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Updated: Apr 16, 2026

Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Prognostic and predictive biomarkers in thymic epithelial tumors: beyond traditional staging: a narrative review
Luis Cabezón-Gutiérrez1,2, Vilma Pacheco-Barcia1,2, Sara Custodio-Cabello1
1Department of Medical Oncology, Hospital Universitario de Torrejón, Madrid, Spain.
Background And Objective:
Thymic epithelial tumors (TETs), encompassing thymomas (TMs) and thymic carcinomas (TCs), are rare and heterogeneous mediastinal malignancies with variable clinical behaviors and prognoses. Current prognostic assessment primarily relies on histological classification (WHO) and anatomical staging systems (Masaoka-Koga, TNM). However, the rarity and complex biology of TETs necessitate the identification of novel prognostic and predictive biomarkers to improve risk stratification and guide personalized treatment strategies. This narrative review aims to summarize and discuss emerging prognostic and predictive biomarkers in TETs beyond traditional staging systems.
Methods:
For this narrative review, we searched EMBASE and MEDLINE up to 4 September 2025. The terms used in the search included TM, TC, TETs, prognosis and predictive biomarkers.
Key Content And Findings:
Traditional staging systems (Masaoka-Koga, TNM) and histological classification retain strong prognostic value. Clinical factors (including age, resection status, and lymph node involvement) further refine risk stratification. Molecular markers such as programmed death-ligand 1 (PD-L1) expression, tumor mutational burden (TMB), DNA methylation profiles, Hippo pathway components, and Ki-67 show promise as prognostic and/or predictive biomarkers, although prospective validation remains limited. Predictive biomarkers for immunotherapy and targeted agents are under active investigation, with preliminary evidence supporting the role of TMB, PD-L1 expression, and c-kit mutations.
Conclusions:
Prognosis in TETs relies primarily on histology and staging, whereas molecular and immunological biomarkers represent emerging tools for risk stratification and treatment selection. Multiparametric models integrating clinical, pathological, and molecular data may pave the way for precision oncology in TETs.
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