Biology subjects

Korbel, J.

Publications and source records attributed to Korbel, J..

9 recordsLinked to original sources

bioRxiv2026

Hierarchical classification of hematologic malignancies using epigenetic and genetic information

Molecular testing in hematology requires different assays for disease subgroup identification, risk stratification and selection of appropriate treatment regimens. Yet, molecular tests are not necessarily standardized between diagnostic laboratories, resulting in varying turnaround times and potentially divergent results. To resolve this issue and enable single-assay molecular testing, we have developed a hierarchical classification framework that combines epigenetic and genetic data from whole genome nanopore sequencing (WGNS) with machine learning to determine disease entities, epigenetic subgroups (epitypes) and genetic aberrations in hematopoietic neoplasms. We curated DNA methylation data from 5,420 samples and trained a classifier allowing entity-level diagnostics featuring 21 conditions, including healthy controls, acute and chronic myeloid and lymphoid neoplasms. This classifier was subsequently combined with entity-specific epitype classifiers predicting 44 therapeutically or prognostically relevant states, followed by integration of genetic data. Benchmarking of the combined (epi-)genetic testing strategy using WGNS confirmed high accuracy in the detection of diagnostic groups and risk stratification, and identified diagnosis-defining molecular alterations that were not reported by standard-of-care work-up.

cancer biology↗

bioRxiv2025

Single cell whole genome and transcriptome sequencing links somatic mutations to cell identity and ancestry

The role of somatic mutations in human development and disease is obscured by difficulties in characterizing mutations at the single cell level and identifying cell types carrying them. Here we analysed somatic genomes of clonal iPSC lines and of single-cells after whole-genome amplification (scWGA) by PTA and ResolveOme from skin fibroblasts, blood and urine of a live donor. Mutation burden and spectra converged across approaches, revealing heterogeneous mutational footprints across cells driven by environmental exposures (UV damage and chemotherapy) and lymphocyte differentiation. Aneuploidies in single cells were detected by all the approaches and were orthogonally validated by Strand-seq. Uniquely, ResolveOme enabled cell-type identification using single-cell transcriptomes. Using a newly developed method accounting for noise and allele drop-out in scWGA, we de novo reconstructed the cell phylogenetic tree for this donor. Together, scWGA establishes a powerful foundation for comprehensive, cell type-aware, lineage-aware profiling of somatic mutations at single cell level.

genomics↗

bioRxiv2025

Nucleotide insufficiency induced by p53 deficiency leads to replication stress driving genomic instability

P53 prevents DNA damage by inducing repair processes, cell cycle arrest or apoptosis. P53 loss leads to replication stress and genomic instability, yet the mechanisms underlying these effects and their contribution to catastrophic genomic events such as chromothripsis remain poorly understood. Using patient-derived fibroblasts with germline p53 variants, that spontaneously undergo chromothripsis, and p53-downregulated fibroblasts, we discovered that p53 loss leads to aberrant transcriptional upregulation, increasing nucleotide consumption while simultaneously decreasing nucleotide biosynthesis. This imbalance in production and consumption results in insufficient nucleotide pools, leading to replication stress and genomic instability, which are rescued by nucleoside supplementation or transcription normalization. The replication stress triggers telomere dysfunction, micronuclei formation, and ultimately chromothripsis. Emerging dominant chromothriptic clones exhibit normal DNA replication, telomere stabilization, and ecDNA, highlighting critical features for clonal selection. Hence, p53 coordinates transcription and nucleotide pools, crucial for maintaining genomic stability and preventing early cancer development.

cancer biology↗

bioRxiv2024

SVarp: pangenome-based structural variant discovery

The linear human reference genome that we use today does not represent the haplotypic diversity of the global human population. This raises bias in genomic read alignment and limits our ability to call large structural variations (SV), especially at highly polymorphic loci. Thus, many SV alleles remain unresolved. Recent efforts to transition to a graph-based reference genome resulted in the generation of the first draft human pangenome reference, but tools to call SVs relative to the pangenome reference are presently lacking. In this study, we present the SVarp algorithm, aiming to discover haplotype resolved SVs on top of a pangenome reference using long sequencing reads. SVarp outputs local assemblies of SV alleles, termed svtigs, instead of a VCF file of SV breakpoints, which we propose as a general exchange format allowing for flexible downstream analyses. In order to assess the accuracy of svtigs, we used simulated and real human genomes. Simulations allowed us to make exact breakpoint comparisons against the true callsets. We observed [~]96% recall with deletions, insertions and duplications larger than 1,000bp, showing that SVarp can reliably detect genomic structural variants not yet represented in the graph. On the other hand, we compared SVarp output for ONT sequencing data at 20X coverage against independent genome assemblies of the same samples and found that [~]82% of our svtig predictions are validated by the assemblies by a match with more than 85% sequence identity. SVarp was implemented using C++ and its source code is available at https://github.com/asylvz/SVarp under MIT license.

bioinformatics↗

bioRxiv2020

Pangenome-based genome inference

AO_SCPLOWBSTRACTC_SCPLOWTypical analysis workflows map reads to a reference genome in order to detect genetic variants. Generating such alignments introduces references biases, in particular against insertion alleles absent in the reference and comes with substantial computational burden. In contrast, recent k-mer-based genotyping methods are fast, but struggle in repetitive or duplicated regions of the genome. We propose a novel algorithm, called PanGenie, that leverages a pangenome reference built from haplotype-resolved genome assemblies in conjunction with k-mer count information from raw, short-read sequencing data to genotype a wide spectrum of genetic variation. The given haplotypes enable our method to take advantage of linkage information to aid genotyping in regions poorly covered by unique k-mers and provides access to regions otherwise inaccessible by short reads. Compared to classic mapping-based approaches, our approach is more than 4x faster at 30x coverage and at the same time, reached significantly better genotype concordances for almost all variant types and coverages tested. Improvements are especially pronounced for large insertions (> 50bp), where we are able to genotype > 99.9% of all tested variants with over 90% accuracy at 30x short-read coverage, where the best competing tools either typed less than 60% of variants or reached accuracies below 70%. PanGenie now enables the inclusion of this commonly neglected variant type in downstream analyses.

bioinformatics↗

bioRxiv2019

Efficient strategies to detect genome editing and integrity in CRISPR-Cas9 engineered ESCs

CRISPR-mediated genome engineering provides a powerful tool to study the function of genes and proteins. In the past decades, the advances in genome and transcriptome sequencing techniques have shed light on the genetic causes underlying many human diseases, such as neurodevelopmental disabilities or cancer. Sometimes, a single point-mutation in a protein coding gene has been identified as the primary cause of the disease. CRISPR-Cas offers the possibility to introduce or remove such a mutation of interest to understand disease mechanisms and even bears therapeutic potential. We describe the adaptation of an experimental strategy that allows the mutation of protein residues in mouse embryonic stem cells (ESCs) and propose a new screening method, Mismatch-qPCR, to reliably detect editing events in clonal cell lines as an alternative to restriction digest or Sanger sequencing. Finally, we show that RNA-Sequencing (RNA-Seq) data or low-coverage genomic sequencing data can be used to detect large chromosomal deletions and rearrangements that frequently occur at the CRISPR-targeting site.

genetics↗

bioRxiv2018

Oncogenic effects of germline mutations in lysosomal storage disease genes

Clinical observations have indicated that patients with Gaucher disease or Fabry disease are at increased risk of cancer. However, a systematic evaluation of the oncogenic effects of causal mutations of lysosomal storage diseases (LSDs) has been lacking. Here we report a comprehensive association analysis between potentially pathogenic germline mutations in LSD genes and cancer interrogating genomic (or exomic) variant datasets derived from the Pan-Cancer Analysis of Whole Genomes project (case cohort), the 1000 Genomes project (primary control cohort), and the Exome Aggregation Consortium that does not include The Cancer Genome Atlas subset (validation control cohort). We show that potentially pathogenic variants (PPVs) in 42 LSD genes are significantly enriched in cancer patients in a histology-dependent manner, cancer risk is higher in individuals with a greater number of PPVs, and cancer develops earlier in PPV carriers. Analysis of tumor genomic and transcriptomic data from the pancreatic adenocarcinoma cohort revealed potential mechanisms that might be involved in the oncogenic contribution of PPVs. Our findings extend the mechanistic understanding of inherited cancer susceptibility and highlight the promise of harnessing available therapeutic strategies to restore lysosomal function for personalized cancer prevention.

genetics↗

bioRxiv2017

Assessing the Gene Regulatory Landscape in 1,188 Human Tumors

Cancer is characterised by somatic genetic variation, but the effect of the majority of non-coding somatic variants and the interface with the germline genome are still unknown. We analysed the whole genome and RNA-Seq data from 1,188 human cancer patients as provided by the Pan-cancer Analysis of Whole Genomes (PCAWG) project to map cis expression quantitative trait loci of somatic and germline variation and to uncover the causes of allele-specific expression patterns in human cancers. The availability of the first large-scale dataset with both whole genome and gene expression data enabled us to uncover the effects of the non-coding variation on cancer. In addition to confirming known regulatory effects, we identified novel associations between somatic variation and expression dysregulation, in particular in distal regulatory elements. Finally, we uncovered links between somatic mutational signatures and gene expression changes, including TERT and LMO2, and we explained the inherited risk factors in APOBEC-related mutational processes. This work represents the first large-scale assessment of the effects of both germline and somatic genetic variation on gene expression in cancer and creates a valuable resource cataloguing these effects.

cancer biology↗

bioRxiv2017

Framework For Quality Assessment Of Whole Genome, Cancer Sequences

Working with cancer whole genomes sequenced over a period of many years in different sequencing centres requires a validated framework to compare the quality of these sequences. The Pan-Cancer Analysis of Whole Genomes (PCAWG) of the International Cancer Genome Consortium (ICGC), a project a cohort of over 2800 donors provided us with the challenge of assessing the quality of the genome sequences. A non-redundant set of five quality control (QC) measurements were assembled and used to establish a star rating system. These QC measures reflect known differences in sequencing protocol and provide a guide to downstream analyses of these whole genome sequences. The resulting QC measures also allowed for exclusion samples of poor quality, providing researchers within PCAWG, and when the data is released for other researchers, a good idea of the sequencing quality. For a researcher wishing to apply the QC measures for their data we provide a Docker Container of the software used to calculate them. We believe that this is an effective framework of quality measures for whole genome, cancer sequences, which will be a useful addition to analytical pipelines, as it has to the PCAWG project.

genomics↗