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Low-dose anti-inflammatory combinatorial therapy reduced cancer stem cell formation in patient-derived preclinical models for tumour relapse prevention.

Authors :
Khoo, Bee Luan
Grenci, Gianluca
Lim, Joey Sze Yun
Lim, Yan Ping
Fong, July
Yeap, Wei Hseun
Bin Lim, Su
Chua, Song Lin
Wong, Siew Cheng
Yap, Yoon-Sim
Lee, Soo Chin
Lim, Chwee Teck
Han, Jongyoon
Source :
British Journal of Cancer; Feb2019, Vol. 120 Issue 4, p407-423, 17p, 1 Diagram, 5 Graphs
Publication Year :
2019

Abstract

<bold>Background: </bold>Emergence of drug-resistant cancer phenotypes is a challenge for anti-cancer therapy. Cancer stem cells are identified as one of the ways by which chemoresistance develops.<bold>Method: </bold>We investigated the anti-inflammatory combinatorial treatment (DA) of doxorubicin and aspirin using a preclinical microfluidic model on cancer cell lines and patient-derived circulating tumour cell clusters. The model had been previously demonstrated to predict patient overall prognosis.<bold>Results: </bold>We demonstrated that low-dose aspirin with a sub-optimal dose of doxorubicin for 72 h could generate higher killing efficacy and enhanced apoptosis. Seven days of DA treatment significantly reduced the proportion of cancer stem cells and colony-forming ability. DA treatment delayed the inhibition of interleukin-6 secretion, which is mediated by both COX-dependent and independent pathways. The response of patients varied due to clinical heterogeneity, with 62.5% and 64.7% of samples demonstrating higher killing efficacy or reduction in cancer stem cell (CSC) proportions after DA treatment, respectively. These results highlight the importance of using patient-derived models for drug discovery.<bold>Conclusions: </bold>This preclinical proof of concept seeks to reduce the onset of CSCs generated post treatment by stressful stimuli. Our study will promote a better understanding of anti-inflammatory treatments for cancer and reduce the risk of relapse in patients. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00070920
Volume :
120
Issue :
4
Database :
Complementary Index
Journal :
British Journal of Cancer
Publication Type :
Academic Journal
Accession number :
134805720
Full Text :
https://doi.org/10.1038/s41416-018-0301-9