Biology subjects

Schwarz, R. F.

Publications and source records attributed to Schwarz, R. F..

18 recordsLinked to original sources

bioRxiv2026

Chromosomal instability shapes spatial and temporal phenotypic diversity in a malignant peripheral nerve sheath tumour

Understanding the spatial and temporal dynamics of tumour evolution is crucial for determining the drivers of cancer progression. Linking genotype to phenotype across a tumour remains challenging, however. Here, we use single-cell and spatial multi-omics to comprehensively profile a primary malignant peripheral nerve sheath tumour (MPNST) and its multifocal recurrence. Combining the native barcoding system from extensive heterogeneity in copy number alterations with mutation data, we resolve the evolutionary tree of this tumour, revealing a branching structure suggestive of ongoing chromosomal instability. We show that gene dosage effects contribute to phenotypic diversity and scale predominantly linearly with copy number. Using spatial genomics assisted by laser capture microdissection and spatial transcriptomics, we performed in situ lineage tracing in this human tumour, elucidating the relationship between local expansions and interactions between tumour cells and the microenvironment. These findings demonstrate the potential of combined bulk, single-cell and spatial techniques to dissect cancer evolution in time and space and link genotype to phenotype in detail.

genomics↗

bioRxiv2026

pyfraglib: An integrated cfDNA fragmentomics platform

SummaryCell-free DNA (cfDNA) fragmentomics is the analysis of a diverse set of cfDNA fragment features, e.g. fragment length profiles, windowed protection scores, and end motifs. As such it requires software tooling for fragment extraction, statistical feature modeling, and cohort-level comparative analysis. In silico simulations can facilitate the development and validation of new methods by generating testing datasets with known ground truth. Existing tools address individual aspects of this workflow but none provide all necessary capabilities within a single package. ResultsWe present pyfraglib, a platform integrating fragment extraction from short- and long-read sequencing, statistical feature modeling (Gaussian mixture and NMF decomposition of fragment length profiles, end motif diversity, windowed protection scores), cohort-level differential testing of said features, and a simulation module. The library is exposed through a command-line interface, a Python API, and a Nextflow pipeline. We demonstrate pyfraglib in two ways. First, on two simulated 20-sample cohorts we show that pyfraglibs per-sample and cohort-level analyses recover the differences introduced by construction. Second, we apply pyfraglib to 88 cfDNA samples from a central nervous system lymphoma (CNSL) study and construct a fragmentomics score combining an NMF signature with end motif and WPS summaries via a classifier trained on cerebrospinal fluid and healthy donor plasma samples. As a proof of concept and applied to 66 baseline patient plasma samples, the score identifies a high-risk subgroup with worse failure-free survival (log-rank p=0.0247). Conclusionspyfraglib integrates sample- and cohort-level fragmentomics analyses as well as in silico simulation within a consistently engineered Python framework. pyfraglib source code and documentation are available at https://github.com/schwarzlab-ccb/pyfraglib.

bioinformatics↗

bioRxiv2026

Haplotype-resolved genome architecture mapping uncovers pervasive structural heterogeneity between human homologous chromosomes

Resolving the three-dimensional structure of chromatin with haplotype specificity remains a fundamental challenge for understanding genetic and epigenetic contributions to gene regulation in healthy development and in disease. Phasing of chromatin contacts derived by ligation-dependent methods relies on high single-nucleotide variant (SNV) density, which limits its applicability in human genomes where SNVs are sparse. Here, we present CoPhasing, a strategy that leverages the intrinsic haplotype fidelity of Genome Architecture Mapping (GAM), a ligation-free method that inherently captures haplotype-matched genomic neighborhoods in thin nuclear slices. CoPhasing assigns SNV-free reads to their haplotype through proximity to informative reads, enabling efficient and accurate phasing even in SNV-sparse regions. Applying CoPhasing genome-wide reveals extensive, previously inaccessible structural heterogeneity between human homologous chromosomes, demonstrating the power of GAM CoPhasing to investigate allele-specific differences in 3D genome organization and their relevance to gene regulation and disease.

genomics↗

bioRxiv2026

SC-BIG: A Hierarchical Bayesian Model for Bulk-Informed Single Nucleotide Variant Calling in Single Cells

Single-cell DNA sequencing (scDNA-seq) has emerged as a primary method for studying the evolution of cancer genomes and intra-tumor heterogeneity. However, despite technological advances, scDNA-seq remains noisy and is affected by amplification biases and allelic dropouts. Accurately determining the presence or absence of candidate somatic nucleotide variants (SNVs) in individual cancer cells therefore remains challenging. One strategy to alleviate this issue is to perform bulk whole-genome sequencing simultaneously with single-cell sequencing. To date, only few computational methods have been developed for bulk-informed detection of somatic SNVs in single cells, and existing methods do not adequately account for somatic copy-number alterations or clonal admixtures. We here present SC-BIG, a hierarchical Bayesian model that leverages bulk sequencing data from a representative tumor sample to improve SNV detection. SC-BIG propagates uncertainty across multiple biological parameters, including copy number alterations, sample purity, and SNV clonality. In a first step, the cancer cell fraction (CCF) of a SNV is jointly estimated from bulk and single-cell data. The CCF in turn then acts as a prior in the second inference step to calculate per-cell posterior probabilities for the presence of the SNV. We demonstrate that across simulated scenarios of varying CCFs, SC-BIG outperforms both naive thresholding and ProSolo, the only bulk-informed single-cell mutation caller described so far. Importantly, SC-BIG produces well-calibrated posterior probabilities that provide interpretable uncertainty quantification, enabling direct integration into downstream analyses such as phylogenetic reconstruction.

bioinformatics↗

bioRxiv2026

Deciphering selection patterns of somatic copy-number events

Somatic copy-number alterations (SCNAs) are pervasive across cancers, arising through diverse mutational mechanisms and subsequently shaped by selection according to fitness advantages [1-4]. These alterations can drive tumourigenesis via gene-dosage changes in oncogenes and tumour suppressors [5, 6]. However, observed SCNA profiles do not uniquely define the genomic events that led to them, which is a major obstacle to understanding the selective forces shaping cancer genomes. Here we present SPICE, Selection Patterns In somatic Copy-number Events, an event-level framework that infers discrete copy-number events from allele-specific profiles and models focal selection from first principles, incorporating uniform breakpoint formation and locus-specific selective pressures. Applied to 5,966 samples from the TCGA dataset, SPICE quantifies the full spectrum of SCNA events, recapitulates known mutational processes, and tracks systematic shifts in event types before and after whole-genome duplication (WGD). Next, SPICE employs a generative selection model to identify the location of oncogenes and tumour-suppressors. Unlike previous approaches that detected peaks in aggregated copy-number signals, our model operates directly on inferred events and treats genome-wide breakpoint formation as a neutral reference against which locus-specific selection is detected. This analysis reveals 460 loci under selection which are highly enriched for known oncogenes and tumour suppressors, recapitulating most previously reported sites and uncovering many novel regions, and simultaneously showing that most internal copy-number events are not subject to focal selection. These results establish a unified framework that deconvolves copy-number profiles into their underlying evolutionary events and greatly expand the catalogue of loci implicated in cancer development. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=45 SRC="FIGDIR/small/708809v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@7d471corg.highwire.dtl.DTLVardef@c78feeorg.highwire.dtl.DTLVardef@1ba52a8org.highwire.dtl.DTLVardef@188ab0c_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2025

PhytClust: efficient and optimal node clustering in phylogenetic trees

Phylogenetic trees play a fundamental role in elucidating evolutionary relationships among taxa. Clustering taxa remains a major challenge across diverse biological domains such as cancer genomics, microbial systematics, and phylogenomics. Several methods partition taxa in phylogenetic trees into clusters, but these approaches face key limitations. Many rely on user-defined distance thresholds, whose choice often depends on prior knowledge and may be difficult to justify consistently across datasets. More broadly, existing methods often differ in how clusters are defined and commonly rely on heuristics or user-specified criteria to make the search space tractable for large trees. Here, we present PhytClust, a threshold-free algorithm that partitions trees into monophyletic subtrees (clusters) by identifying groups of taxa with low within-cluster dispersion. For a fixed number of clusters, PhytClust finds the exact global optimum under this objective and then selects the optimal number of clusters using a cluster-validity index. The resulting partitions are reproducible and reflect both tree topology and branch lengths. In simulated datasets, PhytClust outperforms existing methods in both speed and accuracy and scales to trees with more than a hundred thousand taxa. We apply PhytClust across cancer genomics, avian phylogenomics, bacterial and archaea phylogenetics, and plant genomics to demonstrate PhytClusts varied applicability. By providing a standardized method for taxa clustering within phylogenetic trees, PhytClust yields reproducible, optimal and computationally efficient clusters.

evolutionary biology↗

bioRxiv2024

CNSistent integration and feature extraction from somatic copy number profiles

The vast majority of cancers exhibit Somatic Copy Number Alterations (SCNAs)--gains and losses of variable regions of DNA. SCNAs can shape the phenotype of cancer cells, e.g. by increasing their proliferation rates, removing tumor suppressor genes, or immortalizing cells. While many SCNAs are unique to a patient, certain recurring patterns emerge as a result of shared selectional constraints or common mutational processes. To discover such patterns in a robust way, the size of the dataset is essential, which necessitates combining SCNA profiles from different cohorts, a non-trivial task. To achieve this, we developed CNSistent, a Python package for imputation, filtering, consistent segmentation, feature extraction, and visualization of cancer copy number profiles from heterogeneous datasets. We demonstrate the utility of CNSistent by applying it to the publicly available TCGA, PCAWG, and TRACERx cohorts. We compare different segmentation and aggregation strategies on cancer type and subtype classification tasks using deep convolutional neural networks. We demonstrate an increase in accuracy over training on individual cohorts and efficient transfer learning between cohorts. Using integrated gradients we investigate lung cancer classification results, highlighting SOX2 amplifications as the dominant copy number alteration in lung squamous cell carcinoma.

genomics↗

bioRxiv2024

Ongoing genome doubling promotes evolvability and immune dysregulation in ovarian cancer

Whole-genome doubling (WGD) is a critical driver of tumor development and is linked to drug resistance and metastasis in solid malignancies. Here, we demonstrate that WGD is an ongoing mutational process in tumor evolution. Using single-cell whole-genome sequencing, we measured and modeled how WGD events are distributed across cellular populations within tumors and associated WGD dynamics with properties of genome diversification and phenotypic consequences of innate immunity. We studied WGD evolution in 65 high-grade serous ovarian cancer (HGSOC) tissue samples from 40 patients, yielding 29,481 tumor cell genomes. We found near-ubiquitous evidence of WGD as an ongoing mutational process promoting cell-cell diversity, high rates of chromosomal missegregation, and consequent micronucleation. Using a novel mutation-based WGD timing method, doubleTime, we delineated specific modes by which WGD can drive tumor evolution: (i) unitary evolutionary origin followed by significant diversification, (ii) independent WGD events on a pre-existing background of copy number diversity, and (iii) evolutionarily late clonal expansions of WGD populations. Additionally, through integrated single-cell RNA sequencing and high-resolution immunofluorescence microscopy, we found that inflammatory signaling and cGAS-STING pathway activation result from ongoing chromosomal instability and are restricted to tumors that remain predominantly diploid. This contrasted with predominantly WGD tumors, which exhibited significant quiescent and immunosuppressive phenotypic states. Together, these findings establish WGD as an evolutionarily active mutational process that promotes evolvability and dysregulated immunity in late stage ovarian cancer.

cancer biology↗

bioRxiv2024

Predictive biomarkers of breast ductal carcinoma in situ may underestimate the risk of recurrence due to de novo ipsilateral breast carcinoma development

Development of ipsilateral breast carcinoma following diagnosis of breast ductal carcinoma in situ (DCIS) has been assumed to represent recurrence of the primary tumour. However, this may not be the case and it is important to know how often recurrences are new tumours. Ipsilateral primary-recurrence pairs (n=78) were sequenced to test their clonal relatedness. Shared genetic events were identified from whole exome sequencing (n=54 pairs) using haplotype-specific copy number and phylogenetic analysis. The remaining pairs were sequenced by a targeted panel or low-coverage whole genome sequencing. We included 32 non-recurrent DCIS to compare recurrent and non-recurrent disease. We found that 7% of DCIS recurrences were non-clonal by whole exome sequencing, indicative of a new breast carcinoma. Lower resolution methods detected a higher non-clonality rate (29%). Comparing primary DCIS with their recurrences found that evolution of DCIS to invasive disease was associated with increased ploidy and copy number events. TP53 mutations were enriched in DCIS with clonal recurrence compared with non-recurrent DCIS. Our results verify that de novo "recurrent tumours" of independent origin occur in patients who may be at high risk.

cancer biology↗

bioRxiv2024

Extensive folding variability between homologous chromosomes in mammalian cells

Genetic variation and 3D chromatin structure have major roles in gene regulation. Due to challenges in mapping chromatin conformation with haplotype-specific resolution, the effects of genetic sequence variation on 3D genome structure and gene expression imbalance remain understudied. Here, we applied Genome Architecture Mapping (GAM) to a hybrid mouse embryonic stem cell (mESC) line with high density of single nucleotide polymorphisms (SNPs). GAM resolved haplotype-specific 3D genome structures with high sensitivity, revealing extensive allelic differences in chromatin compartments, topologically associating domains (TADs), long-range enhancer-promoter contacts, and CTCF loops. Architectural differences often coincide with allele-specific differences in gene expression, mediated by Polycomb repression. We show that histone genes are expressed with allelic imbalance in mESCs, are involved in haplotype-specific chromatin contact marked by H3K27me3, and are targets of Polycomb repression through conditional knockouts of Ezh2 or Ring1b. Our work reveals highly distinct 3D folding structures between homologous chromosomes, and highlights their intricate connections with allelic gene expression.

genomics↗

bioRxiv2023

Spontaneous single-nucleotide substitutions and microsatellite mutations have distinct distributions of fitness effects

The fitness effects of new mutations determine key properties of evolutionary processes. Beneficial mutations drive evolution, yet selection is also shaped by the frequency of small-effect deleterious mutations, whose combined effect can burden otherwise adaptive lineages and alter evolutionary trajectories and outcomes in clonally evolving organisms such as viruses, microbes, and tumors. The small effect sizes of these important mutations have made accurate measurements of their rates difficult. In microbes, assessing the effect of mutations on growth can be especially instructive, as this complex phenotype is closely linked to fitness in clonally evolving organisms. Here, we perform high-throughput time-lapse microscopy on cells from mutation-accumulation strains to precisely infer the distribution of mutational effects on growth rate in the budding yeast, Saccharomyces cerevisiae. We show that mutational effects on growth rate are overwhelmingly negative, highly skewed towards very small effect sizes, and frequent enough to suggest that deleterious hitchhikers may impose a significant burden on evolving lineages. By using lines that accumulated mutations in either wild-type or slippage repair-defective backgrounds, we further disentangle the effects of two common types of mutations, single-nucleotide substitutions and simple sequence repeat indels, and show that they have distinct effects on yeast growth rate. Although the average effect of a simple sequence repeat mutation is very small ([~]0.3%), many do alter growth rate, implying that this class of frequent mutations has an important evolutionary impact.

evolutionary biology↗

bioRxiv2023

Intercellular extrachromosomal DNA copy number heterogeneity drives cancer cell state diversity

Neuroblastoma is characterised by extensive inter- and intra-tumour genetic heterogeneity and varying clinical outcomes. One possible driver for this heterogeneity are extrachromosomal DNAs (ecDNA), which segregate independently to the daughter cells during cell division and can lead to rapid amplification of oncogenes. While ecDNA-mediated oncogene amplification has been shown to be associated with poor prognosis in many cancer entities, the effects of ecDNA copy number heterogeneity on intermediate phenotypes are still poorly understood. Here, we leverage DNA and RNA sequencing data from the same single cells in cell lines and neuroblastoma patients to investigate these effects. We utilise ecDNA amplicon structures to determine precise ecDNA copy numbers and reveal extensive intercellular ecDNA copy number heterogeneity. We further provide direct evidence for the effects of this heterogeneity on gene expression of cargo genes, including MYCN and its downstream targets, and the overall transcriptional state of neuroblastoma cells. These results highlight the potential for rapid adaptability of cellular states within a tumour cell population mediated by ecDNA copy number, emphasising the need for ecDNA-specific treatment strategies to tackle tumour formation and adaptation.

cancer biology↗

bioRxiv2022

Refphase: Multi-sample reference phasing reveals haplotype-specific copy number heterogeneity

Most computational methods that infer somatic copy number alterations (SCNAs) from bulk sequencing of DNA analyse tumour samples individually. However, the sequencing of multiple tumour samples from a patients disease is an increasingly common practice. We introduce Refphase, an algorithm that leverages this multi-sampling approach to infer haplotype-specific copy numbers through multi-sample reference phasing. We demonstrate Refphases ability to infer haplotype-specific SCNAs and characterise their intra-tumour heterogeneity, to uncover previously undetected allelic imbalance in low purity samples, and to identify parallel evolution in the context of whole genome doubling in a pan-cancer cohort of 336 samples from 99 tumours.

bioinformatics↗

bioRxiv2022

The rate of spontaneous mutations in yeast deficient for MutSβ function

Mutations in simple sequence repeat loci underlie many inherited disorders in humans, and are increasingly recognized as important determinants of natural phenotypic variation. In eukaryotes, mutations in these sequences are primarily repaired by the MutS{beta} mismatch repair complex. To better understand the role of this complex in mismatch repair and the determinants of simple sequence repeat mutation predisposition, we performed mutation accumulation in yeast strains with abrogated MutS{beta} function. We demonstrate that mutations in simple sequence repeat loci in the absence of mismatch repair are primarily deletions. We also show that mutations accumulate at drastically different rates in short (<8 bp) and longer repeat loci. These data lend support to a model in which the mismatch repair complex is responsible for repair primarily in longer simple sequence repeats.

genomics↗

bioRxiv2022

Copy-number dosage regulates telomere maintenance and disease-associated pathways in neuroblastoma

Telomere maintenance in neuroblastoma is linked to poor outcome and caused by either TERT activation or through alternative lengthening of telomeres (ALT). In contrast to TERT activation, commonly caused by genomic rearrangements or MYCN amplification, ALT is less well understood. Alterations at the ATRX locus are key drivers of ALT but only present in [~]50% of ALT tumors. To identify potential new pathways to telomere maintenance, we investigate allele-specific gene dosage effects from whole genomes and transcriptomes in 115 primary neuroblastomas. We show that copy-number dosage deregulates telomere maintenance, genomic stability, and neuronal pathways and identify upregulation of variants of histone H3 and H2A as a potential alternative pathway to ALT. We investigate the interplay between TERT activation, overexpression and copy-number dosage and reveal loss of imprinting at the RTL1 gene associated with poor clinical outcome. These results highlight the importance of gene dosage in key oncogenic mechanisms in neuroblastoma.

genomics↗

bioRxiv2022

SMITH: Spatially Constrained Stochastic Model for Simulation of Intra-Tumour Heterogeneity

MotivationSimulations of cancer evolution and cellular growth have proven highly useful to study, in detail, the various aspects of intra-tumour heterogeneity, including the effect of selection, mutation rates, and spatial constraints. However, most methods are computationally expensive lattice-embedded models which cannot simulate tumours with a realistic number of cells and rely on various simplifications. Alternatively, well-mixed stochastic models, while efficient and scalable, do not typically include spatial constraints and cannot reproduce the rich clonal dynamics observed in real-world tumours. ResultsWe present SMITH, a simple, efficient, and explainable model of cancer evolution that combines the advantages of well-mixed stochastic models with a new confinement mechanism which limits the growth of clones based on the overall tumour size. We demonstrate that this confinement mechanism is sufficient to induce the rich clonal dynamics observed in spatial models, while allowing for a clear geometric interpretation and efficient simulation of one billion cells within a few minutes on a desktop PC. We explore the extent of stochasticity and rigorously assess the effects of cell turnover, mutation rate, fitness effects and confinement on the resulting clonal structures. Availability and ImplementationSMITH is implemented in C# and freely available at bitbucket.org/schwarzlab/smith together with binaries for all major platforms. For rich visualisations of the simulated clonal dynamics we provide an accompanying Python package PyFish at bitbucket.org/schwarzlab/pyfish. Supplementary informationAll supplementary figures are in the supplementary document.

bioinformatics↗

bioRxiv2021

Whole-genome doubling-aware copy number phylogenies for cancer evolution with MEDICC2

Chromosomal instability (CIN) and somatic copy-number alterations (SCNA) play a key role in the evolutionary process that shapes cancer genomes. SC-NAs comprise many classes of clinically relevant events, such as localised amplifications, gains, losses, loss-of-heterozygosity (LOH) events, and recently discovered parallel evolutionary events revealed by multi-sample phasing. These events frequently appear jointly with whole genome doubling (WGD), a transformative event in tumour evolution involving tetraploidization of genomes preceded or followed by individual chromosomal copy-number changes and associated with an overall increase in structural CIN. While SCNAs have been leveraged for phylogeny reconstruction in the past, existing methods do not take WGD events into account and cannot model parallel evolution. They frequently make use of the infinite sites assumption, do not model horizontal dependencies between adjacent genomic loci and can not infer ancestral genomes. Here we present MEDICC2, a new phylogeny inference algorithm for allele-specific SCNA data that addresses these shortcomings. MEDICC2 dispenses with the infinite sites assumption, models parallel evolution and accurately identifies clonal and subclonal WGD events. It times SCNAs relative to each other, quantifies SCNA burden in single-sample studies and infers phylogenetic trees and ancestral genomes in multi-sample or single-cell sequencing scenarios with thousands of cells. We demonstrate MEDICC2s ability on simulated data, real-world data of 2,778 single sample tumours from the Pan-cancer analysis of whole genomes (PCAWG), 10 bulk multi-region prostate cancer patients and two recent single-cell datasets of triple-negative breast cancer comprising several thousands of single cells.

bioinformatics↗

bioRxiv2020

GAMIBHEAR: whole-genome haplotype reconstruction from Genome Architecture Mapping data

MotivationGenome Architecture Mapping (GAM) was recently introduced as a digestion- and ligation-free method to detect chromatin conformation. Orthogonal to existing approaches based on chromatin conformation capture (3C), GAMs ability to capture both inter- and intra-chromosomal contacts from low amounts of input data makes it particularly well suited for allele-specific analyses in a clinical setting. Allele-specific analyses are powerful tools to investigate the effects of genetic variants on many cellular phenotypes including chromatin conformation, but require the haplotypes of the individuals under study to be known a-priori. So far however, no algorithm exists for haplotype reconstruction and phasing of genetic variants from GAM data, hindering the allele-specific analysis of chromatin contact points in non-model organisms or individuals with unknown haplotypes. ResultsWe present GAMIBHEAR, a tool for accurate haplotype reconstruction from GAM data. GAMIBHEAR aggregates allelic co-observation frequencies from GAM data and employs a GAM-specific probabilistic model of haplotype capture to optimise phasing accuracy. Using a hybrid mouse embryonic stem cell line with known haplotype structure as a benchmark dataset, we assess correctness and completeness of the reconstructed haplotypes, and demonstrate the power of GAMIBHEAR to infer accurate genome-wide haplotypes from GAM data. AvailabilityGAMIBHEAR is available as an R package under the open source GPL-2 license at https://bitbucket.org/schwarzlab/gamibhear Maintainer: julia.markowski@mdc-berlin.de

bioinformatics↗