Phenotype molding of stromal cells in the lung tumor microenvironment
Abstract
Cancer cells are embedded in the tumor microenvironment (TME), a complex ecosystem of stromal cells. Here, we present a 52,698-cell catalog of the TME transcriptome in human lung tumors at single-cell resolution, validated in independent samples where 40,250 additional cells were sequenced. By comparing with matching non-malignant lung samples, we reveal a highly complex TME that profoundly molds stromal cells. We identify 52 stromal cell subtypes, including novel subpopulations in cell types hitherto considered to be homogeneous, as well as transcription factors underlying their heterogeneity. For instance, we discover fibroblasts expressing different collagen sets, endothelial cells downregulating immune cell homing and genes coregulated with established immune checkpoint transcripts and correlating with T-cell activity. By assessing marker genes for these cell subtypes in bulk RNA-sequencing data from 1,572 patients, we illustrate how these correlate with survival, while immunohistochemistry for selected markers validates them as separate cellular entities in an independent series of lung tumors. Hence, in providing a comprehensive catalog of stromal cells types and by characterizing their phenotype and co-optive behavior, this resource provides deeper insights into lung cancer biology that will be helpful in advancing lung cancer diagnosis and therapy.
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There is a recurrent observation of distinct CAFs exhibiting either a matrix-producing contractile phenotype or an immunomodulating secretome, often termed myoCAFs and iCAFs.
“Nonetheless, there is a recurrent observation of distinct CAFs exhibiting either a matrix-producing contractile phenotype or an immunomodulating secretome — often termed ‘myoCAFs’ and ‘iCAFs’, with the prefixes alluding to a myofibroblast phenotype and regulation of inflammation, respectively.”
From A framework for advancing our understanding of cancer-associated fibroblasts (2020) · cites this paper as [133]
Conventional bulk RNA sequencing methods process millions of cells to average out underlying differences, whereas scRNA-seq can reveal changes in individual cell types.
“Although conventional bulk RNA sequencing methods process millions of cells to average out underlying differences, scRNA-seq can reveal changes in individual cell types. 5”
From Single-Cell RNA Sequencing to Dissect the Immunological Network of Autoimmune Myocarditis. (2020) · cites this paper as [5]
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- Authors
- Lambrechts, D., Wauters, E., Boeckx, B., Aibar, S., Nittner, D., Burton, O., Bassez, A., Decaluwé, H., Pircher, A., Van den Eynde, K., Weynand, B., Verbeken, E., De Leyn, P., Liston, A., Vansteenkiste, J., Carmeliet, P., Aerts, S., & Thienpont, B.
- Affiliations
- VIB-KU Leuven Center for Cancer Biology (BE)
- KU Leuven (BE)
- VIB-KU Leuven Center for Brain & Disease Research (BE)
- Sun Yat-sen University (CN)
- Corresponding author
- Lambrechts, Diether
- Thienpont, Bernard
- ORCID
- Venue
- Nature Medicine
- Published
- 2018-07-09
- Volume
- 24
- Issue
- 8
- Pages
- 1277-1289
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- OpenAlex
- W2865352686
- Cited by
- 1,648
- References
- 11
- Citations by year
- 2026: 125 · 2025: 172 · 2024: 238 · 2023: 242 · 2022: 296 · 2021: 247 · 2020: 204 · 2019: 100 · 2018: 21
- FWCI
- 51.04
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Nature Medicine (publishedVersion)
- PubMed (publishedVersion)
- Lirias (submittedVersion)
- Funders
- Vlaamse regering
- Fondation contre le Cancer
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
- Health Sciences › Medicine › Oncology › Cancer Cells and Metastasis
- Keywords
- lung cancer, tumor microenvironment, stromal cells, single-cell transcriptome, fibroblasts, collagen expression, endothelial cells, immune checkpoint genes, T cell function, immune cell homing, transcription factors, prognostic factors
- MeSH terms
- B-Lymphocytes, Fibroblasts, Humans, Lung, Lung Neoplasms, Neoplasms, Phenotype, T-Lymphocytes, Biomarkers, Tumor, Down-Regulation, Survival Analysis, Stromal Cells, Sequence Analysis, RNA, Myeloid Cells, Endothelial Cells, Single-Cell Analysis, Tumor Microenvironment
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