Publications by authors named "Capper D"

Tumor Treating Fields (TTFields) has emerged as a significant adjunctive component in the treatment of high-grade gliomas following the EF-14 trial in 2017. The incorporation of TTFields, alongside cyclic temozolomide therapy, has demonstrated improved patient outcomes when the usage exceeds 18 h per day (75% usage). analysis of the EF-14 trial has demonstrated that therapy usage exceeding 90% is associated with an additional benefit, while rates above 50% have also proven effective in literature.

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Isocitrate dehydrogenase (IDH)-mutant gliomas, comprising both astrocytomas and oligodendrogliomas, represent a distinct group of tumors that pose an interdisciplinary challenge. Addressing the needs of affected patients requires close collaboration among various disciplines, including neuropathology, neuroradiology, neurosurgery, radiation oncology, neurology, medical oncology, and other relevant specialties when necessary. Interdisciplinary tumor boards are central in determining the ideal diagnostic and therapeutic strategies for these patients.

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Article Synopsis
  • IDH-mutant gliomas are the most common malignant brain tumors in young adults, causing significant challenges for patients, including cognitive deficits and high mortality due to tumor progression.
  • Current treatments like surgery, radiation, and chemotherapy enhance survival but can have negative impacts on cognitive function and quality of life.
  • The recent FDA approval of vorasidenib, a drug targeting mutant IDH1/2 proteins, represents a promising new approach, with ongoing trials exploring its use alongside other therapies for better patient outcomes.
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Background: Intracerebral schwannomas are rare tumors resembling their peripheral nerve sheath counterparts but localized in the CNS. They are not classified as a separate tumor type in the 2021 WHO classification. This study aimed to compile and characterize these rare neoplasms morphologically and molecularly.

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  • This study focuses on the role of Thyroid Transcription Factor-1 (TTF-1) in lung adenocarcinoma (LUAD) brain metastases, specifically its predictive value and relationship with patient outcomes.
  • Researchers analyzed data from 245 patients who had brain metastases, looking at factors like tumor volume and survival rates based on TTF-1 expression status.
  • Results indicate that TTF-1 negative patients had larger tumors, higher proliferation rates, and worse survival outcomes, suggesting that TTF-1 negativity signifies a more aggressive form of the disease, warranting further study on its underlying mechanisms.
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Meningiomas are the most common primary intracranial tumors of adults. For meningiomas that progress or recur despite surgical resection and radiotherapy, additional treatment options are limited due to lack of proven efficacy. Meningiomas show recurring molecular aberrations, which may serve as predictive markers for systemic pharmacotherapies with targeted drugs or immunotherapy, radiotherapy or radioligand therapy.

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Background: We have recently constructed a DNA methylation classifier that can discriminate between pancreatic ductal adenocarcinoma (PAAD) liver metastasis and intrahepatic cholangiocarcinoma (iCCA) with high accuracy (PAAD-iCCA-Classifier). PAAD is one of the leading causes of cancer of unknown primary and diagnosis is based on exclusion of other malignancies. Therefore, our focus was to investigate whether the PAAD-iCCA-Classifier can be used to diagnose PAAD metastases from other sites.

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  • - The study validates a new DNA methylation-based predictor for meningiomas that has been enhanced for use with modern methylation arrays and shows improved accuracy over the standard 2021 WHO grading system.
  • - It uses data from 1,347 meningioma cases, including prospective cases and an external cohort, demonstrating that both the new and original models effectively predict early postoperative recurrence, especially within specific risk subgroups.
  • - The new predictor, which is simpler with fewer features, allows for better clinical decision-making, including the use of adjuvant radiotherapy for high-risk patients, and is available as an easy-to-use tool for improved patient stratification in clinical trials.
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Aims: DNA methylation profiling, recently endorsed by the World Health Organisation (WHO) as a pivotal diagnostic tool for brain tumours, most commonly relies on bead arrays. Despite its widespread use, limited data exist on the technical reproducibility and potential cross-institutional differences. The LOGGIC Core BioClinical Data Bank registry conducted a prospective laboratory comparison trial with 12 international laboratories to enhance diagnostic accuracy for paediatric low-grade gliomas, focusing on technical aspects of DNA methylation data generation and profile interpretation under clinical real-time conditions.

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The standard of care for adult patients with gliomas, glioneuronal and neuronal tumors consists of combinations of surgery, radiotherapy, and chemotherapy. For many systemic cancers, targeted treatments are a major part of the standard treatment, however, the predictive significance of most of the targets for treatment in systemic cancer are less well established in central nervous system (CNS) tumors . In 2023 the EANO Guideline Committee presented evidence based recommendations for rational testing of molecular targets for targeted treatments.

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  • Tumors of salivary glands vary widely and can overlap, making them challenging to diagnose, despite advances in molecular testing.
  • A study examined 363 cases of 20 different salivary gland tumors and found distinct DNA methylation patterns that help classify these tumors, achieving high accuracy with a machine learning algorithm.
  • The research identified specific epigenetic signatures, distinguishing certain tumor types, and suggested that DNA methylation could aid in diagnosing and potentially uncovering new tumor classes in the future.
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  • - Meningiomas are the most common primary brain tumors, with most being benign, but around 25% are higher-grade and require better risk assessment through an integrated risk score (IRS) based on tumor biology.
  • - The study involved 160 patients and utilized machine learning with preoperative MRI scans to develop classifiers that predict the IRS, achieving a high accuracy of 90% when distinguishing low-risk from medium/high-risk patients.
  • - The results highlight that specific imaging characteristics, like "sphericity," can effectively predict the molecular low-risk classification of meningiomas, making critical prognostic information more accessible through imaging techniques.
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Background: Although cavitating ultrasonic aspirators are commonly used in neurosurgical procedures, the suitability of ultrasonic aspirator-derived tumor material for diagnostic procedures is still controversial. Here, we explore the feasibility of using ultrasonic aspirator-resected tumor tissue to classify otherwise discarded sample material by fast DNA methylation-based analysis using low pass nanopore whole genome sequencing.

Methods: Ultrasonic aspirator-derived specimens from pediatric patients undergoing brain tumor resection were subjected to low-pass nanopore whole genome sequencing.

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Neural-tumor interactions drive glioma growth as evidenced in preclinical models, but clinical validation is limited. We present an epigenetically defined neural signature of glioblastoma that independently predicts patients' survival. We use reference signatures of neural cells to deconvolve tumor DNA and classify samples into low- or high-neural tumors.

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The combination of DNA methylation analysis with histopathological and genetic features allows for a more accurate risk stratification and classification of meningiomas. Nevertheless, the implications of this classification for patients with grade 2 meningiomas, a particularly heterogeneous tumor entity, are only partially understood. We correlate the outcomes of histopathologically confirmed grade 2 meningioma with an integrated molecular-morphologic risk stratification and determine its clinical implications.

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Background: Tumor embolism is a very rare primary manifestation of cancers and the diagnosis is challenging, especially if located in the pulmonary arteries, where it can mimic nonmalignant pulmonary embolism. Intimal sarcoma is one of the least commonly reported primary tumors of vessels with only a few cases reported worldwide. A typical location of this malignancy is the pulmonary artery.

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DNA methylation analysis based on supervised machine learning algorithms with static reference data, allowing diagnostic tumour typing with unprecedented precision, has quickly become a new standard of care. Whereas genome-wide diagnostic methylation profiling is mostly performed on microarrays, an increasing number of institutions additionally employ nanopore sequencing as a faster alternative. In addition, methylation-specific parallel sequencing can generate methylation and genomic copy number data.

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Article Synopsis
  • The morphological features that are often linked with glioblastoma can also be seen in various other tumor types, complicating the diagnosis.
  • The 2021 WHO Classification of CNS Tumors has begun to categorize some of these tumors separately from glioblastoma, IDH-wildtype, prompting a need for more precise diagnostics.
  • This study analyzed a wealth of DNA methylation data and identified nine distinct methylation classes of tumors likely to be diagnosed as glioblastoma, IDH-wildtype, emphasizing the ongoing complexity and heterogeneity within this diagnostic category.
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  • The study aims to validate an epigenetic methylation signature associated with glioblastomas (GBM) that contact the subventricular zone (SVZ).
  • Methylation array analysis was conducted on GBM patients, focusing on specific tumor subtypes and revealing a strong correlation between the epigenetic signature and SVZ contact through MRI assessment.
  • The findings suggest that SVZ contact is linked to poorer survival outcomes, indicating the potential of methylation analysis to serve as a diagnostic tool alongside MRI, though further studies are needed to confirm its clinical applicability.
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Background: The aim of this study was to analyse transcriptomic differences between primary and recurrent high-grade serous ovarian carcinoma (HGSOC) to identify prognostic biomarkers.

Methods: We analysed 19 paired primary and recurrent HGSOC samples using targeted RNA sequencing. We selected the best candidates using in silico survival and pathway analysis and validated the biomarkers using immunohistochemistry on a cohort of 44 paired samples, an additional cohort of 504 primary HGSOCs and explored their function.

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