Biology subjects

Alexandrov, L. B.

Publications and source records attributed to Alexandrov, L. B..

At least 19 recordsLinked to original sources

bioRxiv2026

Benchmarked sequencing methods reveal hidden mosaic variation in the human brain

Mosaic variants (MVs) record cellular lineages, but their recovery is strongly method-dependent. Here, we benchmarked four duplex sequencing methods alongside bulk and single-neuron genome sequencing in matched human brain tissue. The methods differed in genome representation, MV yield and mutational profiles, with limited caller-level concordance. However, read-level interrogation revealed substantially greater cross-method molecular support. To systematically recover this evidence, we developed Read-level Integration for Variant Evidence Recovery (RIVER), expanding the pool of cross-method-supported candidates by approximately two orders of magnitude relative to caller-level overlap. Orthogonal sequencing at ~10,000x across brain and peripheral tissues validated approximately half of the tested RIVER-rescued candidates. MVs recovered from a single brain region resolved broadly distributed and regionally restricted clones and provided lineage information among individual neurons. These findings show that RIVER substantially expands the pool of endogenous lineage markers available to investigate human development, aging, disease-associated mosaicism and clonal evolution.

genomics↗

bioRxiv2026

A Compendium of 49 Experimental SBS Signatures for Decoding Human Cancer Mutational Processes

Human cancer genomes harbor distinct mutational patterns that reflect past processes of DNA damage and repair. However, the precise attribution of these signatures to specific chemical carcinogens lacks a standardized experimental reference framework. To address this gap, we curated 4,282 genome-wide sequencing datasets from 42 model systems across five species exposed to 146 cancer-risk agents. This platform yielded 49 robust experimental single-base substitution signatures (eSS), with 28 matching 19 established COSMIC signatures and 21 defining novel mutational processes. We reconstructed 24 COSMIC signatures, assigning candidate etiologies to five signatures of unknown origin and revising two contested assignments. Pan-cancer decomposition detected four eSS-like mutational processes enriched in smokers across 4,951 tumors. Lastly, independent single-molecule sequencing of primary human organoids reproduced these profiles with high fidelity, confirming true platform-independent biological reproducibility across complex human models. This eSS repertoire provides a reference that links human mutational processes to mechanistic classes of DNA damage.

genomics↗

bioRxiv2026

Evaluating Aggregated Gene Level eQTL Scores

Genetic feature engineering, used in methods such as transcriptome-wide association study, supports gene-trait association testing by aggregating single variants into gene-level features predictive of expression. To evaluate how different model architectures, LD filtering thresholds, and variant prioritization methods affect expression prediction quality, we trained over 3 million models and evaluated their performance in independent cohorts. Using the best performing models to impute expression and immunotherapy response as an example trait, we found a significant association with the reactive oxygen species pathway (p=0.032). Our model training workflow will support genetic feature engineering towards improved complex trait modeling.

bioinformatics↗

bioRxiv2026

Mutagenic bypass of uracil-derived abasic sites underlies the SBS17 mutational signature

Understanding how mutations arise is central to predicting and preventing cancer, yet most of the mutational signatures found in cancer genomes still have no known mechanistic cause. Among these, single-base-substitution signature 17 (SBS17) is the dominant mutational process in gastric and oesophageal adenocarcinoma. SBS17 has been linked to oxidative damage and to the chemotherapeutic agent 5-fluorouracil (5-FU), but the molecular events that generate it are unknown. Here we demonstrate that SBS17 causes driver mutations in gastrointestinal cancer. We then combine genetics in cell lines and organoids with whole-genome sequencing to define the mechanistic aetiology of SBS17 mutagenesis. We disprove the prevailing oxidative damage hypothesis and instead show that SBS17 arises through the misincorporation of dUTP during DNA replication, followed by uracil excision by the glycosylase UNG and mutagenic bypass of the resulting abasic (AP) sites by the translesion synthesis machinery. Importantly, we reveal that the same mechanism underlies both spontaneous and chemotherapy-induced mutations. Together, our results resolve the origin of gastric mutagenesis, opening therapeutic avenues for curbing ongoing mutagenesis, tumour evolution and drug resistance in gastrointestinal cancers.

molecular biology↗

bioRxiv2025

The Impact of Variant Calling on Substitution Mutational Signature Inference

Identifying mutational signatures is a key component of cancer genomics studies, yet the influence of variant calling strategies on signature extraction has not been systematically evaluated. Here, we analyzed over 8,900 whole exomes from The Cancer Genome Atlas (TCGA) and over 1,800 whole genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium to assess how mutation callers shape de novo single-base substitution (SBS) signatures. We found that consensus calling yielded stable de novo signatures across reference genomes and pipeline versions, whereas individual callers introduced false-positive SBSs that manifested as artifactual signatures that were reproducibly detected by three independent signatures extraction tools. A minimal consensus approach requiring agreement between only two variant calling algorithms effectively removed these artifacts while preserving true biological signal. Together, these results establish consensus variant calling as essential for robust inference of de novo SBS mutational signatures and provide practical guidelines for distinguishing genuine mutational processes from technical artifacts.

bioinformatics↗

bioRxiv2025

Multi-omics profiling reveals microenvironmental remodeling as a key driver of house dust mite-induced lung cancer progression

Chronic exposure to the common aeroallergen house dust mite (HDM) induces lung inflammation and DNA damage, but its impact on lung cancer development remains largely unexplored. Using whole-genome sequencing, RNA-seq, and DNA methylation profiling, we assessed HDM effects in lung epithelial cell lines and a mouse orthotopic lung cancer model. HDM accelerated tumor growth without altering mutational burden. Transcriptomic and epigenetic analyses revealed tissue-specific effects: in normal lung, HDM enhanced pro-inflammatory and immune activation programs, whereas in tumors it suppressed T cell responses, antigen presentation, and chemokine signaling. Immune deconvolution showed a shift toward myeloid enrichment and lymphoid suppression, with reduced cytotoxic T and NK signatures. Notably, HDM-driven tumor promotion was abolished in Il17a-/- but not Il1b-/-mice, identifying IL-17A as a critical mediator. These findings demonstrate that chronic aeroallergen exposure reshapes the lung microenvironment to promote immune suppression and accelerate lung cancer progression. HighlightsChronic house dust mite (HDM) exposure accelerates lung tumor growth through non-mutagenic, immune-mediated mechanisms. HDM activates pro-inflammatory and immune programs in normal lung tissue but suppresses antitumor T cell responses in tumors. Multi-omics profiling reveals epigenetic silencing of immune genes and a myeloid-enriched, lymphoid-deficient tumor microenvironment. HDM-driven tumor promotion depends on IL-17A but not IL-1{beta}, establishing IL-17A as a central driver of lung tumor promotion.

cancer biology↗

bioRxiv2025

The DREAM complex links somatic mutation, lifespan, and disease

The DREAM complex has emerged as a central repressor of DNA repair, raising questions as to whether such repression exerts long-term effects on human health. Here we establish that DREAM activity significantly impacts lifetime somatic mutation burden, and that such effects are linked to altered lifespan and age-related disease pathology. First, joint profiling of DREAM activity and somatic mutations across a single-cell atlas of 21 mouse tissues shows that cellular niches with lower DREAM activity have decreased mutation rates. Second, DREAM activity predicts the varied lifespans observed across 92 mammals, with low activity marking longer-lived species. Third, reduced DREAM activity in Alzheimers patients predicts late disease onset and decreased risk for severe neuropathology. Finally, we show DREAM knockout protects against mutation accumulation in vivo, reducing single-base substitutions by 4.2% and insertion/deletions by 19.6% in brains of mice. These findings position DREAM as a key regulator of aging.

bioinformatics↗

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↗

bioRxiv2025

Identification and Validation of a Previously Missed Mutational Signature in Colorectal Cancer

Mutational signature analysis has greatly enhanced our understanding of the mutagenic processes found in cancer and normal tissues. As part of a recent study, we analyzed 802 treatment-naive, microsatellite-stable colorectal cancers (CRC) and identified a de novo signature, SBS_D, which was conservatively decomposed into SBS18, a signature associated with reactive oxygen species. Here, we re-evaluate this decomposition and provide evidence that SBS_D represents a distinct mutational process from that of SBS18. Through an independent analysis of 2,616 whole-genome sequenced microsatellite-stable CRCs across three distinct cohorts, we demonstrate that SBS_D is consistently present at a similar prevalence, suggesting that this signature may have been previously overlooked. Using a naive decomposition approach, we demonstrate that the pattern of SBS_D better aligns with signatures previously associated with deficiencies in DNA polymerase delta (POLD1) proofreading and mismatch repair. However, multiple lines of evidence, including the absence of pathogenic mutations in the exonuclease domain of POLD1 or in mismatch repair-associated genes, indicate that SBS_D is not driven by canonical defects in these DNA repair pathways. Overall, this study identifies a previously unrecognized mutational signature in microsatellite-stable CRC and proposes that its etiology may be linked to DNA repair infidelity emerging late in tumor development in samples without canonical defects in DNA repair pathways.

cancer biology↗

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↗

bioRxiv2025

Community assembly modeling of microbial evolution within Barrett's esophagus and esophageal adenocarcinoma

Mathematical modeling of somatic evolution, a process impacting both host cells and microbial communities in the human body, can capture important dynamics driving carcinogenesis. Here we considered models for esophageal adenocarcinoma (EAC), a cancer that has dramatically increased in incidence over the past few decades in Western populations, with high case fatality rates due to late-stage diagnoses. Despite advancements in genomic analyses of the precursor Barretts esophagus (BE), prevention of late-stage EAC remains a significant clinical challenge. Previous microbiome studies in BE and EAC have focused on quantifying static microbial abundance differences rather than evolutionary dynamics. Using whole genome sequencing data from esophageal tissues, we first applied a robust bioinformatics pipeline to extract non-host DNA reads, mapped these putative reads to microbial taxa, and retained those taxa with high genomic coverage. When applying mathematical models of microbial evolution to sequential stages of progression to EAC, we observed evidence of neutral dynamics in community assembly within normal esophageal tissue and BE, but not EAC. In a case-control study of BE patients who progressed to EAC cancer outcomes (CO) versus those who had non-cancer outcomes (NCO) during follow-up (mean=10.5 years), we found that Helicobacter pylori deviated significantly from the neutral expectation in BE NCO, suggesting that factors related to H. pylori or H. pylori infection itself may influence EAC risk. Additionally, simulations incorporating selection recapitulated non-neutral behaviors observed in the datasets. Formally modeling dynamics during progression holds promise in clinical applications by offering a deeper understanding of microbial involvement in cancer development.

cancer biology↗

bioRxiv2024

Human cancer genomes harbor the mutational signature of tobacco-specific nitrosamines NNN and NNK

Tobacco usage is linked to multiple cancer types and accounts for a quarter of all cancer-related deaths. Tobacco smoke contains various carcinogenic compounds, including polycyclic aromatic hydrocarbons (PAH), though the mutagenic potential of many tobacco-related chemicals remains largely unexplored. In particular, the highly carcinogenic tobacco-specific nitrosamines NNN and NNK form pre-mutagenic pyridyloxobutyl (POB) DNA adducts. In the study presented here, we identified genome-scale POB-induced mutational signatures in cell lines and rat tumors, while also investigating their role in human cancer. These signatures are characterized by T>N and C>T mutations forming from specific POB adducts damaging dT and dC residues. Analysis of 2,780 cancer genomes uncovered POB signatures in [~]180 tumors; from cancer types distinct from the ones linked to smoking-related signatures SBS4 and SBS92. This suggests that, unlike PAH compounds, the POB pathway may contribute uniquely to the mutational landscapes of certain hematological malignancies and cancers of the kidney, breast, prostate and pancreas.

genomics↗

bioRxiv2024

Accelerated Hematopoietic Stem Cell Aging in Space

Stem cell aging is accelerated by macroenvironmental and microenvironmental stressors, including inflammation. Previously, the NASA Twins study revealed inflammatory cytokine upregulation, chromosomal alterations, and telomere changes suggestive of accelerated aging in low-Earth orbit (LEO). To investigate the effects of spaceflight on human hematopoietic stem and progenitor cell (HSPC) aging, the NASA-supported Integrated Space Stem Cell Orbital Research team performed four independent 30- to 45-day NASA missions with matched flight and ground HSPC nanobioreactors in automated CubeLabs. These experiments revealed loss of HSPC dormancy, reduced self-renewal capacity, mitochondrial DNA amplification, APOBEC3-induced C-to-T mutagenesis, reduced ADAR1p150 expression, and alterations in the expression of repetitive elements. These molecular changes are indicative of accelerated HSPC aging and pre-leukemia stem cell generation in space and may be predictable and preventable.

cancer biology↗

bioRxiv2024

Evaluating topography of mutational signatures with SigProfilerTopography

The mutations found in a cancer genome are shaped by diverse processes, each displaying a characteristic mutational signature that may be influenced by the genomes architecture. While prior analyses have evaluated the effect of topographical genomic features on mutational signatures, there has been no computational tool that can comprehensively examine this interplay. Here, we present SigProfilerTopography, a Python package that allows evaluating the effect of chromatin organization, histone modifications, transcription factor binding, DNA replication, and DNA transcription on the activities of different mutational processes. SigProfilerTopography elucidates the unique topographical characteristics of mutational signatures, unveiling their underlying biological and molecular mechanisms.

bioinformatics↗

bioRxiv2023

Assigning mutational signatures to individual samples and individual somatic mutations with SigProfilerAssignment

Analysis of mutational signatures is a powerful approach for understanding the mutagenic processes that have shaped the evolution of a cancer genome. Here we present SigProfilerAssignment, a desktop and an online computational framework for assigning all types of mutational signatures to individual samples. SigProfilerAssignment is the first tool that allows both analysis of copy-number signatures and probabilistic assignment of signatures to individual somatic mutations. As its computational engine, the tool uses a custom implementation of the forward stagewise algorithm for sparse regression and nonnegative least squares for numerical optimization. Analysis of 2,700 synthetic cancer genomes with and without noise demonstrates that SigProfilerAssignment outperforms four commonly used approaches for assigning mutational signatures. SigProfilerAssignment is freely available at https://github.com/AlexandrovLab/SigProfilerAssignment with a web implementation at https://cancer.sanger.ac.uk/signatures/assignment/.

bioinformatics↗

bioRxiv2023

Arsenic is a potent co-mutagen of ultraviolet light

Environmental co-exposures are widespread and are major contributors to carcinogenic mechanisms. Two well-established environmental agents causing skin cancer are ultraviolet radiation (UVR) and arsenic. Arsenic is a known co-carcinogen that enhances UVRs carcinogenicity. However, the mechanisms of arsenic co-carcinogenesis are not well understood. In this study, we utilized primary human keratinocytes and a hairless mouse model to investigate the carcinogenic and mutagenic properties of co-exposure to arsenic and UVR. In vitro and in vivo exposures revealed that, on its own, arsenic is neither mutagenic nor carcinogenic. However, in combination with UVR, arsenic exposure has a synergistic effect leading to an accelerated mouse skin carcinogenesis as well as to more than 2-fold enrichment of UVR mutational burden. Notably, mutational signature ID13, previously found only in UVR-associated human skin cancers, was observed exclusively in mouse skin tumors and cell lines jointly exposed to arsenic and UVR. This signature was not observed in any model system exposed purely to arsenic or purely to UVR, making ID13 the first co-exposure signature to be reported using controlled experimental conditions. Analysis of existing genomics data from basal cell carcinomas and melanomas revealed that only a subset of human skin cancers harbor ID13 and, consistent with our experimental observations, these cancers exhibited an elevated UVR mutagenesis. Our results provide the first report of a unique mutational signature caused by a co-exposure to two environmental carcinogens and the first comprehensive evidence that arsenic is a potent co-mutagen and co-carcinogen of UVR. Importantly, our findings suggest that a large proportion of human skin cancers are not formed purely due to UVR exposure but rather due to a co-exposure of UVR and other co-mutagens such as arsenic.

cancer biology↗