Biology subjects

Pugh, T. J.

Publications and source records attributed to Pugh, T. J..

17 recordsLinked to original sources

bioRxiv2026

Therapy-associated mutagenesis at CTCF binding sites is shaped by chromatin context and DNA repair capacity

Genotoxic cancer therapies introduce DNA damage that can be fixed as somatic mutations in surviving tumor cells. However, the impact of therapy-associated mutagenesis on regulatory elements remains unclear. CTCF binding sites (CBS) are chromatin architectural elements that exhibit recurrent localized mutation enrichment in cancer genomes. We asked whether treatment exposure is associated with increased mutagenesis at CBS in 4,870 whole-genome sequences from metastatic tumors across 17 cancer types and 45 therapies. Radiotherapy and trifluridine exposure in metastatic colorectal cancer were associated with increased mutation enrichment at CBS. This enrichment was pronounced at motif-containing sites and in low-expression or late-replicating genomic contexts. Alterations in DNA damage response genes, including BRCA2, were associated with increased CBS mutation enrichment following radiotherapy. Together, these findings indicate that therapy-associated mutagenesis at CTCF binding sites is shaped by chromatin context and DNA repair capacity, extending the mutational consequences of cancer treatment to regulatory genome architecture.

cancer biology↗

bioRxiv2025

Consensus molecular subtypes define distinct evolutionary trajectories of biliary tract cancers

Introduction Biliary tract cancer (BTC) comprises a family of rare malignancies subclassified by anatomy and pathology. However, this scheme may obscure shared biology and limit patient stratification. Objectives We tested whether BTC heterogeneity can be explained by coherent latent axes and evaluated the potential to unify diverse clinical and genomic factors under a tractable biological framework Methods We performed whole-genome and transcriptome sequencing of 180 tumors enriched for tumor cells by laser capture microdissection to identify shared programs in BTC. Results Network integration across transcriptomic classes identified two consensus cancer subtypes (CCS). CCS segregated with anatomical location of primary tumor and gene expression marker analyses suggest subtypes reflect tumor cell of origin differences. CCS displayed strikingly divergent molecular landscapes, explaining more variance than anatomical location of primary tumor. CCS-B tumors were mutationally loaded with clock-like and APOBEC signatures and extrachromosomal DNA, whereas CCS-A tumors were characterized by chromosome-arm deletions. Conclusion Our approach showed that harnessing the genomic and transcriptomic diversity of BTC uncovers novel biology and improves stratification.

cancer biology↗

bioRxiv2025

Immunogenomic profiling of circulating T cells in pediatric cancer patients during standard-of-care

While pediatric cancer patients receive intensive chemotherapy, its impact on peripheral T cells and subsequently to disease outcomes are not fully characterized. Here, we assessed T-cell dynamics during treatment, identifying associations with outcomes through immune phenotyping and T-cell Receptor (TCR) sequencing in pediatric solid and hematologic malignancies. We show that while levels of immune checkpoint proteins (PD-1, LAG3, and TIM3) at baseline were highest in lymphomas compared to other cancer groups, they increased significantly in response to therapy in all cancers. Levels of Central Memory (CM) T cells increased in leukemias and solid tumors, while naive T cells and cell-free TCR diversity decreased in lymphomas. By combining immune cell and TCR repertoire features across all timepoints, we proposed the Dynamic Immunogenomic Score (DIS) to measure patient-specific effects of therapy on the peripheral T-cell population. Higher DIS was associated with high-risk cancer types and logistic regression analysis revealed it may predict incidence of relapse in leukemia patients. TCR specificity analysis revealed patient-specific clonal dynamics and differential detection of virally-associated TCRs in cancer patients compared to healthy individuals. Our results highlight the potential of early upfront immunogenomic profiling in identifying high-risk patients that may be predictive in light of emerging cellular immunotherapies.

cancer biology↗

bioRxiv2024

An updated reference genome sequence and annotation reveals gene losses and gains underlying naked mole-rat biology

The naked mole-rat (NMR; Heterocephalus glaber) is a eusocial subterranean rodent with a highly unusual set of physiological traits that has attracted great interest amongst the scientific community. However, the genetic basis of most of these traits has not been elucidated. To facilitate our understanding of the molecular mechanisms underlying NMR physiology and behaviour, we generated a long-read chromosomal-level genome assembly of the NMR. This genome was subsequently annotated and incorporated into multiple whole genome alignments in the Ensembl database. Our long-read assembly identified thousands of repeats and genes that were previously unassembled in the NMR and improved the results of routinely used short-read sequencing-based experiments such as RNA-seq, snRNA-seq, and ATAC-seq. We identified several spermatozoa related gene losses that may underlie the unique degenerative sperm phenotype in NMRs (IRGC, FSCB, AKAP3, MROH2B, CATSPER1, DCDC2C, ATP1A4, TEKT5, and ZAN), and an additional gene loss related to the established NK-cell absence in NMRs (PILRB). We resolved several tandem duplications in genes related to pathways underlying unique NMR adaptations including hypoxia tolerance, oxidative stress, and nervous system protection (TINF2, TCP1, KYAT1). Lastly, we describe our ongoing efforts to generate a reference telomere-to-telomere assembly in the NMR which includes the resolution of complex gene families. This new reference genome should accelerate the discovery of the genetic underpinnings of NMR physiology and adaptation.

genomics↗

bioRxiv2023

MEDIPIPE: an automated and comprehensive pipeline for cfMeDIP-seq data quality control and analysis

Summarycell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq) has emerged as a promising non-invasive technology to detect cancers and monitor treatments. Several bioinformatics tools are available for cfMeDIP-seq data analysis. However, an easy to implement and flexible pipeline, particularly, for large-scale cfMeDIP-seq profiling, is still lacking. Here we present the MEDIPIPE, which provides a one-stop solution for cfMeDIP-seq data quality control, methylation quantification and sample aggregation. The major advantages of MEDIPIPE are: 1) it is easy to implement and reproduce with automatically deployed execution environments; 2) it can handle different experimental settings with a single input configuration file; 3) it is computationally efficient for large-scale cfMeDIP-seq profiling data analysis and aggregation. Availability and implementationThis pipeline is an open-source software under the MIT license and it is freely available at https://github.com/yzeng-lol/MEDIPIPE. Contactyzeng@uhnresearch.ca or trevor.pugh@utoronto.ca or hansenhe@uhnresearch.ca Supplementary informationSupplementary data are appended.

bioinformatics↗

bioRxiv2022

Meta-analysis of preclinical pharmacogenomic studies to discover robust and translatable biomarkers of drug response

Preclinical pharmacogenomic studies provide an opportunity to discover novel biomarkers for drug response. However, pharamcogenomic studies linking gene expression profiles to drug response do not always agree on the significance or strength of biomarkers. We apply a statistical meta-analysis approach to 7 large independent pharmacogenomic studies, testing for tissue-specific gene expression markers predictive of response among cancer cell lines. We found 4,338 statistically-significant biomarkers across 8 tissue types and 34 drugs. Significant biomarkers were found to be closer than random to drug targets in a gene network built on pathway co-membership (average distance of 2 vs 2.9). However, functional relationships with the drug target did not predict reproducibility across studies. To validate these biomarkers, we utilized 10 clinical datasets, allowing 42/4338 biomarkers to be assessed for clinical translation. Of the 42 candidate biomarkers, the expression of ODC1 was found to be significantly predictive of Paclitaxel response as a neoadjuvant treatment of breast carcinoma across 2 independent clinical studies of >200 patients each. We expect that as more clinical transcriptomics data matched with response are available, our results can be used to prioritize which genes to evaluate as clinical biomarkers of drug response.

cancer biology↗

bioRxiv2022

Transcriptional immunogenomic analysis reveals distinct immunological clusters in pediatric nervous system tumours

To inform immunotherapy approaches in children, we performed an immunogenomic analysis of RNA-seq data from 925 treatment-naive pediatric nervous system tumours (pedNST) spanning 12 cancer types from three public data sets. Within pedNST, we uncovered four broad immune clusters: Pediatric Inflamed (10%), Myeloid Predominant (30%), Immune Neutral (43%) and Immune Excluded (17%). We validated these clusters using immunohistochemistry, methylation immune inference, and segmentation analysis of tissue images. We report shared biology of these immune clusters within and across cancer types, and characterization of specific immune-cell frequencies as well as T- and B-cell repertoires. We found no associations between immune infiltration levels and tumour mutational burden, although molecular cancer entities were enriched within specific immune clusters. Given the heterogeneity within pedNST, our findings suggest personalized immunogenomic profiling is needed to guide selection of immunotherapeutic strategies.

genomics↗

bioRxiv2021

Single-cell transcriptional analysis of the immune tumour microenvironment during myeloma disease evolution

Multiple myeloma is universally preceded by a premalignant disease state. However, efforts to develop preventative therapeutic strategies are hindered by an incomplete understanding of the immune mechanisms associated with progression. Using single-cell RNA-sequencing, we profiled 104,880 cells derived from the bone marrow of V{kappa}*MYC mice across the myeloma progression spectrum, of which 97,720 were identified as non-malignant cells of the tumour microenvironment. Analysis of the non-malignant cells comprising the immune microenvironment identified mechanisms associated with disease progression in innate and adaptive immune cell populations. This included activation of IL-17 signaling in myeloid cells from precursor mice, accompanied by upregulation of Il6 gene expression in basophils. In the T/Natural killer cell compartment, we identified Tox-expressing CD8+ T cells enriched in the tumour microenvironment of mice with overt disease, with co-expression of LAG3 and PD-1, as well as elevated T cell exhaustion signatures in mice with early disease. We subsequently showed that early intervention with combinatorial blockade of LAG3 and PD-1 using neutralizing monoclonal antibodies delayed tumor progression and improved survival of V{kappa}*MYC mice. Together, this work provides insight into the biology of myeloma evolution and nominates a treatment strategy for early disease.

cancer biology↗

bioRxiv2021

A comparison of data integration methods for single-cell RNA sequencing of cancer samples

Tumours are routinely profiled with single-cell RNA sequencing (scRNA-seq) to characterize their diverse cellular ecosystems of malignant, immune, and stromal cell types. When combining data from multiple samples or studies, batch-specific technical variation can confound biological signals. However, scRNA-seq batch integration methods are often not designed for, or benchmarked, on datasets containing cancer cells. Here, we compare 5 data integration tools applied to 171,206 cells from 5 tumour scRNA-seq datasets. Based on our results, STACAS and fastMNN are the most suitable methods for integrating tumour datasets, demonstrating robust batch effect correction while preserving relevant biological variability in the malignant compartment. This comparison provides a framework for evaluating how well single-cell integration methods correct for technical variability while preserving biological heterogeneity of malignant and non-malignant cell populations.

bioinformatics↗

bioRxiv2021

CanDIG: Secure Federated Genomic Queries and Analyses Across Jurisdictions

Rapid expansions of bioinformatics and computational biology have broadened the collection and use of -omics data including genomic, transcriptomic, methylomic and a myriad of other health data types, in the clinic and the laboratory. Both clinical and research uses of such data require co-analysis with large datasets, for which participant privacy and the need for data custodian controls must remain paramount. This is particularly challenging in multi-jurisdictional settings, such as Canada, where health privacy and security requirements are often heterogeneous. Data federation presents a solution to this, allowing for integration and analysis of large datasets from various sites while abiding by local policies. The Canadian Distributed Infrastructure for Genomics platform (CanDIG) enables federated querying and analysis of -omics and health data while keeping that data local and under local control. It builds upon existing infrastructures to connect five health and research institutions across Canada, relies heavily on standards and tooling brought together by the Global Alliance for Genomics and Health (GA4GH), implements a clear division of responsibilities among its participants and adheres to international data sharing standards. Participating researchers and clinicians can therefore contribute to and quickly access a critical mass of -omics data across a national network in a manner that takes into account the multi-jurisdictional nature of our privacy and security policies. Through this, CanDIG gives medical and research communities the tools needed to use and analyze the ever-growing amount of -omics data available to them in order to improve our understanding and treatment of various conditions and diseases. CanDIG is being used to make genomic and phenotypic data available for querying across Canada as part of data sharing for five leading pan-Canadian projects including the Terry Fox Comprehensive Cancer Care Centre Consortium Network (TF4CN) and Terry Fox PRecision Oncology For Young peopLE (PROFYLE), and making data from provincial projects such as POG (Personalized Onco- Genomics) more widely available.

genomics↗

bioRxiv2020

CReSCENT: CanceR Single Cell ExpressioN Toolkit

CReSCENTCanceR Single Cell ExpressioN Toolkit (https://crescent.cloud), is an intuitive and scalable web portal incorporating a containerized pipeline execution engine for standardized analysis of single-cell RNA sequencing (scRNA-seq) data. While scRNA-seq data for tumour specimens are readily generated, subsequent analysis requires high-performance computing infrastructure and user expertise to build analysis pipelines and tailor interpretation for cancer biology. CReSCENT uses public data sets and preconfigured pipelines that are accessible to computational biology non-experts and are user-editable to allow optimization, comparison, and reanalysis for specific experiments. Users can also upload their own scRNA-seq data for analysis and results can be kept private or shared with other users.

bioinformatics↗

bioRxiv2019

Karyotypic divergence confounds cellular phenotypes in large pharmacogenomic studies

BackgroundSomatic copy-number alterations that affect large genomic regions are a major source of genomic diversity in cancer and can impact cellular phenotypes. Clonal heterogeneity within cancer cell lines can affect phenotypic presentation, including drug response.\n\nMethodsWe aggregated and analyzed SNP and copy number profiles from six pharmacogenomic datasets encompassing 1,691 cell lines screened for 13 molecules. To look for sources of genotype and karyotype discordances, we compared SNP genotypes and segmental copy-ratios across 5 kb genomic bins. To assess the impact of genomic discordances on pharmacogenomic studies, we assessed gene expression and drug sensitivity data for compared discordant and concordant lines.\n\nResultsWe found 6/1,378 (0.4%) cell lines profiled in two studies to be discordant in both genotypic and karyotypic identity, 51 (3.7%) discordant in genotype, 97 (7.0%) discordant in karyotype, and 125 (9.1%) potential misidentifications. We highlight cell lines REH, NCI-H23 and PSN1 as having drug response discordances that may hinge on divergent copy-number q\n\nConclusionsOur study highlights the low level of misidentification as evidence of effective cell line authentication standards in recent pharmacogenomic studies. However, the proclivity of cell lines to acquire somatic copy-number variants can alter the cellular phenotype, resulting in a biological and predictable effects on drug sensitivity. These findings highlight the need for verification of cell line copy number profiles to inform interpretation of drug sensitivity data in biomedical studies.

cancer biology↗

bioRxiv2019

Evaluation of methods to assign cell type labels to cell clusters from single-cell RNA-sequencing data

Identification of cell type subpopulations from complex cell mixtures using single-cell RNA-sequencing (scRNA-seq) data includes automated computational steps like data normalization, dimensionality reduction and cell clustering. However, assigning cell type labels to cell clusters is still conducted manually by most researchers, resulting in limited documentation, low reproducibility and uncontrolled vocabularies. Two bottlenecks to automating this task are the scarcity of reference cell type gene expression signatures and that some dedicated methods are available only as web servers with limited cell type gene expression signatures. In this study, we benchmarked four methods (CIBERSORT, GSEA, GSVA, and ORA) for the task of assigning cell type labels to cell clusters from scRNA-seq data. We used scRNA-seq datasets from liver, peripheral blood mononuclear cells and retinal neurons for which reference cell type gene expression signatures were available. Our results show that, in general, all four methods show a high performance in the task as evaluated by Receiver Operating Characteristic curve analysis (average AUC = 0.94, sd = 0.036), whereas Precision-Recall curve analyses show a wide variation depending on the method and dataset (average AUC = 0.53, sd = 0.24). CIBERSORT and GSVA were the top two performers. Additionally, GSVA was the fastest of the four methods and was more robust in cell type gene expression signature subsampling simulations. We provide an extensible framework to evaluate other methods and datasets at https://github.com/jdime/scRNAseq_cell_cluster_labeling.

bioinformatics↗

bioRxiv2018

High-resolution structural genomics reveals new therapeutic vulnerabilities in glioblastoma

We investigated the role of 3D genome architecture in instructing functional properties of glioblastoma stem cells (GSCs) by generating the highest-resolution 3D genome maps to-date for this cancer. Integration of DNA contact maps with chromatin and transcriptional profiles identified specific mechanisms of gene regulation, including individual physical interactions between regulatory regions and their target genes. Residing in structurally conserved regions in GSCs was CD276, a gene known to play a role in immuno-modulation. We show that, unexpectedly, CD276 is part of a stemness network in GSCs and can be targeted with an antibody-drug conjugate to curb self-renewal, a key stemness property. Our results demonstrate that integrated structural genomics datasets can be employed to rationally identify therapeutic vulnerabilities in self-renewing cells.\n\nSIGNIFICANCEIn adult GBM, GSCs act as therapy-resistant reservoirs to nucleate tumor recurrence. New therapeutic approaches that target these cell populations hold the potential of significantly improving patient care and overall prognosis for this always-lethal cancer. Our work describes new links between 3D genome architecture and stemness properties in GSCs. In particular, through integration of multiple genomics and structural genomics datasets, we found an unexpected connection between immune-related genes and self-renewal programs in GBM. Among these, we show that targeting CD276 with knockdown strategies or specific antibody-drug conjugates achieve suppression of self-renewal. Strategies to target CD276+ cells are currently in clinical trials for solid tumors. Our results indicate that CD276-targeting agents could be deployed in GBM to specifically target GSC populations.\n\nHIGHLIGHTSO_LIWe generated high (sub-5 kb) resolution Hi-C maps for stem-like cells from GBM patients.\nC_LIO_LIIntegration of Hi-C and genomics datasets dissects mechanisms of gene regulation.\nC_LIO_LI3D genomes poise immune-related genes, including CD276, for expression.\nC_LIO_LITargeting CD276 curbs self-renewal properties of GBM cells.\nC_LI

cancer biology↗

bioRxiv2018

Subclonal architecture, evolutionary trajectories and patterns of inheritance of germline variants in pediatric glioblastoma

Pediatric glioblastoma (pGBM) is a lethal cancer with no effective therapies. Intratumoral genetic heterogeneity and mode of tumor evolution have not been systematically addressed for this cancer. Whole-genome sequencing of germline-tumor pairs showed that pGBM is characterized by intratumoral genetic heterogeneity and consequent subclonal architecture. We found that pGBM undergoes extreme evolutionary trajectories, with primary and recurrent tumors having different subclonal compositions. Analysis of variant allele frequencies supported a model of tumor growth involving slow-cycling cancer stem cells that give rise to fast-proliferating progenitor-like cells and to non-dividing cells. pGBM patients germlines had subclonal structural variants, some of which underwent dynamic frequency fluctuations during tumor evolution. By sequencing germlines of mother-father-patient trios, we found that inheritance of deleterious germline variants from healthy parents cooperate with de novo germline and somatic events to the tumorigenic process. Our studies therefore challenge the current notion that pGBM is a relatively homogeneous molecular entity.

cancer biology↗

bioRxiv2018

Genome analysis and data sharing informs timing of molecular events in pancreatic neuroendocrine tumour

Neuroendocrine tumours (NETs) are rare, slow growing cancers that present in a diversity of tissues. To understand molecular underpinnings of gastrointestinal (GINET) and pancreatic NETs (PNETs), we profiled 45 tumours combining exome, RNA, and shallow whole genome sequencing, as well as fluorescent in situ hybridization. In addition to expected somatic mutations and copy number alterations, we found that PNETs contained a highly consistent copy neutral loss-of-heterozygosity (CN-LOH) profile affecting over half of the genome; a greater percentage than any cancer analyzed to date. Our data indicates that onset of extreme autozygosity may be progressive, associated with metastasis, and initially triggered by the loss of DAXX/ATRX, and subsequent biallelic loss of MEN1. We confirmed this molecular timing model using targeted clinical sequencing data from an additional 43 NETs made available by the AACR GENIE project. Against this background of CN-LOH, several chromosomal regions consistently retained heterozygosity, suggesting selection for crucial allele-specific components specific to PNET progression and potential new therapeutic targets.\n\nStatement of significanceWe have discovered that pancreatic neuroendocrine tumours contain a characteristic pattern of copy neutral loss-of-heterozygosity affecting the majority of the genome following mutations of MEN1 and ATRX/DAXX. Against this background of loss-of-heterozygosity, specific genomic regions are consistently retained and may therefore contain vulnerable therapeutic targets for pancreatic neuroendocrine tumours.

cancer biology↗

bioRxiv2017

Bamgineer: Introduction of simulated allele-specific copy number variants into exome and targeted sequence data sets

Somatic copy number variations (CNVs) play a crucial role in development of many human cancers. The broad availability of next-generation sequencing (NGS) data has enabled the development of algorithms to computationally infer CNV profiles from a variety of data types including exome and targeted sequence data; currently the most prevalent types of cancer genomics data. However, systemic evaluation and comparison of these tools remains challenging due to a lack of ground truth reference sets. To address this need, we have developed Bamgineer, a tool written in Python to introduce user-defined haplotype-phased allele-specific copy number events into an existing Binary Alignment Mapping (BAM) file, with a focus on targeted and exome sequencing experiments. As input, this tool requires a read alignment file (BAM format), lists of non-overlapping genome coordinates for introduction of gains and losses (bed file), and an optional file defining known haplotypes (vcf format). To improve runtime performance, Bamgineer introduces the desired CNVs in parallel using queuing and parallel processing on a local machine or on a high-performance computing cluster. As proof-of-principle, we applied Bamgineer to a single high-coverage (mean: 220X) exome sequence file from a blood sample to simulate copy number profiles of 3 exemplar tumours from each of 10 tumour types at 5 tumour cellularity levels (20-100%, 150 BAM files in total). In addition to these reference sets, we expect Bamgineer to be of use for systematic benchmarking of CNV calling algorithms using their own data and expected tumour content for a variety of applications. The source code and reference datasets are freely available at http://github.org/pughlab/bamgineer.

bioinformatics↗