Biology subjects

Kabeer, F.

Publications and source records attributed to Kabeer, F..

13 recordsLinked to original sources

bioRxiv2026

Chromosomal mutational signatures of DNA damaging agents at single cell resolution

The chromosomal-scale mutational spectrum of small molecules that interact with DNA has been hard to study at scale, as mutational events are distributed in location and occur in parallel in different cells. Here, we present a framework that pairs phylogenetic ancestry reconstruction with mutational signature decomposition to characterise recent, cell-private copy number alteration (CNA) mutational patterns at single-cell resolution. We used this framework to characterise the cell-wise mutational spectrum of contemporaneous CNAs generated by double-strand-break-inducing chemotherapeutic drugs. We demonstrate that platinum salts, G-quadruplex stabilizers and topoisomerase II inhibitors, although mechanistically distinct, converge on a mutational signature dominated by telomere-bounded copy-number gains and losses. This signature is observed in different genetic backgrounds and in vivo in drug-treated patient-derived xenografts. We also observe a high rate of endogenous telomere-bounded mutational foreground in BRCA1 deficient cells. We show that the single cell genome derived signature exposures are drug dose-dependent, and use this to identify the decay of mutational load after drug withdrawal. We observe that both cisplatin and a G4 binder molecule (CX5461) exhibit foreground mutational signature persistence for at least 3 weeks after drug withdrawal, suggesting that residual effects of exposure may last longer than anticipated. Finally, extending the framework to serially drug-treated patient-derived xenograft (PDX) models, we show that telomere-bounded CNA signature exposure is associated with tumoural response to drug, consistent with loss of mutational activity on the genome after acquired resistance emerges. Together, our results show that our framework applied on scWGS identifies contemporaneous chromosomal mutation patterns induced by small molecules in human tissues.

cancer biology↗

bioRxiv2026

Minimally invasive monitoring of clonal evolution through integrated single cell and ctDNA analysis

Circulating cell-free DNA (cfDNA) offers a minimally invasive lens into tumor evolution. However, the accurate quantification of clonal composition from cfDNA remains challenging. Existing methods for clone tracking using liquid biopsies are constrained by issues such as their reliance on bulk tissue references, incomplete representations of clonal architecture or limited breadth of mutation panels. To address these limitations, we developed cfClone, a Bayesian framework that integrates single-cell whole-genome sequencing (scWGS) derived clonal structures with cfDNA whole-genome sequencing (cfDNA-WGS) data to enable high-resolution tissue-informed clonal tracking. By jointly modeling local copy-number alterations and haplotype-specific signals via Bayesian model selection and Markov chain Monte Carlo (MCMC) sampling, the algorithm yields uncertainty-aware estimates of clonal prevalence and tumour fraction (TF). Applied to longitudinal clinical cohorts, cfClone can be used to reconstruct evolutionary trajectories and uncovers clonal selection driving therapeutic resistance, including the de novo detection of emergent clonal populations. We demonstrate that cfClone achieves accurate TF estimates and circulating tumor DNA (ctDNA) detection in malignancies with varying degrees of copy-number variant (CNV) burden using semi-synthetic data. We then compare to the state of the art scWGS informed panel based approach, and demonstrate cfClone provides comparable accuracy while allowing for the tracking of more clones and detection of novel clones. Finally, we show how cfClone can be used to quantitatively track clonal dynamics in response to treatment in high grade serous ovarian cancer. Github link: https://github.com/Roth-Lab/cfclone

cancer biology↗

bioRxiv2025

Somatic copy number mutations contribute to fitness in transplantation models of spontaneous human breast cancer metastasis

The contribution of somatic gene dosage mutations (CNA) to breast cancer metastasis remains poorly defined. Using 9 transplantable human neoadjuvant-naive triple-negative breast cancer xenografts, we studied the fitness of copy number clones in spontaneous metastasis from orthotopic transplant sites. Metastatic site preference was strongly patient-dependent, and the emergence of metastases exhibited a general trend toward slower growth at the orthotopic site. In our models, single-cell whole-genome sequencing of primary and metastatic sites showed that distant metastases were most often the result of minor prevalence clones at the orthotopic site, suggesting that some metastatic phenotypes may be weakly negatively fit at the primary site. We validated the existence of a fitness hierarchy of copy number clones using a previously established paradigm of remixing and retransplanting clones. Single-cell clone analysis of competitive repopulation and re-emergence of metastases showed that CNAs arising in cancer evolution can mediate metastatic fitness. Moreover, some clones displaying strong metastatic tendency exhibited weaker survival at the primary site, consistent with the notion that metastatic phenotypes could have a fitness cost at the primary site. Finally, we conducted RNA-seq analysis combined with DriverNet analysis to dissect the contribution of CNA-mediated versus genome-independent transcriptional states. CNA mutations appeared to contribute strongly to transcriptional differences between clones. Among clones of high metastatic potential, we observed CNA-mediated and CNA-independent convergence on pathways such as epithelial-mesenchymal transition (EMT), established as mediators of metastatic cell survival at distant sites. Taken together, our data point to a contribution of CNA-mediated cancer evolution to metastatic states and identify distant-site context as a key determinant of CNA-mediated fitness.

cancer biology↗

bioRxiv2023

Single cell decoding of drug induced transcriptomic reprogramming in triple negative breast cancers

BackgroundThe encoding of cell intrinsic resistance states in breast cancer reflects the contributions of genomic and non-genomic variation. However, identifying the potential contributions of each requires accurate measurement and subtraction of the contribution of clonal fitness from co-measurement of transcriptional states. Somatic genomic variation in gene dosage, copy number variation, is the dominant mutational mechanism in breast cancer contributing to transcriptional variation and has recently been shown to contribute to platinum chemotherapy resistance states. Here we deploy time series measurements of triple negative breast cancer single cell transcriptomes in conjunction with co-measured single cell copy number associated clonal fitness to identify the contributions of genomic and non-genomic mechanisms to drug associated transcription states. ResultsWe generated serial scRNA-seq data (126,556 cells) from triple negative breast cancer (TNBC) patient-derived xenograft (PDX) experiments over 2.5 years in duration, and matched it against genomic copy number single cell data from the same biological samples. We show that the cell memory of transcriptional states of TNBC tumors serially exposed to platinum identifies distinct clonal responses within individual tumours. Copy-number clones with high drug fitness leading to clonal sweeps exhibit less transcriptional reversion, whereas clones with weak drug fitness exhibit highly dynamic transcription on drug withdrawal. Pathway analysis shows that copy number associated and copy number independent transcripts converge on epithelial-mesenchymal transition (EMT) and cytokine signaling states associated with resistance. We show from trajectory analysis that transcriptional reversion exhibits hysteresis, indicating that new intermediate transcriptional states are generated by platinum exposure. ConclusionsWe discovered that copy number clones with strong genotype associated fitness under platinum became fixed in their states, resulting in minimal transcriptional reversion on drug withdrawal. In contrast clones with weaker fitness undergo non-genomic transcriptional plasticity and these distinct responses co-exist within single tumours. Together the data suggest that copy number associated and copy number independent transcriptional states may contribute to platinum drug resistance within individual tumours. The dominance of genomic or non-genomic mechanisms within individual polyclonal tumours has implications for approaches to restoration of drug sensitivity and re-treatment strategies. Data availabilityUploaded Data URL: https://ega-archive.org/studies/EGAS00001007242 Github manuscript: https://github.com/molonc/drug_resistant_material/

genomics↗

bioRxiv2022

GeneVector: Identification of transcriptional programs using dense vector representations defined by mutual information.

Deciphering individual cell phenotypes from cell-specific transcriptional processes requires high dimensional single cell RNA sequencing. However, current dimensionality reduction methods aggregate sparse gene information across cells, without directly measuring the relationships that exist between genes. By performing dimensionality reduction with respect to gene co-expression, low-dimensional features can model these gene-specific relationships and leverage shared signal to overcome sparsity. We describe GeneVector, a scalable framework for dimensionality reduction implemented as a vector space model using mutual information between gene expression. Unlike other methods, including principal component analysis and variational autoencoders, GeneVector uses latent space arithmetic in a lower dimensional gene embedding to identify transcriptional programs and classify cell types. In this work, we show in four single cell RNA-seq datasets that GeneVector was able to capture phenotypespecific pathways, perform batch effect correction, interactively annotate cell types, and identify pathway variation with treatment over time.

bioinformatics↗

bioRxiv2021

Evolutionary tracking of cancer haplotypes at single-cell resolution

Cancer genomes exhibit extensive chromosomal copy number changes and structural variation, yet how allele specific alterations drive cancer genome evolution remains unclear. Here, through application of a new computational approach we report allele specific copy number alterations in 11,097 single cell whole genomes from genetically engineered mammary epithelial cells and 21,852 cells from high grade serous ovarian and triple negative breast cancers. Resolving single cell copy number profiles to individual alleles uncovered genomic background distributions of gains, losses and loss of heterozygosity, yielding evidence of positive selection of specific chromosomal alterations. In addition specific genomic loci in maternal and paternal alleles were commonly found to be altered in parallel with convergent phenotypic transcriptional effects. Finally we show that haplotype specific alterations trace the cyclical etiology of high level amplifications and reveal clonal haplotype decomposition of complex structures. Together, our results illuminate how allele and haplotype specific alterations, here determined across thousands of single cell cancer genomes, impact the etiology and evolution of structural variations in human tumours.

cancer biology↗

bioRxiv2021

The impact of mutational processes on structural genomic plasticity in cancer cells

Structural genome alterations are determinants of cancer ontogeny and therapeutic response. While bulk genome sequencing has enabled delineation of structural variation (SV) mutational processes which generate patterns of DNA damage, we have little understanding of how these processes lead to cell-to-cell variations which underlie selection and rates of accrual of different genomic lesions. We analysed 309 high grade serous ovarian and triple negative breast cancer genomes to determine their mutational processes, selecting 22 from which we sequenced >22,000 single cell whole genomes across a spectrum of mutational processes. We show that distinct patterns of cell-to-cell variation in aneuploidy, copy number alteration (CNA) and segment length occur in homologous recombination deficiency (HRD) and fold-back inversion (FBI) phenotypes. Widespread aneuploidy through induction of HRD through BRCA1 and BRCA2 inactivation was mirrored by continuous whole genome duplication in HRD tumours, contrasted with early ploidy fixation in FBI. FBI tumours exhibited copy number distributions skewed towards gains, widespread clone-specific variation in amplitude of high-level amplifications, often impacting oncogenes, and break-point variability consistent with progressive genomic diversification, which we termed serriform structural variation (SSV). SSVs were consistent with a CNA-based molecular clock reflecting a continual and distributed process across clones within tumours. These observations reveal previously obscured genome plasticity and evolutionary properties with implications for cancer evolution, therapeutic targeting and response.

cancer biology↗

bioRxiv2020

Single cell fitness landscapes induced by genetic and pharmacologic perturbations in cancer

Tumour fitness landscapes underpin selection in cancer, impacting etiology, evolution and response to treatment. Progress in defining fitness landscapes has been impeded by a lack of timeseries perturbation experiments over realistic intervals at single cell resolution. We studied the nature of clonal dynamics induced by genetic and pharmacologic perturbation with a quantitative fitness model developed to ascribe quantitative selective coefficients to individual cancer clones, enable prediction of clone-specific growth potential, and forecast competitive clonal dynamics over time. We applied the model to serial single cell genome (>60,000 cells) and transcriptome (>58,000 cells) experiments ranging from 10 months to 2.5 years in duration. We found that genetic perturbation of TP53 in epithelial cell lines induces multiple forms of copy number alteration that confer increased fitness to clonal populations with measurable consequences on gene expression. In patient derived xenografts, predicted selective coefficients accurately forecasted clonal competition dynamics, that were validated with timeseries sampling of experimentally engineered mixtures of low and high fitness clones. In cisplatin-treated patient derived xenografts, the fitness landscape was inverted in a time-dependent manner, whereby a drug resistant clone emerged from a phylogenetic lineage of low fitness clones, and high fitness clones were eradicated. Moreover, clonal selection mediated reversible drug response early in the selection process, whereas late dynamics in genomically fixed clones were associated with transcriptional plasticity on a fixed clonal genotype. Together, our findings outline causal mechanisms with implication for interpreting how mutations and multi-faceted drug resistance mechanisms shape the etiology and cellular fitness of human cancers.

cancer biology↗

bioRxiv2019

Dissociation of solid tumour tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase associated stress responses

BackgroundSingle-cell RNA sequencing (scRNAseq) is a powerful tool for studying complex biological systems, such as tumour heterogeneity and tissue microenvironments. However, the sources of technical and biological variation in primary solid tumour tissues and patient-derived mouse xenografts for scRNAseq, are not well understood. Here, we used low temperature (6{degrees}C) protease and collagenase (37{degrees}C) to identify the transcriptional signatures associated with tissue dissociation across a diverse scRNAseq dataset comprising 128,481 cells from patient cancer tissues, patient-derived breast cancer xenografts and cancer cell lines.\n\nResultsWe observe substantial variation in standard quality control (QC) metrics of cell viability across conditions and tissues. From FACS sorted populations gated for cell viability, we identify a sub-population of dead cells that would pass standard data filtering practices, and quantify the extent to which their transcriptomes differ from live cells. We identify a further subpopulation of transcriptomically \"dying\" cells that exhibit up-regulation of MHC class I transcripts, in contrast with live and fully dead cells. From the contrast between tissue protease dissociation at 37{degrees}C or 6{degrees}C, we observe that collagenase digestion results in a stress response. We derive a core gene set of 512 heat shock and stress response genes, including FOS and JUN, induced by collagenase (37{degrees}C), which are minimized by dissociation with a cold active protease (6{degrees}C). While induction of these genes was highly conserved across all cell types, cell type-specific responses to collagenase digestion were observed in patient tissues. We observe that the yield of cancer and non-cancer cell types varies between tissues and dissociation methods.\n\nConclusionsThe method and conditions of tumour dissociation influence cell yield and transcriptome state and are both tissue and cell type dependent. Interpretation of stress pathway expression differences in cancer single cell studies, including components of surface immune recognition such as MHC class I, may be especially confounded. We define a core set of 512 genes that can assist with identification of such effects in dissociated scRNA-seq experiments.

genomics↗

bioRxiv2018

Epiclomal: probabilistic clustering of sparse single-cell DNA methylation data

We present Epiclomal, a probabilistic clustering method arising from a hierarchical mixture model to simultaneously cluster sparse single-cell DNA methylation data and impute missing values. Using synthetic and published single-cell CpG datasets we show that Epiclomal outperforms non-probabilistic methods and is able to handle the inherent missing data feature which dominates single-cell CpG genome sequences. Using a recently published single-cell 5mCpG sequencing method (PBAL), we show that Epiclomal discovers sub-clonal patterns of methylation in aneuploid tumour genomes, thus defining epiclones. We show that epiclones may transcend copy number determined clonal lineages, thus opening this important form of clonal analysis in cancer. Epiclomal is written in R and Python and is available at https://github.com/shahcompbio/Epiclomal.

genomics↗

bioRxiv2018

Resource: Scalable whole genome sequencing of 40,000 single cells identifies stochastic aneuploidies, genome replication states and clonal repertoires

Essential features of cancer tissue cellular heterogeneity such as negatively selected genome topologies, sub-clonal mutation patterns and genome replication states can only effectively be studied by sequencing single-cell genomes at scale and high fidelity. Using an amplification-free single-cell genome sequencing approach implemented on commodity hardware (DLP+) coupled with a cloud-based computational platform, we define a resource of 40,000 single-cell genomes characterized by their genome states, across a wide range of tissue types and conditions. We show that shallow sequencing across thousands of genomes permits reconstruction of clonal genomes to single nucleotide resolution through aggregation analysis of cells sharing higher order genome structure. From large-scale population analysis over thousands of cells, we identify rare cells exhibiting mitotic mis-segregation of whole chromosomes. We observe that tissue derived scWGS libraries exhibit lower rates of whole chromosome anueploidy than cell lines, and loss of p53 results in a shift in event type, but not overall prevalence in breast epithelium. Finally, we demonstrate that the replication states of genomes can be identified, allowing the number and proportion of replicating cells, as well as the chromosomal pattern of replication to be unambiguously identified in single-cell genome sequencing experiments. The combined annotated resource and approach provide a re-implementable large scale platform for studying lineages and tissue heterogeneity.

genomics↗

bioRxiv2018

clonealign: statistical integration of independent single-cell RNA & DNA-seq from human cancers

Measuring gene expression of genomically defined tumour clones at single cell resolution would associate functional consequences to somatic alterations, as a prelude to elucidating pathways driving cell population growth, resistance and relapse. In the absence of scalable methods to simultaneously assay DNA and RNA from the same single cell, independent sampling of cell populations for parallel measurement of single cell DNA and single cell RNA must be computationally mapped for genome-transcriptome association. Here we present clonealign, a robust statistical framework to assign gene expression states to cancer clones using single-cell RNA-seq and DNA-seq independently sampled from an heterogeneous cancer cell population. We apply clonealign to triple-negative breast cancer patient derived xenografts and high-grade serous ovarian cancer cell lines and discover clone-specific dysregulated biological pathways not visible using either DNA-Seq or RNA-Seq alone.

bioinformatics↗

bioRxiv2016

An evolving cancer instigates clonally unrelated neighboring cells to form distant metastases.

Based on the clonal evolution theory of cancer formation, a single cell within a tissue gains a cancer-driving mutation and thus a growth advantage. From this expanded cellular mass, another cell gains a new mutation allowing this newly mutated cell to gain new competitive advantage and to expand in number (thus clonal expansion). Another clone then emerges. Eventually all required mutations are gained, and a cancer forms. Consequently, while a primary lesion may harbor divergent subclones, all the subclones within the primary cancer as well as all metastatic growths in secondary organs share at least the very first oncogenic mutation that initiates the primary cancer. However, by tracking genetically marked mammary epithelial cells that suffered the initiating oncogenic mutation--and their neighboring mammary cells that did not-in several mouse models of human breast cancer, we found that genetically unrelated mammary epithelial cells can be colluded by neighboring mutated cells to disseminate, and that they can even undergo de novo tumorigenic transformation and form distant metastases. Therefore, clonally unrelated epithelial cells may contribute to cancer progression and to the heterogeneity of the systemic disease. The non-linear cancer spread has important implications in cancer prevention, treatment, and therapeutic resistance.

cancer biology↗