 8 CD8+ epitopes: (FMYSDFHFI, GILGFVFTL, ILRGSVAHK, FYIQMCTEL, ILKGKFQTA, YYLEKANKI, VSDGGPNLY, YSHGTGTGY) and the same 3 CD4+ epitopes. Our USA-specific vaccine has a population protection coverage (portion of the population potentially responsive to one or more component epitopes of the vaccine, PPC) of over 96 and 95% coverage of observed influenza subtypes. The universal vaccine has a PPC value of over 97 and 88% coverage of observed subtypes.Availability and Implementation: http://imed.med.ucm.es/Tools/episopt.html.Contact: d.r.flower@aston.ac.uk

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“…We used agent-based modeling (ABM) approach to build the simulation framework and taking into account our previous successful experiences in simulating the dynamics of the immune system in several pathologies. In particular, we adapted and extended our immune system simulation Cells 2020, 9, 586 5 of 23 framework [16,[26][27][28], named "Universal Immune Simulator Framework" (UISS), to model the dynamics of the immune system in MS along with the therapeutic effects of teriflunomide, fingolimod, IFN-β1a, ocrelizumab, and natalizumab. The advantages of this modeling methodology are well known: entities, as well as biological functions and interactions, can be described very closely to the biological reality; at the same time, this approach allows a mathematician or a computer scientist to describe the scenario using a logical and rational framework.…”

Section: The Computational Modelmentioning

confidence: 99%

The Potential of Computational Modeling to Predict Disease Course and Treatment Response in Patients with Relapsing Multiple Sclerosis

Pappalardo

Russo

Pennisi

et al. 2020

Cells

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As of today, 20 disease-modifying drugs (DMDs) have been approved for the treatment of relapsing multiple sclerosis (MS) and, based on their efficacy, they can be grouped into moderate-efficacy DMDs and high-efficacy DMDs. The choice of the drug mostly relies on the judgment and experience of neurologists and the evaluation of the therapeutic response can only be obtained by monitoring the clinical and magnetic resonance imaging (MRI) status during follow up. In an era where therapies are focused on personalization, this study aims to develop a modeling infrastructure to predict the evolution of relapsing MS and the response to treatments. We built a computational modeling infrastructure named Universal Immune System Simulator (UISS), which can simulate the main features and dynamics of the immune system activities. We extended UISS to simulate all the underlying MS pathogenesis and its interaction with the host immune system. This simulator is a multi-scale, multi-organ, agent-based simulator with an attached module capable of simulating the dynamics of specific biological pathways at the molecular level. We simulated six MS patients with different relapsing–remitting courses. These patients were characterized based on their age, sex, presence of oligoclonal bands, therapy, and MRI lesion load at the onset. The simulator framework is made freely available and can be used following the links provided in the availability section. Even though the model can be further personalized employing immunological parameters and genetic information, we generated a few simulation scenarios for each patient based on the available data. Among these simulations, it was possible to find the scenarios that realistically matched the real clinical and MRI history. Moreover, for two patients, the simulator anticipated the timing of subsequent relapses, which occurred, suggesting that UISS may have the potential to assist MS specialists in predicting the course of the disease and the response to treatment.

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Induction of T-cell memory by a dendritic cell vaccine: a computational model

Abstract: The model is fully described in the article.

Cited by 37 publications

References 47 publications

Modeling putative therapeutic implications of exosome exchange between tumor and immune cells

Modeling putative therapeutic implications of exosome exchange between tumor and immune cells

Towards the knowledge-based design of universal influenza epitope ensemble vaccines

The Potential of Computational Modeling to Predict Disease Course and Treatment Response in Patients with Relapsing Multiple Sclerosis

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