Abstract

Studying the cellular geographic distribution in non-small cell lung cancer is essential to understand the roles of cell populations in this type of tumor. In this study, we characterize the spatial cellular distribution of immune cell populations using 23 makers placed in five multiplex immunofluorescence panels and their associations with clinicopathologic variables and outcomes. Our results demonstrate two cellular distribution patterns—an unmixed pattern mostly related to immunoprotective cells and a mixed pattern mostly related to immunosuppressive cells. Distance analysis shows that T-cells expressing immune checkpoints are closer to malignant cells than other cells. Combining the cellular distribution patterns with cellular distances, we can identify four groups related to inflamed and not-inflamed tumors. Cellular distribution patterns and distance are associated with survival in univariate and multivariable analyses. Spatial distribution is a tool to better understand the tumor microenvironment, predict outcomes, and may can help select therapeutic interventions.

The spatial distribution of cellular compartments within the tumour microenvironment in non-small cell lung cancer (NSCLC) remains to be investigated. Here, the authors identify distinct cell populations of tumour cells and tumour-associated immune cell phenotypes with different spatial distributions in NSCLC.

Details

Title
Immune cellular patterns of distribution affect outcomes of patients with non-small cell lung cancer
Author
Parra, Edwin Roger 1   VIAFID ORCID Logo  ; Zhang, Jiexin 2   VIAFID ORCID Logo  ; Jiang, Mei 1 ; Tamegnon, Auriole 1 ; Pandurengan, Renganayaki Krishna 1 ; Behrens, Carmen 2 ; Solis, Luisa 1   VIAFID ORCID Logo  ; Haymaker, Cara 1   VIAFID ORCID Logo  ; Heymach, John Victor 3   VIAFID ORCID Logo  ; Moran, Cesar 4   VIAFID ORCID Logo  ; Lee, Jack J. 5   VIAFID ORCID Logo  ; Gibbons, Don 6   VIAFID ORCID Logo  ; Wistuba, Ignacio Ivan 7 

 The University of Texas MD Anderson Cancer Center, Departments of Translational Molecular Pathology, Houston, USA (GRID:grid.240145.6) (ISNI:0000 0001 2291 4776) 
 The University of Texas MD Anderson Cancer Center, Departments of Bioinformatics and Computational Biology, Houston, USA (GRID:grid.240145.6) (ISNI:0000 0001 2291 4776) 
 The University of Texas MD Anderson Cancer Center, Departments of Thoracic/Head and Neck Medical Oncology, Houston, USA (GRID:grid.240145.6) (ISNI:0000 0001 2291 4776) 
 The University of Texas MD Anderson Cancer Center, Departments of Pathology, Houston, USA (GRID:grid.240145.6) (ISNI:0000 0001 2291 4776) 
 The University of Texas MD Anderson Cancer Center, Departments of Biostatistics, Houston, USA (GRID:grid.240145.6) (ISNI:0000 0001 2291 4776) 
 The University of Texas MD Anderson Cancer Center, Departments of Thoracic/Head and Neck Medical Oncology, Houston, USA (GRID:grid.240145.6) (ISNI:0000 0001 2291 4776); The University of Texas MD Anderson Cancer Center, Departments of Molecular and Cellular Oncology, Houston, USA (GRID:grid.240145.6) (ISNI:0000 0001 2291 4776) 
 The University of Texas MD Anderson Cancer Center, Departments of Translational Molecular Pathology, Houston, USA (GRID:grid.240145.6) (ISNI:0000 0001 2291 4776); The University of Texas MD Anderson Cancer Center, Departments of Thoracic/Head and Neck Medical Oncology, Houston, USA (GRID:grid.240145.6) (ISNI:0000 0001 2291 4776) 
Pages
2364
Publication year
2023
Publication date
2023
Publisher
Nature Publishing Group
e-ISSN
20411723
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2805752461
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.