Publications by authors named "Tabea Hartung"

Background: Neurofibromatosis type 1 (NF1) is a rare genetic disorder affecting multiple bodily systems that predisposes to the development of tumors. It affects approximately 1 in 3000 newborns in Germany. Its clinical manifestations are diverse and complex, and its diagnostic and therapeutic management call for specialized knowledge and experience.

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Background/aim: Neurofibromatosis type 1 (NF1) is a genetic disorder with an incidence of approximately one in 3,000. More than half of the patients have new de novo pathogenic variants of the NF1 gene. In most family cases, all family members share an identical NF1-variant.

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Background/aim: Plexiform neurofibromas are the hallmark of neurofibromatosis type 1, an autosomal dominantly inherited multisystem disorder. Spinal plexiform neurofibromas can particularly cause severe neurological symptoms. Treatment options are limited due to invasive growth, and targeted therapy with selumetinib is only approved for inoperable tumors in children.

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Background: The biological understanding of glioblastoma (GB) with gliomatosis cerebri (GC) pattern is poor due to the absence of GC-specific studies. Here, we aimed to identify molecular or clinical parameters that drive GC growth.

Methods: From our methylome database of IDH (isocitrate dehydrogenase)-wildtype GB, we identified 158 non-GC and 65 GC cases.

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Background: DNA methylation-based tumor classification allows an enhanced distinction into subgroups of glioblastoma. However, the clinical benefit of DNA methylation-based stratification of glioblastomas remains inconclusive.

Methods: Multicentric cohort study including 430 patients with newly diagnosed glioblastoma subjected to global DNA methylation profiling.

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Uveal Melanoma (UM) is a rare disease; however, it is the most common primary intraocular malignant tumor in adults. Hematogenous metastasis, occurring in up to 50% of cases, mainly to the liver (90%), is associated with poor clinical course and treatment failure. In contrast to dramatic benefits of immunotherapy in many tumor entities, as seen in cutaneous melanoma, immune checkpoint inhibitors (ICI) do not achieve comparable results in Metastatic UM (MUM).

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Background: Therapies based on targeting immune checkpoints have revolutionized the treatment of metastatic melanoma in recent years. Still, biomarkers predicting long-term therapy responses are lacking.

Methods: A novel approach of reference-free deconvolution of large-scale DNA methylation data enabled us to develop a machine learning classifier based on CpG sites, specific for latent methylation components (LMC), that allowed for patient allocation to prognostic clusters.

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