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Quantification of spatial and phenotypic heterogeneity in an agent-based model of tumour-macrophage interactions

We introduce a new spatial statistic, the weighted pair correlation function (wPCF). The wPCF extends the existing pair correlation function (PCF) and cross-PCF to describe spatial relationships between points marked with combinations of discrete and continuous labels. We validate its use through ap...

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Autores principales: Bull, Joshua A., Byrne, Helen M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10079237/
https://www.ncbi.nlm.nih.gov/pubmed/36972297
http://dx.doi.org/10.1371/journal.pcbi.1010994
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author Bull, Joshua A.
Byrne, Helen M.
author_facet Bull, Joshua A.
Byrne, Helen M.
author_sort Bull, Joshua A.
collection PubMed
description We introduce a new spatial statistic, the weighted pair correlation function (wPCF). The wPCF extends the existing pair correlation function (PCF) and cross-PCF to describe spatial relationships between points marked with combinations of discrete and continuous labels. We validate its use through application to a new agent-based model (ABM) which simulates interactions between macrophages and tumour cells. These interactions are influenced by the spatial positions of the cells and by macrophage phenotype, a continuous variable that ranges from anti-tumour to pro-tumour. By varying model parameters that regulate macrophage phenotype, we show that the ABM exhibits behaviours which resemble the ‘three Es of cancer immunoediting’: Equilibrium, Escape, and Elimination. We use the wPCF to analyse synthetic images generated by the ABM. We show that the wPCF generates a ‘human readable’ statistical summary of where macrophages with different phenotypes are located relative to both blood vessels and tumour cells. We also define a distinct ‘PCF signature’ that characterises each of the three Es of immunoediting, by combining wPCF measurements with the cross-PCF describing interactions between vessels and tumour cells. By applying dimension reduction techniques to this signature, we identify its key features and train a support vector machine classifier to distinguish between simulation outputs based on their PCF signature. This proof-of-concept study shows how multiple spatial statistics can be combined to analyse the complex spatial features that the ABM generates, and to partition them into interpretable groups. The intricate spatial features produced by the ABM are similar to those generated by state-of-the-art multiplex imaging techniques which distinguish the spatial distribution and intensity of multiple biomarkers in biological tissue regions. Applying methods such as the wPCF to multiplex imaging data would exploit the continuous variation in biomarker intensities and generate more detailed characterisation of the spatial and phenotypic heterogeneity in tissue samples.
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spelling pubmed-100792372023-04-07 Quantification of spatial and phenotypic heterogeneity in an agent-based model of tumour-macrophage interactions Bull, Joshua A. Byrne, Helen M. PLoS Comput Biol Research Article We introduce a new spatial statistic, the weighted pair correlation function (wPCF). The wPCF extends the existing pair correlation function (PCF) and cross-PCF to describe spatial relationships between points marked with combinations of discrete and continuous labels. We validate its use through application to a new agent-based model (ABM) which simulates interactions between macrophages and tumour cells. These interactions are influenced by the spatial positions of the cells and by macrophage phenotype, a continuous variable that ranges from anti-tumour to pro-tumour. By varying model parameters that regulate macrophage phenotype, we show that the ABM exhibits behaviours which resemble the ‘three Es of cancer immunoediting’: Equilibrium, Escape, and Elimination. We use the wPCF to analyse synthetic images generated by the ABM. We show that the wPCF generates a ‘human readable’ statistical summary of where macrophages with different phenotypes are located relative to both blood vessels and tumour cells. We also define a distinct ‘PCF signature’ that characterises each of the three Es of immunoediting, by combining wPCF measurements with the cross-PCF describing interactions between vessels and tumour cells. By applying dimension reduction techniques to this signature, we identify its key features and train a support vector machine classifier to distinguish between simulation outputs based on their PCF signature. This proof-of-concept study shows how multiple spatial statistics can be combined to analyse the complex spatial features that the ABM generates, and to partition them into interpretable groups. The intricate spatial features produced by the ABM are similar to those generated by state-of-the-art multiplex imaging techniques which distinguish the spatial distribution and intensity of multiple biomarkers in biological tissue regions. Applying methods such as the wPCF to multiplex imaging data would exploit the continuous variation in biomarker intensities and generate more detailed characterisation of the spatial and phenotypic heterogeneity in tissue samples. Public Library of Science 2023-03-27 /pmc/articles/PMC10079237/ /pubmed/36972297 http://dx.doi.org/10.1371/journal.pcbi.1010994 Text en © 2023 Bull, Byrne https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Bull, Joshua A.
Byrne, Helen M.
Quantification of spatial and phenotypic heterogeneity in an agent-based model of tumour-macrophage interactions
title Quantification of spatial and phenotypic heterogeneity in an agent-based model of tumour-macrophage interactions
title_full Quantification of spatial and phenotypic heterogeneity in an agent-based model of tumour-macrophage interactions
title_fullStr Quantification of spatial and phenotypic heterogeneity in an agent-based model of tumour-macrophage interactions
title_full_unstemmed Quantification of spatial and phenotypic heterogeneity in an agent-based model of tumour-macrophage interactions
title_short Quantification of spatial and phenotypic heterogeneity in an agent-based model of tumour-macrophage interactions
title_sort quantification of spatial and phenotypic heterogeneity in an agent-based model of tumour-macrophage interactions
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10079237/
https://www.ncbi.nlm.nih.gov/pubmed/36972297
http://dx.doi.org/10.1371/journal.pcbi.1010994
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