Publications by authors named "Stephan Brock"

Aim: Endometrial cancer (EC) is heterogeneous with respect to epidemiology, clinical course, histopathology and tumor biology. Recently, The Cancer Genome Atlas (TCGA) network has identified four molecular subtypes with distinct clinical courses by an integrated multi-omics approach. These subtypes are of critical importance in the clinical management of EC.

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In an era of unparalleled technical advancement, the pharmaceutical industry is struggling to transform data into increased research and development efficiency, and, as a corollary, new drugs for patients. Here, we briefly review some of the commonly discussed issues around this counterintuitive innovation crisis. Looking at both industry- and science-related factors, we posit that traditional preclinical research is front-loading the development pipeline with data and drug candidates that are unlikely to succeed in patients.

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Background: The Cancer Genome Atlas (TCGA) network (United States National Cancer Institute) identified four molecular endometrial cancer (EC) subtypes using an extensive multi-method approach. The aim of this study was to determine the four TCGA EC molecular subtypes using a single-method whole-exome sequencing (WES)-based approach provided by MH Guide (Molecular Health, Heidelberg, Germany).

Methods: WES and clinical data of n = 232 EC patients were obtained from TCGA.

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The COVID-19 pandemic has caused more than 6 million deaths worldwide since its first outbreak in December 2019 and continues to be a major health problem. Several studies have established that the infection by SARS-CoV-2 can be categorized in a viremic, acute and recovery or severe phase. Hyperinflammation during the acute pneumonia phase is a major cause of severe disease progression and death.

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Article Synopsis
  • - The fight against COVID-19 requires understanding the complex relationship between the disease’s diverse symptoms and the underlying molecular mechanisms of the SARS-CoV-2 virus, which is crucial for developing effective treatments.
  • - Researchers created a comprehensive knowledge model by compiling existing data, which has been validated through recent findings, indicating the model's usefulness in generating and testing new hypotheses related to COVID-19.
  • - The model is accessible via the "COVID-19 Explorer" webserver and aims to provide insights across different organ systems, potentially leading to the discovery of new drug candidates and biomarkers to enhance therapeutic strategies.
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Article Synopsis
  • The COVID-19 pandemic, beginning in early 2020, has caused over half a billion infections and 6 million deaths, highlighting a fragmented understanding of the disease's diverse symptoms and mechanisms.
  • Despite vaccines being available, effective treatments for severe cases are still urgently needed, especially with new variants emerging, due to COVID-19's complex effects on various body tissues and organs.
  • Utilizing the Dataome platform, researchers created an integrated model linking COVID-19 symptoms to molecular behavior and intercellular communication, which can aid in drug development and risk factor identification, all of which is accessible through the open-source COVID-19 Explorer.
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  • The study evaluates a new computational tool, MH BRCA, for classifying genetic variants in hereditary breast and/or ovarian cancer (HBOC), demonstrating a high accuracy in identifying pathogenic variants.
  • The tool showed a 99.98% agreement with existing classification databases, significantly reducing uncertainty in variant interpretation and identifying several cases with potential family risks for HBOC.
  • The findings also suggest a strong link between the classification of variants and their predicted efficacy in PARP inhibitor treatment, underscoring the importance of accurate variant assessment in clinical settings.
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Pancreatic ductal adenocarcinoma (PDAC) is a tumor with an extremely poor prognosis, predominantly as a result of chemotherapy resistance and numerous somatic mutations. Consequently, PDAC is a prime candidate for the use of sequencing to identify causative mutations, facilitating subsequent administration of targeted therapy. In a feasibility study, we retrospectively assessed the therapeutic recommendations of a novel, evidence-based software that analyzes next-generation sequencing (NGS) data using a large panel of pharmacogenomic biomarkers for efficacy and toxicity.

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While recombinant human erythropoietin (rhEpo) has been widely used to treat anemia in cancer patients, concerns about its adverse effects on patient survival have emerged. A lack of correlation between expression of the canonical EpoR and rhEpo's effects on cancer cells prompted us to consider the existence of an alternative Epo receptor. Here, we identified EphB4 as an Epo receptor that triggers downstream signaling via STAT3 and promotes rhEpo-induced tumor growth and progression.

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The initiation of protein translation in bacteria requires in addition to mRNA, fMet-tRNA, and ribosomal subunits three protein factors, the initiation factor 1 (IF1), initiation factor 2 (IF2), and initiation factor 3 (IF3). The genes coding for IF1 and IF3 from Thermus thermophilus have been identified and cloned into pET expression vector and were expressed as soluble proteins in Escherichia coli. IF1 was purified by a DEAE-cellulose chromatography, followed by heat denaturation, chromatography on Hydroxylapatit, and gel permeation chromatography using Sephacryl 200HR.

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