Ancestry and somatic profile predict acral melanoma origin and prognosis.

Acral melanoma, which is not ultraviolet (UV)-associated, is the most common type of melanoma in several low- and middle-income countries including Mexico. Latin American samples are significantly underrepresented in global cancer genomics studies, which directly affects patients in these regions as it is known that cancer risk and incidence may be influenced by ancestry and environmental exposures. To address this, here we characterise the genome and transcriptome of 128 acral melanoma tumours from 96 Mexican patients, a population notable because of its genetic admixture. Compared with other studies of melanoma, we found fewer frequent mutations in classical driver genes such as BRAF , NRAS or NF1 . While most patients had predominantly Amerindian genetic ancestry, those with higher European ancestry had increased frequency of BRAF mutations and a lower number of structural variants. These BRAF -mutated tumours have a transcriptional profile similar to cutaneous non-volar melanocytes, suggesting that acral melanomas in these patients may arise from a distinct cell of origin compared to other tumours arising in these locations. KIT mutations were found in a subset of these tumours, and transcriptional profiling defined three expression clusters; these characteristics were associated with overall survival. We highlight novel low-frequency drivers, such as SPHKAP , which correlate with a distinct genomic profile and clinical characteristics. Our study enhances knowledge of this understudied disease and underscores the importance of including samples from diverse ancestries in cancer genomics studies.

Preclinical evaluation of avutometinib and defactinib in high-grade endometrioid endometrial cancer.

BACKGROUND: High-grade endometrial cancers (EAC) are aggressive tumors with a high risk of progression after treatment. As EAC may harbor mutations in the RAS/MAPK pathways, we evaluated the preclinical in vitro and in vivo efficacy of avutometinib, a RAF/MEK clamp, in combination with the focal adhesion kinase (FAK) inhibitors defactinib or VS-4718, against multiple primary EAC cell lines and xenografts. METHODS: Whole-exome sequencing (WES) was used to evaluate the genetic landscape of five primary EAC cell lines. The in vitro activity of avutometinib and defactinib as single agents and in combination was evaluated using cell viability, cell cycle, and cytotoxicity assays. Mechanistic studies were performed using Western blot assays while in vivo experiments were completed in UTE10 engrafted mice treated with either vehicle, avutometinib, VS-4718, or their combination through oral gavage. RESULTS: WES results demonstrated multiple EAC cell lines to harbor genetic derangements in the RAS/MAPK pathway including KRAS/PTEN/PIK3CA/BRAF/ARID1A, potentially sensitizing to FAK and RAF/MEK inhibition. Five out of five of the EAC cell lines demonstrated in vitro sensitivity to FAK and/or RAF/MEK inhibition. By Western blot assays, exposure of EAC cell lines to defactinib, avutometinib, and their combination demonstrated decreased phosphorylated FAK (p-FAK) as well as decreased p-MEK and p-ERK. In vivo the combination of avutometinib/VS-4718 demonstrated superior tumor growth inhibition compared to single-agent treatment and controls starting at Day 9 (p < 0.02 and p < 0.04) in UTE10 xenografts. CONCLUSIONS: Avutometinib, defactinib, and to a larger extent their combinations, demonstrated promising in vitro and in vivo activity against EAC cell lines and xenografts. These preclinical data support the potential clinical evaluation of this combination in high-grade EAC patients.

CarD-T: Interpreting Carcinomic Lexicon via Transformers.

The identification and classification of carcinogens is critical in cancer epidemiology, necessitating updated methodologies to manage the burgeoning biomedical literature. Current systems, like those run by the International Agency for Research on Cancer (IARC) and the National Toxicology Program (NTP), face challenges due to manual vetting and disparities in carcinogen classification spurred by the volume of emerging data. To address these issues, we introduced the Carcinogen Detection via Transformers (CarD-T) framework, a text analytics approach that combines transformer-based machine learning with probabilistic statistical analysis to efficiently nominate carcinogens from scientific texts. CarD-T uses Named Entity Recognition (NER) trained on PubMed abstracts featuring known carcinogens from IARC groups and includes a context classifier to enhance accuracy and manage computational demands. Using this method, journal publication data indexed with carcinogenicity & carcinogenesis Medical Subject Headings (MeSH) terms from the last 25 years was analyzed, identifying potential carcinogens. Training CarD-T on 60% of established carcinogens (Group 1 and 2A carcinogens, IARC designation), CarD-T correctly to identifies all of the remaining Group 1 and 2A designated carcinogens from the analyzed text. In addition, CarD-T nominates roughly 1500 more entities as potential carcinogens that have at least two publications citing evidence of carcinogenicity. Comparative assessment of CarD-T against GPT-4 model reveals a high recall (0.857 vs 0.705) and F1 score (0.875 vs 0.792), and comparable precision (0.894 vs 0.903). Additionally, CarD-T highlights 554 entities that show disputing evidence for carcinogenicity. These are further analyzed using Bayesian temporal Probabilistic Carcinogenic Denomination (PCarD) to provide probabilistic evaluations of their carcinogenic status based on evolving evidence. Our findings underscore that the CarD-T framework is not only robust and effective in identifying and nominating potential carcinogens within vast biomedical literature but also efficient on consumer GPUs. This integration of advanced NLP capabilities with vital epidemiological analysis significantly enhances the agility of public health responses to carcinogen identification, thereby setting a new benchmark for automated, scalable toxicological investigations.

The genomic landscape of 2,023 colorectal cancers.

Colorectal carcinoma (CRC) is a common cause of mortality1, but a comprehensive description of its genomic landscape is lacking2-9. Here we perform whole-genome sequencing of 2,023 CRC samples from participants in the UK 100,000 Genomes Project, thereby providing a highly detailed somatic mutational landscape of this cancer. Integrated analyses identify more than 250 putative CRC driver genes, many not previously implicated in CRC or other cancers, including several recurrent changes outside the coding genome. We extend the molecular pathways involved in CRC development, define four new common subgroups of microsatellite-stable CRC based on genomic features and show that these groups have independent prognostic associations. We also characterize several rare molecular CRC subgroups, some with potential clinical relevance, including cancers with both microsatellite and chromosomal instability. We demonstrate a spectrum of mutational profiles across the colorectum, which reflect aetiological differences. These include the role of Escherichia colipks+ colibactin in rectal cancers10 and the importance of the SBS93 signature11-13, which suggests that diet or smoking is a risk factor. Immune-escape driver mutations14 are near-ubiquitous in hypermutant tumours and occur in about half of microsatellite-stable CRCs, often in the form of HLA copy number changes. Many driver mutations are actionable, including those associated with rare subgroups (for example, BRCA1 and IDH1), highlighting the role of whole-genome sequencing in optimizing patient care.

Detecting HRD in whole-genome and whole-exome sequenced breast and ovarian cancers.

Breast and ovarian cancers harboring homologous recombination deficiency (HRD) are sensitive to PARP inhibitors and platinum chemotherapy. Conventionally, detecting HRD involves screening for defects in BRCA1, BRCA2, and other relevant genes. Recent analyses have shown that HRD cancers exhibit characteristic mutational patterns due to the activities of HRD-associated mutational signatures. At least three machine learning tools exist for detecting HRD based on mutational patterns. Here, using sequencing data from 1,043 breast and 182 ovarian cancers, we trained Homologous Recombination Proficiency Profiler (HRProfiler), a machine learning method for detecting HRD using six mutational features. HRProfiler's performance is assessed against prior approaches using additional independent datasets of 417 breast and 115 ovarian cancers, including retrospective data from a clinical trial involving patients treated with PARP inhibitors. Our results demonstrate that HRProfiler is the only tool that robustly and consistently predicts clinical response from whole-exome sequenced breast and ovarian cancers.

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.

Deep Learning Artificial Intelligence Predicts Homologous Recombination Deficiency and Platinum Response From Histologic Slides.

PURPOSE: Cancers with homologous recombination deficiency (HRD) can benefit from platinum salts and poly(ADP-ribose) polymerase inhibitors. Standard diagnostic tests for detecting HRD require molecular profiling, which is not universally available. METHODS: We trained DeepHRD, a deep learning platform for predicting HRD from hematoxylin and eosin (H&E)-stained histopathological slides, using primary breast (n = 1,008) and ovarian (n = 459) cancers from The Cancer Genome Atlas (TCGA). DeepHRD was compared with four standard HRD molecular tests using breast (n = 349) and ovarian (n = 141) cancers from multiple independent data sets, including platinum-treated clinical cohorts with RECIST progression-free survival (PFS), complete response (CR), and overall survival (OS) endpoints. RESULTS: DeepHRD predicted HRD from held-out H&E-stained breast cancer slides in TCGA with an AUC of 0.81 (95% CI, 0.77 to 0.85). This performance was confirmed in two independent primary breast cancer cohorts (AUC, 0.76 [95% CI, 0.71 to 0.82]). In an external platinum-treated metastatic breast cancer cohort, samples predicted as HRD had higher complete CR (AUC, 0.76 [95% CI, 0.54 to 0.93]) with 3.7-fold increase in median PFS (14.4 v 3.9 months; P = .0019) and hazard ratio (HR) of 0.45 (P = .0047). There were no significant differences in nonplatinum treatment outcome by predicted HRD status in three breast cancer cohorts, including CR (AUC, 0.39) and PFS (HR, 0.98, P = .95) in taxane-treated metastatic breast cancer. Through transfer learning to high-grade serous ovarian cancer, DeepHRD-predicted HRD samples had better OS after first-line (HR, 0.46; P = .030) and neoadjuvant (HR, 0.49; P = .015) platinum therapy in two cohorts. CONCLUSION: DeepHRD can predict HRD in breast and ovarian cancers directly from routine H&E slides across multiple external cohorts, slide scanners, and tissue fixation variables. When compared with molecular testing, DeepHRD classified 1.8- to 3.1-fold more patients with HRD, which exhibited better OS in high-grade serous ovarian cancer and platinum-specific PFS in metastatic breast cancer.

The evolution of lung adenocarcinoma precursors is associated with chromosomal instability and transition from innate to adaptive immune response/evasion.

Studying lung adenocarcinoma (LUAD) early carcinogenesis is challenging, primarily due to the lack of LUAD precursors specimens. We amassed multi-omics data from 213 LUAD and LUAD precursors to identify molecular features underlying LUAD precancer evolution. We observed progressively increasing mutations, chromosomal aberrations, whole genome doubling and genomic instability from precancer to invasive LUAD, indicating aggravating chromosomal instability (CIN). Telomere shortening, a crucial genomic alteration linked to CIN, emerged at precancer stage. Moreover, later-stage lesions demonstrated increasing cancer stemness and decreasing alveolar identity, suggesting epithelial de-differentiation during early LUAD carcinogenesis. The innate immune cells progressively diminished from precancer to invasive LUAD, concomitant with a gradual recruitment of adaptive immune cells (except CD8+ and gamma-delta T cells that decreased in later stages) and upregulation of numerous immune checkpoints, suggesting LUAD precancer evolution is associated with a shift from innate to adaptive immune response and immune evasion mediated by various mechanisms.

The mutagenic forces shaping the genomic landscape of lung cancer in never smokers.

Lung cancer in never smokers (LCINS) accounts for up to 25% of all lung cancers and has been associated with exposure to secondhand tobacco smoke and air pollution in observational studies. Here, we evaluate the mutagenic exposures in LCINS by examining deep whole-genome sequencing data from a large international cohort of 871 treatment-naïve LCINS recruited from 28 geographical locations within the Sherlock-Lung study. KRAS mutations were 3.8-fold more common in adenocarcinomas of never smokers from North America and Europe, while a 1.6-fold higher prevalence of EGFR and TP53 mutations was observed in adenocarcinomas from East Asia. Signature SBS40a, with unknown cause, was found in most samples and accounted for the largest proportion of single base substitutions in adenocarcinomas, being enriched in EGFR-mutated cases. Conversely, the aristolochic acid signature SBS22a was almost exclusively observed in patients from Taipei. Even though LCINS exposed to secondhand smoke had an 8.3% higher mutational burden and 5.4% shorter telomeres, passive smoking was not associated with driver mutations in cancer driver genes or the activities of individual mutational signatures. In contrast, patients from regions with high levels of air pollution were more likely to have TP53 mutations while exhibiting shorter telomeres and an increase in most types of somatic mutations, including a 3.9-fold elevation of signature SBS4 (q-value=3.1 × 10-5), previously linked mainly to tobacco smoking, and a 76% increase of clock-like signature SBS5 (q-value=5.0 × 10-5). A positive dose-response effect was observed with air pollution levels, which correlated with both a decrease in telomere length and an elevation in somatic mutations, notably attributed to signatures SBS4 and SBS5. Our results elucidate the diversity of mutational processes shaping the genomic landscape of lung cancer in never smokers.

The Complexity of Tobacco Smoke-Induced Mutagenesis in Head and Neck Cancer.

Tobacco smoke, alone or combined with alcohol, is the predominant cause of head and neck cancer (HNC). Here, we further explore how tobacco exposure contributes to cancer development by mutational signature analysis of 265 whole-genome sequenced HNC from eight countries. Six tobacco-associated mutational signatures were detected, including some not previously reported. Differences in HNC incidence between countries corresponded with differences in mutation burdens of tobacco-associated signatures, consistent with the dominant role of tobacco in HNC causation. Differences were found in the burden of tobacco-associated signatures between anatomical subsites, suggesting that tissue-specific factors modulate mutagenesis. We identified an association between tobacco smoking and three additional alcohol-related signatures indicating synergism between the two exposures. Tobacco smoking was associated with differences in the mutational spectra and repertoire of driver mutations in cancer genes, and in patterns of copy number change. Together, the results demonstrate the multiple pathways by which tobacco smoke can influence the evolution of cancer cell clones.

APOBEC shapes tumor evolution and age at onset of lung cancer in smokers.

APOBEC enzymes are part of the innate immunity and are responsible for restricting viruses and retroelements by deaminating cytosine residues1,2. Most solid tumors harbor different levels of somatic mutations attributed to the off-target activities of APOBEC3A (A3A) and/or APOBEC3B (A3B)3-6. However, how APOBEC3A/B enzymes shape the tumor evolution in the presence of exogenous mutagenic processes is largely unknown. Here, by combining deep whole-genome sequencing with multi-omics profiling of 309 lung cancers from smokers with detailed tobacco smoking information, we identify two subtypes defined by low (LAS) and high (HAS) APOBEC mutagenesis. LAS are enriched for A3B-like mutagenesis and KRAS mutations, whereas HAS for A3A-like mutagenesis and TP53 mutations. Unlike APOBEC3A, APOBEC3B expression is strongly associated with an upregulation of the base excision repair pathway. Hypermutation by unrepaired A3A and tobacco smoking mutagenesis combined with TP53-induced genomic instability can trigger senescence7, apoptosis8, and cell regeneration9, as indicated by high expression of pulmonary healing signaling pathway, stemness markers and distal cell-of-origin in HAS. The expected association of tobacco smoking variables (e.g., time to first cigarette) with genomic/epigenomic changes are not observed in HAS, a plausible consequence of frequent cell senescence or apoptosis. HAS have more neoantigens, slower clonal expansion, and older age at onset compared to LAS, particularly in heavy smokers, consistent with high proportions of newly generated, unmutated cells and frequent immuno-editing. These findings show how heterogeneity in mutational burden across co-occurring mutational processes and cell types contributes to tumor development, with important clinical implications.

Integrated mutational landscape analysis of poorly differentiated high-grade neuroendocrine carcinoma of the uterine cervix.

High-grade neuroendocrine cervical cancers (NETc) are exceedingly rare, highly aggressive tumors. We analyzed 64 NETc tumor samples by whole-exome sequencing (WES). Human papillomavirus DNA was detected in 65.6% (42/64) of the tumors. Recurrent mutations were identified in PIK3CA, KMT2D/MLL2, K-RAS, ARID1A, NOTCH2, and RPL10. The top mutated genes included RB1, ARID1A, PTEN, KMT2D/MLL2, and WDFY3, a gene not yet implicated in NETc. Somatic CNV analysis identified two copy number gains (3q27.1 and 19q13.12) and five copy number losses (1p36.21/5q31.3/6p22.2/9q21.11/11p15.5). Also, gene fusions affecting the ACLY-CRHR1 and PVT1-MYC genes were identified in one of the eight samples subjected to RNA sequencing. To resolve evolutionary history, multiregion WES in NETc admixed with adenocarcinoma cells was performed (i.e., mixed-NETc). Phylogenetic analysis of mixed-NETc demonstrated that adenocarcinoma and neuroendocrine elements derive from a common precursor with mutations typical of adenocarcinomas. Over one-third (22/64) of NETc demonstrated a mutator phenotype of C > T at CpG consistent with deficiencies in MBD4, a member of the base excision repair (BER) pathway. Mutations in the PI3K/AMPK pathways were identified in 49/64 samples. We used two patient-derived-xenografts (PDX) (i.e., NET19 and NET21) to evaluate the activity of pan-HER (afatinib), PIK3CA (copanlisib), and ATR (elimusertib) inhibitors, alone and in combination. PDXs harboring alterations in the ERBB2/PI3K/AKT/mTOR/ATR pathway were sensitive to afatinib, copanlisib, and elimusertib (P < 0.001 vs. controls). However, combinations of copanlisib/afatinib and copanlisib/elimusertib were significantly more effective in controlling NETc tumor growth. These findings define the genetic landscape of NETc and suggest that a large subset of these highly lethal malignancies might benefit from existing targeted therapies.

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 genome's 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.

Nivolumab for Patients With High-Risk Oral Leukoplakia: A Nonrandomized Controlled Trial.

Importance: Proliferative verrucous leukoplakia (PVL) is an aggressive oral precancerous disease characterized by a high risk of transformation to invasive oral squamous cell carcinoma (OSCC), and no therapies have been shown to affect its natural history. A recent study of the PVL immune landscape revealed a cytotoxic T-cell-rich microenvironment, providing strong rationale to investigate immune checkpoint therapy. Objective: To determine the safety and clinical activity of anti-programmed cell death 1 protein (PD-1) therapy to treat high-risk PVL. Design, Setting, and Participants: This nonrandomized, open-label, phase 2 clinical trial was conducted from January 2019 to December 2021 at a single academic medical center; median (range) follow-up was 21.1 (5.4-43.6) months. Participants were a population-based sample of patients with PVL (multifocal, contiguous, or a single lesion ≥4 cm with any degree of dysplasia). Intervention: Patients underwent pretreatment biopsy (1-3 sites) and then received 4 doses of nivolumab (480 mg intravenously) every 28 days, followed by rebiopsy and intraoral photographs at each visit. Main Outcomes and Measures: The primary end point was the change in composite score (size and degree of dysplasia) from before to after treatment (major response [MR]: >80% decrease in score; partial response: 40%-80% decrease). Secondary analyses included immune-related adverse events, cancer-free survival (CFS), PD-1 ligand 1 (PD-L1) expression, 9p21.3 deletion, and other exploratory immunologic and genomic associations of response. Results: A total of 33 patients were enrolled (median [range] age, 63 [32-80] years; 18 [55%] were female), including 8 (24%) with previously resected early-stage OSCC. Twelve patients (36%) (95% CI, 20.4%-54.8%) had a response by composite score (3 MRs [9%]), 4 had progressive disease (>10% composite score increase, or cancer). Nine patients (27%) developed OSCC during the trial, with a 2-year CFS of 73% (95% CI, 53%-86%). Two patients (6%) discontinued because of toxic effects; 7 (21%) experienced grade 3 to 4 immune-related adverse events. PD-L1 combined positive scores were not associated with response or CFS. Of 20 whole-exome sequenced patients, all 6 patients who had progression to OSCC after nivolumab treatment exhibited 9p21.3 somatic copy-number loss on pretreatment biopsy, while only 4 of the 14 patients (29%) who did not develop OSCC had 9p21.3 loss. Conclusions and Relevance: This immune checkpoint therapy precancer nonrandomized clinical trial met its prespecified response end point, suggesting potential clinical activity for nivolumab in high-risk PVL. Findings identified immunogenomic associations to inform future trials in this precancerous disease with unmet medical need that has been difficult to study. Trial Registration: ClinicalTrials.gov Identifier: NCT03692325.

Arsenic and UVR co-exposure results in unique gene expression profile identifying key co-carcinogenic mechanisms.

Changes in gene expression underlie many pathogenic endpoints including carcinogenesis. Metals, like arsenic, alter gene expression; however, the consequences of co-exposures of metals with other stressors are less understood. Although arsenic acts as a co-carcinogen by enhancing the development of UVR skin cancers, changes in gene expression in arsenic UVR co-carcinogenesis have not been investigated. We performed RNA-sequencing analysis to profile changes in gene expression distinct from arsenic or UVR exposures alone. A large number of differentially expressed genes (DEGs) were identified after arsenic exposure alone, while after UVR exposure alone fewer genes were changed. A distinct increase in the number of DEGs was identified after exposure to combined arsenic and UVR exposure that was synergistic rather than additive. In addition, a majority of these DEGs were unique from arsenic or UVR alone suggesting a distinct response to combined arsenic-UVR exposure. Globally, arsenic alone and arsenic plus UVR exposure caused a global downregulation of genes while fewer genes were upregulated. Gene Ontology analysis using the DEGs revealed cellular processes related to chromosome instability, cell cycle, cellular transformation, and signaling were targeted by combined arsenic and UVR exposure, distinct from UVR alone and arsenic alone, while others were related to epigenetic mechanisms such as the modification of histones. This result suggests the cellular functions we identified in this study may be key in understanding how arsenic enhances UVR carcinogenesis and that arsenic-enhanced gene expression changes may drive co-carcinogenesis of UVR exposure.

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

MOTIVATION: Analysis of mutational signatures is a powerful approach for understanding the mutagenic processes that have shaped the evolution of a cancer genome. To evaluate the mutational signatures operative in a cancer genome, one first needs to quantify their activities by estimating the number of mutations imprinted by each signature. RESULTS: 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 2700 synthetic cancer genomes with and without noise demonstrates that SigProfilerAssignment outperforms four commonly used approaches for assigning mutational signatures. AVAILABILITY AND IMPLEMENTATION: SigProfilerAssignment is available under the BSD 2-clause license at https://github.com/AlexandrovLab/SigProfilerAssignment with a web implementation at https://cancer.sanger.ac.uk/signatures/assignment/.

Arsenic is a potent co-mutagen of ultraviolet light.

Arsenic enhances the carcinogenicity of ultraviolet radiation (UVR). However, the mechanisms of arsenic-driven oncogenesis are not well understood. Here, we utilize experimental systems to investigate the carcinogenic and mutagenic properties of co-exposure to arsenic and UVR. In vitro and in vivo exposures indicate that, by itself, arsenic is not mutagenic. However, in combination with UVR, arsenic exposure has a synergistic effect leading to an accelerated mouse skin carcinogenesis and to more than 2-fold enrichment of UVR mutational burden. Notably, mutational signature ID13, previously found only in UVR-associated human skin cancers, is 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, to the best of our knowledge, the first co-exposure signature to be reported using controlled experimental conditions. Analysis of existing skin cancer genomics data reveals that only a subset of cancers harbor ID13 and these exhibit an elevated UVR mutagenesis. Our results report a unique mutational signature caused by a co-exposure to two environmental carcinogens and provide comprehensive evidence that arsenic is a potent co-mutagen and co-carcinogen of UVR.

APOBEC3B regulates R-loops and promotes transcription-associated mutagenesis in cancer.

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 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 genes overexpressed in tumors and splice factor mutant tumors preferentially, and APOBEC3-attributed kataegis are enriched in RTCW motifs 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 regulates R-loops and contributes to R-loop mutagenesis in cancer.

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 tumors 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 etiologies being similarly affected. Certain signatures exhibit periodic behaviors or cancer-type-specific enrichments/depletions near topographical features, revealing further information about the processes that imprinted them. Our findings, disseminated via the COSMIC (Catalog of Somatic Mutations in Cancer) signatures database, provide a comprehensive online resource for exploring the interactions between mutational signatures and topographical features across human cancer.