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Hazelaar, D. M.

Publications and source records attributed to Hazelaar, D. M..

2 recordsLinked to original sources

From Background to Signal: Simultaneously derived Copy Numbers from Methylation-Dependent Sequencing cell-free DNA

Cell-free DNA (cfDNA) analysis offers a powerful, minimally invasive approach to improve cancer care by measuring tumor-specific genomic and epigenetic alterations. Here, we demonstrate the versatility of MeD-seq, a methylation-dependent sequencing assay, for comprehensive cfDNA analysis, including DNA methylation profiling, chromosomal copy number (CN) alterations, and tumor fraction (TF) estimation. MeD-seq-derived CN profiles and TF estimates from 38 colorectal cancer with liver metastases (CRLM) and 5 ovarian cancer patients were highly comparable to shallow whole-genome sequencing (sWGS) validating our approach. For 120 CRLM patients we used MeD-seq CN and TF information in an improved Differential Methylation Model which detected additional significantly Differentially Methylated Regions (DMRs) correlating with TF estimates. Using the identified DMR sets we were subsequently able to distinguish healthy blood donors from CRLM patients with low amounts of circulating tumor DNA (ctDNA) as well. These findings show MeD-seq as an affordable platform for detecting cancer-specific signals directly from plasma without prior tissue-based information. Future work could expand its application to other cancer types, solidifying MeD-seq as a versatile tool for cfDNA profiling. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=159 SRC="FIGDIR/small/633371v2_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@bd440eorg.highwire.dtl.DTLVardef@1befbb7org.highwire.dtl.DTLVardef@17fe9d2org.highwire.dtl.DTLVardef@d0101a_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract of MeD-seq and Copy Number Informed Differential Methylation Analysis Liquid biopsy samples are collected, followed by cfDNA extraction, methylation-dependent enzymatic digestion, Whole Genome Sequencing (WGS) and in silico read selection. Background reads (lacking the recognition site of the methylation-dependent restriction ezyme) are utilized for CN profiling and TF estimation. Methylated reads (with the recognition site of the methylation-dependent restriction enzyme) are counted in CpG islands and integrated with CN information to perform Differential Methylation profiling, enabling a comprehensive assessment of methylation in cfDNA. C_FIG

bioinformatics↗

Katdetectr: utilising unsupervised changepoint analysis for robust kataegis detection

BackgroundKataegis refers to the occurrence of regional genomic hypermutation in cancer and is a phenomenon that has been observed in a wide range of malignancies. A kataegis locus constitutes a genomic region with a high mutation rate, i.e., a higher frequency of closely interspersed somatic variants than the overall mutational background. It has been shown that kataegis is of biological significance and possibly clinically relevant. Therefore, an accurate and robust workflow for kataegis detection is paramount. FindingsHere we present Katdetectr, an open-source R/Bioconductor-based package for the robust yet flexible and fast detection of kataegis loci in genomic data. In addition, Katdetectr houses functionalities to characterize and visualize kataegis and provides results in a standardized format useful for subsequent analysis. In brief, Katdetectr imports industry-standard formats (MAF, VCF, and VRanges), determines the intermutation distance of the genomic variants and performs unsupervised changepoint analysis utilizing the Pruned Exact Linear Time search algorithm followed by kataegis calling according to user-defined parameters. We used synthetic data and an a priori labeled pan-cancer dataset of Whole Genome Sequenced malignancies for the performance evaluation of Katdetectr and five publicly available kataegis detection packages. Our performance evaluation shows that Katdetectr is robust regarding tumor mutational burden (TMB) and shows the fastest mean computation time. Additionally, Katdetectr reveals the highest accuracy (0.99, 0.99) and normalized Matthews Correlation Coefficient (0.98, 0.92) of all evaluated tools for both datasets. ConclusionsKatdetectr is a robust workflow for the detection, characterization, and visualization of kataegis and is available on Bioconductor: https://doi.org/doi:10.18129/B9.bioc.katdetectr

bioinformatics↗