Biology subjects

Huebschmann, D.

Publications and source records attributed to Huebschmann, D..

At least 19 recordsLinked to original sources

bioRxiv2026

Global signatures of Ribonucleotide Excision Repair deficiency and its therapeutic vulnerabilities

Cancer therapies increasingly exploit DNA repair deficiencies, yet many clinically actionable repair-deficient states remain undetectable because they lack robust biomarkers and are not identifiable through gene-based diagnostics. Here, we show that deficiency of ribonucleotide excision repair (RER), a pathway that removes misincorporated ribonucleotides from genomic DNA, generates a distinct genome-wide mutational phenotype comprising characteristic insertion-deletion, substitution, and structural variation signatures. Using these features, we developed RERDetect, a whole-genome classifier that identifies RER-deficient tumors across diverse cancer types. Application of RERDetect to more than 11,000 primary and metastatic cancers revealed a recurrent subset of tumors with evidence of RER deficiency, the majority lacking detectable genetic alterations in RNASEH2 genes. We identified promoter hypermethylation of RNASEH2C as a recurrent mechanism of pathway inactivation. RER-deficient cells exhibited distinct vulnerabilities, including sensitivity to EXO1 and FANCM inhibition. These findings establish RER deficiency as a previously unrecognized and therapeutically actionable DNA repair-deficient state in cancer, demonstrating the power of mutational scars to identify clinically relevant tumor phenotypes that evade conventional genomic diagnostics.

genomics↗

bioRxiv2026

Magel2 deficiency drives microglial synaptic engulfment and alters neuron-microglia interactions in Prader-Willi syndrome

Prader-Willi syndrome (PWS) is a neurodevelopmental disorder characterized by hypothalamic dysfunction, developmental delay, hyperphagia, and cognitive impairment, yet the cellular mechanisms underlying circuit abnormalities remain unclear. Here, we show that loss of the imprinted gene Magel2 remodels microglial state and function in a mouse model of PWS. Three-dimensional morphometric analysis of hypothalamic microglia revealed reduced structural complexity and contracted territorial domains, emerging early in development and largely independent of obesity. Single-nucleus RNA sequencing of hypothalamic brain tissue identified extensive transcriptional reprogramming involving immune signaling, cellular communication, ribosome function, synaptic organization, and protein translation, with stronger effects in females. Magel2 deficiency also increased microglia-neuron contacts and synaptic engulfment, accompanied by reduced synaptic marker density, suggesting aberrant refinement of hypothalamic circuits. Analyses of human post-mortem hypothalamic tissue from individuals with PWS confirmed microglial structural alterations and increased complement engagement. Ligand-receptor inference further revealed reorganization of microglia-centered communication networks, including altered adhesion, trophic, and immune signaling consistent with enhanced phagocytic states. Together, these findings identify microglial remodeling and increased synaptic engagement as central features of Magel2 deficiency, implicate microglia in hypothalamic circuit dysfunction in PWS, and highlight excessive synaptic pruning as a novel dysfunctional mechanism of PWS.

neuroscience↗

bioRxiv2026

Multiscale biological interactions define clinical trajectories in acute myeloid leukemia

Cancer is characterized by complex interactions across genetic, cellular, and microenvironmental scales. However, a quantitative understanding of how these interactions shape clinical trajectories remains limited. Here, we present a multi-scale single-cell dataset from 184 treatment-naive acute myeloid leukemia (AML) patients spanning all major genetic subtypes, together with an analytical framework to dissect interactions across biological scales. We show that distinct clinical outcomes are encoded by specific cross-scale, cross-compartment interactions present at diagnosis: response to induction therapy is governed by interactions between genetic alterations and leukemic differentiation state; relapse following chemotherapy is associated with non-genetic programs linked to metabolism; and relapse after allogeneic stem cell transplantation is driven by interactions between the immune microenvironment and residual healthy hematopoiesis. Together, our study provides a framework to resolve intra- and inter-patient heterogeneity in cancer and supports a model in which clinical trajectories in AML emerge from defined interactions across biological scales.

cancer biology↗

bioRxiv2026

Prospective pan-cancer phosphoproteomics at clinical scale extends therapeutic options in precision oncology

Genomics-guided precision oncology has improved survival in cancer entities with actionable mutations but cannot capture oncogenic signaling that manifests at the protein level. Here, we report a prospective, real-world pan-cancer study profiling proteomes and phosphoproteomes of 1,998 tumor samples from adults and children with rare or advanced cancers enrolled in the German precision oncology programs DKFZ/NCT/DKTK MASTER, CATCH and INFORM and their molecular tumor boards (MTBs). We developed tumor proteome activity status (TOPAS) scores for 46 clinically relevant kinases, an immune activity score capturing antigen presentation and T-cell activation and identified therapeutically targetable cell-surface proteins for 94% of patients. These readouts enhance MTB recommendations by exposing actionable non-genomic kinase activity, refining interpretation of oncogenic genome alterations, and highlighting cell-surface treatment options. Three proof-of-concept analyses indicate clinical utility including kinase activity-stratified pazopanib response in sarcoma, immune activity score-tracked checkpoint-inhibitor outcomes pan-cancer, and a phosphoproteomic biomarker distinguishing EGFR-inhibitor response in chordoma.

cancer biology↗

bioRxiv2026

Alternative lengthening of telomeres stratifies complex karyotype sarcomas into distinct genomic and transcriptomic states

Complex karyotype sarcomas (CKS) are heterogeneous mesenchymal malignancies that typically lack recurrent actionable oncogenic drivers and remain therapeutically challenging. Loss of ATRX is a recurrent feature of CKS and defines a particularly high-risk subgroup. ATRX loss is also associated with activation of the alternative lengthening of telomeres (ALT) pathway, and ALT-positive sarcomas have been linked to poor clinical outcomes. However, the molecular underpinnings underlying ALT-status-dependent differences in CKS, as well as the therapeutic vulnerabilities associated with ALT, remain poorly defined. By integrating C-circle-based ALT detection across 776 sarcoma samples with multi-modal sequencing of five CKS subtypes, we find that ALT activity is associated with enriched hallmarks of genomic instability. ALT-positive transcriptomes are dominated by a coordinated DNA damage response and mitotic program, in contrast to oncogenic signaling pathways that drive TERT activation in ALT-negative tumors. Long-read sequencing reveals telomere repeat clusters and telomere-mediated healing at structural breakpoints in ALT-positive tumors. These events also occur on extrachromosomal DNA (ecDNA), linking ALT activity to ecDNA biology. Together, our findings position ALT status as an important stratifying feature of CKS and identify ALT-associated transcriptional programs as potential therapeutic targets.

cancer biology↗

bioRxiv2026

Systematic functional drug testing in patient-derived models reveals ex vivo sensitivities associated with clinical outcome in rare solid tumors

Rare cancers are individually uncommon but collectively represent a substantial share of cancer burden, with limited systemic treatment options for many entities. Molecular profiling identifies targetable alterations, but actionable findings are limited and responses can vary despite a matched target. This motivates complementary approaches that directly assess tumor drug response. Here, we establish a biopsy-compatible ex vivo drug sensitivity testing platform optimized for low input and reproducibility. Patient-derived material was tested either directly or following ex vivo expansion. Functional profiling was performed within clinically relevant timelines across models from 126 patients with rare advanced solid tumors. Drug responses were consistent between model types. In most samples, we identified at least one potentially active compound, supporting feasibility at biopsy-scale. High in vitro sensitivity was associated with clinical benefit and progression-free survival. These findings support functional drug sensitivity testing as a complementary component in precision oncology for adults with rare cancers. Statement of SignificanceThis study presents a biopsy-compatible drug sensitivity testing platform for phenotype-based therapy stratification in rare cancers. It identifies actionable ex vivo drug responses and shows associations with clinical outcome in patients treated with screened therapies. These findings support functional testing as a complementary additional layer of stratification for therapeutic prioritization.

cancer biology↗

bioRxiv2025

A kinetics-based model of hematopoiesis reveals extrinsic regulation of skewed lineage output from stem cells

Residing at the top of the hematopoietic hierarchy, long-term hematopoietic stem cells (HSCs) are capable of self-renewal and sustained blood cell regeneration. Over the past decades, single-cell and clonal analyses have revealed substantial functional and molecular heterogeneity within this compartment, challenging the notion that self-renewal is inherently tied to balanced, multi-lineage blood production. However, a cohesive model that explains the relationships among these diverse HSC states remains elusive. Here, we combined single-cell transplantations of over 1,000 highly purified murine long-term HSCs with in-depth phenotyping of their clonal progeny to achieve a detailed, time-resolved understanding of heterogeneous reconstitution outcomes. We identified reconstitution kinetics as an overall unifying metric of HSC functional potency, with the most potent HSCs displaying the greatest delay in hematopoietic reconstitution. Importantly, a progressive acceleration in reconstitution kinetics was also associated with a gradual shift in mature cell production from platelet and erythro-myeloid bias to balanced, and eventually lymphoid bias. Serial single-cell transplantations of HSCs revealed a unidirectional acceleration in reconstitution kinetics accompanied by a gradual decline in functional potency of daughter HSCs, aligning diverse phenotypes along a linear hierarchical trajectory. Mathematical modeling, together with experimental modulation of lineage-biased blood production, demonstrated that apparent lineage biases actually arise from cell-extrinsic feedback regulation and clonal competition between slow- and fast-engrafting clones to occupy the limited compartment sizes of mature lineages. Our study reconciles multiple layers of HSC heterogeneity into a unifying framework, prompting a reevaluation of the meaning of lineage biases in both normal and diseased hematopoiesis, with broad implications for other regenerating tissues during development, homeostasis, and repair.

cell biology↗

bioRxiv2023

Multiple myeloma long-term survivors display sustained immune alterations decades after first line therapy

The long-term consequences of cancer or cancer therapy on the patients immune system years after cancer-free survival remain poorly understood. Here, we have performed an in-depth characterization of the bone marrow ecosystem of multiple myeloma long-term survivors at initial diagnosis and up to 17 years following cancer-free survival. Using comparative single-cell analyses in combination with molecular, genomic and functional approaches, we demonstrate that multiple myeloma long-term survivors display pronounced alterations in their bone marrow microenvironment associated with impaired immunity. These immunological alterations were frequently driven by an inflammatory immune circuit fueled by the long-term persistence or resurgence of residual myeloma cells. Notably, even in the complete absence of any detectable residual disease for decades, sustained changes in the immune system were observed, suggesting an irreversible immunological scarring caused by the initial exposure to the cancer and therapy. Collectively, our study provides key insights into the molecular and cellular bone marrow ecosystem of multiple myeloma long-term survivors, revealing reversible and irreversible alterations of the immune compartment, which can serve as diagnostic and predictive tools. Statement of significanceLarge-scale single-cell profiling of a unique cohort of multiple myeloma long-term survivors uncovered that exposure to cancer and its treatment causes both reversible and irreversible immune alterations associated with impaired immunity. These findings have far-reaching implications for the understanding of long-term immune alterations in cancer, which need to be considered also in the context of immune therapeutic approaches. Furthermore, our study demonstrates how cancer-associated immune trafficking can be used to predict disease re-initiation in the bone marrow, opening new avenues for minimally invasive disease monitoring.

cancer biology↗

bioRxiv2023

An AI-based segmentation and analysis pipeline for high-field MR monitoring of cerebral organoids

BackgroundCerebral organoids simulate the structure and function of the developing human brain in vitro, offering a large potential for personalized therapeutic strategies. The enormous growth of this research area over the past decade with its capability for clinical translation makes a non-invasive, automated analysis pipeline of organoids highly desirable. PurposeThis work presents the first application of magnetic resonance imaging (MRI) for the non-invasive quantification and quality assessment of cerebral organoids using an automated analysis tool. Three specific objectives are addressed, namely organoid segmentation to investigate organoid development over time, global cysticity classification, and local cyst segmentation. MethodsNine wildtype cerebral organoids were imaged over nine weeks using high-field 9.4T MRI including a 3D T2*-w and 2D diffusion tensor imaging (DTI) sequence. This dataset was used to train a deep learning-based 3D U-Net for organoid and local cyst segmentation. For global cysticity classification, we developed a new metric, compactness, to separate low- and high-quality organoids. ResultsThe 3D U-Net achieved a Dice score of 0.92{+/-}0.06 (mean {+/-} SD) for organoid segmentation in the T2*-w sequence. For global cysticity classification, compactness separated low- and high-quality organoids with high accuracy (ROC AUC 0.98). DTI showed that low-quality organoids have a significantly higher diffusion than high-quality organoids (p < .001). For local cyst segmentation in T2*-w, the 3D U-Net achieved a Dice score of 0.63{+/-}0.15 (mean {+/-} SD). ConclusionWe present a novel non-invasive approach to monitor and analyze cerebral organoids over time using high-field MRI and state-of-the-art tools for automated image analysis, offering a comparative pipeline for personalized medicine. We show that organoid growth can be monitored reliably over time and low- and high-quality organoids can be separated with high accuracy. Local cyst segmentation is feasible but could be further improved in the future.

developmental biology↗

bioRxiv2023

ZygosityPredictor

SummaryZygosityPredictor provides functionality to evaluate how many copies of a gene are affected by mutations in next generation sequencing data. In cancer samples, the tool processes both somatic and germline mutations. In particular, ZygosityPredictor computes the number of affected copies for single nucleotide variants and small insertions and deletions (Indels). In addition, the tool integrates information at gene level via phasing of several variants and subsequent logic to derive how strongly a gene is affected by mutations and provides a measure of confidence. This information is of particular interest in precision oncology, e.g. when assessing whether unmutated copies of tumor-suppressor genes remain. Availability and implementationZygosityPredictor was implemented as an R-package and is available via Bioconductor at https://bioconductor.org/packages/ZygosityPredictor. Detailed documentation is provided in the vignette including application to an example genome.

bioinformatics↗

bioRxiv2022

aRgus: multilevel visualization of non-synonymous single nucleotide variants & advanced pathogenicity score modeling for genetic vulnerability assessment

The widespread use of high-throughput sequencing techniques is leading to a rapidly increasing number of disease-associated variants of unknown significance and candidate genes. Integration of knowledge concerning their genetic, protein as well as functional and conservational aspects is necessary for an exhaustive assessment of their relevance and for prioritization of further clinical and functional studies investigating their role in human disease. In order to collect the necessary information, a multitude of different databases has to be accessed and data extraction from the original sources commonly is not user-friendly and requires advanced bioinformatics skills. This leads to a decreased data accessibility for a relevant number of potential users such as clinicians, geneticist, and clinical researchers. Here, we present aRgus (https://argus.urz.uni-heidelberg.de/), a standalone webtool for simple extraction and intuitive visualization of multi-layered gene, protein, variant, and variant effect prediction data. aRgus provides interactive exploitation of these data within seconds for any known gene of the human genome. In contrast to existing online platforms for compilation of variant data, aRgus complements visualization of chromosomal exon-intron structure and protein domain annotation with ClinVar and gnomAD variant distributions as well as position-specific variant effect prediction score modeling. aRgus thereby enables timely assessment of protein regions vulnerable to variation with single amino acid resolution and provides numerous applications in variant and protein domain interpretation as well as in the design of in vitro experiments.

bioinformatics↗

bioRxiv2022

rGREAT: an R/Bioconductor package for functional enrichment on genomic regions

SummaryGREAT is a widely used tool for functional enrichment on genomic regions. However, as an online tool, it has limitations of outdated annotation data, small numbers of supported organisms and gene set collections, and not being extensible for users. Here we developed a new R/Bioconductor package named rGREAT which implements the GREAT algorithm locally. rGREAT by default supports more than 500 organisms and a large number of gene set collections, as well as self-provided gene sets and organisms from users. Additionally, it implements a general method for dealing with background regions. Availability and implementationThe package rGREAT is freely available from the Bioconductor project: https://bioconductor.org/packages/rGREAT/. The development version is available at https://github.com/jokergoo/rGREAT. Gene Ontology gene sets for 556 organisms are freely available at https://jokergoo.github.io/rGREAT_genesets/. Contactz.gu@dkfz.de or d.huebschmann@dkfz.de Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2022

pkgndep: a tool for heaviness analysis on R package dependencies

SummaryNumerous R packages have been developed for bioinformatics analysis in the last decade and dependencies among packages have become critical issues to consider. In this work, we proposed a new metric named dependency heaviness that measures the number of unique dependencies that a parent brings to a package, and we proposed possible solutions for reducing the complexity of dependencies by optimizing the use of heavy parents. We implemented the metric in a new R package pkgndep which provides an intuitive way for dependency heaviness analysis. Based on pkgndep, we additionally performed a global analysis of dependency heaviness on CRAN and Bioconductor ecosystems and we revealed top packages that have significant contributions of high dependency heaviness to their child packages. Availability and implementationThe package pkgndep and documentations are freely available from the Comprehensive R Archive Network (CRAN) https://cran.r-project.org/package=pkgndep. The dependency heaviness analysis for all 21,741 CRAN and Bioconductor packages retrieved on 2021-10-28 are available at https://pkgndep.github.io/.

bioinformatics↗

bioRxiv2021

Comparison of extraction methods for intracellular metabolomics

Measurements of metabolic compounds inside cells or tissues are of high informative potential since they represent the endpoint of biological information flow and a snapshot of the integration of many regulatory processes. However, it requires careful extraction to quantify their abundance. Here we present a comprehensive study using ten extraction protocols on four human sample types (liver tissue, bone marrow, HL60 and HEK cells) targeting 630 metabolites of different chemical classes. We show that the extraction efficiency and stability is highly variable across protocols and tissues by using different quality metrics including the limit of detection and variability between replicates as well as the sum of concentration as a global estimate of extraction stability. The profile of extracted metabolites depends on the used solvents - an observation which has implications for measurements of different sample types and metabolic compounds of interest. To identify the optimal extraction method for future metabolomics studies, the benchmark dataset was implemented in an easy-to-use, interactive and flexible online resource (R/shiny app MetaboExtract).

biochemistry↗

bioRxiv2021

Comprehensive analysis of mutational signatures in pediatric cancers

Analysis of mutational signatures can reveal the underlying molecular mechanisms of the processes that have imprinted the somatic mutations found in a cancer genome. Here, we present a pan-cancer mutational signatures analysis of single base substitutions (SBS) and small insertion and deletions (ID) in pediatric cancers encompassing 537 whole genome sequenced tumors from 20 molecularly defined cancer subtypes. We identified only a small number of mutational signatures active in pediatric cancers when compared to the previously analyzed adult cancers. Further, we report a significant difference in the proportion of pediatric tumors which show homologous recombination repair defect signature SBS3 compared to prior analyses. Correlating genomic alterations with signature activities, we identified an association of TP53 mutation status with substitution signatures SBS2, SBS8, SBS13 and indel signatures ID2 and ID9, as well as chromothripsis associated with SBS8, SBS40 and ID9. This analysis provides a systematic overview of COSMIC v.3 SBS and ID mutational signatures active across pediatric cancers, which is highly relevant for understanding tumor biology as well as enabling future research in defining biomarkers of treatment response.

cancer biology↗

bioRxiv2021

Improve consensus partitioning via a hierarchical procedure

Consensus partitioning is an unsupervised method widely used in high throughput data analysis for revealing subgroups and assigns stability for the classification. However, standard consensus partitioning procedures are weak to identify large numbers of stable subgroups. There are two main issues. 1. Subgroups with small differences are difficult to separate if they are simultaneously detected with subgroups with large differences. And 2. stability of classification generally decreases as the number of subgroups increases. In this work, we proposed a new strategy to solve these two issues by applying consensus partitionings in a hierarchical procedure. We demonstrated hierarchical consensus partitioning can be efficient to reveal more subgroups. We also tested the performance of hierarchical consensus partitioning on revealing a great number of subgroups with a DNA methylation dataset. The hierarchical consensus partitioning is implemented in the R package cola with comprehensive functionality for analysis and visualizations. It can also automate the analysis only with a minimum of two lines of code, which generates a detailed HTML report containing the complete analysis.

bioinformatics↗

bioRxiv2021

spiralize: an R Package for Visualizing Data on Spirals

SummarySpiral layout has two major advantages for data visualization. First, it is able to visualize data with long axes, which greatly improves the resolution of visualization. Second, it is efficient for time series data to reveal periodic patterns. Here we present the R package spiralize that provides a general solution for visualizing data on spirals. spiralize implements numerous graphics functions so that self-defined high-level graphics can be easily implemented by users. The flexibility and power of spiralize are demonstrated by five examples from real-world datasets. Availability and implementationThe spiralize package and documentations are freely available at the Comprehensive R Archive Network (CRAN) https://CRAN.R-project.org/package=spiralize. Contactz.gu@dkfz.de or d.huebschmann@dkfz.de Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2021

Single-cell proteo-genomic reference maps of the hematopoietic system enable the purification and massive profiling of precisely defined cell states

Single-cell genomics has transformed our understanding of complex cellular systems. However, excessive costs and a lack of strategies for the purification of newly identified cell types impede their functional characterization and large-scale profiling. Here, we have generated high content single-cell proteo-genomic reference maps of human blood and bone marrow that quantitatively link the expression of up to 197 surface markers to cellular identities and biological processes across all major hematopoietic cell types in healthy aging and leukemia. These reference maps enable the automatic design of cost-effective high-throughput cytometry schemes that outperform state-of-the-art approaches, accurately reflect complex topologies of cellular systems, and permit the purification of precisely defined cell states. The systematic integration of cytometry and proteo-genomic data enables measuring the functional capacities of precisely mapped cell states at the single-cell level. Our study serves as an accessible resource and paves the way for a data-driven era in cytometry.

immunology↗