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.

Hyperthermia (HT) combined with irradiation is a well-known concept to improve the curative potential of radiotherapy. Technological progress has opened new avenues for thermoradiotherapy, even for recurrent head and neck squamous cell carcinomas (HNSCC). Preclinical evaluation of the curative radiosensitizing potential of various HT regimens remains ethically, economically, and technically challenging. One key objective of our study was to refine an advanced 3-D assay setup for HT + RT research and treatment testing. For the first time, HT-induced radiosensitization was systematically examined in two differently radioresponsive HNSCC spheroid models using the unique in vitro “curative” analytical endpoint of spheroid control probability. We further investigated the cellular stress response mechanisms underlying the HT-related radiosensitization process with the aim to unravel the impact of HT-induced proteotoxic stress on the overall radioresponse. HT disrupted the proteome’s thermal stability, causing severe proteotoxic stress. It strongly enhanced radiation efficacy and affected paramount survival and stress response signaling networks. Transcriptomics, q-PCR, and western blotting data revealed that HT + RT co-treatment critically triggers the heat shock response (HSR). Pre-treatment with chemical chaperones intensified the radiosensitizing effect, thereby suppressing HT-induced Hsp27 expression. Our data suggest that HT-induced radiosensitization is adversely affected by the proteotoxic stress response. Hence, we propose the inhibition of particular heat shock proteins as a targeting strategy to improve the outcome of combinatorial HT + RT.

Background: Multicellular tumor spheroids (MCTS) are advanced cell culture systems for assessing the impact of combinatorial radio(chemo)therapy as they exhibit therapeutically relevant in vivo–like characteristics from 3-dimensional cell–cell and cell–matrix interactions to radial pathophysiological gradients. State-of-the-art assays quantify long-term curative endpoints based on collected brightfield image time series from large treated spheroid populations per irradiation dose and treatment arm. This analyses require laborious spheroid segmentation of up to 100,000 images per treatment arm to extract relevant structural information from the images (e.g., diameter, area, volume, and circularity). While several image analysis algorithms are available for spheroid segmentation, they all focus on compact MCTS with a clearly distinguishable outer rim throughout growth. However, they often fail for the common case of treated MCTS, which may partly be detached and destroyed and are usually obscured by dead cell debris. Results: To address these issues, we successfully train 2 fully convolutional networks, UNet and HRNet, and optimize their hyperparameters to develop an automatic segmentation for both untreated and treated MCTS. We extensively test the automatic segmentation on larger, independent datasets and observe high accuracy for most images with Jaccard indices around 90%. For cases with lower accuracy, we demonstrate that the deviation is comparable to the interobserver variability. We also test against previously published datasets and spheroid segmentations. Conclusions: The developed automatic segmentation can not only be used directly but also integrated into existing spheroid analysis pipelines and tools. This facilitates the analysis of 3-dimensional spheroid assay experiments and contributes to the reproducibility and standardization of this preclinical in vitro model.

Understanding the complex dynamics of tumor growth to develop more efficient therapeutic strategies is one of the most challenging problems in biomedicine. Three-dimensional (3D) tumor spheroids, reflecting avascular microregions within a tumor, are an advanced in vitro model system to assess the curative effect of combinatorial radio(chemo)therapy. Tumor spheroids exhibit particular crucial pathophysiological characteristics such as a radial oxygen gradient that critically affect the sensitivity of the malignant cell population to treatment. However, spheroid experiments remain laborious, and determining long-term radio(chemo)therapy outcomes is challenging. Mathematical models of spheroid dynamics have the potential to enhance the informative value of experimental data, and can support study design; however, they typically face one of two limitations: while non-spatial models are computationally cheap, they lack the spatial resolution to predict oxygen-dependent radioresponse, whereas models that describe spatial cell dynamics are computationally expensive and often heavily parameterized, impeding the required calibration to experimental data. Here, we present an effectively one-dimensional mathematical model based on the cell dynamics within and across radial spheres which fully incorporates the 3D dynamics of tumor spheroids by exploiting their approximate rotational symmetry. We demonstrate that this radial-shell (RS) model reproduces experimental spheroid growth curves of several cell lines with and without radiotherapy, showing equal or better performance than published models such as 3D agent-based models. Notably, the RS model is sufficiently efficient to enable multi-parametric optimization within previously reported and/or physiologically reasonable ranges based on experimental data. Analysis of the model reveals that the characteristic change of dynamics observed in experiments at small spheroid volume originates from the spatial scale of cell interactions. Based on the calibrated parameters, we predict the spheroid volumes at which this behavior should be observable. Finally, we demonstrate how the generic parameterization of the model allows direct parameter transfer to 3D agent-based models.

Objective. Mathematical modeling can offer valuable insights into the behavior of biological systems upon treatment. Different mathematical models (empirical, semi-empirical, and mechanistic) have been designed to predict the efficacy of either hyperthermia (HT), radiotherapy (RT), or their combination. However, mathematical approaches capable of modeling cell survival from shared general principles for both mono-treatments alone and their co-application are rare. Moreover, some cell cultures show dose-dependent saturation in response to HT or RT, manifesting in survival curve flattenings. An advanced survival model must, therefore, appropriately reflect such behavior. Approach. We propose a mathematical approach to model the effect of both treatments based on the general principle of sublethal damage (SLD) accumulation for the induction of cell death and irreversible proliferation arrest. Our approach extends Jung’s model on heat-induced cellular inactivation by incorporating dose-dependent recovery rates that delineate changes in SLD restoration. Main results. The resulting unified model (Umodel) accurately describes HT and RT survival outcomes, applies to simultaneous thermoradiotherapy modeling, and is particularly suited to reproduce survival curve flattening phenomena. We demonstrate the Umodel’s robust performance (R2 0.95) based on numerous clonogenic cell survival data sets from the literature and our experimental studies. Significance. The proposed Umodel allows using a single unified mathematical function based on generalized principles of accumulation of SLD with implemented radiosensitization, regardless of the type of energy deposited and the mechanism of action. It can reproduce various patterns of clonogenic survival curves, including any flattening, thus encompassing the variability of cell reactions to therapy, thereby potentially better reflecting overall tumor responses. Our approach opens a range of options for further model developments and strategic therapy outcome predictions of sequential treatments applied in different orders and varying recovery intervals between them.