Year: 2024
Type: article
Study design: Observational Study
Abstract: BACKGROUND: Despite advances in diagnosis and treatment in lung cancer, therapies still fail to improve patient management due to resistance mechanisms and relapses. As Cancer stem cells (CSCs) directly contribute to tumor growth and therapeutic resistance, their clinical detection represents a major challenge. However specific and additional CSC markers lack. Thus, our aim was to achieve selective detection of CSCs with specific glycan patterns and assess the CSCs burden to predict the risk of relapse in NSCLC tumors. METHODS: The lung CSCs detection and sorting with a lectin MIX were assessed and compared to CD133 in vitro. Then, its putative role as CSC biomarker was evaluated in vivo and its clinical significance on 221 NSCLC patients. RESULTS: We showed a significant CSCs enrichment in the MIX+ sorted fraction compared to CD133+ cells and confirmed its high tumorigenic capacity. The MIX prognostic value on the overall survival from early stages patients was validated suggesting its potential for detecting CSCs directly linked to tumor aggressiveness. CONCLUSION: The MIX could be more relevant for detecting and sorting CSCs than CD133. Moreover, its prognosis value could enable clinicians to better classify early-stage patients at high risk of relapse in order to tailor therapeutic decisions.
Source: British Journal of Cancer
Authors: Boutaîna Chandouri, Thomas Naves, May Yassine, Léa Ikhlef, Jérémy Tricard, Alain Chaunavel, Zeinab Homayed, Julie Pannequin, Nicolas Girard, Stéphanie Durand, Vincent Carré, Fabrice Lalloué
Institutions: Inserm, Contrôle de l’Activation cellulaire, Progression Tumorale et Résistance thérapeutique, Université de Limoges, Centre Hospitalier Universitaire de Limoges, Hôpital Universitaire Dupuytren, Centre National de la Recherche Scientifique, Institut de Génomique Fonctionnelle, Université de Versailles Saint-Quentin-en-Yvelines, Institut Curie
Language: English
Topic: Cancer Cells and Metastasis
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $4,990
Award: G8939549768
OpenAlex ID: W4402681588
