The potential of a spheroid tumor model composed of cells in different proliferative and metabolic states for the development of new anticancer strategies has been amply demonstrated. However, there is little or no information in the literature on the problems of reproducibility of data originating from experiments using 3D models. Our analyses, carried out using a novel open source software capable of performing an automatic image analysis of 3D tumor colonies, showed that a number of morphology parameters affect the response of large spheroids to treatment. In particular, we found that both spheroid volume and shape may be a source of variability. We also compared some commercially available viability assays specifically designed for 3D models. In conclusion, our data indicate the need for a pre-selection of tumor spheroids of homogeneous volume and shape to reduce data variability to a minimum before use in a cytotoxicity test. In addition, we identified and validated a cytotoxicity test capable of providing meaningful data on the damage induced in large tumor spheroids of up to diameter in 650 μm by different kinds of treatments.

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.

Despite the gradual decrease in incidence, gastric cancer is still the third leading cause of cancer death worldwide. Although chemotherapy enhances overall survival and quality of life in advanced disease, the median overall survival is < 12 months. In recent years, the human epidermal growth factor receptor (ErbB) family has been extensively investigated in gastric cancer. The ErbB family is composed of four closely-related members: ErbB-1 (HER1 or epidermal growth factor receptor, EGFR), ErbB-2 (HER2), ErbB-3 (HER3), and ErbB-4 (HER4), all of which play a critical role in regulating cell growth, proliferation and migration of tumors. It is well known that gastric cancer overexpresses HER in a heterogeneous pattern, especially EGFR, and HER2. HER3 is another important member of the ErbB family that preferentially activates the phosphatidylinositol 3-kinase (PI3K) pathway. Furthermore, its heterodimerization with HER2 seems fundamental for steering HER2-overexpressing breast cancer tumor growth. Less is known about the impact of HER4 on gastric cancer. Improved survival from the use of trastuzumab has paved the way for ErbB receptor family-targeted treatments in gastric cancer. However, unlike trastuzumab, ErbB receptor-targeted drugs have not consistently maintained the encouraging results obtained in preclinical and early clinical trials. This may be attributable to the intrinsic heterogeneity of gastric cancer and/or to the lack of standardized test quality for established biomarkers used to evaluate these biological targets. This review presents an overview of the most recent clinical studies on agents targeting the ErbB family in gastric cancer.

Whatever the target of an experiment in cell biology, cell counting and viability assessment are always computed. The Trypan Blue (TB) assay was proposed about a century ago and is still the most widely used method to perform cell viability analysis. Furthermore, the combined use of TB with a haemocytometer is also considered the standard approach to estimate cell population density. There are numerous research articles reporting the use of TB assays to compute cell number and viability of 2D and 3D cultures. However, the literature still lacks studies regarding the reliability of the TB assay in terms of assessment of its repeatability and reproducibility. We compared the TB assay's measurements obtained by two biologists who analysed 105 different samples in double-blind for a total of 210 counts performed. We measured: (a) the repeatability of the count performed by the same operator; (b) the reproducibility of counts performed by the two operators. There were no significant differences in the results obtained with 2D and 3D cell cultures: we estimated an approximate variability of 5% when the TB assay was used to assess the viability of the culture, and a variability of around 20% when it was used to determine the cell population density. The main aim of this study was to make researchers aware of potential measurement errors when TB is used with a haemocytometer for counting and viability measurements in 2D and 3D cultures. We believe that these results can help researchers to determine whether the expected reliability of the TB assay is compliant with their applications.

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.

Spheroids are three-dimensional cellular models with widespread basic and translational application across academia and industry. However, methodological transparency and guidelines for spheroid research have not yet been established. The MISpheroID Consortium developed a crowdsourcing knowledgebase that assembles the experimental parameters of 3,058 published spheroid-related experiments. Interrogation of this knowledgebase identified heterogeneity in the methodological setup of spheroids. Empirical evaluation and interlaboratory validation of selected variations in spheroid methodology revealed diverse impacts on spheroid metrics. To facilitate interpretation, stimulate transparency and increase awareness, the Consortium defines the MISpheroID string, a minimum set of experimental parameters required to report spheroid research. Thus, MISpheroID combines a valuable resource and a tool for three-dimensional cellular models to mine experimental parameters and to improve reproducibility.