SPARTIN: a Bayesian method for the quantification and characterization of cell type interactions in spatial pathology data

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dc.citation.articleNumber1175603
dc.citation.journalTitleFrontiers in Genetics
dc.citation.volumeNumber14
dc.contributor.authorOsher, Nathaniel
dc.contributor.authorKang, Jian
dc.contributor.authorKrishnan, Santhoshi
dc.contributor.authorRao, Arvind
dc.contributor.authorBaladandayuthapani, Veerabhadran
dc.date.accessioned2023-07-21T16:13:51Z
dc.date.available2023-07-21T16:13:51Z
dc.date.issued2023
dc.description.abstractIntroduction: 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.
dc.identifier.citationOsher, Nathaniel, Kang, Jian, Krishnan, Santhoshi, et al.. "SPARTIN: a Bayesian method for the quantification and characterization of cell type interactions in spatial pathology data." <i>Frontiers in Genetics,</i> 14, (2023) Frontiers Media S.A.: https://doi.org/10.3389/fgene.2023.1175603.
dc.identifier.digitalfgene-14-1175603
dc.identifier.doihttps://doi.org/10.3389/fgene.2023.1175603
dc.identifier.urihttps://hdl.handle.net/1911/114992
dc.language.isoeng
dc.publisherFrontiers Media S.A.
dc.rightsExcept where otherwise noted, this work is licensed under a Creative Commons Attribution (CC BY) license.  Permission to reuse, publish, or reproduce the work beyond the terms of the license or beyond the bounds of Fair Use or other exemptions to copyright law must be obtained from the copyright holder.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleSPARTIN: a Bayesian method for the quantification and characterization of cell type interactions in spatial pathology data
dc.typeJournal article
dc.type.dcmiText
dc.type.publicationpublisher version
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