Biology subjects

Culibrk, L.

Publications and source records attributed to Culibrk, L..

7 recordsLinked to original sources

bioRxiv2026

A context dependent hierarchy of APOBEC3A and APOBEC3B mutators in lung adenocarcinoma

APOBEC3 cytosine deaminases are major sources of cancer mutations. In lung adenocarcinoma (LUAD), APOBEC3 activity drives tumor evolution and therapy resistance, nominating these enzymes as therapeutic targets. Although APOBEC3A drives the major portion of APOBEC3-associated mutational signature burdens across cancers, the relative contributions of individual APOBEC3 paralogs in LUAD remain unclear, limiting effective targeting. Here, we define the roles of endogenously misregulated APOBEC3 deaminases in LUAD using CRISPR-Cas9 knockouts and whole-genome sequencing of 197 long-term-propagated single-cell clones. We show that APOBEC3A and APOBEC3B both generate mutations but display marked heterogeneity, ranging from single-enzyme to dual activity or minimal mutagenesis, which is not predicted by commonly used mRNA and protein levels, or deaminase assays. Using these genetically defined systems, we provide the first causal evidence that endogenous APOBEC3A and APOBEC3B generate the indel signature InD9a in tumors, linking it to uracil excision following cytosine deamination. Together, these findings reveal heterogeneous APOBEC3 contributions to LUAD mutagenesis and highlight the need for biomarkers that resolve individual APOBEC3 activities for their effective targeting.

cancer biology↗

bioRxiv2025

A Universal Duplex Sequencing Approach for Accurate Detection of Somatic Mutations

Ultra-accurate detection of rare somatic mutations is critical for understanding mutational processes in human disease, aging, and environmental exposures, yet current methods are limited by error rates, restricted genome coverage, and high DNA input. We present UDSeq, a duplex sequencing protocol combining random fragmentation, efficient UMI ligation, and quantitative input control to achieve near-complete genome/exome representation from as little as 100 pg DNA. Benchmarking in human sperm estimates a UDSeq error rate of [~]2.5x10-9 per base pair. UDSeq captures mutational signatures from heterogeneous populations without clonal expansion, reproduces exposure-specific patterns in cell lines and rodent models, and enables cross-species profiling. Compared with prior duplex methods, UDSeq yields up to fourfold more usable duplex molecules, improves library conversion, and remains cost-effective. We include a step-by-step protocol with quality-control checkpoints for fragment size, ligation yield, library conversion, and duplication rate. UDSeq provides a scalable, low-input platform for accurate profiling of somatic mutagenesis.

genomics↗

bioRxiv2025

Improved Mutation Detection in Duplex Sequencing Data with Sample-Specific Error Profiles

Duplex sequencing enables highly accurate detection of rare somatic mutations, but existing variant callers often rely on protocol-specific heuristics that limit sensitivity, reproducibility, and cross-study comparability. We present DupCaller, a probabilistic variant caller that builds sample-specific error profiles and applies a strand-aware statistical model for mutation detection. Across 50 synthetic datasets, DupCaller identified 1.25-fold more single-base substitutions (SBSs) and 1.41-fold more indels than a state-of-the-art method, while exhibiting equal or better precision. In three duplex-sequenced cell lines treated with aristolochic acid, it recovered expected mutational signatures while detecting 3.5-fold more SBSs and 2.8-fold more indels. In 93 tissue samples-- including neurons, cord blood, sperm, saliva, and blood--DupCaller showed consistent gains, detecting 1.21- to 2.7-fold more mutations. Sensitivity scaled with sample duplication rate, yielding approximately 1.5-fold more mutations under optimal conditions and over 3-fold more in low-duplication samples where other tools falter. These results establish DupCaller as a robust and scalable solution for somatic mutation profiling in duplex sequencing across diverse biological and technical contexts.

bioinformatics↗

bioRxiv2023

Genomic structures and regulation patterns at HPV integration sites in cervical cancer

Human papillomavirus (HPV) integration has been implicated in transforming HPV infection into cancer, but its genomic consequences have been difficult to study using short-read technologies. To resolve the dysregulation associated with HPV integration, we performed long-read sequencing on 63 cervical cancer genomes. We identified six categories of integration events based on HPV-human genomic structures. Of all HPV integrants, defined as two HPV-human breakpoints bridged by an HPV sequence, 24% contained variable copies of HPV between the breakpoints, a phenomenon we termed heterologous integration. Analysis of DNA methylation within and in proximity to the HPV genome at individual integration events revealed relationships between methylation status of the integrant and its orientation and structure. Dysregulation of the human epigenome and neighboring gene expression in cis with the HPV-integrated allele was observed over megabase-ranges of the genome. By elucidating the structural, epigenetic, and allele-specific impacts of HPV integration, we provide insight into the role of integrated HPV in cervical cancer.

genomics↗

bioRxiv2023

IMPALA: A Comprehensive Pipeline for Detecting and Elucidating Mechanisms of Allele Specific Expression in Cancer

SummaryAllele-specific expression (ASE), where transcripts from one allele are more abundant than transcripts from the other, can arise from various genetic mechanisms and has implications for gene regulation and disease. We present IMPALA (Integrated Mapping and Profiling of Allelically-expressed Loci with Annotations), a versioned and containerized pipeline for detecting ASE in samples including cancer genomes. IMPALA leverages RNA sequencing data and, optionally, phased variant, copy number variant (CNV), allelic methylation, and mutation data to identify ASE genes and uncover underlying regulatory mechanisms. IMPALA incorporates the MBASED framework for ASE detection, and outputs a comprehensive summary table and informative figures to visualize the genomic distribution of ASE genes and their correlation with potential regulatory causes. We applied IMPALA to a cancer sample and identified thousands of genes with ASE and highlighted potential somatic events that may have influenced ASE of these genes. ASE data can be used to detect the downstream consequences of genomic alterations, which facilitates the identification of dysregulated cancer-related genes. IMPALA thus provides researchers with a powerful tool for both ASE analysis and for investigating genetic factors correlated with ASE. Availability and implementationIMPALA is licensed under GNU General Public License v3.0 and freely available at https://github.com/bcgsc/IMPALA and https://doi.org/10.5281/zenodo.8019168 with documentation and tutorial. Contactsjones@bcgsc.ca Supplemental informationSupplemental materials are available at Bioinformatics online. Issue section: Gene expression

bioinformatics↗

bioRxiv2021

Ploidetect enables pan-cancer analysis of the causes and impacts of chromosomal instability

Cancers routinely exhibit chromosomal instability that results in copy number variants (CNVs), namely changes in the abundance of genomic material. Unfortunately, the detection of these variants in cancer genomes is difficult. We present Ploidetect, a software package that effectively identifies CNVs within whole-genome sequenced tumors. Ploidetect utilizes a coarse-to-fine segmentation approach which yields highly contiguous segments while allowing for focal CNVs to be detected with high sensitivity. We benchmark Ploidetect against popular CNV tools using synthetic data, cell line data, and real-world metastatic tumor data, and find an improvement in sensitivity and accuracy while maintaining low levels of oversegmentation. We show that the improved CNV sensitivity enables Ploidetect to recover recurrent homozygous deletions and genes associated with chromosomal instability in a multi-cancer cohort of 687 patients. Using highly contiguous CNV calls afforded by Ploidetect, we also demonstrate the use of segment N50 as a novel metric for the measurement of chromosomal instability within tumor biopsies. We propose that increasingly accurate determination of CNVs is critical for their productive study in cancer, and our work demonstrates advances made possible by progress in this regard.

cancer biology↗

bioRxiv2018

Text-mining clinically relevant cancer biomarkers for curation into the CIViC database

Precision oncology involves analysis of individual cancer samples to understand the genes and pathways involved in the development and progression of a cancer. To improve patient care, knowledge of diagnostic, prognostic, predisposing and drug response markers is essential. Several knowledgebases have been created by different groups to collate evidence for these associations. These include the open-access Clinical Interpretation of Variants in Cancer (CIViC) knowledgebase. These databases rely on time-consuming manual curation from skilled experts who read and interpret the relevant biomedical literature. To aid in this curation and provide the greatest coverage for these databases, particularly CIViC, we propose the use of text mining approaches to extract these clinically relevant biomarkers from all available published literature. To this end, a group of cancer genomics experts annotated biomarkers and their clinical associations discussed in 800 sentences and achieved good inter-annotator agreement. We then used a supervised learning approach to construct the CIViCmine knowledgebase (http://bionlp.bcgsc.ca/civicmine/) extracting 128,857 relevant sentences from PubMed abstracts and Pubmed Central Open Access full text papers. CIViCmine contains over 90,992 biomarkers associated with 7,866 genes, 402 drugs and 557 cancer types, representing 29,153 abstracts and 40,551 full-text publications. Through integration with CIVIC, we provide a prioritised list of curatable biomarkers as well as a resource that is valuable to other knowledgebases and precision cancer analysts in general.

bioinformatics↗