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Get Free AccessAbstract Triple-negative breast cancer (TNBC) represents the most heterogeneous and aggressive subtype of breast carcinomas, characterized by the absence of clinical biomarkers (ERα, PR, and HER2) and the lack of targeted therapies. In this regard, several clinical trials have consistently failed to effectively stratify patients and identify specific treatments that elicit substantial responses. This study aims to pinpoint biomarkers expressed exclusively in the basal mammary epithelial compartment, facilitating a refined subclassification of this breast cancer subtype. Using computational analyses of single-cell RNA sequencing data, we have identified a list of genes associated with basal identity (BC-markers). Histological validation in 137 human samples has enabled us to categorize TNBC patients into BC-positive and BC-negative TNBC subtypes. Significantly, the presence of these markers correlates with a poorer prognosis in TNBC patients. Functional analyses have revealed a pivotal role for TAGLN in cell migration, likely influencing tumor aggressiveness. Further, we discovered that BC-marker expression is associated with the mesenchymal phenotype and increased sensitivity to the tyrosine kinase inhibitor dasatinib, particularly in BC-positive TNBC, suggesting novel therapeutic avenues. In our study, TAGLN emerged as a potential predictive biomarker for dasatinib responsiveness, offering new directions for personalized therapy for TNBC patients.
Daniel Ortega-Álvarez, David Tébar-García, Marta Casado-Peláez, E Castillo-Agea, J Balibrea-Rull, David Olivares-Osuna, D Pérez-Parra, Cristina Guardia, Eva Musulén, Elena Vinuesa-Pitarch, Elsa Sánchez‐López, Fátima Postigo-Corrales, Ester Ballana, Anna Martínez‐Cardús, Mireia Margelí Vila, Manel Esteller, PL Fernández-Ruiz, Ginés Luengo‐Gil, Elisabetta Mereu, Eva María Galán‐Moya, Verónica Rodilla (2024). Discovery and evaluation of novel biomarkers reveal dasatinib as a potential treatment for a specific subtype of Triple-Negative Breast Cancer. , DOI: https://doi.org/10.1101/2024.07.24.603752.
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Type
Preprint
Year
2024
Authors
21
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.1101/2024.07.24.603752
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