Biology subjects

Feuerbach, L.

Publications and source records attributed to Feuerbach, L..

8 recordsLinked to original sources

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↗

bioRxiv2025

Nanopore whole-genome sequencing reveals conserved chromosome-specific telomere architecture across tissues and populations

Telomeres are repetitive nucleoprotein structures that cap the ends of linear chromosomes and are essential for maintaining genomic stability. While individual chromosome ends maintain distinct telomere lengths, the extent of this conservation across tissues and populations remains unclear due to the difficulty of analyzing repetitive telomeric sequences. Here, we show that high-coverage whole-genome Nanopore sequencing enables robust measurement of telomere length at the level of individual chromosomes. Nanopore reads yield reproducible telomere length estimates across replicates, in contrast to PacBio HiFi reads. Across > 250 individuals from 1000 Genomes and the SMaHT projects, chromosome-specific telomere length patterns are conserved across individuals and tissues, with tissues from the same individual showing highly similar patterns. This conserved landscape suggests coordinated regulation, whose disruption may contribute to genomic instability. Nanopore sequencing also allows simultaneous detection of structural variants, including disruption of TERT and NHP2 that drive global telomere shortening. Furthermore, our quantification of telomere variant repeats in positional context indicates active telomerase-mediated elongation. Our integrated profiling of telomere length and structural variation enables inference of variant effects on chromosome-specific telomere dynamics and may uncover risk factors for short telomere syndromes and cancer. Importantly, positionally fully resolved telomeric variant repeat patterns may predict activated telomere maintenance mechanisms with high accuracy. SignificanceResolving chromosome-specific telomere length and variant-repeat architecture across tissues and individuals provides a framework to dissect coordinated telomere maintenance, its disruption by genetic variants, and how this shapes telomere mosaicism and disease risk.

genomics↗

bioRxiv2024

Identifying, Prioritizing, and Visualizing Functional Promoter SNVs with the Recurrence-agnostic REMIND-Cancer Pipeline and pSNV Hunter

Cancer is a heterogeneous disease that arises due to mutations that drive cancer progression. However, the identification of these functional mutations has typically focused only on protein-coding DNA. Among non-coding mutations, only a few have been clearly associated with cancer. We hypothesize that this gap in discovery is partly due to the limitations of current methods requiring high recurrence of mutations. To support candidate selection for experimental validation of lowly recurrent and singleton promoter mutations, new computational approaches for the integrated analysis of multi-omics data are required. To address this challenge, the REMIND-Cancer Pipeline leverages whole-genome sequencing and RNA-Seq data to extract and prioritize functional promoter mutations, regardless of their recurrence status. Subsequently, pSNV Hunter aggregates and visualizes comprehensive information for each candidate. We demonstrate the functionality of both tools by applying it to the PCAWG dataset. This workflow successfully identified and prioritized known highly-recurrent mutations, as well as, novel singletons and lowly recurrent candidates. Hence, the output of our workflow directly supports hypothesis generation for subsequent experimental validation to overcome limitations of recurrence-based approaches.

cancer biology↗

bioRxiv2024

Quantifying immune cell telomere content at single-cell resolution in context of PD-1 checkpoint immunotherapy

Biological processes such as aging, carcinogenesis, and immune responses depend on the ability of cells to maintain or rapidly expand populations. This capacity is constrained by a cells replicative potential, which is reflected in its telomere content. Despite the central role of telomeres in cancer and immunity, their analysis at single-cell resolution across diverse cell types remains challenging. Here we show that scATAC-seq data enables quantitative telomeromics when key technical and biological confounders are accounted for. We present a computational framework that addresses read sparsity, telomere representation, and chromatin-state-dependent competition for sequencing signal, enabling robust estimation of telomere content and telomeric variant repeat composition from scATAC-seq data. By inferring global chromatin condensation directly from scATAC-seq profiles, our approach corrects for cell-cycle-associated biases while simultaneously capturing chromatin-state dysregulation in cancer. Applied to a large cancer atlas, this framework reveals patient-specific telomere maintenance phenotypes, including telomerase-associated and alternative lengthening of telomeres (ALT)-like profiles preserved across subclonal populations. Extending beyond cancer cells, we observe that telomere content in exhausted T cell subpopulations prior to immunotherapy is predictive for effective response to PD-1 checkpoint blockade. Together, these results establish scATAC-seq as a robust platform for single-cell telomeromics in cancer and immunity. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=78 SRC="FIGDIR/small/609339v2_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@4f9498org.highwire.dtl.DTLVardef@a4b787org.highwire.dtl.DTLVardef@d1e25org.highwire.dtl.DTLVardef@136196b_HPS_FORMAT_FIGEXP M_FIG C_FIG Created in BioRender. Popp, F. (2026) https://BioRender.com/mi2jove

immunology↗

bioRxiv2024

The promoter mutation paucity as part of the dark matter of the cancer genome

Cancer is a heterogeneous disease caused by genetic alterations. Computational analysis of cancer genomes led to the expansion of the catalog of driver mutations. While individual high-impact mutations have been discovered also in gene promoters, frequency-based approaches have only characterized a few novel candidates. To investigate the promoter mutation paucity in cancer, we developed the REMIND-Cancer workflow to predict activating promoter mutations in silico, irrespective of their recurrence frequency, and applied it to the PCAWG dataset. We positively validated 7 candidates by luciferase assay including mutations within the promoters of ANKRD53 and MYB. Our analysis indicates that particular mutational signatures and necessary co-alterations constrain the creation and positive selection of functional promoter mutations. We conclude that activating promoter mutations are more frequent in the PCAWG dataset than previously observed, which has potential implications for personalized oncology.

bioinformatics↗

bioRxiv2017

Discovery and characterization of coding and non-coding driver mutations in more than 2,500 whole cancer genomes

Discovery of cancer drivers has traditionally focused on the identification of protein-coding genes. Here we present a comprehensive analysis of putative cancer driver mutations in both protein-coding and non-coding genomic regions across >2,500 whole cancer genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. We developed a statistically rigorous strategy for combining significance levels from multiple driver discovery methods and demonstrate that the integrated results overcome limitations of individual methods. We combined this strategy with careful filtering and applied it to protein-coding genes, promoters, untranslated regions (UTRs), distal enhancers and non-coding RNAs. These analyses redefine the landscape of non-coding driver mutations in cancer genomes, confirming a few previously reported elements and raising doubts about others, while identifying novel candidate elements across 27 cancer types. Novel recurrent events were found in the promoters or 5UTRs of TP53, RFTN1, RNF34, and MTG2, in the 3UTRs of NFKBIZ and TOB1, and in the non-coding RNA RMRP. We provide evidence that the previously reported non-coding RNAs NEAT1 and MALAT1 may be subject to a localized mutational process. Perhaps the most striking finding is the relative paucity of point mutations driving cancer in non-coding genes and regulatory elements. Though we have limited power to discover infrequent non-coding drivers in individual cohorts, combined analysis of promoters of known cancer genes show little excess of mutations beyond TERT.

genomics↗

bioRxiv2017

Genomic footprints of activated telomere maintenance mechanisms in cancer

Cancers require telomere maintenance mechanisms for unlimited replicative potential. We dissected whole-genome sequencing data of over 2,500 matched tumor-control samples from 36 different tumor types to characterize the genomic footprints of these mechanisms. While the telomere content of tumors with ATRX or DAXX mutations (ATRX/DAXXtrunc) was increased, tumors with TERT modifications showed a moderate decrease of telomere content. One quarter of all tumor samples contained somatic integrations of telomeric sequences into non-telomeric DNA. With 80% prevalence, ATRX/DAXXtrunc tumors display a 3-fold enrichment of telomere insertions. A systematic analysis of telomere composition identified aberrant telomere variant repeat (TVR) distribution as a genomic marker of ATRX/DAXXtrunc tumors. In this clinically relevant subgroup, singleton TTCGGG and TTTGGG TVRs (previously undescribed) were significantly enriched or depleted, respectively. Overall, our findings provide new insight into the recurrent genomic alterations that are associated with the establishment of different telomere maintenance mechanisms in cancer.

genomics↗

bioRxiv2017

Unique genomic features and deeply-conserved functions of long non-coding RNAs in the Cancer LncRNA Census (CLC)

Long non-coding RNAs (lncRNAs) that drive tumorigenesis are a growing focus of cancer genomics studies. To facilitate further discovery, we have created the \"Cancer LncRNA Census\" (CLC), a manually-curated and strictly-defined compilation of lncRNAs with causative roles in cancer. CLC has two principle applications: first, as a resource for training and benchmarking de novo identification methods; and second, as a dataset for studying the fundamental properties of these genes.\n\nCLC Version 1 comprises 122 lncRNAs implicated in 29 distinct cancers. LncRNAs are included based on functional or genetic evidence for causative roles in cancer progression. All belong to the GENCODE reference annotation, to enable integration across projects and datasets. For each entry, the evidence type, biological activity (oncogene or tumour suppressor), source reference and cancer type are recorded. Supporting its usefulness, CLC genes are significantly enriched amongst de novo predicted driver genes from PCAWG. CLC genes are distinguished from other lncRNAs by a series of features consistent with biological function, including gene length, high expression and sequence conservation of both exons and promoters. We identify a trend for CLC genes to be co-localised with known protein-coding cancer genes along the human genome. Finally, by integrating data from transposon-mutagenesis functional screens, we show that mouse orthologues of CLC genes tend also to be cancer genes.\n\nThus CLC represents a valuable resource for research into long non-coding RNAs in cancer. Their evolutionary and genomic properties have implications for understanding disease mechanisms and point to conserved functions across ~80 million years of evolution.

bioinformatics↗