Identification of somatically acquired rearrangements in cancer using genome-wide massively parallel paired-end sequencing

Novel candidate cancer genes identified by a large-scale cross-species comparative oncogenomics approach

Comparative genomic hybridization (CGH) can reveal important disease genes but the large regions identified could sometimes contain hundreds of genes. Here we combine high-resolution CGH analysis of 598 human cancer cell lines with insertion sites isolated from 1,005 mouse tumors induced with the murine leukemia virus (MuLV). This cross-species oncogenomic analysis revealed candidate tumor suppressor genes and oncogenes mutated in both human and mouse tumors, making them strong candidates for novel cancer genes. A significant number of these genes contained binding sites for the stem cell transcription factors Oct4 and Nanog. Notably, mice carrying tumors with insertions in or near stem cell module genes, which are thought to participate in cell self-renewal, died significantly faster than mice without these insertions. A comparison of the profile we identified to that induced with the Sleeping Beauty (SB) transposon system revealed significant differences in the profile of recurrently mutated genes. Collectively, this work provides a rich catalogue of new candidate cancer genes for functional analysis.

Simplistic pathways or complex networks?

Signaling events are frequently described in textbooks as linear cascades. However, in reality, input cues are processed by dynamic and context-specific networks, which are assembled from numerous signaling molecules. Diseases, such as cancer, are typically associated with multiple genomic alterations that likely change the structure and dynamics of cellular signaling networks. To assess the impact of such genomic alterations on the structure of signaling networks and on the ability of cells to accurately translate environmental cues into phenotypic changes, we argue studies must be conducted on a network level. Advances in technologies and computational approaches for data integration have permitted network studies of signaling events in both cancer and normal cells. Here we will review recent advances and how they have impacted our view on signaling networks with a specific angle on signal processing in cancer.

A comprehensive catalogue of somatic mutations from a human cancer genome

All cancers carry somatic mutations. A subset of these somatic alterations, termed driver mutations, confer selective growth advantage and are implicated in cancer development, whereas the remainder are passengers. Here we have sequenced the genomes of a malignant melanoma and a lymphoblastoid cell line from the same person, providing the first comprehensive catalogue of somatic mutations from an individual cancer. The catalogue provides remarkable insights into the forces that have shaped this cancer genome. The dominant mutational signature reflects DNA damage due to ultraviolet light exposure, a known risk factor for malignant melanoma, whereas the uneven distribution of mutations across the genome, with a lower prevalence in gene footprints, indicates that DNA repair has been preferentially deployed towards transcribed regions. The results illustrate the power of a cancer genome sequence to reveal traces of the DNA damage, repair, mutation and selection processes that were operative years before the cancer became symptomatic.

A small-cell lung cancer genome with complex signatures of tobacco exposure

Cancer is driven by mutation. Worldwide, tobacco smoking is the principal lifestyle exposure that causes cancer, exerting carcinogenicity through >60 chemicals that bind and mutate DNA. Using massively parallel sequencing technology, we sequenced a small-cell lung cancer cell line, NCI-H209, to explore the mutational burden associated with tobacco smoking. A total of 22,910 somatic substitutions were identified, including 134 in coding exons. Multiple mutation signatures testify to the cocktail of carcinogens in tobacco smoke and their proclivities for particular bases and surrounding sequence context. Effects of transcription-coupled repair and a second, more general, expression-linked repair pathway were evident. We identified a tandem duplication that duplicates exons 3-8 of CHD7 in frame, and another two lines carrying PVT1-CHD7 fusion genes, indicating that CHD7 may be recurrently rearranged in this disease. These findings illustrate the potential for next-generation sequencing to provide unprecedented insights into mutational processes, cellular repair pathways and gene networks associated with cancer.

Genome variation discovery with high-throughput sequencing data

The advent of high-throughput sequencing (HTS) technologies is enabling sequencing of human genomes at a significantly lower cost. The availability of these genomes is hoped to enable novel medical diagnostics and treatment, specific to the individual, thus launching the era of personalized medicine. The data currently generated by HTS machines require extensive computational analysis in order to identify genomic variants present in the sequenced individual. In this paper, we overview HTS technologies and discuss several of the plethora of algorithms and tools designed to analyze HTS data, including algorithms for read mapping, as well as methods for identification of single-nucleotide polymorphisms, insertions/deletions and large-scale structural variants and copy-number variants from these mappings.

A simple algebraic cancer equation: calculating how cancers may arise with normal mutation rates

Background

The purpose of this article is to present a relatively easy to understand cancer model where transformation occurs when the first cell, among many at risk within a colon, accumulates a set of driver mutations. The analysis of this model yields a simple algebraic equation, which takes as inputs the number of stem cells, mutation and division rates, and the number of driver mutations, and makes predictions about cancer epidemiology.

Methods

The equation [p = 1 - (1 - (1 - (1 - u)^d)^k)^Nm ] calculates the probability of cancer (p) and contains five parameters: the number of divisions (d), the number of stem cells (N × m), the number of critical rate-limiting pathway driver mutations (k), and the mutation rate (u). In this model progression to cancer "starts" at conception and mutations accumulate with cell division. Transformation occurs when a critical number of rate-limiting pathway mutations first accumulates within a single stem cell.

Results

When applied to several colorectal cancer data sets, parameter values consistent with crypt stem cell biology and normal mutation rates were able to match the increase in cancer with aging, and the mutation frequencies found in cancer genomes. The equation can help explain how cancer risks may vary with age, height, germline mutations, and aspirin use. APC mutations may shorten pathways to cancer by effectively increasing the numbers of stem cells at risk.

Conclusions

The equation illustrates that age-related increases in cancer frequencies may result from relatively normal division and mutation rates. Although this equation does not encompass all of the known complexity of cancer, it may be useful, especially in a teaching setting, to help illustrate relationships between small and large cancer features.

Allelic genome structural variations in maize detected by array comparative genome hybridization

DNA polymorphisms such as insertion/deletions and duplications affecting genome segments larger than 1 kb are known as copy-number variations (CNVs) or structural variations (SVs). They have been recently studied in animals and humans by using array-comparative genome hybridization (aCGH), and have been associated with several human diseases. Their presence and phenotypic effects in plants have not been investigated on a genomic scale, although individual structural variations affecting traits have been described. We used aCGH to investigate the presence of CNVs in maize by comparing the genome of 13 maize inbred lines to B73. Analysis of hybridization signal ratios of 60,472 60-mer oligonucleotide probes between inbreds in relation to their location in the reference genome (B73) allowed us to identify clusters of probes that deviated from the ratio expected for equal copy-numbers. We found CNVs distributed along the maize genome in all chromosome arms. They occur with appreciable frequency in different germplasm subgroups, suggesting ancient origin. Validation of several CNV regions showed both insertion/deletions and copy-number differences. The nature of CNVs detected suggests CNVs might have a considerable impact on plant phenotypes, including disease response and heterosis.

Genomics of medulloblastoma: from Giemsa-banding to next-generation sequencing in 20 years

Advances in the field of genomics have recently enabled the unprecedented characterization of the cancer genome, providing novel insight into the molecular mechanisms underlying malignancies in humans. The application of high-resolution microarray platforms to the study of medulloblastoma has revealed new oncogenes and tumor suppressors and has implicated changes in DNA copy number, gene expression, and methylation state in its etiology. Additionally, the integration of medulloblastoma genomics with patient clinical data has confirmed molecular markers of prognostic significance and highlighted the potential utility of molecular disease stratification. The advent of next-generation sequencing technologies promises to greatly transform our understanding of medulloblastoma pathogenesis in the next few years, permitting comprehensive analyses of all aspects of the genome and increasing the likelihood that genomic medicine will become part of the routine diagnosis and treatment of medulloblastoma.

Copy number variations in the human genome and strategies for analysis

The structure and sequence of the genome is immensely variable in the human population. Segmental copy number variants (CNVs) contribute to the extensive phenotypic diversity among humans and have been shown to associate with disease susceptibility. In this article, we provide a detailed review of human genetic variations and the experimental approaches used to discover, catalog, and genotype CNVs.

A census of amplified and overexpressed human cancer genes

Integrated genome-wide screens of DNA copy number and gene expression in human cancers have accelerated the rate of discovery of amplified and overexpressed genes. However, the biological importance of most of the genes identified in such studies remains unclear. In this Analysis, we propose a weight-of-evidence based classification system for identifying individual genes in amplified regions that are selected for during tumour development. In a census of the published literature we have identified 77 genes for which there is good evidence of involvement in the development of human cancer.

Our changing view of the genomic landscape of cancer

Sporadic tumours, which account for the majority of all human cancers, arise from the acquisition of somatic, genetic and epigenetic alterations leading to changes in gene sequence, structure, copy number and expression. Within the last decade, the availability of a complete sequence-based map of the human genome, coupled with significant technological advances, has revolutionized the search for somatic alterations in tumour genomes. Recent landmark studies, which resequenced all coding exons within breast, colorectal, brain and pancreatic cancers, have shed new light on the genomic landscape of cancer. Within a given tumour type there are many infrequently mutated genes and a few frequently mutated genes, resulting in incredible genetic heterogeneity. However, when the altered genes are placed into biological processes and biochemical pathways, this complexity is significantly reduced and shared pathways that are affected in significant numbers of tumours can be discerned. The advent of next-generation sequencing technologies has opened up the potential to resequence entire tumour genomes to interrogate protein-encoding genes, non-coding RNA genes, non-genic regions and the mitochondrial genome. During the next decade it is anticipated that the most common forms of human cancer will be systematically surveyed to identify the underlying somatic changes in gene copy number, sequence and expression. The resulting catalogues of somatic alterations will point to candidate cancer genes requiring further validation to determine whether they have a causal role in tumourigenesis. The hope is that this knowledge will fuel improvements in cancer diagnosis, prognosis and therapy, based on the specific molecular alterations that drive individual tumours. In this review, I will provide a historical perspective on the identification of somatic alterations in the pre- and post-genomic eras, with a particular emphasis on recent pioneering studies that have provided unprecedented insights into the genomic landscape of human cancer.

Simplified DGS procedure for large-scale genome structural study

Ditag genome scanning (DGS) uses next-generation DNA sequencing to sequence the ends of ditag fragments produced by restriction enzymes. These sequences are compared to known genome sequences to determine their structure. In order to use DGS for large-scale genome structural studies, we have substantially revised the original protocol by replacing the in vivo genomic DNA cloning with in vitro adaptor ligation, eliminating the ditag concatemerization steps, and replacing the 454 sequencer with Solexa or SOLiD sequencers for ditag sequence collection. This revised protocol further increases genome coverage and resolution and allows DGS to be used to analyze multiple genomes simultaneously.

Mutational heterogeneity in human cancers: origin and consequences

Cancer recapitulates Darwinian evolution. Mutations acquired during life that provide cells with a growth or survival advantage will preferentially multiply to form a tumor. As a result of The Cancer Genome Atlas Project, we have gathered detailed information on the nucleotide sequence changes in a number of human cancers. The sources of mutations in cancer are diverse, and the complexity of those found to be clonally present in tumors has increasingly made it difficult to identify key rate-limiting genes for tumor growth that could serve as potential targets for directed therapies. The impact of DNA sequencing on future cancer research and personalized therapy is likely to be profound and merits critical evaluation.

Cancer genome analysis informatics

The analysis of cancer genomes has benefited from the advances in technology that enable data to be generated on an unprecedented scale, describing a tumour genome's sequence and composition at increasingly high resolution and reducing cost. This progress is likely to increase further over the coming years as next-generation sequencing approaches are applied to the study of cancer genomes, in tandem with large-scale efforts such as the Cancer Genome Atlas and recently announced International Cancer Genome Consortium efforts to complement those already established such as the Sanger Institute Cancer Genome Project. This presents challenges for the cancer researcher and the research community in general, in terms of analysing the data generated in one's own projects and also in coordinating and interrogating data that are publicly available. This review aims to provide a brief overview of some of the main informatics resources currently available and their use, and some of the informatics approaches that may be applied in the study of cancer genomes.

Nucleotide-resolution analysis of structural variants using BreakSeq and a breakpoint library

Structural variants (SVs) are a major source of human genomic variation; however, characterizing them at nucleotide resolution remains challenging. Here we assemble a library of breakpoints at nucleotide resolution from collating and standardizing ~2,000 published SVs. For each breakpoint, we infer its ancestral state (through comparison to primate genomes) and its mechanism of formation (e.g., nonallelic homologous recombination, NAHR). We characterize breakpoint sequences with respect to genomic landmarks, chromosomal location, sequence motifs and physical properties, finding that the occurrence of insertions and deletions is more balanced than previously reported and that NAHR-formed breakpoints are associated with relatively rigid, stable DNA helices. Finally, we demonstrate an approach, BreakSeq, for scanning the reads from short-read sequenced genomes against our breakpoint library to accurately identify previously overlooked SVs, which we then validate by PCR. As new data become available, we expect our BreakSeq approach will become more sensitive and facilitate rapid SV genotyping of personal genomes.

Complex landscapes of somatic rearrangement in human breast cancer genomes

Multiple somatic rearrangements are often found in cancer genomes; however, the underlying processes of rearrangement and their contribution to cancer development are poorly characterized. Here we use a paired-end sequencing strategy to identify somatic rearrangements in breast cancer genomes. There are more rearrangements in some breast cancers than previously appreciated. Rearrangements are more frequent over gene footprints and most are intrachromosomal. Multiple rearrangement architectures are present, but tandem duplications are particularly common in some cancers, perhaps reflecting a specific defect in DNA maintenance. Short overlapping sequences at most rearrangement junctions indicate that these have been mediated by non-homologous end-joining DNA repair, although varying sequence patterns indicate that multiple processes of this type are operative. Several expressed in-frame fusion genes were identified but none was recurrent. The study provides a new perspective on cancer genomes, highlighting the diversity of somatic rearrangements and their potential contribution to cancer development.

Whole-genome cancer analysis as an approach to deeper understanding of tumour biology

Recent advances in DNA sequencing technology are providing unprecedented opportunities for comprehensive analysis of cancer genomes, exomes, transcriptomes, as well as epigenomic components. The integration of these data sets with well-annotated phenotypic and clinical data will expedite improved interventions based on the individual genomics of the patient and the specific disease.

Next-generation sequencing

Next-generation sequencing (also known as massively parallel sequencing) technologies are revolutionising our ability to characterise cancers at the genomic, transcriptomic and epigenetic levels. Cataloguing all mutations, copy number aberrations and somatic rearrangements in an entire cancer genome at base pair resolution can now be performed in a matter of weeks. Furthermore, massively parallel sequencing can be used as a means for unbiased transcriptomic analysis of mRNAs, small RNAs and noncoding RNAs, genome-wide methylation assays and high-throughput chromatin immunoprecipitation assays. Here, I discuss the potential impact of this technology on breast cancer research and the challenges that come with this technological breakthrough.

High-throughput molecular analysis in lung cancer: insights into biology and potential clinical applications

During the last decade, high-throughput technologies including genomic, epigenomic, transcriptomic and proteomic have been applied to further our understanding of the molecular pathogenesis of this heterogeneous disease, and to develop strategies that aim to improve the management of patients with lung cancer. Ultimately, these approaches should lead to sensitive, specific and noninvasive methods for early diagnosis, and facilitate the prediction of response to therapy and outcome, as well as the identification of potential novel therapeutic targets. Genomic studies were the first to move this field forward by providing novel insights into the molecular biology of lung cancer and by generating candidate biomarkers of disease progression. Lung carcinogenesis is driven by genetic and epigenetic alterations that cause aberrant gene function; however, the challenge remains to pinpoint the key regulatory control mechanisms and to distinguish driver from passenger alterations that may have a small but additive effect on cancer development. Epigenetic regulation by DNA methylation and histone modifications modulate chromatin structure and, in turn, either activate or silence gene expression. Proteomic approaches critically complement these molecular studies, as the phenotype of a cancer cell is determined by proteins and cannot be predicted by genomics or transcriptomics alone. The present article focuses on the technological platforms available and some proposed clinical applications. We illustrate herein how the "-omics" have revolutionised our approach to lung cancer biology and hold promise for personalised management of lung cancer.

A high-resolution integrated analysis of genetic and expression profiles of breast cancer cell lines

Tumour cell lines derived from breast cancer patients constitute one of the cornerstones of breast cancer research. To characterise breast cancer cell lines at the genetic level, we have developed a full tiling path bacterial artificial chromosome (BAC) array collection for comparative genomic hybridisation (aCGH). This aCGH BAC collection covers 98% of the entire human genome at a resolution of 40-60 kbp. We have used this platform alongside an in-house produced 17 K cDNA microarray set to characterise the genetic and transcriptomic profiles of 24 breast cancer cell lines, as well as cell types derived from non-diseased breast. We demonstrate that breast cancer cell lines have genomic and transcriptomic features that recapitulate those of primary breast cancers and can be reliably subclassified into basal-like and luminal subgroups. By overlaying aCGH and transcriptomic data, we have identified 753 genes whose expression correlate with copy number; this list comprised numerous oncogenes recurrently amplified and overexpressed in breast cancer (e.g., HER2, MYC, CCND1 and AURKA). Finally, we demonstrate that although breast cancer cell lines have genomic features usually found in grade III breast cancers (i.e., gains of 1q, 8q and 20q), basal-like and luminal cell lines are characterised by distinct genomic aberrations.

Genomic and proteomic biomarkers for cancer: a multitude of opportunities

Biomarkers are molecular indicators of a biological status, and as biochemical species can be assayed to evaluate the presence of cancer and therapeutic interventions. Through a variety of mechanisms cancer cells provide the biomarker material for their own detection. Biomarkers may be detectable in the blood, other body fluids, or tissues. The expectation is that the level of an informative biomarker is related to the specific type of disease present in the body. Biomarkers have potential both as diagnostic indicators and monitors of the effectiveness of clinical interventions. Biomarkers are also able to stratify cancer patients to the most appropriate treatment. Effective biomarkers for the early detection of cancer should provide a patient with a better outcome which in turn will translate into more efficient delivery of healthcare. Technologies for the early detection of cancer have resulted in reductions in disease-associated mortalities from cancers that are otherwise deadly if allowed to progress. Such screening technologies have proven that early detection will decrease the morbidity and mortality from cancer. An emerging theme in biomarker research is the expectation that panels of biomarker analytes rather than single markers will be needed to have sufficient sensitivity and specificity for the presymptomatic detection of cancer. Biomarkers may provide prognostic information of disease enabling interventions using targeted therapeutic agents as well as course-corrections in cancer treatment. Novel genomic, proteomic and metabolomic technologies are being used to discover and validate tumor biomarkers individually and in panels.

Population genomic inferences from sparse high-throughput sequencing of two populations of Drosophila melanogaster

Short-read sequencing techniques provide the opportunity to capture genome-wide sequence data in a single experiment. A current challenge is to identify questions that shallow-depth genomic data can address successfully and to develop corresponding analytical methods that are statistically sound. Here, we apply the Roche/454 platform to survey natural variation in strains of Drosophila melanogaster from an African (n = 3) and a North American (n = 6) population. Reads were aligned to the reference D. melanogaster genomic assembly, single nucleotide polymorphisms were identified, and nucleotide variation was quantified genome wide. Simulations and empirical results suggest that nucleotide diversity can be accurately estimated from sparse data with as little as 0.2x coverage per line. The unbiased genomic sampling provided by random short-read sequencing also allows insight into distributions of transposable elements and copy number polymorphisms found within populations and demonstrates that short-read sequencing methods provide an efficient means to quantify variation in genome organization and content. Continued development of methods for statistical inference of shallow-depth genome-wide sequencing data will allow such sparse, partial data sets to become the norm in the emerging field of population genomics.

Computational methods for discovering structural variation with next-generation sequencing

In the last several years, a number of studies have described large-scale structural variation in several genomes. Traditionally, such methods have used whole-genome array comparative genome hybridization or single-nucleotide polymorphism arrays to detect large regions subject to copy-number variation. Later techniques have been based on paired-end mapping of Sanger sequencing data, providing better resolution and accuracy. With the advent of next-generation sequencing, a new generation of methods is being developed to tackle the challenges of short reads, while taking advantage of the high coverage the new sequencing technologies provide. In this survey, we describe these methods, including their strengths and their limitations, and future research directions.

ChimerDB 2.0--a knowledgebase for fusion genes updated

Chromosome translocations and gene fusions are frequent events in the human genome and have been found to cause diverse types of tumor. ChimerDB is a knowledgebase of fusion genes identified from bioinformatics analysis of transcript sequences in the GenBank and various other public resources such as the Sanger cancer genome project (CGP), OMIM, PubMed and the Mitelman’s database. In this updated version, we significantly modified the algorithm of identifying fusion transcripts. Specifically, the new algorithm is more sensitive and has detected 2699 fusion transcripts with high confidence. Furthermore, it can identify interchromosomal translocations as well as the intrachromosomal deletions or inversions of large DNA segments. Importantly, results from the analysis of next-generation sequencing data in the short read archives are incorporated as well. We updated and integrated all contents (GenBank, Sanger CGP, OMIM, PubMed publications and the Mitelman’s database), and the user-interface has been improved to support diverse types of searches and to enhance the user convenience especially in browsing PubMed articles. We also developed a new alignment viewer that should facilitate examining reliability of fusion transcripts and inferring functional significance. We expect ChimerDB 2.0, available at http://ercsb.ewha.ac.kr/fusiongene, to be a valuable tool in identifying biomarkers and drug targets.

COSMIC (the Catalogue of Somatic Mutations in Cancer): a resource to investigate acquired mutations in human cancer

The catalogue of Somatic Mutations in Cancer (COSMIC) (http://www.sanger.ac.uk/cosmic/) is the largest public resource for information on somatically acquired mutations in human cancer and is available freely without restrictions. Currently (v43, August 2009), COSMIC contains details of 1.5-million experiments performed through 13 423 genes in almost 370 000 tumours, describing over 90 000 individual mutations. Data are gathered from two sources, publications in the scientific literature, (v43 contains 7797 curated articles) and the full output of the genome-wide screens from the Cancer Genome Project (CGP) at the Sanger Institute, UK. Most of the world’s literature on point mutations in human cancer has now been curated into COSMIC and while this is continually updated, a greater emphasis on curating fusion gene mutations is driving the expansion of this information; over 2700 fusion gene mutations are now described. Whole-genome sequencing screens are now identifying large numbers of genomic rearrangements in cancer and COSMIC is now displaying details of these analyses also. Examination of COSMIC’s data is primarily web-driven, focused on providing mutation range and frequency statistics based upon a choice of gene and/or cancer phenotype. Graphical views provide easily interpretable summaries of large quantities of data, and export functions can provide precise details of user-selected data.

Genomic aberrations in 80 cases of primary glioblastoma multiforme: Pathogenetic heterogeneity and putative cytogenetic pathways

Screening the whole glioblastoma multiforme (GBM) genome for aberrations is a good starting point when looking for molecular markers that could potentially stratify patients according to prognosis and optimal treatment. We investigated 80 primary untreated GBM using both G-banding analysis and high-resolution comparative genomic hybridization (HR-CGH). Abnormal karyotypes were found in 83% of the tumors. The most common numerical chromosome aberrations were +7, -10, -13, -14, -15, +20, and -22. Structural abnormalities most commonly involved chromosomes 1 and 3, and the short arm of chromosome 9. HR-CGH verified these findings and revealed additional frequent losses at 1p34-36, 6q22-27, and 19q12-13 and gains of 3q26 and 12q13-15. Although most karyotypes and gain/loss patterns were complex, there was also a distinct subset of tumors displaying simple karyotypic changes only. There was a statistically significant association between trisomy 7 and monosomy 10, and also between +7/-10 as putative primary aberrations and secondary losses of 1p, 9p, 13q, and 22q. The low number of tumors in the rarer histological tumor subgroups precludes definite conclusions, but there did not seem to be any clear-cut cytogenetic-pathological correlations, perhaps with the exception of ring chromosomes in giant cell glioblastomas. Our findings demonstrate that although GBM is a pathogenetically very heterogeneous group of diseases, distinct genomic aberration patterns exist.

High-throughput sequencing of microdissected chromosomal regions

The linkage of disease gene mapping with DNA sequencing is an essential strategy for defining the genetic basis of a disease. New massively parallel sequencing procedures will greatly facilitate this process, although enrichment for the target region before sequencing remains necessary. For this step, various DNA capture approaches have been described that rely on sequence-defined probe sets. To avoid making assumptions on the sequences present in the targeted region, we accessed specific cytogenetic regions in preparation for next-generation sequencing. We directly microdissected the target region in metaphase chromosomes, amplified it by degenerate oligonucleotide-primed PCR, and obtained sufficient material of high quality for high-throughput sequencing. Sequence reads could be obtained from as few as six chromosomal fragments. The power of cytogenetic enrichment followed by next-generation sequencing is that it does not depend on earlier knowledge of sequences in the region being studied. Accordingly, this method is uniquely suited for situations in which the sequence of a reference region of the genome is not available, including population-specific or tumor rearrangements, as well as previously unsequenced genomic regions such as centromeres.

Next-generation sequencing of cancer genomes: back to the future

The systematic karyotyping of bone marrow cells was the first genomic approach used to personalize therapy for patients with leukemia. The paradigm established by cytogenetic studies in leukemia (from gene discovery to therapeutic intervention) now has the potential to be rapidly extended with the use of whole-genome sequencing approaches for cancer, which are now possible. We are now entering a period of exponential growth in cancer gene discovery that will provide many novel therapeutic targets for a large number of cancer types. Establishing the pathogenetic relevance of individual mutations is a major challenge that must be solved. However, after thousands of cancer genomes have been sequenced, the genetic rules of cancer will become known and new approaches for diagnosis, risk stratification and individualized treatment of cancer patients will surely follow.

LookSeq: a browser-based viewer for deep sequencing data

Sequencing a genome to great depth can be highly informative about heterogeneity within an individual or a population. Here we address the problem of how to visualize the multiple layers of information contained in deep sequencing data. We propose an interactive AJAX-based web viewer for browsing large data sets of aligned sequence reads. By enabling seamless browsing and fast zooming, the LookSeq program assists the user to assimilate information at different levels of resolution, from an overview of a genomic region to fine details such as heterogeneity within the sample. A specific problem, particularly if the sample is heterogeneous, is how to depict information about structural variation. LookSeq provides a simple graphical representation of paired sequence reads that is more revealing about potential insertions and deletions than are conventional methods.

Mutational evolution in a lobular breast tumour profiled at single nucleotide resolution

Recent advances in next generation sequencing have made it possible to precisely characterize all somatic coding mutations that occur during the development and progression of individual cancers. Here we used these approaches to sequence the genomes (>43-fold coverage) and transcriptomes of an oestrogen-receptor-alpha-positive metastatic lobular breast cancer at depth. We found 32 somatic non-synonymous coding mutations present in the metastasis, and measured the frequency of these somatic mutations in DNA from the primary tumour of the same patient, which arose 9 years earlier. Five of the 32 mutations (in ABCB11, HAUS3, SLC24A4, SNX4 and PALB2) were prevalent in the DNA of the primary tumour removed at diagnosis 9 years earlier, six (in KIF1C, USP28, MYH8, MORC1, KIAA1468 and RNASEH2A) were present at lower frequencies (1-13%), 19 were not detected in the primary tumour, and two were undetermined. The combined analysis of genome and transcriptome data revealed two new RNA-editing events that recode the amino acid sequence of SRP9 and COG3. Taken together, our data show that single nucleotide mutational heterogeneity can be a property of low or intermediate grade primary breast cancers and that significant evolution can occur with disease progression.

Targeted next-generation sequencing of a cancer transcriptome enhances detection of sequence variants and novel fusion transcripts

Targeted RNA-Seq combines next-generation sequencing with capture of sequences from a relevant subset of a transcriptome. When testing by capturing sequences from a tumor cDNA library by hybridization to oligonucleotide probes specific for 467 cancer-related genes, this method showed high selectivity, improved mutation detection enabling discovery of novel chimeric transcripts, and provided RNA expression data. Thus, targeted RNA-Seq produces an enhanced view of the molecular state of a set of "high interest" genes.

High-resolution identification of balanced and complex chromosomal rearrangements by 4C technology

Balanced chromosomal rearrangements can cause disease, but techniques for their rapid and accurate identification are missing. Here we demonstrate that chromatin conformation capture on chip (4C) technology can be used to screen large genomic regions for balanced and complex inversions and translocations at high resolution. The 4C technique can be used to detect breakpoints also in repetitive DNA sequences as it uniquely relies on capturing genomic fragments across the breakpoint. Using 4C, we uncovered LMO3 as a potentially leukemogenic translocation partner of TRB@. We developed multiplex 4C to simultaneously screen for translocation partners of multiple selected loci. We identified unsuspected translocations and complex rearrangements. Furthermore, using 4C we detected translocations even in small subpopulations of cells. This strategy opens avenues for the rapid fine-mapping of cytogenetically identified translocations and inversions, and the efficient screening for balanced rearrangements near candidate loci, even when rearrangements exist only in subpopulations of cells.

Origins and functional impact of copy number variation in the human genome

Structural variations of DNA greater than 1 kilobase in size account for most bases that vary among human genomes, but are still relatively under-ascertained. Here we use tiling oligonucleotide microarrays, comprising 42 million probes, to generate a comprehensive map of 11,700 copy number variations (CNVs) greater than 443 base pairs, of which most (8,599) have been validated independently. For 4,978 of these CNVs, we generated reference genotypes from 450 individuals of European, African or East Asian ancestry. The predominant mutational mechanisms differ among CNV size classes. Retrotransposition has duplicated and inserted some coding and non-coding DNA segments randomly around the genome. Furthermore, by correlation with known trait-associated single nucleotide polymorphisms (SNPs), we identified 30 loci with CNVs that are candidates for influencing disease susceptibility. Despite this, having assessed the completeness of our map and the patterns of linkage disequilibrium between CNVs and SNPs, we conclude that, for complex traits, the heritability void left by genome-wide association studies will not be accounted for by common CNVs.

Deciphering the genetic landscape of cancer--from genes to pathways

Advances in genomic technologies have made it possible to screen the entire cancer genome for mutations, leading to a better understanding of the genetic landscape of cancer. Emerging results suggest that the cancer genome is composed of a few commonly mutated genes and many infrequently mutated genes. Although the number of mutated genes in any one tumor is limited, there is much heterogeneity in the genes mutated in two tumors of even the same class because of the large number of infrequently mutated genes. This could explain the wide variation in tumor behavior to chemotherapeutic intervention. Pathway analysis suggests this large collection of cancer genes functions in a few signaling pathways, providing a simplifying picture of cancer, and indicating the possibility of treating cancer using target-based therapeutics directed against the deregulated signaling pathways themselves rather than the individually mutated genes.

Anticancer drug discovery in the future: an evolutionary perspective

Identification of agents that are pharmacologically active against human cancer has depended largely on the screening of natural products and their analogs. Many anticancer drugs have been discovered fortuitously through random investigation of organisms; indeed, serendipity remains important in anticancer drug discovery. Although it is broadly accepted that cancers comprise an evolutionary microcosm, this idea has not been advanced to understand and control carcinogenic progression. Here, we address anticancer drug discovery from an evolutionary perspective and present a series of case studies that demonstrate that the rate of anticancer drug discovery can be increased greatly by targeted screening of natural compounds from ancient species.

Yeast genome analysis identifies chromosomal translocation, gene conversion events and several sites of Ty element insertion

Paired end mapping of chromosomal fragments has been used in human cells to identify numerous structural variations in chromosomes of individuals and of cancer cell lines; however, the molecular, biological and bioinformatics methods for this technology are still in development. Here, we present a parallel bioinformatics approach to analyze chromosomal paired-end tag (ChromPET) sequence data and demonstrate its application in identifying gene rearrangements in the model organism Saccharomyces cerevisiae. We detected several expected events, including a chromosomal rearrangement of the nonessential arm of chromosome V induced by selective pressure, rearrangements introduced during strain construction and gene conversion at the MAT locus. In addition, we discovered several unannotated Ty element insertions that are present in the reference yeast strain, but not in the reference genome sequence, suggesting a few revisions are necessary in the latter. These data demonstrate that application of the chromPET technique to a genetically tractable organism like yeast provides an easy screen for studying the mechanisms of chromosomal rearrangements during the propagation of a species.

Targeted capture and massively parallel sequencing of 12 human exomes

Genome-wide association studies suggest that common genetic variants explain only a modest fraction of heritable risk for common diseases, raising the question of whether rare variants account for a significant fraction of unexplained heritability. Although DNA sequencing costs have fallen markedly, they remain far from what is necessary for rare and novel variants to be routinely identified at a genome-wide scale in large cohorts. We have therefore sought to develop second-generation methods for targeted sequencing of all protein-coding regions ('exomes'), to reduce costs while enriching for discovery of highly penetrant variants. Here we report on the targeted capture and massively parallel sequencing of the exomes of 12 humans. These include eight HapMap individuals representing three populations, and four unrelated individuals with a rare dominantly inherited disorder, Freeman-Sheldon syndrome (FSS). We demonstrate the sensitive and specific identification of rare and common variants in over 300 megabases of coding sequence. Using FSS as a proof-of-concept, we show that candidate genes for Mendelian disorders can be identified by exome sequencing of a small number of unrelated, affected individuals. This strategy may be extendable to diseases with more complex genetics through larger sample sizes and appropriate weighting of non-synonymous variants by predicted functional impact.

Applications of new sequencing technologies for transcriptome analysis

Transcriptome analysis has been a key area of biological inquiry for decades. Over the years, research in the field has progressed from candidate gene-based detection of RNAs using Northern blotting to high-throughput expression profiling driven by the advent of microarrays. Next-generation sequencing technologies have revolutionized transcriptomics by providing opportunities for multidimensional examinations of cellular transcriptomes in which high-throughput expression data are obtained at a single-base resolution.

Sequence and structural variation in a human genome uncovered by short-read, massively parallel ligation sequencing using two-base encoding

We describe the genome sequencing of an anonymous individual of African origin using a novel ligation-based sequencing assay that enables a unique form of error correction that improves the raw accuracy of the aligned reads to >99.9%, allowing us to accurately call SNPs with as few as two reads per allele. We collected several billion mate-paired reads yielding approximately 18x haploid coverage of aligned sequence and close to 300x clone coverage. Over 98% of the reference genome is covered with at least one uniquely placed read, and 99.65% is spanned by at least one uniquely placed mate-paired clone. We identify over 3.8 million SNPs, 19% of which are novel. Mate-paired data are used to physically resolve haplotype phases of nearly two-thirds of the genotypes obtained and produce phased segments of up to 215 kb. We detect 226,529 intra-read indels, 5590 indels between mate-paired reads, 91 inversions, and four gene fusions. We use a novel approach for detecting indels between mate-paired reads that are smaller than the standard deviation of the insert size of the library and discover deletions in common with those detected with our intra-read approach. Dozens of mutations previously described in OMIM and hundreds of nonsynonymous single-nucleotide and structural variants in genes previously implicated in disease are identified in this individual. There is more genetic variation in the human genome still to be uncovered, and we provide guidance for future surveys in populations and cancer biopsies.

Personalized copy number and segmental duplication maps using next-generation sequencing

Despite their importance in gene innovation and phenotypic variation, duplicated regions have remained largely intractable due to difficulties in accurately resolving their structure, copy number and sequence content. We present an algorithm (mrFAST) to comprehensively map next-generation sequence reads allowing for the prediction of absolute copy-number variation of duplicated segments and genes. We examine three human genomes and experimentally validate genome-wide copy-number differences. We estimate that 73–87 genes will be on average copy-number variable between two human genomes and find that these genic differences overwhelmingly correspond to segmental duplications (OR=135; p<2.2e-16). Our method can distinguish between different copies of highly identical genes, providing a more accurate census of gene content and insight into functional constraint without the limitations of array-based technology.

Statistical aspects of discerning indel-type structural variation via DNA sequence alignment

Background

Structural variations in the form of DNA insertions and deletions are an important aspect of human genetics and especially relevant to medical disorders. Investigations have shown that such events can be detected via tell-tale discrepancies in the aligned lengths of paired-end DNA sequencing reads. Quantitative aspects underlying this method remain poorly understood, despite its importance and conceptual simplicity. We report the statistical theory characterizing the length-discrepancy scheme for Gaussian libraries, including coverage-related effects that preceding models are unable to account for.

Results

Deletion and insertion statistics both depend heavily on physical coverage, but otherwise differ dramatically, refuting a commonly held doctrine of symmetry. Specifically, coverage restrictions render insertions much more difficult to capture. Increased read length has the counterintuitive effect of worsening insertion detection characteristics of short inserts. Variance in library insert length is also a critical factor here and should be minimized to the greatest degree possible. Conversely, no significant improvement would be realized in lowering fosmid variances beyond current levels. Detection power is examined under a straightforward alternative hypothesis and found to be generally acceptable. We also consider the proposition of characterizing variation over the entire spectrum of variant sizes under constant risk of false-positive errors. At 1% risk, many designs will leave a significant gap in the 100 to 200 bp neighborhood, requiring unacceptably high redundancies to compensate. We show that a few modifications largely close this gap and we give a few examples of feasible spectrum-covering designs.

Conclusion

The theory resolves several outstanding issues and furnishes a general methodology for designing future projects from the standpoint of a spectrum-wide constant risk.

Chimeric transcript discovery by paired-end transcriptome sequencing

Recurrent gene fusions are a prevalent class of mutations arising from the juxtaposition of 2 distinct regions, which can generate novel functional transcripts that could serve as valuable therapeutic targets in cancer. Therefore, we aim to establish a sensitive, high-throughput methodology to comprehensively catalog functional gene fusions in cancer by evaluating a paired-end transcriptome sequencing strategy. Not only did a paired-end approach provide a greater dynamic range in comparison with single read based approaches, but it clearly distinguished the high-level "driving" gene fusions, such as BCR-ABL1 and TMPRSS2-ERG, from potential lower level "passenger" gene fusions. Also, the comprehensiveness of a paired-end approach enabled the discovery of 12 previously undescribed gene fusions in 4 commonly used cell lines that eluded previous approaches. Using the paired-end transcriptome sequencing approach, we observed read-through mRNA chimeras, tissue-type restricted chimeras, converging transcripts, diverging transcripts, and overlapping mRNA transcripts. Last, we successfully used paired-end transcriptome sequencing to detect previously undescribed ETS gene fusions in prostate tumors. Together, this study establishes a highly specific and sensitive approach for accurately and comprehensively cataloguing chimeras within a sample using paired-end transcriptome sequencing.

Acquired copy number alterations in adult acute myeloid leukemia genomes

Cytogenetic analysis of acute myeloid leukemia (AML) cells has accelerated the identification of genes important for AML pathogenesis. To complement cytogenetic studies and to identify genes altered in AML genomes, we performed genome-wide copy number analysis with paired normal and tumor DNA obtained from 86 adult patients with de novo AML using 1.85 million feature SNP arrays. Acquired copy number alterations (CNAs) were confirmed using an ultra-dense array comparative genomic hybridization platform. A total of 201 somatic CNAs were found in the 86 AML genomes (mean, 2.34 CNAs per genome), with French-American-British system M6 and M7 genomes containing the most changes (10-29 CNAs per genome). Twenty-four percent of AML patients with normal cytogenetics had CNA, whereas 40% of patients with an abnormal karyotype had additional CNA detected by SNP array, and several CNA regions were recurrent. The mRNA expression levels of 57 genes were significantly altered in 27 of 50 recurrent CNA regions <5 megabases in size. A total of 8 uniparental disomy (UPD) segments were identified in the 86 genomes; 6 of 8 UPD calls occurred in samples with a normal karyotype. Collectively, 34 of 86 AML genomes (40%) contained alterations not found with cytogenetics, and 98% of these regions contained genes. Of 86 genomes, 43 (50%) had no CNA or UPD at this level of resolution. In this study of 86 adult AML genomes, the use of an unbiased high-resolution genomic screen identified many genes not previously implicated in AML that may be relevant for pathogenesis, along with many known oncogenes and tumor suppressor genes.

A geometric approach for classification and comparison of structural variants

Motivation: Structural variants, including duplications, insertions, deletions and inversions of large blocks of DNA sequence, are an important contributor to human genome variation. Measuring structural variants in a genome sequence is typically more challenging than measuring single nucleotide changes. Current approaches for structural variant identification, including paired-end DNA sequencing/mapping and array comparative genomic hybridization (aCGH), do not identify the boundaries of variants precisely. Consequently, most reported human structural variants are poorly defined and not readily compared across different studies and measurement techniques.

Results: We introduce Geometric Analysis of Structural Variants (GASV), a geometric approach for identification, classification and comparison of structural variants. This approach represents the uncertainty in measurement of a structural variant as a polygon in the plane, and identifies measurements supporting the same variant by computing intersections of polygons. We derive a computational geometry algorithm to efficiently identify all such intersections. We apply GASV to sequencing data from nine individual human genomes and several cancer genomes. We obtain better localization of the boundaries of structural variants, distinguish genetic from putative somatic structural variants in cancer genomes, and integrate aCGH and paired-end sequencing measurements of structural variants. This work presents the first general framework for comparing structural variants across multiple samples and measurement techniques, and will be useful for studies of both genetic structural variants and somatic rearrangements in cancer.

Availability:http://cs.brown.edu/people/braphael/software.html

Contact:braphael@brown.edu

Therapeutic options for triple-negative breast cancers with defective homologous recombination

Breast cancer is the most common malignancy among women in developed countries, affecting more than a million women per year worldwide. Over the last decades, our increasing understanding of breast cancer biology has led to the development of endocrine agents against hormone receptor-positive tumors and targeted therapeutics against HER2-expressing tumors. However, no targeted therapy is available for patients with triple-negative breast cancer, lacking expression of hormone receptors and HER2. Overlap between BRCA1-mutated breast cancers and triple-negative tumors suggests that an important part of the triple-negative tumors may respond to therapeutics targeting BRCA1-deficient cells. Here, we review the features shared between triple-negative, basal-like and BRCA1-related breast cancers. We also discuss the development of novel therapeutic strategies to target BRCA1-mutated tumors and triple-negative tumors with BRCA1-like features. Finally, we highlight the utility of mouse models for BRCA1-mutated breast cancer to optimize (combination) therapy and to understand drug resistance.

Gene copy number and malaria biology

Alteration in gene copy number provides a simple way to change expression levels and alter phenotype. This was fully appreciated by bacteriologists more than 25 years ago, but the extent and implications of copy number polymorphism (CNP) have only recently become apparent in other organisms. New methods demonstrate the ubiquity of CNPs in eukaryotes and their medical importance in humans. CNP is also widespread in the Plasmodium falciparum genome and has an important and underappreciated role in determining phenotype. In this review, we summarize the distribution of CNP, its evolutionary dynamics within populations, its functional importance and its mode of evolution.