Metastasis-on-a-chip mimicking the progression of kidney cancer in the liver for predicting treatment efficacy

Yimin Wang, Di Wu, Guohua Wu, Jianguo Wu, Siming Lu, James Lo, Yong He, Chao Zhao, Xin Zhao, Hongbo Zhang, ShuQi Wang*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

53 Citations (Scopus)
23 Downloads (Pure)

Abstract

Metastasis is one of the most important factors that lead to poor prognosis in cancer patients, and effective suppression of the growth of primary cancer cells in a metastatic site is paramount in averting cancer progression. However, there is a lack of biomimetic three-dimensional (3D) in vitro models that can closely mimic the continuous growth of metastatic cancer cells in an organ-specific extracellular microenvironment (ECM) for assessing effective therapeutic strategies. Methods: In this metastatic tumor progression model, kidney cancer cells (Caki-1) and hepatocytes (i.e., HepLL cells) were co-cultured at an increasing ratio from 1:9 to 9:1 in a decellularized liver matrix (DLM)/gelatin methacryloyl (GelMA)-based biomimetic liver microtissue in a microfluidic device. Results: Via this model, we successfully demonstrated a linear anti-cancer relationship between the concentration of anti-cancer drug 5-Fluorouracil (5-FU) and the percentage of Caki-1 cells in the co-culture system (R 2 = 0.89). Furthermore, the Poly(lactide-co-glycolide) (PLGA)-poly(ethylene glycol) (PEG)-based delivery system showed superior efficacy to free 5-FU in killing Caki-1 cells. Conclusions: In this study, we present a novel 3D metastasis-on-a-chip model mimicking the progression of kidney cancer cells metastasized to the liver for predicting treatment efficacy. Taken together, our study proved that the tumor progression model based on metastasis-on-a-chip with organ-specific ECM would provide a valuable tool for rapidly assessing treatment regimens and developing new chemotherapeutic agents.

Original languageEnglish
Pages (from-to)300-311
Number of pages1
JournalTheranostics
Volume10
Issue number1
DOIs
Publication statusPublished - 2020
MoE publication typeA1 Journal article-refereed

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