Linked read sequencing resolves complex genomic rearrangements in gastric cancer metastases

Somatic mutation phasing and haplotype extension using linked-reads in multiple myeloma

Somatic mutation phasing informs our understanding of cancer-related events, like driver mutations. We generated linked-read whole genome sequencing data for 23 samples across disease stages from 14 multiple myeloma (MM) patients and systematically assigned somatic mutations to haplotypes using linked-reads. Here, we report the reconstructed cancer haplotypes and phase blocks from several MM samples and show how phase block length can be extended by integrating samples from the same individual. We also uncover phasing information in genes frequently mutated in MM, including DIS3 , HIST1H1E , KRAS , NRAS , and TP53 , phasing 79.4% of 20,705 high-confidence somatic mutations. In some cases, this enabled us to interpret clonal evolution models at higher resolution using pairs of phased somatic mutations. For example, our analysis of one patient suggested that two NRAS hotspot mutations occurred on the same haplotype but were independent events in different subclones. Given sufficient tumor purity and data quality, our framework illustrates how haplotype-aware analysis of somatic mutations in cancer can be beneficial for some cancer cases.

Neotelomeres and telomere-spanning chromosomal arm fusions in cancer genomes revealed by long-read sequencing

Alterations in the structure and location of telomeres are pivotal in cancer genome evolution. Here, we applied both long-read and short-read genome sequencing to assess telomere repeat-containing structures in cancers and cancer cell lines. Using long-read genome sequences that span telomeric repeats, we defined four types of telomere repeat variations in cancer cells: neotelomeres where telomere addition heals chromosome breaks, chromosomal arm fusions spanning telomere repeats, fusions of neotelomeres, and peri-centromeric fusions with adjoined telomere and centromere repeats. These results provide a framework for the systematic study of telomeric repeats in cancer genomes, which could serve as a model for understanding the somatic evolution of other repetitive genomic elements.

Circulating Tumor DNA Enables Sensitive Detection of Actionable Gene Fusions and Rearrangements Across Cancer Types

PURPOSE

Genomic rearrangements can generate potent oncogenic drivers or disrupt tumor suppressor genes. This study examines the landscape of fusions and rearrangements detected by liquid biopsy (LBx) of circulating tumor DNA (ctDNA) across different cancer types.

EXPERIMENTAL DESIGN

LBx from 53,842 patients with 66 solid tumor types were profiled using FoundationOneLiquid CDx, a hybrid-capture sequencing platform that queries 324 cancer-related genes. Tissue biopsies (TBx) profiled using FoundationOneCDx were used as a comparator.

RESULTS

Among all LBx, 7,377 (14%) had ≥1 pathogenic rearrangement detected. A total of 3,648 (6.8%) LBx had ≥1 gain-of-function (GOF) oncogene rearrangement, and 4,428 (8.2%) LBx had ≥1 loss-of-function rearrangement detected. Cancer types with higher prevalence of GOF rearrangements included those with canonical fusion drivers: prostate cancer (19%), cholangiocarcinoma (6.4%), bladder (5.5%), and non-small cell lung cancer (4.4%). Although the prevalence of driver rearrangements was lower in LBx than TBx overall, the frequency of detection was comparable in LBx with a tumor fraction (TF) ≥1%. Rearrangements in FGFR2, BRAF, RET, and ALK, were detected across cancer types, but tended to be clonal variants in some cancer types and potential acquired resistance variants in others.

CONCLUSIONS

In contrast to some prior literature, this study reports detection of a wide variety of rearrangements in ctDNA. The prevalence of driver rearrangements in tissue and LBx was comparable when TF ≥1%. LBx presents a viable alternative when TBx is not available, and there may be less value in confirmatory testing when TF is sufficient.

LRTK: a platform agnostic toolkit for linked-read analysis of both human genome and metagenome

BACKGROUND

Linked-read sequencing technologies generate high-base quality short reads that contain extrapolative information on long-range DNA connectedness. These advantages of linked-read technologies are well known and have been demonstrated in many human genomic and metagenomic studies. However, existing linked-read analysis pipelines (e.g., Long Ranger) were primarily developed to process sequencing data from the human genome and are not suited for analyzing metagenomic sequencing data. Moreover, linked-read analysis pipelines are typically limited to 1 specific sequencing platform.

FINDINGS

To address these limitations, we present the Linked-Read ToolKit (LRTK), a unified and versatile toolkit for platform agnostic processing of linked-read sequencing data from both human genome and metagenome. LRTK provides functions to perform linked-read simulation, barcode sequencing error correction, barcode-aware read alignment and metagenome assembly, reconstruction of long DNA fragments, taxonomic classification and quantification, and barcode-assisted genomic variant calling and phasing. LRTK has the ability to process multiple samples automatically and provides users with the option to generate reproducible reports during processing of raw sequencing data and at multiple checkpoints throughout downstream analysis. We applied LRTK on linked reads from simulation, mock community, and real datasets for both human genome and metagenome. We showcased LRTK's ability to generate comparative performance results from preceding benchmark studies and to report these results in publication-ready HTML document plots.

CONCLUSIONS

LRTK provides comprehensive and flexible modules along with an easy-to-use Python-based workflow for processing linked-read sequencing datasets, thereby filling the current gap in the field caused by platform-centric genome-specific linked-read data analysis tools.

Neotelomeres and Telomere-Spanning Chromosomal Arm Fusions in Cancer Genomes Revealed by Long-Read Sequencing

Alterations in the structure and location of telomeres are key events in cancer genome evolution. However, previous genomic approaches, unable to span long telomeric repeat arrays, could not characterize the nature of these alterations. Here, we applied both long-read and short-read genome sequencing to assess telomere repeat-containing structures in cancers and cancer cell lines. Using long-read genome sequences that span telomeric repeat arrays, we defined four types of telomere repeat variations in cancer cells: neotelomeres where telomere addition heals chromosome breaks, chromosomal arm fusions spanning telomere repeats, fusions of neotelomeres, and peri-centromeric fusions with adjoined telomere and centromere repeats. Analysis of lung adenocarcinoma genome sequences identified somatic neotelomere and telomere-spanning fusion alterations. These results provide a framework for systematic study of telomeric repeat arrays in cancer genomes, that could serve as a model for understanding the somatic evolution of other repetitive genomic elements.

Linked-read based analysis of the medulloblastoma genome

Introduction

Medulloblastoma is the most common type of malignant pediatric brain tumor with group 4 medulloblastomas (G4 MBs) accounting for 40% of cases. However, the molecular mechanisms that underlie this subgroup are still poorly understood. Point mutations are detected in a large number of genes at low incidence per gene while the detection of complex structural variants in recurrently affected genes typically requires the application of long-read technologies.

Methods

Here, we applied linked-read sequencing, which combines the long-range genome information of long-read sequencing with the high base pair accuracy of short read sequencing and very low sample input requirements.

Results

We demonstrate the detection of complex structural variants and point mutations in these tumors, and, for the first time, the detection of extrachromosomal DNA (ecDNA) with linked-reads. We provide further evidence for the high heterogeneity of somatic mutations in G4 MBs and add new complex events associated with it.

Discussion

We detected several enhancer-hijacking events, an ecDNA containing the MYCN gene, and rare structural rearrangements, such a chromothripsis in a G4 medulloblastoma, chromoplexy involving 8 different chromosomes, a TERT gene rearrangement, and a PRDM6 duplication.

Nanopore long-read next-generation sequencing for detection of mitochondrial DNA large-scale deletions

Primary mitochondrial diseases are progressive genetic disorders affecting multiple organs and characterized by mitochondrial dysfunction. These disorders can be caused by mutations in nuclear genes coding proteins with mitochondrial localization or by genetic defects in the mitochondrial genome (mtDNA). The latter include point pathogenic variants and large-scale deletions/rearrangements. MtDNA molecules with the wild type or a variant sequence can exist together in a single cell, a condition known as mtDNA heteroplasmy. MtDNA single point mutations are typically detected by means of Next-Generation Sequencing (NGS) based on short reads which, however, are limited for the identification of structural mtDNA alterations. Recently, new NGS technologies based on long reads have been released, allowing to obtain sequences of several kilobases in length; this approach is suitable for detection of structural alterations affecting the mitochondrial genome. In the present work we illustrate the optimization of two sequencing protocols based on long-read Oxford Nanopore Technology to detect mtDNA structural alterations. This approach presents strong advantages in the analysis of mtDNA compared to both short-read NGS and traditional techniques, potentially becoming the method of choice for genetic studies on mtDNA.

From Samples to Germline and Somatic Sequence Variation: A Focus on Next-Generation Sequencing in Melanoma Research

Next-generation sequencing (NGS) applications have flourished in the last decade, permitting the identification of cancer driver genes and profoundly expanding the possibilities of genomic studies of cancer, including melanoma. Here we aimed to present a technical review across many of the methodological approaches brought by the use of NGS applications with a focus on assessing germline and somatic sequence variation. We provide cautionary notes and discuss key technical details involved in library preparation, the most common problems with the samples, and guidance to circumvent them. We also provide an overview of the sequence-based methods for cancer genomics, exposing the pros and cons of targeted sequencing vs. exome or whole-genome sequencing (WGS), the fundamentals of the most common commercial platforms, and a comparison of throughputs and key applications. Details of the steps and the main software involved in the bioinformatics processing of the sequencing results, from preprocessing to variant prioritization and filtering, are also provided in the context of the full spectrum of genetic variation (SNVs, indels, CNVs, structural variation, and gene fusions). Finally, we put the emphasis on selected bioinformatic pipelines behind (a) short-read WGS identification of small germline and somatic variants, (b) detection of gene fusions from transcriptomes, and (c) de novo assembly of genomes from long-read WGS data. Overall, we provide comprehensive guidance across the main methodological procedures involved in obtaining sequencing results for the most common short- and long-read NGS platforms, highlighting key applications in melanoma research.

Efficient detection and assembly of non-reference DNA sequences with synthetic long reads

Recent pan-genome studies have revealed an abundance of DNA sequences in human genomes that are not present in the reference genome. A lion's share of these non-reference sequences (NRSs) cannot be reliably assembled or placed on the reference genome. Improvements in long-read and synthetic long-read (aka linked-read) technologies have great potential for the characterization of NRSs. While synthetic long reads require less input DNA than long-read datasets, they are algorithmically more challenging to use. Except for computationally expensive whole-genome assembly methods, there is no synthetic long-read method for NRS detection. We propose a novel integrated alignment-based and local assembly-based algorithm, Novel-X, that uses the barcode information encoded in synthetic long reads to improve the detection of such events without a whole-genome de novo assembly. Our evaluations demonstrate that Novel-X finds many non-reference sequences that cannot be found by state-of-the-art short-read methods. We applied Novel-X to a diverse set of 68 samples from the Polaris HiSeq 4000 PGx cohort. Novel-X discovered 16 691 NRS insertions of size > 300 bp (total length 18.2 Mb). Many of them are population specific or may have a functional impact.

Application of next-generation sequencing in the diagnosis of gastric cancer

Objectives: Gastric cancer (GC) is a disease with high mortality, poor prognosis and numerous risk factors. GC has an asymptomatic nature in early stages of the diseases, making timely diagnosis complicated using common conventional approaches, namely pathological examinations and imaging tests. Recently, molecular profiling of GC using next generation sequencing (NGS) has opened new doors to efficient prognostic, diagnostic, and therapeutic strategies. The current review aims to thoroughly discuss and compare the current NGS techniques and commercial platforms utilized for GC diagnosis and treatment, highlighting the most recent NGS-based GC studies. Furthermore, this review addresses the challenges of clinical implementation of NGS in GC.Materials and methods: This review was conducted according to the eligible studies identified via search of Web of Science, PubMed, Scopus, Embase and the Cochrane Library. In the present study, data on gastric cancer patients and NGS methods used to diagnose the disease were reviewed.Conclusion: Given the ever-rising advancements in NGS technologies, bioinformatics, healthcare guidelines and refined classifications, it is hoped that these technologies can actualize their advantages and optimize GC patients' experience.

A multi-platform reference for somatic structural variation detection

Accurate detection of somatic structural variation (SV) in cancer genomes remains a challenging problem. This is in part due to the lack of high-quality, gold-standard datasets that enable the benchmarking of experimental approaches and bioinformatic analysis pipelines. Here, we performed somatic SV analysis of the paired melanoma and normal lymphoblastoid COLO829 cell lines using four different sequencing technologies. Based on the evidence from multiple technologies combined with extensive experimental validation, we compiled a comprehensive set of carefully curated and validated somatic SVs, comprising all SV types. We demonstrate the utility of this resource by determining the SV detection performance as a function of tumor purity and sequence depth, highlighting the importance of assessing these parameters in cancer genomics projects. The truth somatic SV dataset as well as the underlying raw multi-platform sequencing data are freely available and are an important resource for community somatic benchmarking efforts.

Linked-read whole-genome sequencing resolves common and private structural variants in multiple myeloma

Linked-read WGS can be performed without DNA purification and allows for resolution of the diverse structural variants found in MM.

Linked-read WGS can, as a standalone assay, provide comprehensive genetics in myeloma and other diseases with complex genomes.

Multiple myeloma (MM) is an incurable and aggressive plasma cell malignancy characterized by a complex karyotype with multiple structural variants (SVs) and copy-number variations (CNVs). Linked-read whole-genome sequencing (lrWGS) allows for refined detection and reconstruction of SVs by providing long-range genetic information from standard short-read sequencing. This makes lrWGS an attractive solution for capturing the full genomic complexity of MM. Here we show that high-quality lrWGS data can be generated from low numbers of cells subjected to fluorescence-activated cell sorting (FACS) without DNA purification. Using this protocol, we analyzed MM cells after FACS from 37 patients with MM using lrWGS. We found high concordance between lrWGS and fluorescence in situ hybridization (FISH) for the detection of recurrent translocations and CNVs. Outside of the regions investigated by FISH, we identified >150 additional SVs and CNVs across the cohort. Analysis of the lrWGS data allowed for resolution of the structure of diverse SVs affecting the MYC and t(11;14) loci, causing the duplication of genes and gene regulatory elements. In addition, we identified private SVs causing the dysregulation of genes recurrently involved in translocations with the IGH locus and show that these can alter the molecular classification of MM. Overall, we conclude that lrWGS allows for the detection of aberrations critical for MM prognostics and provides a feasible route for providing comprehensive genetics. Implementing lrWGS could provide more accurate clinical prognostics, facilitate genomic medicine initiatives, and greatly improve the stratification of patients included in clinical trials.

Application of long-read sequencing to the detection of structural variants in human cancer genomes

In recent years, the so-called long-read sequencing technology has had a substantial impact on various aspects of genome sciences. Here, we introduce recent studies of cancerous structural variants (SVs) using long-read sequencing technologies, namely Pacific Biosciences (PacBio) sequencers, Oxford Nanopore Technologies (ONT) sequencers, and linked-read methods. By taking advantage of long-read lengths, these technologies have enabled the precise detection of SVs, including long insertions by transposable elements, such as LINE-1. In addition to SV detection, the epigenome status (including DNA methylation and haplotype information) surrounding SV loci has also been unveiled by long-read sequencing technologies, to identify the effects of SVs. Among the various research fields in which long-read sequencing has been applied, cancer genomics has shown the most remarkable advances. In fact, many studies are beginning to shed light on the detection of SVs and the elucidation of their complex structures in various types of cancer. In the particular case of cancers, we summarize the technical limitations of the application of this technology to the analysis of clinical samples. We will introduce recent achievements from this viewpoint. However, a similar approach will be started for other applications in the near future. Therefore, by complementing the current short-read sequencing analysis, long-read sequencing should reveal the complex nature of human genomes in their healthy and disease states, which will open a new opportunity for a better understanding of disease development and for a novel strategy for drug development.

Mako: A Graph-based Pattern Growth Approach to Detect Complex Structural Variants

Complex structural variants (CSVs) are genomic alterations that have more than two breakpoints and are considered as the simultaneous occurrence of simple structural variants. However, detecting the compounded mutational signals of CSVs is challenging through a commonly used model-match strategy. As a result, there has been limited progress for CSV discovery compared with simple structural variants. Here, we systematically analyzed the multi-breakpoint connection feature of CSVs, and proposed Mako, utilizing a bottom-up guided model-free strategy, to detect CSVs from paired-end short-read sequencing. Specifically, we implemented a graph-based pattern growth approach, where the graph depicts potential breakpoint connections, and pattern growth enables CSV detection without pre-defined models. Comprehensive evaluations on both simulated and real datasets revealed that Mako outperformed other algorithms. Notably, validation rates of CSVs on real data based on experimental and computational validations as well as manual inspections are around 70%, where the medians of experimental and computational breakpoint shift are 13 bp and 26 bp, respectively. Moreover, the Mako CSV subgraph effectively characterized the breakpoint connections of a CSV event and uncovered a total of 15 CSV types, including two novel types of adjacent segment swap and tandem dispersed duplication. Further analysis of these CSVs also revealed the impact of sequence homology on the formation of CSVs. Mako is publicly available at https://github.com/xjtu-omics/Mako.

Integrative single-cell analysis of allele-specific copy number alterations and chromatin accessibility in cancer

Cancer progression is driven by both somatic copy number aberrations (CNAs) and chromatin remodeling, yet little is known about the interplay between these two classes of events in shaping the clonal diversity of cancers. We present Alleloscope, a method for allele-specific copy number estimation that can be applied to single-cell DNA- and/or transposase-accessible chromatin-sequencing (scDNA-seq, ATAC-seq) data, enabling combined analysis of allele-specific copy number and chromatin accessibility. On scDNA-seq data from gastric, colorectal and breast cancer samples, with validation using matched linked-read sequencing, Alleloscope finds pervasive occurrence of highly complex, multiallelic CNAs, in which cells that carry varying allelic configurations adding to the same total copy number coevolve within a tumor. On scATAC-seq from two basal cell carcinoma samples and a gastric cancer cell line, Alleloscope detected multiallelic copy number events and copy-neutral loss-of-heterozygosity, enabling dissection of the contributions of chromosomal instability and chromatin remodeling to tumor evolution.

Comparison of Structural and Short Variants Detected by Linked-Read and Whole-Exome Sequencing in Multiple Myeloma

Linked-read sequencing was developed to aid the detection of large structural variants (SVs) from short-read sequencing efforts. We performed a systematic evaluation to determine if linked-read exome sequencing provides more comprehensive and clinically relevant information than whole-exome sequencing (WES) when applied to the same set of multiple myeloma patient samples. We report that linked-read sequencing detected a higher number of SVs (n = 18,455) than WES (n = 4065). However, linked-read predictions were dominated by inversions (92.4%), leading to poor detection of other types of SVs. In contrast, WES detected 56.3% deletions, 32.6% insertions, 6.7% translocations, 3.3% duplications and 1.2% inversions. Surprisingly, the quantitative performance assessment suggested a higher performance for WES (AUC = 0.791) compared to linked-read sequencing (AUC = 0.766) for detecting clinically validated cytogenetic alterations. We also found that linked-read sequencing detected more short variants (n = 704) compared to WES (n = 109). WES detected somatic mutations in all MM-related genes while linked-read sequencing failed to detect certain mutations. The comparison of somatic mutations detected using linked-read, WES and RNA-seq revealed that WES and RNA-seq detected more mutations than linked-read sequencing. These data indicate that WES outperforms and is more efficient than linked-read sequencing for detecting clinically relevant SVs and MM-specific short variants.

Is Gene-Size an Issue for the Diagnosis of Skeletal Muscle Disorders?

Human genes have a variable length. Those having a coding sequence of extraordinary length and a high number of exons were almost impossible to sequence using the traditional Sanger-based gene-by-gene approach. High-throughput sequencing has partly overcome the size-related technical issues, enabling a straightforward, rapid and relatively inexpensive analysis of large genes.

Several large genes (e.g. TTN, NEB, RYR1, DMD) are recognized as disease-causing in patients with skeletal muscle diseases. However, because of their sheer size, the clinical interpretation of variants in these genes is probably the most challenging aspect of the high-throughput genetic investigation in the field of skeletal muscle diseases.

The main aim of this review is to discuss the technical and interpretative issues related to the diagnostic investigation of large genes and to reflect upon the current state of the art and the future advancements in the field.

New insights of extrachromosomal DNA in tumorigenesis and therapeutic resistance of cancer

In the past few decades, the studies of extrachromosomal DNA (ecDNA), which existed independently of chromosomes, were tepid. However, recent studies on ecDNA rekindled the enthusiasm of oncologists for further studying ecDNA. In this review, we summarized the recent advances of ecDNA in oncogenesis and oncotherapy. ecDNA consists of highly open chromatin, and its circular structure enables ultra-long-range chromatin contacts. ecDNA is not inherited in accordance with Mendel's laws. Furthermore, ecDNA is widely existed in cancer cells, but almost never found in normal cells. It has been found that ecDNA played important roles in tumorigenesis and tumor progression, including oncogene amplification, tumor heterogeneity, enhancer hijacking and genomic rearrangement. More importantly, ecDNA is closely related to cancer treatment resistance. In hence, further understanding of ecDNA would contribute to developing innovative targeting ecDNA therapies.

Integrative analysis of structural variations using short-reads and linked-reads yields highly specific and sensitive predictions

Genetic diseases are driven by aberrations of the human genome. Identification of such aberrations including structural variations (SVs) is key to our understanding. Conventional short-reads whole genome sequencing (cWGS) can identify SVs to base-pair resolution, but utilizes only short-range information and suffers from high false discovery rate (FDR). Linked-reads sequencing (10XWGS) utilizes long-range information by linkage of short-reads originating from the same large DNA molecule. This can mitigate alignment-based artefacts especially in repetitive regions and should enable better prediction of SVs. However, an unbiased evaluation of this technology is not available. In this study, we performed a comprehensive analysis of different types and sizes of SVs predicted by both the technologies and validated with an independent PCR based approach. The SVs commonly identified by both the technologies were highly specific, while validation rate dropped for uncommon events. A particularly high FDR was observed for SVs only found by 10XWGS. To improve FDR and sensitivity, statistical models for both the technologies were trained. Using our approach, we characterized SVs from the MCF7 cell line and a primary breast cancer tumor with high precision. This approach improves SV prediction and can therefore help in understanding the underlying genetics in various diseases.

Cancer and many other diseases are often driven by structural rearrangements in the patients. Their precise identification is necessary to understand evolution and cure for the disease. In this study, we have compared two sequencing technologies for the identification of structural variations i.e. Illumina’s short-reads and 10X Genomics linked-reads sequencing. Short-reads sequencing is already known to have high false discovery rate for structural variations, while, an unbiased performance evaluation of linked-reads sequencing is missing. Hence, we evaluate the performance of these two technologies using computational and PCR based methodologies. Moreover, we also present a statistical approach to increase their performance, supporting better detection of structural variations and thus further research into disease biology.

Structural variant analysis for linked-read sequencing data with gemtools

SUMMARY

Linked-read sequencing generates synthetic long reads which are useful for the detection and analysis of structural variants (SVs). The software associated with 10× Genomics linked-read sequencing, Long Ranger, generates the essential output files (BAM, VCF, SV BEDPE) necessary for downstream analyses. However, to perform downstream analyses requires the user to customize their own tools to handle the unique features of linked-read sequencing data. Here, we describe gemtools, a collection of tools for the downstream and in-depth analysis of SVs from linked-read data. Gemtools uses the barcoded aligned reads and the Megabase-scale phase blocks to determine haplotypes of SV breakpoints and delineate complex breakpoint configurations at the resolution of single DNA molecules. The gemtools package is a suite of tools that provides the user with the flexibility to perform basic functions on their linked-read sequencing output in order to address even more questions.

AVAILABILITY AND IMPLEMENTATION

The gemtools package is freely available for download at: https://github.com/sgreer77/gemtools.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

An integrated Asian human SNV and indel benchmark established using multiple sequencing methods

Sequencing technologies have been rapidly developed recently, leading to the breakthrough of sequencing-based clinical diagnosis, but accurate and complete genome variation benchmark would be required for further assessment of precision medicine applications. Despite the human cell line of NA12878 has been successfully developed to be a variation benchmark, population-specific variation benchmark is still lacking. Here, we established an Asian human variation benchmark by constructing and sequencing a stabilized cell line of a Chinese Han volunteer. By using seven different sequencing strategies, we obtained ~3.88 Tb clean data from different laboratories, hoping to reach the point of high sequencing depth and accurate variation detection. Through the combination of variations identified from different sequencing strategies and different analysis pipelines, we identified 3.35 million SNVs and 348.65 thousand indels, which were well supported by our sequencing data and passed our strict quality control, thus should be high confidence variation benchmark. Besides, we also detected 5,913 high-quality SNVs which had 969 sites were novel and  located in the high homologous regions supported by long-range information in both the co-barcoding single tube Long Fragment Read (stLFR) data and PacBio HiFi CCS data. Furthermore, by using the long reads data (stLFR and HiFi CCS), we were able to phase more than 99% heterozygous SNVs, which helps to improve the benchmark to be haplotype level. Our study provided comprehensive sequencing data as well as the integrated variation benchmark of an Asian derived cell line, which would be valuable for future sequencing-based clinical development.

Structural variation in the sequencing era

Identifying structural variation (SV) is essential for genome interpretation but has been historically difficult due to limitations inherent to available genome technologies. Detection methods that use ensemble algorithms and emerging sequencing technologies have enabled the discovery of thousands of SVs, uncovering information about their ubiquity, relationship to disease and possible effects on biological mechanisms. Given the variability in SV type and size, along with unique detection biases of emerging genomic platforms, multiplatform discovery is necessary to resolve the full spectrum of variation. Here, we review modern approaches for investigating SVs and proffer that, moving forwards, studies integrating biological information with detection will be necessary to comprehensively understand the impact of SV in the human genome.

Refined detection and phasing of structural aberrations in pediatric acute lymphoblastic leukemia by linked-read whole-genome sequencing

Structural chromosomal rearrangements that can lead to in-frame gene-fusions are a leading source of information for diagnosis, risk stratification, and prognosis in pediatric acute lymphoblastic leukemia (ALL). Traditional methods such as karyotyping and FISH struggle to accurately identify and phase such large-scale chromosomal aberrations in ALL genomes. We therefore evaluated linked-read WGS for detecting chromosomal rearrangements in primary samples of from 12 patients diagnosed with ALL. We assessed the effect of input DNA quality on phased haplotype block size and the detectability of copy number aberrations and structural variants in the ALL genomes. We found that biobanked DNA isolated by standard column-based extraction methods was sufficient to detect chromosomal rearrangements even at low 10x sequencing coverage. Linked-read WGS enabled precise, allele-specific, digital karyotyping at a base-pair resolution for a wide range of structural variants including complex rearrangements and aneuploidy assessment. With use of haplotype information from the linked-reads, we also identified previously unknown structural variants, such as a compound heterozygous deletion of ERG in a patient with the DUX4-IGH fusion gene. We conclude that linked-read WGS allows detection of important pathogenic variants in ALL genomes at a resolution beyond that of traditional karyotyping and FISH.

Genome sequencing in cytogenetics: Comparison of short-read and linked-read approaches for germline structural variant detection and characterization

BACKGROUND

Structural variants (SVs) include copy number variants (CNVs) and apparently balanced chromosomal rearrangements (ABCRs). Genome sequencing (GS) enables SV detection at base-pair resolution, but the use of short-read sequencing is limited by repetitive sequences, and long-read approaches are not yet validated for diagnosis. Recently, 10X Genomics proposed Chromium, a technology providing linked-reads to reconstruct long DNA fragments and which could represent a good alternative. No study has compared short-read to linked-read technologies to detect SVs in a constitutional diagnostic setting yet. The aim of this work was to determine whether the 10X Genomics technology enables better detection and comprehension of SVs than short-read WGS.

METHODS

We included 13 patients carrying various SVs. Whole genome analyses were performed using paired-end HiSeq X sequencing with (linked-read strategy) or without (short-read strategy) Chromium library preparation. Two different bioinformatic pipelines were used: Variants are called using BreakDancer for short-read strategy and LongRanger for long-read strategy. Variant interpretations were first blinded.

RESULTS

The short-read strategy allowed diagnosis of known SV in 10/13 patients. After unblinding, the linked-read strategy identified 10/13 SVs, including one (patient 7) missed by the short-read strategy.

CONCLUSION

In conclusion, regarding the results of this study, 10X Genomics solution did not improve the detection and characterization of SV.

Structural variation and its potential impact on genome instability: Novel discoveries in the EGFR landscape by long-read sequencing

Structural variation (SV) is typically defined as variation within the human genome that exceeds 50 base pairs (bp). SV may be copy number neutral or it may involve duplications, deletions, and complex rearrangements. Recent studies have shown SV to be associated with many human diseases. However, studies of SV have been challenging due to technological constraints. With the advent of third generation (long-read) sequencing technology, exploration of longer stretches of DNA not easily examined previously has been made possible. In the present study, we utilized third generation (long-read) sequencing techniques to examine SV in the EGFR landscape of four haplotypes derived from two human samples. We analyzed the EGFR gene and its landscape (+/- 500,000 base pairs) using this approach and were able to identify a region of non-coding DNA with over 90% similarity to the most common activating EGFR mutation in non-small cell lung cancer. Based on previously published Alu-element genome instability algorithms, we propose a molecular mechanism to explain how this non-coding region of DNA may be interacting with and impacting the stability of the EGFR gene and potentially generating this cancer-driver gene. By these techniques, we were also able to identify previously hidden structural variation in the four haplotypes and in the human reference genome (hg38). We applied previously published algorithms to compare the relative stabilities of these five different EGFR gene landscape haplotypes to estimate their relative potentials to generate the EGFR exon 19, 15 bp canonical deletion. To our knowledge, the present study is the first to use the differences in genomic architecture between targeted cancer-linked phased haplotypes to estimate their relative potentials to form a common cancer-linked driver mutation.

A new era of long-read sequencing for cancer genomics

Cancer is a disease largely caused by genomic aberrations. Utilizing many rapidly emerging sequencing technologies, researchers have studied cancer genomes to understand the molecular statuses of cancer cells and to reveal their vulnerabilities, such as driver mutations or gene expression. Long-read technologies enable us to identify and characterize novel types of cancerous mutations, including complicated structural variants in haplotype resolution. In this review, we introduce three representative platforms for long-read sequencing and research trends of cancer genomics with long-read data. Further, we describe that aberrant transcriptome and epigenome statuses, namely, fusion transcripts, as well as aberrant transcript isoforms and the phase information of DNA methylation, are able to be elucidated by long-read sequencers. Long-read sequencing may shed light on novel types of aberrations in cancer genomics that are being missed by conventional short-read sequencing analyses.

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.

Haplotype-resolved and integrated genome analysis of the cancer cell line HepG2

HepG2 is one of the most widely used human cancer cell lines in biomedical research and one of the main cell lines of ENCODE. Although the functional genomic and epigenomic characteristics of HepG2 are extensively studied, its genome sequence has never been comprehensively analyzed and higher order genomic structural features are largely unknown. The high degree of aneuploidy in HepG2 renders traditional genome variant analysis methods challenging and partially ineffective. Correct and complete interpretation of the extensive functional genomics data from HepG2 requires an understanding of the cell line’s genome sequence and genome structure. Using a variety of sequencing and analysis methods, we identified a wide spectrum of genome characteristics in HepG2: copy numbers of chromosomal segments at high resolution, SNVs and Indels (corrected for aneuploidy), regions with loss of heterozygosity, phased haplotypes extending to entire chromosome arms, retrotransposon insertions and structural variants (SVs) including complex and somatic genomic rearrangements. A large number of SVs were phased, sequence assembled and experimentally validated. We re-analyzed published HepG2 datasets for allele-specific expression and DNA methylation and assembled an allele-specific CRISPR/Cas9 targeting map. We demonstrate how deeper insights into genomic regulatory complexity are gained by adopting a genome-integrated framework.

Gene surgery: Potential applications for human diseases

Gene therapy became in last decade a new emerging therapeutic era showing promising results against different diseases such as cancer, cardiovascular diseases, diabetes, and neurological disorders. Recently, the genome editing technique for eukaryotic cells called CRISPR-Cas (Clustered Regulatory Interspaced Short Palindromic Repeats) has enriched the field of gene surgery with enhanced applications. In the present review, we summarized the different applications of gene surgery for treating human diseases such as cancer, diabetes, nervous, and cardiovascular diseases, besides the molecular mechanisms involved in these important effects. Several studies support the important therapeutic applications of gene surgery in a large number of health disorders and diseases including β-thalassemia, cancer, immunodeficiencies, diabetes, and neurological disorders. In diabetes, gene surgery was shown to be effective in type 1 diabetes by triggering different signaling pathways. Furthermore, gene surgery, especially that using CRISPR-Cas possessed important application on diagnosis, screening and treatment of several cancers such as lung, liver, pancreatic and colorectal cancer. Nevertheless, gene surgery still presents some limitations such as the design difficulties and costs regarding ZFNs (Zinc Finger Nucleases) and TALENs (Transcription Activator-Like Effector Nucleases) use, off-target effects, low transfection efficiency, in vivo delivery-safety and ethical issues.

Investigational fibroblast growth factor receptor 2 antagonists in early phase clinical trials to treat solid tumors

Introduction: Fibroblast growth factor receptor 2 (FGFR2) is a highly conserved transmembrane tyrosine kinase receptor. FGFR2 dysregulation occurs in numerous human solid tumors and overexpression is closely associated with tumor progression. FGFR2 has recently been reported as a therapeutic target for cancer. Several targeted therapies are being investigated to disrupt FGFR2 activity; these include multi-target tyrosine kinase inhibitors (TKIs), pan-FGFR targeted TKIs and FGFR2 monoclonal antibodies. Areas: This review examines FGFR2 regulation and function in cancer and its potential as a target for cancer treatment. Expert opinion: Highly specific FGFR2 blockers have not yet been developed and moreover, resistance to FGFR2-targeted therapies is a challenge. More sophisticated patient selection strategies would help improve FGFR2-targeted therapies and combination therapy is considered the most promising approach for cancer patients with FGFR2 alterations.

miR-671-5p Blocks The Progression Of Human Esophageal Squamous Cell Carcinoma By Suppressing FGFR2

Esophageal cancer is the eighth most common malignant tumor worldwide, of which esophageal squamous cell carcinoma (ESCC) is the dominant histological subtype. A drug shortage for ESCC therapy triggered us to explore the roles of fibroblast growth factor receptor 2 (FGFR2) and its upstream regulator miR-671-5p in ESCC progression. We compared the levels of FGFR2 and miR-671-5p between human ESCC tissues and their matched normal esophageal tissues and found an association between higher levels of FGFR2 and lower levels of miR-671-5p in ESCC tissues. High levels of FGFR2 resulted in the activation of the ERK and AKT pathways and a promotion of ESCC progression. High levels of miR-671-5p specifically reduced the expression of FGFR2 and suppressed ESCC progression in both in vitro and in vivo models. Therefore, suppressing FGFR2 and enhancing miR-671-5p expression may be the right approaches for ESCC therapy.

A survey of localized sequence rearrangements in human DNA

Genomes mutate and evolve in ways simple (substitution or deletion of bases) and complex (e.g. chromosome shattering). We do not fully understand what types of complex mutation occur, and we cannot routinely characterize arbitrarily-complex mutations in a high-throughput, genome-wide manner. Long-read DNA sequencing methods (e.g. PacBio, nanopore) are promising for this task, because one read may encompass a whole complex mutation. We describe an analysis pipeline to characterize arbitrarily-complex 'local' mutations, i.e. intrachromosomal mutations encompassed by one DNA read. We apply it to nanopore and PacBio reads from one human cell line (NA12878), and survey sequence rearrangements, both real and artifactual. Almost all the real rearrangements belong to recurring patterns or motifs: the most common is tandem multiplication (e.g. heptuplication), but there are also complex patterns such as localized shattering, which resembles DNA damage by radiation. Gene conversions are identified, including one between hemoglobin gamma genes. This study demonstrates a way to find intricate rearrangements with any number of duplications, deletions, and repositionings. It demonstrates a probability-based method to resolve ambiguous rearrangements involving highly similar sequences, as occurs in gene conversion. We present a catalog of local rearrangements in one human cell line, and show which rearrangement patterns occur.

Resolving the full spectrum of human genome variation using Linked-Reads

Large-scale population analyses coupled with advances in technology have demonstrated that the human genome is more diverse than originally thought. To date, this diversity has largely been uncovered using short-read whole-genome sequencing. However, these short-read approaches fail to give a complete picture of a genome. They struggle to identify structural events, cannot access repetitive regions, and fail to resolve the human genome into haplotypes. Here, we describe an approach that retains long range information while maintaining the advantages of short reads. Starting from ∼1 ng of high molecular weight DNA, we produce barcoded short-read libraries. Novel informatic approaches allow for the barcoded short reads to be associated with their original long molecules producing a novel data type known as "Linked-Reads". This approach allows for simultaneous detection of small and large variants from a single library. In this manuscript, we show the advantages of Linked-Reads over standard short-read approaches for reference-based analysis. Linked-Reads allow mapping to 38 Mb of sequence not accessible to short reads, adding sequence in 423 difficult-to-sequence genes including disease-relevant genes STRC, SMN1, and SMN2 Both Linked-Read whole-genome and whole-exome sequencing identify complex structural variations, including balanced events and single exon deletions and duplications. Further, Linked-Reads extend the region of high-confidence calls by 68.9 Mb. The data presented here show that Linked-Reads provide a scalable approach for comprehensive genome analysis that is not possible using short reads alone.

Comprehensive, integrated, and phased whole-genome analysis of the primary ENCODE cell line K562

K562 is widely used in biomedical research. It is one of three tier-one cell lines of ENCODE and also most commonly used for large-scale CRISPR/Cas9 screens. Although its functional genomic and epigenomic characteristics have been extensively studied, its genome sequence and genomic structural features have never been comprehensively analyzed. Such information is essential for the correct interpretation and understanding of the vast troves of existing functional genomics and epigenomics data for K562. We performed and integrated deep-coverage whole-genome (short-insert), mate-pair, and linked-read sequencing as well as karyotyping and array CGH analysis to identify a wide spectrum of genome characteristics in K562: copy numbers (CN) of aneuploid chromosome segments at high-resolution, SNVs and indels (both corrected for CN in aneuploid regions), loss of heterozygosity, megabase-scale phased haplotypes often spanning entire chromosome arms, structural variants (SVs), including small and large-scale complex SVs and nonreference retrotransposon insertions. Many SVs were phased, assembled, and experimentally validated. We identified multiple allele-specific deletions and duplications within the tumor suppressor gene FHIT. Taking aneuploidy into account, we reanalyzed K562 RNA-seq and whole-genome bisulfite sequencing data for allele-specific expression and allele-specific DNA methylation. We also show examples of how deeper insights into regulatory complexity are gained by integrating genomic variant information and structural context with functional genomics and epigenomics data. Furthermore, using K562 haplotype information, we produced an allele-specific CRISPR targeting map. This comprehensive whole-genome analysis serves as a resource for future studies that utilize K562 as well as a framework for the analysis of other cancer genomes.

Minerva: an alignment- and reference-free approach to deconvolve Linked-Reads for metagenomics

Emerging Linked-Read technologies (aka read cloud or barcoded short-reads) have revived interest in short-read technology as a viable approach to understand large-scale structures in genomes and metagenomes. Linked-Read technologies, such as the 10x Chromium system, use a microfluidic system and a specialized set of 3' barcodes (aka UIDs) to tag short DNA reads sourced from the same long fragment of DNA; subsequently, the tagged reads are sequenced on standard short-read platforms. This approach results in interesting compromises. Each long fragment of DNA is only sparsely covered by reads, no information about the ordering of reads from the same fragment is preserved, and 3' barcodes match reads from roughly 2-20 long fragments of DNA. However, compared to long-read technologies, the cost per base to sequence is far lower, far less input DNA is required, and the per base error rate is that of Illumina short-reads. In this paper, we formally describe a particular algorithmic issue common to Linked-Read technology: the deconvolution of reads with a single 3' barcode into clusters that represent single long fragments of DNA. We introduce Minerva, a graph-based algorithm that approximately solves the barcode deconvolution problem for metagenomic data (where reference genomes may be incomplete or unavailable). Additionally, we develop two demonstrations where the deconvolution of barcoded reads improves downstream results, improving the specificity of taxonomic assignments and of k-mer-based clustering. To the best of our knowledge, we are the first to address the problem of barcode deconvolution in metagenomics.

Structural Alterations Driving Castration-Resistant Prostate Cancer Revealed by Linked-Read Genome Sequencing

Nearly all prostate cancer deaths are from metastatic castration-resistant prostate cancer (mCRPC), but there have been few whole-genome sequencing (WGS) studies of this disease state. We performed linked-read WGS on 23 mCRPC biopsy specimens and analyzed cell-free DNA sequencing data from 86 patients with mCRPC. In addition to frequent rearrangements affecting known prostate cancer genes, we observed complex rearrangements of the AR locus in most cases. Unexpectedly, these rearrangements include highly recurrent tandem duplications involving an upstream enhancer of AR in 70%-87% of cases compared with <2% of primary prostate cancers. A subset of cases displayed AR or MYC enhancer duplication in the context of a genome-wide tandem duplicator phenotype associated with CDK12 inactivation. Our findings highlight the complex genomic structure of mCRPC, nominate alterations that may inform prostate cancer treatment, and suggest that additional recurrent events in the non-coding mCRPC genome remain to be discovered.

Molecular Cytogenetics Guides Massively Parallel Sequencing of a Radiation-Induced Chromosome Translocation in Human Cells

Chromosome rearrangements are large-scale structural variants that are recognized drivers of oncogenic events in cancers of all types. Cytogenetics allows for their rapid, genome-wide detection, but does not provide gene-level resolution. Massively parallel sequencing (MPS) promises DNA sequence-level characterization of the specific breakpoints involved, but is strongly influenced by bioinformatics filters that affect detection efficiency. We sought to characterize the breakpoint junctions of chromosomal translocations and inversions in the clonal derivatives of human cells exposed to ionizing radiation. Here, we describe the first successful use of DNA paired-end analysis to locate and sequence across the breakpoint junctions of a radiation-induced reciprocal translocation. The analyses employed, with varying degrees of success, several well-known bioinformatics algorithms, a task made difficult by the involvement of repetitive DNA sequences. As for underlying mechanisms, the results of Sanger sequencing suggested that the translocation in question was likely formed via microhomology-mediated non-homologous end joining (mmNHEJ). To our knowledge, this represents the first use of MPS to characterize the breakpoint junctions of a radiation-induced chromosomal translocation in human cells. Curiously, these same approaches were unsuccessful when applied to the analysis of inversions previously identified by directional genomic hybridization (dGH). We conclude that molecular cytogenetics continues to provide critical guidance for structural variant discovery, validation and in "tuning" analysis filters to enable robust breakpoint identification at the base pair level.

Optical mapping reveals a higher level of genomic architecture of chained fusions in cancer

Genomic rearrangements are common in cancer, with demonstrated links to disease progression and treatment response. These rearrangements can be complex, resulting in fusions of multiple chromosomal fragments and generation of derivative chromosomes. Although methods exist for detecting individual fusions, they are generally unable to reconstruct complex chained events. To overcome these limitations, we adopted a new optical mapping approach, allowing megabase-length genome maps to be reconstructed and rearranged genomes to be visualized without loss of integrity. Whole-genome mapping (Bionano Genomics) of a well-studied highly rearranged liposarcoma cell line resulted in 3338 assembled consensus genome maps, including 72 fusion maps. These fusion maps represent 112.3 Mb of highly rearranged genomic regions, illuminating the complex architecture of chained fusions, including content, order, orientation, and size. Spanning the junction of 147 chromosomal translocations, we found a total of 28 Mb of interspersed sequences that could not be aligned to the reference genome. Traversing these interspersed sequences using short-read sequencing breakpoint calls, we were able to identify and place 399 sequencing fragments within the optical mapping gaps, thus illustrating the complementary nature of optical mapping and short-read sequencing. We demonstrate that optical mapping provides a powerful new approach for capturing a higher level of complex genomic architecture, creating a scaffold for renewed interpretation of sequencing data of particular relevance to human cancer.

Structural Variation Detection and Analysis Using Bionano Optical Mapping

The need to accurately identify the complete structural variation profile of genomes is becoming increasingly evident. In contrast to reference-based methods like sequencing or comparative methods like aCGH, optical mapping is a de novo assembly-based method that enables better realization of true genomic structure. It allows for independently detecting balanced and unbalanced structural variants (SVs) from separate alleles and for discovering de novo events. Here we show how Bionano Genome Mapping creates de novo assemblies from native and intact, megabase-scale DNA molecules and uses those assemblies to detect a wide range of structural variants.