Single-cell spatial landscapes of the lung tumour immune microenvironment

被引:0
作者
Mark Sorin
Morteza Rezanejad
Elham Karimi
Benoit Fiset
Lysanne Desharnais
Lucas J. M. Perus
Simon Milette
Miranda W. Yu
Sarah M. Maritan
Samuel Doré
Émilie Pichette
William Enlow
Andréanne Gagné
Yuhong Wei
Michele Orain
Venkata S. K. Manem
Roni Rayes
Peter M. Siegel
Sophie Camilleri-Broët
Pierre Olivier Fiset
Patrice Desmeules
Jonathan D. Spicer
Daniela F. Quail
Philippe Joubert
Logan A. Walsh
机构
[1] McGill University,Rosalind and Morris Goodman Cancer Institute
[2] McGill University,Department of Human Genetics
[3] University of Toronto,Department of Psychology
[4] University of Toronto,Department of Computer Science
[5] McGill University,Department of Physiology, Faculty of Medicine
[6] McGill University,Department of Medicine, Division of Experimental Medicine
[7] McGill University,Faculty of Medicine and Health Sciences
[8] Institut Universitaire de Cardiologie et de Pneumologie de Québec,Department of Mathematics and Computer Science
[9] Laval University,Department of Biochemistry, Faculty of Medicine
[10] University of Quebec at Trois-Rivières,Department of Pathology
[11] McGill University,Department of Surgery
[12] McGill University,undefined
[13] McGill University Health Center,undefined
来源
Nature | 2023年 / 614卷
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学科分类号
摘要
Single-cell technologies have revealed the complexity of the tumour immune microenvironment with unparalleled resolution1–9. Most clinical strategies rely on histopathological stratification of tumour subtypes, yet the spatial context of single-cell phenotypes within these stratified subgroups is poorly understood. Here we apply imaging mass cytometry to characterize the tumour and immunological landscape of samples from 416 patients with lung adenocarcinoma across five histological patterns. We resolve more than 1.6 million cells, enabling spatial analysis of immune lineages and activation states with distinct clinical correlates, including survival. Using deep learning, we can predict with high accuracy those patients who will progress after surgery using a single 1-mm2 tumour core, which could be informative for clinical management following surgical resection. Our dataset represents a valuable resource for the non-small cell lung cancer research community and exemplifies the utility of spatial resolution within single-cell analyses. This study also highlights how artificial intelligence can improve our understanding of microenvironmental features that underlie cancer progression and may influence future clinical practice.
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页码:548 / 554
页数:6
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