Systemic autoimmunity describes a group of detrimental conditions, characterized by loss of immunologic self-tolerance. Pattern recognition receptors (PRR) detect recurrent microbial structures including nucleic acids. Nucleic acid-specific PRRs are not well equipped to discriminate between self and non-self nucleic acids and their aberrant activation leads to autoimmune conditions, driven by chronic activation of the type I interferon (IFN) system. This concept has been established by research on the molecular mechanisms underlying the rare Aicardi-Goutières syndrome (AGS). Loss of function mutations in the gene SAMHD1 cause AGS. SAMHD1 was first described as a deoxyribonucleotide (dNTP) triphosphohydrolase (dNTPase) and its activity is tightly regulated during the cell cycle to ensure the correct cellular supply of dNTPs. Cells decrease SAMHD1 dNTPase activity during S phase where the DNA must be replicated and once the S phase is over, dNTPase activity is restored and the dNTP levels are reduced. Loss of SAMHD1 causes an increase of the cellular dNTP concentration during phases of the cell cycle, a well-known trigger for DNA damage, but its consequences has not been addressed yet thoroughly. Recently, SAMHD1 has been also reported to promote homologous recombination directly at the site of DNA double strand breaks (DSB) upon genotoxic stress. By interacting with the protein CtIP, SAMHD1 helped to coordinate the MRN complex and promotes DSB repair. Loss of SAMHD1 impaired this repair mechanism, causing genome instability. Interestingly, this activity of SAMHD1 has also been recently shown to promote restart of stalled replication forks. Lack of SAMHD1 lead to an increase of stalled replication forks and DSBs. How the different activities of SAMHD1 remain balanced and are activated under specific conditions still remains unknown. Additionally, inactivating mutations in SAMHD1 have recurrently been identified in various types of cancers, raising the question, if the protein might function as a tumour suppressor. However, up to date, no in vivo study has addressed the role of SAMHD1 in preventing DNA damage or cancer development, and its relationship to an uncontrolled type I IFN response. In this work, Samhd1-deficient mice were screened in search for sign of DNA damage and an increase in micronucleated erythrocytes, a hallmark for genome instability, was found in comparison with their littermate controls. This increase was still present upon inactivation of nucleic acid sensing pathways, indicating that it was independent of the status of type I IFN response. HSC competitive transplantation experiments with Samhd1-deficient and control HSCs showed a minor contribution of Samhd1 in maintaining lymphogenesis. Despite these findings, Samhd1-deficient mice do not develop any autoimmune disease nor cancer up to 2 years of observation. Previous reports showed a possible relation between loss of SAMHD1 and p53 activation. We inactivated p53 in Samhd1 ko mice, which resulted in accelerated lethality and an earlier onset of tumour formation when compared with p53 ko mice. However, the underlying molecular mechanisms of both observations remains to be fully elucidated. In contrast to the results with p53, inactivation of DNA mismatch repair (knockout of Pms2) in Samhd1 ko mice, had no effect on the tumour-free survival in comparison with Pms2 ko mice. Inactivation of either p53 or Pms2 in Samhd1 ko mice did not altered the spontaneous type I IFN activation. To understand better the different activities described for Samhd1 – dNTPase and DNA damage-related activities –, the dNTPase-inactivating mutations HD238/239AA were knocked into the endogenous Samhd1 gene using CRISPR/Cas9. Using this mouse model, we found that the mutant Samhd1 protein is rapidly degraded in the proteasome, leading to almost complete absence of Samhd1 in the new mouse strain, as seen in patient with similar mutation in Samhd1. These results demonstrated that in patients with mutations in the dNTPase domain, the phenotype is most likely driven by a complete absence of Samhd1 and only by a selective loss of the dNTPase activity. Our work provides new insights in the understanding of Samhd1 as regulator of DNA damage and establishes new ground for further research on the link between DNA damage and type I IFN response.

Adrenocortical carcinoma (ACC) is a rare, but highly aggressive endocrine malignancy. Tumor-related hypercortisolism is present in 60 % of patients and associated with worse outcome. While cancer immunotherapies have revolutionized the treatment of many cancer entities, the results of initial studies of different immune checkpoint inhibitors in ACC were heterogeneous. Up to now, five small clinical trials with a total of 121 patients have been published and demonstrated an objective response in only 17 patients. However, one of the studies, by Raj et al., reported a clinically meaningful disease control rate of 52 % and a median overall survival of almost 25 months suggesting that a subgroup of ACC patients may benefit from immunotherapeutic approaches. Following the hypothesis that some ACCs are characterized by a glucocorticoid-induced T lymphocytes depletion, several studies were performed as part of the presented thesis. First, the immune cell infiltration in a large cohort of 146 ACC specimens was investigated. It was demonstrated for the first time, and against the common assumption, that ACCs were infiltrated not only by FoxP3+ regulatory T cells (49.3 %), but also that a vast majority of tumor samples was infiltrated by CD4+ TH cells (74 %) and CD8+ cytotoxic T cells (84.3 %), albeit the immune cell number varied heterogeneously and was rather low (median: 7.7 CD3+ T cells / high power field, range: 0.1-376). Moreover, the presence of CD3+-, CD4+- and CD8+ ACC-infiltrating lymphocytes was associated with an improved recurrence-free (HR: 0.31 95 % CI 0.11-0.82) and overall survival (HR: 0.47 96 % CI 0.25-0.87). Particularly, patients with tumor-infiltrating CD4+ TH cells without glucocorticoid excess had a significantly longer overall survival compared to patients with T cell-depleted ACC and hypercortisolism (121 vs. 27 months, p = 0.004). Hence, the impact of glucocorticoids might to some extent be responsible for the modest immunogenicity in ACC as hypercortisolism was reversely correlated with the number of CD4+ TH cells. Accordingly, CD3+ T cells co-cultured with steroidogenic NCI-H295R ACC cells demonstrated in vitro an enhanced anti-tumoral cytotoxicity by secreting 747.96 ±225.53 pg/ml IFN-γ in a therapeutically hormone-depleted microenvironment (by incubation with metyrapone), versus only 276.02 ±117.46 pg/ml IFN-γ in a standard environment with glucocorticoid excess. Other potential biomarkers to predict response to immunotherapies are the immunomodulatory checkpoint molecules, programmed cell death 1 (PD-1) and its ligand PD-L1, since both are targets of antibodies used therapeutically in different cancer entities. In a subcohort of 129 ACCs, expressions of both molecules were heterogeneous (PD-1 17.4 %, range 1-15; PD-L1 24.4 %, range 1 - 90) and rather low. Interestingly, PD-1 expression significantly influenced ACC patients´ overall (HR: 0.21 95 % CI 0.53-0.84) and progression- free survival (HR: 0.30 95 % CI 0.13-0.72) independently of established factors, like ENSAT tumor stage, resection status, Ki67 proliferation index and glucocorticoid excess, while PD-L1 had no impact. In conclusion, this study provides several potential explanations for the heterogeneous results of the immune checkpoint therapy in advanced ACC. In addition, the establishment of PD-1 as prognostic marker can be easily applied in routine clinical care, because it is nowadays anyway part of a detailed histo-pathological work-up. Furthermore, these results provide the rationale and will pave the way towards a combination therapy using immune checkpoint inhibitors as well as glucocorticoid blockers. This will increase the likelihood of re-activating the immunological anti-tumor potential in ACC. However, this will have to be demonstrated by additional preclinical in vivo experiments and finally in clinical trials with patients. / Das Nebennierenrindenkarzinom (ACC) ist ein seltenes, aber äußerst aggressives endokrines Malignom. Ein tumorbedingter Hyperkortisolismus liegt bei 60 % der Patienten vor und ist mit einer schlechteren Prognose assoziiert. Während Krebsimmuntherapien die Behandlung vieler Krebsentitäten revolutioniert haben, waren die Ergebnisse der ersten Studien zu verschiedenen Immun-Checkpoint-Inhibitoren beim ACC heterogen. Die fünf klinischen Studien mit insgesamt 121 Patienten zeigten ein objektives Ansprechen bei nur 17 Patienten. Eine Studie von Raj et al. berichtete über eine klinisch bedeutsame Krankheitskontrollrate von 52 % und ein medianes Gesamtüberleben von fast 25 Monaten. Diese beträchtliche Anti-Tumor-Aktivität legt nahe, dass eine Subgruppe von ACC-Patienten von immuntherapeutischen Ansätzen profitieren könnte. Der Hypothese folgend, dass einige ACCs durch eine Glukokortikoid-induzierte T Lymphozyten-Depletion gekennzeichnet sind, wurden im Rahmen der vorliegenden Arbeit mehrere Studien durchgeführt. Zunächst wurde die Immunzellinfiltration in einer großen Kohorte von 146 ACC-Proben untersucht. Entgegen der verbreiteten Annahme konnte erstmals gezeigt werden, dass ACCs nicht nur von FoxP3+ regulatorischen T Zellen (49,3 %), sondern die Mehrheit der ACCs von CD4+ TH (74 %) und CD8+ zytotoxischen T Zellen (84,3 %) infiltriert wurde, wenngleich die Immunzellanzahl heterogen und eher gering war (7,7 CD3+ T Zellen/HPF). Darüber hinaus war die Präsenz von CD3+-, CD4+- und CD8+ ACC-infiltrierenden Lymphozyten mit einem rezidivfreien (HR:0,31;95%CI0,11-0,82) und verbesserten Gesamtüberleben (HR:0,47;95%CI 0,25-0,87) assoziiert. Insbesondere Patienten mit tumorinfiltrierenden CD4+ TH Zellen ohne Glukokortikoid-Überschuss hatten im Vergleich zu Patienten mit T Zell-depletiertem ACC und Hyperkortisolismus ein signifikant län- geres Gesamtüberleben (121 vs. 27 Monate; p = 0.004). Daher könnte die Wirkung von Glukokortikoiden für die moderate Immunogenität verantwortlich sein, da Hyperkortisolismus umgekehrt mit der Zahl der CD4+ TH Zellen korreliert war. Dementsprechend zeigten CD3+ T Zellen, die mit steroid-produzierenden NCI-H295R ACC-Zellen co-kultiviert wurden, in vitro eine erhöhte anti-tumorale Zytotoxizität in einem durch Metyrapon-induzierten Mikromilieu ohne Glukokortikoide im Vergleich zu einem Glukokortikoid- Überschuss (IFN-γ Sekretion: 747,96 pg/ml vs. 276,02 pg/ml). Andere potenzielle Biomarker zur Vorhersage des Ansprechens auf Immuntherapien sind die immunmodulatorischen Checkpoint-Moleküle, Programmed cell death 1 (PD-1) und sein Ligand PD-L1, da beide Ziele von Antikörpern sind, die therapeutisch bei verschiedenen Krebsentitäten eingesetzt werden. In einer Subkohorte von 129 ACCs waren die Expressionen beider Moleküle heterogen (PD-1 17,4 %, PD-L1 24,4 %). Interessanterweise beeinflusste die PD-1-Expression signifikant das Gesamtüberleben (HR: 0,21; 95 % CI 0,53-0,84) und das progressionsfreie Überleben (HR: 0,30; 95 % CI 0,13-0.72) unabhängig von etablierten Faktoren, wie dem ENSAT Tumorstadium, Resektionsstatus, Ki67 Proliferationsindex und Glukokortikoid-Überschuss, während PD-L1 keinen Einfluss hatte. Zusammenfassend liefert diese Studie mehrere mögliche Erklärungen für die heterogenen Ergebnisse der Immun-Checkpoint-Therapie bei fortgeschrittenem ACC. Darüber hinaus ist die Etablierung von PD-1 als prognostischer Marker anwendbar als Teil einer detaillierten histo-pathologischen Untersuchung. Zudem liefern diese Ergebnisse die Rationale und ebnen den Weg für eine Kombinationstherapie von Immun-Check- point-Inhibitoren sowie Anti-Glukokortikoiden, um die Wahrscheinlichkeit einer Reaktivierung des immu- nologischen Anti-Tumor-Potenzials beim ACC zu erhöhen. Dies muss jedoch durch zusätzliche präklinische in vivo Experimente und schließlich in klinischen Studien mit Patienten nachgewiesen werden.

Populationen kanzeröser Zellen können aus verschiedenen Subpopulationen mit distinkten phänotypischen Profilen bestehen. Diese Dissertation verwendet mathematische Modellierung sowie die Analyse von Einzelzell-Genexpressionsdaten zur Beantwortung von Fragen über die Entstehung, das Wachstum und die Behandlung von Tumoren im Kontext einer solchen intratumoralen Heterogenität. / Tumoural cell populations may consist of different subpopulations with distinct phenotypic profiles. This thesis applies mathematical modelling as well as the analysis of single-cell gene expression data to questions related to the emergence, growth and treatment of tumours in the context of such an intratumoural heterogeneity.

In dieser Arbeit ist die Synthese von funktionalisiertem Nanodiamant mit bioaktiven Substanzen, welche vor allem als Wirkstofftransporter eingesetzt werden sollen, beschrieben. Dazu werden zum einen bereits bekannte Anbindungsmöglichkeiten an Nanodiamant, wie zum Beispiel die Klick-Reaktion, sowie die Ausbildung von Amidbrücken verwendet. Zum anderen werden neuartige Funktionalisierungsmöglichkeiten wie Protein Ligation und Thioharnstoffbrücken verwendet und somit das Repertoire an bekannten Anbindungsreaktion erweitert. Des weiteren wurde ein multifunktionales Nanodiamantsystem synthetisiert. Dieses ist in der Lage, zwei verschiedene Moleküle auf einem Partikel zu immobilisieren. Die verwendeten Methoden ermöglichen die Anbindung verschiedener Substanzen aus unterschiedlichen Molekülgruppen an Nanodiamanten und sind somit universell einsetzbar. / Because of its unique physical and chemical properties, nanodiamond can be used in a variety of scientific fields, such as in medical and biological research. In this thesis, new ways to covalently bind substances to the nanodiamond surface have been explored, which also can be used in biological systems. ...

Molecular genetic analyses, such as mutation analyses, are becoming increasingly important in the tumor field, especially in the context of therapy stratification. The identification of the underlying tumor entity is crucial, but can sometimes be difficult, for example in the case of metastases or the so-called Cancer of Unknown Primary (CUP) syndrome. In recent years, methylome and transcriptome utilizing machine learning (ML) approaches have been developed to enable fast and reliable tumor and tumor subtype identification. However, so far only methylome analysis have become widely used in routine diagnostics. The present work addresses the utility of publicly available RNA-sequencing data to determine the underlying tumor entity, possible subgroups, and potential therapy options. Identification of these by ML - in particular random forest (RF) models - was the first task. The results with test accuracies of up to 99% provided new, previously unknown insights into the trained models and the corresponding entity prediction. Reducing the input data to the top 100 mRNA transcripts resulted in a minimal loss of prediction quality and could potentially enable application in clinical or real-world settings. By introducing the ratios of these top 100 genes to each other as a new database for RF models, a novel method was developed enabling the use of trained RF models on data from other sources. Further analysis of the transcriptomic differences of metastatic samples by visual clustering showed that there were no differences specific for the site of metastasis. Similarly, no distinct clusters were detectable when investigating primary tumors and metastases of cutaneous skin melanoma (SKCM). Subsequently, more than half of the validation datasets had a prediction accuracy of at least 80%, with many datasets even achieving a prediction accuracy of – or close to – 100%. To investigate the applicability of the used methods for subgroup identification, the TCGA-KIPAN dataset, consisting of the three major kidney cancer subgroups, was used. The results revealed a new, previously unknown subgroup consisting of all histopathological groups with clinically relevant characteristics, such as significantly different survival. Based on significant differences in gene expression, potential therapeutic options of the identified subgroup could be proposed. Concludingly, in exploring the potential applicability of RNA-sequencing data as a basis for therapy prediction, it was shown that this type of data is suitable to predict entities as well as subgroups with high accuracy. Clinical relevance was also demonstrated for a novel subgroup in renal cell carcinoma. The reduction of the number of genes required for entity prediction to 100 genes, enables panel sequencing and thus demonstrates potential applicability in a real-life setting. / Molekulargenetische Analysen, wie z. B. Mutationsanalysen, gewinnen im Tumorbereich zunehmend an Bedeutung, insbesondere im Zusammenhang mit der Therapiestratifizierung. Die Identifizierung der zugrundeliegenden Tumorentität ist von entscheidender Bedeutung, kann sich aber manchmal als schwierig erweisen, beispielsweise im Falle von Metastasen oder dem sogenannten Cancer of Unknown Primary (CUP)-Syndrom. In den letzten Jahren wurden Methylom- und Transkriptom-Ansätze mit Hilfe des maschinellen Lernens (ML) entwickelt, die eine schnelle und zuverlässige Identifizierung von Tumoren und Tumorsubtypen ermöglichen. Bislang werden jedoch nur Methylomanalysen in der Routinediagnostik eingesetzt. Die vorliegende Arbeit befasst sich mit dem Nutzen öffentlich zugänglicher RNA-Sequenzierungsdaten zur Bestimmung der zugrunde liegenden Tumorentität, möglicher Untergruppen und potenzieller Therapieoptionen. Die Identifizierung dieser durch ML - insbesondere Random-Forest (RF)-Modelle - war die erste Aufgabe. Die Ergebnisse mit Testgenauigkeiten von bis zu 99 % lieferten neue, bisher unbekannte Erkenntnisse über die trainierten Modelle und die entsprechende Entitätsvorhersage. Die Reduktion der Eingabedaten auf die 100 wichtigsten mRNA-Transkripte führte zu einem minimalen Verlust an Vorhersagequalität und könnte eine Anwendung in klinischen oder realen Umgebungen ermöglichen. Durch die Einführung des Verhältnisses dieser Top 100 Gene zueinander als neue Datenbasis für RF-Modelle wurde eine neuartige Methode entwickelt, die die Verwendung trainierter RF-Modelle auf Daten aus anderen Quellen ermöglicht. Eine weitere Analyse der transkriptomischen Unterschiede von metastatischen Proben durch visuelles Clustering zeigte, dass es keine für den Ort der Metastasierung spezifischen Unterschiede gab. Auch bei der Untersuchung von Primärtumoren und Metastasen des kutanen Hautmelanoms (SKCM) konnten keine unterschiedlichen Cluster festgestellt werden. Mehr als die Hälfte der Validierungsdatensätze wiesen eine Vorhersagegenauigkeit von mindestens 80% auf, wobei viele Datensätze sogar eine Vorhersagegenauigkeit von 100% oder nahezu 100% erreichten. Um die Anwendbarkeit der verwendeten Methoden zur Identifizierung von Untergruppen zu untersuchen, wurde der TCGA-KIPAN-Datensatz verwendet, welcher die drei wichtigsten Nierenkrebs-Untergruppen umfasst. Die Ergebnisse enthüllten eine neue, bisher unbekannte Untergruppe, die aus allen histopathologischen Gruppen mit klinisch relevanten Merkmalen, wie z. B. einer signifikant unterschiedlichen Überlebenszeit, besteht. Auf der Grundlage signifikanter Unterschiede in der Genexpression konnten potenzielle therapeutische Optionen für die identifizierte Untergruppe vorgeschlagen werden. Zusammenfassend lässt sich sagen, dass bei der Untersuchung der potenziellen Anwendbarkeit von RNA-Sequenzierungsdaten als Grundlage für die Therapievorhersage gezeigt werden konnte, dass diese Art von Daten geeignet ist, sowohl Entitäten als auch Untergruppen mit hoher Genauigkeit vorherzusagen. Die klinische Relevanz wurde auch für eine neue Untergruppe beim Nierenzellkarzinom demonstriert. Die Verringerung der für die Entitätsvorhersage erforderlichen Anzahl von Genen auf 100 Gene ermöglicht die Sequenzierung von Panels und zeigt somit die potenzielle Anwendbarkeit in der Praxis.

Lung cancer is the main cause of cancer-related deaths worldwide. Despite the availability of several targeted therapies and immunotherapies in the clinics, the prognosis for lung cancer remains poor. A major problem for the low benefit of these therapies is intrinsic and acquired resistance, asking for pre-clinical models for closer investigation of predictive biomarkers for refined personalized medicine and testing of possible combination therapies as well as novel therapeutic approaches to break resistances. One third of all lung adenocarcinoma harbor mutations in the KRAS gene, of which 39 % are transitions from glycine to cysteine in codon 12 (KRASG12C). Being considered “undruggable” in previous decades, KRASG12C-inhibitors now paved the way into the standard-of-care for lung adenocarcinoma treatment in the clinics. Still, the overall response rates as well as overall survival of patients treated with KRASG12C-inhibitors are sobering. Therefore, 3D KRASG12C-biomarker in vitro models were developed based on a decellularized porcine jejunum (SISmuc) using commercial and PDX-derived cell lines and characterized in regards of epithelial-mesenchymal-transition (EMT), stemness, proliferation, invasion and c-MYC expression as well as the sensitivity towards KRASG12C-inhibiton. The phenotype of lung tumors harboring KRAS mutations together with a c-MYC overexpression described in the literature regarding invasion and proliferation for in vivo models was well represented in the SISmuc models. A higher resistance towards targeted therapies was validated in the 3D models compared to 2D cultures, while reduced viability after treatment with combination therapies were exclusively observed in the 3D models. In the test system neither EMT, stemness nor the c-MYC expression were directly predictive for drug sensitivity. Testing of a panel of combination therapies, a sensitizing effect of the aurora kinase A (AURKA) inhibitor alisertib for the KRASG12C-inhibitor ARS-1620 directly correlating with the level of c-MYC expression in the corresponding 3D models was observed. Thereby, the capability of SISmuc tumor models as an in vitro test system for patient stratification was demonstrated, holding the possibility to reduce animal experiments. Besides targeted therapies the treatment of NSCLC with oncolytic viruses (OVs) is a promising approach. However, a lack of in vitro models to test novel OVs limits the transfer from bench to bedside. In this study, 3D NSCLC models based on the SISmuc were evaluated for their capability to perform efficacy and risk assessment of oncolytic viruses (OVs) in a pre-clinical setting. Hereby, the infection of cocultures of tumor cells and fibroblasts on the SISmuc with provided viruses demonstrated that in contrast to a wildtype herpes simplex virus 1 (HSV-1) based OV, the attenuated version of the OV exhibited specificity for NSCLC cells with a more advanced and highly proliferative phenotype, while fibroblasts were no longer permissive for infection. This approach introduced SISmuc tumor models as novel test system for in vitro validation of OVs. Finally, a workflow for validating the efficacy of anti-cancer therapies in 3D tumor spheroids was established for the transfer to an automated platform based on a two-arm-robot system. In a proof-of-concept process, H358 spheroids were characterized and treated with the KRASG12C-inhibitor ARS-1620. A time- and dose-dependent reduction of the spheroid area after treatment was defined together with a live/dead-staining as easy-to-perform and cost-effective assays for automated drug testing that can be readily performed in situ in an automated system. / Lungentumoren sind die Hauptursache für krebsbedingte Todesfälle weltweit. Trotz der Verfügbarkeit diverser zielgerichteter Therapien und Immuntherapien im klinischen Alltag ist die Prognose für Lungenkrebs nach wie vor schlecht. Eine Hauptursache hierfür sind intrinsische und erworbene Resistenzen. Hieraus ergibt sich ein Bedarf für präklinische Modelle zur genaueren Untersuchung prädiktiver Biomarker für eine verbesserte personalisierte Medizin und zur Testung von Kombinationstherapien sowie neuartiger therapeutischer Ansätze, um bestehende Resistenzen zu brechen. Ein Drittel aller Lungen-Adenokarzinome weisen Mutationen im KRAS-Gen auf, von denen 39 % Transitionen von Glycin zu Cystein in Codon 12 (KRASG12C) darstellen. Obwohl KRAS in den vergangenen Jahrzehnten als "unbehandelbar" galt, haben sich KRASG12C-Inhibitoren nun den Weg in die klinische Standardbehandlung von Lungen-Adenokarzinomen gebahnt. Jedoch sind die Ansprech- und Überlebensraten von Patienten, die mit KRASG12C-Inhibitoren behandelt werden, ernüchternd. Daher wurden in dieser Arbeit 3D KRASG12C-Biomarker in vitro Modelle basierend auf dezellularisierten Schweinedünndarm (SISmuc) unter Verwendung kommerzieller und PDX-abgeleiteter Zelllinien aufgebaut und hinsichtlich der epithelial-mesenchymalen Transition (EMT), Stammzell-Eigenschaften, Proliferation, Invasion und c MYC-Expression sowie der Sensitivität gegenüber KRASG12C-Inhibitoren charakterisiert. Der in der Literatur für in vivo Modelle beschriebene Phänotyp von Lungentumoren mit KRAS-Mutationen und c-MYC-Überexpression in Bezug auf Invasion und Proliferation war in den SISmuc-Modellen reproduzierbar. Während in den 3D Modellen erhöhte Resistenz gegenüber zielgerichteten Therapien im Vergleich zu 2D beobachtet wurde, konnte eine verringerte Viabilität nach der Behandlung mit Kombinationstherapien ausschließlich in den 3D Modellen beobachtet werden. Im Test-System zeigten sich weder EMT noch die c-MYC-Expression als direkt prädiktiv für die Sensitivität gegenüber KRASG12C-Inhibitoren. Bei der Prüfung von verschiedenen Kombinationstherapien, wurde eine sensibilisierende Wirkung des Aurora-Kinase A (AURKA)-Inhibitors Alisertib für den KRASG12C-Inhibitor ARS-1620 beobachtet, welche direkt mit dem Grad der c-MYC-Expression in den entsprechenden 3D-Modellen korrelierte. Hierdurch konnte die Eignung von SISmuc Tumor Modellen als in vitro Test-System zur Patienten-Stratifizierung gezeigt werden, welches die Möglichkeit einer Reduktion von Tierversuchen birgt. Neben zielgerichteten Therapien ist die Behandlung von NSCLC mit onkolytischen Viren (OVs) ein vielversprechender Ansatz. Es mangelt jedoch an in vitro Modellen, um neue OVs in einer präklinischen Umgebung zu testen. Hierfür wurden 3D-NSCLC-Modelle auf der Grundlage der SISmuc bezüglich ihrer Eignung zur Durchführung von Wirksamkeits- und Risikobewertungen von OVs untersucht. Dabei zeigte die Infektion von Kokulturen aus Tumorzellen und Fibroblasten auf der SISmuc mit bereitgestellten Viren, dass die abgeschwächte Version des OV im Gegensatz zu einem auf dem Wildtyp des Herpes Simplex Virus 1 (HSV-1) basierenden OV eine Spezifität für NSCLC-Zellen mit einem fortgeschritteneren und stark proliferativen Phänotyp aufwies, während Fibroblasten sich für eine Infektion nicht länger permissiv zeigten. Dieser Ansatz stellt unter Beweis, dass SISmuc-Tumormodelle sich als neues Test-System zur in vitro Prüfung von OVs eignen. Schließlich wurde ein Arbeitsablauf zur Validierung der Wirksamkeit von Krebstherapien in 3D-Tumor-Sphäroiden für die Übertragung auf eine automatisierte Plattform auf der Grundlage eines zweiarmigen Robotersystems entwickelt. In einem Proof-of-Concept-Prozess wurden H358-Sphäroide charakterisiert und mit dem KRASG12C-Inhibitor ARS-1620 behandelt. Eine zeit- und dosisabhängige Reduktion der Sphäroid-Fläche nach der Behandlung wurde zusammen mit einer Lebend/Tot-Färbung als einfach durchzuführender und kostengünstiger Assay für automatisierte Medikamententests definiert, welche in situ in einer automatisierten Umgebung durchgeführt werden können.

In many European countries, migrants utilize cancer screening less often than non-migrants. In Germany, in contrast, higher rates of utilization among migrants as compared with non-migrants have been reported. The role of demographic and socioeconomic factors potentially confounding the association between migration status and participation in screening, however, could not be studied. The present study aims to investigate the utilization of cancer screening among migrant and nonmigrant women residing in Germany, adjusting for potential confounders. We used self-reported information from women surveyed on whether they have ever participated in screening for cancer (n = 11,709). The data was collected as part of a cross-sectional representative telephone survey conducted by the Robert Koch-Institute in 2010. We distinguished between three groups of women: (1) respondents of non-German nationality, those who had immigrated to Germany after their birth or those who have two foreign-born parents (“migrants with two-sided migration background”), (2) respondents who only have one foreign-born parent (“migrant with one-sided migration background”), and (3) all others (“non-migrants”). To account for confounders, logistic regression analysis was performed. Only individuals proficient in German were included in the survey, allowing to control for a bias arising from poor language proficiency. 84.9% of nonmigrant women, 82.1% of women with a one-sided, and 70.5% of women with a two-sided migration background had utilized screening for cancer at least once in their lifetime before the survey. The adjusted odds ratios (OR) as compared with nonmigrant women were 0.99 (95% confidence interval [95% CI]: 0.77–1.27) and 0.55 (95% CI: 0.47–0.64), respectively. The study shows that migrant women with a two-sided migration background residing in Germany utilize screening for cancer less often than nonmigrant women—independently of demographic and socioeconomic factors. This is in line with findings from other countries. Likely, barriers that migrant women encounter limit them from taking informed choices. These barriers need to be identified and appropriate measures aiming to enhance informed decision making must be implemented.

Proteins and their interactions are fundamental to cellular life. Disruption of protein-protein, protein-RNA, or protein-DNA interactions can lead to disease, by affecting the function of protein complexes or by affecting gene regulation. A better understanding of these interactions on the molecular level gives rise to new methods to predict protein interaction, and is critical for the rational design of new therapeutic agents that disrupt disease-causing interactions. This thesis consists of three parts that focus on various aspects of protein interactions and their prediction in the context of disease. In the first part of this thesis, we classify interfaces of protein-protein interactions. We do so by systematically computing all binding sites between protein domains in protein complex structures solved by X-ray crystallography. The result is SCOPPI, the Structural Classification of Protein Protein Interfaces. Clustering and classification of geometrically similar interfaces reveals interesting examples comprising viral mimicry of human interface binding sites, gene fusion events, conservation of interface residues, and diversity of interface localisations. We then develop a novel method to predict protein interactions which is based on these structural interface templates from SCOPPI. The method is applied in three use cases covering osteoclast differentiation, which is relevant for osteoporosis, the microtubule-associated network in meiosis, and proteins found deregulated in pancreatic cancer. As a result, we are able to reconstruct many interactions known to the expert molecular biologist, and predict novel high confidence interactions backed up by structural or experimental evidence. These predictions can facilitate the generation of hypotheses, and provide knowledge on binding sites of promising disease-relevant candidates for targeted drug development. In the second part, we present a novel algorithm to search for protein binding sites in RNA sequences. The algorithm combines RNA structure prediction with sequence motif scanning and evolutionary conservation to identify binding sites on candidate messenger RNAs. It is used to search for binding sites of the PTBP1 protein, an important regulator of glucose secretion in the pancreatic beta cell. First, applied to a benchmark set of mRNAs known to be regulated by PTBP1, the algorithm successfully finds significant binding sites in all benchmark mRNAs. Second, collaborators carried out a screen to identify changes in the proteome of beta cells upon glucose stimulation while inhibiting gene expression. Analysing this set of post-transcriptionally controlled candidate mRNAs for PTBP1 binding, the algorithm produced a ranked list of 11 high confident potential PTBP1 binding sites. Experimental validation of predicted targets is ongoing. Overall, identifying targets of PTBP1 and hence regulators of insulin secretion may contribute to the treatment of diabetes by providing novel protein drug targets or by aiding in the design of novel RNA-binding therapeutics. The third part of this thesis deals with gene regulation in disease. One of the great challenges in medicine is to correlate genotypic data, such as gene expression measurements, and other covariates, such as age or gender, to a variety of phenotypic data from the patient. Here, we address the problem of survival prediction based on microarray data in cancer patients. To this end, a computational approach was devised to find genes in human cancer tissue samples whose expression is predictive for the survival outcome of the patient. The central idea of the approach is the incorporation of background knowledge information in form of a network, and the use of an algorithm similar to Google s PageRank. Applied to pancreas cancer, it identifies a set of eight genes that allows to predict whether a patient has a poor or good prognosis. The approach shows an accuracy comparable to studies that were performed in breast cancer or lymphatic malignancies. Yet, no such study was done for pancreatic cancer. Regulatory networks contain information of transcription factors that bind to DNA in order to regulate genes. We find that including background knowledge in form of such regulatory networks gives highest improvement on prediction accuracy compared to including protein interaction or co-expression networks. Currently, our collaborators test the eight identified genes for their predictive power for survival in an independent group of 150 patients. Under a therapeutic perspective, reliable survival prediction greatly improves the correct choice of therapy. Whereas the live expectancy of some patients might benefit from extensive therapy such as surgery and chemotherapy, for other patients this may only be a burden. Instead, for this group, a less aggressive or different treatment could result in better quality of the remaining lifetime. Conclusively, this thesis contributes novel analytical tools that provide insight into disease-relevant interactions of proteins. Furthermore, this thesis work contributes a novel algorithm to deal with noisy microarray measurements, which allows to considerably improve prediction of survival of cancer patients from gene expression data.

In many European countries, migrants utilize cancer screening less often than non-migrants. In Germany, in contrast, higher rates of utilization among migrants as compared with non-migrants have been reported. The role of demographic and socioeconomic factors potentially confounding the association between migration status and participation in screening, however, could not be studied. The present study aims to investigate the utilization of cancer screening among migrant and nonmigrant women residing in Germany, adjusting for potential confounders. We used self-reported information from women surveyed on whether they have ever participated in screening for cancer (n = 11,709). The data was collected as part of a cross-sectional representative telephone survey conducted by the Robert Koch-Institute in 2010. We distinguished between three groups of women: (1) respondents of non-German nationality, those who had immigrated to Germany after their birth or those who have two foreign-born parents (“migrants with two-sided migration background”), (2) respondents who only have one foreign-born parent (“migrant with one-sided migration background”), and (3) all others (“non-migrants”). To account for confounders, logistic regression analysis was performed. Only individuals proficient in German were included in the survey, allowing to control for a bias arising from poor language proficiency. 84.9% of nonmigrant women, 82.1% of women with a one-sided, and 70.5% of women with a two-sided migration background had utilized screening for cancer at least once in their lifetime before the survey. The adjusted odds ratios (OR) as compared with nonmigrant women were 0.99 (95% confidence interval [95% CI]: 0.77–1.27) and 0.55 (95% CI: 0.47–0.64), respectively. The study shows that migrant women with a two-sided migration background residing in Germany utilize screening for cancer less often than nonmigrant women—independently of demographic and socioeconomic factors. This is in line with findings from other countries. Likely, barriers that migrant women encounter limit them from taking informed choices. These barriers need to be identified and appropriate measures aiming to enhance informed decision making must be implemented.

Our aim was to determine the characteristics, treatments and outcomes of patients with primary spinal glioblastomas (GB) or gliosarcomas (GS) reported in literature until March 2015. PubMed and Web of Science were searched for peer-reviewed articles pertaining to cases of glioblastomas / gliosarcomas with primary spinal origin, using predefined search terms. Furthermore we performed hand searches tracking the references from the selected papers. Eighty-two articles published between 1938 and March 2015 were eligible. They reported on 157 patients. Median age at diagnosis was 22 years. The proportion of patients who received adjuvant chemo- or radiotherapy clearly increased from the time before 1980 until present. Median overall survival from diagnosis was 8.0 ± 0.9 months. On univariate analysis age influenced overall survival, whereas tumor location, gender and the extent of initial resection did not. Outcomes did not differ between children (< 18 years) and adults. However, the patients who were treated after 1980 achieved longer survival times than the patients treated before. On multivariable analysis only age (< 60 years) and the time period of treatment (>1980) were confirmed as positive independent prognostic factors. In conclusion, primary spinal GB / GS mainly affect younger patients and are associated with a dismal prognosis. However, most likely due to the increasing use of adjuvant treatment, modest therapeutic progress has been achieved over recent decades. The characteristics and treatments of primary spinal glioblastomas should be entered into a central registry in order to gain more information about the ideal treatment approach in the future.