Enhancer landscape of lung neuroendocrine tumors reveals regulatory and developmental signatures with potential theranostic implications.
FGFR signaling
enhancers
epigenomics
neuroendocrine tumors
pulmonary carcinoids
Journal
Proceedings of the National Academy of Sciences of the United States of America
ISSN: 1091-6490
Titre abrégé: Proc Natl Acad Sci U S A
Pays: United States
ID NLM: 7505876
Informations de publication
Date de publication:
08 Oct 2024
08 Oct 2024
Historique:
medline:
4
10
2024
pubmed:
4
10
2024
entrez:
3
10
2024
Statut:
ppublish
Résumé
Well-differentiated low-grade lung neuroendocrine tumors (lung carcinoids or LNETs) are histopathologically classified as typical and atypical LNETs, but each subtype is still heterogeneous at both the molecular level and its clinical manifestation. Here, we report genome-wide profiles of primary LNETs' cis-regulatory elements by H3K27ac ChIP-seq with matching RNA-seq profiles. Analysis of these regulatory landscapes revealed three regulatory subtypes, independent of the typical/atypical classification. We identified unique differentiation signals that delineate each subtype. The "proneuronal" subtype emerges under the influence of ASCL1, SOX4, and TCF4 transcription factors, embodying a pronounced proneuronal signature. The "luminal-like" subtype is characterized by gain of acetylation at markers of luminal cells and GATA2 activation and loss of LRP5 and OTP. The "HNF+" subtype is characterized by a robust enhancer landscape driven by HNF1A, HNF4A, and FOXA3, with notable acetylation and expression of FGF signaling genes, especially FGFR3 and FGFR4, pivotal components of the FGF pathway. Our findings not only deepen the understanding of LNETs' regulatory and developmental diversity but also spotlight the HNF+ subtype's reliance on FGFR signaling. We demonstrate that targeting this pathway with FGF inhibitors curtails tumor growth both in vitro and in xenograft models, unveiling a potential vulnerability and paving the way for targeted therapies. Overall, our work provides an important resource for studying LNETs to reveal regulatory networks, differentiation signals, and therapeutically relevant dependencies.
Identifiants
pubmed: 39361648
doi: 10.1073/pnas.2405001121
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e2405001121Subventions
Organisme : Neuroendocrine Tumor Research Foundation (NETRF)
ID : NA
Organisme : Council for Higher Education (CHE)
ID : NA
Déclaration de conflit d'intérêts
Competing interests statement:The authors declare no competing interest.