Simulating Cytotoxic T-Lymphocyte and Cancer Cells Interactions: An LSTM-Based Approach to Surrogate an Agent-Based Model

2021 ◽  
pp. 41-46
Author(s):  
David Bernard ◽  
Anthony Kobanda ◽  
Sylvain Cussat-Blanc
Keyword(s):  
Cancer Cells ◽  
T Lymphocyte ◽  
Cytotoxic T Lymphocyte ◽  
Agent Based Model ◽  
Agent Based

scholarly journals Agent based models to investigate cooperation between cancer cells

2015 ◽  
Author(s):  
Joao Xavier ◽  
William Chang
Keyword(s):  
Growth Factor ◽  
Cancer Cells ◽  
Solid Tumor ◽  
Kin Selection ◽  
Evolution Of Cooperation ◽  
Emergent Properties ◽  
Agent Based Model ◽  
Agent Based ◽  
Chemical Gradients ◽  
Close Proximity

We present a type of agent-based model that uses off-lattice spheres to represent individual cells in a solid tumor. The model calculates chemical gradients and determines the dynamics of the tumor as emergent properties of the interactions between the cells. As an example, we present an investigation of cooperation among cancer cells where cooperators secrete a growth factor that is costly to synthesize. Simulations reveal that cooperation is favored when cancer cells from the same lineage stay in close proximity. The result supports the hypothesis that kin selection, a theory that explains the evolution of cooperation in animals, also applies to cancers.

An Agent-Based Model of the Spread of Devil Facial Tumor Disease in an Isolated Population of Tasmanian Devils

2012 ◽  
Vol 4 (4) ◽  
pp. 1-16
Author(s):  
Charles E. Knadler
Keyword(s):  
Life History ◽  
Cancer Cells ◽  
Tasmanian Devil ◽  
Isolated Population ◽  
Agent Based Model ◽  
Disease Mechanism ◽  
Agent Based ◽  
In The Wild ◽  
Devil Facial Tumor Disease

The Tasmanian devil population is being reduced in the wild at an alarming rate due to an epidemic, which is the result of an unusual disease mechanism. Infected animals “inject” cancer cells into other devils, which then clone the cells, developing tumors. These tumors are invariably fatal. Field observers have developed hypotheses that include a life- history change for the species. It is hypothesized that this change has the potential to improve the population’s survivability. An agent-based model of Tasmanian devils is used to evaluate these hypotheses. The model results suggest that the devils’ intra-gender aggression as well as their aggressive mating practices render the life-history change hypotheses’ correctness improbable.

Establishment of shared antigen reactive cytotoxic T lymphocyte using co-stimulatory molecule introduced autologous cancer cells

2010 ◽  
Vol 88 (1) ◽  
pp. 128-132 ◽  
Author(s):  
Yoshihiko Hirohashi ◽  
Toshihiko Torigoe ◽  
Itaru Hirai ◽  
Yasuaki Tamura ◽  
Munehide Nakatsugawa ◽  
...  
Keyword(s):  
Cancer Cells ◽  
T Lymphocyte ◽  
Cytotoxic T Lymphocyte

scholarly journals An Agent-Based Model of monocyte differentiation into Tumour-Associated Macrophages in Chronic Lymphocytic Leukemia

2021 ◽  
Author(s):  
Nina Verstraete ◽  
Malvina Marku ◽  
Marcin Domagala ◽  
Julie Bordenave ◽  
H&eacutelène Arduin ◽  
...  
Keyword(s):  
Chronic Lymphocytic Leukemia ◽  
Cancer Cells ◽  
Mononuclear Cells ◽  
Lymphocytic Leukemia ◽  
Leukemic Cells ◽  
Patient Specific ◽  
Model Parameters ◽  
Agent Based Model ◽  
Agent Based ◽  
Monocyte Differentiation

Monocyte-derived macrophages are immune cells which help maintain tissue homeostasis and defend the organism against pathogens. In solid tumours, recent studies have uncovered complex macrophage populations, among which tumour-associated macrophages, supporting tumorigenesis through multiple cancer hallmarks such as immunosuppression, angiogenesis or matrix remodelling. In the case of chronic lymphocytic leukemia, these macrophages are known as nurse-like cells and have been shown to protect leukemic cells from spontaneous apoptosis and contribute to their chemoresistance. We propose an agent-based model of monocytes differentiation into nurse-like cells upon contact with leukemic B cells in-vitro. We studied monocyte differentiation and cancer cells survival dynamics depending on diverse hypotheses on monocytes and cancer cells relative proportions, sensitivity to their surrounding environment and cell-cell interactions. Peripheral blood mononuclear cells from patients were cultured and monitored during 13 days to calibrate the model parameters, such as phagocytosis efficiency, death rates or protective effect from the nurse-like cells. Our model is able to reproduce experimental results and predict cancer cells survival dynamics in a patient-specific manner. Our results shed light on important factors at play in cancer cells survival, highlighting a potentially important role of phagocytosis.

scholarly journals Low Dose Decitabine Treatment Induces CD80 Expression in Cancer Cells and Stimulates Tumor Specific Cytotoxic T Lymphocyte Responses

PLoS ONE ◽  
2013 ◽  
Vol 8 (5) ◽  
pp. e62924 ◽  
Author(s):  
Li-Xin Wang ◽  
Zhen-Yang Mei ◽  
Ji-Hao Zhou ◽  
Yu-Shi Yao ◽  
Yong-Hui Li ◽  
...  
Keyword(s):  
Cancer Cells ◽  
T Lymphocyte ◽  
Low Dose ◽  
Cytotoxic T Lymphocyte ◽  
Lymphocyte Responses

scholarly journals Evaluation of CD8 T cell killing models with computer simulations of 2-photon imaging experiments

2019 ◽  
Author(s):  
Ananya Rastogi ◽  
Philippe Robert ◽  
Stephan Halle ◽  
Michael Meyer-Hermann
Keyword(s):  
T Cells ◽  
T Cell ◽  
T Lymphocyte ◽  
Cytotoxic T Lymphocyte ◽  
Cytotoxic T Cells ◽  
Signal Integration ◽  
Agent Based ◽  
Killing Activity ◽  
Infected Cells

AbstractIn vivo imaging of cytotoxic T lymphocyte (CTL) killing activity revealed that infected cells have a higher observed probability of dying after multiple contacts with CTLs, suggesting memory effect in CTLs or infected cells. We developed a three-dimensional agent-based model of CTL killing activity to discriminate different hypotheses about how infected cells get killed based on quantitative 2-photon in vivo observations. We compared a constant CTL killing probability with mechanisms of signal integration in CTL or infected cells. The most likely scenario implied increased susceptibility of infected cells with increasing number of CTL contacts where the total number of contacts was a critical factor as opposed to signal integration over many contacts. However, when allowing in silico T cells to interact with apoptotic target cells (zombie contacts), a contact history independent killing mechanism was also in agreement with the experimental datasets. We showed that contacts that take place between CTLs and dying infected cells impact the observed killing dynamics because even in absence of modulation of cell properties, we saw an increase of the observed probability of killing infected cells with more interactions. The duration taken by an infected cell to die and the per capita killing rate (PCKR) of CTLs, parameters hard to measure directly, were determined from the model and turned out predictive to distinguish the different CTL killing models in future experiments. The comparison of observed datasets to simulation results, revealed limitations in interpreting 2-photon data, and provided prediction for additional measurements to distinguish CTL killing models.HighlightsKilling of infected cells by cytotoxic T cells typically involves more than a single contact.Cytotoxic T cells or infected cells integrate signals from multiple interactions.T cell contacts with dying infected cells have a major impact on in vivo data interpretation.Significance StatementDespite having a clear understanding of cytotoxic T lymphocyte (CTL) mediated cytotoxicity mechanisms, the quantitative dynamics remain unexplored at a cellular level. We developed an agent-based model to compare different hypotheses for mechanisms of CTL mediated cytotoxicity that could lead to an increase in observed probability of killing infected cells at higher interactions with CTLs as seen in vivo. We showed that this behaviour can be explained by modulation of properties by infected cells or CTLs with increasing number of contacts. For the modulation, we compared two modes of signal integration and showed that time is not a relevant parameter in signal integration. We also studied the impact of contacts between CTLs and apoptotic infected cells on observed killing properties.

scholarly journals A computational multiscale agent-based model for simulating spatio-temporal tumour immune response to PD1 and PDL1 inhibition

2017 ◽  
Vol 14 (134) ◽  
pp. 20170320 ◽  
Author(s):  
Chang Gong ◽  
Oleg Milberg ◽  
Bing Wang ◽  
Paolo Vicini ◽  
Rajesh Narwal ◽  
...  
Keyword(s):  
Cancer Cells ◽  
Immune Checkpoint ◽  
Temporal Dynamics ◽  
Checkpoint Inhibitors ◽  
Tumour Progression ◽  
Cytotoxic T Cells ◽  
Three Dimensional ◽  
Agent Based Model ◽  
Agent Based ◽  
Tumour Development

When the immune system responds to tumour development, patterns of immune infiltrates emerge, highlighted by the expression of immune checkpoint-related molecules such as PDL1 on the surface of cancer cells. Such spatial heterogeneity carries information on intrinsic characteristics of the tumour lesion for individual patients, and thus is a potential source for biomarkers for anti-tumour therapeutics. We developed a systems biology multiscale agent-based model to capture the interactions between immune cells and cancer cells, and analysed the emergent global behaviour during tumour development and immunotherapy. Using this model, we are able to reproduce temporal dynamics of cytotoxic T cells and cancer cells during tumour progression, as well as three-dimensional spatial distributions of these cells. By varying the characteristics of the neoantigen profile of individual patients, such as mutational burden and antigen strength, a spectrum of pretreatment spatial patterns of PDL1 expression is generated in our simulations, resembling immuno-architectures obtained via immunohistochemistry from patient biopsies. By correlating these spatial characteristics with in silico treatment results using immune checkpoint inhibitors, the model provides a framework for use to predict treatment/biomarker combinations in different cancer types based on cancer-specific experimental data.

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