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SPARTIN: a Bayesian method for the quantification and characterization of cell type interactions in spatial pathology data
Introduction: The acquisition of high-resolution digital pathology imaging data has sparked the development of methods to extract context-specific features from such complex data. In the context of cancer, this has led to increased exploration of the tumor microenvironment with respect to the presen...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10232864/ https://www.ncbi.nlm.nih.gov/pubmed/37274781 http://dx.doi.org/10.3389/fgene.2023.1175603 |
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author | Osher, Nathaniel Kang, Jian Krishnan, Santhoshi Rao, Arvind Baladandayuthapani, Veerabhadran |
author_facet | Osher, Nathaniel Kang, Jian Krishnan, Santhoshi Rao, Arvind Baladandayuthapani, Veerabhadran |
author_sort | Osher, Nathaniel |
collection | PubMed |
description | Introduction: The acquisition of high-resolution digital pathology imaging data has sparked the development of methods to extract context-specific features from such complex data. In the context of cancer, this has led to increased exploration of the tumor microenvironment with respect to the presence and spatial composition of immune cells. Spatial statistical modeling of the immune microenvironment may yield insights into the role played by the immune system in the natural development of cancer as well as downstream therapeutic interventions. Methods: In this paper, we present SPatial Analysis of paRtitioned Tumor-Immune imagiNg (SPARTIN), a Bayesian method for the spatial quantification of immune cell infiltration from pathology images. SPARTIN uses Bayesian point processes to characterize a novel measure of local tumor-immune cell interaction, Cell Type Interaction Probability (CTIP). CTIP allows rigorous incorporation of uncertainty and is highly interpretable, both within and across biopsies, and can be used to assess associations with genomic and clinical features. Results: Through simulations, we show SPARTIN can accurately distinguish various patterns of cellular interactions as compared to existing methods. Using SPARTIN, we characterized the local spatial immune cell infiltration within and across 335 melanoma biopsies and evaluated their association with genomic, phenotypic, and clinical outcomes. We found that CTIP was significantly (negatively) associated with deconvolved immune cell prevalence scores including CD8+ T-Cells and Natural Killer cells. Furthermore, average CTIP scores differed significantly across previously established transcriptomic classes and significantly associated with survival outcomes. Discussion: SPARTIN provides a general framework for investigating spatial cellular interactions in high-resolution digital histopathology imaging data and its associations with patient level characteristics. The results of our analysis have potential implications relevant to both treatment and prognosis in the context of Skin Cutaneous Melanoma. The R-package for SPARTIN is available at https://github.com/bayesrx/SPARTIN along with a visualization tool for the images and results at: https://nateosher.github.io/SPARTIN. |
format | Online Article Text |
id | pubmed-10232864 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102328642023-06-02 SPARTIN: a Bayesian method for the quantification and characterization of cell type interactions in spatial pathology data Osher, Nathaniel Kang, Jian Krishnan, Santhoshi Rao, Arvind Baladandayuthapani, Veerabhadran Front Genet Genetics Introduction: The acquisition of high-resolution digital pathology imaging data has sparked the development of methods to extract context-specific features from such complex data. In the context of cancer, this has led to increased exploration of the tumor microenvironment with respect to the presence and spatial composition of immune cells. Spatial statistical modeling of the immune microenvironment may yield insights into the role played by the immune system in the natural development of cancer as well as downstream therapeutic interventions. Methods: In this paper, we present SPatial Analysis of paRtitioned Tumor-Immune imagiNg (SPARTIN), a Bayesian method for the spatial quantification of immune cell infiltration from pathology images. SPARTIN uses Bayesian point processes to characterize a novel measure of local tumor-immune cell interaction, Cell Type Interaction Probability (CTIP). CTIP allows rigorous incorporation of uncertainty and is highly interpretable, both within and across biopsies, and can be used to assess associations with genomic and clinical features. Results: Through simulations, we show SPARTIN can accurately distinguish various patterns of cellular interactions as compared to existing methods. Using SPARTIN, we characterized the local spatial immune cell infiltration within and across 335 melanoma biopsies and evaluated their association with genomic, phenotypic, and clinical outcomes. We found that CTIP was significantly (negatively) associated with deconvolved immune cell prevalence scores including CD8+ T-Cells and Natural Killer cells. Furthermore, average CTIP scores differed significantly across previously established transcriptomic classes and significantly associated with survival outcomes. Discussion: SPARTIN provides a general framework for investigating spatial cellular interactions in high-resolution digital histopathology imaging data and its associations with patient level characteristics. The results of our analysis have potential implications relevant to both treatment and prognosis in the context of Skin Cutaneous Melanoma. The R-package for SPARTIN is available at https://github.com/bayesrx/SPARTIN along with a visualization tool for the images and results at: https://nateosher.github.io/SPARTIN. Frontiers Media S.A. 2023-05-18 /pmc/articles/PMC10232864/ /pubmed/37274781 http://dx.doi.org/10.3389/fgene.2023.1175603 Text en Copyright © 2023 Osher, Kang, Krishnan, Rao and Baladandayuthapani. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Osher, Nathaniel Kang, Jian Krishnan, Santhoshi Rao, Arvind Baladandayuthapani, Veerabhadran SPARTIN: a Bayesian method for the quantification and characterization of cell type interactions in spatial pathology data |
title | SPARTIN: a Bayesian method for the quantification and characterization of cell type interactions in spatial pathology data |
title_full | SPARTIN: a Bayesian method for the quantification and characterization of cell type interactions in spatial pathology data |
title_fullStr | SPARTIN: a Bayesian method for the quantification and characterization of cell type interactions in spatial pathology data |
title_full_unstemmed | SPARTIN: a Bayesian method for the quantification and characterization of cell type interactions in spatial pathology data |
title_short | SPARTIN: a Bayesian method for the quantification and characterization of cell type interactions in spatial pathology data |
title_sort | spartin: a bayesian method for the quantification and characterization of cell type interactions in spatial pathology data |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10232864/ https://www.ncbi.nlm.nih.gov/pubmed/37274781 http://dx.doi.org/10.3389/fgene.2023.1175603 |
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