{"database":"biostudies-arrayexpress","file_versions":[],"scores":null,"additional":{"submitter":["Catherine VOEGELE"],"organism":["Homo sapiens"],"software":["SpaceRanger v1.3.0"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/E-MTAB-17055"],"description":["This dataset contains spatial transcriptomics data of four lung neuroendocrine tumours (lung NETs), a rare and understudied type of lung cancer. The dataset consists of raw sequencing data, metadata, and gene expression matrices.  It is part of the lungNENomics project. See https://doi.org/10.5281/zenodo.19366762 for processed data for the series, including downstream analyses data for the four samples in this dataset, such as inferred CNVs and aneuploidy status, and spatial domain and cell proportions for each spot. The lungNENomics project also generated other molecular data for a series of more than 200 samples. The raw sequencing data (fastq files for RNA-seq, cram files for WGS, and idat files for methylation arrays) is hosted on the European Genome-Phenome Archive website, study EGAS00001005979. Medical imaging data (Hematoxylin & Eosin stained whole-slide images) from the lungNENomics project is hosted in the EBI bioImage Archive (10.6019/S-BIAD3143)."],"repository":["biostudies-arrayexpress"],"sample_protocol":["Nucleic Acid Extraction - Each sample was placed on a 10x Genomics Visium slide followed by deparaffinisation, H&E staining and decrosslinking steps, according to 10x Genomics guidelines.","Library Construction - Human probes targeting approximately 18,000 genes were hybridised overnight on the slides and captured on each spot after ligation between the LHS and RHS probes. Libraries were produced for each sample following 10x Genomics protocols.","Sample Collection - Formalin-fixed, paraffin-embedded (FFPE) tumour tissues were collected at diagnosis during surgical resection from contributing centres. Patients provided informed consent for tissue collection and its use in histopathological and molecular analyses (including somatic and germline whole-genome sequencing), as well as for the collection of de-identified clinical data. This study was approved by the International Agency for Research on Cancer Ethics Committee (project number 19-07).","Sequencing - Libraries were prepared and sequenced on an Illumina NovaSeq 6000 machine with a target sequencing depth of 50,000 reads per spot."],"figure_sub":["Organization","MINSEQE Score","Assays and Data","Processed Data","MAGE-TAB Files"],"data_protocol":["Data Transformation - Data was processed using SpaceRanger (v1.3.0) to generate both raw and filtered matrices."],"omics_type":["Metabolomics","Unknown","Transcriptomics","Genomics","Proteomics"],"instrument_platform":["Illumina NovaSeq 6000"],"pubmed_abstract":["Lung neuroendocrine tumours (NETs, also known as carcinoids) are rapidly rising in incidence worldwide but have unknown aetiology and limited therapeutic options beyond surgery. We conducted multi-omic analyses on over 300 lung NETs including whole-genome sequencing (WGS), transcriptome profiling, methylation arrays, spatial RNA sequencing, and spatial proteomics. The integration of multi-omic data provides definitive proof of the existence of four strikingly different molecular groups that vary in patient characteristics, genomic and transcriptomic profiles, microenvironment, and morphology, as much as distinct diseases. Among these, we identify a new molecular group, enriched for highly aggressive supra-carcinoids, that displays an immune-rich microenvironment linked to tumour—macrophage crosstalk, and we uncover an undifferentiated cell population within supra-carcinoids, explaining their molecular and behavioural link to high-grade lung neuroendocrine carcinomas. Deep learning models accurately identified the Ca A1, Ca A2, and Ca B groups based on morphology alone, outperforming current histological criteria. The characteristic tumour microenvironment of supra-carcinoids and the validation of a panel of immunohistochemistry markers for the other three molecular groups demonstrates that these groups can be accurately identified based solely on morphological features, facilitating their implementation in the clinical setting. Our proposed morpho-molecular classification highlights group-specific therapeutic opportunities, including DLL3, FGFR, TERT, and BRAF inhibitors. Overall, our findings unify previously proposed molecular classifications and refine the lung cancer map by revealing novel tumour types and potential treatments, with significant implications for prognosis and treatment decision-making."],"study_type":["spatial transcriptomics by high-throughput sequencing"],"species":["Homo sapiens"],"pubmed_title":["A clinically relevant morpho-molecular classification of lung neuroendocrine tumours"],"pubmed_authors":["Catherine VOEGELE","Nicolas ALCALA","Lynnette FERNANDEZ-CUESTA","Alexandra Sexton-Oates, Émilie Mathian, Noah Candeli, Yuliya Lim, Catherine Voegele, Alex Di Genova, Laurane Mangé, Zhaozhi Li, Tijmen van Weert, Lisa M. Hillen, Ricardo Blázquez-Encinas, Abel Gonzalez-Perez, Maike L. Morrison, Eleonora Lauricella, Lise Mangiante, Lisa Bonheme, Laura Moonen, Gudrun Absenger, Janine Altmuller, Cyril Degletagne, Odd Terje Brustugun, Vincent Cahais, Giovanni Centonze, Amélie Chabrier, Cyrille Cuenin, Francesca Damiola, Vincent Thomas de Montpréville, Jean-François Deleuze, Anne-Marie C. Dingemans, Élie Fadel, Nicolas Gadot, Akram Ghantous, Paolo Graziano, Paul Hofman, Véronique Hofman, Alejandro Ibáñez-Costa, Stéphanie Lacomme, Nuria Lopez-Bigas, Marius Lund-Iversen, Massimo Milione, Lucia Anna Muscarella, Sergio Pedraza-Arevalo, Corinne Perrin, Gaetane Planchard, Helmut Popper, Luca Roz, Angelo Sparaneo, Wieneke Buikhuisen, José van den Berg, Margot Tesselaar, Jaehee Kim, Ernst Jan M Speel, Séverine Tabone-Eglinger, Thomas Walter, Gavin M. Wright, Justo P. Castaño, Lara Chalabreysse, Liming Chen, Christophe Caux, Marco Volante, Nicolas Girard, Jean-Michel Vignaud, Esther Conde, Audrey Mansuet-Lupo, Luka Brcic, Giuseppe Pelosi, Mauro Giulio Papotti, Sylvie Lantuejoul, Jules Derks, Talya Dayton, Nicolas Alcala, Matthieu Foll, Lynnette Fernandez-Cuesta","Matthieu FOLL"],"additional_accession":[]},"is_claimable":false,"name":"Spatial transcriptomics data from the lungNENomics cohort of lung neuroendocrine tumours","description":"This dataset contains spatial transcriptomics data of four lung neuroendocrine tumours (lung NETs), a rare and understudied type of lung cancer. The dataset consists of raw sequencing data, metadata, and gene expression matrices.  It is part of the lungNENomics project. See https://doi.org/10.5281/zenodo.19366762 for processed data for the series, including downstream analyses data for the four samples in this dataset, such as inferred CNVs and aneuploidy status, and spatial domain and cell proportions for each spot. The lungNENomics project also generated other molecular data for a series of more than 200 samples. The raw sequencing data (fastq files for RNA-seq, cram files for WGS, and idat files for methylation arrays) is hosted on the European Genome-Phenome Archive website, study EGAS00001005979. Medical imaging data (Hematoxylin & Eosin stained whole-slide images) from the lungNENomics project is hosted in the EBI bioImage Archive (10.6019/S-BIAD3143).","dates":{"release":"2026-05-22T00:00:00Z","modification":"2026-05-22T01:02:24.429Z","creation":"2026-05-18T14:54:04.275Z"},"accession":"E-MTAB-17055","cross_references":{"ENA":["ERP193570"],"EFO":["EFO_0002944","EFO_0004170","EFO_0030005","EFO_0005518","EFO_0003816","EFO_0004184"],"doi":["10.1101/2025.07.18.25331556"]}}