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Patient-Derived Tumoroid for the Prediction of Radiotherapy and Chemotherapy Responses in Non-Small-Cell Lung Cancer

Authors :
Anasse Nounsi
Joseph Seitlinger
Charlotte Ponté
Julien Demiselle
Ysia Idoux-Gillet
Erwan Pencreach
Michèle Beau-Faller
Véronique Lindner
Jean-Marc Balloul
Eric Quemeneur
Hélène Burckel
Georges Noël
Anne Olland
Florence Fioretti
Pierre-Emmanuel Falcoz
Nadia Benkirane-Jessel
Guoqiang Hua
Source :
Biomedicines, Vol 11, Iss 7, p 1824 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

Radiation therapy and platinum-based chemotherapy are common treatments for lung cancer patients. Several factors are considered for the low overall survival rate of lung cancer, such as the patient’s physical state and the complex heterogeneity of the tumor, which leads to resistance to the treatment. Consequently, precision medicines are needed for the patients to improve their survival and their quality of life. Until now, no patient-derived tumoroid model has been reported to predict the efficiency of radiation therapy in non-small-cell lung cancer. Using our patient-derived tumoroid model, we report that this model could be used to evaluate the efficiency of radiation therapy and cisplatin-based chemotherapy in non-small-cell lung cancer. In addition, these results can be correlated to clinical outcomes of patients, indicating that this patient-derived tumoroid model can predict the response to radiotherapy and chemotherapy in non-small-cell lung cancer.

Details

Language :
English
ISSN :
22279059
Volume :
11
Issue :
7
Database :
Directory of Open Access Journals
Journal :
Biomedicines
Publication Type :
Academic Journal
Accession number :
edsdoj.bd8ffc5895445ddb7df859cf885a4d9
Document Type :
article
Full Text :
https://doi.org/10.3390/biomedicines11071824