Decoding neuroendocrine tumors: a cross-site multiomic approach unveils clinically relevant molecular subtypes

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.

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Journal
Genome Medicine
Published
2026-11-05
DOI
https://doi.org/10.1186/s13073-026-01826-4
Primary Topic
Neuroendocrine Tumor Research Advances
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article
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article

Decoding neuroendocrine tumors: a cross-site multiomic approach unveils clinically relevant molecular subtypes

David Gómez-Sánchez, Beatriz Rubio-Cuesta, Cristian Perna, Lourdes Gomez‐Izquierdo et al.
Genome Medicine
Neuroendocrine Tumor Research Advances
article

Decoding neuroendocrine tumors: a cross-site multiomic approach unveils clinically relevant molecular subtypes

David Gómez-Sánchez, Beatriz Rubio-Cuesta, Cristian Perna, Lourdes Gomez‐Izquierdo, Benjamin Gibert, Anna La Salvia, Paula Espinosa-Olarte, Fátima Al‐Shahrour, Ana Teijo, Carlos Carretero-Puche, Paula Jiménez‐Fonseca, Marcos Rubio‐Fernández, Rocio García‐Carbonero, Gonzalo Goméz-López, Alberto Lens‐Pardo, Mercedes Robledo, Beatriz Soldevilla, Santiago García‐Martín, Alejandra Caminoa, Yolanda Rodriguez-Gil, María José Jiménez‐Santos, Beatriz Antón-Pascual, Marta Benavent
article en

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.

Genome Medicine
Université Claude Bernard Lyon 1 (FR), Universidad Complutense de Madrid (ES), Centre National de la Recherche Scientifique (FR), Memorial Sloan Kettering Cancer Center (US), Inserm (FR), Universidad de Alcalá (ES), Istituto Superiore di Sanità (IT), Instituto de Salud Carlos III (ES), Research Institute Hospital 12 de Octubre (ES), Spanish National Cancer Research Centre (ES), Hospital Universitario 12 De Octubre (ES), Centre Léon Bérard (FR), Centre for Biomedical Network Research on Rare Diseases (ES), Centre de Recherche en Cancérologie de Lyon (FR), Instituto Ramón y Cajal de Investigación Sanitaria (ES), Instituto de Biomedicina de Sevilla (ES), Hospital Universitario Central de Asturias (ES), Hospital Universitario Virgen del Rocío (ES), Hospital Universitario Ramón y Cajal (ES), Instituto de Investigación Sanitaria del Principado de Asturias (ES)
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Neuroendocrine Tumor Research Advances
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