The consensus molecular subtypes of esophageal squamous cell carcinoma
Summary
Esophageal squamous cell carcinoma (ESCC) lacks a standardized classification system, resulting in inconsistent clinical management and a suboptimal prognosis. This study addresses the urgent need for a robust consensus taxonomy to facilitate precision treatment for ESCC. We employed a network-based approach to elucidate the interconnections among eight existing classification systems, leading to the identification of four distinct consensus molecular subtypes (ECMSs): ECMS1-MET (metabolic
Content
# The consensus molecular subtypes of esophageal squamous cell carcinoma
*Published: 2026 Feb 19*
Esophageal squamous cell carcinoma (ESCC) lacks a standardized classification
system, resulting in inconsistent clinical management and a suboptimal
prognosis. This study addresses the urgent need for a robust consensus taxonomy
to facilitate precision treatment for ESCC. We employed a network-based approach
to elucidate the interconnections among eight existing classification systems,
leading to the identification of four distinct consensus molecular subtypes
(ECMSs): ECMS1-MET (metabolic), characterized by dysregulated metabolic pathways
and NFE2L2 activation; ECMS2-CLS (classical), featuring upregulated cell cycle
and canonical signaling pathways; ECMS3-IM (immunomodulatory), marked by robust
immune activation and elevated PD-1 expression; and ECMS4-MES (mesenchymal),
associated with mesenchymal transition, stromal activation, and VEGF signaling.
To improve clinical applicability, we developed an image-based framework
(imECMS) that utilizes spatial organization features (SOFs) quantified from
autodelineated hematoxylin‒eosin (H&E)-stained whole-slide images through deep
learning algorithms. The imECMS classifier assigns patients to one of the four
ECMS subtypes, which correlate with distinct molecular characteristics,
prognoses, and responses to neoadjuvant chemotherapy and immunotherapy.
Validation across multiple independent cohorts confirmed that the imECMS
accurately classifies ESCC subtypes from histopathological images, offering a
robust and effective tool for precision medicine. In summary, the ECMS/imECMS
subtyping systems we developed are the most robust frameworks for ESCC to date,
providing clear biological insights and a foundation for clinical stratification
and targeted therapies.
DOI: 10.1038/s41392-026-02577-9