Decoding neuroendocrine tumors: a cross-site multiomic approach unveils clinically relevant molecular subtypes
Year: 2026
Type: article
Study design: Observational Study
Abstract: Neuroendocrine tumors (NETs) comprise a heterogeneous family of malignancies of wide anatomic distribution but shared clinical and molecular features. Although tissue-specific biology has been well characterized for some common primary sites, comprehensive analyses capable of defining biologically meaningful, site-independent subtypes are lacking. This study aimed to identify and validate cross-site molecular subtypes of NETs and to evaluate their clinical relevance. Multi-omic factor analysis (MOFA) and unsupervised clustering were used to integrate tumor gene expression and DNA methylation data from 194 patients with lung, stomach, pancreas, small intestine, and colorectal NETs. Latent factors representing biologically meaningful sources of variation were leveraged to generate a robust consensus clustering derived from eight unsupervised algorithms. Each molecular subtype was comprehensively characterized by integrating clinical, pathological and molecular features with survival through multivariable Cox proportional hazards models. Validation was performed in independent external cohorts of lung, small intestine, and pancreatic NETs with publicly available multi-omic datasets. We identified three distinct molecular subtypes (NS1–3). NS1 exhibited a strong immune phenotype and was enriched in metabolic and nutrient-digestion pathways. NS2 showed pronounced neuroendocrine differentiation with enrichment of neuronal and secretory programs. NS3 was characterized by global DNA hypermethylation, enrichment in DNA-replication pathways, and significantly poorer prognosis (10-year OS: 51.9% vs. 83.2–83.9% for NS3 vs NS1/NS2; p = 3 × 10 −4 ). NSs were independent of primary tumor location and retained independent prognostic significance in multivariable models adjusting for age, sex, TNM stage and tumor site. Validation in an external cohort confirmed the robustness of these findings. This new cross-site classification of NETs reveals convergent biological traits and common potentially targetable vulnerabilities, providing a framework for biomarker development and the rational design of novel therapeutic strategies.
Source: Genome Medicine
Authors: Carlos Carretero-Puche, Beatriz Anton-Pascual, Alberto Lens-Pardo, Gonzalo Gomez-Lopez, Marcos Rubio-Fernandez, Beatriz Rubio-Cuesta, Marta Benavent, Lourdes Gomez-Izquierdo, Paula Jimenez-Fonseca, Ana Teijo, Yolanda Rodriguez-Gil, Cristian Perna, Alejandra Caminoa, Paula Espinosa-Olarte, Anna La Salvia, David Gomez-Sanchez, Maria Jose Jimenez-Santos, Santiago Garcia-Martin, Benjamin Gibert, Mercedes Robledo, Fatima Al-Shahrour, Beatriz Soldevilla, Rocio Garcia-Carbonero
Institutions: Research Institute Hospital 12 de Octubre, Hospital Universitario 12 De Octubre, Spanish National Cancer Research Centre, Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocío, Hospital Universitario Central de Asturias, Instituto de Investigación Sanitaria del Principado de Asturias, Universidad de Alcalá, Instituto Ramón y Cajal de Investigación Sanitaria, Hospital Universitario Ramón y Cajal, Istituto Superiore di Sanità, Memorial Sloan Kettering Cancer Center, Lyon 1 Université, Centre National de la Recherche Scientifique, Inserm, Centre Léon Bérard, Centre de Recherche en Cancérologie de Lyon, Instituto de Salud Carlos III, Centre for Biomedical Network Research on Rare Diseases, Universidad Complutense de Madrid
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $5,290
OpenAlex ID: W7225322762