A Compendium of Mutational Signatures of Environmental Agents.

Whole-genome-sequencing (WGS) of human tumors has revealed distinct mutation patterns that hint at the causative origins of cancer. We examined mutational signatures in 324 WGS human-induced pluripotent stem cells exposed to 79 known or suspected environmental carcinogens. Forty-one yielded characteristic substitution mutational signatures. Some were similar to signatures found in human tumors. Additionally, six agents produced double-substitution signatures and eight produced indel signatures. Investigating mutation asymmetries across genome topography revealed fully functional mismatch and transcription-coupled repair pathways. DNA damage induced by environmental mutagens can be resolved by disparate repair and/or replicative pathways, resulting in an assortment of signature outcomes even for a single agent. This compendium of experimentally induced mutational signatures permits further exploration of roles of environmental agents in cancer etiology and underscores how human stem cell DNA is directly vulnerable to environmental agents. VIDEO ABSTRACT.

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.

Author Correction: Landscape of somatic mutations in 560 breast cancer whole-genome sequences.

In the Methods section of this Article, 'greater than' should have been 'less than' in the sentence 'Putative regions of clustered rearrangements were identified as having an average inter-rearrangement distance that was at least 10 times greater than the whole-genome average for the individual sample. '. The Article has not been corrected.

Landscape of somatic mutations in 560 breast cancer whole-genome sequences.

We analysed whole-genome sequences of 560 breast cancers to advance understanding of the driver mutations conferring clonal advantage and the mutational processes generating somatic mutations. We found that 93 protein-coding cancer genes carried probable driver mutations. Some non-coding regions exhibited high mutation frequencies, but most have distinctive structural features probably causing elevated mutation rates and do not contain driver mutations. Mutational signature analysis was extended to genome rearrangements and revealed twelve base substitution and six rearrangement signatures. Three rearrangement signatures, characterized by tandem duplications or deletions, appear associated with defective homologous-recombination-based DNA repair: one with deficient BRCA1 function, another with deficient BRCA1 or BRCA2 function, the cause of the third is unknown. This analysis of all classes of somatic mutation across exons, introns and intergenic regions highlights the repertoire of cancer genes and mutational processes operating, and progresses towards a comprehensive account of the somatic genetic basis of breast cancer.

Noncanonical secondary structures arising from non-B DNA motifs are determinants of mutagenesis.

Somatic mutations show variation in density across cancer genomes. Previous studies have shown that chromatin organization and replication time domains are correlated with, and thus predictive of, this variation. Here, we analyze 1809 whole-genome sequences from 10 cancer types to show that a subset of repetitive DNA sequences, called non-B motifs that predict noncanonical secondary structure formation can independently account for variation in mutation density. Combined with epigenetic factors and replication timing, the variance explained can be improved to 43%-76%. Approximately twofold mutation enrichment is observed directly within non-B motifs, is focused on exposed structural components, and is dependent on physical properties that are optimal for secondary structure formation. Therefore, there is mounting evidence that secondary structures arising from non-B motifs are not simply associated with increased mutation density-they are possibly causally implicated. Our results suggest that they are determinants of mutagenesis and increase the likelihood of recurrent mutations in the genome. This analysis calls for caution in the interpretation of recurrent mutations and highlights the importance of taking non-B motifs that can simply be inferred from the reference sequence into consideration in background models of mutability henceforth.

ChromoTrace: Computational reconstruction of 3D chromosome configurations for super-resolution microscopy.

The 3D structure of chromatin plays a key role in genome function, including gene expression, DNA replication, chromosome segregation, and DNA repair. Furthermore the location of genomic loci within the nucleus, especially relative to each other and nuclear structures such as the nuclear envelope and nuclear bodies strongly correlates with aspects of function such as gene expression. Therefore, determining the 3D position of the 6 billion DNA base pairs in each of the 23 chromosomes inside the nucleus of a human cell is a central challenge of biology. Recent advances of super-resolution microscopy in principle enable the mapping of specific molecular features with nanometer precision inside cells. Combined with highly specific, sensitive and multiplexed fluorescence labeling of DNA sequences this opens up the possibility of mapping the 3D path of the genome sequence in situ. Here we develop computational methodologies to reconstruct the sequence configuration of all human chromosomes in the nucleus from a super-resolution image of a set of fluorescent in situ probes hybridized to the genome in a cell. To test our approach, we develop a method for the simulation of DNA in an idealized human nucleus. Our reconstruction method, ChromoTrace, uses suffix trees to assign a known linear ordering of in situ probes on the genome to an unknown set of 3D in-situ probe positions in the nucleus from super-resolved images using the known genomic probe spacing as a set of physical distance constraints between probes. We find that ChromoTrace can assign the 3D positions of the majority of loci with high accuracy and reasonable sensitivity to specific genome sequences. By simulating appropriate spatial resolution, label multiplexing and noise scenarios we assess our algorithms performance. Our study shows that it is feasible to achieve genome-wide reconstruction of the 3D DNA path based on super-resolution microscopy images.

Short inverted repeats contribute to localized mutability in human somatic cells.

Selected repetitive sequences termed short inverted repeats (SIRs) have the propensity to form secondary DNA structures called hairpins. SIRs comprise palindromic arm sequences separated by short spacer sequences that form the hairpin stem and loop respectively. Here, we show that SIRs confer an increase in localized mutability in breast cancer, which is domain-dependent with the greatest mutability observed within spacer sequences (∼1.35-fold above background). Mutability is influenced by factors that increase the likelihood of formation of hairpins such as loop lengths (of 4-5 bp) and stem lengths (of 7-15 bp). Increased mutability is an intrinsic property of SIRs as evidenced by how almost all mutational processes demonstrate a higher rate of mutagenesis of spacer sequences. We further identified 88 spacer sequences showing enrichment from 1.8- to 90-fold of local mutability distributed across 283 sites in the genome that intriguingly, can be used to inform the biological status of a tumor.

VEGAWES: variational segmentation on whole exome sequencing for copy number detection.

BACKGROUND: Copy number variations are important in the detection and progression of significant tumors and diseases. Recently, Whole Exome Sequencing is gaining popularity with copy number variations detection due to low cost and better efficiency. In this work, we developed VEGAWES for accurate and robust detection of copy number variations on WES data. VEGAWES is an extension to a variational based segmentation algorithm, VEGA: Variational estimator for genomic aberrations, which has previously outperformed several algorithms on segmenting array comparative genomic hybridization data. RESULTS: We tested this algorithm on synthetic data and 100 Glioblastoma Multiforme primary tumor samples. The results on the real data were analyzed with segmentation obtained from Single-nucleotide polymorphism data as ground truth. We compared our results with two other segmentation algorithms and assessed the performance based on accuracy and time. CONCLUSIONS: In terms of both accuracy and time, VEGAWES provided better results on the synthetic data and tumor samples demonstrating its potential in robust detection of aberrant regions in the genome.

The genome as a record of environmental exposure.

Whole genome sequencing of human tumours has revealed distinct patterns of mutation that hint at the causative origins of cancer. Experimental investigations of the mutations and mutation spectra induced by environmental mutagens have traditionally focused on single genes. With the advent of faster cheaper sequencing platforms, it is now possible to assess mutation spectra in experimental models across the whole genome. As a proof of principle, we have examined the whole genome mutation profiles of mouse embryo fibroblasts immortalised following exposure to benzo[a]pyrene (BaP), ultraviolet light (UV) and aristolochic acid (AA). The results reveal that each mutagen induces a characteristic mutation signature: predominantly G→T mutations for BaP, C→T and CC→TT for UV and A→T for AA. The data are not only consistent with existing knowledge but also provide additional information at higher levels of genomic organisation. The approach holds promise for identifying agents responsible for mutations in human tumours and for shedding light on the aetiology of human cancer.

VegaMC: a R/bioconductor package for fast downstream analysis of large array comparative genomic hybridization datasets.

SUMMARY: Identification of genetic alterations of tumor cells has become a common method to detect the genes involved in development and progression of cancer. In order to detect driver genes, several samples need to be simultaneously analyzed. The Cancer Genome Atlas (TCGA) project provides access to a large amount of data for several cancer types. TGCA is an invaluable source of information, but analysis of this huge dataset possess important computational problems in terms of memory and execution times. Here, we present a R/package, called VegaMC (Vega multi-channel), that enables fast and efficient detection of significant recurrent copy number alterations in very large datasets. VegaMC is integrated with the output of the common tools that convert allele signal intensities in log R ratio and B allele frequency. It also enables the detection of loss of heterozigosity and provides in output two web pages allowing a rapid and easy navigation of the aberrant genes. Synthetic data and real datasets are used for quantitative and qualitative evaluation purposes. In particular, we demonstrate the ability of VegaMC on two large TGCA datasets: colon adenocarcinoma and glioblastoma multiforme. For both the datasets, we provide the list of aberrant genes which contain previously validated genes and can be used as basis for further investigations. AVAILABILITY: VegaMC is a R/Bioconductor Package, available at http://bioconductor.org/packages/release/bioc/html/VegaMC.html. CONTACT: morganella@unisannio.it SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Finding recurrent copy number alterations preserving within-sample homogeneity.

MOTIVATION: Copy number alterations (CNAs) represent an important component of genetic variation and play a significant role in many human diseases. Development of array comparative genomic hybridization (aCGH) technology has made it possible to identify CNAs. Identification of recurrent CNAs represents the first fundamental step to provide a list of genomic regions which form the basis for further biological investigations. The main problem in recurrent CNAs discovery is related to the need to distinguish between functional changes and random events without pathological relevance. Within-sample homogeneity represents a common feature of copy number profile in cancer, so it can be used as additional source of information to increase the accuracy of the results. Although several algorithms aimed at the identification of recurrent CNAs have been proposed, no attempt of a comprehensive comparison of different approaches has yet been published. RESULTS: We propose a new approach, called Genomic Analysis of Important Alterations (GAIA), to find recurrent CNAs where a statistical hypothesis framework is extended to take into account within-sample homogeneity. Statistical significance and within-sample homogeneity are combined into an iterative procedure to extract the regions that likely are involved in functional changes. Results show that GAIA represents a valid alternative to other proposed approaches. In addition, we perform an accurate comparison by using two real aCGH datasets and a carefully planned simulation study. AVAILABILITY: GAIA has been implemented as R/Bioconductor package. It can be downloaded from the following page http://bioinformatics.biogem.it/download/gaia. CONTACT: ceccarelli@unisannio.it; morganella@unisannio.it. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Hydroxymethylation profile of cell-free DNA is a biomarker for early colorectal cancer.

Early detection of cancer will improve survival rates. The blood biomarker 5-hydroxymethylcytosine has been shown to discriminate cancer. In a large covariate-controlled study of over two thousand individual blood samples, we created, tested and explored the properties of a 5-hydroxymethylcytosine-based classifier to detect colorectal cancer (CRC). In an independent validation sample set, the classifier discriminated CRC samples from controls with an area under the receiver operating characteristic curve (AUC) of 90% (95% CI [87, 93]). Sensitivity was 55% at 95% specificity. Performance was similar for early stage 1 (AUC 89%; 95% CI [83, 94]) and late stage 4 CRC (AUC 94%; 95% CI [89, 98]). The classifier could detect CRC even when the proportion of tumor DNA in blood was undetectable by other methods. Expanding the classifier to include information about cell-free DNA fragment size and abundance across the genome led to gains in sensitivity (63% at 95% specificity), with similar overall performance (AUC 91%; 95% CI [89, 94]). We confirm that 5-hydroxymethylcytosine can be used to detect CRC, even in early-stage disease. Therefore, the inclusion of 5-hydroxymethylcytosine in multianalyte testing could improve sensitivity for the detection of early-stage cancer.

VEGA: variational segmentation for copy number detection.

MOTIVATION: Genomic copy number (CN) information is useful to study genetic traits of many diseases. Using array comparative genomic hybridization (aCGH), researchers are able to measure the copy number of thousands of DNA loci at the same time. Therefore, a current challenge in bioinformatics is the development of efficient algorithms to detect the map of aberrant chromosomal regions. METHODS: We describe an approach for the segmentation of copy number aCGH data. Variational estimator for genomic aberrations (VEGA) adopt a variational model used in image segmentation. The optimal segmentation is modeled as the minimum of an energy functional encompassing both the quality of interpolation of the data and the complexity of the solution measured by the length of the boundaries between segmented regions. This solution is obtained by a region growing process where the stop condition is completely data driven. RESULTS: VEGA is compared with three algorithms that represent the state of the art in CN segmentation. Performance assessment is made both on synthetic and real data. Synthetic data simulate different noise conditions. Results on these data show the robustness with respect to noise of variational models and the accuracy of VEGA in terms of recall and precision. Eight mantle cell lymphoma cell lines and two samples of glioblastoma multiforme are used to evaluate the behavior of VEGA on real biological data. Comparison between results and current biological knowledge shows the ability of the proposed method in detecting known chromosomal aberrations. AVAILABILITY: VEGA has been implemented in R and is available at the address http://www.dsba.unisannio.it/Members/ceccarelli/vega in the section Download.

Genome-wide analysis of copy number alterations led to the characterisation of PDCD10 as oncogene in ovarian cancer.

Copy Number Alterations (CNAs) represent the most common genetic alterations identified in ovarian cancer cells, being responsible for the extensive genomic instability observed in this cancer. Here we report the identification of CNAs in a cohort of Italian patients affected by ovarian cancer performed by SNP-based array. Our analysis allowed the identification of 201 significantly altered chromosomal bands (70 copy number gains; 131 copy number losses). The 3300 genes subjected to CNA identified here were compared to those present in the TCGA dataset. The analysis allowed the identification of 11 genes with increased CN and mRNA expression (PDCD10, EBAG9, NUDCD1, ENY2, CSNK2A1, TBC1D20, ZCCHC3, STARD3, C19orf12, POP4, UQCRFS1). PDCD10 was selected for further studies because of the highest frequency of CNA. PDCD10 was found, by immunostaining of three different Tissue Micro Arrays, to be over-expressed in the majority of ovarian primary cancer samples and in metastatic lesions. Moreover, significant correlations were found in specific subsets of patients, between increased PDCD10 expression and grade (p < 0.005), nodal involvement (p < 0.05) or advanced FIGO stage (p < 0.01). Finally, manipulation of PDCD10 expression by shRNA in ovarian cancer cells (OVCAR-5 and OVCA429) demonstrated a positive role for PDCD10 in the control of cell growth and motility in vitro and tumorigenicity in vivo. In conclusion, this study allowed the identification of novel genes subjected to copy number alterations in ovarian cancer. In particular, the results reported here point to a prominent role of PDCD10 as a bona fide oncogene.

TimeDelay-ARACNE: Reverse engineering of gene networks from time-course data by an information theoretic approach.

BACKGROUND: One of main aims of Molecular Biology is the gain of knowledge about how molecular components interact each other and to understand gene function regulations. Using microarray technology, it is possible to extract measurements of thousands of genes into a single analysis step having a picture of the cell gene expression. Several methods have been developed to infer gene networks from steady-state data, much less literature is produced about time-course data, so the development of algorithms to infer gene networks from time-series measurements is a current challenge into bioinformatics research area. In order to detect dependencies between genes at different time delays, we propose an approach to infer gene regulatory networks from time-series measurements starting from a well known algorithm based on information theory. RESULTS: In this paper we show how the ARACNE (Algorithm for the Reconstruction of Accurate Cellular Networks) algorithm can be used for gene regulatory network inference in the case of time-course expression profiles. The resulting method is called TimeDelay-ARACNE. It just tries to extract dependencies between two genes at different time delays, providing a measure of these dependencies in terms of mutual information. The basic idea of the proposed algorithm is to detect time-delayed dependencies between the expression profiles by assuming as underlying probabilistic model a stationary Markov Random Field. Less informative dependencies are filtered out using an auto calculated threshold, retaining most reliable connections. TimeDelay-ARACNE can infer small local networks of time regulated gene-gene interactions detecting their versus and also discovering cyclic interactions also when only a medium-small number of measurements are available. We test the algorithm both on synthetic networks and on microarray expression profiles. Microarray measurements concern S. cerevisiae cell cycle, E. coli SOS pathways and a recently developed network for in vivo assessment of reverse engineering algorithms. Our results are compared with ARACNE itself and with the ones of two previously published algorithms: Dynamic Bayesian Networks and systems of ODEs, showing that TimeDelay-ARACNE has good accuracy, recall and F-score for the network reconstruction task. CONCLUSIONS: Here we report the adaptation of the ARACNE algorithm to infer gene regulatory networks from time-course data, so that, the resulting network is represented as a directed graph. The proposed algorithm is expected to be useful in reconstruction of small biological directed networks from time course data.

A practical framework and online tool for mutational signature analyses show inter-tissue variation and driver dependencies.

Mutational signatures are patterns of mutations that arise during tumorigenesis. We present an enhanced, practical framework for mutational signature analyses. Applying these methods on 3,107 whole genome sequenced (WGS) primary cancers of 21 organs reveals known signatures and nine previously undescribed rearrangement signatures. We highlight inter-organ variability of signatures and present a way of visualizing that diversity, reinforcing our findings in an independent analysis of 3,096 WGS metastatic cancers. Signatures with a high level of genomic instability are dependent on TP53 dysregulation. We illustrate how uncertainty in mutational signature identification and assignment to samples affects tumor classification, reinforcing that using multiple orthogonal mutational signature data is not only beneficial, it is essential for accurate tumor stratification. Finally, we present a reference web-based tool for cancer and experimentally-generated mutational signatures, called Signal (https://signal.mutationalsignatures.com), that also supports performing mutational signature analyses.

IRIS: a method for reverse engineering of regulatory relations in gene networks.

BACKGROUND: The ultimate aim of systems biology is to understand and describe how molecular components interact to manifest collective behaviour that is the sum of the single parts. Building a network of molecular interactions is the basic step in modelling a complex entity such as the cell. Even if gene-gene interactions only partially describe real networks because of post-transcriptional modifications and protein regulation, using microarray technology it is possible to combine measurements for thousands of genes into a single analysis step that provides a picture of the cell's gene expression. Several databases provide information about known molecular interactions and various methods have been developed to infer gene networks from expression data. However, network topology alone is not enough to perform simulations and predictions of how a molecular system will respond to perturbations. Rules for interactions among the single parts are needed for a complete definition of the network behaviour. Another interesting question is how to integrate information carried by the network topology, which can be derived from the literature, with large-scale experimental data. RESULTS: Here we propose an algorithm, called inference of regulatory interaction schema (IRIS), that uses an iterative approach to map gene expression profile values (both steady-state and time-course) into discrete states and a simple probabilistic method to infer the regulatory functions of the network. These interaction rules are integrated into a factor graph model. We test IRIS on two synthetic networks to determine its accuracy and compare it to other methods. We also apply IRIS to gene expression microarray data for the Saccharomyces cerevisiae cell cycle and for human B-cells and compare the results to literature findings. CONCLUSIONS: IRIS is a rapid and efficient tool for the inference of regulatory relations in gene networks. A topological description of the network and a matrix of gene expression profiles are required as input to the algorithm. IRIS maps gene expression data onto discrete values and then computes regulatory functions as conditional probability tables. The suitability of the method is demonstrated for synthetic data and microarray data. The resulting network can also be embedded in a factor graph model.

GARFIELD classifies disease-relevant genomic features through integration of functional annotations with association signals.

Loci discovered by genome-wide association studies predominantly map outside protein-coding genes. The interpretation of the functional consequences of non-coding variants can be greatly enhanced by catalogs of regulatory genomic regions in cell lines and primary tissues. However, robust and readily applicable methods are still lacking by which to systematically evaluate the contribution of these regions to genetic variation implicated in diseases or quantitative traits. Here we propose a novel approach that leverages genome-wide association studies' findings with regulatory or functional annotations to classify features relevant to a phenotype of interest. Within our framework, we account for major sources of confounding not offered by current methods. We further assess enrichment of genome-wide association studies for 19 traits within Encyclopedia of DNA Elements- and Roadmap-derived regulatory regions. We characterize unique enrichment patterns for traits and annotations driving novel biological insights. The method is implemented in standalone software and an R package, to facilitate its application by the research community.

Partially methylated domains are hypervariable in breast cancer and fuel widespread CpG island hypermethylation.

Global loss of DNA methylation and CpG island (CGI) hypermethylation are key epigenomic aberrations in cancer. Global loss manifests itself in partially methylated domains (PMDs) which extend up to megabases. However, the distribution of PMDs within and between tumor types, and their effects on key functional genomic elements including CGIs are poorly defined. We comprehensively show that loss of methylation in PMDs occurs in a large fraction of the genome and represents the prime source of DNA methylation variation. PMDs are hypervariable in methylation level, size and distribution, and display elevated mutation rates. They impose intermediate DNA methylation levels incognizant of functional genomic elements including CGIs, underpinning a CGI methylator phenotype (CIMP). Repression effects on tumor suppressor genes are negligible as they are generally excluded from PMDs. The genomic distribution of PMDs reports tissue-of-origin and may represent tissue-specific silent regions which tolerate instability at the epigenetic, transcriptomic and genetic level.