Most large structural variants in cancer genomes can be detected without long reads.

Short-read sequencing is the workhorse of cancer genomics yet is thought to miss many structural variants (SVs), particularly large chromosomal alterations. To characterize missing SVs in short-read whole genomes, we analyzed 'loose ends'-local violations of mass balance between adjacent DNA segments. In the landscape of loose ends across 1,330 high-purity cancer whole genomes, most large (>10-kb) clonal SVs were fully resolved by short reads in the 87% of the human genome where copy number could be reliably measured. Some loose ends represent neotelomeres, which we propose as a hallmark of the alternative lengthening of telomeres phenotype. These pan-cancer findings were confirmed by long-molecule profiles of 38 breast cancer and melanoma cases. Our results indicate that aberrant homologous recombination is unlikely to drive the majority of large cancer SVs. Furthermore, analysis of mass balance in short-read whole genome data provides a surprisingly complete picture of cancer chromosomal structure.

When 3D genome changes cause disease: the impact of structural variations in congenital disease and cancer.

Large structural variations (SV) are a class of mutations that have long been known to cause a wide range of genetic diseases, from rare congenital disease to cancer. Many of these SVs do not directly disrupt disease-related genes and determining causal genotype-phenotype relationships has been challenging to disentangle in the past. This has started to change with our increased understanding of the 3D genome folding. The pathophysiologies of the different types of genetic diseases influence the type of SVs observed and their genetic consequences, and how these are connected to 3D genome folding. We propose guiding principles for interpreting disease-associated SVs based on our current understanding of 3D chromatin architecture and the gene-regulatory and physiological mechanisms disrupted in disease.

Long-read sequencing identifies novel structural variations in colorectal cancer.

Structural variations (SVs) are a key type of cancer genomic alterations, contributing to oncogenesis and progression of many cancers, including colorectal cancer (CRC). However, SVs in CRC remain difficult to be reliably detected due to limited SV-detection capacity of the commonly used short-read sequencing. This study investigated the somatic SVs in 21 pairs of CRC samples by Nanopore whole-genome long-read sequencing. 5200 novel somatic SVs from 21 CRC patients (494 SVs / patient) were identified. A 4.9-Mbp long inversion that silences APC expression (confirmed by RNA-seq) and an 11.2-kbp inversion that structurally alters CFTR were identified. Two novel gene fusions that might functionally impact the oncogene RNF38 and the tumor-suppressor SMAD3 were detected. RNF38 fusion possesses metastasis-promoting ability confirmed by in vitro migration and invasion assay, and in vivo metastasis experiments. This work highlighted the various applications of long-read sequencing in cancer genome analysis, and shed new light on how somatic SVs structurally alter critical genes in CRC. The investigation on somatic SVs via nanopore sequencing revealed the potential of this genomic approach in facilitating precise diagnosis and personalized treatment of CRC.

Structural variants drive context-dependent oncogene activation in cancer.

Higher-order chromatin structure is important for the regulation of genes by distal regulatory sequences1,2. Structural variants (SVs) that alter three-dimensional (3D) genome organization can lead to enhancer-promoter rewiring and human disease, particularly in the context of cancer3. However, only a small minority of SVs are associated with altered gene expression4,5, and it remains unclear why certain SVs lead to changes in distal gene expression and others do not. To address these questions, we used a combination of genomic profiling and genome engineering to identify sites of recurrent changes in 3D genome structure in cancer and determine the effects of specific rearrangements on oncogene activation. By analysing Hi-C data from 92 cancer cell lines and patient samples, we identified loci affected by recurrent alterations to 3D genome structure, including oncogenes such as MYC, TERT and CCND1. By using CRISPR-Cas9 genome engineering to generate de novo SVs, we show that oncogene activity can be predicted by using 'activity-by-contact' models that consider partner region chromatin contacts and enhancer activity. However, activity-by-contact models are only predictive of specific subsets of genes in the genome, suggesting that different classes of genes engage in distinct modes of regulation by distal regulatory elements. These results indicate that SVs that alter 3D genome organization are widespread in cancer genomes and begin to illustrate predictive rules for the consequences of SVs on oncogene activation.

Dynamic Interplay between Structural Variations and 3D Genome Organization in Pancreatic Cancer.

Structural variations (SVs) are the greatest source of variations in the genome and can lead to oncogenesis. However, the identification and interpretation of SVs in human cancer remain technologically challenging. Here, long-read sequencing is first employed to depict the signatures of structural variations in carcinogenesis of human pancreatic ductal epithelium. Then widespread reprogramming of the 3D chromatin architecture is revealed by an in situ Hi-C technique. Integrative analyses indicate that the distribution pattern of SVs among the 3D genome is highly cell-type specific and the bulk remodeling effects of SVs in the chromatin organization partly depend on intercellular genomic heterogeneity. Meanwhile, contact domains tend to minimize these disrupting effects of SVs within local adjacent genomic regions to maintain overall stability. Notably, complex genomic rearrangements involving two key driver genes CDKN2A and SMAD4 are identified, and their influence on the expression of oncogenes MIR31HG, MYO5B, etc., are further elucidated from both a linear view and 3D perspective. Overall, this work provides a genome-wide resource and highlights the impact, complexity, and dynamicity of the interplay between structural variations and high-order chromatin organization, which expands the current understanding of the pathogenesis of SVs in human cancer.

Searching thousands of genomes to classify somatic and novel structural variants using STIX.

Structural variants are associated with cancers and developmental disorders, but challenges with estimating population frequency remain a barrier to prioritizing mutations over inherited variants. In particular, variability in variant calling heuristics and filtering limits the use of current structural variant catalogs. We present STIX, a method that, instead of relying on variant calls, indexes and searches the raw alignments from thousands of samples to enable more comprehensive allele frequency estimation.

Rearrangement-mediated cis-regulatory alterations in advanced patient tumors reveal interactions with therapy.

The global impact of somatic structural variants (SVs) on gene regulation in advanced tumors with complex treatment histories has been mostly uncharacterized. Here, using whole-genome and RNA sequencing from 570 recurrent or metastatic tumors, we report the altered expression of hundreds of genes in association with nearby SV breakpoints, including oncogenes and G-protein-coupled receptor-related genes such as PLEKHG2. A significant fraction of genes with SV-expression associations correlate with worse patient survival in primary and advanced cancers, including SRD5A1. In many instances, SV-expression associations involve retrotransposons being translocated near genes. High overall SV burden is associated with treatment with DNA alkylating agents or taxanes and altered expression of metabolism-associated genes. SV-expression associations within tumors from topoisomerase I inhibitor-treated patients include chromatin-related genes. Within anthracycline-treated tumors, SV breakpoints near chromosome 1p genes include PDE4B. Patient treatment and history can help understand the widespread SV-mediated cis-regulatory alterations found in cancer.

Virus-derived variation in diverse human genomes.

Acquisition of genetic material from viruses by their hosts can generate inter-host structural genome variation. We developed computational tools enabling us to study virus-derived structural variants (SVs) in population-scale whole genome sequencing (WGS) datasets and applied them to 3,332 humans. Although SVs had already been cataloged in these subjects, we found previously-overlooked virus-derived SVs. We detected non-germline SVs derived from squirrel monkey retrovirus (SMRV), human immunodeficiency virus 1 (HIV-1), and human T lymphotropic virus (HTLV-1); these variants are attributable to infection of the sequenced lymphoblastoid cell lines (LCLs) or their progenitor cells and may impact gene expression results and the biosafety of experiments using these cells. In addition, we detected new heritable SVs derived from human herpesvirus 6 (HHV-6) and human endogenous retrovirus-K (HERV-K). We report the first solo-direct repeat (DR) HHV-6 likely to reflect DR rearrangement of a known full-length endogenous HHV-6. We used linkage disequilibrium between single nucleotide variants (SNVs) and variants in reads that align to HERV-K, which often cannot be mapped uniquely using conventional short-read sequencing analysis methods, to locate previously-unknown polymorphic HERV-K loci. Some of these loci are tightly linked to trait-associated SNVs, some are in complex genome regions inaccessible by prior methods, and some contain novel HERV-K haplotypes likely derived from gene conversion from an unknown source or introgression. These tools and results broaden our perspective on the coevolution between viruses and humans, including ongoing virus-to-human gene transfer contributing to genetic variation between humans.

Integrative reconstruction of cancer genome karyotypes using InfoGenomeR.

Annotation of structural variations (SVs) and base-level karyotyping in cancer cells remains challenging. Here, we present Integrative Framework for Genome Reconstruction (InfoGenomeR)-a graph-based framework that can reconstruct individual SVs into karyotypes based on whole-genome sequencing data, by integrating SVs, total copy number alterations, allele-specific copy numbers, and haplotype information. Using whole-genome sequencing data sets of patients with breast cancer, glioblastoma multiforme, and ovarian cancer, we demonstrate the analytical potential of InfoGenomeR. We identify recurrent derivative chromosomes derived from chromosomes 11 and 17 in breast cancer samples, with homogeneously staining regions for CCND1 and ERBB2, and double minutes and breakage-fusion-bridge cycles in glioblastoma multiforme and ovarian cancer samples, respectively. Moreover, we show that InfoGenomeR can discriminate private and shared SVs between primary and metastatic cancer sites that could contribute to tumour evolution. These findings indicate that InfoGenomeR can guide targeted therapies by unravelling cancer-specific SVs on a genome-wide scale.

Exome sequencing identifies SLIT2 variants in primary CNS lymphoma.

SLIT2 constitutes a known tumour suppressor gene, which has not yet been implicated in the pathogenesis of primary central nervous system lymphoma (PCNSL). Performing exome sequencing on paired blood and tumour DNA samples from six treatment-naïve PCNSL patients, we identified novel SLIT2 variants (p.N63S, p.T590M, p.T732S) that were associated with shorter progression-free survival in our cohort and shorter overall survival in a large validation cohort of lymphoid malignancies from the cBio Cancer Genomics Portal. WNT- and NF-κB-reporter luciferase assays suggest detected alterations are loss-of-function variants. Given the possible prognostic implications, the role of SLIT2 in PCNSL pathogenesis and progression warrants further investigation.

Novel pathogenic genomic variants leading to autosomal dominant and recessive Robinow syndrome.

Robinow syndrome (RS) is a genetically heterogeneous disorder characterized by skeletal dysplasia and a distinctive facial appearance. Previous studies have revealed locus heterogeneity with rare variants in DVL1, DVL3, FZD2, NXN, ROR2, and WNT5A underlying the etiology of RS. The aforementioned "Robinow-associated genes" and their gene products all play a role in the WNT/planar cell polarity signaling pathway. We performed gene-targeted Sanger sequencing, exome sequencing, genome sequencing, and array comparative genomic hybridization on four subjects with a clinical diagnosis of RS who had not had prior DNA testing. Individuals in our cohort were found to carry pathogenic or likely pathogenic variants in three RS related genes: DVL1, ROR2, and NXN. One subject was found to have a nonsense variant (c.817C > T [p.Gln273*]) in NXN in trans with an ~1 Mb telomeric deletion on chromosome 17p containing NXN, which supports our contention that biallelic NXN variant alleles are responsible for a novel autosomal recessive RS locus. These findings provide increased understanding of the role of WNT signaling in skeletal development and maintenance. These data further support the hypothesis that dysregulation of the noncanonical WNT pathway in humans gives rise to RS.

Distinct Classes of Complex Structural Variation Uncovered across Thousands of Cancer Genome Graphs.

Cancer genomes often harbor hundreds of somatic DNA rearrangement junctions, many of which cannot be easily classified into simple (e.g., deletion) or complex (e.g., chromothripsis) structural variant classes. Applying a novel genome graph computational paradigm to analyze the topology of junction copy number (JCN) across 2,778 tumor whole-genome sequences, we uncovered three novel complex rearrangement phenomena: pyrgo, rigma, and tyfonas. Pyrgo are "towers" of low-JCN duplications associated with early-replicating regions, superenhancers, and breast or ovarian cancers. Rigma comprise "chasms" of low-JCN deletions enriched in late-replicating fragile sites and gastrointestinal carcinomas. Tyfonas are "typhoons" of high-JCN junctions and fold-back inversions associated with expressed protein-coding fusions, breakend hypermutation, and acral, but not cutaneous, melanomas. Clustering of tumors according to genome graph-derived features identified subgroups associated with DNA repair defects and poor prognosis.

Identification of unique and shared mitochondrial DNA mutations in neurodegeneration and cancer by single-cell mitochondrial DNA structural variation sequencing (MitoSV-seq).

BACKGROUND: Point mutations and structural variations (SVs) in mitochondrial DNA (mtDNA) contribute to many neurodegenerative diseases. Technical limitations and heteroplasmy, however, have impeded their identification, preventing these changes from being examined in neurons in healthy and disease states. METHODS: We have developed a high-resolution technique-Mitochondrial DNA Structural Variation Sequencing (MitoSV-seq)-that identifies all types of mtDNA SVs and single-nucleotide variations (SNVs) in single neurons and novel variations that have been undetectable with conventional techniques. FINDINGS: Using MitoSV-seq, we discovered SVs/SNVs in dopaminergic neurons in the Ifnar1-/- murine model of Parkinson disease. Further, MitoSV-seq was found to have broad applicability, delivering high-quality, full-length mtDNA sequences in a species-independent manner from human PBMCs, haematological cancers, and tumour cell lines, regardless of heteroplasmy. We characterised several common SVs in haematological cancers (AML and MDS) that were linked to the same mtDNA region, MT-ND5, using only 10 cells, indicating the power of MitoSV-seq in determining single-cancer-cell ontologies. Notably, the MT-ND5 hotspot, shared between all examined cancers and Ifnar1-/- dopaminergic neurons, suggests that its mutations have clinical value as disease biomarkers. INTERPRETATION: MitoSV-seq identifies disease-relevant mtDNA mutations in single cells with high resolution, rendering it a potential drug screening platform in neurodegenerative diseases and cancers. FUNDING: The Lundbeck Foundation, Danish Council for Independent Research-Medicine, and European Union Horizon 2020 Research and Innovation Programme.

De Novo SOX6 Variants Cause a Neurodevelopmental Syndrome Associated with ADHD, Craniosynostosis, and Osteochondromas.

SOX6 belongs to a family of 20 SRY-related HMG-box-containing (SOX) genes that encode transcription factors controlling cell fate and differentiation in many developmental and adult processes. For SOX6, these processes include, but are not limited to, neurogenesis and skeletogenesis. Variants in half of the SOX genes have been shown to cause severe developmental and adult syndromes, referred to as SOXopathies. We here provide evidence that SOX6 variants also cause a SOXopathy. Using clinical and genetic data, we identify 19 individuals harboring various types of SOX6 alterations and exhibiting developmental delay and/or intellectual disability; the individuals are from 17 unrelated families. Additional, inconstant features include attention-deficit/hyperactivity disorder (ADHD), autism, mild facial dysmorphism, craniosynostosis, and multiple osteochondromas. All variants are heterozygous. Fourteen are de novo, one is inherited from a mosaic father, and four offspring from two families have a paternally inherited variant. Intragenic microdeletions, balanced structural rearrangements, frameshifts, and nonsense variants are predicted to inactivate the SOX6 variant allele. Four missense variants occur in residues and protein regions highly conserved evolutionarily. These variants are not detected in the gnomAD control cohort, and the amino acid substitutions are predicted to be damaging. Two of these variants are located in the HMG domain and abolish SOX6 transcriptional activity in vitro. No clear genotype-phenotype correlations are found. Taken together, these findings concur that SOX6 haploinsufficiency leads to a neurodevelopmental SOXopathy that often includes ADHD and abnormal skeletal and other features.

Integrated paired-end enhancer profiling and whole-genome sequencing reveals recurrent CCNE1 and IGF2 enhancer hijacking in primary gastric adenocarcinoma.

OBJECTIVE: Genomic structural variations (SVs) causing rewiring of cis-regulatory elements remain largely unexplored in gastric cancer (GC). To identify SVs affecting enhancer elements in GC (enhancer-based SVs), we integrated epigenomic enhancer profiles revealed by paired-end H3K27ac ChIP-sequencing from primary GCs with tumour whole-genome sequencing (WGS) data (PeNChIP-seq/WGS). DESIGN: We applied PeNChIP-seq to 11 primary GCs and matched normal tissues combined with WGS profiles of >200 GCs. Epigenome profiles were analysed alongside matched RNA-seq data to identify tumour-associated enhancer-based SVs with altered cancer transcription. Functional validation of candidate enhancer-based SVs was performed using CRISPR/Cas9 genome editing, chromosome conformation capture assays (4C-seq, Capture-C) and Hi-C analysis of primary GCs. RESULTS: PeNChIP-seq/WGS revealed ~150 enhancer-based SVs in GC. The majority (63%) of SVs linked to target gene deregulation were associated with increased tumour expression. Enhancer-based SVs targeting CCNE1, a key driver of therapy resistance, occurred in 8% of patients frequently juxtaposing diverse distal enhancers to CCNE1 proximal regions. CCNE1-rearranged GCs were associated with high CCNE1 expression, disrupted CCNE1 topologically associating domain (TAD) boundaries, and novel TAD interactions in CCNE1-rearranged primary tumours. We also observed IGF2 enhancer-based SVs, previously noted in colorectal cancer, highlighting a common non-coding genetic driver alteration in gastric and colorectal malignancies. CONCLUSION: Integrated paired-end NanoChIP-seq and WGS of gastric tumours reveals tumour-associated regulatory SV in regions associated with both simple and complex genomic rearrangements. Genomic rearrangements may thus exploit enhancer-hijacking as a common mechanism to drive oncogene expression in GC.

SVSR: A Program to Simulate Structural Variations and Generate Sequencing Reads for Multiple Platforms.

Structural variation accounts for a major fraction of mutations in the human genome and confers susceptibility to complex diseases. Next generation sequencing along with the rapid development of computational methods provides a cost-effective procedure to detect such variations. Simulation of structural variations and sequencing reads with real characteristics is essential for benchmarking the computational methods. Here, we develop a new program, SVSR, to simulate five types of structural variations (indels, tandem duplication, CNVs, inversions, and translocations) and SNPs for the human genome and to generate sequencing reads with features from popular platforms (Illumina, SOLiD, 454, and Ion Torrent). We adopt a selection model trained from real data to predict copy number states, starting from the first site of a particular genome to the end. Furthermore, we utilize references of microbial genomes to produce insertion fragments and design probabilistic models to imitate inversions and translocations. Moreover, we create platform-specific errors and base quality profiles to generate normal, tumor, or normal-tumor mixture reads. Experimental results show that SVSR could capture more features that are realistic and generate datasets with satisfactory quality scores. SVSR is able to evaluate the performance of structural variation detection methods and guide the development of new computational methods.

Detecting inherited and novel structural variants in low-coverage parent-child sequencing data.

Structural variants (SVs) are a class of genomic variation shared by members of the same species. Though relatively rare, they represent an increasingly important class of variation, as SVs have been associated with diseases and susceptibility to some types of cancer. Common approaches to SV detection require the sequencing and mapping of fragments from a test genome to a high-quality reference genome. Candidate SVs correspond to fragments with discordant mapped configurations. However, because errors in the sequencing and mapping will also create discordant arrangements, many of these predictions will be spurious. When sequencing coverage is low, distinguishing true SVs from errors is even more challenging. In recent work, we have developed SV detection methods that exploit genome information of closely related individuals - parents and children. Our previous approaches were based on the assumption that any SV present in a child's genome must have come from one of their parents. However, using this strict restriction may have resulted in failing to predict any rare but novel variants present only in the child. In this work, we generalize our previous approaches to allow the child to carry novel variants. We consider a constrained optimization approach where variants in the child are of two types either inherited - and therefore must be present in a parent - or novel. For simplicity, we consider only a single parent and single child each of which have a haploid genome. However, even in this restricted case, our approach has the power to improve variant prediction. We present results on both simulated candidate variant regions, parent-child trios from the 1000 Genomes Project, and a subset of the 17 Platinum Genomes.

LinkedSV for detection of mosaic structural variants from linked-read exome and genome sequencing data.

Linked-read sequencing provides long-range information on short-read sequencing data by barcoding reads originating from the same DNA molecule, and can improve detection and breakpoint identification for structural variants (SVs). Here we present LinkedSV for SV detection on linked-read sequencing data. LinkedSV considers barcode overlapping and enriched fragment endpoints as signals to detect large SVs, while it leverages read depth, paired-end signals and local assembly to detect small SVs. Benchmarking studies demonstrate that LinkedSV outperforms existing tools, especially on exome data and on somatic SVs with low variant allele frequencies. We demonstrate clinical cases where LinkedSV identifies disease-causal SVs from linked-read exome sequencing data missed by conventional exome sequencing, and show examples where LinkedSV identifies SVs missed by high-coverage long-read sequencing. In summary, LinkedSV can detect SVs missed by conventional short-read and long-read sequencing approaches, and may resolve negative cases from clinical genome/exome sequencing studies.

Targeted short read sequencing and assembly of re-arrangements and candidate gene loci provide megabase diplotypes.

The human genome is composed of two haplotypes, otherwise called diplotypes, which denote phased polymorphisms and structural variations (SVs) that are derived from both parents. Diplotypes place genetic variants in the context of cis-related variants from a diploid genome. As a result, they provide valuable information about hereditary transmission, context of SV, regulation of gene expression and other features which are informative for understanding human genetics. Successful diplotyping with short read whole genome sequencing generally requires either a large population or parent-child trio samples. To overcome these limitations, we developed a targeted sequencing method for generating megabase (Mb)-scale haplotypes with short reads. One selects specific 0.1-0.2 Mb high molecular weight DNA targets with custom-designed Cas9-guide RNA complexes followed by sequencing with barcoded linked reads. To test this approach, we designed three assays, targeting the BRCA1 gene, the entire 4-Mb major histocompatibility complex locus and 18 well-characterized SVs, respectively. Using an integrated alignment- and assembly-based approach, we generated comprehensive variant diplotypes spanning the entirety of the targeted loci and characterized SVs with exact breakpoints. Our results were comparable in quality to long read sequencing.

Validation of concurrent preimplantation genetic testing for polygenic and monogenic disorders, structural rearrangements, and whole and segmental chromosome aneuploidy with a single universal platform.

Preimplantation genetic testing (PGT) has been successfully applied to reduce the risk of miscarriage, improve IVF success rates, and prevent inheritance of monogenic disease and unbalanced translocations. The present study provides the first method capable of simultaneous testing of aneuploidy (PGT-A), structural rearrangements (PGT-SR), and monogenic (PGT-M) disorders using a single platform. Using positive controls to establish performance characteristics, accuracies of 97 to >99% for each type of testing were observed. In addition, this study expands PGT to include predicting the risk of polygenic disorders (PGT-P) for the first time. Performance was established for two common diseases, hypothyroidism and type 1 diabetes, based upon availability of positive control samples from commercially available repositories. Data from the UK Biobank, eMERGE, and T1DBASE were used to establish and validate SNP-based predictors of each disease (7,311 SNPs for hypothyroidism and 82 for type 1 diabetes). Area under the curve of disease status prediction from genotypes alone were 0.71 for hypothyroidism and 0.68 for type 1 diabetes. The availability of expanded PGT to evaluate the risk of polygenic disorders in the preimplantation embryo has the potential to lower the prevalence of common genetic disease in humans.