Multi-scale spatial modeling of immune cell distributions enables survival prediction in primary central nervous system lymphoma

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

doi:10.1016/j.isci.2023.107331

Multi-scale spatial modeling of immune cell distributions enables survival prediction in primary central nervous system lymphoma

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^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

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Abstract

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.

Original language	English
Article number	107331
Journal	iScience
Volume	26
Issue number	8
DOIs	https://doi.org/10.1016/j.isci.2023.107331
Publication status	Published - 18 Aug 2023

Bibliographical note

Funding Information:
The project was supported by Dutch cancer society (KWF) grant KWF VU 2015-7925 and Leukemia Research Foundation, Hollis Brownstein Research grant. We acknowledge the Microscopy and Cytometry Core Facility at the Amsterdam UMC - location VUmc for providing assistance with the multiplex IHC / Vectra Polaris experiments and analyses. We also acknowledge the clinical co-principle investigator of the HOVON105/ALLG NHL 24 study Samar Issa (Department of Hematology, Middlemore Hospital, Auckland, New Zealand). We thank the collaborators at the HOVON Data Center, specifically Katerina Bakunina (HOVON Data Center, Department of Hematology, Erasmus MC Cancer Institute, Rotterdam, Netherlands) for providing excellent clinical data and support. We also would like to acknowledge Eric J. Meershoek and Danielle Hoogmoed of Leica Biosystems, Amsterdam, the Netherlands, for their invaluable contribution to the development of the 9p24.1/PD-L1/PD-L2 FISH assay and support in obtaining FISH data for this study. Conceptulization, M.G.M.R. D.dJ. and Y.K.; Methodolgy, T.vdB. E.B. M.vdW. and Y.K.; Software, T.vdB. E.B. and Y.K.; Investigation, D.B. N.H. P.S. and M.G.M.R.; Data collection: D.B. N.H. P.S. K.W. M.G.M.R.; Clinical data collection: J.D. J.B.; Writing-Original Draft, M.G.M.R. B.Y. D.dJ. and Y.K.; Writing - Review & Editing, Y.K. and D.dJ.; Resources, K.W. J.D. J.B. B.Y.; Supervision, M.vdW. Y.K. and D.dJ. The authors have no conflicts of interest.

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© 2023 The Authors

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Roemer, M. G. M., van de Brug, T., Bosch, E., Berry, D., Hijmering, N., Stathi, P., Weijers, K., Doorduijn, J., Bromberg, J., van de Wiel, M., Ylstra, B., de Jong, D., & Kim, Y. (2023). Multi-scale spatial modeling of immune cell distributions enables survival prediction in primary central nervous system lymphoma. iScience, 26(8), Article 107331. https://doi.org/10.1016/j.isci.2023.107331

@article{6a0656e123d6478c90c7d4515eae709f,

title = "Multi-scale spatial modeling of immune cell distributions enables survival prediction in primary central nervous system lymphoma",

abstract = "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.",

author = "Roemer, {Margaretha G.M.} and {van de Brug}, Tim and Erik Bosch and Daniella Berry and Nathalie Hijmering and Phylicia Stathi and Karin Weijers and Jeannette Doorduijn and Jacoline Bromberg and {van de Wiel}, Mark and Bauke Ylstra and {de Jong}, Daphne and Yongsoo Kim",

note = "Funding Information: The project was supported by Dutch cancer society (KWF) grant KWF VU 2015-7925 and Leukemia Research Foundation, Hollis Brownstein Research grant. We acknowledge the Microscopy and Cytometry Core Facility at the Amsterdam UMC - location VUmc for providing assistance with the multiplex IHC / Vectra Polaris experiments and analyses. We also acknowledge the clinical co-principle investigator of the HOVON105/ALLG NHL 24 study Samar Issa (Department of Hematology, Middlemore Hospital, Auckland, New Zealand). We thank the collaborators at the HOVON Data Center, specifically Katerina Bakunina (HOVON Data Center, Department of Hematology, Erasmus MC Cancer Institute, Rotterdam, Netherlands) for providing excellent clinical data and support. We also would like to acknowledge Eric J. Meershoek and Danielle Hoogmoed of Leica Biosystems, Amsterdam, the Netherlands, for their invaluable contribution to the development of the 9p24.1/PD-L1/PD-L2 FISH assay and support in obtaining FISH data for this study. Conceptulization, M.G.M.R. D.dJ. and Y.K.; Methodolgy, T.vdB. E.B. M.vdW. and Y.K.; Software, T.vdB. E.B. and Y.K.; Investigation, D.B. N.H. P.S. and M.G.M.R.; Data collection: D.B. N.H. P.S. K.W. M.G.M.R.; Clinical data collection: J.D. J.B.; Writing-Original Draft, M.G.M.R. B.Y. D.dJ. and Y.K.; Writing - Review & Editing, Y.K. and D.dJ.; Resources, K.W. J.D. J.B. B.Y.; Supervision, M.vdW. Y.K. and D.dJ. The authors have no conflicts of interest. Publisher Copyright: {\textcopyright} 2023 The Authors",

year = "2023",

month = aug,

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doi = "10.1016/j.isci.2023.107331",

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Roemer, MGM, van de Brug, T, Bosch, E, Berry, D, Hijmering, N, Stathi, P, Weijers, K, Doorduijn, J , Bromberg, J, van de Wiel, M, Ylstra, B, de Jong, D & Kim, Y 2023, 'Multi-scale spatial modeling of immune cell distributions enables survival prediction in primary central nervous system lymphoma', iScience, vol. 26, no. 8, 107331. https://doi.org/10.1016/j.isci.2023.107331

TY - JOUR

T1 - Multi-scale spatial modeling of immune cell distributions enables survival prediction in primary central nervous system lymphoma

AU - Roemer, Margaretha G.M.

AU - van de Brug, Tim

AU - Bosch, Erik

AU - Berry, Daniella

AU - Hijmering, Nathalie

AU - Stathi, Phylicia

AU - Weijers, Karin

AU - Doorduijn, Jeannette

AU - Bromberg, Jacoline

AU - van de Wiel, Mark

AU - Ylstra, Bauke

AU - de Jong, Daphne

AU - Kim, Yongsoo

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PY - 2023/8/18

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N2 - 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.

AB - 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.

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DO - 10.1016/j.isci.2023.107331

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Multi-scale spatial modeling of immune cell distributions enables survival prediction in primary central nervous system lymphoma

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