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