Fibroblast subsets in non-small cell lung cancer : associations with survival, mutations, and immune features
(2023) In Journal of the National Cancer Institute 115(1). p.71-82- Abstract
BACKGROUND: Cancer-associated fibroblasts (CAFs) are molecularly heterogeneous mesenchymal cells that interact with malignant cells and immune cells and confer both anti- and pro-tumorigenic functions. Prior in situ profiling studies of human CAFs have largely relied on scoring single markers, thus presenting a very limited view of their molecular complexity. Our objective was to study the complex spatial tumor microenvironment of non-small cell lung cancer (NSCLC) with multiple CAF biomarkers, identify novel CAF subsets and explore their associations with patient outcome.
METHODS: Multiplex fluorescence immunohistochemistry (mfIHC) was employed to spatially profile the CAF landscape in two population-based NSCLC cohorts (n = 636)... (More)
BACKGROUND: Cancer-associated fibroblasts (CAFs) are molecularly heterogeneous mesenchymal cells that interact with malignant cells and immune cells and confer both anti- and pro-tumorigenic functions. Prior in situ profiling studies of human CAFs have largely relied on scoring single markers, thus presenting a very limited view of their molecular complexity. Our objective was to study the complex spatial tumor microenvironment of non-small cell lung cancer (NSCLC) with multiple CAF biomarkers, identify novel CAF subsets and explore their associations with patient outcome.
METHODS: Multiplex fluorescence immunohistochemistry (mfIHC) was employed to spatially profile the CAF landscape in two population-based NSCLC cohorts (n = 636) using antibodies against four fibroblast markers: Platelet-derived growth factor receptor-alpha (PDGFRA) and -beta (PDGFRB), fibroblast activation protein (FAP), and alpha-smooth muscle actin (αSMA). The CAF subsets were analyzed for their correlations with mutations, immune characteristics, clinical variables as well as overall survival (OS).
RESULTS: Two CAF subsets, CAF7 (PDGFRA-/PDGFRB+/FAP+/αSMA+) and CAF13 (PDGFRA+/PDGFRB+/FAP-/αSMA+), showed significant but opposite associations with tumor histology, driver mutations (TP53 and EGFR), immune features (PD-L1 and CD163), and prognosis. In patients with early-stage tumors (pTNM IA-IB), CAF7 and CAF13 acted as independent prognostic factors.
CONCLUSIONS: Multi-marker-defined CAF subsets were identified through high-content spatial profiling. The robust associations of CAFs with driver mutations, immune features, and outcome suggest CAFs as essential factors in NSCLC progression and warrant further studies to explore their potential as biomarkers or therapeutic targets. This study also highlights mfIHC-based CAF profiling as a powerful tool for the discovery of clinically relevant CAF subsets.
(Less)
- author
- organization
- publishing date
- 2023
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Journal of the National Cancer Institute
- volume
- 115
- issue
- 1
- pages
- 71 - 82
- publisher
- Oxford University Press
- external identifiers
-
- pmid:36083003
- scopus:85151506005
- ISSN
- 1460-2105
- DOI
- 10.1093/jnci/djac178
- language
- English
- LU publication?
- yes
- additional info
- © The Author(s) 2022. Published by Oxford University Press.
- id
- 31378719-aa04-4dbf-8eab-214a9beffdc2
- date added to LUP
- 2022-10-19 16:13:23
- date last changed
- 2024-09-07 09:08:26
@article{31378719-aa04-4dbf-8eab-214a9beffdc2, abstract = {{<p>BACKGROUND: Cancer-associated fibroblasts (CAFs) are molecularly heterogeneous mesenchymal cells that interact with malignant cells and immune cells and confer both anti- and pro-tumorigenic functions. Prior in situ profiling studies of human CAFs have largely relied on scoring single markers, thus presenting a very limited view of their molecular complexity. Our objective was to study the complex spatial tumor microenvironment of non-small cell lung cancer (NSCLC) with multiple CAF biomarkers, identify novel CAF subsets and explore their associations with patient outcome.</p><p>METHODS: Multiplex fluorescence immunohistochemistry (mfIHC) was employed to spatially profile the CAF landscape in two population-based NSCLC cohorts (n = 636) using antibodies against four fibroblast markers: Platelet-derived growth factor receptor-alpha (PDGFRA) and -beta (PDGFRB), fibroblast activation protein (FAP), and alpha-smooth muscle actin (αSMA). The CAF subsets were analyzed for their correlations with mutations, immune characteristics, clinical variables as well as overall survival (OS).</p><p>RESULTS: Two CAF subsets, CAF7 (PDGFRA-/PDGFRB+/FAP+/αSMA+) and CAF13 (PDGFRA+/PDGFRB+/FAP-/αSMA+), showed significant but opposite associations with tumor histology, driver mutations (TP53 and EGFR), immune features (PD-L1 and CD163), and prognosis. In patients with early-stage tumors (pTNM IA-IB), CAF7 and CAF13 acted as independent prognostic factors.</p><p>CONCLUSIONS: Multi-marker-defined CAF subsets were identified through high-content spatial profiling. The robust associations of CAFs with driver mutations, immune features, and outcome suggest CAFs as essential factors in NSCLC progression and warrant further studies to explore their potential as biomarkers or therapeutic targets. This study also highlights mfIHC-based CAF profiling as a powerful tool for the discovery of clinically relevant CAF subsets.</p>}}, author = {{Pellinen, Teijo and Paavolainen, Lassi and Martín-Bernabé, Alfonso and Papatella Araujo, Renata and Strell, Carina and Mezheyeuski, Artur and Backman, Max and La Fleur, Linnea and Brück, Oscar and Sjölund, Jonas and Holmberg, Erik and Välimäki, Katja and Brunnström, Hans and Botling, Johan and Moreno-Ruiz, Pablo and Kallioniemi, Olli and Micke, Patrick and Östman, Arne}}, issn = {{1460-2105}}, language = {{eng}}, number = {{1}}, pages = {{71--82}}, publisher = {{Oxford University Press}}, series = {{Journal of the National Cancer Institute}}, title = {{Fibroblast subsets in non-small cell lung cancer : associations with survival, mutations, and immune features}}, url = {{http://dx.doi.org/10.1093/jnci/djac178}}, doi = {{10.1093/jnci/djac178}}, volume = {{115}}, year = {{2023}}, }