Whole Genome Analysis of Ovarian Granulosa Cell Tumors Reveals Tumor Heterogeneity and a High-Grade TP53-Specific Subgroup

Adult granulosa cell tumors (AGCTs) harbor a somatic FOXL2 c.402C>G mutation in ~95% of cases and are mainly surgically removed due to limited systemic treatment effect. In this study, potentially targetable genomic alterations in AGCTs were investigated by whole genome sequencing on 46 tumor samples and matched normal DNA. Copy number variant (CNV) analysis confirmed gain of chromosome 12 and 14, and loss of 22. Pathogenic TP53 mutations were identified in three patients with highest tumor mutational burden and mitotic activity, defining a high-grade AGCT subgroup. Within-patient tumor comparisons showed 29–80% unique somatic mutations per sample, suggesting tumor heterogeneity. A higher mutational burden was found in recurrent tumors, as compared to primary AGCTs. FOXL2-wildtype AGCTs harbored DICER1, TERT(C228T) and TP53 mutations and similar CNV profiles as FOXL2-mutant tumors. Our study confirms that absence of the FOXL2 c.402C>G mutation does not exclude AGCT diagnosis. The lack of overlapping variants in targetable cancer genes indicates the need for personalized treatment for AGCT patients

Distinct Genomic Profiles Are Associated with Treatment Response and Survival in Ovarian Cancer

SIMPLE SUMMARY: In most patients with ovarian cancer, their disease eventually becomes resistant to chemotherapy. The timing and type of treatment given are therefore highly important. Currently, treatment choice is mainly based on the subtype of cancer (from a histological point of view), prior response to chemotherapy, and the time it takes for the disease to recur. In this study, we combined complete genome data of the tumor with clinical data to better understand treatment responses. In total, 132 tumor samples were included, all from patients with disease that had spread beyond the primary location. By clustering the samples based on genetic characteristics, we have identified subgroups with distinct response rates and survival outcomes. We suggest that in the future, this data can be used to make more informed treatment choices for individuals with ovarian cancer. ABSTRACT: The majority of patients with ovarian cancer ultimately develop recurrent chemotherapy-resistant disease. Treatment stratification is mainly based on histological subtype and stage, prior response to platinum-based chemotherapy, and time to recurrent disease. Here, we integrated clinical treatment, treatment response, and survival data with whole-genome sequencing profiles of 132 solid tumor biopsies of metastatic epithelial ovarian cancer to explore genome-informed stratification opportunities. Samples from primary and recurrent disease harbored comparable numbers of single nucleotide variants and structural variants. Mutational signatures represented platinum exposure, homologous recombination deficiency, and aging. Unsupervised hierarchical clustering based on genomic input data identified specific ovarian cancer subgroups, characterized by homologous recombination deficiency, genome stability, and duplications. The clusters exhibited distinct response rates and survival probabilities which could thus potentially be used for genome-informed therapy stratification for more personalized ovarian cancer treatment

Ubc13 dosage is critical for immunoglobulin gene conversion and gene targeting in vertebrate cells

In contrast to lower eukaryotes, most vertebrate cells are characterized by a moderate efficiency of homologous recombination (HR) and limited feasibility of targeted genetic modifications. As a notable exception, the chicken DT40 B cell line is distinguished by efficient homology-mediated repair of DNA lesions during Ig gene conversion, and also shows exceptionally high gene-targeting efficiencies. The molecular basis of these phenomena is elusive. Here we show that the activity levels of Ubc13, the E2 enzyme responsible for non-canonical K63-linked polyubiquitination, are critical for high efficiency of Ig gene conversion and gene targeting in DT40. Ubc13+/− cells show substantially lower homology-mediated repair, yet do not display changes in somatic hypermutation, overall DNA repair or cell proliferation. Our results suggest that modulation of the activity of K63-linked polyubiquitination may be used to customize HR efficiencies in vertebrate cells

A multi-platform reference for somatic structural variation detection

Accurate detection of somatic structural variation (SV) in cancer genomes remains a challenging problem. This is in part due to the lack of high-quality, gold-standard datasets that enable the benchmarking of experimental approaches and bioinformatic analysis pipelines. Here, we performed somatic SV analysis of the paired melanoma and normal lymphoblastoid COLO829 cell lines using four different sequencing technologies. Based on the evidence from multiple technologies combined with extensive experimental validation, we compiled a comprehensive set of carefully curated and validated somatic SVs, comprising all SV types. We demonstrate the utility of this resource by determining the SV detection performance as a function of tumor purity and sequence depth, highlighting the importance of assessing these parameters in cancer genomics projects. The truth somatic SV dataset as well as the underlying raw multi-platform sequencing data are freely available and are an important resource for community somatic benchmarking efforts

Computational interaction proteomics: from proteome to complexome

Modern measurement methods and computational data analysis tools allow protein exploration that covers all proteins in an organism. Especially discovering the interactions between the proteins is crucial for a better understanding of the cell, because proteins in the cell do not carry out their function alone but in close cooperation with other proteins. Protein interaction takes place at different levels. At a low level, proteins are bound firmly, building stable and small complexes. At a higher level, the small stable complexes themselves interact with other complexes to build larger assemblies, and one such complex is often part of many different assemblies. Considering the different levels of protein interactions has caused a shift of thinking in modern interaction proteomics. The focus changed from treating interactions equally to a subtle distinction between binary interactions, stable small complexes and larger assemblies up to the interaction between protein complexes. This paradigm change is called the transition from proteomics to complexomics and strives for observing a broader picture of protein interactions. This thesis follows that novel way of thinking and combines my complexome research of the last four years. It contains an overview of IP/MS based interaction proteomics and presents several novel methods to detect protein interaction at the different interaction levels

Qualitätsverbesserungen im Bereich Treibersoftware und hardwarenaher Programmierung am Beispiel automatischer Komponenten-, Integrations- undRegressionstests bei der Firma Garz&Fricke

Der Funktionsumfang von kleinen und eingebetteten Systemen nimmt stetig zu. Das Testen dieser Gerätekategorie muss dem Rechnung tragen. Der Testprozess eines Herstellers von Embedded-Geräten mit dem Betriebssystem Windows CE soll mittels ausgewählter Testmethoden verbessert werden. Am Beispiel eines CAN-Treibers werden Tests mit hohem Automatisierungsgrad dargestellt und angewandt. Auf der Komponentenebene als auch auf der Integrationsebene werden Testfälle erstellt. Die Ergebnisse bzgl. Aufwand und Qualität werden reflektiert und auf den Entwicklungsprozess weiterer Komponenten in diesem Bereich übertragen.The functionality of small embedded devices gets more complex. The testing of these must take care of this development. The testprocess of a designer and manufacturer of embedded hardware devices with Windows CE shall be improved in quality by choosen testmethods. Tests with high automatism will be illustrated and executed on an example part of the system (CAN driver). On the component and the integration layer testcases will be designed. The results will be analyzed with the focus on effort and quality and will be assigned to the development process of further components in this area

Variant-DB: A Tool for Efficiently Exploring Millions of Human Genetic Variants and Their Annotations

Next Generation Sequencing (NGS) allows sequencing of a human genome within hours, enabling large scale applications such as sequencing the genome of each patient in a clinical study. Each individual human genome has about 3.5 Million genetic differences to the so called reference genome, the consensus genome of a healthy human. These differences, called variants, determine individual phenotypes, and certain variants are known to indicate disease predispositions. Finding associations from variant patterns and affected genes to these diseases requires combined analysis of variants from multiple individuals and hence, efficient solutions for accessing and filtering the variant data. We present Variant-DB, our in-house database solution that allows such efficient access to millions of variants from hundreds to thousands of individuals. Variant-DB stores individual variant genotypes and annotations. It features a REST-API and a web-based front-end for filtering variants based on annotations, individuals, families and studies. We explain Variant-DB and its front-end and demonstrate how the Variant-DB API can be included in data integration workflows

Inferring protein-protein interaction complexes from immunoprecipitation data

BACKGROUND: Protein inverted question markprotein interactions in cells are widely explored using small inverted question markscale experiments. However, the search for protein complexes and their interactions in data from high throughput experiments such as immunoprecipitation is still a challenge. We present "4N", a novel method for detecting protein complexes in such data. Our method is a heuristic algorithm based on Near Neighbor Network (3N) clustering. It is written in R, it is faster than model-based methods, and has only a small number of tuning parameters. We explain the application of our new method to real immunoprecipitation results and two artificial datasets. We show that the method can infer protein complexes from protein immunoprecipitation datasets of different densities and sizes. FINDINGS: 4N was applied on the immunoprecipitation dataset that was presented by the authors of the original 3N in Cell 145:787 inverted question mark799, 2011. The test with our method shows that it can reproduce the original clustering results with fewer manually adapted parameters and, in addition, gives direct insight into the complex inverted question markcomplex interactions. We also tested 4N on the human "Tip49a/b" dataset. We conclude that 4N can handle the contaminants and can correctly infer complexes from this very dense dataset. Further tests were performed on two artificial datasets of different sizes. We proved that the method predicts the reference complexes in the two artificial datasets with high accuracy, even when the number of samples is reduced. CONCLUSIONS: 4N has been implemented in R. We provide the sourcecode of 4N and a user-friendly toolbox including two example calculations. Biologists can use this 4N-toolbox even if they have a limited knowledge of R. There are only a few tuning parameters to set, and each of these parameters has a biological interpretation. The run times for medium scale datasets are in the order of minutes on a standard desktop PC. Large datasets can typically be analyzed within a few hours