We report the use of thin film poly(dimethylsiloxane) (PDMS) prints for the arrayed mass production of highly uniform 3-D human HT29 colon carcinoma spheroids. The spheroids have an organotypic density and, as determined by 3-axis imaging, were genuinely spherical. Critically, the array density impacts growth kinetics and can be tuned to produce spheroids ranging in diameter from 200 to 550 µm. The diffusive limit of competition for media occurred with a pitch of ≥1250 µm and was used for the optimal array-based culture of large, viable spheroids. During sustained culture mass transfer gradients surrounding and within the spheroids are established, and lead to growth cessation, altered expression patterns and the formation of a central secondary necrosis. These features reflect the microenvironment of avascularised tumours, making the array format well suited for the production of model tumours with defined sizes and thus defined spatio-temporal pathophysiological gradients. Experimental windows, before and after the onset of hypoxia, were identified and used with an enzyme activity-based viability assay to measure the chemosensitivity towards irinotecan. Compared to monolayer cultures, a marked reduction in the drug efficacy towards the different spheroid culture states was observed and attributed to cell cycle arrest, the 3-D character, scale and/or hypoxia factors. In summary, spheroid culture using the array format has great potential to support drug discovery and development, as well as tumour biology research. / Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich.

At the forefront of cancer treatment development and evaluation, three-dimensional Tumour Spheroid Control Experiments play a pivotal role in the battle against cancer. Conducting and evaluating in vitro experiments are time-consuming processes. This thesis details the development, implementation, and validation of an algorithmic model that classifies spheroids as either controlled or relapsed by assessing the success of their treatments based on criteria rooted in biological insights. The introduction of this model is crucial for biologists to accurately and efficiently predict treatment efficacy in 3D in vitro experiments. The motivation for this research is driven by the need to improve the objectivity and efficiency of treatment outcome evaluations, which have traditionally depended on manual and subjective assessments by biologists. The research involved creating a comprehensive dataset from multiple 60-day in vitro experiments by combining data from various sources, focusing on the growth dynamics of tumour spheroids subjected to different treatment regimens. Through preprocessing and analysis, growth characteristics were extracted and utilized as input features for the model. A feature selection and optimization technique was applied to refine the software model and improve its predictive accuracy. The model is based on a handful of comprehensive criteria, calibrated by employing a grid search mechanism for hyperparameter tuning to optimize accuracy. The validation process, conducted via independent test sets, confirmed the model’s capability to predict treatment outcomes with a high degree of reliability and an accuracy of about 99%. The findings reveal that algorithmic classification models can make a significant contribution to the standardization and automation of treatment efficacy assessment in tumour spheroid experiments. Not only does this approach reduce the potential for human error and variability, but it also provides a scalable and objective means of evaluating treatment outcomes.:1 Introduction 1.1 Background and Motivation 1.2 Biological Background 1.3 Iteration Methodology 1.4 Objective of the Thesis 2 Definition of basic Notation and Concepts 2.1 Time Series Analysis 2.2 Linear Interpolation 2.3 Simple Exponential Smoothing 2.4 Volume of a Spheroid 2.5 Heavyside Function 2.6 Least Squares Method 2.7 Linear Regression 2.8 Exponential Approximation 2.9 Grid Search 2.10 Binary Regression 2.11 Pearson Correlation Coefficient 3 Observation Data 3.1 General Overview 3.1.1 Structure of the Data 3.1.2 Procedure of Data Processing using 3D-Analysis 3.2 Data Engineering 3.2.1 Data Consolidation and Sanitization 3.2.2 Extension and Interpolation 3.2.3 Variance Reduction 4 Model Development 4.1 Modeling of Various Classification-Relevant Aspects 4.1.1 Primary Criteria 4.1.2 Secondary Criteria 4.1.3 Statistical Learning Approaches 4.2 Day of Relapse Estimation 4.3 Model Implementation 4.3.1 Combination of Approaches 4.3.2 Implementation in Python 4.4 Model Calibration 4.4.1 Consecutive Growth 4.4.2 Quintupling 4.4.3 Secondary Criteria 4.4.4 Combined Approach 5 Model Testing 5.1 Evaluation Methods 5.1.1 Applying the Model to New Data 5.1.2 Spheroid Control Probability 5.1.3 Kaplan-Meier Survival Analysis 5.1.4 Analysis of Classification Mismatches 5.2 Model Benchmark 5.2.1 Comparison to Human Raters 5.2.2 Comparison to Binary Regression Model 5.3 Robustness 5.3.1 Test using different Segmentation 5.3.2 Feature Reduction 5.3.3 Sensitivity 5.3.4 Calibration Templates 6 Discussion 6.1 Practical Application Opportunities 6.2 Evaluation of the Algorithmic Model 6.3 Limitations 7 Conclusion 7.1 Summary 7.2 Future Research Directions / Dreidimensionale Experimente zur Kontrolle von Tumorsphäroiden sind zentral für die Entwicklung und Evaluierung von Krebstherapien. Die Durchführung und Auswertung von In-vitro-Experimenten ist jedoch zeitaufwendig. Diese Arbeit beschreibt die Entwicklung, Implementierung und Validierung eines algorithmischen Modells zur Einstufung von Sphäroiden als kontrolliert oder rezidivierend. Das Modell bewertet den Behandlungserfolg anhand biologisch fundierter Kriterien. Diese Innovation ist entscheidend für die präzise und effiziente Vorhersage der Wirksamkeit von Behandlungen in 3D-In-vitro-Experimenten und zielt darauf ab, die Objektivität und Effizienz der Beurteilung von Behandlungsergebnissen zu verbessern, die traditionell von manuellen, subjektiven Einschätzungen der Biologen abhängen. Die Forschung umfasste die Erstellung eines umfassenden Datensatzes aus mehreren 60-tägigen In-vitro-Experimenten, bei denen die Wachstumsdynamik von Tumorsphäroiden unter verschiedenen Behandlungsschemata untersucht wurde. Durch Vorverarbeitung und Analyse wurden Wachstumscharakteristika extrahiert und als Eingangsmerkmale für das Modell verwendet. Das Modell basiert auf wenigen umfassenden Kriterien, die mithilfe eines Gittersuchmechanismus zur Abstimmung der Hyperparameter kalibriert wurden, um die Genauigkeit zu optimieren. Der Validierungsprozess bestätigte die Fähigkeit des Modells, Behandlungsergebnisse mit hoher Zuverlässigkeit und einer Genauigkeit von etwa 99 % vorherzusagen. Die Ergebnisse zeigen, dass algorithmische Klassifizierungsmodelle einen wesentlichen Beitrag zur Standardisierung und Automatisierung der Bewertung der Behandlungseffektivität in Tumorsphäroid-Experimenten leisten können. Dieser Ansatz verringert nicht nur das Potenzial für menschliche Fehler und Schwankungen, sondern bietet auch ein skalierbares und objektives Mittel zur Bewertung von Behandlungsergebnissen.:1 Introduction 1.1 Background and Motivation 1.2 Biological Background 1.3 Iteration Methodology 1.4 Objective of the Thesis 2 Definition of basic Notation and Concepts 2.1 Time Series Analysis 2.2 Linear Interpolation 2.3 Simple Exponential Smoothing 2.4 Volume of a Spheroid 2.5 Heavyside Function 2.6 Least Squares Method 2.7 Linear Regression 2.8 Exponential Approximation 2.9 Grid Search 2.10 Binary Regression 2.11 Pearson Correlation Coefficient 3 Observation Data 3.1 General Overview 3.1.1 Structure of the Data 3.1.2 Procedure of Data Processing using 3D-Analysis 3.2 Data Engineering 3.2.1 Data Consolidation and Sanitization 3.2.2 Extension and Interpolation 3.2.3 Variance Reduction 4 Model Development 4.1 Modeling of Various Classification-Relevant Aspects 4.1.1 Primary Criteria 4.1.2 Secondary Criteria 4.1.3 Statistical Learning Approaches 4.2 Day of Relapse Estimation 4.3 Model Implementation 4.3.1 Combination of Approaches 4.3.2 Implementation in Python 4.4 Model Calibration 4.4.1 Consecutive Growth 4.4.2 Quintupling 4.4.3 Secondary Criteria 4.4.4 Combined Approach 5 Model Testing 5.1 Evaluation Methods 5.1.1 Applying the Model to New Data 5.1.2 Spheroid Control Probability 5.1.3 Kaplan-Meier Survival Analysis 5.1.4 Analysis of Classification Mismatches 5.2 Model Benchmark 5.2.1 Comparison to Human Raters 5.2.2 Comparison to Binary Regression Model 5.3 Robustness 5.3.1 Test using different Segmentation 5.3.2 Feature Reduction 5.3.3 Sensitivity 5.3.4 Calibration Templates 6 Discussion 6.1 Practical Application Opportunities 6.2 Evaluation of the Algorithmic Model 6.3 Limitations 7 Conclusion 7.1 Summary 7.2 Future Research Directions

High-throughput drug screening (HTS) in live cells is often a vital part of the preclinical anticancer drug discovery process. So far, two-dimensional (2D) monolayer cell cultures have been the most prevalent model in HTS endeavors. However, 2D cell cultures often fail to recapitulate the complex microenvironments of in vivo tumors. Monolayer cultures are highly proliferative and generally do not contain quiescent cells, thought to be one of the main reasons for the anticancer therapy failure in clinic. Thus, there is a need for in vitro cellular models that would increase predictive value of preclinical research results. The utilization of more complex three-dimensional (3D) cell cultures, such as multicellular tumor spheroids (MCTS), which contain both proliferating and quiescent cells, has therefore been proposed. However, difficult handling and high costs still pose significant hurdles for application of MCTS for HTS. In this work, we aimed to develop novel assays to apply MCTS for HTS and drug evaluation. We also set out to identify cellular processes that could be targeted to selectively eradicate quiescent cancer cells. In Paper I, we developed a novel MCTS-based HTS assay and found that nutrient-deprived and hypoxic cancer cells are selectively vulnerable to treatment with inhibitors of mitochondrial oxidative phosphorylation (OXPHOS). We also identified nitazoxanide, an FDA-approved anthelmintic agent, to act as an OXPHOS inhibitor and to potentiate the effects of standard chemotherapy in vivo. Subsequently, in Paper II we applied the high-throughput gene-expression profiling method for MCTS-based drug screening. This led to discovery that quiescent cells up-regulate the mevalonate pathway upon OXPHOS inhibition and that the combination of OXPHOS inhibitors and mevalonate pathway inhibitors (statins) results in synergistic toxicity in this cell population. In Paper III, we developed a novel spheroid-based drug combination-screening platform and identified a set of molecules that synergize with nitazoxanide to eradicate quiescent cancer cells. Finally, in Paper IV, we applied our MCTS-based methods to evaluate the effects of phosphodiesterase (PDE) inhibitors in PDE3A-expressing cell lines. In summary, this work illustrates how MCTS-based HTS yields potential to reveal and exploit previously unrecognized tumor-specific vulnerabilities. It also underscores the importance of cell culture conditions in preclinical drug discovery endeavors.