Multi-scale spatial modeling of immune cell distributions enables survival prediction in primary central nervous system lymphoma
Summary: To understand the clinical significance of the tumor microenvironment (TME), it is essential to study the interactions between malignant and non-malignant cells in clinical specimens. Here, we established a computational framework for a multiplex imaging system to comprehensively characteri...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-08-01
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| Series: | iScience |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004223014086 |
| _version_ | 1797772415632146432 |
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| author | Margaretha G.M. Roemer Tim van de Brug Erik Bosch Daniella Berry Nathalie Hijmering Phylicia Stathi Karin Weijers Jeannette Doorduijn Jacoline Bromberg Mark van de Wiel Bauke Ylstra Daphne de Jong Yongsoo Kim |
| author_facet | Margaretha G.M. Roemer Tim van de Brug Erik Bosch Daniella Berry Nathalie Hijmering Phylicia Stathi Karin Weijers Jeannette Doorduijn Jacoline Bromberg Mark van de Wiel Bauke Ylstra Daphne de Jong Yongsoo Kim |
| author_sort | Margaretha G.M. Roemer |
| collection | DOAJ |
| description | Summary: To understand the clinical significance of the tumor microenvironment (TME), it is essential to study the interactions between malignant and non-malignant cells in clinical specimens. Here, we established a computational framework for a multiplex imaging system to comprehensively characterize spatial contexts of the TME at multiple scales, including close and long-distance spatial interactions between cell type pairs. We applied this framework to a total of 1,393 multiplex imaging data newly generated from 88 primary central nervous system lymphomas with complete follow-up data and identified significant prognostic subgroups mainly shaped by the spatial context. A supervised analysis confirmed a significant contribution of spatial context in predicting patient survival. In particular, we found an opposite prognostic value of macrophage infiltration depending on its proximity to specific cell types. Altogether, we provide a comprehensive framework to analyze spatial cellular interaction that can be broadly applied to other technologies and tumor contexts. |
| first_indexed | 2024-03-12T21:50:30Z |
| format | Article |
| id | doaj.art-b93f9e05d3bf4bc2823b75234a29a36d |
| institution | Directory Open Access Journal |
| issn | 2589-0042 |
| language | English |
| last_indexed | 2024-03-12T21:50:30Z |
| publishDate | 2023-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj.art-b93f9e05d3bf4bc2823b75234a29a36d2023-07-26T04:09:34ZengElsevieriScience2589-00422023-08-01268107331Multi-scale spatial modeling of immune cell distributions enables survival prediction in primary central nervous system lymphomaMargaretha G.M. Roemer0Tim van de Brug1Erik Bosch2Daniella Berry3Nathalie Hijmering4Phylicia Stathi5Karin Weijers6Jeannette Doorduijn7Jacoline Bromberg8Mark van de Wiel9Bauke Ylstra10Daphne de Jong11Yongsoo Kim12Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Pathology, Cancer Center Amsterdam, Amsterdam, The NetherlandsDepartment of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The NetherlandsDepartment of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Department of Mathematics, Vrije Universiteit Amsterdam, Amsterdam, The NetherlandsAmsterdam UMC, Vrije Universiteit Amsterdam, Department of Pathology, Cancer Center Amsterdam, Amsterdam, The NetherlandsAmsterdam UMC, Vrije Universiteit Amsterdam, Department of Pathology, Cancer Center Amsterdam, Amsterdam, The Netherlands; HOVON Pathology Facility and Biobank (HOP), Department of Pathology, Amsterdam University Medical Centre, Amsterdam, The NetherlandsAmsterdam UMC, Vrije Universiteit Amsterdam, Department of Pathology, Cancer Center Amsterdam, Amsterdam, The NetherlandsAmsterdam UMC, Vrije Universiteit Amsterdam, Department of Pathology, Cancer Center Amsterdam, Amsterdam, The NetherlandsDepartment of Hematology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The NetherlandsDepartment of Neuro-Oncology, Erasmus MC Cancer Institute, Brain Tumor Center, University Medical Center Rotterdam, Rotterdam, The NetherlandsDepartment of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The NetherlandsAmsterdam UMC, Vrije Universiteit Amsterdam, Department of Pathology, Cancer Center Amsterdam, Amsterdam, The NetherlandsAmsterdam UMC, Vrije Universiteit Amsterdam, Department of Pathology, Cancer Center Amsterdam, Amsterdam, The Netherlands; Corresponding authorAmsterdam UMC, Vrije Universiteit Amsterdam, Department of Pathology, Cancer Center Amsterdam, Amsterdam, The Netherlands; Corresponding authorSummary: To understand the clinical significance of the tumor microenvironment (TME), it is essential to study the interactions between malignant and non-malignant cells in clinical specimens. Here, we established a computational framework for a multiplex imaging system to comprehensively characterize spatial contexts of the TME at multiple scales, including close and long-distance spatial interactions between cell type pairs. We applied this framework to a total of 1,393 multiplex imaging data newly generated from 88 primary central nervous system lymphomas with complete follow-up data and identified significant prognostic subgroups mainly shaped by the spatial context. A supervised analysis confirmed a significant contribution of spatial context in predicting patient survival. In particular, we found an opposite prognostic value of macrophage infiltration depending on its proximity to specific cell types. Altogether, we provide a comprehensive framework to analyze spatial cellular interaction that can be broadly applied to other technologies and tumor contexts.http://www.sciencedirect.com/science/article/pii/S2589004223014086OncologyNeuroscienceCell biologyMathematical biosciencesComputational bioinformatics |
| spellingShingle | Margaretha G.M. Roemer Tim van de Brug Erik Bosch Daniella Berry Nathalie Hijmering Phylicia Stathi Karin Weijers Jeannette Doorduijn Jacoline Bromberg Mark van de Wiel Bauke Ylstra Daphne de Jong Yongsoo Kim Multi-scale spatial modeling of immune cell distributions enables survival prediction in primary central nervous system lymphoma iScience Oncology Neuroscience Cell biology Mathematical biosciences Computational bioinformatics |
| title | Multi-scale spatial modeling of immune cell distributions enables survival prediction in primary central nervous system lymphoma |
| title_full | Multi-scale spatial modeling of immune cell distributions enables survival prediction in primary central nervous system lymphoma |
| title_fullStr | Multi-scale spatial modeling of immune cell distributions enables survival prediction in primary central nervous system lymphoma |
| title_full_unstemmed | Multi-scale spatial modeling of immune cell distributions enables survival prediction in primary central nervous system lymphoma |
| title_short | Multi-scale spatial modeling of immune cell distributions enables survival prediction in primary central nervous system lymphoma |
| title_sort | multi scale spatial modeling of immune cell distributions enables survival prediction in primary central nervous system lymphoma |
| topic | Oncology Neuroscience Cell biology Mathematical biosciences Computational bioinformatics |
| url | http://www.sciencedirect.com/science/article/pii/S2589004223014086 |
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