Targeting chemoresistant senescent pancreatic cancer cells improves conventional treatment efficacy

Pancreatic cancer is one of the deadliest cancers owing to its late diagnosis and of the strong resistance to available treatments. Despite a better understanding of the disease in the last two decades, no significant improvement in patient care has been made. Senescent cells are characterized by a...

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Veröffentlicht in:Molecular biomedicine 2023-02, Vol.4 (1), p.4-4, Article 4
Hauptverfasser: Jaber, Sara, Warnier, Marine, Leers, Christopher, Vernier, Mathieu, Goehrig, Delphine, Médard, Jean-Jacques, Vindrieux, David, Ziegler, Dorian V., Bernard, David
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Sprache:eng
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Zusammenfassung:Pancreatic cancer is one of the deadliest cancers owing to its late diagnosis and of the strong resistance to available treatments. Despite a better understanding of the disease in the last two decades, no significant improvement in patient care has been made. Senescent cells are characterized by a stable proliferation arrest and some resistance to cell death. Increasing evidence suggests that multiple lines of antitumor therapy can induce a senescent-like phenotype in cancer cells, which may participate in treatment resistance. In this study, we describe that gemcitabine, a clinically-used drug against pancreatic cancer, induces a senescent-like phenotype in highly chemoresistant pancreatic cancer cells in vitro and in xenografted tumors in vivo. The use of ABT-263, a well-described senolytic compound targeting Bcl2 anti-apoptotic proteins, killed pancreatic gemcitabine-treated senescent-like cancer cells in vitro. In vivo, the combination of gemcitabine and ABT-263 decreased tumor growth, whereas their individual administration had no effect. Together these data highlight the possibility of improving the efficacy of conventional chemotherapies against pancreatic cancer by eliminating senescent-like cancer cells through senolytic intervention. Further studies testing different senolytics or their combination with available treatments will be necessary to optimize preclinical data in mouse models before transferring these findings to clinical trials.
ISSN:2662-8651
2662-8651
DOI:10.1186/s43556-023-00116-4