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Abstract 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

Abstract 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.

Abstract Background All 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 computationally efficient bioinformatics tool that allows visualizing and exploring these large-scale mutational events. Results Here, 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. Conclusions The 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/ .

Abstract With the continued promise of immunotherapy for treating cancer, understanding how host genetics contributes to the tumor immune microenvironment (TIME) is essential to tailoring cancer screening and treatment strategies. Here, we study 1084 eQTLs affecting the TIME found through analysis of The Cancer Genome Atlas and literature curation. These TIME eQTLs are enriched in areas of active transcription, and associate with gene expression in specific immune cell subsets, such as macrophages and dendritic cells. Polygenic score models built with TIME eQTLs reproducibly stratify cancer risk, survival and immune checkpoint blockade (ICB) response across independent cohorts. To assess whether an eQTL-informed approach could reveal potential cancer immunotherapy targets, we inhibit CTSS, a gene implicated by cancer risk and ICB response-associated polygenic models; CTSS inhibition results in slowed tumor growth and extended survival in vivo. These results validate the potential of integrating germline variation and TIME characteristics for uncovering potential targets for immunotherapy.

Nail polish dryers commonly emit ultraviolet A (UVA) light, but the effects of this irradiation on mammalian cells remain unclear. Here, the authors examine the effects of UVA irradiation by a nail polish dryer on the genomes of mammalian cell lines, finding high levels of reactive oxygen species and related mutational signatures.

Li-Fraumeni syndrome (LFS) is associated with pathogenic germline TP53 variants and predisposes patients to cancer; understanding the evolution and drivers of LFS-related tumours remains crucial. Here, the authors analyse 22 LFS tumours using whole-genome sequencing and reconstruct the evolution and timing of somatic driver alterations.

Summary: 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.

Abstract Background The mutational profile of cancer reflects the activity of the mutagenic processes which have been operative throughout the lineage of the cancer cell. These processes leave characteristic profiles of somatic mutations called mutational signatures. Mutational signatures, including single-base substitution (SBS) signatures, may reflect the effects of exogenous or endogenous exposures. Methods We used polygenic risk scores (PRS) to summarize common germline variation associated with cancer risk and other cancer-related traits and examined the association between somatic mutational profiles and germline PRS in 12 cancer types from The Cancer Genome Atlas. Somatic mutational profiles were constructed from whole-exome sequencing data of primary tumors. PRS were calculated for the 12 selected cancer types and 9 non-cancer traits, including cancer risk determinants, hormonal factors, and immune-mediated inflammatory diseases, using germline genetic data and published summary statistics from genome-wide association studies. Results We found 17 statistically significant associations between somatic mutational profiles and germline PRS after Bonferroni correction (p < 3.15 × 10−5), including positive associations between germline inflammatory bowel disease PRS and number of somatic mutations attributed to signature SBS1 in prostate cancer and APOBEC-related signatures in breast cancer. Positive associations were also found between age at menarche PRS and mutation counts of SBS1 in overall and estrogen receptor-positive breast cancer. Consistent with prior studies that found an inverse association between the pubertal development PRS and risk of prostate cancer, likely reflecting hormone-related mechanisms, we found an inverse association between age at menarche PRS and mutation counts of SBS1 in prostate cancer. Inverse associations were also found between several cancer PRS and tumor mutation counts. Conclusions Our analysis suggests that there are robust associations between tumor somatic mutational profiles and germline PRS. These may reflect the mechanisms through hormone regulation and immune responses that contribute to cancer etiology and drive cancer progression.

Serrated polyposis syndrome (SPS) is one of the most frequent polyposis syndromes characterized by an increased risk for developing colorectal cancer (CRC). Although SPS etiology has been mainly associated with environmental factors, germline predisposition to SPS could also be relevant for cases with familial aggregation or a family history of SPS/CRC. After whole-exome sequencing of 39 SPS patients from 16 families, we identified a heterozygous germline frameshift variant in the POLD1 gene (c.1941delG, p.(Lys648fs*46)) in a patient with SPS and CRC. Tumor presented an ultra-hypermutated phenotype and microsatellite instability. The POLD1 germline variant segregated in three additional SPS-affected family members. We attempted to create yeast and cellular models for this variant but were no viable. Alternatively, we generated patient-derived organoids (PDOs) from healthy rectal tissue of the index case, as well as from a control donor. Then, we challenged PDOs with a DNA-damaging agent to induce replication stress. No significant differences were observed in the DNA damage response between control and POLD1-Lys648fs PDOs, nor specific mutational signatures were observed. Our results do not support the pathogenicity of the analyzed POLD1 frameshift variant. One possible explanation is that haplosufficiency of the wild-type allele may be compensating for the absence of expression of the frameshift allele. Overall, future work is required to elucidate if functional consequences could be derived from POLD1 alterations different from missense variants in their proofreading domain. To our knowledge, our study presents the first organoid model for germline POLD1 variants and establishes the basis for its use as a model for disease in SPS, CRC and other malignancies.

Although Barrett’s metaplasia of the esophagus (BE) is the only known precursor lesion to esophageal adenocarcinomas (EACs), drivers of cellular transformation in BE remain incompletely understood. We use an artificial intelligence–guided network approach to study EAC initiation and progression. Key predictions are subsequently validated in a human organoid model, in patient-derived biopsy specimens of BE, a case-control study of genomics of BE progression, and in a cross-sectional study of 113 patients with BE and EACs. Our model classified healthy esophagus from BE and BE from EACs in several publicly available gene expression data sets (n = 932 samples). The model confirmed that all EACs must originate from BE and pinpointed a CXCL8/IL8↔neutrophil immune microenvironment as a driver of cellular transformation in EACs and gastroesophageal junction adenocarcinomas. This driver is prominent in White individuals but is notably absent in African Americans (AAs). Network-derived gene signatures, independent signatures of neutrophil processes, CXCL8/IL8 expression, and an absolute neutrophil count (ANC) are associated with risk of progression. SNPs associated with changes in ANC by ethnicity (e.g., benign ethnic neutropenia [BEN]) modify that risk. Findings define a racially influenced immunological basis for cell transformation and suggest that BEN in AAs may be a deterrent to BE→EAC progression.

Heterogeneity in leiomyosarcomas (LMS) makes treatment of the disease challenging. Here the authors analyze LMS heterogeneity and molecular LMS subtypes using genomics and transcriptomics, finding origins in distinct lineages and associations with survival, in addition to the early emergence of metastatic clones.

Abstract Background 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. Results 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 2144 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 ± 2 bp 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. Conclusions SigProfilerSimulator’s 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. SigProfilerSimulator is freely available at https://github.com/AlexandrovLab/SigProfilerSimulator with an extensive documentation at https://osf.io/usxjz/wiki/home/ .

Here the authors show that stronger immune selection and immune editing in females and younger patients lead to the accumulation of poorly presented driver mutations in tumors. These results may explain why young and female patients are characterized by lower response rates to immune checkpoint blockade therapies.

Many tumours are heterogenous, which calls into question whether multiple biopsies are required to accurately assess the cancer. Here, the authors show that papillary renal cell carcinoma is clonal in nature, suggesting that in this cancer one biopsy is sufficient for diagnosis and molecular analyses.

The initial mutational processes and how these lead to progression in multiple myeloma (MM) are unclear. Here, the authors identify mutational signatures that occur over time in a large cohort of MM patients and suggest features that may help in early diagnosis.

Mutational signatures induced by ionising radiation remain largely unexplored. Here in TP53 mutant mice, the authors characterise the genomic landscape of tumours induced by high- and low-energy radiation.

Tobacco use is one of the major risk factors for Head and neck squamous cell carcinoma (HNSCC). Here, the authors report an immune-competent syngeneic mouse model that mimics human tobacco-related HNSCC, and develops tumors in the tongue, and report a high response rate to anti-CTLA-4 therapy.

BACKGROUND The aberrant activation of the PI3K/mTOR signaling circuitry is one of the most frequently dysregulated signaling events in head and neck squamous cell carcinoma (HNSCC). Here, we conducted a single-arm, open-label phase IIa clinical trial in individuals with oral premalignant lesions (OPLs) to explore the potential of metformin to target PI3K/mTOR signaling for HNSCC prevention.METHODS Individuals with OPLs, but who were otherwise healthy and without diabetes, underwent pretreatment and posttreatment clinical exam and biopsy. Participants received metformin for 12 weeks (week 1, 500 mg; week 2, 1000 mg; weeks 3–12, 2000 mg daily). Pretreatment and posttreatment biopsies, saliva, and blood were obtained for biomarker analysis, including IHC assessment of mTOR signaling and exome sequencing.RESULTS Twenty-three participants were evaluable for response. The clinical response rate (defined as a ≥50% reduction in lesion size) was 17%. Although lower than the proposed threshold for favorable clinical response, the histological response rate (improvement in histological grade) was 60%, including 17% complete responses and 43% partial responses. Logistic regression analysis revealed that when compared with never smokers, current and former smokers had statistically significantly increased histological responses (P = 0.016). Remarkably, a significant correlation existed between decreased mTOR activity (pS6 IHC staining) in the basal epithelial layers of OPLs and the histological (P = 0.04) and clinical (P = 0.01) responses.CONCLUSION To our knowledge this is the first phase II trial of metformin in individuals with OPLs, providing evidence that metformin administration results in encouraging histological responses and mTOR pathway modulation, thus supporting its further investigation as a chemopreventive agent.TRIAL REGISTRATION NCT02581137FUNDING NIH contract HHSN261201200031I, grants R01DE026644 and R01DE026870

Abstract Background Cancer 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. Results Here, 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. Conclusions SigProfilerMatrixGenerator 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/.

In cancer, global DNA methylation loss and CpG island hypermethylation are commonly observed. Here, in breast cancer the authors find that hyper-variability of partially methylated domains is the prime source of DNA methylation variation and that these domains fuel CpG island hypermethylation.

It is known cutaneous squamous cell carcinoma (cSCC) involves a high tumour mutation burden. Here the authors identify common cSCC mutated genes, copy number changes, altered pathways and report the presence of a novel mutation signature associated with chronic exposure to the immunosuppressive drug azathioprine.

The molecular nature of large-cell neuroendocrine lung carcinomas (LCNEC) has remained unclear. Here, the authors show LCNECs represent a distinct transcriptional subgroup among lung cancers and comprise two molecular subgroups, type I (TP53 and STK11/KEAP1 alterations) and type II (TP53 and RB1 inactivation).

Chordoma is a rare often incurable malignant bone tumour. Here, the authors investigate driver mutations of sporadic chordoma in 104 cases, revealing duplications in notochordal transcription factor brachyury (T), PI3K signalling mutations, and mutations in LYST, a potential novel cancer gene in chordoma.

Abstract Both a DNA lesion and an intermediate for antibody maturation, uracil is primarily processed by base excision repair (BER), either initiated by uracil-DNA glycosylase (UNG) or by single-strand selective monofunctional uracil DNA glycosylase (SMUG1). The relative in vivo contributions of each glycosylase remain elusive. To assess the impact of SMUG1 deficiency, we measured uracil and 5-hydroxymethyluracil, another SMUG1 substrate, in Smug1 −/− mice. We found that 5-hydroxymethyluracil accumulated in Smug1 −/− tissues and correlated with 5-hydroxymethylcytosine levels. The highest increase was found in brain, which contained about 26-fold higher genomic 5-hydroxymethyluracil levels than the wild type. Smug1 −/− mice did not accumulate uracil in their genome and Ung −/− mice showed slightly elevated uracil levels. Contrastingly, Ung −/− Smug1 −/− mice showed a synergistic increase in uracil levels with up to 25-fold higher uracil levels than wild type. Whole genome sequencing of UNG/SMUG1-deficient tumours revealed that combined UNG and SMUG1 deficiency leads to the accumulation of mutations, primarily C to T transitions within CpG sequences. This unexpected sequence bias suggests that CpG dinucleotides are intrinsically more mutation prone. In conclusion, we showed that SMUG1 efficiently prevent genomic uracil accumulation, even in the presence of UNG, and identified mutational signatures associated with combined UNG and SMUG1 deficiency.

Osteosarcoma is a primary malignancy of bone that affects children and adults. Here, the authors sequence childhood and adult osteosarcomas, identifying mutations in insulin-like growth factor signalling genes and distinct genomic rearrangement profiles characterized by chromothripsis-amplification.

8-Oxoguanine, a common mutagenic DNA lesion, generates G:C>T:A transversions via mispairing with adenine during DNA replication. When operating normally, the MUTYH DNA glycosylase prevents 8-oxoguanine-related mutagenesis by excising the incorporated adenine. Biallelic MUTYH mutations impair this enzymatic function and are associated with colorectal cancer (CRC) in MUTYH-Associated Polyposis (MAP) syndrome. Here, we perform whole-exome sequencing that reveals a modest mutator phenotype in MAP CRCs compared to sporadic CRC stem cell lines or bulk tumours. The excess G:C>T:A transversion mutations in MAP CRCs exhibits a novel mutational signature, termed Signature 36, with a strong sequence dependence. The MUTYH mutational signature reflecting persistent 8-oxoG:A mismatches occurs frequently in the APC, KRAS, PIK3CA, FAT4, TP53, FAT1, AMER1, KDM6A, SMAD4 and SMAD2 genes that are associated with CRC. The occurrence of Signature 36 in other types of human cancer indicates that DNA 8-oxoguanine-related mutations might contribute to the development of cancer in other organs.

Ionizing radiation may induce irreparable DNA damage leading to cancer. Here, the authors identify a specific signature of mutations arising in patients exposed to ionizing radiation and suggest that radiation-induced tumorigenesis is associated with higher rates of genome-wide deletions and balanced inversions.

Deleterious germline variants in the MC1Rgene are associated with red hair and freckles, and with an increased risk of developing melanoma. Here, the authors investigate melanoma samples from patients with and without these variants and find that their presence is predictive of a higher overall mutation prevalence.

Mutational signatures provide evidence of the mechanism of action of a given mutagen and are found in cancer cells. Here, using 560 breast cancer genomes, the authors demonstrate that mutational signatures are frequently associated with genomic architecture including nucleosome positioning and replication timing.

Chronic myelomonocytic leukaemia is treated with agents that modify DNA methylation but whether they have direct cytotoxic effects is unclear. Here, the authors show that cells from treated patients show marked methylation changes without altered somatic mutation burden, suggesting that cytotoxicity is not a major factor in therapeutic efficacy.

The genome of a cancer cell carries somatic mutations that are the cumulative consequences of the DNA damage and repair processes operative during the cellular lineage between the fertilized egg and the cancer cell. Remarkably, these mutational processes are poorly characterized. Global sequencing initiatives are yielding catalogs of somatic mutations from thousands of cancers, thus providing the unique opportunity to decipher the signatures of mutational processes operative in human cancer. However, until now there have been no theoretical models describing the signatures of mutational processes operative in cancer genomes and no systematic computational approaches are available to decipher these mutational signatures. Here, by modeling mutational processes as a blind source separation problem, we introduce a computational framework that effectively addresses these questions. Our approach provides a basis for characterizing mutational signatures from cancer-derived somatic mutational catalogs, paving the way to insights into the pathogenetic mechanism underlying all cancers.