AbdulJabbar, K. orcid.org/0000-0002-4411-4435, Raza, S.E.A. orcid.org/0000-0002-1097-1738, Rosenthal, R. et al. (20 more authors) (2020) Geospatial immune variability illuminates differential evolution of lung adenocarcinoma. Nature Medicine, 26 (7). pp. 1054-1062. ISSN 1078-8956
Abstract
Remarkable progress in molecular analyses has improved our understanding of the evolution of cancer cells toward immune escape1–5. However, the spatial configurations of immune and stromal cells, which may shed light on the evolution of immune escape across tumor geographical locations, remain unaddressed. We integrated multiregion exome and RNA-sequencing (RNA-seq) data with spatial histology mapped by deep learning in 100 patients with non-small cell lung cancer from the TRACERx cohort6. Cancer subclones derived from immune cold regions were more closely related in mutation space, diversifying more recently than subclones from immune hot regions. In TRACERx and in an independent multisample cohort of 970 patients with lung adenocarcinoma, tumors with more than one immune cold region had a higher risk of relapse, independently of tumor size, stage and number of samples per patient. In lung adenocarcinoma, but not lung squamous cell carcinoma, geometrical irregularity and complexity of the cancer–stromal cell interface significantly increased in tumor regions without disruption of antigen presentation. Decreased lymphocyte accumulation in adjacent stroma was observed in tumors with low clonal neoantigen burden. Collectively, immune geospatial variability elucidates tumor ecological constraints that may shape the emergence of immune-evading subclones and aggressive clinical phenotypes.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 The author(s). This is an author-produced version of a paper subsequently published in Nature Medicine. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Adenocarcinoma of Lung; Antigens, Neoplasm; Biomarkers, Tumor; Carcinoma, Non-Small-Cell Lung; Deep Learning; Exome; Female; Humans; Male; Middle Aged; Mutation; Neoplasm Recurrence, Local; Neoplasm Staging; RNA-Seq; Recurrence; Tumor Microenvironment; Exome Sequencing |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > The Medical School (Sheffield) > Division of Genomic Medicine (Sheffield) > Department of Oncology and Metabolism (Sheffield) The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Medicine and Population Health The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 22 May 2024 11:10 |
Last Modified: | 22 May 2024 11:10 |
Status: | Published |
Publisher: | Springer Science and Business Media LLC |
Refereed: | Yes |
Identification Number: | 10.1038/s41591-020-0900-x |
Related URLs: | |
Sustainable Development Goals: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:212692 |