VaccImm: simulating peptide vaccination in cancer therapy

Eichborn, Joachim von; Woelke, Anna Lena; Castiglione, Filippo; Preißner, Robert

doi:10.1186/1471-2105-14-127

Cited by 13 publications

(7 citation statements)

References 22 publications

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“…It exploits immunoinformatics tools to calculate B-cell epitopes and TCR peptides of antigenic sequences, and a generic contact potential to estimate the affinity between T-cells receptors and peptides presented by antigen-presenting cells, infected cells, or, as in the present case, malignant (tumor) cells. 40 41 Besides that, the model accepts in input any pre-calculated list of peptides associated with a “ranked likelihood” to bind a certain HLA, 42 for example, NetMHCstabpan. 39 Moreover, an arbitrary value is introduced in the modeling to evaluate the conformational similarity between the paired peptides.…”

Section: Methodsmentioning

confidence: 99%

Identification and validation of viral antigens sharing sequence and structural homology with tumor-associated antigens (TAAs).

Ragone

Manolio

Cavalluzzo

et al. 2021

J Immunother Cancer

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BackgroundThe host’s immune system develops in equilibrium with both cellular self-antigens and non-self-antigens derived from microorganisms which enter the body during lifetime. In addition, during the years, a tumor may arise presenting to the immune system an additional pool of non-self-antigens, namely tumor antigens (tumor-associated antigens, TAAs; tumor-specific antigens, TSAs).MethodsIn the present study, we looked for homology between published TAAs and non-self-viral-derived epitopes. Bioinformatics analyses and ex vivo immunological validations have been performed.ResultsSurprisingly, several of such homologies have been found. Moreover, structural similarities between paired TAAs and viral peptides as well as comparable patterns of contact with HLA and T cell receptor (TCR) α and β chains have been observed. Therefore, the two classes of non-self-antigens (viral antigens and tumor antigens) may converge, eliciting cross-reacting CD8+ T cell responses which possibly drive the fate of cancer development and progression.ConclusionsAn established antiviral T cell memory may turn out to be an anticancer T cell memory, able to control the growth of a cancer developed during the lifetime if the expressed TAA is similar to the viral epitope. This may ultimately represent a relevant selective advantage for patients with cancer and may lead to a novel preventive anticancer vaccine strategy.

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Section: Methodsmentioning

confidence: 99%

Identification and validation of viral antigens sharing sequence and structural homology with tumor-associated antigens (TAAs).

Ragone

Manolio

Cavalluzzo

et al. 2021

J Immunother Cancer

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“…or EBV [21], [22]), cancer immuno-prevention [23], [24], type 1 hypersensitivity [25] and the chronic inflammation associated to type 2 diabetes [26] as well as specific aspects of the immune dynamics such as lymphocytes homing in lymph nodes [27], the gene regulation leading to cell differentiation [28], clonal dominance in heterologous immune responses [29] and also vaccination-eliciting fish immunity [30]. Relevant to the present study, the model has recently being used to simulate the response to a multi-epitope vaccine against SARS-CoV-2 [31], [32].…”

Section: Virus Definitionmentioning

confidence: 99%

From infection to immunity: understanding the response to SARS-CoV2 through in-silico modeling

Castiglione

Deb

Srivastava

et al. 2020

Preprint

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BackgroundImmune system conditions of the patient is a key factor in COVID-19 infection survival. A growing number of studies have focused on immunological determinants to develop better biomarkers for therapies.AimThe dynamics of the insurgence of immunity is at the core of the both SARS-CoV-2 vaccine development and therapies. This paper addresses a fundamental question in the management of the infection: can we describe the insurgence (and the span) of immunity in COVID-19? The in-silico model developed here answers this question at individual (personalized) and population levels.We simulate the immune response to SARS-CoV-2 and analyze the impact of infecting viral load, affinity to the ACE2 receptor and age in the artificially infected population on the course of the disease.MethodsWe use a stochastic agent-based immune simulation platform to construct a virtual cohort of infected individuals with age-dependent varying degree of immune competence. We use a parameter setting to reproduce known inter-patient variability and general epidemiological statistics.ResultsWe reproduce in-silico a number of clinical observations and we identify critical factors in the statistical evolution of the infection. In particular we evidence the importance of the humoral response over the cytotoxic response and find that the antibody titers measured after day 25 from the infection is a prognostic factor for determining the clinical outcome of the infection.Our modeling framework uses COVID-19 infection to demonstrate the actionable effectiveness of simulating the immune response at individual and population levels. The model developed is able to explain and interpret observed patterns of infection and makes verifiable temporal predictions.Within the limitations imposed by the simulated environment, this work proposes in a quantitative way that the great variability observed in the patient outcomes in real life can be the mere result of subtle variability in the infecting viral load and immune competence in the population.In this work we i) show the power of model predictions, ii) identify the clinical end points that could be more suitable for computational modeling of COVID-19 immune response, iii) define the resolution and amount of data required to empower this class of models for translational medicine purposes and, iv) we exemplify how computational modeling of immune response provides an important view to discuss hypothesis and design new experiments, in particular paving the way to further investigations about the duration of vaccine-elicited immunity especially in the view of the blundering effect of immunesenescence.

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“…It is also observed that memory against cancer will be helpful to reduce initial phase of cancer or to destroy tumour cells. In [75], von Eichborn et al proposed an ABM, termed as VaccImm for cancer immunotherapy vaccination. Agents such as tumour antigens, immune cells, and molecules such as IL-2, antibodies with the help of their biological rules were simulated in VaccImm that models adaptive response.…”

Section: Modelling Of Immune Response To Cancermentioning

confidence: 99%

Review of the systems biology of the immune system using agent‐based models

Shinde

Kurhekar

2018

IET syst. biol.

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The immune system is an inherent protection system in vertebrate animals including human beings that exhibit properties such as self-organisation, self-adaptation, learning, and recognition. It interacts with the other allied systems such as the gut and lymph nodes. There is a need for immune system modelling to know about its complex internal mechanism, to understand how it maintains the homoeostasis, and how it interacts with the other systems. There are two types of modelling techniques used for the simulation of features of the immune system: equation-based modelling (EBM) and agent-based modelling. Owing to certain shortcomings of the EBM, agent-based modelling techniques are being widely used. This technique provides various predictions for disease causes and treatments; it also helps in hypothesis verification. This study presents a review of agent-based modelling of the immune system and its interactions with the gut and lymph nodes. The authors also review the modelling of immune system interactions during tuberculosis and cancer. In addition, they also outline the future research directions for the immune system simulation through agent-based techniques such as the effects of stress on the immune system, evolution of the immune system, and identification of the parameters for a healthy immune system.

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VaccImm: simulating peptide vaccination in cancer therapy

Cited by 13 publications

References 22 publications

Identification and validation of viral antigens sharing sequence and structural homology with tumor-associated antigens (TAAs).

Identification and validation of viral antigens sharing sequence and structural homology with tumor-associated antigens (TAAs).

From infection to immunity: understanding the response to SARS-CoV2 through in-silico modeling

Review of the systems biology of the immune system using agent‐based models

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