Biology subjects

De Preter, K.

Publications and source records attributed to De Preter, K..

12 recordsLinked to original sources

bioRxiv2026

Benchmarking fragmentation-derived artificial cfDNA reference standards

An important step toward clinical implementation of (epi-)genomic assays on liquid biopsies is their validation on identical samples within and across laboratories. For these validation studies, there is a need for cell-free DNA (cfDNA) samples with defined tumor fractions and (epi-)genomic aberrations. However, the amount of circulating cfDNA isolated from patient samples is often limited, especially in pediatric cases. Additionally, patient samples contain a high degree of variability in cfDNA yield and tumor fraction. Several commercial artificial cfDNA products are available for validation studies, however their use is restricted to specific assays, aberrations and/or tumor entities. Alternatively, artificial cfDNA samples can be produced by fragmenting genomic DNA to mimic highly fragmented cfDNA derived from both tumor and healthy blood, followed by mixing artificial tumoral and healthy cfDNA at defined fractions. In this study, we compared native cfDNA with artificial cfDNA generated by three different fragmentation methods, including sonication and two enzymatic digestions using micrococcal nuclease and double-stranded deoxyribonuclease (dsDNase). We assessed fragment length profiles, end motifs and nucleosome occupancy patterns from shallow whole-genome sequencing data, as well as coverage profiles from targeted panel sequencing, together with a small-scale mixing experiment of tumor and healthy cell derived artificial cfDNA. Although sonication remains a convenient high-throughput approach to generate artificial cfDNA for certain downstream applications, enzymatic fragmentation, particularly the dsDNase-based method, more faithfully reproduced native cfDNA characteristics.

genomics↗

bioRxiv2025

A benchmark of DNA methylation deconvolution methods for tumoral fraction estimation using DecoNFlow

In cancer patients, circulating cell-free DNA (cfDNA) is released into body fluids from both healthy and cancer cells. The proportion of tumor-derived cfDNA serves as a surrogate marker of tumor burden allowing disease monitoring. Tumoral cfDNA can be distinguished based on patient specific tumoral mutations or using more general tumor specific DNA methylation patterns, that are preserved on tumoral cfDNA. DNAm profiling of cfDNA thus enables non-invasive cancer detection and monitoring. However, accurately determining tumour fractions remains challenging due to the heterogeneous mixture of cfDNA sources in body fluids. Computational DNAm deconvolution methods address this by inferring cell-type contributions either with or without reference methylomes. While several tools exist and multiple benchmarking studies have been performed, none have specifically evaluated the sensitivity and accuracy of tumour-fraction estimation in cfDNA-focused contexts. Here, we benchmarked 10 reference-based and 2 reference-free DNAm deconvolution tools using 3,690 in silico mixtures spanning multiple tumour types, different bisulfite-based sequencing strategies and several sequencing depths. Overall, CelFiE showed the most accurate tumour-fraction estimation across the different conditions. Interestingly, reference-free methods demonstrated superior sensitivity for tumour detection, but consistent over-estimation of tumoral fraction. We further observed that sequencing depth strongly affects performance until sufficient saturation is achieved. To enable reproducible evaluation and tool selection within this benchmark, we developed DecoNFlow, a scalable Nextflow pipeline integrating 12 deconvolution tools and 3 marker selection methods, making it the most comprehensive pipeline for sequencing-based deconvolution up to date. Together, our findings provide practical guidance for tool selection in cfDNA tumour monitoring and establish DecoNFlow as a robust framework for benchmarking and applying DNAm deconvolution.

bioinformatics↗

bioRxiv2023

Evaluation of single-sample network inference methods for precision oncology

A major challenge in precision oncology is to identify targetable cancer vulnerabilities in individual patients. Modelling high-throughput omics data in biological networks allows identifying key molecules and processes of tumorigenesis. Traditionally, network inference methods rely on many samples to contain sufficient information for learning and predicting gene interactions for a group of patients. However, to implement patient-tailored approaches in precision oncology, we need to interpret omics data at the level of the individual patient. Several single-sample network inference methods have been developed that infer biological networks for an individual sample from bulk RNA-seq data. However, only a limited comparison of these methods has been made. Moreover, many methods rely on normal tissue samples as reference point for the tumor samples, which is not always available. Here, we conducted an evaluation of the single-sample network inference methods SSN, LIONESS, iENA, CSN and SSPGI using expression profiles of lung and brain cancer cell lines from the CCLE database. The methods constructed networks with distinct network topologies, as observed by edge weight distributions and other network characteristics. Further, hub gene analyses revealed different degrees of subtype-specificity across methods. Single-sample networks were able to distinguish between tumor subtypes, as exemplified by edge weight clustering, enrichment of known subtype-specific driver genes among hub gene sets, and differential node importance. Finally, we show that single-sample networks correlate better to other omics data from the same cell line as compared to aggregate networks. Our results point to the important role of single-sample network inference in precision medicine.

systems biology↗

bioRxiv2020

Recurrent chromosomal imbalances provide selective advantage to human embryonic stem cells under enhanced replicative stress conditions

Human embryonic stem cells (hESCs) and embryonal tumors share a number of common features including a compromised G1/S checkpoint. Consequently, these rapidly dividing hESCs and cancer cells undergo elevated levels of replicative stress which is known to induce genomic instability causing chromosomal imbalances. In this context, it is of interest that long-term in vitro cultured hESCs exhibit a remarkable high incidence of segmental DNA copy number gains, some of which are also highly recurrent in certain malignancies such as 17q gain (17q+). The selective advantage of DNA copy number changes in these cells has been attributed to several underlying processes including enhanced proliferation. We hypothesized that these recurrent chromosomal imbalances become rapidly embedded in the cultured hESCs through a replicative stress driven Darwinian selection process. To this end, we compared the effect of hydroxyurea induced replicative stress versus normal growth conditions in an equally-mixed cell population of isogenic euploid and 17q+ hESCs. We could show that 17q+ hESCs rapidly overtook normal hESCs. Our data suggest that recurrent chromosomal segmental gains provide a proliferative advantage to hESCs under increased replicative stress, a process that may also explain the highly recurrent nature of certain imbalances in cancer.

cancer biology↗

bioRxiv2020

When DNA gets in the way in RNA-seq experiments, a sequel

Using a newly developed method dubbed SILVER-Seq--enabling extracellular RNA sequencing (exRNA-seq) directly from a small volume of human serum or plasma-- Yan et al. recently reported in Current Biology a potential exRNA biomarker for the early diagnosis of Alzheimers disease [1]. After the publication of the initial paper describing the SILVER-Seq method [2], we reported our concern regarding potential DNA contamination in their datasets [3]. Although the authors replied they were able to successfully treat RNA samples with DNase to avoid such contamination, they did not address our observations of the majority of reads without evidence of being derived from RNA, nor documented verified absence of DNA after DNase treatment [4]. To assess whether the newly data generated may suffer from DNA contamination, we downloaded the publicly available sequencing data and evaluated two quality control metrics (i.e., fraction of exonic and splice reads), which were not reported in the paper. We found that both quality metrics were much lower than expected for RNA-seq data (6.28% exonic and 0.478% splice reads), in line with our previous findings on the first SILVER-Seq paper. These observations suggest the data and results presented by Yan et al. are affected by DNA contamination, an issue that may be inherent to the SILVER-Seq technology.

molecular biology↗

bioRxiv2020

SOX11 is a lineage-dependency factor and master epigenetic regulator in neuroblastoma

The pediatric extra-cranial tumor neuroblastoma (NB) is characterised by a low mutation burden while copy number alterations are present in most high-risk cases. We identified SOX11 as a strong lineage dependency transcription factor in adrenergic NB based on recurrent chromosome 2p focal gains and amplifications, its specific expression in the normal sympatho-adrenal lineage and adrenergic NBs and its regulation by multiple adrenergic specific cis-interacting (super-)enhancers. Adrenergic NBs are strongly dependent on high SOX11 expression levels for growth and proliferation. Through genome-wide DNA-binding and transcriptome analysis, we identified and validated functional SOX11 target genes, several of which implicated in chromatin remodeling and epigenetic modification. SOX11 controls chromatin accessibility predominantly affecting distal adrenergic lineage-specific enhancers marked by binding sites of the adrenergic core regulatory circuitry. During normal sympathoblast differentiation we find expression of SOX11 prior to members of the adrenergic core regulatory circuitry. Given the broad control of SOX11 of multiple epigenetic regulatory complexes and its presumed pioneer factor function, we propose that adrenergic NB cells have co-opted the normal role of SOX11 as a crucial regulator of chromatin accessibility and cell identity.

cancer biology↗

bioRxiv2020

Genome-wide study of the effect of blood collection tubes on the cell-free DNA methylome

BackgroundThe methylation pattern of cfDNA, isolated from liquid biopsies, is gaining substantial interest for diagnosis and monitoring of diseases. We have evaluated the impact of type of blood collection tube and time delay between blood draw and plasma preparation on bisulfite-based cfDNA methylation profiling. Methods15 tubes of blood were drawn from three healthy volunteer subjects (BD Vacutainer K2E EDTA spray tubes, Streck Cell-Free DNA BCT tubes, PAXgene Blood ccfDNA tubes, Roche Cell-Free DNA Collection tubes and Biomatrica LBgard blood tubes in triplicate). Samples were either immediately processed or stored at room temperature for 24 or 72 hours before plasma preparation. DNA fragment size was evaluated by capillary electrophoresis. Reduced representation bisulfite sequencing was performed on the cell-free DNA isolated from these plasma samples. We evaluated the impact of blood tube and time delay on several quality control metrics. ResultsAll preservation tubes performed similar on the quality metrics that were evaluated. Furthermore, a considerable increase in cfDNA concentration and the fraction of it derived from NK cells was observed after a 72-hour time delay in EDTA tubes. ConclusionThe methylation pattern of cfDNA is robust and reproducible in between the different preservation tubes. EDTA tubes processed as soon as possible, preferably within 24 hours, are the most cost effective. If immediate processing is not possible, preservation tubes are valid alternatives.

genomics↗

bioRxiv2020

Comprehensive benchmarking of computational deconvolution of transcriptomics data

Many computational methods to infer cell type proportions from bulk transcriptomics data have been developed. Attempts comparing these methods revealed that the choice of reference marker signatures is far more important than the method itself. However, a thorough evaluation of the combined impact of data transformation, pre-processing, marker selection, cell type composition and choice of methodology on the results is still lacking. Using different single-cell RNA-sequencing (scRNA-seq) datasets, we generated hundreds of pseudo-bulk mixtures to evaluate the combined impact of these factors on the deconvolution results. Along with methods to perform deconvolution of bulk RNA-seq data we also included five methods specifically designed to infer the cell type composition of bulk data using scRNA-seq data as reference. Both bulk and single-cell deconvolution methods perform best when applied to data in linear scale and the choice of normalization can have a dramatic impact on the performance of some, but not all methods. Overall, single-cell methods have comparable performance to the best performing bulk methods and bulk methods based on semi-supervised approaches showed higher error and lower correlation values between the computed and the expected proportions. Moreover, failure to include cell types in the reference that are present in a mixture always led to substantially worse results, regardless of any of the previous choices. Taken together, we provide a thorough evaluation of the combined impact of the different factors affecting the computational deconvolution task across different datasets and propose general guidelines to maximize its performance.

bioinformatics↗

bioRxiv2019

The RNA Atlas, a single nucleotide resolution map of the human transcriptome

The human transcriptome consists of various RNA biotypes including multiple types of non-coding RNAs (ncRNAs). Current ncRNA compendia remain incomplete partially because they are almost exclusively derived from the interrogation of small- and polyadenylated RNAs. Here, we present a more comprehensive atlas of the human transcriptome that is derived from matching polyA-, total-, and small-RNA profiles of a heterogeneous collection of nearly 300 human tissues and cell lines. We report on thousands of novel RNA species across all major RNA biotypes, including a hitherto poorly-cataloged class of non-polyadenylated single-exon long non-coding RNAs. In addition, we exploit intron abundance estimates from total RNA-sequencing to test and verify functional regulation by novel non-coding RNAs. Our study represents a substantial expansion of the current catalogue of human ncRNAs and their regulatory interactions. All data, analyses, and results are available in the R2 web portal and serve as a basis to further explore RNA biology and function.

genetics↗

bioRxiv2019

Minimally invasive classification of pediatric solid tumors using reduced representation bisulfite sequencing of cell-free DNA: a proof-of-principle study

In the clinical management of pediatric solid tumors, histological examination of tumor tissue obtained by a biopsy remains the gold standard to establish a conclusive pathological diagnosis. The DNA methylation pattern of a tumor is known to correlate with the histopathological diagnosis across cancer types and is showing promise in the diagnostic workup of tumor samples. This methylation pattern can be detected in the cell-free DNA. Here, we provide proof-of-concept of histopathologic classification of pediatric tumors using cell-free reduced representation bisulfite sequencing (cf-RRBS) from retrospectively collected plasma and cerebrospinal fluid samples. We determined the correct tumor type in 49 out of 60 (81.6%) samples starting from minute amounts (less than 10 ng) of cell-free DNA. We demonstrate that the majority of misclassifications were associated with sample quality and not with the extent of disease. Our approach has the potential to help tackle some of the remaining diagnostic challenges in pediatric oncology in a cost-effective and minimally invasive manner. Translational relevanceObtaining a correct diagnosis in pediatric oncology can be challenging in some tumor types, especially in renal tumors or central nervous system tumors. Furthermore, the diagnostic odyssey can result in anxiety and discomfort for these children. By applying a novel technique, reduced representation bisulfite sequencing on cell-free DNA (cf-RRBS), we show the feasibility of obtaining the histopathological diagnosis with a minimally invasive test on either plasma or cerebrospinal fluid. Furthermore, we were able to derive the copy number profile or tumor subtype from the same assay. Given that primary tumor material might be difficult to obtain, in particular in critically ill children or depending on the tumor location, and might be limited in terms of quantity or quality, our assay could become complementary to the classical tissue biopsy in difficult cases.

genomics↗

bioRxiv2019

A versatile method for circulating cell-free DNA methylome profiling by reduced representation bisulfite sequencing

Methylation profiling of circulating cell-free DNA (cfDNA) is of great interest as a liquid biopsy assay for the detection and monitoring of cancer and other pathologies. Here we describe circulating cell-free DNA reduced representation bisulfite sequencing (cf-RRBS), enabling the use of highly effective RRBS on fragmented plasma cfDNA. This method enriches the CpG-rich RRBS target regions by enzymatic degradation of all off-target DNA rather than by targeted capture, in contrast to previous methods. Critical steps are fully enzymatic in a single-tube, making it rapid, cost-effective, robust, and easily implemented on a liquid-handling station for high-throughput sample preparation. We benchmark cf-RRBS results to those obtained by previous more complex methods and exemplify its use for accurate non-invasive subtyping of lung cancer, a frequent onco-pathology task. cf-RRBS enables any molecular pathology lab to tap into the cfDNA methylome, only making use of off-the-shelf reagents and open-source data analysis tools. One Sentence SummaryNovel methodology enables facile and cost-effective methylation profiling of fragmented plasma DNA, allowing for routine liquid biopsy clinical diagnostics, exemplified here for differential diagnosis of lung cancer subtypes.

genomics↗

bioRxiv2018

Circulating microRNA biomarkers for metastatic disease in neuroblastoma patients

In this study, the circulating miRNome from diagnostic neuroblastoma serum was assessed for identification of non-invasive biomarkers with potential in monitoring metastatic disease. After determining the circulating neuroblastoma miRNome, 743 miRNAs were screened in two 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. Differential expression analysis between patients with metastatic and localized disease revealed 9 miRNAs strongly associated with metastatic stage 4 disease in both patient cohorts. Increasing levels of these miRNAs were also observed in serum from xenografted mice bearing human neuroblastoma tumors. Moreover, murine serum miRNA levels were strongly associated with tumor volume, suggesting this miRNA signature may be applied to monitor disease burden.

cancer biology↗