3D-culture models as drug-testing platforms in canine lymphoma and their cross talk with lymph node-derived stromal cells

3D 培养模型作为犬淋巴瘤药物测试平台及其与淋巴结衍生基质细胞的相互作用

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作者:Ju Hyun An, Woo Jin Song, Qiang Li, Dong Ha Bhang, Hwa Young Youn

Background

Malignant lymphoma is the most common hematopoietic malignancy in dogs, and relapse is frequently seen despite aggressive initial treatment. In order for the treatment of these recurrent lymphomas in dogs to be effective, it is important to choose a personalized and sensitive anticancer agent. To provide a reliable tool for drug development and for personalized cancer therapy, it is critical to maintain key characteristics of the original tumor. Objectives: In this study, we established a model of hybrid tumor/stromal spheroids and investigated the association between canine lymphoma cell line (GL-1) and canine lymph node (LN)-derived stromal cells (SCs).

Conclusions

SCs upregulated multidrug resistance genes in TCs and influenced apoptosis and the cell cycle of TCs in the presence of anticancer drugs. This study revealed that understanding the interaction between the tumor microenvironment and TCs is essential in designing experimental approaches to personalized medicine and to predict the effect of drugs.

Methods

A hybrid spheroid model consisting of GL-1 cells and LN-derived SC was created using ultra low attachment plate. The relationship between SCs and tumor cells (TCs) was investigated using a coculture system.

Results

TCs cocultured with SCs were found to have significantly upregulated multidrug resistance genes, such as P-qp, MRP1, and BCRP, compared with TC monocultures. Additionally, it was revealed that coculture with SCs reduced doxorubicin-induced apoptosis and G2/M cell cycle arrest of GL-1 cells. Conclusions: SCs upregulated multidrug resistance genes in TCs and influenced apoptosis and the cell cycle of TCs in the presence of anticancer drugs. This study revealed that understanding the interaction between the tumor microenvironment and TCs is essential in designing experimental approaches to personalized medicine and to predict the effect of drugs.

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