Deep molecular profiling of lung neuroendocrine tumours and supra-carcinoids

BACKGROUND: Lung neuroendocrine tumours (NETs, also known as carcinoids) are rapidly rising in incidence worldwide but have unknown aetiology and limited therapeutic options beyond surgery. The current WHO classification, based on mitotic count and presence or absence of necrosis, divides lung NETs into grade-1 typical, and grade-2 atypical tumours. This dichotomous classification however does not account for recently described molecular entities nor is it sufficient for clinical management. METHODS: Here we conducted integrative multi-omic analyses on over 300 lung NETs including whole-genome sequencing, transcriptome profiling, and DNA methylation arrays, followed by archetype analysis, to identify and characterise molecular groups. We further investigated molecular groups using spatial RNA sequencing and proteomics, and deep learning analysis of whole slide images. RESULTS: The integration of multi-omic data provided definitive proof of the existence of four strikingly different molecular groups that vary in patient characteristics, genomic and transcriptomic profiles, microenvironment, and morphology. Among these, we identified a new molecular group, enriched for highly aggressive supra-carcinoids that displayed an immune-rich microenvironment linked to tumour-macrophage crosstalk. We uncovered an undifferentiated cell population within supra-carcinoids and show the transcriptomic similarities between supra-carcinoids and the recently identified atypical small cell lung cancer tumours, further demonstrating their molecular link to high-grade lung neuroendocrine carcinomas. Multi-regional genomic analyses identified distinct evolutionary trajectories, suggesting that molecular groups are determined early in tumourigenesis by genomic events, and that transitions between groups, though infrequent, are possible for supra-carcinoids. Deep learning models accurately identified these groups based on morphology alone, outperforming current histological criteria. Together with the validation of a panel of immunohistochemistry markers, we demonstrated that these molecular groups can be accurately identified based on morphological features, facilitating their future implementation in the clinical setting. Our proposed morpho-molecular classification highlights potential group-specific therapeutic opportunities, with differences in expression to DLL3, EGFR, FGFR and TERT inhibitor targets. CONCLUSIONS: Overall, our findings unify previously proposed molecular classifications and refine the lung cancer map by revealing novel tumour phenotypes with potential implications for prognosis and therapeutic management.

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Journal
Molecular Cancer
Published
2026-08-27
DOI
https://doi.org/10.1186/s12943-026-02721-7
Primary Topic
Neuroendocrine Tumor Research Advances
Type
article
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article

Deep molecular profiling of lung neuroendocrine tumours and supra-carcinoids

A. Di Genova, Francesca Damiola, Massimo Milione, Cyril Dégletagne et al.
Molecular Cancer
Neuroendocrine Tumor Research Advances
article

Deep molecular profiling of lung neuroendocrine tumours and supra-carcinoids

A. Di Genova, Francesca Damiola, Massimo Milione, Cyril Dégletagne, Anne‐Marie C. Dingemans, Vincent Cahais, A. Ghantous, G. Planchard, Christophe Caux, Stéphanie Lacomme, Cyrille Cuenin, Lucia Anna Muscarella, Jean-Michel Vignaud, Janine Altmüller, Paolo Graziano, Angelo Sparaneo, Catherine Voegele, Amélie Chabrier, Alejandro Ibáñez‐Costa, Giuseppe Pelosi, Odd Terje Brustugun, Núria López-Bigas, Lise Mangiante, Giovanni Centonze, Luca Roz, Nicolas Gadot, Gudrun Absenger, José van den Berg, E Lauricella, Marius Lund‐Iversen, Hofman, Matthieu Foll, Maike L. Morrison, Lipika Kalson, Laurane Mangé, Noah Candeli, G Wright, Corinne Perrin, Tijmen van Weert, Lisa Bonheme, Jules Derks, E. J. M. Speel, Vincent Thomas de Montpreville, Jaehee Kim, Laura Moonen, Lisa M. Hillen, Talya Dayton, Yuliya Lim, Justo P. Castaño, Alexandra Sexton-Oates, Paul Hofman, Thomas Walter, Marco Volante, Mauro Giulio Papotti, Séverine Tabone-Eglinger, Sergio Pedraza-Arevalo, Liming Chen, Lara Chalabreysse, Zhaozhi Li, Luka Brcic, Audrey Mansuet-Lupo, Nicolas Girard, Jean-François Deleuze, Nicolas Alcala, Tiffany M. Delhomme, Élie Fadel, Esther Conde, Wieneke Buikhuisen, Lynnette Fernandez-Cuesta, Émilie Mathian, Abel Gonzalez-Perez, Ricardo Blázquez-Encinas, Helmut Popper, Margot Tesselaar, Sylvie Lantuejoul
article en

Abstract

BACKGROUND: Lung neuroendocrine tumours (NETs, also known as carcinoids) are rapidly rising in incidence worldwide but have unknown aetiology and limited therapeutic options beyond surgery. The current WHO classification, based on mitotic count and presence or absence of necrosis, divides lung NETs into grade-1 typical, and grade-2 atypical tumours. This dichotomous classification however does not account for recently described molecular entities nor is it sufficient for clinical management. METHODS: Here we conducted integrative multi-omic analyses on over 300 lung NETs including whole-genome sequencing, transcriptome profiling, and DNA methylation arrays, followed by archetype analysis, to identify and characterise molecular groups. We further investigated molecular groups using spatial RNA sequencing and proteomics, and deep learning analysis of whole slide images. RESULTS: The integration of multi-omic data provided definitive proof of the existence of four strikingly different molecular groups that vary in patient characteristics, genomic and transcriptomic profiles, microenvironment, and morphology. Among these, we identified a new molecular group, enriched for highly aggressive supra-carcinoids that displayed an immune-rich microenvironment linked to tumour-macrophage crosstalk. We uncovered an undifferentiated cell population within supra-carcinoids and show the transcriptomic similarities between supra-carcinoids and the recently identified atypical small cell lung cancer tumours, further demonstrating their molecular link to high-grade lung neuroendocrine carcinomas. Multi-regional genomic analyses identified distinct evolutionary trajectories, suggesting that molecular groups are determined early in tumourigenesis by genomic events, and that transitions between groups, though infrequent, are possible for supra-carcinoids. Deep learning models accurately identified these groups based on morphology alone, outperforming current histological criteria. Together with the validation of a panel of immunohistochemistry markers, we demonstrated that these molecular groups can be accurately identified based on morphological features, facilitating their future implementation in the clinical setting. Our proposed morpho-molecular classification highlights potential group-specific therapeutic opportunities, with differences in expression to DLL3, EGFR, FGFR and TERT inhibitor targets. CONCLUSIONS: Overall, our findings unify previously proposed molecular classifications and refine the lung cancer map by revealing novel tumour phenotypes with potential implications for prognosis and therapeutic management.

Molecular CancerVol. 25(1)
Université Claude Bernard Lyon 1 (FR), École Centrale de Lyon (FR), Institució Catalana de Recerca i Estudis Avançats (ES), Oslo University Hospital (NO), European Organisation for Rare Diseases (FR), Centre National de la Recherche Scientifique (FR), Santa Fe Institute (US), Inserm (FR), The University of Melbourne (AU), Universitat Pompeu Fabra (ES), University of Oslo (NO), Institut Universitaire de France (FR), University of Milan (IT), Université de Versailles Saint-Quentin-en-Yvelines (FR), Medical University of Graz (AT), Max Delbrück Center (DE), Cornell University (US), Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR), Université Paris-Saclay (FR), Casa Sollievo della Sofferenza (IT), Maastricht University Medical Centre (NL), Drammen Hospital (NO), Vestre Viken Hospital Trust (NO), Instituto de Salud Carlos III (ES), Spanish Biomedical Research Centre in Physiopathology of Obesity and Nutrition (ES), Erasmus MC (NL), The Netherlands Cancer Institute (NL), Centre National de Recherche en Génomique Humaine (FR), Research Institute Hospital 12 de Octubre (ES), St Vincent's Hospital Melbourne (AU), Ludwig Boltzmann Institute for Lung Vascular Research (AT), Hôpital Cochin (FR), Instituto Maimónides de Investigación Biomédica de Córdoba (ES), Graz University Hospital (AT), European Molecular Biology Laboratory (ES), Assistance Publique – Hôpitaux de Paris (FR), Centre Hospitalier Régional et Universitaire de Nancy (FR), Hospices Civils de Lyon (FR), Hospital Universitario 12 De Octubre (ES), Centre Léon Bérard (FR), Hôpital Marie Lannelongue (FR), Institute for Research in Biomedicine (ES), Hospital Universitario Reina Sofía (ES), Centre de Recherche en Cancérologie de Lyon (FR), CEA Paris-Saclay (FR), Hôpital Paris Saint-Joseph (FR), Berlin Institute of Health at Charité - Universitätsmedizin Berlin (DE), Bases, Corpus, Langage (FR), University of O'Higgins (CL), Erasmus MC Cancer Institute (NL), Miguel de Cervantes University (CL), Fondazione IRCCS Istituto Nazionale dei Tumori (IT), Centro de Investigación Biomédica en Red de Cáncer (ES), Centre international de recherche sur le cancer (FR), Center for Mathematical Modeling (CL), University of Córdoba (ES), University of Turin (IT), University of Bari Aldo Moro (IT), Medical University of Vienna (AT), Institut Curie (FR), Sapienza University of Rome (IT), Université Grenoble Alpes (FR), Stanford University (US), Université de Caen Normandie (FR)
Neuroendocrine Tumor Research Foundation, Università degli Studi di Milano, Agence Nationale de la Recherche, Institut National de la Santé et de la Recherche Médicale, Ministero della Salute, Ligue Contre le Cancer, KWF Kankerbestrijding, Ministerio de Ciencia e Innovación, Associazione Italiana per la Ricerca sul Cancro, Institut National Du Cancer, Hospices Civils de Lyon, Università degli Studi di Torino, Worldwide Cancer Research, Karl-Franzens-Universität Graz, Direction Générale de l’offre de Soins, Medizinische Universität Graz, Grand Équipement National De Calcul Intensif, Centre Léon Bérard, National Cancer Institute
Openalex Percentile: Top 11%
Neuroendocrine Tumor Research Advances
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