This thesis deals with the differential response of healthy and cancerous cells to surface topography at the nanoscale and the microscale. Using a statistical method we developed we studied the interactions of cells with grooves of nanoscale depth. We demonstrate that healthy cells have a greater ability to align with deeper grooves, whereas cancerous cells are more sensitive to shallow grooves. Analysis reveals that the nucleus follows the alignment of the cell body more closely in cancerous cells, and that the nucleus of cancerous cells is more sensitive to shallow grooves.On microscale pillars we demonstrate for the first time that osteosarcoma cells deform to adopt the surface topography and that the deformation extends to the interior of the cell and in particular to the nucleus. We show that healthy cells only deform during the initial stages of adhesion and that immortalized cells show intermediate deformation between the healthy and cancerous cells. When the spacing between the pillars is reduced, differences in the deformation of different cancerous cell lines are detected. Deformation was also found to be related to the malignancy in keratinocytes, and related to the expression of Cdx2 in adenocarcinoma. The mechanism of deformation is tentatively attributed to the cytoskeleton and attempts to identify the main actors of deformation were performed using confocal microscopy and cytoskeleton inhibitors. Live cell imaging experiments reveal that the deformed cells are very mobile on the surfaces, loss of deformation is necessary for mitosis to occur and deformation after mitosis is more rapid than initial deformation upon adhesion to surfaces.

Zellen aus Karzinomen sind erwiesenermaßen weicher als Epithelzellen ihres Ursprungsgewebes. Es wurde vermutet, dass dieses Weicherwerden Zellen dabei hilft, aus dem Primärtumor auszubrechen und Metastasen zu bilden, was allerdings erst von wenigen Belegen bestärkt wird. Weiterhin wird die Entwicklung von Karzinomen allgemein als von einer epithelial-mesenchymalen Transition (EMT) angetrieben angesehen, ein Prozess, der die Umformung von Epithelgeweben steuert und stark in das Zytoskelett eingreift. Ich habe daher die Hypothese aufgestellt, dass EMT Karzinomzellen weicher macht und somit aggressive und invasive Tumore erzeugt. In der vorgelegten Arbeit gehe ich dem Nachweis dieser Hypothese nach. Ich habe den Einfluss der EMT auf Zellweichheit in vitro untersucht, allerdings kein gerichtetes Weicherwerden mit Fortschritt der EMT feststellen können. Mit vitalen Einzelzellen, die ich aus Operationsresektaten isoliert habe, verglich ich die mechanischen Eigenschaften von invasiven und nicht-invasiven Tumoren ex vivo und konnte eine klare Korrelation von Aggressivität mit Zellweichheit in vier verschiedenen Arten von Karzinomen aufzeigen. Membrangebundenes E-cadherin, das mir als Marker für den Fortschritt der EMT diente, war jedoch weder mit der Aggressivität der Karzinome noch mit der Weichheit derer Zellen korreliert. Ich benutzte maschinelles Lernen (ML), um Krebs-zellen in silico auf Basis ihrer mechanischer Eigenschaften zu klassifizieren, stieß aber auf klare Grenzen. In dieser Arbeit habe ich zum ersten Mal ex vivo gezeigt, dass das Weicherwerden von Krebszellen ein kontinuierlicher Prozess in Karzinomen ist, und dass erhöhte Aggressivität mit erhöhter Zellweichheit einhergeht. Ich habe außerdem EMT, die lange Zeit als entscheidend für Zellinvasion galt, als mögliche Ursache für dieses Weicherwerden ausgeschlossen. Zusammengenommen mit meinen Resultaten der ML Klassifikation deutet dies darauf hin, dass eine erhöhte Heterogenität von mechanischen Eigenschaften von Krebszellen, ausgelöst von allgemeiner Deregulation, die Invasion von Karzinomen antreibt.:1 Introduction 1 2 Background 11 2.1 The cytoskeleton of eukaryotic cells 12 2.2 The actin-E-cadherin-complex 17 2.2.1 E-cadherin 17 2.2.2 The Wnt/β-catenin pathway 18 2.2.3 Actin-E-cadherin dynamics 19 2.3 The epithelial to mesenchymal transition (EMT) 21 2.3.1 Epithelial and mesenchymal cells 21 2.3.2 Classical EMT 22 2.3.3 EMT in carcinoma development 23 2.4 Carcinoma development 25 2.4.1 Growth and spread 25 2.4.2 Tumor grading and staging 26 2.4.3 Carcinoma development outside of EMT 29 2.5 Cell mechanics in migration and invasion 31 3 Materials & methods 37 3.1 The Optical Stretcher as a main measurement device for cellular softness and E-cadherin level 38 3.1.1 Deformation by radiation pressure 39 3.1.2 Viability in an OS 43 3.1.3 Data acquisition and evaluation 46 3.2 Kelvin Voigt (KV) modeling 50 3.3 Machine learning 53 3.3.1 Interpreting and evaluating classications 54 3.3.2 Data preparation 58 3.3.3 Support vector machines (SVM) 58 3.3.4 Random forest (RF) 64 3.3.5 Permutation importance 67 3.4 Statistical analysis 68 3.4.1 Two one-sided tests (TOST) as a statistical test for equivalence 69 3.5 In vitro model systems for eukaryotic cells, their culture, and preparation 71 3.5.1 Cell lines 71 3.5.2 Cell culture 73 3.5.3 Fluorescent labeling of E-cadherin 73 3.6 Isolation of cancer cells from primary samples 75 3.6.1 Isolation of cancer cells from blood samples 75 3.6.2 Isolation of cancer cells from surgical resections 77 4 Results & discussion 79 4.1 In vitro growth factor induced EMT 81 4.1.1 EGF induced EMT is not correlated to cell softening in MCF 10A epithelial cells 82 4.1.2 TGFβ1 induced EMT is not correlated to cell softening in MCF 10A epithelial cells 87 4.1.3 Summary 91 4.2 Ex vivo vital tumor cells from liquid biopsies and surgical resections 94 4.2.1 Database analysis reveals that there is no systematic change of EMT related markers over the course of carcinoma progression 96 4.2.2 Vital single cells isolated from liquid biopsies of breast cancer patients can be distinguished from healthy cells of their natural surrounding 99 4.2.3 Cell softening is correlated to aggressiveness in tumor cells isolated from surgical resections 110 4.2.4 EMT progression is connected to neither cell softening nor aggressiveness in tumor cells isolated from surgical resections 120 4.2.5 Summary 123 4.3 In silico Machine learning as means to assess the predictive power of cell mechanics 127 4.3.1 Parameters from OS measurements 128 4.3.2 In vitro discrimination of cell types in a breast cancer cell line panel 129 4.3.3 Ex vivo discrimination of breast cancer cells and PBMC isolated from liquid biopsies 136 4.3.4 Summary 143 5 Conclusion & outlook 147 A Additional data and information 161 A.1 Optimization of support vector machines (SVM) and random forest (RF) machine learning approaches 161 A.1.1 Optimization of the training set size in SVM and RF machine learning approaches 161 A.1.2 Optimization of the SVM machine learning algorithm 161 A.1.3 Optimization of the RF machine learning algorithm 163 A.2 List of features for machine learning based classication 164 A.2.1 List of features used for classication of my in vitro cell line panel 164 A.2.2 List of features for classication of circulating tumor cells isolated from the blood of patients with mamma carcinoma 166 A.3 Activity parameter A of cells isolated from the blood samples of breast cancer patients 170 B Materials and reagents 171 B.1 Cell culture media 171 B.1.1 Medium for MCF 10A cells 171 B.1.2 Medium for MDA-MB-436 and MDA-MB-231 cells 171 B.1.3 Medium for NIH/3T3 cells 172 B.2 Ringer lactate buer for tissue transport and storage 172 B.3 MACS buffer 172 C Protocols 173 C.1 In vitro culture of cell lines 173 C.1.1 Passage of cell lines cultured in vitro 173 C.1.2 Cryogenic storage and thawing of cell lines 174 C.2 Immunouorescent labeling of E-cadherin 174 C.3 Growth factor treatment of MCF 10A epithelial cells 175 C.3.1 Treatment with increasing concentrations of epidermal growth factor (EGF) 175 C.3.2 Treatment with constant concentration of epidermal growth factor (EGF) 176 C.3.3 Treatment with transforming growth factor β1 (TGFβ1) 177 C.4 Isolation of vital cells from patient samples 178 C.4.1 Negative depletion of specic populations from cell suspensions by magnetic bead sorting 178 C.4.2 Isolation of vital circulating tumor cells (CTC) from the blood of patients with mamma carcinoma 179 C.4.3 Isolation of healthy peripheral blood mononuclear cells (PBMC) from the blood of patients and donors 179 C.4.4 Isolation of vital cancer cells from tumor samples of surgical resections of various carcinomas 180 C.5 Immunohistochemical staining of paranized tissue slices of tumor tissue 82 Bibliography 186 / Carcinoma cells have been shown to be softer than cells from their tissue of origin, healthy epithelia. This softening effect has been predicted to drive tumor cell migration and ergo metastases, but only circumstantial evidence exists for this. Carcinoma development is also generally viewed as driven by an epithelial to mesenchymal transition (EMT), a process that governs epithelial restructuring and heavily interferes with the cytoskeleton. I therefore hypothesized that EMT drives cell softening in carcinomas, which in turn leads to aggressive and invasive tumors. In the presented work, I pursue the verification of this hypothesis. I investigated the influence of EMT on cell softening in vitro, yet found no directed development of cell body softness with EMT progression. With vital single cancer cells that I isolated from surgical resections, I explored the mechanics of invasive, and non-invasive tumors ex vivo and saw a clear correlation of tumor aggressiveness with cell softness in four different types of carcinomas. There was however no correlation between E-cadherin in the cell membrane of isolated cancer cells, which I used as a marker for EMT progression, and the aggressiveness of the respective carcinomas or the softness of their cells. I employed machine learning (ML) to classify cancer cells based on their mechanical properties in silico, but found clear limits to that approach. In this work, I have shown for the very first time ex vivo how cell softening is an ongoing process during carcinoma development and increased aggressiveness is linked to increased softness. I also excluded EMT, which has long been deemed a driver of cell invasion, as a possible origin for cell softening. Together with results from ML classification, this points to increased heterogeneity in mechanical properties of cancer cells by deregulation as a main contributor to carcinoma invasion.:1 Introduction 1 2 Background 11 2.1 The cytoskeleton of eukaryotic cells 12 2.2 The actin-E-cadherin-complex 17 2.2.1 E-cadherin 17 2.2.2 The Wnt/β-catenin pathway 18 2.2.3 Actin-E-cadherin dynamics 19 2.3 The epithelial to mesenchymal transition (EMT) 21 2.3.1 Epithelial and mesenchymal cells 21 2.3.2 Classical EMT 22 2.3.3 EMT in carcinoma development 23 2.4 Carcinoma development 25 2.4.1 Growth and spread 25 2.4.2 Tumor grading and staging 26 2.4.3 Carcinoma development outside of EMT 29 2.5 Cell mechanics in migration and invasion 31 3 Materials & methods 37 3.1 The Optical Stretcher as a main measurement device for cellular softness and E-cadherin level 38 3.1.1 Deformation by radiation pressure 39 3.1.2 Viability in an OS 43 3.1.3 Data acquisition and evaluation 46 3.2 Kelvin Voigt (KV) modeling 50 3.3 Machine learning 53 3.3.1 Interpreting and evaluating classications 54 3.3.2 Data preparation 58 3.3.3 Support vector machines (SVM) 58 3.3.4 Random forest (RF) 64 3.3.5 Permutation importance 67 3.4 Statistical analysis 68 3.4.1 Two one-sided tests (TOST) as a statistical test for equivalence 69 3.5 In vitro model systems for eukaryotic cells, their culture, and preparation 71 3.5.1 Cell lines 71 3.5.2 Cell culture 73 3.5.3 Fluorescent labeling of E-cadherin 73 3.6 Isolation of cancer cells from primary samples 75 3.6.1 Isolation of cancer cells from blood samples 75 3.6.2 Isolation of cancer cells from surgical resections 77 4 Results & discussion 79 4.1 In vitro growth factor induced EMT 81 4.1.1 EGF induced EMT is not correlated to cell softening in MCF 10A epithelial cells 82 4.1.2 TGFβ1 induced EMT is not correlated to cell softening in MCF 10A epithelial cells 87 4.1.3 Summary 91 4.2 Ex vivo vital tumor cells from liquid biopsies and surgical resections 94 4.2.1 Database analysis reveals that there is no systematic change of EMT related markers over the course of carcinoma progression 96 4.2.2 Vital single cells isolated from liquid biopsies of breast cancer patients can be distinguished from healthy cells of their natural surrounding 99 4.2.3 Cell softening is correlated to aggressiveness in tumor cells isolated from surgical resections 110 4.2.4 EMT progression is connected to neither cell softening nor aggressiveness in tumor cells isolated from surgical resections 120 4.2.5 Summary 123 4.3 In silico Machine learning as means to assess the predictive power of cell mechanics 127 4.3.1 Parameters from OS measurements 128 4.3.2 In vitro discrimination of cell types in a breast cancer cell line panel 129 4.3.3 Ex vivo discrimination of breast cancer cells and PBMC isolated from liquid biopsies 136 4.3.4 Summary 143 5 Conclusion & outlook 147 A Additional data and information 161 A.1 Optimization of support vector machines (SVM) and random forest (RF) machine learning approaches 161 A.1.1 Optimization of the training set size in SVM and RF machine learning approaches 161 A.1.2 Optimization of the SVM machine learning algorithm 161 A.1.3 Optimization of the RF machine learning algorithm 163 A.2 List of features for machine learning based classication 164 A.2.1 List of features used for classication of my in vitro cell line panel 164 A.2.2 List of features for classication of circulating tumor cells isolated from the blood of patients with mamma carcinoma 166 A.3 Activity parameter A of cells isolated from the blood samples of breast cancer patients 170 B Materials and reagents 171 B.1 Cell culture media 171 B.1.1 Medium for MCF 10A cells 171 B.1.2 Medium for MDA-MB-436 and MDA-MB-231 cells 171 B.1.3 Medium for NIH/3T3 cells 172 B.2 Ringer lactate buer for tissue transport and storage 172 B.3 MACS buffer 172 C Protocols 173 C.1 In vitro culture of cell lines 173 C.1.1 Passage of cell lines cultured in vitro 173 C.1.2 Cryogenic storage and thawing of cell lines 174 C.2 Immunouorescent labeling of E-cadherin 174 C.3 Growth factor treatment of MCF 10A epithelial cells 175 C.3.1 Treatment with increasing concentrations of epidermal growth factor (EGF) 175 C.3.2 Treatment with constant concentration of epidermal growth factor (EGF) 176 C.3.3 Treatment with transforming growth factor β1 (TGFβ1) 177 C.4 Isolation of vital cells from patient samples 178 C.4.1 Negative depletion of specic populations from cell suspensions by magnetic bead sorting 178 C.4.2 Isolation of vital circulating tumor cells (CTC) from the blood of patients with mamma carcinoma 179 C.4.3 Isolation of healthy peripheral blood mononuclear cells (PBMC) from the blood of patients and donors 179 C.4.4 Isolation of vital cancer cells from tumor samples of surgical resections of various carcinomas 180 C.5 Immunohistochemical staining of paranized tissue slices of tumor tissue 82 Bibliography 186

L'objet de cette thèse est l'étude comparative de la réponse de cellules saines et malignes à la micro- et la nano-topographie de surface. L'interaction avec des stries de profondeur nanométrique est étudiée grâce à une méthode statistique. Nous démontrons que les cellules saines s'alignent plutôt sur des stries profondes, et que les cellules cancéreuses sont plus sensibles aux stries peu profondes. L'analyse des noyaux révèle qu’ils suivent l'alignement des corps cellulaires plus fidèlement dans le cas des cellules cancéreuses et que les noyaux de ces dernières sont plus sensibles aux stries de faible profondeur. Sur des micro-piliers nous démontrons que les cellules d’ostéosarcomes sont capables de se déformer et de faire adopter à leurs noyaux la forme de l'espace entre les piliers. Ceci ne se produit que durant la phase initiale d'adhésion pour les cellules saines. Les cellules immortalisées présentent un niveau intermédiaire de déformation. Quand l'espacement entre piliers est réduit, des différences de déformation sont révélées entre les lignées cancéreuses testées. La déformation est aussi liée au caractère cancéreux de kératinocytes et à l'expression de Cdx2 dans des lignées d'adénocarcinomes. Nous avons tenté d'expliquer ce mécanisme de déformation en l'attribuant au cytosquelette grâce à des analyses en microscopie confocale et avec des inhibiteurs du cytosquelette. L'imagerie de cellules vivantes a permis d'observer que les cellules sont très mobiles même quand elles sont déformées, que la mitose nécessite la perte de la déformation et que la déformation après mitose est plus rapide que la déformation pendant l'adhésion initiale des cellules. / This thesis deals with the differential response of healthy and cancerous cells to surface topography at the nanoscale and the microscale. Using a statistical method we developed we studied the interactions of cells with grooves of nanoscale depth. We demonstrate that healthy cells have a greater ability to align with deeper grooves, whereas cancerous cells are more sensitive to shallow grooves. Analysis reveals that the nucleus follows the alignment of the cell body more closely in cancerous cells, and that the nucleus of cancerous cells is more sensitive to shallow grooves.On microscale pillars we demonstrate for the first time that osteosarcoma cells deform to adopt the surface topography and that the deformation extends to the interior of the cell and in particular to the nucleus. We show that healthy cells only deform during the initial stages of adhesion and that immortalized cells show intermediate deformation between the healthy and cancerous cells. When the spacing between the pillars is reduced, differences in the deformation of different cancerous cell lines are detected. Deformation was also found to be related to the malignancy in keratinocytes, and related to the expression of Cdx2 in adenocarcinoma. The mechanism of deformation is tentatively attributed to the cytoskeleton and attempts to identify the main actors of deformation were performed using confocal microscopy and cytoskeleton inhibitors. Live cell imaging experiments reveal that the deformed cells are very mobile on the surfaces, loss of deformation is necessary for mitosis to occur and deformation after mitosis is more rapid than initial deformation upon adhesion to surfaces.

Besides biochemical and molecular regulation, the migration and invasion of cells is controlled by the environmental mechanics and cellular mechanics. Hence, the mechanical phenotype of cells, such as fibroblasts, seems to be crucial for the migratory capacity in confined 3D extracellular matrices. Recently, we have shown that the migratory and invasive capacity of mouse embryonic fibroblasts depends on the expression of the Rho-GTPase Rac1, similarly it has been demonstrated that the Rho-GTPase Cdc42 affects cell motility. The p21-activated kinase (PAK) is an effector down-stream target of both Rho-GTPases Rac1 and Cdc42, and it can activate via the LIM kinase-1 its down-stream target cofilin and subsequently support the cell migration and invasion through the polymerization of actin filaments. Since Rac1 deficient cells become mechanically softer than controls, we investigated the effect of group I PAKs and PAK1 inhibition on cell mechanics in the presence and absence of Rac1. Therefore, we determined whether mouse embryonic fibroblasts, in which Rac1 was knockedout, and control cells, displayed cell mechanical alterations after treatment with group I PAKs or PAK1 inhibitors using a magnetic tweezer (adhesive cell state) and an optical cell stretcher (non-adhesive cell state). In fact, we found that group I PAKs and Pak1 inhibition decreased the stiffness and the Young’s modulus of fibroblasts in the presence of Rac1 independent of their adhesive state. However, in the absence of Rac1 the effect was abolished in the adhesive cell state for both inhibitors and in their nonadhesive state, the effect was abolished for the FRAX597 inhibitor, but not for the IPA3 inhibitor. The migration and invasion were additionally reduced by both PAK inhibitors in the presence of Rac1. In the absence of Rac1, only FRAX597 inhibitor reduced their invasiveness, whereas IPA3 had no effect. These findings indicate that group I PAKs and PAK1 inhibition is solely possible in the presence of Rac1 highlighting Rac1/PAK I (PAK1, 2, and 3) as major players in cell mechanics.

No description available.

No description available.