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Single-cell spatial landscapes of the lung tumour immune microenvironment
by
Sorin, Mark
, Rezanejad, Morteza
, Siegel, Peter M.
, Karimi, Elham
, Desmeules, Patrice
, Orain, Michele
, Spicer, Jonathan D.
, Yu, Miranda W.
, Camilleri-Broët, Sophie
, Gagné, Andréanne
, Manem, Venkata S. K.
, Maritan, Sarah M.
, Joubert, Philippe
, Perus, Lucas J. M.
, Milette, Simon
, Fiset, Benoit
, Pichette, Émilie
, Wei, Yuhong
, Doré, Samuel
, Enlow, William
, Rayes, Roni
, Fiset, Pierre Olivier
, Walsh, Logan A.
, Quail, Daniela F.
, Desharnais, Lysanne
in
14/105
/ 631/67/1612/1350
/ 631/67/327
/ Adenocarcinoma
/ Adenocarcinoma of Lung - diagnosis
/ Adenocarcinoma of Lung - immunology
/ Adenocarcinoma of Lung - pathology
/ Adenocarcinoma of Lung - surgery
/ Antigens
/ Artificial intelligence
/ Cancer research
/ Carcinoma, Non-Small-Cell Lung - diagnosis
/ Carcinoma, Non-Small-Cell Lung - immunology
/ Carcinoma, Non-Small-Cell Lung - pathology
/ Carcinoma, Non-Small-Cell Lung - surgery
/ Cells
/ Clinical outcomes
/ Cytometry
/ Deep Learning
/ Disease Progression
/ Humanities and Social Sciences
/ Humans
/ Immunology
/ Lung - pathology
/ Lung - surgery
/ Lung cancer
/ Lung diseases
/ Lung Neoplasms - diagnosis
/ Lung Neoplasms - immunology
/ Lung Neoplasms - pathology
/ Lung Neoplasms - surgery
/ Lymphocytes
/ Microenvironments
/ multidisciplinary
/ Neutrophils
/ Non-small cell lung carcinoma
/ Patients
/ Phenotypes
/ Prognosis
/ Science
/ Science (multidisciplinary)
/ Single-Cell Analysis
/ Small cell lung carcinoma
/ Spatial analysis
/ Spatial discrimination
/ Spatial resolution
/ Subgroups
/ Tumor Microenvironment - immunology
/ Tumors
2023
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Single-cell spatial landscapes of the lung tumour immune microenvironment
by
Sorin, Mark
, Rezanejad, Morteza
, Siegel, Peter M.
, Karimi, Elham
, Desmeules, Patrice
, Orain, Michele
, Spicer, Jonathan D.
, Yu, Miranda W.
, Camilleri-Broët, Sophie
, Gagné, Andréanne
, Manem, Venkata S. K.
, Maritan, Sarah M.
, Joubert, Philippe
, Perus, Lucas J. M.
, Milette, Simon
, Fiset, Benoit
, Pichette, Émilie
, Wei, Yuhong
, Doré, Samuel
, Enlow, William
, Rayes, Roni
, Fiset, Pierre Olivier
, Walsh, Logan A.
, Quail, Daniela F.
, Desharnais, Lysanne
in
14/105
/ 631/67/1612/1350
/ 631/67/327
/ Adenocarcinoma
/ Adenocarcinoma of Lung - diagnosis
/ Adenocarcinoma of Lung - immunology
/ Adenocarcinoma of Lung - pathology
/ Adenocarcinoma of Lung - surgery
/ Antigens
/ Artificial intelligence
/ Cancer research
/ Carcinoma, Non-Small-Cell Lung - diagnosis
/ Carcinoma, Non-Small-Cell Lung - immunology
/ Carcinoma, Non-Small-Cell Lung - pathology
/ Carcinoma, Non-Small-Cell Lung - surgery
/ Cells
/ Clinical outcomes
/ Cytometry
/ Deep Learning
/ Disease Progression
/ Humanities and Social Sciences
/ Humans
/ Immunology
/ Lung - pathology
/ Lung - surgery
/ Lung cancer
/ Lung diseases
/ Lung Neoplasms - diagnosis
/ Lung Neoplasms - immunology
/ Lung Neoplasms - pathology
/ Lung Neoplasms - surgery
/ Lymphocytes
/ Microenvironments
/ multidisciplinary
/ Neutrophils
/ Non-small cell lung carcinoma
/ Patients
/ Phenotypes
/ Prognosis
/ Science
/ Science (multidisciplinary)
/ Single-Cell Analysis
/ Small cell lung carcinoma
/ Spatial analysis
/ Spatial discrimination
/ Spatial resolution
/ Subgroups
/ Tumor Microenvironment - immunology
/ Tumors
2023
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Single-cell spatial landscapes of the lung tumour immune microenvironment
by
Sorin, Mark
, Rezanejad, Morteza
, Siegel, Peter M.
, Karimi, Elham
, Desmeules, Patrice
, Orain, Michele
, Spicer, Jonathan D.
, Yu, Miranda W.
, Camilleri-Broët, Sophie
, Gagné, Andréanne
, Manem, Venkata S. K.
, Maritan, Sarah M.
, Joubert, Philippe
, Perus, Lucas J. M.
, Milette, Simon
, Fiset, Benoit
, Pichette, Émilie
, Wei, Yuhong
, Doré, Samuel
, Enlow, William
, Rayes, Roni
, Fiset, Pierre Olivier
, Walsh, Logan A.
, Quail, Daniela F.
, Desharnais, Lysanne
in
14/105
/ 631/67/1612/1350
/ 631/67/327
/ Adenocarcinoma
/ Adenocarcinoma of Lung - diagnosis
/ Adenocarcinoma of Lung - immunology
/ Adenocarcinoma of Lung - pathology
/ Adenocarcinoma of Lung - surgery
/ Antigens
/ Artificial intelligence
/ Cancer research
/ Carcinoma, Non-Small-Cell Lung - diagnosis
/ Carcinoma, Non-Small-Cell Lung - immunology
/ Carcinoma, Non-Small-Cell Lung - pathology
/ Carcinoma, Non-Small-Cell Lung - surgery
/ Cells
/ Clinical outcomes
/ Cytometry
/ Deep Learning
/ Disease Progression
/ Humanities and Social Sciences
/ Humans
/ Immunology
/ Lung - pathology
/ Lung - surgery
/ Lung cancer
/ Lung diseases
/ Lung Neoplasms - diagnosis
/ Lung Neoplasms - immunology
/ Lung Neoplasms - pathology
/ Lung Neoplasms - surgery
/ Lymphocytes
/ Microenvironments
/ multidisciplinary
/ Neutrophils
/ Non-small cell lung carcinoma
/ Patients
/ Phenotypes
/ Prognosis
/ Science
/ Science (multidisciplinary)
/ Single-Cell Analysis
/ Small cell lung carcinoma
/ Spatial analysis
/ Spatial discrimination
/ Spatial resolution
/ Subgroups
/ Tumor Microenvironment - immunology
/ Tumors
2023
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Single-cell spatial landscapes of the lung tumour immune microenvironment
Journal Article
Single-cell spatial landscapes of the lung tumour immune microenvironment
2023
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Overview
Single-cell technologies have revealed the complexity of the tumour immune microenvironment with unparalleled resolution
1
–
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-mm
2
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.
Using imaging mass cytometry, the tumour and immunological spatial landscapes of 416 lung adenocarcinomas are characterized, which, when combined with deep learning, can predict clinical outcomes with high accuracy.
Publisher
Nature Publishing Group UK,Nature Publishing Group
Subject
/ Adenocarcinoma of Lung - diagnosis
/ Adenocarcinoma of Lung - immunology
/ Adenocarcinoma of Lung - pathology
/ Adenocarcinoma of Lung - surgery
/ Antigens
/ Carcinoma, Non-Small-Cell Lung - diagnosis
/ Carcinoma, Non-Small-Cell Lung - immunology
/ Carcinoma, Non-Small-Cell Lung - pathology
/ Carcinoma, Non-Small-Cell Lung - surgery
/ Cells
/ Humanities and Social Sciences
/ Humans
/ Non-small cell lung carcinoma
/ Patients
/ Science
/ Tumor Microenvironment - immunology
/ Tumors
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