Publications by authors named "Anneleen Decock"

Extracellular RNA (cell-free RNA; exRNA) from blood-derived liquid biopsies is an appealing, minimally invasive source of disease biomarkers. As pre-analytical variables strongly influence exRNA measurements, their reporting is essential for meaningful interpretation and replication of results. The aim of this review was to chart to what extent pre-analytical variables are documented, to pinpoint shortcomings and to improve future reporting.

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The analysis of extracellular vesicles (EV) in blood samples is under intense investigation and holds the potential to deliver clinically meaningful biomarkers for health and disease. Technical variation must be minimized to confidently assess EV-associated biomarkers, but the impact of pre-analytics on EV characteristics in blood samples remains minimally explored. We present the results from the first large-scale EV Blood Benchmarking (EVBB) study in which we systematically compared 11 blood collection tubes (BCT; six preservation and five non-preservation) and three blood processing intervals (BPI; 1, 8 and 72 h) on defined performance metrics (n = 9).

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Background: Blood plasma, one of the most studied liquid biopsies, contains various molecules that have biomarker potential for cancer detection, including cell-free DNA (cfDNA) and cell-free RNA (cfRNA). As the vast majority of cell-free nucleic acids in circulation are non-cancerous, a laboratory workflow with a high detection sensitivity of tumor-derived nucleic acids is a prerequisite for precision oncology. One way to meet this requirement is by the combined analysis of cfDNA and cfRNA from the same liquid biopsy sample.

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While cell-free DNA (cfDNA) is widely being investigated, free circulating RNA (extracellular RNA, exRNA) has the potential to improve cancer therapy response monitoring and detection due to its dynamic nature. However, it remains unclear in which blood subcompartment tumour-derived exRNAs primarily reside. We developed a host-xenograft deconvolution framework, exRNAxeno, with mapping strategies to either a combined human-mouse reference genome or both species genomes in parallel, applicable to exRNA sequencing data from liquid biopsies of human xenograft mouse models.

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We assess the performance of mRNA capture sequencing to identify fusion transcripts in FFPE tissue of different sarcoma types, followed by RT-qPCR confirmation. To validate our workflow, six positive control tumors with a specific chromosomal rearrangement were analyzed using the TruSight RNA Pan-Cancer Panel. Fusion transcript calling by FusionCatcher confirmed these aberrations and enabled the identification of both fusion gene partners and breakpoints.

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Comprehensive transcriptome analysis of extracellular RNA (exRNA) purified from human biofluids is challenging because of the low RNA concentration and compromised RNA integrity. Here, we describe an optimized workflow to (1) isolate exRNA from different types of biofluids and (2) to prepare messenger RNA (mRNA)-enriched sequencing libraries using complementary hybridization probes. Importantly, the workflow includes 2 sets of synthetic spike-in RNA molecules as processing controls for RNA purification and sequencing library preparation and as an alternative data normalization strategy.

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RNA profiling has emerged as a powerful tool to investigate the biomarker potential of human biofluids. However, despite enormous interest in extracellular nucleic acids, RNA sequencing methods to quantify the total RNA content outside cells are rare. Here, we evaluate the performance of the SMARTer Stranded Total RNA-Seq method in human platelet-rich plasma, platelet-free plasma, urine, conditioned medium, and extracellular vesicles (EVs) from these biofluids.

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In this study, the circulating miRNome from diagnostic neuroblastoma serum was assessed for identification of noninvasive biomarkers with potential in monitoring metastatic disease. After determining the circulating neuroblastoma miRNome, 743 miRNAs were screened in 2 independent cohorts of 131 and 54 patients. Evaluation of serum miRNA variance in a model testing for tumor stage, MYCN status, age at diagnosis, and overall survival revealed tumor stage as the most significant factor impacting miRNA abundance in neuroblastoma serum.

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In recent years, technological advances in transcriptome profiling revealed that the repertoire of human RNA molecules is more diverse and extended than originally thought. This diversity and complexity mainly derive from a large ensemble of noncoding RNAs. Because of their key roles in cellular processes important for normal development and physiology, disruption of noncoding RNA expression is intrinsically linked to human disease, including cancer.

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Stage 4S neuroblastoma (NB) is a special type of NB found in infants with metastases at diagnosis and is associated with an excellent outcome due to its remarkable capacity to undergo spontaneous regression. As genomics have not been able to explain this intriguing clinical presentation, we here aimed at profiling the DNA methylome of stage 4S NB to better understand this phenomenon. To this purpose, differential methylation analyses between International Neuroblastoma Staging System (INSS) stage 4S, stage 4 and stage 1/2 were performed, using methyl-CpG-binding domain (MBD) sequencing data of 14 stage 4S, 14 stage 4, and 13 stage 1/2 primary NB tumors (all MYCN non-amplified in order not to confound results).

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The systemic and resistant nature of metastatic neuroblastoma renders it largely incurable with current multimodal treatment. Clinical progression stems mainly from the increasing burden of metastatic colonization. Therapeutically inhibiting the migration-invasion-metastasis cascade would be of great benefit, but the mechanisms driving this cycle are as yet poorly understood.

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Comprehensive genome-wide DNA methylation studies in neuroblastoma (NB), a childhood tumor that originates from precursor cells of the sympathetic nervous system, are scarce. Recently, we profiled the DNA methylome of 102 well-annotated primary NB tumors by methyl-CpG-binding domain (MBD) sequencing, in order to identify prognostic biomarker candidates. In this data descriptor, we give details on how this data set was generated and which bioinformatics analyses were applied during data processing.

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Article Synopsis
  • This study focuses on developing new DNA methylation biomarkers to improve the prediction of neuroblastoma outcomes.
  • A total of 396 tumors were analyzed, and 87 underwent detailed methylation profiling, leading to the creation of methylation-specific PCR assays for 78 key regions.
  • The research identified a robust 58-marker methylation signature that successfully predicts overall and event-free survival in neuroblastoma patients, marking a significant advancement in prognostic assessment.
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Article Synopsis
  • The study focuses on improving risk assessment for neuroblastoma patients by identifying DNA methylation biomarkers that could predict outcomes.
  • Two advanced techniques were used to analyze the methylation patterns in neuroblastoma cell lines and primary tumors, leading to the identification of 43 potential biomarkers.
  • Among the identified biomarkers, specific genes like HIST1H3C and GNAS showed strong associations with patient survival rates, suggesting their potential role in developing a new prognostic classification system for neuroblastoma.
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Neuroblastoma (NB) is a childhood tumor originating from sympathetic nervous system cells. Although recently new insights into genes involved in NB have emerged, the molecular basis of neuroblastoma development and progression still remains poorly understood. The best-characterized genetic alterations include amplification of the proto-oncogene MYCN, ALK activating mutations or amplification, gain of chromosome arm 17q and losses of 1p, 3p, and 11q.

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