Complex Tumor Spheroids, a Tissue-Mimicking Tumor Model, for Drug Discovery and Precision Medicine

Malignant tumors are complex tissues composed of malignant cells, vascular cells, structural mesenchymal cells including pericytes and carcinoma-associated fibroblasts, infiltrating immune cells, and others, collectively called the tumor stroma. The number of stromal cells in a tumor is often much greater than the number of malignant cells. The physical associations among all these cell types are critical to tumor growth, survival, and response to therapy. Most cell-based screens for cancer drug discovery and precision medicine validation use malignant cells in isolation as monolayers, embedded in a matrix, or as spheroids in suspension. Medium- and high-throughput screening with multiple cell lines requires a scalable, reproducible, robust cell-based assay. Complex spheroids include malignant cells and two normal cell types, human umbilical vein endothelial cells and highly plastic mesenchymal stem cells, which rapidly adapt to the malignant cell microenvironment. The patient-derived pancreatic adenocarcinoma cell line, K24384-001-R, was used to explore complex spheroid structure and response to anticancer agents in a 96-well format. We describe the development of the complex spheroid assay as well as the growth and structure of complex spheroids over time. Subsequently, we demonstrate successful assay miniaturization to a 384-well format and robust performance in a high-throughput screen. Implementation of the complex spheroid assay was further demonstrated with 10 well-established pancreatic cell lines. By incorporating both human stromal and tumor components, complex spheroids might provide an improved model for tumor response in vivo.

The Extensive and Expensive Impacts of HEp-2 [HeLa], Intestine 407 [HeLa], and Other False Cell Lines in Journal Publications

Cell lines are essential models for biomedical research. However, they have a common and important problem that needs to be addressed. Cell lines can be misidentified, meaning that they no longer correspond to the donor from whom the cells were first obtained. This problem may arise due to cross-contamination: the accidental introduction of cells from another culture. The contaminant, which is often a rapidly dividing cell line, will overgrow and replace the original culture. The end result is a false cell line, also known as a misidentified or imposter cell line. False cell lines may come from an entirely different species, tissue, or cell type than the original donor. If undetected, false cell lines produce unreliable and irreproducible results that pollute the biomedical literature and threaten the development of reliable drug discovery and meaningful patient treatments. The goal of this study was to ascertain how widespread this problem is and how it affects the literature, as well as to estimate how much funding has been used to produce pools of scientific literature of questionable value. We focus on HEp-2 [HeLa] and Intestine 407 [HeLa], two false cell lines that are widely used in the scientific literature but were shown to be cross-contaminated in 1967. These two cell lines have been used in 8497 and 1397 published articles and extensively described as laryngeal cancer and normal intestine, respectively, rather than their true identity: the cervical cancer cell line HeLa. Discussed are tools, approaches, and resources that can address this issue—both retrospectively and prospectively.

Flexible Fitting of PROTAC Concentration–Response Curves with Changepoint Gaussian Processes

A proteolysis-targeting chimera (PROTAC) is a new technology that marks proteins for degradation in a highly specific manner. During screening, PROTAC compounds are tested in concentration–response (CR) assays to determine their potency, and parameters such as the half-maximal degradation concentration (DC50) are estimated from the fitted CR curves. These parameters are used to rank compounds, with lower DC50 values indicating greater potency. However, PROTAC data often exhibit biphasic and polyphasic relationships, making standard sigmoidal CR models inappropriate. A common solution includes manual omitting of points (the so-called masking step), allowing standard models to be used on the reduced data sets. Due to its manual and subjective nature, masking becomes a costly and nonreproducible procedure. We therefore used a Bayesian changepoint Gaussian processes model that can flexibly fit both nonsigmoidal and sigmoidal CR curves without user input. Parameters such as the DC50, maximum effect Dmax, and point of departure (PoD) are estimated from the fitted curves. We then rank compounds based on one or more parameters and propagate the parameter uncertainty into the rankings, enabling us to confidently state if one compound is better than another. Hence, we used a flexible and automated procedure for PROTAC screening experiments. By minimizing subjective decisions, our approach reduces time and cost and ensures reproducibility of the compound-ranking procedure. The code and data are provided on GitHub ( https://github.com/elizavetasemenova/gp_concentration_response ).

Treating Cells as Reagents to Design Reproducible Assays

The reproducibility of high-throughput cell-based assays is dependent on having a consistent source of cells for each experiment. Developing an understanding of the nature of cells growing in vitro and factors that influence their responsiveness to test compounds will contribute to the development of reproducible cell-based assays. Using good cell culture practices and establishing standard operating procedures (SOPs) for handling cultures can eliminate several potential contributors to variability in the responsiveness and performance of cells. The SOPs for handling each cell type must have clear and detailed instructions that can be understood and followed among different laboratories. The SOPs should include documenting the source of cells and authenticating their identity, both of which have become required to achieve peer acceptance of experimental data. Variability caused by biological issues such as phenotypic drift can be reduced by using standardized subculture procedures or using cryopreserved cells to set up experiments. Variability caused by inconsistent dispensing of cells per well and edge effects can be identified by measuring how many cells are present and whether they are alive or dead. Multiplex methods for real-time measurement of viable or dead cell number in each sample can be used for normalizing data and determining if proliferation or cytotoxicity has occurred during the experiment. Following good cell culture practices will go a long way toward executing reproducible cell-based assays. Resources will be included describing good cell culture practices, cell line authentication, and multiplex determination of cell number as an internal control.

Addressing Unusual Assay Variability with Robust Statistics

Well-behaved, in vitro bioassays generally produce normally distributed values in their primary (efficacy) data. Accordingly, the best practices for statistical analysis are well documented and understood. However, assays may occasionally display unusually high variability and fall outside the assumptions inherent in these standard analyses. These assays may still be in the optimization phase, in which the source of variation could be identified and addressed. They might also represent the best available option to address the biological process being examined. In these cases, the use of robust statistical methods may provide a more appropriate set of tools for both data analysis and assay optimization. This article provides guidance on best practices for the use of robust statistical methods for the analysis of bioassay data as an alternative to standard methods. Impacts on experimental design and interpretation will be discussed.

Luminescence Energy Transfer–Based Screening and Target Engagement Approaches for Chemical Biology and Drug Discovery

Luminescence is characterized by the spontaneous emission of light resulting from either chemical or biological reactions. Because of their high sensitivity, reduced background interference, and applicability to numerous situations, luminescence-based assay strategies play an essential role in early-stage drug discovery. Newer developments in luminescence-based technologies have dramatically affected the ability of researchers to investigate molecular binding events. At the forefront of these developments are the nano bioluminescence resonance energy transfer (NanoBRET) and amplified luminescent proximity homogeneous assay (Alpha) technologies. These technologies have opened up numerous possibilities for analyzing the molecular biophysical properties of complexes in environments such as cell lysates. Moreover, NanoBRET enables the validation and quantitation of the interactions between therapeutic targets and small molecules in live cells, representing an essential benchmark for preclinical drug discovery. Both techniques involve proximity-based luminescence energy transfer, in which excited-state energy is transferred from a donor to an acceptor, where the efficiency of transfer depends on proximity. Both approaches can be applied to high-throughput compound screening in biological samples, with the NanoBRET assay providing opportunities for live-cell screening. Representative applications of both technologies for assessing physical interactions and associated challenges are discussed.