Single-cell spatial landscapes of the lung tumour immune microenvironment

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 () and Logan A. Walsh ()
Additional contact information
Mark Sorin: McGill University
Morteza Rezanejad: University of Toronto
Elham Karimi: McGill University
Benoit Fiset: McGill University
Lysanne Desharnais: McGill University
Lucas J. M. Perus: McGill University
Simon Milette: McGill University
Miranda W. Yu: McGill University
Sarah M. Maritan: McGill University
Samuel Doré: McGill University
Émilie Pichette: McGill University
William Enlow: Institut Universitaire de Cardiologie et de Pneumologie de Québec, Laval University
Andréanne Gagné: Institut Universitaire de Cardiologie et de Pneumologie de Québec, Laval University
Yuhong Wei: McGill University
Michele Orain: Institut Universitaire de Cardiologie et de Pneumologie de Québec, Laval University
Venkata S. K. Manem: Institut Universitaire de Cardiologie et de Pneumologie de Québec, Laval University
Roni Rayes: McGill University
Peter M. Siegel: McGill University
Sophie Camilleri-Broët: McGill University
Pierre Olivier Fiset: McGill University
Patrice Desmeules: Institut Universitaire de Cardiologie et de Pneumologie de Québec, Laval University
Jonathan D. Spicer: McGill University
Daniela F. Quail: McGill University
Philippe Joubert: Institut Universitaire de Cardiologie et de Pneumologie de Québec, Laval University
Logan A. Walsh: McGill University

Nature, 2023, vol. 614, issue 7948, 548-554

Abstract: Abstract 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.

Date: 2023
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DOI: 10.1038/s41586-022-05672-3

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