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Lung carcinoids (L‐CDs) are rare, poorly characterised neuroendocrine tumours (NETs). L‐CDs are more common in women and are not the consequence of cigarette smoking. They are classified histologically as typical carcinoids (TCs) or atypical carcinoids (ACs). ACs confer a worse survival. Histological classification is imperfect, and there is increasing interest in molecular markers. We therefore investigated global transcriptomic and epigenomic profiles of 15 L‐CDs resected with curative intent at Royal Brompton Hospital. We identified underlying mutations and structural abnormalities through whole‐exome sequencing (WES) and single nucleotide polymorphism (SNP) genotyping. Transcriptomic clustering algorithms identified two distinct L‐CD subtypes. These showed similarities either to pancreatic or neuroendocrine tumours at other sites and so were named respectively L‐CD‐PanC and L‐CD‐NeU. L‐CD‐PanC tumours featured upregulation of pancreatic and metabolic pathway genes matched by promoter hypomethylation of genes for beta cells and insulin secretion (p < 1 × 10−6). These tumours were centrally located and showed mutational signatures of activation‐induced deaminase/apolipoprotein B editing complex activity, together with genome‐wide DNA methylation loss enriched in repetitive elements (p = 2.2 × 10−16). By contrast, the L‐CD‐NeU group exhibited upregulation of neuronal markers (adjusted p < 0.01) and was characterised by focal spindle cell morphology (p = 0.04), peripheral location (p = 0.01), high mutational load (p = 2.17 × 10−4), recurrent copy number alterations, and enrichment for ACs. Mutations affected chromatin remodelling and SWI/SNF complex pathways. L‐CD‐NeU tumours carried a mutational signature attributable to aflatoxin and aristolochic acid (p = 0.05), suggesting a possible environmental exposure in their pathogenesis. Immunologically, myeloid and T‐cell markers were enriched in L‐CD‐PanC and B‐cell markers in L‐CD‐NeU tumours. The substantial epigenetic and non‐coding differences between L‐CD‐PanC and L‐CD‐NeU open new possibilities for biomarker selection and targeted treatment of L‐CD. © 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland. [ABSTRACT FROM AUTHOR]

Background: Error-corrected next-generation sequencing (ecNGS) technologies have enabled the direct evaluation of genome-wide mutations after exposure to mutagens. Previously, we reported an ecNGS methodology, Hawk-Seq™, and demonstrated its utility in evaluating mutagenicity. The evaluation of technical transferability is essential to further evaluate the reliability of ecNGS-based assays. However, cutting-edge sequencing platforms are continually evolving, which can affect the sensitivity of ecNGS. Therefore, the effect of differences in sequencing instruments on mutation data quality should be evaluated. Results: We assessed the performance of four sequencing platforms (HiSeq2500, NovaSeq6000, NextSeq2000, and DNBSEQ-G400) with the Hawk-Seq™ protocol for mutagenicity evaluation using DNA samples from mouse bone marrow exposed to benzo[a]pyrene (BP). The overall mutation (OM) frequencies per 106 bp in vehicle-treated samples were 0.22, 0.36, 0.46, and 0.26 for HiSeq2500, NovaSeq6000, NextSeq2000, and DNBSEQ-G400, respectively. The OM frequency of NextSeq2000 was significantly higher than that of HiSeq2500, suggesting the difference to be based on the platform. The relatively higher value in NextSeq2000 was a consequence of the G:C to C:G mutations in NextSeq2000 data (0.67 per 106 G:C bp), which was higher than the mean of the four platforms by a ca. of 0.25 per 106 G:C bp. A clear dose-dependent increase in G:C to T:A mutation frequencies was observed in all four sequencing platforms after BP exposure. The cosine similarity values of the 96-dimensional trinucleotide mutation patterns between HiSeq and the three other platforms were 0.93, 0.95, and 0.92 for NovaSeq, NextSeq, and DNBSeq, respectively. These results suggest that all platforms can provide equivalent data that reflect the characteristics of the mutagens. Conclusions: All platforms sensitively detected mutagen-induced mutations using the Hawk-Seq™ analysis. The substitution types and frequencies of the background errors differed depending on the platform. The effects of sequencing platforms on mutagenicity evaluation should be assessed before experimentation. [ABSTRACT FROM AUTHOR]

The genome of a cell is continuously battered by a plethora of exogenous and endogenous processes that can lead to damaged DNA. Repair mechanisms correct this damage most of the time, but failure to do so leaves mutations. Mutations do not occur in random manner, but rather typically follow a more or less specific pattern due to known or imputed mutational processes. Mutational signature analysis is the process by which the predominant mutational process can be inferred for a cancer and can be used in several contexts to study both the genesis of cancer and its response to therapy. Recent pan-cancer genomic efforts such as "The Cancer Genome Atlas" have identified numerous mutational signatures that can be categorized into single base substitutions, doublet base substitutions, or small insertions/deletions. Understanding these mutational signatures as they occur in non-small lung cancer could improve efforts at prevention, predict treatment response to personalized treatments, and guide the development of therapies targeting tumor evolution. For non-small cell lung cancer, several mutational signatures have been identified that correlate with exposures such as tobacco smoking and radon and can also reflect endogenous processes such as aging, APOBEC activity, and loss of mismatch repair. Herein, we provide an overview of the current knowledge of mutational signatures in non-small lung cancer. [ABSTRACT FROM AUTHOR]

Time always leaves its mark, and our genome is no exception. Mutations in the genome of somatic cells were first hypothesized to be the cause of aging in the 1950s, shortly after the molecular structure of DNA had been described. Somatic mutation theories of aging are based on the fact that mutations in DNA as the ultimate template for all cellular functions are irreversible. However, it took until the 1990s to develop the methods to test if DNA mutations accumulate with age in different organs and tissues and estimate the severity of the problem. By now, numerous studies have documented the accumulation of somatic mutations with age in normal cells and tissues of mice, humans, and other animals, showing clock-like mutational signatures that provide information on the underlying causes of the mutations. In this review, we will first briefly discuss the recent advances in next-generation sequencing that now allow quantitative analysis of somatic mutations. Second, we will provide evidence that the mutation rate differs between cell types, with a focus on differences between germline and somatic mutation rate. Third, we will discuss somatic mutational signatures as measures of aging, environmental exposure, and activities of DNA repair processes. Fourth, we will explain the concept of clonally amplified somatic mutations, with a focus on clonal hematopoiesis. Fifth, we will briefly discuss somatic mutations in the transcriptome and in our other genome, i.e., the genome of mitochondria. We will end with a brief discussion of a possible causal contribution of somatic mutations to the aging process. [ABSTRACT FROM AUTHOR]

Neuroblastoma is the most prevalent solid tumor in early childhood, with a 5-year overall survival rate of 40–60% in high-risk cases. Therefore, the identification of novel biomarkers for the diagnosis, prognosis, and therapy of neuroblastoma is crucial for improving the clinical outcomes of these patients. In this study, we conducted the whole-exome sequencing of 48 freshly frozen tumor samples obtained from the Biobank. Somatic variants were identified and selected using a bioinformatics analysis pipeline. The mutational signatures were determined using the Mutalisk online tool. Cancer driver genes and druggable mutations were predicted using the Cancer Genome Interpreter. The most common mutational signature was single base substitution 5. MUC4, MUC16, and FLG were identified as the most frequently mutated genes. Using the Cancer Genome Interpreter, we identified five recurrent cancer driver mutations spanning MUC16, MUC4, ALK, and CTNND1, with the latter being novel and containing a missense mutation, R439C. We also identified 11 putative actionable mutations including NF1 Q1798*, Q2616*, and S636X, ALK F1174L and R1275Q, SETD2 P10L and Q1829E, BRCA1 R612S, NOTCH1 D1670V, ATR S1372L, and FGFR1 N577K. Our findings provide a comprehensive overview of the novel information relevant to the underlying molecular pathogenesis and therapeutic targets of neuroblastoma. [ABSTRACT FROM AUTHOR]

Simple Summary: This review provides a comprehensive overview of the current knowledge on mutational signatures in colorectal cancer (CRC), highlighting their potential clinical applications. It discusses the challenges and limitations in translating these analyses into clinical practice and proposes strategies to overcome these obstacles. Additionally, it provides insights into future directions and emerging proof-of-concept studies that highlight the translational potential of mutational signature analysis in improving patient care and outcomes in CRC. A multitude of exogenous and endogenous processes have the potential to result in DNA damage. While the repair mechanisms are typically capable of correcting this damage, errors in the repair process can result in mutations. The findings of research conducted in 2012 indicate that mutations do not occur randomly but rather follow specific patterns that can be attributed to known or inferred mutational processes. The process of mutational signature analysis allows for the inference of the predominant mutational process for a given cancer sample, with significant potential for clinical applications. A deeper comprehension of these mutational signatures in CRC could facilitate enhanced prevention strategies, facilitate the comprehension of genotoxic drug activity, predict responses to personalized treatments, and, in the future, inform the development of targeted therapies in the context of precision oncology. The efforts of numerous researchers have led to the identification of several mutational signatures, which can be categorized into different mutational signature references. In CRC, distinct mutational signatures are identified as correlating with mismatch repair deficiency, polymerase mutations, and chemotherapy treatment. In this context, a mutational signature analysis offers considerable potential for enhancing minimal residual disease (MRD) tests in stage II (high-risk) and stage III CRC post-surgery, stratifying CRC based on the impacts of genetic and epigenetic alterations for precision oncology, identifying potential therapeutic vulnerabilities, and evaluating drug efficacy and guiding therapy, as illustrated in a proof-of-concept clinical trial. [ABSTRACT FROM AUTHOR]

Abstract Background Error-corrected next-generation sequencing (ecNGS) technologies have enabled the direct evaluation of genome-wide mutations after exposure to mutagens. Previously, we reported an ecNGS methodology, Hawk-Seq™, and demonstrated its utility in evaluating mutagenicity. The evaluation of technical transferability is essential to further evaluate the reliability of ecNGS-based assays. However, cutting-edge sequencing platforms are continually evolving, which can affect the sensitivity of ecNGS. Therefore, the effect of differences in sequencing instruments on mutation data quality should be evaluated. Results We assessed the performance of four sequencing platforms (HiSeq2500, NovaSeq6000, NextSeq2000, and DNBSEQ-G400) with the Hawk-Seq™ protocol for mutagenicity evaluation using DNA samples from mouse bone marrow exposed to benzo[a]pyrene (BP). The overall mutation (OM) frequencies per 106 bp in vehicle-treated samples were 0.22, 0.36, 0.46, and 0.26 for HiSeq2500, NovaSeq6000, NextSeq2000, and DNBSEQ-G400, respectively. The OM frequency of NextSeq2000 was significantly higher than that of HiSeq2500, suggesting the difference to be based on the platform. The relatively higher value in NextSeq2000 was a consequence of the G:C to C:G mutations in NextSeq2000 data (0.67 per 106 G:C bp), which was higher than the mean of the four platforms by a ca. of 0.25 per 106 G:C bp. A clear dose-dependent increase in G:C to T:A mutation frequencies was observed in all four sequencing platforms after BP exposure. The cosine similarity values of the 96-dimensional trinucleotide mutation patterns between HiSeq and the three other platforms were 0.93, 0.95, and 0.92 for NovaSeq, NextSeq, and DNBSeq, respectively. These results suggest that all platforms can provide equivalent data that reflect the characteristics of the mutagens. Conclusions All platforms sensitively detected mutagen-induced mutations using the Hawk-Seq™ analysis. The substitution types and frequencies of the background errors differed depending on the platform. The effects of sequencing platforms on mutagenicity evaluation should be assessed before experimentation.

Abstract Background Gastric cancer (GC), a molecularly heterogeneous disease, is the third leading cause of cancer death worldwide. The majority of GC cases worldwide occur in East Asia, predominantly China. Mutational Signature Framework offers an elegant approach to identify mutational processes present in tumors. Methods To identify mutational signature patterns, we conducted whole exome sequencing (WES) analysis in Chinese patients with GC. Mutect2 and MutsigCV were used to identify significantly mutated genes in 175 Chinese GC cases using paired tumor-normal tissues. We investigated mutational signatures using Catalogue of Somatic Mutations in Cancer (COSMIC) Version 2 (V2) and Version 3 (V3). Results We identified 104 mutated genes with P

The field of cancer genomics and transcriptomics has evolved from targeted profiling to swift sequencing of individual tumor genome and transcriptome. The steady growth in genome, epigenome, and transcriptome datasets on a genome-wide scale has significantly increased our capability in capturing signatures that represent both the intrinsic and extrinsic biological features of tumors. These biological differences can help in precise molecular subtyping of cancer, predicting tumor progression, metastatic potential, and resistance to therapeutic agents. In this review, we summarized the current development of genomic, methylomic, transcriptomic, proteomic and metabolic signatures in the field of cancer research and highlighted their potentials in clinical applications to improve diagnosis, prognosis, and treatment decision in cancer patients.

Accurate detection and analysis of somatic variants in cancer involve multiple third-party tools with complex dependencies and configurations, leading to laborious, error-prone, and time-consuming data conversions. This approach lacks accuracy, reproducibility, and portability, limiting clinical application. Musta was developed to address these issues as an end-to-end pipeline for detecting, classifying, and interpreting cancer mutations. Musta is based on a Python command-line tool designed to manage tumor-normal samples for precise somatic mutation analysis. The core is a Snakemake-based workflow that covers all key cancer genomics steps, including variant calling, mutational signature deconvolution, variant annotation, driver gene detection, pathway analysis, and tumor heterogeneity estimation. Musta is easy to install on any system via Docker, with a Makefile handling installation, configuration, and execution, allowing for full or partial pipeline runs. Musta has been validated at the CRS4-NGS Core facility and tested on large datasets from The Cancer Genome Atlas and the Beijing Institute of Genomics. Musta has proven robust and flexible for somatic variant analysis in cancer. It is user-friendly, requiring no specialized programming skills, and enables data processing with a single command line. Its reproducibility ensures consistent results across users following the same protocol. [ABSTRACT FROM AUTHOR]

Mutational patterns caused by APOBEC3 cytidine deaminase activity are evident throughout human cancer genomes. In particular, the APOBEC3A family member is a potent genotoxin that causes substantial DNA damage in experimental systems and human tumors. However, the mechanisms that ensure genome stability in cells with active APOBEC3A are unknown. Through an unbiased genome-wide screen, we define the Structural Maintenance of Chromosomes 5/6 (SMC5/6) complex as essential for cell viability when APOBEC3A is active. We observe an absence of APOBEC3A mutagenesis in human tumors with SMC5/6 dysfunction, consistent with synthetic lethality. Cancer cells depleted of SMC5/6 incur substantial genome damage from APOBEC3A activity during DNA replication. Further, APOBEC3A activity results in replication tract lengthening which is dependent on PrimPol, consistent with re-initiation of DNA synthesis downstream of APOBEC3A-induced lesions. Loss of SMC5/6 abrogates elongated replication tracts and increases DNA breaks upon APOBEC3A activity. Our findings indicate that replication fork lengthening reflects a DNA damage response to APOBEC3A activity that promotes genome stability in an SMC5/6-dependent manner. Therefore, SMC5/6 presents a potential therapeutic vulnerability in tumors with active APOBEC3A. Synopsis: APOBEC3A is a prominent source of mutagenesis across many cancer types. This study defines a novel role of the SMC5/6 complex in maintaining genomic stability and viability of cancer cells in which APOBEC3A is active, revealing a potential therapeutic vulnerability. In the absence of functional SMC5/6, APOBEC3A activity results in genotoxic cell death SMC5/6 dysfunction augments APOBEC3A-mediated DNA damage during replication PrimPol generates single-strand DNA gaps upon APOBEC3A activity at replication forks SMC5/6 may play a critical role in maintaining genome stability during APOBEC3A activity by stabilization of replication forks containing single-strand DNA gaps Maintaining genome stability in the presence of active APOBEC3A involves SMC5/6-dependent stabilization of forks with single-strand gaps, suggesting a potential vulnerability of cancer cells. [ABSTRACT FROM AUTHOR]

Tumor mutational signatures have gained prominence in cancer research, yet the lack of standardized methods hinders reproducibility and robustness. Leveraging colorectal cancer (CRC) as a model, we explored the influence of computational parameters on mutational signature analyses across 230 CRC cell lines and 152 CRC patients. Results were validated in three independent datasets: 483 endometrial cancer patients stratified by mismatch repair (MMR) status, 35 lung cancer patients by smoking status and 12 patient-derived organoids (PDOs) annotated for colibactin exposure. Assessing various bioinformatic tools, reference datasets and input data sizes including whole genome sequencing, whole exome sequencing and a pan-cancer gene panel, we demonstrated significant variability in the results. We report that the use of distinct algorithms and references led to statistically different results, highlighting how arbitrary choices may induce variability in the mutational signature contributions. Furthermore, we found a differential contribution of mutational signatures between coding and intergenic regions and defined the minimum number of somatic variants required for reliable mutational signature assignment. To facilitate the identification of the most suitable workflows, we developed Comparative Mutational Signature analysis on Coding and Extragenic Regions (CoMSCER), a bioinformatic tool which allows researchers to easily perform comparative mutational signature analysis by coupling the results from several tools and public reference datasets and to assess mutational signature contributions in coding and non-coding genomic regions. In conclusion, our study provides a comparative framework to elucidate the impact of distinct computational workflows on mutational signatures. [ABSTRACT FROM AUTHOR]

BackgroundCurrently developed molecular markers can predict the effectiveness of cancer immunotherapy and screen beneficiaries to some extent, but they are not stable enough. Therefore, there is an urgent need for discovering novel biomarkers. At the same time, sex factor plays a vital role in the response to immunotherapy, so it is particularly important to identify sex-related molecular indicators.MethodsWe integrated a pan-cancer cohort consisting of 2348 cancer patients who received immune checkpoint inhibitors and targeted sequencing. Using somatic mutation profiles, we identified mutational signatures, molecular subtypes, and frequently mutated genes, and analyzed their relationships with immunotherapeutic outcomes. We also explored sex disparities of determined biomarkers in response to treatments.ResultsWe found that male patients exhibited better immunotherapy outcomes and higher tumor mutational burden. A total of seven mutational signatures were identified, among which signatures 1 and 3 were associated with worse immunotherapy outcomes, while signatures 2 and 6 correlated with better outcomes. Gender-based analysis revealed that mutational signature 1 continued to show a worse immunotherapy outcome in female patients, whereas signature 6 demonstrated a better outcome in male patients. Based on mutational activities, we identified four potential molecular subtypes with gender differences and relevance to treatment outcomes. PI3K-AKT, RAS signaling pathways, and 68 significantly mutated genes were identified to be associated with immunotherapy outcomes, with nine genes (i.e., ATM, ATRX, DOT1L, EP300, EPHB1, NOTCH1, PBRM1, RBM10, and SETD2) exhibiting gender differences. Finally, we discovered co-mutated gene pairs and TP53 p.R282W mutations related to treatment outcomes, highlighting their gender-specific differences.ConclusionThis study identified several molecular biomarkers related to cancer immunotherapy outcomes in terms of mutational signatures, molecular subtypes, and mutated genes, and explored their gender-relatedness in order to provide clues and basis for clinical treatment efficacy evaluation and patient selection.

Abstract Although colorectal cancer (CRC) remains the second leading cause of cancer-related death in the United States, the overall incidence and mortality from the disease have declined in recent decades. In contrast, there has been a steady increase in the incidence of CRC in individuals under 50 years of age. Hereditary syndromes contribute disproportionately to early onset CRC (EOCRC). These include microsatellite instability high (MSI+) tumors arising in patients with Lynch Syndrome. However, most EOCRCs are not associated with familial syndromes or MSI+ genotypes. Comprehensive genomic profiling has provided the basis of improved more personalized treatments for older CRC patients. However, less is known about the basis of sporadic EOCRC. To define the genomic landscape of EOCRC we used DNA content flow sorting to isolate diploid and aneuploid tumor fractions from 21 non-hereditary cases. We then generated whole exome mutational profiles for each case and whole genome copy number, telomere length, and EGFR immunohistochemistry (IHC) analyses on subsets of samples. These results discriminate the molecular features of diploid and aneuploid EOCRC and provide a basis for larger population-based studies and the development of effective strategies to monitor and treat this emerging disease.

Abstract Background Nonalcoholic fatty liver disease (NAFLD) is a leading public health problem worldwide. Approximately one fourth of patients with nonalcoholic fatty liver (NAFL) progress to nonalcoholic steatohepatitis (NASH), an advanced stage of NAFLD. Hence, there is an urgent need to make a better understanding of NAFLD heterogeneity and facilitate personalized management of high-risk NAFLD patients who may benefit from more intensive surveillance and preventive intervene. Methods In this study, a series of bioinformatic methods were performed to identify NAFLD progression-specific pathways and genes, and three machine learning approaches were combined to construct a risk-stratification gene signature to quantify risk assessment. In addition, bulk RNA-seq, single-cell RNA-seq (scRNA-seq) transcriptome profiling data and whole-exome sequencing (WES) data were comprehensively analyzed to reveal the genomic alterations and altered pathways between distinct molecular subtypes. Results Two distinct subtypes of NAFL were identified with the NAFLD progression-specific genes, and one subtype has a high similarity of the inflammatory pattern and fibrotic potential with NASH. The established risk-stratification gene signature could discriminate advanced samples from overall NAFLD. COL1A2, one key gene closely related to NAFLD progression, is specifically expressed in fibroblasts involved in hepatocellular carcinoma (HCC), and significantly correlated with EMT and angiogenesis in pan-cancer. Moreover, the β-catenin/COL1A2 axis might play a critical role in fibrosis severity and inflammatory response during NAFLD-HCC progression. Conclusion In summary, our study provided evidence for the necessity of molecular classification and established a risk-stratification gene signature to quantify risk assessment of NAFLD, aiming to identify different risk subsets and to guide personalized treatment.

Simple Summary: Genomic instability is an important feature of cancer, including multiple myeloma, which is the second most common hematological malignancy. There are several sources of genomic instability in multiple myeloma, including mutations in DNA repair genes and genotoxic therapy. Non-canonical secondary DNA structures (such as four-stranded G-quadruplex structures) may contribute to this process by interfering with DNA replication and repair and leading to the accumulation of mutations at specific sites in the genome. Here, we address the question of whether G-quadruplex structures have any impact on the accumulation of mutations in multiple myeloma cells. We discuss the possible consequences of defects in G-quadruplex unwinding for the specificity of somatic mutations in MM. Understanding the role of G-quadruplex structures in the disease may lead to the development of new diagnostic and therapeutic strategies for multiple myeloma and other cancers. Multiple myeloma (MM) is the second most common hematological malignancy, which remains incurable despite recent advances in treatment strategies. Like other forms of cancer, MM is characterized by genomic instability, caused by defects in DNA repair. Along with mutations in DNA repair genes and genotoxic drugs used to treat MM, non-canonical secondary DNA structures (four-stranded G-quadruplex structures) can affect accumulation of somatic mutations and chromosomal abnormalities in the tumor cells of MM patients. Here, we tested the hypothesis that G-quadruplex structures may influence the distribution of somatic mutations in the tumor cells of MM patients. We sequenced exomes of normal and tumor cells of 11 MM patients and analyzed the data for the presence of G4 context around points of somatic mutations. To identify molecular mechanisms that could affect mutational profile of tumors, we also analyzed mutational signatures in tumor cells as well as germline mutations for the presence of specific SNPs in DNA repair genes or in genes regulating G-quadruplex unwinding. In several patients, we found that sites of somatic mutations are frequently located in regions with G4 context. This pattern correlated with specific germline variants found in these patients. We discuss the possible implications of these variants for mutation accumulation and specificity in MM and propose that the extent of G4 context enrichment around somatic mutation sites may be a novel metric characterizing mutational processes in tumors. [ABSTRACT FROM AUTHOR]

Early-onset breast cancer (EoBC), defined by a diagnosis <40 years of age, is associated with poor prognosis. This study investigated the mutational landscape of non-metastatic EoBC and the prognostic relevance of mutational signatures using 100 tumour samples from Alberta, Canada. The MutationalPatterns package in R/Bioconductor was used to extract de novo single-base substitution (SBS) and insertion–deletion (indel) mutational signatures and to fit COSMIC SBS and indel signatures. We assessed associations between these signatures and clinical characteristics of disease, in addition to recurrence-free (RFS) and overall survival (OS). Five SBS and two indel signatures were extracted. The SBS13-like signature had higher relative contributions in the HER2-enriched subtype. Patients with higher than median contribution tended to have better RFS after adjustment for other prognostic factors (HR = 0.29; 95% CI: 0.08–1.06). An unsupervised clustering algorithm based on absolute contribution revealed three clusters of fitted COSMIC SBS signatures, but cluster membership was not associated with clinical variables or survival outcomes. The results of this exploratory study reveal various SBS and indel signatures may be associated with clinical features of disease and prognosis. Future studies with larger samples are required to better understand the mechanistic underpinnings of disease progression and treatment response in EoBC. [ABSTRACT FROM AUTHOR]

Although colorectal cancer (CRC) remains the second leading cause of cancer-related death in the United States, the overall incidence and mortality from the disease have declined in recent decades. In contrast, there has been a steady increase in the incidence of CRC in individuals under 50 years of age. Hereditary syndromes contribute disproportionately to early onset CRC (EOCRC). These include microsatellite instability high (MSI+) tumors arising in patients with Lynch Syndrome. However, most EOCRCs are not associated with familial syndromes or MSI+ genotypes. Comprehensive genomic profiling has provided the basis of improved more personalized treatments for older CRC patients. However, less is known about the basis of sporadic EOCRC. To define the genomic landscape of EOCRC we used DNA content flow sorting to isolate diploid and aneuploid tumor fractions from 21 non-hereditary cases. We then generated whole exome mutational profiles for each case and whole genome copy number, telomere length, and EGFR immunohistochemistry (IHC) analyses on subsets of samples. These results discriminate the molecular features of diploid and aneuploid EOCRC and provide a basis for larger population-based studies and the development of effective strategies to monitor and treat this emerging disease. [ABSTRACT FROM AUTHOR]

Lynch syndrome (LS) predisposes to cancer in adulthood and is caused by heterozygous germline variants in a mismatch repair (MMR) gene. Recent studies show an increased prevalence of LS among children with cancer, suggesting a causal relationship. For LS‐spectrum (LSS) cancers, including high‐grade gliomas and colorectal cancer, causality has been supported by typical MMR‐related tumor characteristics, but for non‐LSS cancers, causality is unclear. We characterized 20 malignant tumors of 18 children with LS, including 16 non‐LSS tumors. We investigated second hits, tumor mutational load, mutational signatures and MMR protein expression. In all LSS tumors and three non‐LSS tumors, we detected MMR deficiency caused by second hit somatic alterations. Furthermore, these MMR‐deficient tumors carried driver variants that likely originated as a consequence of MMR deficiency. However, in 13 non‐LSS tumors (81%), a second hit and MMR deficiency were absent, thus a causal link between LS and cancer development in these children is lacking. These findings demonstrate that causality of LS in children with cancer, which can be determined by molecular tumor characterization, seems to be restricted to specific tumor types. Large molecular and epidemiological studies are needed to further refine the tumor spectrum in children with LS. [ABSTRACT FROM AUTHOR]

While mutational processes operating in the Escherichia coli genome have been revealed by multiple laboratory experiments, the contribution of these processes to accumulation of bacterial polymorphism and evolution in natural environments is unknown. To address this question, we reconstruct signatures of distinct mutational processes from experimental data on E. coli hypermutators, and ask how these processes contribute to differences between naturally occurring E. coli strains. We show that both mutations accumulated in the course of evolution of wild-type strains in nature and in the lab-grown nonmutator laboratory strains are explained predominantly by the low fidelity of DNA polymerases II and III. By contrast, contributions specific to disruption of DNA repair systems cannot be detected, suggesting that temporary accelerations of mutagenesis associated with such disruptions are unimportant for within-species evolution. These observations demonstrate that accumulation of diversity in bacterial strains in nature is predominantly associated with errors of DNA polymerases. [ABSTRACT FROM AUTHOR]

Background: Nonalcoholic fatty liver disease (NAFLD) is a leading public health problem worldwide. Approximately one fourth of patients with nonalcoholic fatty liver (NAFL) progress to nonalcoholic steatohepatitis (NASH), an advanced stage of NAFLD. Hence, there is an urgent need to make a better understanding of NAFLD heterogeneity and facilitate personalized management of high-risk NAFLD patients who may benefit from more intensive surveillance and preventive intervene. Methods: In this study, a series of bioinformatic methods were performed to identify NAFLD progression-specific pathways and genes, and three machine learning approaches were combined to construct a risk-stratification gene signature to quantify risk assessment. In addition, bulk RNA-seq, single-cell RNA-seq (scRNA-seq) transcriptome profiling data and whole-exome sequencing (WES) data were comprehensively analyzed to reveal the genomic alterations and altered pathways between distinct molecular subtypes. Results: Two distinct subtypes of NAFL were identified with the NAFLD progression-specific genes, and one subtype has a high similarity of the inflammatory pattern and fibrotic potential with NASH. The established risk-stratification gene signature could discriminate advanced samples from overall NAFLD. COL1A2, one key gene closely related to NAFLD progression, is specifically expressed in fibroblasts involved in hepatocellular carcinoma (HCC), and significantly correlated with EMT and angiogenesis in pan-cancer. Moreover, the β-catenin/COL1A2 axis might play a critical role in fibrosis severity and inflammatory response during NAFLD-HCC progression. Conclusion: In summary, our study provided evidence for the necessity of molecular classification and established a risk-stratification gene signature to quantify risk assessment of NAFLD, aiming to identify different risk subsets and to guide personalized treatment. [ABSTRACT FROM AUTHOR]

Mutational signature analysis is commonly performed in cancer genomic studies. Here, we present SigProfilerExtractor, an automated tool for de novo extraction of mutational signatures, and benchmark it against another 13 bioinformatics tools by using 34 scenarios encompassing 2,500 simulated signatures found in 60,000 synthetic genomes and 20,000 synthetic exomes. For simulations with 5% noise, reflecting high-quality datasets, SigProfilerExtractor outperforms other approaches by elucidating between 20% and 50% more true-positive signatures while yielding 5-fold less false-positive signatures. Applying SigProfilerExtractor to 4,643 whole-genome- and 19,184 whole-exome-sequenced cancers reveals four novel signatures. Two of the signatures are confirmed in independent cohorts, and one of these signatures is associated with tobacco smoking. In summary, this report provides a reference tool for analysis of mutational signatures, a comprehensive benchmarking of bioinformatics tools for extracting signatures, and several novel mutational signatures, including one putatively attributed to direct tobacco smoking mutagenesis in bladder tissues.

The sparsity of mutations observed across tumours hinders our ability to study mutation rate variability at nucleotide resolution. To circumvent this, here we investigated the propensity of mutational processes to form mutational hotspots as a readout of their mutation rate variability at single base resolution. Mutational signatures 1 and 17 have the highest hotspot propensity (5--78 times higher than other processes). After accounting for trinucleotide mutational probabilities, sequence composition and mutational heterogeneity at 10 Kbp, most (94--95%) signature 17 hotspots remain unexplained, suggesting a significant role of local genomic features. For signature 1, the inclusion of genome-wide distribution of methylated CpG sites into models can explain most (80--100%) of the hotspot propensity. There is an increased hotspot propensity of signature 1 in normal tissues and de novo germline mutations. We demonstrate that hotspot propensity is a useful readout to assess the accuracy of mutation rate models at nucleotide resolution. This new approach and the findings derived from it open up new avenues for a range of somatic and germline studies investigating and modelling mutagenesis. [ABSTRACT FROM AUTHOR]

Abstract Background Homologous recombination is a robust, broadly error-free mechanism of double-strand break repair, and deficiencies lead to PARP inhibitor sensitivity. Patients displaying homologous recombination deficiency can be identified using ‘mutational signatures’. However, these patterns are difficult to reliably infer from exome sequencing. Additionally, as mutational signatures are a historical record of mutagenic processes, this limits their utility in describing the current status of a tumour. Methods We apply two methods for characterising homologous recombination deficiency in breast cancer to explore the features and heterogeneity associated with this phenotype. We develop a likelihood-based method which leverages small insertions and deletions for high-confidence classification of homologous recombination deficiency for exome-sequenced breast cancers. We then use multinomial elastic net regression modelling to develop a transcriptional signature of heterogeneous homologous recombination deficiency. This signature is then applied to single-cell RNA-sequenced breast cancer cohorts enabling analysis of homologous recombination deficiency heterogeneity and differential patterns of tumour microenvironment interactivity. Results We demonstrate that the inclusion of indel events, even at low levels, improves homologous recombination deficiency classification. Whilst BRCA-positive homologous recombination deficient samples display strong similarities to those harbouring BRCA1/2 defects, they appear to deviate in microenvironmental features such as hypoxic signalling. We then present a 228-gene transcriptional signature which simultaneously characterises homologous recombination deficiency and BRCA1/2-defect status, and is associated with PARP inhibitor response. Finally, we show that this signature is applicable to single-cell transcriptomics data and predict that these cells present a distinct milieu of interactions with their microenvironment compared to their homologous recombination proficient counterparts, typified by a decreased cancer cell response to TNFα signalling. Conclusions We apply multi-scale approaches to characterise homologous recombination deficiency in breast cancer through the development of mutational and transcriptional signatures. We demonstrate how indels can improve homologous recombination deficiency classification in exome-sequenced breast cancers. Additionally, we demonstrate the heterogeneity of homologous recombination deficiency, especially in relation to BRCA1/2-defect status, and show that indications of this feature can be captured at a single-cell level, enabling further investigations into interactions between DNA repair deficient cells and their tumour microenvironment.

Abstract Background Mutagenicity, the ability of chemical agents to cause mutations and potentially lead to cancer, is a critical aspect of substance safety assessment for protecting human health and the environment. Metabolic enzymes activate multiple mutagens in living organisms, thus in vivo animal models provide highly important information for evaluating mutagenicity in human. Rats are considered suitable models as they share a similar metabolic pathway with humans for processing toxic chemical and exhibit higher responsiveness to chemical carcinogens than mice. To assess mutagenicity in rats, transgenic rodents (TGRs) are widely used for in vivo gene mutation assays. However, such assays are labor-intensive and could only detect transgene mutations inserted into the genome. Therefore, introducing a technology to directly detect in vivo mutagenicity in rats would be necessary. The next-generation sequencing (NGS) based error-corrected sequencing technique is a promising approach for such purposes. Results We investigated the applicability of paired-end and complementary consensus sequencing (PECC-Seq), an error-corrected sequencing technique, for detecting in vivo mutagenicity in the rat liver samples. PECC-Seq allows for the direct detection of ultra-rare somatic mutations in the genomic DNA without being constrained by the genomic locus, tissue, or organism. We tested PECC-Seq feasibility in rats treated with diethylnitrosamine (DEN), a mutagenic compound. Interestingly, the mutation and mutant frequencies between PECC-Seq and the TGR assay displayed a promising correlation. Our results also demonstrated that PECC-Seq could successfully detect the A:T > T:A mutation in rat liver samples, consistent with the TGR assay. Furthermore, we calculated the trinucleotide mutation frequency and proved that PECC-Seq accurately identified the DEN treatment-induced mutational signatures. Conclusions Our study provides the first evidence of using PECC-Seq for in vivo mutagenicity detection in rat liver samples. This approach could provide a valuable alternative to conventional TGR assays as it is labor- and time-efficient and eliminates the need for transgenic rodents. Error-corrected sequencing techniques, such as PECC-Seq, represent promising approaches for enhancing mutagenicity assessment and advancing regulatory science.

Abstract Background Germline variants affecting the proofreading activity of polymerases epsilon and delta cause a hereditary cancer and adenomatous polyposis syndrome characterized by tumors with a high mutational burden and a specific mutational spectrum. In addition to the implementation of multiple pieces of evidence for the classification of gene variants, POLE and POLD1 variant classification is particularly challenging given that non-disruptive variants affecting the proofreading activity of the corresponding polymerase are the ones associated with cancer. In response to an evident need in the field, we have developed gene-specific variant classification recommendations, based on the ACMG/AMP (American College of Medical Genetics and Genomics/Association for Molecular Pathology) criteria, for the assessment of non-disruptive variants located in the sequence coding for the exonuclease domain of the polymerases. Methods A training set of 23 variants considered pathogenic or benign was used to define the usability and strength of the ACMG/AMP criteria. Population frequencies, computational predictions, co-segregation data, phenotypic and tumor data, and functional results, among other features, were considered. Results Gene-specific variant classification recommendations for non-disruptive variants located in the exonuclease domain of POLE and POLD1 were defined. The resulting recommendations were applied to 128 exonuclease domain variants reported in the literature and/or public databases. A total of 17 variants were classified as pathogenic or likely pathogenic, and 17 as benign or likely benign. Conclusions Our recommendations, with room for improvement in the coming years as more information become available on carrier families, tumor molecular characteristics and functional assays, are intended to serve the clinical and scientific communities and help improve diagnostic performance, avoiding variant misclassifications.

Accurate detection and analysis of somatic variants in cancer involve multiple third-party tools with complex dependencies and configurations, leading to laborious, error-prone, and time-consuming data conversions. This approach lacks accuracy, reproducibility, and portability, limiting clinical application. Musta was developed to address these issues as an end-to-end pipeline for detecting, classifying, and interpreting cancer mutations. Musta is based on a Python command-line tool designed to manage tumor-normal samples for precise somatic mutation analysis. The core is a Snakemake-based workflow that covers all key cancer genomics steps, including variant calling, mutational signature deconvolution, variant annotation, driver gene detection, pathway analysis, and tumor heterogeneity estimation. Musta is easy to install on any system via Docker, with a Makefile handling installation, configuration, and execution, allowing for full or partial pipeline runs. Musta has been validated at the CRS4-NGS Core facility and tested on large datasets from The Cancer Genome Atlas and the Beijing Institute of Genomics. Musta has proven robust and flexible for somatic variant analysis in cancer. It is user-friendly, requiring no specialized programming skills, and enables data processing with a single command line. Its reproducibility ensures consistent results across users following the same protocol.

Abstract Background Familial breast cancer is in most cases unexplained due to the lack of identifiable pathogenic variants in the BRCA1 and BRCA2 genes. The somatic mutational landscape and in particular the extent of BRCA-like tumour features (BRCAness) in these familial breast cancers where germline BRCA1 or BRCA2 mutations have not been identified is to a large extent unknown. Methods We performed whole-genome sequencing on matched tumour and normal samples from high-risk non-BRCA1/BRCA2 breast cancer families to understand the germline and somatic mutational landscape and mutational signatures. We measured BRCAness using HRDetect. As a comparator, we also analysed samples from BRCA1 and BRCA2 germline mutation carriers. Results We noted for non-BRCA1/BRCA2 tumours, only a small proportion displayed high HRDetect scores and were characterized by concomitant promoter hypermethylation or in one case a RAD51D splice variant previously reported as having unknown significance to potentially explain their BRCAness. Another small proportion showed no features of BRCAness but had mutationally active tumours. The remaining tumours lacked features of BRCAness and were mutationally quiescent. Conclusions A limited fraction of high-risk familial non-BRCA1/BRCA2 breast cancer patients is expected to benefit from treatment strategies against homologue repair deficient cancer cells.

Background: Mutagenicity, the ability of chemical agents to cause mutations and potentially lead to cancer, is a critical aspect of substance safety assessment for protecting human health and the environment. Metabolic enzymes activate multiple mutagens in living organisms, thus in vivo animal models provide highly important information for evaluating mutagenicity in human. Rats are considered suitable models as they share a similar metabolic pathway with humans for processing toxic chemical and exhibit higher responsiveness to chemical carcinogens than mice. To assess mutagenicity in rats, transgenic rodents (TGRs) are widely used for in vivo gene mutation assays. However, such assays are labor-intensive and could only detect transgene mutations inserted into the genome. Therefore, introducing a technology to directly detect in vivo mutagenicity in rats would be necessary. The next-generation sequencing (NGS) based error-corrected sequencing technique is a promising approach for such purposes. Results: We investigated the applicability of paired-end and complementary consensus sequencing (PECC-Seq), an error-corrected sequencing technique, for detecting in vivo mutagenicity in the rat liver samples. PECC-Seq allows for the direct detection of ultra-rare somatic mutations in the genomic DNA without being constrained by the genomic locus, tissue, or organism. We tested PECC-Seq feasibility in rats treated with diethylnitrosamine (DEN), a mutagenic compound. Interestingly, the mutation and mutant frequencies between PECC-Seq and the TGR assay displayed a promising correlation. Our results also demonstrated that PECC-Seq could successfully detect the A:T > T:A mutation in rat liver samples, consistent with the TGR assay. Furthermore, we calculated the trinucleotide mutation frequency and proved that PECC-Seq accurately identified the DEN treatment-induced mutational signatures. Conclusions: Our study provides the first evidence of using PECC-Seq for in vivo mutagenicity detection in rat liver samples. This approach could provide a valuable alternative to conventional TGR assays as it is labor- and time-efficient and eliminates the need for transgenic rodents. Error-corrected sequencing techniques, such as PECC-Seq, represent promising approaches for enhancing mutagenicity assessment and advancing regulatory science. [ABSTRACT FROM AUTHOR]

Background: Homologous recombination is a robust, broadly error-free mechanism of double-strand break repair, and deficiencies lead to PARP inhibitor sensitivity. Patients displaying homologous recombination deficiency can be identified using 'mutational signatures'. However, these patterns are difficult to reliably infer from exome sequencing. Additionally, as mutational signatures are a historical record of mutagenic processes, this limits their utility in describing the current status of a tumour. Methods: We apply two methods for characterising homologous recombination deficiency in breast cancer to explore the features and heterogeneity associated with this phenotype. We develop a likelihood-based method which leverages small insertions and deletions for high-confidence classification of homologous recombination deficiency for exome-sequenced breast cancers. We then use multinomial elastic net regression modelling to develop a transcriptional signature of heterogeneous homologous recombination deficiency. This signature is then applied to single-cell RNA-sequenced breast cancer cohorts enabling analysis of homologous recombination deficiency heterogeneity and differential patterns of tumour microenvironment interactivity. Results: We demonstrate that the inclusion of indel events, even at low levels, improves homologous recombination deficiency classification. Whilst BRCA-positive homologous recombination deficient samples display strong similarities to those harbouring BRCA1/2 defects, they appear to deviate in microenvironmental features such as hypoxic signalling. We then present a 228-gene transcriptional signature which simultaneously characterises homologous recombination deficiency and BRCA1/2-defect status, and is associated with PARP inhibitor response. Finally, we show that this signature is applicable to single-cell transcriptomics data and predict that these cells present a distinct milieu of interactions with their microenvironment compared to their homologous recombination proficient counterparts, typified by a decreased cancer cell response to TNFα signalling. Conclusions: We apply multi-scale approaches to characterise homologous recombination deficiency in breast cancer through the development of mutational and transcriptional signatures. We demonstrate how indels can improve homologous recombination deficiency classification in exome-sequenced breast cancers. Additionally, we demonstrate the heterogeneity of homologous recombination deficiency, especially in relation to BRCA1/2-defect status, and show that indications of this feature can be captured at a single-cell level, enabling further investigations into interactions between DNA repair deficient cells and their tumour microenvironment. [ABSTRACT FROM AUTHOR]

Single-base substitution (SBS) mutational signatures have become standard practice in cancer genomics. In lieu of de novo signature extraction, reference signature assignment allows users to estimate the activities of pre-established SBS signatures within individual malignancies. Several tools have been developed for this purpose, each with differing methodologies. However, due to a lack of standardization, there may be inter-tool variability in signature assignment. We deeply characterized three assignment strategies and five SBS signature assignment tools. We observed that assignment strategy choice can significantly influence results and interpretations. Despite varying recommendations by tools, Refit performed best by reducing overfitting and maximizing reconstruction of the original mutational spectra. Even after uniform application of Refit, tools varied remarkably in signature assignments both qualitatively (Jaccard index = 0.38–0.83) and quantitatively (Kendall tau-b = 0.18–0.76). This phenomenon was exacerbated for 'flat' signatures such as the homologous recombination deficiency signature SBS3. An ensemble approach (EnsembleFit), which leverages output from all five tools, increased SBS3 assignment accuracy in BRCA1/2 -deficient breast carcinomas. After generating synthetic mutational profiles for thousands of pan-cancer tumors, EnsembleFit reduced signature activity assignment error 15.9–24.7% on average using Catalogue of Somatic Mutations In Cancer and non-standard reference signature sets. We have also released the EnsembleFit web portal (https://www.ensemblefit.pittlabgenomics.com) for users to generate or download ensemble-based SBS signature assignments using any strategy and combination of tools. Overall, we show that signature assignment heterogeneity across tools and strategies is non-negligible and propose a viable, ensemble solution. [ABSTRACT FROM AUTHOR]

Background: Primary small cell neuroendocrine carcinomas of the cervix and endometrium are rare gynecological malignancies with limited treatment options. This study aimed to improve the understanding of the carcinogenesis process and identify potential therapeutic targets for these two tumor types by constructing the mutational landscape at the whole exome level. Methods: Primary tumor tissues and their matched blood samples were obtained from 10 patients with small cell cervical neuroendocrine carcinoma (NECC) and five patients with small cell endometrial neuroendocrine carcinoma (NECE). Whole exome sequencing was performed to construct the somatic mutation profiles. Mutational signature and recurrent mutated gene analysis were used to identify tumor subtypes and common carcinogenesis processes. Results: Based on the burden of different mutational signatures, the NECCs in this work can be divided into two subtypes, including the mismatch repair deficiency like (dMMR-like) type (4/10) and the high spontaneous deamination type (6/10). Components of the PI3K/AKT signaling and RAS signaling were exclusively mutated in these two subtypes, respectively. The integration of human papillomavirus made a limited contribution to tumorigenesis in NECC (20%). The dysfunction of the mismatch repair system and microsatellite instability are the major features of NECE. PI3K/AKT, JAK/STAT signaling, and chromatin remodeling activity were the common mutated pathways in NECE. PIK3CA, WNK2, and KMT2B underwent mutations in both the dMMR-like subtype of NECC (50% - 75%) and in NECE (60% - 80%) specimens, while exhibiting infrequent mutational occurrences in publicly available data pertaining to neuroendocrine carcinomas of the lung or bladder (< 10%). Conclusion: We identified the two subtypes of NECC with distinct mutated pathways and potential therapy targets. The dMMR-like type NECC and NECE may share a similar carcinogenesis process that include dysfunction of PI3K/AKT signaling, cell cycle, antiapoptotic processes, and chromatin remodeling activity. [ABSTRACT FROM AUTHOR]

Background: Germline variants affecting the proofreading activity of polymerases epsilon and delta cause a hereditary cancer and adenomatous polyposis syndrome characterized by tumors with a high mutational burden and a specific mutational spectrum. In addition to the implementation of multiple pieces of evidence for the classification of gene variants, POLE and POLD1 variant classification is particularly challenging given that non-disruptive variants affecting the proofreading activity of the corresponding polymerase are the ones associated with cancer. In response to an evident need in the field, we have developed gene-specific variant classification recommendations, based on the ACMG/AMP (American College of Medical Genetics and Genomics/Association for Molecular Pathology) criteria, for the assessment of non-disruptive variants located in the sequence coding for the exonuclease domain of the polymerases. Methods: A training set of 23 variants considered pathogenic or benign was used to define the usability and strength of the ACMG/AMP criteria. Population frequencies, computational predictions, co-segregation data, phenotypic and tumor data, and functional results, among other features, were considered. Results: Gene-specific variant classification recommendations for non-disruptive variants located in the exonuclease domain of POLE and POLD1 were defined. The resulting recommendations were applied to 128 exonuclease domain variants reported in the literature and/or public databases. A total of 17 variants were classified as pathogenic or likely pathogenic, and 17 as benign or likely benign. Conclusions: Our recommendations, with room for improvement in the coming years as more information become available on carrier families, tumor molecular characteristics and functional assays, are intended to serve the clinical and scientific communities and help improve diagnostic performance, avoiding variant misclassifications. [ABSTRACT FROM AUTHOR]

Background: Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is a rare hematopoietic disease derived from plasmacytoid dendritic lineage cells. The disease typically shows skin as well as frequent bone marrow and peripheral blood involvement. However, the pathogenesis of this disease is still not well understood. While somatic point mutations and genetic rearrangements have been described in BPDCN, the types and origins of these mutations and relationships to other cancer types is not well understood. Materials and Methods: To probe the origins of BPDCN, we analyzed the exome sequence data of 9 tumor‐normal pair cases of BPDCN. We utilized SignatureAnalyzer, SigProfiler and a custom microbial analysis pipeline to understand the relevance of endogenous and environmental mutagenic processes. Results: Our results identified a significant tobacco exposure and aging genetic signature as well as signatures related to nucleotide excision repair deficiency, ultra violet (UV) exposure, and endogenous deamination in BPDCN. We also assessed the samples for microbial infectious disease organisms but did not find a link to a microbial etiology. Conclusion: The identification of a tobacco exposure and aging genetic signature in patients with BPDCN suggests that environmental and endogenous genetic changes may be central to the oncogenesis of BPDCN. [ABSTRACT FROM AUTHOR]

Abstract Background The spectrum of mutations in a collection of cancer genomes can be described by a mixture of a few mutational signatures. The mutational signatures can be found using non-negative matrix factorization (NMF). To extract the mutational signatures we have to assume a distribution for the observed mutational counts and a number of mutational signatures. In most applications, the mutational counts are assumed to be Poisson distributed, and the rank is chosen by comparing the fit of several models with the same underlying distribution and different values for the rank using classical model selection procedures. However, the counts are often overdispersed, and thus the Negative Binomial distribution is more appropriate. Results We propose a Negative Binomial NMF with a patient specific dispersion parameter to capture the variation across patients and derive the corresponding update rules for parameter estimation. We also introduce a novel model selection procedure inspired by cross-validation to determine the number of signatures. Using simulations, we study the influence of the distributional assumption on our method together with other classical model selection procedures. We also present a simulation study with a method comparison where we show that state-of-the-art methods are highly overestimating the number of signatures when overdispersion is present. We apply our proposed analysis on a wide range of simulated data and on two real data sets from breast and prostate cancer patients. On the real data we describe a residual analysis to investigate and validate the model choice. Conclusions With our results on simulated and real data we show that our model selection procedure is more robust at determining the correct number of signatures under model misspecification. We also show that our model selection procedure is more accurate than the available methods in the literature for finding the true number of signatures. Lastly, the residual analysis clearly emphasizes the overdispersion in the mutational count data. The code for our model selection procedure and Negative Binomial NMF is available in the R package SigMoS and can be found at https://github.com/MartaPelizzola/SigMoS .

Abstract Background There has been a growing appreciation recently that mutagenic processes can be studied through the lenses of mutational signatures, which represent characteristic mutation patterns attributed to individual mutagens. However, the causal links between mutagens and observed mutation patterns as well as other types of interactions between mutagenic processes and molecular pathways are not fully understood, limiting the utility of mutational signatures. Methods To gain insights into these relationships, we developed a network-based method, named GeneSigNet that constructs an influence network among genes and mutational signatures. The approach leverages sparse partial correlation among other statistical techniques to uncover dominant influence relations between the activities of network nodes. Results Applying GeneSigNet to cancer data sets, we uncovered important relations between mutational signatures and several cellular processes that can shed light on cancer-related processes. Our results are consistent with previous findings, such as the impact of homologous recombination deficiency on clustered APOBEC mutations in breast cancer. The network identified by GeneSigNet also suggest an interaction between APOBEC hypermutation and activation of regulatory T Cells (Tregs), as well as a relation between APOBEC mutations and changes in DNA conformation. GeneSigNet also exposed a possible link between the SBS8 signature of unknown etiology and the Nucleotide Excision Repair (NER) pathway. Conclusions GeneSigNet provides a new and powerful method to reveal the relation between mutational signatures and gene expression. The GeneSigNet method was implemented in python, and installable package, source codes and the data sets used for and generated during this study are available at the Github site https://github.com/ncbi/GeneSigNet.

Little is known about the mutational processes that shape the genetic landscape of gliomas. Numerous mutational processes leave marks on the genome in the form of mutations, copy number alterations, rearrangements or their combinations. To explore gliomagenesis, we hypothesized that gliomas with different underlying oncogenic mechanisms would have differences in the burden of various forms of these genomic alterations. This was an analysis on adult diffuse gliomas, but IDH-mutant gliomas as well as diffuse midline gliomas H3-K27M were excluded to search for the possible presence of new entities among the very heterogenous group of IDH-WT glioblastomas. The cohort was divided into two molecular subsets: (1) Molecularly-defined GBM (mGBM) as those that carried molecular features of glioblastomas (including TERT promoter mutations, 7/10 pattern, or EGFR-amplification), and (2) those who did not (others). Whole exome sequencing was performed for 37 primary tumors and matched blood samples as well as 8 recurrences. Single nucleotide variations (SNV), short insertion or deletions (indels) and copy number alterations (CNA) were quantified using 5 quantitative metrics (SNV burden, indel burden, copy number alteration frequency-wGII, chromosomal arm event ratio-CAER, copy number amplitude) as well as 4 parameters that explored underlying oncogenic mechanisms (chromothripsis, double minutes, microsatellite instability and mutational signatures). Findings were validated in the TCGA pan-glioma cohort. mGBM and Others differed significantly in their SNV (only in the TCGA cohort) and CNA metrics but not indel burden. SNV burden increased with increasing age at diagnosis and at recurrences and was driven by mismatch repair deficiency. On the contrary, indel and CNA metrics remained stable over increasing age at diagnosis and with recurrences. Copy number alteration frequency (wGII) correlated significantly with chromothripsis while CAER and CN amplitude correlated significantly with the presence of double minutes, suggesting separate underlying mechanisms for different forms of CNA.

Multiple myeloma (MM) is the second most common hematological malignancy, which remains incurable despite recent advances in treatment strategies. Like other forms of cancer, MM is characterized by genomic instability, caused by defects in DNA repair. Along with mutations in DNA repair genes and genotoxic drugs used to treat MM, non-canonical secondary DNA structures (four-stranded G-quadruplex structures) can affect accumulation of somatic mutations and chromosomal abnormalities in the tumor cells of MM patients. Here, we tested the hypothesis that G-quadruplex structures may influence the distribution of somatic mutations in the tumor cells of MM patients. We sequenced exomes of normal and tumor cells of 11 MM patients and analyzed the data for the presence of G4 context around points of somatic mutations. To identify molecular mechanisms that could affect mutational profile of tumors, we also analyzed mutational signatures in tumor cells as well as germline mutations for the presence of specific SNPs in DNA repair genes or in genes regulating G-quadruplex unwinding. In several patients, we found that sites of somatic mutations are frequently located in regions with G4 context. This pattern correlated with specific germline variants found in these patients. We discuss the possible implications of these variants for mutation accumulation and specificity in MM and propose that the extent of G4 context enrichment around somatic mutation sites may be a novel metric characterizing mutational processes in tumors.

Summary: Neuroblastoma is a pediatric solid tumor characterized by strong clinical heterogeneity. Although clinical risk-defining genomic alterations exist in neuroblastomas, the mutational processes involved in their generation remain largely unclear. By examining the topography and mutational signatures derived from all variant classes, we identified co-occurring mutational footprints, which we termed mutational scenarios. We demonstrate that clinical neuroblastoma heterogeneity is associated with differences in the mutational processes driving these scenarios, linking risk-defining pathognomonic variants to distinct molecular processes. Whereas high-risk MYCN-amplified neuroblastomas were characterized by signs of replication slippage and stress, homologous recombination-associated signatures defined high-risk non-MYCN-amplified patients. Non-high-risk neuroblastomas were marked by footprints of chromosome mis-segregation and TOP1 mutational activity. Furthermore, analysis of subclonal mutations uncovered differential activity of these processes through neuroblastoma evolution. Thus, clinical heterogeneity of neuroblastoma patients can be linked to differences in the mutational processes that are active in their tumors.

Simple Summary: There is a lack of molecular biomarkers that would allow better characterisation and categorisation of gastric tumours. Distinct mutational patterns have been observed at both the whole genome and exome levels and have been referred to as mutational signatures. Some of these characteristic mutational patterns have been associated with defects in DNA repair mechanisms or linked to exogenous mutagens. The mutational signatures found in gastric tumours could be used as prognostic biomarkers and could provide new information about the drivers of gastric carcinogenesis, which might be useful for the improvement in disease treatment options. This review summarises mutational signatures found in gastric cancer and their clinical potential. Gastric cancer is characterised by high inter- and intratumour heterogeneity. The majority of patients are older than 65 years and the global burden of this disease is increasing due to the aging of the population. The disease is usually diagnosed at advanced stages, which is a consequence of nonspecific symptoms. Few improvements have been made at the level of noninvasive molecular diagnosis of sporadic gastric cancer, and therefore the mortality rate remains high. A new field of mutational signatures has emerged in the past decade with advances in the genome sequencing technology. These distinct mutational patterns in the genome, caused by exogenous and endogenous mutational processes, can be associated with tumour aetiology and disease progression, and could provide novel perception on the treatment possibilities. This review assesses the mutational signatures found in gastric cancer and summarises their potential for use in clinical setting as diagnostic or prognostic biomarkers. Associated treatment options and biomarkers already implemented in clinical use are discussed, together with those that are still being explored or are in clinical studies. [ABSTRACT FROM AUTHOR]

A small number of driver mutations cause cancer. The identification of driver mutations is challenging, both experimentally and computationally, especially for rare drivers. The main limitation of computational methods to predict driver mutations lies in the lack of an objective gold standard benchmark. Knowledge about mutational processes can help to predict drivers. Driver mutations are associated with clinical phenotype and can be used as cancer biomarkers. Cancer cells accumulate many genetic alterations throughout their lifetime, but only a few of them drive cancer progression, termed driver mutations. Driver mutations may vary between cancer types and patients, can remain latent for a long time and become drivers at particular cancer stages, or may drive oncogenesis only in conjunction with other mutations. The high mutational, biochemical, and histological tumor heterogeneity makes driver mutation identification very challenging. In this review we summarize recent efforts to identify driver mutations in cancer and annotate their effects. We underline the success of computational methods to predict driver mutations in finding novel cancer biomarkers, including in circulating tumor DNA (ctDNA). We also report on the boundaries of their applicability in clinical research. [ABSTRACT FROM AUTHOR]