CD44 drives aggressiveness and chemoresistance of a metastatic human osteosarcoma xenograft model.
Oncotarget
; 8(69): 114095-114108, 2017 Dec 26.
Article
em En
| MEDLINE
| ID: mdl-29371972
ABSTRACT
BACKGROUND:
Osteosarcoma is the most common primary malignant bone tumor with a 5 year survival rate of up to 70%. However, patients with metastatic disease have still a very poor prognosis. Osteosarcoma metastasis models are essential to develop novel treatment strategies for advanced disease.METHODS:
Based on a serial transplantation approach, we have established a U-2 OS osteosarcoma xenograft model with increased metastatic potential and compared it to other metastatic osteosarcoma models from international sources. Subclones with differing invasive potential were compared for genomic gains and losses as well as gene expression changes by several bioinformatic approaches. Based on the acquired results, the effects of a shRNA-mediated CD44 mRNA knockdown on migration, invasion and chemosensitivity were evaluated.RESULTS:
The CD44 gene was part of an amplified region at chromosome 11p found in both U-2 OS subclones with enhanced metastatic potential but not in parental U-2 OS cells, corresponding with distinct CD44 overexpression. Accordingly, shRNA-mediated CD44 knockdown significantly attenuated osteosarcoma cell migration, invasion, and viability especially in the metastatic subclones of U-2 OS and Saos-2 cells. Metastatic subclones generally were hypersensitive against the integrin inhibitor cilengitide paralleled by alterations in integrin expression pattern following CD44 knock-down. Additionally, attenuation of CD44 expression sensitized these cell models against osteosarcoma chemotherapy with doxorubicin but not methotrexate and cisplatin.CONCLUSIONS:
The osteosarcoma xenograft models with increased metastatic potential developed in this study can be useful for identification of mechanisms driving metastasis and resistance towards clinically used and novel therapeutic regimens.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Ano de publicação:
2017
Tipo de documento:
Article