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

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

 

 

Introduction
Although significant advances have been made to save cancer patients’ lives, metastasis is still the leading cause of cancer-related mortality, accounting for approximately 90% of global cancer deaths [1]. As reported, the 5-year survival rate of patients with a primary tumor is relatively high, whereas metastasis can considerably reduce the 5-year survival rate to less than 30% [1, 2]. Chemotherapy remains the mainstream treatment strategy for metastatic cancer, particularly when the primary tumor cannot be surgically removed [3, 4]. Although chemotherapy plays a key part in the plethora of options for the clinical management of metastatic cancer, the success rate of developing effective chemo-compounds is less than 5% due to the limitation of conventional models [5]. The selection of highly effective anticancer treatment rather than empirical treatment is much needed for curbing cancer progression in metastatic cancer patients. It is, therefore, essential to identify effective anticancer chemotherapies and optimize drug delivery efficiency for improved clinical outcomes in metastatic cancer patients.

 

Traditionally, two-dimensional (2D) cell cultures and animals have been used as primary tumor and metastatic cancer drug development models. Although 2D cell culture is relatively easy to perform, it lacks the essential physiopathological cues in the tumor microenvironment and metastatic niche [6, 7]. As such, the inadequacies of traditional 2D models lie in their inability to accurately simulate the complex, interact with appropriate physiopathological conditions or predict the in vivo effectiveness of anticancer compounds [8-10]. On the other hand, animal models for drug testing are labor/time intensive, costly, and most importantly, often yield untranslatable results due to the physiological differences between humans and animal models [11-14]. Therefore, the creation of cost-effective, reliable, and pragmatic in vitro models that can be used for accurately screening anticancer drug effects as well as overcoming the drawbacks of conventional models is of great importance for improving the current clinical management of primary tumor and metastatic cancer [15-18].
In recent years, organ-on-chip platforms, due to their 3D nature and cost-effectiveness, have been developed to model the metastatic cascade within conditioned microenvironments [19-23]. Metastasis- on-a-chip models have been used to study the metastatic cascade and offer a feasible platform for drug testing [24-26]. For instance, a metastasis- on-a-chip was constructed to mimic the migration of metastatic tumors from the intestine to the liver and to allow real-time tracking of cell movement and behavior [27]. However, this study only used hyaluronic acid hydrogel without considering organ-specific ECM in the migration model. In another study, normal breast cells are co-cultured with breast cancer cells to simulate cancer models at mild, moderate, and severe stages, in which cell density is found to be highly correlated with the incidence of metastasis [28]. However, this study used a 2D rather than a 3D model to investigate cancer migration and drug screening. Therefore, the progression of post-metastasis tumor within an organ-specific ECM has not been studied.


In this article, we present a new metastasis-on-a-chip model incorporated with organ-specific ECM. This model can mimic the progression of kidney cancer cells in the liver to predict the therapeutic effects and evaluate dosage responses of anticancer drugs in a physiologically relevant liver microenvironment. We cultured kidney cancer cells (Caki-1) in a DLM/GelMA-based 3D biomimetic liver microtissue via continuous perfusion. Within this model, we co-cultured the Caki-1 and HepLL cells in increasing ratios from 1:9 to 9:1 to investigate the progression of metastatic kidney cancer cells in the liver. We observed that there was a linear anticancer relationship between the concentration of 5-Fluorouracil (5-FU) and the percentage of Caki-1 cells in the metastatic tumor progression model, and that the 5-FU-loaded PLGA-PEG nanoparticles (NPs) showed a stronger killing efficacy than free 5-FU. Our findings demonstrate that the tumor progression model can be used to establish 3D metastatic cancer in vitro models and to rapidly assess anti-cancer efficiency and optimize dosage regimes.

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