Search

Cancer is a complex disease in which both genetic defects and microenvironmental components contribute to the development, progression, and metastasization of disease, representing major hurdles in the identification of more effective and safer treatment regimens for patients. Three-dimensional (3D) models are changing the paradigm of preclinical cancer research as they more closely resemble the complex tissue environment and architecture found in clinical tumors than in bidimensional (2D) cell cultures. Among 3D models, spheroids and organoids represent the most versatile and promising models in that they are capable of recapitulating the heterogeneity and pathophysiology of human cancers and of filling the gap between conventional 2D in vitro testing and animal models. Such 3D systems represent a powerful tool for studying cancer biology, enabling us to model the dynamic evolution of neoplastic disease from the early stages to metastatic dissemination and the interactions with the microenvironment. Spheroids and organoids have recently been used in the field of drug discovery and personalized medicine. The combined use of 3D models could potentially improve the robustness and reliability of preclinical research data, reducing the need for animal testing and favoring their transition to clinical practice. In this review, we summarize the recent advances in the use of these 3D systems for cancer modeling, focusing on their innovative translational applications, looking at future challenges, and comparing them with most widely used animal models.

Introduction: Despite the increasing financial outlay on cancer research and drug discovery, many advanced cancers remain incurable. One possible strategy for increasing the approval rate of new anticancer drugs for use in clinical practice could be represented by three-dimensional (3D) tumor models on which to perform in vitro drug screening. There is a general consensus among the scientific community that 3D tumor models more closely recapitulate the complexity of tumor tissue architecture and biology than bi-dimensional cell cultures. In a 3D context, cells are connected to each other through tissue junctions and show proliferative and metabolic gradients that resemble the intricate milieu of organs and tumors. Areas covered: The present review focuses on available techniques for generating tumor spheroids and discusses current and future applications in the field of drug discovery. The article is based on literature obtained from PubMed. Expert opinion: Given the relative simplicity of spheroid models with respect to clinical tumors, we must be careful not to overestimate the reliability of their drug-response prediction capacity. The next challenge is to combine our knowledge of co-culture methodologies with high-content imaging and advanced microfluidic technologies to improve the readout and biomimetic potential of spheroid-based models.

The past two decades have seen a shift from cytotoxic drugs to targeted therapy in medical oncology. Although targeted therapeutic agents have shown more impressive clinical efficacy and minimized adverse effects than traditional treatments, drug resistance has become the main limitation to their benefits. Several preclinical in vitro/in vivo models of acquired resistance to targeted agents in clinical practice have been developed mainly by using two strategies: i) genetic manipulation for modeling genotypes of acquired resistance, and ii) in vitro/in vivo selection of resistant models. In the present work, we propose a unifying framework, for investigating the underlying mechanisms responsible for acquired resistance to targeted therapeutic agents, starting from the generation of drug-resistant cellular subclones to the description of silencing procedures used for restoring the sensitivity to the inhibitor. This simple time- and cost-effective approach is widely applicable, and could be easily extended to investigate resistance mechanisms to other targeted therapeutic drugs in different tumor histotypes.

The analysis of the global thiol-disulfide redox status in tissues and cells is a challenging task since thiols and disulfides can undergo artificial oxido-reductions during sample manipulation. Because of this, the measured values, in particular for disulfides, can have a significant bias. Whereas this methodological problem has already been addressed in samples of red blood cells and solid tissues, a reliable method to measure thiols and disulfides in cell cultures has not been previously reported. Here, we demonstrate that the major artifact occurring during thiol and disulfide analysis in cultured cells is represented by glutathione disulfide (GSSG) and S-glutathionylated proteins (PSSG) overestimation, due to artificial oxidation of glutathione (GSH) during sample manipulation, and that this methodological problem can be solved by the addition of N-ethylmaleimide (NEM) immediately after culture medium removal. Basal levels of GSSG and PSSG in different lines of cultured cells were 3-5 and 10-20 folds higher, respectively, when the cells were processed without NEM. NEM pre-treatment also prevented the artificial reduction of disulfides that occurs during the pre-analytical phase when cells are exposed to an oxidant stimulus. In fact, in the absence of NEM, after medium removal, GSH, GSSG and PSSG levels restored their initial values within 15-30 min, due to the activity of reductases and the lack of the oxidant. The newly developed protocol was used to measure the thiol-disulfide redox status in 16 different line cells routinely used for biomedical research both under basal conditions and after treatment with disulfiram, a thiol-specific oxidant (0-200 μM concentration range). Our data indicate that, in most cell lines, treatment with disulfiram affected the levels of GSH and GSSG only at the highest concentration. On the other hand, PSSG levels increased significantly also at the lower concentrations of the drug, and the rise was remarkable (from 100 to 1000 folds at 200 μM concentration) and dose-dependent for almost all the cell lines. These data support the suitability of the analysis of PSSG in cultured cells as a biomarker of oxidative stress., (Copyright © 2015 Elsevier Inc. All rights reserved.)