Genome-Wide Somatic Alterations in Multiple Myeloma Reveal a Superior Outcome Group

PURPOSE

Multiple myeloma (MM) is accompanied by heterogeneous somatic alterations. The overall goal of this study was to describe the genomic landscape of myeloma using deep whole-genome sequencing (WGS) and develop a model that identifies patients with long survival.

METHODS

We analyzed deep WGS data from 183 newly diagnosed patients with MM treated with lenalidomide, bortezomib, and dexamethasone (RVD) alone or RVD + autologous stem cell transplant (ASCT) in the IFM/DFCI 2009 study (ClinicalTrials.gov identifier: NCT01191060). We integrated genomic markers with clinical data.

RESULTS

We report significant variability in mutational load and processes within MM subgroups. The timeline of observed activation of mutational processes provides the basis for 2 distinct models of acquisition of mutational changes detected at the time of diagnosis of myeloma. Virtually all MM subgroups have activated DNA repair-associated signature as a prominent late mutational process, whereas APOBEC signature targeting C>G is activated in the intermediate phase of disease progression in high-risk MM. Importantly, we identify a genomically defined MM subgroup (17% of newly diagnosed patients) with low DNA damage (low genomic scar score with chromosome 9 gain) and a superior outcome (100% overall survival at 69 months), which was validated in a large independent cohort. This subgroup allowed us to distinguish patients with low- and high-risk hyperdiploid MM and identify patients with prolongation of progression-free survival. Genomic characteristics of this subgroup included lower mutational load with significant contribution from age-related mutations as well as frequent NRAS mutation. Surprisingly, their overall survival was independent of International Staging System and minimal residual disease status.

CONCLUSION

This is a comprehensive study identifying genomic markers of a good-risk group with prolonged survival. Identification of this patient subgroup will affect future therapeutic algorithms and research planning.

Molecular characterization of organoids derived from pancreatic intraductal papillary mucinous neoplasms

Intraductal papillary mucinous neoplasms (IPMNs) are commonly identified non-invasive cyst-forming pancreatic neoplasms with the potential to progress into invasive pancreatic adenocarcinoma. There are few in vitro models with which to study the biology of IPMNs and their progression to invasive carcinoma. Therefore, we generated a living biobank of organoids from seven normal pancreatic ducts and ten IPMNs. We characterized eight IPMN organoid samples using whole genome sequencing and characterized five IPMN organoids and seven normal pancreatic duct organoids using transcriptome sequencing. We identified an average of 11,344 somatic mutations in the genomes of organoids derived from IPMNs, with one sample harboring 61,537 somatic mutations enriched for T→C transitions and T→A transversions. Recurrent coding somatic mutations were identified in 15 genes, including KRAS, GNAS, RNF43, PHF3, and RBM10. The most frequently mutated genes were KRAS, GNAS, and RNF43, with somatic mutations identified in six (75%), four (50%), and three (37.5%) IPMN organoid samples, respectively. On average, we identified 36 structural variants in IPMN derived organoids, and none had an unstable phenotype (> 200 structural variants). Transcriptome sequencing identified 28 genes differentially expressed between normal pancreatic duct organoid and IPMN organoid samples. The most significantly upregulated and downregulated genes were CLDN18 and FOXA1. Immunohistochemical analysis of FOXA1 expression in 112 IPMNs, 113 mucinous cystic neoplasms, and 145 pancreatic ductal adenocarcinomas demonstrated statistically significant loss of expression in low-grade IPMNs (p < 0.0016), mucinous cystic neoplasms (p < 0.0001), and pancreatic ductal adenocarcinoma of any histologic grade (p < 0.0001) compared to normal pancreatic ducts. These data indicate that FOXA1 loss of expression occurs early in pancreatic tumorigenesis. Our study highlights the utility of organoid culture to study the genetics and biology of normal pancreatic duct and IPMNs. © 2020 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

A Practical Guide for Structural Variation Detection in the Human Genome

Profiling genetic variants-including single nucleotide variants, small insertions and deletions, copy number variations, and structural variations (SVs)-from both healthy individuals and individuals with disease is a key component of genetic and biomedical research. SVs are large-scale changes in the genome and involve breakage and rejoining of DNA fragments. They may affect thousands to millions of nucleotides and can lead to loss, gain, and reshuffling of genes and regulatory elements. SVs are known to impact gene expression and potentially result in altered phenotypes and diseases. Therefore, identifying SVs from the human genomes is particularly important. In this review, I describe advantages and disadvantages of the available high-throughput assays for the discovery of SVs, which are the most challenging genetic alterations to detect. A practical guide is offered to suggest the most suitable strategies for discovering different types of SVs including common germline, rare, somatic, and complex variants. I also discuss factors to be considered, such as cost and performance, for different strategies when designing experiments. Last, I present several approaches to identify potential SV artifacts caused by samples, experimental procedures, and computational analysis. © 2020 Wiley Periodicals LLC.

Comprehensive analysis reveals distinct mutational signature and its mechanistic insights of alcohol consumption in human cancers

Alcohol consumption is a critical risk factor for multiple types of cancer. A genome can be attacked and acquire numerous somatic mutations in the environment of alcohol exposure. Mutational signature has the capacity illustrating the complex somatic mutation patterns in cancer genome. Recent studies have discovered distinct mutational signatures associating with alcohol consumption in liver and esophageal cancers. However, their prevalence among diverse cancers, impact of genetic background and origin of alcohol-induced mutational signatures remain unclear. By a comprehensive bioinformatics analysis on somatic mutations from patients of four cancer types with drinking information, we identified nine mutational signatures (signatures B-J), among which signature J (similar to COSMIC signature 16) was distinctive to alcohol drinking. Signature J was associated with HNSC, ESCA and LIHC but not PAAD. Interestingly, patients with mutated allele rs1229984 in ADH1B had lower level of signature J while mutated allele rs671 in ALDH2 exhibited higher signature J abundance, suggesting acetaldehyde is one cause of signature J. Intriguingly, somatic mutations of three potential cancer driver genes (TP53, CUL3 and NSD1) were found the critical contributors for increased mutational load of signature J in alcohol consumption patients. Furthermore, signature J was enriched with early accumulated clonal mutations compared to mutations derived from late tumor growth. This study systematically characterized alcohol-related mutational signature and indicated mechanistic insights into the prevalence, origin and gene-environment interaction regarding the risk oncogenic mutations associated with alcohol intake.

Insights Into the Somatic Mutation Burden of Hepatoblastomas From Brazilian Patients

Hepatoblastoma is a very rare embryonal liver cancer supposed to arise from the impairment of hepatocyte differentiation during embryogenesis. In this study, we investigated by exome sequencing the burden of somatic mutations in a cohort of 10 hepatoblastomas, including a congenital case. Our data disclosed a low mutational background and pointed out to a novel set of candidate genes for hepatoblastoma biology, which were shown to impact gene expression levels. Only three recurrently mutated genes were detected: CTNNB1 and two novel candidates, CX3CL1 and CEP164. A relevant finding was the identification of a recurrent mutation (A235G) in two hepatoblastomas at the CX3CL1 gene; evaluation of RNA and protein expression revealed upregulation of CX3CL1 in tumors. The analysis was replicated in two independents cohorts, substantiating that an activation of the CX3CL1/CX3CR1 pathway occurs in hepatoblastomas. In inflammatory regions of hepatoblastomas, CX3CL1/CX3CR1 were not detected in the infiltrated lymphocytes, in which they should be expressed in normal conditions, whereas necrotic regions exhibited negative labeling in tumor cells, but strongly positive infiltrated lymphocytes. Altogether, these data suggested that CX3CL1/CX3CR1 upregulation may be a common feature of hepatoblastomas, potentially related to chemotherapy response and progression. In addition, three mutational signatures were identified in hepatoblastomas, two of them with predominance of either the COSMIC signatures 1 and 6, found in all cancer types, or the COSMIC signature 29, mostly related to tobacco chewing habit; a third novel mutational signature presented an unspecific pattern with an increase of C>A mutations. Overall, we present here novel candidate genes for hepatoblastoma, with evidence that CX3CL1/CX3CR1 chemokine signaling pathway is likely involved with progression, besides reporting specific mutational signatures.

A Sticky Multinomial Mixture Model of Strand-Coordinated Mutational Processes in Cancer

The characterization of mutational processes in terms of their signatures of activity relies mostly on the assumption that mutations in a given cancer genome are independent of one another. Recently, it was discovered that certain segments of mutations, termed processive groups, occur on the same DNA strand and are generated by a single process or signature. Here we provide a first probabilistic model of mutational signatures that accounts for their observed stickiness and strand coordination. The model conditions on the observed strand for each mutation and allows the same signature to generate a run of mutations. It can both use known signatures or learn new ones. We show that this model provides a more accurate description of the properties of mutagenic processes than independent-mutation achieving substantially higher likelihood on held-out data. We apply this model to characterize the processivity of mutagenic processes across multiple types of cancer.

The repertoire of mutational signatures in human cancer

Somatic mutations in cancer genomes are caused by multiple mutational processes, each of which generates a characteristic mutational signature1. Here, as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium2 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), we characterized mutational signatures using 84,729,690 somatic mutations from 4,645 whole-genome and 19,184 exome sequences that encompass most types of cancer. We identified 49 single-base-substitution, 11 doublet-base-substitution, 4 clustered-base-substitution and 17 small insertion-and-deletion signatures. The substantial size of our dataset, compared with previous analyses3-15, enabled the discovery of new signatures, the separation of overlapping signatures and the decomposition of signatures into components that may represent associated-but distinct-DNA damage, repair and/or replication mechanisms. By estimating the contribution of each signature to the mutational catalogues of individual cancer genomes, we revealed associations of signatures to exogenous or endogenous exposures, as well as to defective DNA-maintenance processes. However, many signatures are of unknown cause. This analysis provides a systematic perspective on the repertoire of mutational processes that contribute to the development of human cancer.

CANCERSIGN: a user-friendly and robust tool for identification and classification of mutational signatures and patterns in cancer genomes

Analysis of cancer mutational signatures have been instrumental in identification of responsible endogenous and exogenous molecular processes in cancer. The quantitative approach used to deconvolute mutational signatures is becoming an integral part of cancer research. Therefore, development of a stand-alone tool with a user-friendly interface for analysis of cancer mutational signatures is necessary. In this manuscript we introduce CANCERSIGN, which enables users to identify 3-mer and 5-mer mutational signatures within whole genome, whole exome or pooled samples. Additionally, this tool enables users to perform clustering on tumor samples based on the proportion of mutational signatures in each sample. Using CANCERSIGN, we analysed all the whole genome somatic mutation datasets profiled by the International Cancer Genome Consortium (ICGC) and identified a number of novel signatures. By examining signatures found in exonic and non-exonic regions of the genome using WGS and comparing this to signatures found in WES data we observe that WGS can identify additional non-exonic signatures that are enriched in the non-coding regions of the genome while the deeper sequencing of WES may help identify weak signatures that are otherwise missed in shallower WGS data.

APOBEC-mediated DNA alterations: A possible new mechanism of carcinogenesis in EBV-positive gastric cancer

Mechanisms of viral oncogenesis are diverse and include the off-target activity of enzymes expressed by the infected cells, which evolved to target viral genomes for controlling their infection. Among these enzymes, the single-strand DNA editing capability of APOBECs represent a well-conserved viral infection response that can also cause untoward mutations in the host DNA. Here we show, after evaluating somatic single-nucleotide variations and transcriptome data in 240 gastric cancer samples, a positive correlation between APOBEC3s mRNA-expression and the APOBEC-mutation signature, both increased in EBV+ tumors. The correlation was reinforced by the observation of APOBEC mutations preferentially occurring in the genomic loci of the most active transcripts. This EBV infection and APOBEC3 mutation-signature axis were confirmed in a validation cohort of 112 gastric cancer patients. Our findings suggest that APOBEC3 upregulation in EBV+ cancer may boost the mutation load, providing further clues to the mechanisms of EBV-induced gastric carcinogenesis. After further validation, this EBV-APOBEC axis may prove to be a secondary driving force in the mutational evolution of EBV+ gastric tumors, whose consequences in terms of prognosis and treatment implications should be vetted.

Discovering novel mutation signatures by latent Dirichlet allocation with variational Bayes inference

MOTIVATION

A cancer genome includes many mutations derived from various mutagens and mutational processes, leading to specific mutation patterns. It is known that each mutational process leads to characteristic mutations, and when a mutational process has preferences for mutations, this situation is called a 'mutation signature.' Identification of mutation signatures is an important task for elucidation of carcinogenic mechanisms. In previous studies, analyses with statistical approaches (e.g. non-negative matrix factorization and latent Dirichlet allocation) revealed a number of mutation signatures. Nonetheless, strictly speaking, these existing approaches employ an ad hoc method or incorrect approximation to estimate the number of mutation signatures, and the whole picture of mutation signatures is unclear.

RESULTS

In this study, we present a novel method for estimating the number of mutation signatures-latent Dirichlet allocation with variational Bayes inference (VB-LDA)-where variational lower bounds are utilized for finding a plausible number of mutation patterns. In addition, we performed cluster analyses for estimated mutation signatures to extract novel mutation signatures that appear in multiple primary lesions. In a simulation with artificial data, we confirmed that our method estimated the correct number of mutation signatures. Furthermore, applying our method in combination with clustering procedures for real mutation data revealed many interesting mutation signatures that have not been previously reported.

AVAILABILITY AND IMPLEMENTATION

All the predicted mutation signatures with clustering results are freely available at http://www.f.waseda.jp/mhamada/MS/index.html. All the C++ source code and python scripts utilized in this study can be downloaded on the Internet (https://github.com/qkirikigaku/MS_LDA).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Computational tools to detect signatures of mutational processes in DNA from tumours: A review and empirical comparison of performance

Mutational signatures refer to patterns in the occurrence of somatic mutations that might be uniquely ascribed to particular mutational process. Tumour mutation catalogues can reveal mutational signatures but are often consistent with the mutation spectra produced by a variety of mutagens. To date, after the analysis of tens of thousands of exomes and genomes from about 40 different cancer types, tens of mutational signatures characterized by a unique probability profile across the 96 trinucleotide-based mutation types have been identified, validated and catalogued. At the same time, several concurrent methods have been developed for either the quantification of the contribution of catalogued signatures in a given cancer sequence or the identification of new signatures from a sample of cancer sequences. A review of existing computational tools has been recently published to guide researchers and practitioners through their mutational signature analyses, but other tools have been introduced since its publication and, a systematic evaluation and comparison of the performance of such tools is still lacking. In order to fill this gap, we have carried out an empirical evaluation of the main packages available to date, using both real and simulated data. Among other results, our empirical study shows that the identification of signatures is more difficult for cancers characterized by multiple signatures each having a small contribution. This work suggests that detection methods based on probabilistic models, especially EMu and bayesNMF, have in general better performance than NMF-based methods.

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

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/ .

HiLDA: a statistical approach to investigate differences in mutational signatures

We propose a hierarchical latent Dirichlet allocation model (HiLDA) for characterizing somatic mutation data in cancer. The method allows us to infer mutational patterns and their relative frequencies in a set of tumor mutational catalogs and to compare the estimated frequencies between tumor sets. We apply our method to two datasets, one containing somatic mutations in colon cancer by the time of occurrence, before or after tumor initiation, and the second containing somatic mutations in esophageal cancer by sex, age, smoking status, and tumor site. In colon cancer, the relative frequencies of mutational patterns were found significantly associated with the time of occurrence of mutations. In esophageal cancer, the relative frequencies were significantly associated with the tumor site. Our novel method provides higher statistical power for detecting differences in mutational signatures.

Hidden Markov models lead to higher resolution maps of mutation signature activity in cancer

Knowing the activity of the mutational processes shaping a cancer genome may provide insight into tumorigenesis and personalized therapy. It is thus important to characterize the signatures of active mutational processes in patients from their patterns of single base substitutions. However, mutational processes do not act uniformly on the genome, leading to statistical dependencies among neighboring mutations. To account for such dependencies, we develop the first sequence-dependent model, SigMa, for mutation signatures. We apply SigMa to characterize genomic and other factors that influence the activity of mutation signatures in breast cancer. We show that SigMa outperforms previous approaches, revealing novel insights on signature etiology. The source code for SigMa is publicly available at https://github.com/lrgr/sigma.

Computational approaches for discovery of mutational signatures in cancer

The accumulation of somatic mutations in a genome is the result of the activity of one or more mutagenic processes, each of which leaves its own imprint. The study of these DNA fingerprints, termed mutational signatures, holds important potential for furthering our understanding of the causes and evolution of cancer, and can provide insights of relevance for cancer prevention and treatment. In this review, we focus our attention on the mathematical models and computational techniques that have driven recent advances in the field.

Modeling clinical and molecular covariates of mutational process activity in cancer

MOTIVATION

Somatic mutations result from processes related to DNA replication or environmental/lifestyle exposures. Knowing the activity of mutational processes in a tumor can inform personalized therapies, early detection, and understanding of tumorigenesis. Computational methods have revealed 30 validated signatures of mutational processes active in human cancers, where each signature is a pattern of single base substitutions. However, half of these signatures have no known etiology, and some similar signatures have distinct etiologies, making patterns of mutation signature activity hard to interpret. Existing mutation signature detection methods do not consider tumor-level clinical/demographic (e.g. smoking history) or molecular features (e.g. inactivations to DNA damage repair genes).

RESULTS

To begin to address these challenges, we present the Tumor Covariate Signature Model (TCSM), the first method to directly model the effect of observed tumor-level covariates on mutation signatures. To this end, our model uses methods from Bayesian topic modeling to change the prior distribution on signature exposure conditioned on a tumor's observed covariates. We also introduce methods for imputing covariates in held-out data and for evaluating the statistical significance of signature-covariate associations. On simulated and real data, we find that TCSM outperforms both non-negative matrix factorization and topic modeling-based approaches, particularly in recovering the ground truth exposure to similar signatures. We then use TCSM to discover five mutation signatures in breast cancer and predict homologous recombination repair deficiency in held-out tumors. We also discover four signatures in a combined melanoma and lung cancer cohort-using cancer type as a covariate-and provide statistical evidence to support earlier claims that three lung cancers from The Cancer Genome Atlas are misdiagnosed metastatic melanomas.

AVAILABILITY AND IMPLEMENTATION

TCSM is implemented in Python 3 and available at https://github.com/lrgr/tcsm, along with a data workflow for reproducing the experiments in the paper.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A practical guide for mutational signature analysis in hematological malignancies

Analysis of mutational signatures is becoming routine in cancer genomics, with implications for pathogenesis, classification, prognosis, and even treatment decisions. However, the field lacks a consensus on analysis and result interpretation. Using whole-genome sequencing of multiple myeloma (MM), chronic lymphocytic leukemia (CLL) and acute myeloid leukemia, we compare the performance of public signature analysis tools. We describe caveats and pitfalls of de novo signature extraction and fitting approaches, reporting on common inaccuracies: erroneous signature assignment, identification of localized hyper-mutational processes, overcalling of signatures. We provide reproducible solutions to solve these issues and use orthogonal approaches to validate our results. We show how a comprehensive mutational signature analysis may provide relevant biological insights, reporting evidence of c-AID activity among unmutated CLL cases or the absence of BRCA1/BRCA2-mediated homologous recombination deficiency in a MM cohort. Finally, we propose a general analysis framework to ensure production of accurate and reproducible mutational signature data.

Somatic mutational signatures in polyposis and colorectal cancer

The somatic mutation spectrum imprinted in the genome of a tumor represents the mutational processes that have been active in that tumor. Large sequencing efforts in various cancer types have resulted in the identification of multiple mutational signatures, of which several have been linked to specific biological mechanisms. Several pan-cancer mutational signatures have been identified, while other signatures are only found in specific tissue types. Research on tumors from individuals with specific DNA repair defects has led to links between specific mutational signatures and mutational processes. Studying mutational signatures in cancers that are likely the result of a genetic predisposition may represent an interesting strategy to identify constitutional DNA repair defects, including those underlying polyposis and colorectal cancer.

Portrait of a cancer: mutational signature analyses for cancer diagnostics

BACKGROUND

In the past decade, systematic and comprehensive analyses of cancer genomes have identified cancer driver genes and revealed unprecedented insight into the molecular mechanisms underlying the initiation and progression of cancer. These studies illustrate that although every cancer has a unique genetic make-up, there are only a limited number of mechanisms that shape the mutational landscapes of cancer genomes, as reflected by characteristic computationally-derived mutational signatures. Importantly, the molecular mechanisms underlying specific signatures can now be dissected and coupled to treatment strategies. Systematic characterization of mutational signatures in a cancer patient's genome may thus be a promising new tool for molecular tumor diagnosis and classification.

RESULTS

In this review, we describe the status of mutational signature analysis in cancer genomes and discuss the opportunities and relevance, as well as future challenges, for further implementation of mutational signatures in clinical tumor diagnostics and therapy guidance.

CONCLUSIONS

Scientific studies have illustrated the potential of mutational signature analysis in cancer research. As such, we believe that the implementation of mutational signature analysis within the diagnostic workflow will improve cancer diagnosis in the future.

Integrated structural variation and point mutation signatures in cancer genomes using correlated topic models

Mutation signatures in cancer genomes reflect endogenous and exogenous mutational processes, offering insights into tumour etiology, features for prognostic and biologic stratification and vulnerabilities to be exploited therapeutically. We present a novel machine learning formalism for improved signature inference, based on multi-modal correlated topic models (MMCTM) which can at once infer signatures from both single nucleotide and structural variation counts derived from cancer genome sequencing data. We exemplify the utility of our approach on two hormone driven, DNA repair deficient cancers: breast and ovary (n = 755 samples total). We show how introducing correlated structure both within and between modes of mutation can increase accuracy of signature discovery, particularly in the context of sparse data. Our study emphasizes the importance of integrating multiple mutation modes for signature discovery and patient stratification, and provides a statistical modeling framework to incorporate additional features of interest for future studies.

Assessment of somatic single-nucleotide variation in brain tissue of cases with schizophrenia

The genetic architecture of schizophrenia (SCZ) includes numerous risk loci across a range of frequencies and sizes, including common and rare single-nucleotide variants and insertions/deletions (indels), as well as rare copy number variants (CNVs). Despite the clear heritability of the disease, monozygotic twins are discordant for SCZ at a significant rate. Somatic variants-genetic changes that arise after fertilization rather than through germline inheritance-are widespread in the human brain and known to contribute to risk for both rare and common neuropsychiatric conditions. The contribution of somatic variants in the brain to risk of SCZ remains to be determined. In this study, we surveyed somatic single-nucleotide variants (sSNVs) in the brains of controls and individuals with SCZ (n = 10 and n = 9, respectively). From each individual, whole-exome sequencing (WES) was performed on DNA from neuronal and non-neuronal nuclei isolated by fluorescence activated nuclear sorting (FANS) from frozen postmortem prefrontal cortex (PFC) samples, as well as DNA extracted from temporal muscle as a reference. We identified an increased burden of sSNVs in cases compared to controls (SCZ rate = 2.78, control rate = 0.70; P = 0.0092, linear mixed effects model), that included a higher rate of non-synonymous and loss-of-function variants (SCZ rate = 1.33, control rate = 0.50; P = 0.047, linear mixed effects model). Our findings suggest sSNVs in the brain may constitute an additional component of the complex genetic architecture of SCZ. This perspective argues for the need to further investigate somatic variation in the brain as an explanation of the discordance in monozygotic twins and a potential guide to the identification of novel therapeutic targets.

Helmsman: fast and efficient mutation signature analysis for massive sequencing datasets

Background

The spectrum of somatic single-nucleotide variants in cancer genomes often reflects the signatures of multiple distinct mutational processes, which can provide clinically actionable insights into cancer etiology. Existing software tools for identifying and evaluating these mutational signatures do not scale to analyze large datasets containing thousands of individuals or millions of variants.

Results

We introduce Helmsman, a program designed to perform mutation signature analysis on arbitrarily large sequencing datasets. Helmsman is up to 300 times faster than existing software. Helmsman’s memory usage is independent of the number of variants, resulting in a small enough memory footprint to analyze datasets that would otherwise exceed the memory limitations of other programs.

Conclusions

Helmsman is a computationally efficient tool that enables users to evaluate mutational signatures in massive sequencing datasets that are otherwise intractable with existing software. Helmsman is freely available at https://github.com/carjed/helmsman.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5264-y) contains supplementary material, which is available to authorized users.

Aristolochic acids and their derivatives are widely implicated in liver cancers in Taiwan and throughout Asia

Many traditional pharmacopeias include Aristolochia and related plants, which contain nephrotoxins and mutagens in the form of aristolochic acids and similar compounds (collectively, AA). AA is implicated in multiple cancer types, sometimes with very high mutational burdens, especially in upper tract urothelial cancers (UTUCs). AA-associated kidney failure and UTUCs are prevalent in Taiwan, but AA's role in hepatocellular carcinomas (HCCs) there remains unexplored. Therefore, we sequenced the whole exomes of 98 HCCs from two hospitals in Taiwan and found that 78% showed the distinctive mutational signature of AA exposure, accounting for most of the nonsilent mutations in known cancer driver genes. We then searched for the AA signature in 1400 HCCs from diverse geographic regions. Consistent with exposure through known herbal medicines, 47% of Chinese HCCs showed the signature, albeit with lower mutation loads than in Taiwan. In addition, 29% of HCCs from Southeast Asia showed the signature. The AA signature was also detected in 13 and 2.7% of HCCs from Korea and Japan as well as in 4.8 and 1.7% of HCCs from North America and Europe, respectively, excluding one U.S. hospital where 22% of 87 "Asian" HCCs had the signature. Thus, AA exposure is geographically widespread. Asia, especially Taiwan, appears to be much more extensively affected, which is consistent with other evidence of patterns of AA exposure. We propose that additional measures aimed at primary prevention through avoidance of AA exposure and investigation of possible approaches to secondary prevention are warranted.

MutationalPatterns: comprehensive genome-wide analysis of mutational processes

Background

Base substitution catalogues represent historical records of mutational processes that have been active in a cell. Such processes can be distinguished by various characteristics, like mutation type, sequence context, transcriptional and replicative strand bias, genomic distribution and association with (epi)-genomic features.

Results

We have created MutationalPatterns, an R/Bioconductor package that allows researchers to characterize a broad range of patterns in base substitution catalogues to dissect the underlying molecular mechanisms. Furthermore, it offers an efficient method to quantify the contribution of known mutational signatures within single samples. This analysis can be used to determine whether certain DNA repair mechanisms are perturbed and to further characterize the processes underlying known mutational signatures.

Conclusions

MutationalPatterns allows for easy characterization and visualization of mutational patterns. These analyses willsupport fundamental research into mutational mechanisms and may ultimately improve cancer diagnosis and treatment strategies. MutationalPatterns is freely available at http://bioconductor.org/packages/MutationalPatterns.

Electronic supplementary material

The online version of this article (10.1186/s13073-018-0539-0) contains supplementary material, which is available to authorized users.

Real-Time Genomic Characterization of Metastatic Pancreatic Neuroendocrine Tumors Has Prognostic Implications and Identifies Potential Germline Actionability

Purpose

We assessed the usefulness of real-time molecular profiling through next-generation sequencing (NGS) in predicting the tumor biology of advanced pancreatic neuroendocrine tumors (panNETs) and in characterizing genomic evolution.

Methods

Patients with metastatic panNETs were recruited in the routine clinical practice setting (between May 2014 and March 2017) for prospective NGS of their tumors as well as for germline analysis using the Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT) sequencing platform. When possible, NGS was performed at multiple time points.

Results

NGS was performed in 96 tumor samples from 80 patients. Somatic alterations were identified in 76 of 80 patients (95%). The most commonly altered genes were MEN1 (56%), DAXX (40%), ATRX (25%), and TSC2 (25%). Alterations could be defined in pathways that included chromatin remodeling factors, histone methyltransferases, and mammalian target of rapamycin pathway genes. Somatic loss of heterozygosity was particularly prevalent (50 of 96 samples [52%]), and the presence of loss of heterozygosity resulted in inferior overall survival (P < .01). Sequencing of pre- and post-treatment samples revealed tumor-grade progression; clonal evolution patterns were also seen (molecular resistance mechanisms and chemotherapy-associated mutagenesis). Germline genetic analysis identified clinically actionable pathogenic or likely pathogenic variants in 14 of 88 patients (16%), including mutations in high-penetrance cancer susceptibility genes (MEN1, TSC2, and VHL).

Conclusion

A clinical NGS platform reveals pertubations of biologic pathways in metastatic panNETs that may inform prognosis and direct therapies. Repeat sequencing at disease progression reveals increasing tumor grade and genetic evolution, demonstrating that panNETs adopt a more aggressive behavior through time and therapies. In addition to frequent somatic mutations in MEN1 and TSC2, germline mutations in these same genes underlie susceptibility to panNETs and highlight the need to re-evaluate whether germline genetic analysis should be performed for all patients with panNETs.

Biochemical and genetic predictors of overall survival in patients with metastatic pancreatic cancer treated with capecitabine and nab-paclitaxel

Pancreatic cancer is a dismal disease with a mortality rate almost similar to its incidence rate. To date, there are neither validated predictive nor prognostic biomarkers for this lethal disease. Thus, the aim of the present study was to retrospectively investigate the capability of biochemical parameters and molecular profiles to predict survival of patients with metastatic pancreatic ductal adenocarcinoma (mPDAC) who participated in a phase II clinical trial to test the safety and efficacy of the combination treatment of capecitabine plus nab-paclitaxel. Herein, we investigated the association of 18 biochemical parameters obtained from routine diagnosis and the clinical outcome of the 30 patients enrolled in the clinical trial. Furthermore, we analysed formalin-fixed paraffin-embedded (FFPE) tumour tissue to identify molecular biomarkers via RNA seq and the Illumina TruSeq Amplicon Cancer panel which covers 48 hotspot genes. Our analysis identified SERPINB7 as a novel transcript and a DNA mutation signature that might predict a poor outcome of disease. Moreover, we identified the bilirubin basal level as an independent predictive factor for overall survival in our study cohort.