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

iScience. 2023 Jul 10;26(8):107331. doi: 10.1016/j.isci.2023.107331. eCollection 2023 Aug 18.

Authors

Affiliations

¹ Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Pathology, Cancer Center Amsterdam, Amsterdam, The Netherlands.
² Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
³ Department of Mathematics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
⁴ HOVON Pathology Facility and Biobank (HOP), Department of Pathology, Amsterdam University Medical Centre, Amsterdam, The Netherlands.
⁵ Department of Hematology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands.
⁶ Department of Neuro-Oncology, Erasmus MC Cancer Institute, Brain Tumor Center, University Medical Center Rotterdam, Rotterdam, The Netherlands.

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.

Keywords: Cell biology; Computational bioinformatics; Mathematical biosciences; Neuroscience; Oncology.