Biology subjects

Bergstrom, E. N.

Publications and source records attributed to Bergstrom, E. N..

12 recordsLinked to original sources

bioRxiv2025

Examining the Role of Extrachromosomal DNA in 1,216 Lung Cancers

The role of extrachromosomal DNA (ecDNA) in lung cancer, particularly in subjects who never smoked (LCINS), remains unclear. Examination of over 1200 whole-genome-sequenced lung cancers identified ecDNA in 18.9% of patients. Recurrent amplification of MDM2 and other oncogenes via ecDNA possibly drives a LCINS subset. Tumors harboring ecDNA showed worse overall survival than tumors harboring other focal amplifications. A strong association with whole-genome doubling suggests most ecDNA reflects genomic instability in treatment-naive lung cancer.

bioinformatics↗

bioRxiv2025

Tobacco smoke carcinogens exacerbate APOBEC mutagenesis and carcinogenesis

Mutational processes are thought to act independently and additively. We challenge this view by demonstrating that DNA-adducting agents sensitize the genome to mutagenesis by APOBEC enzymes. A model tobacco carcinogen (NQO) triggers a 100-fold increase in APOBEC3B- catalyzed signature mutations and elevates oral carcinoma levels in vivo. A hallmark of this unidirectional synergy is strand-coordinated pairs of APOBEC signature mutations within 32nt of each other (didyma). Biochemical experiments show that APOBEC3B can catalyze didyma formation, and genetic studies demonstrate requirements for APOBEC3B and functional nucleotide excision repair (XPA in human cells and Rad14 in yeast). Analyses of lung and head & neck cancers show that APOBEC mutagenesis and didyma are elevated in tumors from smokers compared to non-smokers. Additional tumor types with links to DNA-adducting agents also exhibit didyma. These studies support a model in which DNA-adducting carcinogens activate nucleotide excision repair and amplify mutagenesis by APOBEC enzymes in cancer.

cancer biology↗

bioRxiv2024

The interplay between dormant mutated cells and tumor promotion by chronic tissue damage in determining cancer risk.

While the causal role of mutagenic carcinogens in tumor development is well established, the relative contribution of environmental tumor promoting factors, wounding, and chronic inflammation is still unclear. Recent sequencing studies have suggested that most environmental carcinogens act as promoters rather than through mechanisms that involve direct induction of point mutations, but whether cancer risk factors such as obesity, chronic inflammation, wounding, or tumor promoters contribute directly or indirectly to mutation burden, or induce novel signatures, has not been investigated. Here, we present WGS analysis of over 100 mouse skin tumors to compare the effects of exposure to mutagens, the tumor promoter TPA, chronic wounding, obesity, or chemotherapy, on mutational burden and cancer risk. All tumors initiated by the carcinogen Dimethylbenzanthracene (DMBA) show a very strong A>T mutational signature (SBS.DMBA) attributable to a single exposure to this carcinogen. The number of SBS.DMBA mutations also showed a strong correlation with the "clock" signature SBS5, suggesting that one treatment with this mutagen can induce mutational signatures attributed to endogenous processes. No specific signatures could be attributed to obesity, high fat diet, wounding, or TPA. Cells carrying thousands of mutations persist over very long periods without inducing tumors or causing pathological changes but can give rise to tumors after short term exposure to TPA. Furthermore, normal cell turnover and proliferation during fetal and adult growth, is not sufficient for promotion, but tissue damage followed by regenerative proliferation seems to be required for tumor development. We conclude that tumor promoters, chronic inflammation, wounding, and obesity do not contribute significantly to tumor mutational burden, and that the rate-limiting determinant of tumor growth is exposure to a tumor promoter rather than the nature or number of genomic point mutations. These data are highly relevant to the recent demonstration of persistent oncogenic mutations in histologically normal human tissues during ageing.

cancer biology↗

bioRxiv2023

Visualizing and exploring patterns of large mutational events with SigProfilerMatrixGenerator

BackgroundAll cancers harbor somatic mutations in their genomes. In principle, mutations affecting between one and fifty base pairs are generally classified as small mutational events. Conversely, large mutational events affect more than fifty base pairs, and, in most cases, they encompass copy-number and structural variants affecting many thousands of base pairs. Prior studies have demonstrated that examining patterns of somatic mutations can be leveraged to provide both biological and clinical insights, thus, resulting in an extensive repertoire of tools for evaluating small mutational events. Recently, classification schemas for examining large-scale mutational events have emerged and shown their utility across the spectrum of human cancers. However, there has been no standard bioinformatics tool that allows visualizing and exploring these large-scale mutational events ResultsHere, we present a new version of SigProfilerMatrixGenerator that now delivers integrated capabilities for examining large mutational events. The tool provides support for examining copy-number variants and structural variants under two previously developed classification schemas and it supports data from numerous algorithms and data modalities. SigProfilerMatrixGenerator is written in Python with an R wrapper package provided for users that prefer working in an R environment. ConclusionsThe new version of SigProfilerMatrixGenerator provides the first standardized bioinformatics tool for optimized exploration and visualization of two previously developed classification schemas for copy number and structural variants. The tool is freely available at https://github.com/AlexandrovLab/SigProfilerMatrixGenerator with an extensive documentation at https://osf.io/s93d5/wiki/home/.

bioinformatics↗

bioRxiv2022

Topography of mutational signatures in human cancer

The somatic mutations found in a cancer genome are imprinted by different mutational processes. Each process exhibits a characteristic mutational signature, which can be affected by the genome architecture. However, the interplay between mutational signatures and topographical genomic features has not been extensively explored. Here, we integrate mutations from 5,120 whole-genome sequenced tumours from 40 cancer types with 516 topographical features from ENCODE to evaluate the effect of nucleosome occupancy, histone modifications, CTCF binding, replication timing, and transcription/replication strand asymmetries on the cancer-specific accumulation of mutations from distinct mutagenic processes. Most mutational signatures are affected by topographical features with signatures of related aetiologies being similarly affected. Certain signatures exhibit periodic behaviours or cancer-type specific enrichments/depletions near topographical features, revealing further information about the processes that imprinted them. Our findings, disseminated via COSMIC, provide a comprehensive online resource for exploring the interactions between mutational signatures and topographical features across human cancer. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=175 SRC="FIGDIR/small/493921v2_figu1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@abf065org.highwire.dtl.DTLVardef@33ad99org.highwire.dtl.DTLVardef@ca37b6org.highwire.dtl.DTLVardef@fa7c6_HPS_FORMAT_FIGEXP M_FIG C_FIG HIGHLIGHTSO_LIComprehensive topography analysis of mutational signatures encompassing 82,890,857 somatic mutations in 5,120 whole-genome sequenced tumours integrated with 516 tissue-matched topographical features from the ENCODE project. C_LIO_LIThe accumulation of somatic mutations from most mutational signatures is affected by nucleosome occupancy, histone modifications, CTCF binding sites, transcribed regions, or replication strand/timing. C_LIO_LIMutational signatures with related aetiologies are consistently characterized by similar genome topographies across tissue types. C_LIO_LITopography analysis allows both separating signatures from different aetiologies and understanding the genomic specificity of clustered somatic mutations. C_LIO_LIA comprehensive online resource, disseminate through the COSMIC signatures database, that allows researchers to explore the interactions between somatic mutational processes and genome architecture within and across cancer types. C_LI

cancer biology↗

bioRxiv2022

Examining clustered somatic mutations with SigProfilerClusters

SummaryClustered mutations are found in the human germline as well as in the genomes of cancer and normal somatic cells. Clustered events can be imprinted by a multitude of mutational processes, and they have been implicated in both cancer evolution and development disorders. Existing tools for identifying clustered mutations have been optimized for a particular subtype of clustered event and, in most cases, relied on a predefined inter-mutational distance (IMD) cutoff combined with a piecewise linear regression analysis. Here we present SigProfilerClusters, an automated tool for detecting all types of clustered mutations by calculating a sample-dependent IMD threshold using a simulated background model that takes into account extended sequence context, transcriptional strand asymmetries, and regional mutation densities. SigProfilerClusters disentangles all types of clustered events from non-clustered mutations and annotates each clustered event into an established subclass, including the widely used classes of doublet-base substitutions, multi-base substitutions, omikli, and kataegis. SigProfilerClusters outputs non-clustered mutations and clustered events using standard data formats as well as provides multiple visualizations for exploring the distributions and patterns of clustered mutations across the genome. AvailabilitySigProfilerClusters is freely available at https://github.com/AlexandrovLab/SigProfilerClusters with support across most operating systems and extensive documentation at https://osf.io/qpmzw/wiki/home/. Contactebergstr@eng.ucsd.edu or L2alexandrov@health.ucsd.edu

bioinformatics↗

bioRxiv2021

R-loop homeostasis and cancer mutagenesis promoted by the DNA cytosine deaminase APOBEC3B

The single-stranded DNA cytosine-to-uracil deaminase APOBEC3B is an antiviral protein implicated in cancer. However, its substrates in cells are not fully delineated. Here, APOBEC3B proteomics reveal interactions with a surprising number of R-loop factors. Biochemical experiments show APOBEC3B binding to R-loops in human cells and in vitro. Genetic experiments demonstrate R-loop increases in cells lacking APOBEC3B and decreases in cells overexpressing APOBEC3B. Genome-wide analyses show major changes in the overall landscape of physiological and stimulus-induced R-loops with thousands of differentially altered regions as well as binding of APOBEC3B to many of these sites. APOBEC3 mutagenesis impacts overexpressed genes and splice factor mutant tumors preferentially, and APOBEC3-attributed kataegis are enriched in RTCW consistent with APOBEC3B deamination. Taken together with the fact that APOBEC3B binds single-stranded DNA and RNA and preferentially deaminates DNA, these results support a mechanism in which APOBEC3B mediates R-loop homeostasis and contributes to R-loop mutagenesis in cancer. HighlightsO_LIUnbiased proteomics link antiviral APOBEC3B to R-loop regulation C_LIO_LISystematic alterations of APOBEC3B levels trigger corresponding changes in R-loops C_LIO_LIAPOBEC3B binds R-loops in living cells and in vitro C_LIO_LIBioinformatics analyses support an R-loop deamination and mutation model C_LI

cancer biology↗

bioRxiv2021

Comprehensive analysis of clustered mutations in cancer reveals recurrent APOBEC3 mutagenesis of ecDNA

Clustered somatic mutations are common in cancer genomes with prior analyses revealing several types of clustered single-base substitutions, including doublet- and multi-base substitutions, diffuse hypermutation termed omikli, and longer strand-coordinated events termed kataegis. Here, we provide a comprehensive characterization of clustered substitutions and clustered small insertions and deletions (indels) across 2,583 whole-genome sequenced cancers from 30 cancer types. While only 3.7% of substitutions and 0.9% of indels were found to be clustered, they contributed 8.4% and 6.9% of substitution and indel drivers, respectively. Multiple distinct mutational processes gave rise to clustered indels including signatures enriched in tobacco smokers and homologous-recombination deficient cancers. Doublet-base substitutions were caused by at least 12 mutational processes, while the majority of multi-base substitutions were generated by either tobacco smoking or exposure to ultraviolet light. Omikli events, previously attributed to the activity of APOBEC3 deaminases, accounted for a large proportion of clustered substitutions. However, only 16.2% of omikli matched APOBEC3 patterns with experimental validation confirming additional mutational processes giving rise to omikli. Kataegis was generated by multiple mutational processes with 76.1% of all kataegic events exhibiting AID/APOBEC3-associated mutational patterns. Co-occurrence of APOBEC3 kataegis and extrachromosomal-DNA (ecDNA) was observed in 31% of samples with ecDNA. Multiple distinct APOBEC3 kataegic events were observed on most mutated ecDNA. ecDNA containing known cancer genes exhibited both positive selection and kataegic hypermutation. Our results reveal the diversity of clustered mutational processes in human cancer and the role of APOBEC3 in recurrently mutating and fueling the evolution of ecDNA.

cancer biology↗

bioRxiv2021

The APOBEC3A deaminase drives episodic mutagenesis in cancer cells

The APOBEC3 family of cytidine deaminases is widely speculated to be a major source of somatic mutations in cancer1-3. However, causal links between APOBEC3 enzymes and mutations in human cancer cells have not been established. The identity of the APOBEC3 paralog(s) that may act as prime drivers of mutagenesis and the mechanisms underlying different APOBEC3-associated mutational signatures are unknown. To directly investigate the roles of APOBEC3 enzymes in cancer mutagenesis, candidate APOBEC3 genes were deleted from cancer cell lines recently found to naturally generate APOBEC3-associated mutations in episodic bursts4. Deletion of the APOBEC3A paralog severely diminished the acquisition of mutations of speculative APOBEC3 origins in breast cancer and lymphoma cell lines. APOBEC3 mutational burdens were undiminished in APOBEC3B knockout cell lines. APOBEC3A deletion reduced the appearance of the clustered mutation types kataegis and omikli, which are frequently found in cancer genomes. The uracil glycosylase UNG and the translesion polymerase REV1 were found to play critical roles in the generation of mutations induced by APOBEC3A. These data represent the first evidence for a long-postulated hypothesis that APOBEC3 deaminases generate prevalent clustered and non-clustered mutational signatures in human cancer cells, identify APOBEC3A as a driver of episodic mutational bursts, and dissect the roles of the relevant enzymes in generating the associated mutations in breast cancer and B cell lymphoma cell lines.

cancer biology↗

bioRxiv2020

Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor

Mutational signature analysis is commonly performed in genomic studies surveying cancer and normal somatic tissues. Here we present SigProfilerExtractor, an automated tool for accurate de novo extraction of mutational signatures for all types of somatic mutations. Benchmarking with a total of 34 distinct scenarios encompassing 2,500 simulated signatures operative in more than 60,000 unique synthetic genomes and 20,000 synthetic exomes demonstrates that SigProfilerExtractor outperforms thirteen other tools across all datasets with and without noise. For genome simulations with 5% noise, reflecting high-quality genomic datasets, SigProfilerExtractor outperforms other approaches by elucidating between 20% and 50% more true positive signatures while yielding more than 5-fold less false positive signatures. Applying SigProfilerExtractor to 4,643 whole-genome sequenced and 19,184 whole-exome sequenced cancers reveals four previously missed mutational signatures. Two of the signatures are confirmed in independent cohorts with one of these signatures associating with tobacco smoking. In summary, this report provides a reference tool for analysis of mutational signatures, a comprehensive benchmarking of bioinformatics tools for extracting mutational signatures, and several novel mutational signatures including a signature putatively attributed to direct tobacco smoking mutagenesis in bladder cancer and in normal bladder epithelium.

bioinformatics↗

bioRxiv2020

Generating realistic null hypothesis of cancer mutational landscapes using SigProfilerSimulator

Performing a statistical test requires a null hypothesis. In cancer genomics, a key challenge is the fast generation of accurate somatic mutational landscapes that can be used as a realistic null hypothesis for making biological discoveries. Here we present SigProfilerSimulator, a powerful tool that is capable of simulating the mutational landscapes of thousands of cancer genomes at different resolutions within seconds. Applying SigProfilerSimulator to 2,144 whole-genome sequenced cancers reveals: (i) that most doublet base substitutions are not due to two adjacent single base substitutions but likely occur as single genomic events; (ii) that an extended sequencing context of +/-2bp is required to more completely capture the patterns of substitution mutational signatures in human cancer; (iii) information on false-positive discovery rate of commonly used bioinformatics tools for detecting driver genes. SigProfilerSimulators breadth of features allows one to construct a tailored null hypothesis and use it for evaluating the accuracy of other bioinformatics tools or for downstream statistical analysis for biological discoveries.

genomics↗

bioRxiv2019

SigProfilerMatrixGenerator: a tool for visualizing and exploring patterns of small mutational events

BackgroundCancer genomes are peppered with somatic mutations imprinted by different mutational processes. The mutational pattern of a cancer genome can be used to identify and understand the etiology of the underlying mutational processes. A plethora of prior research has focused on examining mutational signatures and mutational patterns from single base substitutions and their immediate sequencing context. We recently demonstrated that further classification of small mutational events (including substitutions, insertions, deletions, and doublet substitutions) can be used to provide a deeper understanding of the mutational processes that have molded a cancer genome. However, there has been no standard tool that allows fast, accurate, and comprehensive classification for all types of small mutational events\n\nResultsHere, we present SigProfilerMatrixGenerator, a computational tool designed for optimized exploration and visualization of mutational patterns for all types of small mutational events. SigProfilerMatrixGenerator is written in Python with an R wrapper package provided for users that prefer working in an R environment. SigProfilerMatrixGenerator produces fourteen distinct matrices by considering transcriptional strand bias of individual events and by incorporating distinct classifications for single base substitutions, doublet base substitutions, and small insertions and deletions. While the tool provides a comprehensive classification of mutations, SigProfilerMatrixGenerator is also faster and more memory efficient than existing tools that generate only a single matrix.\n\nConclusionsSigProfilerMatrixGenerator provides a standardized method for classifying small mutational events that is both efficient and scalable to large datasets. In addition to extending the classification of single base substitutions, the tool is the first to provide support for classifying doublet base substitutions and small insertions and deletions. SigProfilerMatrixGenerator is freely available at https://github.com/AlexandrovLab/SigProfilerMatrixGenerator with an extensive documentation at https://osf.io/s93d5/wiki/home/.

bioinformatics↗