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Die Analyse von NGS Daten ist ein zentraler Aspekt der modernen genomischen Forschung. Bei der Extraktion von Daten aus den beiden am häufigsten verwendeten Quellorganismen bestehen jedoch vielfältige Problemstellungen. Im ersten Kapitel wird ein neuartiger Ansatz vorgestellt welcher einen Abstand zwischen Krebszellinienkulturen auf Grundlage ihrer kleinen genomischen Varianten bestimmt um die Kulturen zu identifizieren. Eine Voll-Exom sequenzierte Kultur wird durch paarweise Vergleiche zu Referenzdatensätzen identifiziert so ein gemessener Abstand geringer ist als dies bei nicht verwandten Kulturen zu erwarten wäre. Die Wirksamkeit der Methode wurde verifiziert, jedoch verbleiben Einschränkung da nur das Sequenzierformat des Voll-Exoms unterstützt wird. Daher wird im zweiten Kapitel eine publizierte Modifikation des Ansatzes vorgestellt welcher die Unterstützung der weitläufig genutzten Bulk RNA sowie der Panel-Sequenzierung ermöglicht. Die Ausweitung der Technologiebasis führt jedoch zu einer Verstärkung von Störeffekten welche zu Verletzungen der mathematischen Konditionen einer Abstandsmetrik führen. Daher werden die entstandenen Verletzungen durch statistische Verfahren zuerst quantifiziert und danach durch dynamische Schwellwertanpassungen erfolgreich kompensiert. Das dritte Kapitel stellt eine neuartige Daten-Aufwertungsmethode (Data-Augmentation) vor welche das Trainieren von maschinellen Lernmodellen in Abwesenheit von neoplastischen Trainingsdaten ermöglicht. Ein abstraktes Abstandsmaß wird zwischen neoplastischen Entitäten sowie Entitäten gesundem Ursprungs mittels einer transkriptomischen Dekonvolution hergestellt. Die Ausgabe der Dekonvolution erlaubt dann das effektive Vorhersagen von klinischen Eigenschaften von seltenen jedoch biologisch vielfältigen Krebsarten wobei die prädiktive Kraft des Verfahrens der des etablierten Goldstandard ebenbürtig ist. The analysis of NGS data is a central aspect of modern Molecular Genetics and Oncology. The first scientific contribution is the development of a method which identifies Whole-exome-sequenced CCL via the quantification of a distance between their sets of small genomic variants. A distinguishing aspect of the method is that it was designed for the computer-based identification of NGS-sequenced CCL. An identification of an unknown CCL occurs when its abstract distance to a known CCL is smaller than is expected due to chance. The method performed favorably during benchmarks but only supported the Whole-exome-sequencing technology. The second contribution therefore extended the identification method by additionally supporting the Bulk mRNA-sequencing technology and Panel-sequencing format. However, the technological extension incurred predictive biases which detrimentally affected the quantification of abstract distances. Hence, statistical methods were introduced to quantify and compensate for confounding factors. The method revealed a heterogeneity-robust benchmark performance at the trade-off of a slightly reduced sensitivity compared to the Whole-exome-sequencing method. The third contribution is a method which trains Machine-Learning models for rare and diverse cancer types. Machine-Learning models are subsequently trained on these distances to predict clinically relevant characteristics. The performance of such-trained models was comparable to that of models trained on both the substituted neoplastic data and the gold-standard biomarker Ki-67. No proliferation rate-indicative features were utilized to predict clinical characteristics which is why the method can complement the proliferation rate-oriented pathological assessment of biopsies. The thesis revealed that the quantification of an abstract distance can address sources of erroneous NGS data analysis.

Simple Summary Neuroendocrine tumors of the gastrointestinal tract (GEP-NEN) are a rare type of tumor with considerable variability in the course of disease, which makes clinical management particularly challenging. Biomarkers that are able to guide personalized treatment decisions would be of importance, but are not yet available. In this study, we demonstrate that tissue expression, as well as circulating levels of the cytokine Flt3L in advanced and aggressive tumors, predict survival and time to progression. Increased tumoral Flt3L was also associated with upregulation of genes related to immune activation, suggesting Flt3L as a surrogate marker of host anti-tumor immunity. Therefore, Flt3L measurements in serum may hold promise as a biomarker of disease outcome that could support personalized treatment decisions in GEP-NEN patients, potentially guiding researchers towards viable immunotherapies. Abstract Background: The clinical management of high-grade gastroenteropancreatic neuroendocrine neoplasms (GEP-NEN) is challenging due to disease heterogeneity, illustrating the need for reliable biomarkers facilitating patient stratification and guiding treatment decisions. FMS-like tyrosine kinase 3 ligand (Flt3L) is emerging as a prognostic or predictive surrogate marker of host tumoral immune response and might enable the stratification of patients with otherwise comparable tumor features. Methods: We evaluated Flt3L gene expression in tumor tissue as well as circulating Flt3L levels as potential biomarkers in a cohort of 54 patients with GEP-NEN. Results: We detected a prominent induction of Flt3L gene expression in individual G2 and G3 NEN, but not in G1 neuroendocrine tumors (NET). Flt3L mRNA expression levels in tumor tissue predicted the disease-related survival of patients with highly proliferative G2 and G3 NEN more accurately than the conventional criteria of grading or NEC/NET differentiation. High level Flt3L mRNA expression was associated with the increased expression of genes related to immunogenic cell death, lymphocyte effector function and dendritic cell maturation, suggesting a less tolerogenic (more proinflammatory) phenotype of tumors with Flt3L induction. Importantly, circulating levels of Flt3L were also elevated in high grade NEN and correlated with patients’ progression-free and disease-related survival, thereby reflecting the results observed in tumor tissue. Conclusions: We propose Flt3L as a prognostic biomarker for high grade GEP-NEN, harnessing its potential as a marker of an inflammatory tumor microenvironment. Flt3L measurements in serum, which can be easily be incorporated into clinical routine, should be further evaluated to guide patient stratification and treatment decisions.

Detection of epithelial ovarian cancer (EOC) poses a critical medical challenge. However, novel biomarkers for diagnosis remain to be discovered. Therefore, innovative approaches are of the utmost importance for patient outcome. Here, we present a concept for blood-based biomarker discovery, investigating both epithelial and specifically stromal compartments, which have been neglected in search for novel candidates. We queried gene expression profiles of EOC including microdissected epithelium and adjacent stroma from benign and malignant tumours. Genes significantly differentially expressed within either the epithelial or the stromal compartments were retrieved. The expression of genes whose products are secreted yet absent in the blood of healthy donors were validated in tissue and blood from patients with pelvic mass by NanoString analysis. Results were confirmed by the comprehensive gene expression database, CSIOVDB (Ovarian cancer database of Cancer Science Institute Singapore). The top 25% of candidate genes were explored for their biomarker potential, and twelve were able to discriminate between benign and malignant tumours on transcript levels (p < 0.05). Among them T-cell differentiation protein myelin and lymphocyte (MAL), aurora kinase A (AURKA), stroma-derived candidates versican (VCAN), and syndecan-3 (SDC), which performed significantly better than the recently reported biomarker fibroblast growth factor 18 (FGF18) to discern malignant from benign conditions. Furthermore, elevated MAL and AURKA expression levels correlated significantly with a poor prognosis. We identified promising novel candidates and found the stroma of EOC to be a suitable compartment for biomarker discovery.