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Computational immune synapse analysis reveals T-cell interactions in distinct tumor microenvironments.

Authors :
Wang, Victor G.
Liu, Zichao
Martinek, Jan
Foroughi pour, Ali
Zhou, Jie
Boruchov, Hannah
Ray, Kelly
Palucka, Karolina
Chuang, Jeffrey H.
Source :
Communications Biology. 9/28/2024, Vol. 7 Issue 1, p1-17. 17p.
Publication Year :
2024

Abstract

The tumor microenvironment (TME) and the cellular interactions within it can be critical to tumor progression and treatment response. Although technologies to generate multiplex images of the TME are advancing, the many ways in which TME imaging data can be mined to elucidate cellular interactions are only beginning to be realized. Here, we present a novel approach for multipronged computational immune synapse analysis (CISA) that reveals T-cell synaptic interactions from multiplex images. CISA enables automated discovery and quantification of immune synapse interactions based on the localization of proteins on cell membranes. We first demonstrate the ability of CISA to detect T-cell:APC (antigen presenting cell) synaptic interactions in two independent human melanoma imaging mass cytometry (IMC) tissue microarray datasets. We then verify CISA's applicability across data modalities with melanoma histocytometry whole slide images, revealing that T-cell:macrophage synapse formation correlates with T-cell proliferation. We next show the generality of CISA by extending it to breast cancer IMC images, finding that CISA quantifications of T-cell:B-cell synapses are predictive of improved patient survival. Our work demonstrates the biological and clinical significance of spatially resolving cell-cell synaptic interactions in the TME and provides a robust method to do so across imaging modalities and cancer types. Computational immune synapse analysis reveals T-cell synaptic interactions in tumor micro-environments using multiplex spatial protein images. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
23993642
Volume :
7
Issue :
1
Database :
Academic Search Index
Journal :
Communications Biology
Publication Type :
Academic Journal
Accession number :
179970327
Full Text :
https://doi.org/10.1038/s42003-024-06902-2