Molecular Biomedicine (May 2022)
Identifying drivers of breast cancer metastasis in progressively invasive subpopulations of zebrafish-xenografted MDA-MB-231
Abstract
Abstract Cancer metastasis is the primary cause of the high mortality rate among human cancers. Efforts to identify therapeutic agents targeting cancer metastasis frequently fail to demonstrate efficacy in clinical trials despite strong preclinical evidence. Until recently, most preclinical studies used mouse models to evaluate anti-metastatic agents. Mouse models are time-consuming and expensive. In addition, an important drawback is that mouse models inadequately model the early stages of metastasis which plausibly leads to the poor correlation with clinical outcomes. Here, we report an in vivo model based on xenografted zebrafish embryos where we select for progressively invasive subpopulations of MDA-MB-231 breast cancer cells. A subpopulation analogous to circulating tumor cells found in human cancers was selected by injection of MDA-MB-231 cells into the yolk sacs of 2 days post-fertilized zebrafish embryos and selecting cells that migrated to the tail. The selected subpopulation derived from MDA-MB-231 cells were increasingly invasive in zebrafish. Isolation of these subpopulations and propagation in vitro revealed morphological changes consistent with activation of an epithelial-mesenchymal transition program. Differential gene analysis and knockdown of genes identified gene-candidates (DDIT4, MT1X, CTSD, and SERPINE1) as potential targets for anti-metastasis therapeutics. Furthermore, RNA-splicing analysis reinforced the importance of BIRC5 splice variants in breast cancer metastasis. This is the first report using zebrafish to isolate and expand progressively invasive populations of human cancer cells. The model has potential applications in understanding the metastatic process, identification and/or development of therapeutics that specifically target metastatic cells and formulating personalized treatment strategies for individual cancer patients.
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