Next-generation sequencing for cancer diagnostics: a practical perspective

Next-generation sequencing (NGS) is arguably one of the most significant technological advances in the biological sciences of the last 30 years. The second generation sequencing platforms have advanced rapidly to the point that several genomes can now be sequenced simultaneously in a single instrument run in under two weeks. Targeted DNA enrichment methods allow even higher genome throughput at a reduced cost per sample. Medical research has embraced the technology and the cancer field is at the forefront of these efforts given the genetic aspects of the disease. World-wide efforts to catalogue mutations in multiple cancer types are underway and this is likely to lead to new discoveries that will be translated to new diagnostic, prognostic and therapeutic targets. NGS is now maturing to the point where it is being considered by many laboratories for routine diagnostic use. The sensitivity, speed and reduced cost per sample make it a highly attractive platform compared to other sequencing modalities. Moreover, as we identify more genetic determinants of cancer there is a greater need to adopt multi-gene assays that can quickly and reliably sequence complete genes from individual patient samples. Whilst widespread and routine use of whole genome sequencing is likely to be a few years away, there are immediate opportunities to implement NGS for clinical use. Here we review the technology, methods and applications that can be immediately considered and some of the challenges that lie ahead.     

Next generation molecular diagnosis of mitochondrial disorders

Mitochondrial disorders are by far the most genetically heterogeneous group of diseases, involving two genomes, the 16.6 kb mitochondrial genome and ~ 1500 genes encoded in the nuclear genome. For maternally inherited mitochondrial DNA disorders, a complete molecular diagnosis requires several different methods for the detection and quantification of mtDNA point mutations and large deletions. For mitochondrial disorders caused by autosomal recessive, dominant, and X-linked nuclear genes, the diagnosis has relied on clinical, biochemical, and molecular studies to point to a group of candidate genes followed by stepwise Sanger sequencing of the candidate genes one-by-one. The development of Next Generation Sequencing (NGS) has revolutionized the diagnostic approach. Using massively parallel sequencing (MPS) analysis of the entire mitochondrial genome, mtDNA point mutations and deletions can be detected and quantified in one single step. The NGS approach also allows simultaneous analyses of a group of genes or the whole exome, thus, the mutations in causative gene(s) can be identified in one-step. New approaches make genetic analyses much faster and more efficient. Huge amounts of sequencing data produced by the new technologies brought new challenges to bioinformatics, analytical pipelines, and interpretation of numerous novel variants. This article reviews the clinical utility of next generation sequencing for the molecular diagnoses of complex dual genome mitochondrial disorders.     

Understanding genetic variation and function- the applications of next generation sequencing

Next generation sequencing (NGS) technology has had a transformatory effect upon population-level studies linking genetic variation to gene function. In this review, I briefly describe recent studies that have used top-down genome scanning and population genetic approaches to identify loci under recent selection, as well as some examples of how large NGS datasets can be deployed to detect the total level of deleterious, neutral and advantageous variation present in standing genetic variation. I then explore studies that have used some of these approaches to study gene function along with advances in sequencing populations under selection, QTL mapping techniques and emerging methodologies utilising targeted capture and NGS.     

Mutation Detection in Patients with Retinal Dystrophies Using Targeted Next Generation Sequencing

Retinal dystrophies (RD) constitute a group of blinding diseases that are characterized by clinical variability and pronounced genetic heterogeneity. The different nonsyndromic and syndromic forms of RD can be attributed to mutations in more than 200 genes. Consequently, next generation sequencing (NGS) technologies are among the most promising approaches to identify mutations in RD. We screened a large cohort of patients comprising 89 independent cases and families with various subforms of RD applying different NGS platforms. While mutation screening in 50 cases was performed using a RD gene capture panel, 47 cases were analyzed using whole exome sequencing. One family was analyzed using whole genome sequencing. A detection rate of 61% was achieved including mutations in 34 known and two novel RD genes. A total of 69 distinct mutations were identified, including 39 novel mutations. Notably, genetic findings in several families were not consistent with the initial clinical diagnosis. Clinical reassessment resulted in refinement of the clinical diagnosis in some of these families and confirmed the broad clinical spectrum associated with mutations in RD genes.     

An Integrated Approach for Analyzing Clinical Genomic Variant Data from Next-Generation Sequencing

Next-generation sequencing (NGS) technologies provide the potential for developing high-throughput and low-cost platforms for clinical diagnostics. A limiting factor to clinical applications of genomic NGS is downstream bioinformatics analysis for data interpretation. We have developed an integrated approach for end-to-end clinical NGS data analysis from variant detection to functional profiling. Robust bioinformatics pipelines were implemented for genome alignment, single nucleotide polymorphism (SNP), small insertion/deletion (InDel), and copy number variation (CNV) detection of whole exome sequencing (WES) data from the Illumina platform. Quality-control metrics were analyzed at each step of the pipeline by use of a validated training dataset to ensure data integrity for clinical applications. We annotate the variants with data regarding the disease population and variant impact. Custom algorithms were developed to filter variants based on criteria, such as quality of variant, inheritance pattern, and impact of variant on protein function. The developed clinical variant pipeline links the identified rare variants to Integrated Genome Viewer for visualization in a genomic context and to the Protein Information Resource’s iProXpress for rich protein and disease information. With the application of our system of annotations, prioritizations, inheritance filters, and functional profiling and analysis, we have created a unique methodology for downstream variant filtering that empowers clinicians and researchers to interpret more effectively the relevance of genomic alterations within a rare genetic disease.     

Whole-Exome Sequencing to Decipher the Genetic Heterogeneity of Hearing Loss in a Chinese Family with Deaf by Deaf Mating

Inherited deafness has been shown to have high genetic heterogeneity. For many decades, linkage analysis and candidate gene approaches have been the main tools to elucidate the genetics of hearing loss. However, this associated study design is costly, time-consuming, and unsuitable for small families. This is mainly due to the inadequate numbers of available affected individuals, locus heterogeneity, and assortative mating. Exome sequencing has now become technically feasible and a cost-effective method for detection of disease variants underlying Mendelian disorders due to the recent advances in next-generation sequencing (NGS) technologies. In the present study, we have combined both the Deafness Gene Mutation Detection Array and exome sequencing to identify deafness causative variants in a large Chinese composite family with deaf by deaf mating. The simultaneous screening of the 9 common deafness mutations using the allele-specific PCR based universal array, resulted in the identification of the 1555A>G in the mitochondrial DNA (mtDNA) 12S rRNA in affected individuals in one branch of the family. We then subjected the mutation-negative cases to exome sequencing and identified novel causative variants in the MYH14 and WFS1 genes. This report confirms the effective use of a NGS technique to detect pathogenic mutations in affected individuals who were not candidates for classical genetic studies.     

Harvesting the Promising Fruits of Genomics: Applying Genome Sequencing Technologies to Crop Breeding

Next generation sequencing (NGS) technologies are being used to generate whole genome sequences for a wide range of crop species. When combined with precise phenotyping methods, these technologies provide a powerful and rapid tool for identifying the genetic basis of agriculturally important traits and for predicting the breeding value of individuals in a plant breeding population. Here we summarize current trends and future prospects for utilizing NGS-based technologies to develop crops with improved trait performance and increase the efficiency of modern plant breeding. It is our hope that the application of NGS technologies to plant breeding will help us to meet the challenge of feeding a growing world population.

This article is part of the PLOS Biology Collection “The Promise of Plant Translational Research.”

     

Using High-Throughput Sequencing to Leverage Surveillance of Genetic Diversity and Oseltamivir Resistance: A Pilot Study during the 2009 Influenza A(H1N1) Pandemic

Background

Influenza viruses display a high mutation rate and complex evolutionary patterns. Next-generation sequencing (NGS) has been widely used for qualitative and semi-quantitative assessment of genetic diversity in complex biological samples. The “deep sequencing” approach, enabled by the enormous throughput of current NGS platforms, allows the identification of rare genetic viral variants in targeted genetic regions, but is usually limited to a small number of samples.

Methodology and Principal Findings

We designed a proof-of-principle study to test whether redistributing sequencing throughput from a high depth-small sample number towards a low depth-large sample number approach is feasible and contributes to influenza epidemiological surveillance. Using 454-Roche sequencing, we sequenced at a rather low depth, a 307 bp amplicon of the neuraminidase gene of the Influenza A(H1N1) pandemic (A(H1N1)pdm) virus from cDNA amplicons pooled in 48 barcoded libraries obtained from nasal swab samples of infected patients (n  =  299) taken from May to November, 2009 pandemic period in Mexico. This approach revealed that during the transition from the first (May-July) to second wave (September-November) of the pandemic, the initial genetic variants were replaced by the N248D mutation in the NA gene, and enabled the establishment of temporal and geographic associations with genetic diversity and the identification of mutations associated with oseltamivir resistance.

Conclusions

NGS sequencing of a short amplicon from the NA gene at low sequencing depth allowed genetic screening of a large number of samples, providing insights to viral genetic diversity dynamics and the identification of genetic variants associated with oseltamivir resistance. Further research is needed to explain the observed replacement of the genetic variants seen during the second wave. As sequencing throughput rises and library multiplexing and automation improves, we foresee that the approach presented here can be scaled up for global genetic surveillance of influenza and other infectious diseases.     

Next Generation Sequencing in der diagnostischen Praxis

Next generation sequencing (NGS) allows whole exome or whole genome sequencing for a given patient to be performed timely and at reasonable cost. This diagnostic quantum leap not only has various legal, ethical and economical aspects but will naturally also impact upon patient care. Currently, however, the wide-spread introduction of NGS into routine diagnostics is facing many obstacles. In particular, it is to be expected that NGS will identify a large number of rare variants in a given patient that are of (yet) unknown clinical significance. As a first step towards solving this problem, we introduce the concept of a database that will systematically integrate genotypic and phenotypic information from the German health care context. Not only will this resource be of great scientific value, but the database shall also provide human geneticists with the evidence base necessary for the reliable evaluation of their patient-related sequencing data.     

Customisation of the exome data analysis pipeline using a combinatorial approach

The advent of next generation sequencing (NGS) technologies have revolutionised the way biologists produce, analyse and interpret data. Although NGS platforms provide a cost-effective way to discover genome-wide variants from a single experiment, variants discovered by NGS need follow up validation due to the high error rates associated with various sequencing chemistries. Recently, whole exome sequencing has been proposed as an affordable option compared to whole genome runs but it still requires follow up validation of all the novel exomic variants. Customarily, a consensus approach is used to overcome the systematic errors inherent to the sequencing technology, alignment and post alignment variant detection algorithms. However, the aforementioned approach warrants the use of multiple sequencing chemistry, multiple alignment tools, multiple variant callers which may not be viable in terms of time and money for individual investigators with limited informatics know-how. Biologists often lack the requisite training to deal with the huge amount of data produced by NGS runs and face difficulty in choosing from the list of freely available analytical tools for NGS data analysis. Hence, there is a need to customise the NGS data analysis pipeline to preferentially retain true variants by minimising the incidence of false positives and make the choice of right analytical tools easier. To this end, we have sampled different freely available tools used at the alignment and post alignment stage suggesting the use of the most suitable combination determined by a simple framework of pre-existing metrics to create significant datasets.     

Targeted single molecule mutation detection with massively parallel sequencing

Next-generation sequencing (NGS) technologies have transformed genomic research and have the potential to revolutionize clinical medicine. However, the background error rates of sequencing instruments and limitations in targeted read coverage have precluded the detection of rare DNA sequence variants by NGS. Here we describe a method, termed CypherSeq, which combines double-stranded barcoding error correction and rolling circle amplification (RCA)-based target enrichment to vastly improve NGS-based rare variant detection. The CypherSeq methodology involves the ligation of sample DNA into circular vectors, which contain double-stranded barcodes for computational error correction and adapters for library preparation and sequencing. CypherSeq is capable of detecting rare mutations genome-wide as well as those within specific target genes via RCA-based enrichment. We demonstrate that CypherSeq is capable of correcting errors incurred during library preparation and sequencing to reproducibly detect mutations down to a frequency of 2.4 × 10⁻⁷ per base pair, and report the frequency and spectra of spontaneous and ethyl methanesulfonate-induced mutations across the Saccharomyces cerevisiae genome.     

Gene identification using rice genome sequences

Identifying useful gene(s) is one of the most important objectives of plant geneticists. Various strategies can be used, which are based on the characteristics of plant reproduction and available technology. Rice is the first model crop whose whole genome sequence has been reported. In addition, information on the whole genome sequences of two important rice subspecies (japonica and indica rice) is also available. Rice is a self-pollinating crop and methods of artificial crossing are relatively easy to perform; such methods enable the production of numerous seeds for genetic analyses. Based on these features, a map-based cloning (i.e., positional cloning) strategy has been successfully applied over the last decade to identify rice genes. Recently, advanced next-generation sequencing (NGS) technology was used to ascertain the genome sequences of individual plants, opening up a new strategy for gene identification. This strategy has been used successfully to identify the genes responsible for certain qualitative traits in rice. However, to identify the gene(s) involved in a quantitative trait, a map-based cloning strategy is still required after quantitative trait loci analysis using NGS technology. In this review, we discuss both map-based cloning (which is still the primary strategy used to identify rice genes) and NGS-based strategies.     

Genome-wide next-generation DNA and RNA sequencing reveals a mutation that perturbs splicing of the phosphatidylinositol glycan anchor biosynthesis class H gene (PIGH) and causes arthrogryposis in Belgian Blue cattle

Background

Cattle populations are characterized by regular outburst of genetic defects as a result of the extensive use of elite sires. The causative genes and mutations can nowadays be rapidly identified by means of genome-wide association studies combined with next generation DNA sequencing, provided that the causative mutations are conventional loss-of-function variants. We show in this work how the combined use of next generation DNA and RNA sequencing allows for the rapid identification of otherwise difficult to identify splice-site variants.

Results

We report the use of haplotype-based association mapping to identify a locus on bovine chromosome 10 that underlies autosomal recessive arthrogryposis in Belgian Blue Cattle. We identify 31 candidate mutations by resequencing the genome of four cases and 15 controls at ~10-fold depth. By analyzing RNA-Seq data from a carrier fetus, we observe skipping of the second exon of the PIGH gene, which we confirm by RT-PCR to be fully penetrant in tissues from affected calves. We identify - amongst the 31 candidate variants - a C-to-G transversion in the first intron of the PIGH gene (c211-10C > G) that is predicted to affect its acceptor splice-site. The resulting PIGH protein is likely to be non-functional as it lacks essential domains, and hence to cause arthrogryposis.

Conclusions

This work illustrates how the growing arsenal of genome exploration tools continues to accelerate the identification of an even broader range of disease causing mutations, therefore improving the management and control of genetic defects in livestock.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1528-y) contains supplementary material, which is available to authorized users.     

Accurate detection of subclonal single nucleotide variants in whole genome amplified and pooled cancer samples using HaloPlex target enrichment

Background

Target enrichment and resequencing is a widely used approach for identification of cancer genes and genetic variants associated with diseases. Although cost effective compared to whole genome sequencing, analysis of many samples constitutes a significant cost, which could be reduced by pooling samples before capture. Another limitation to the number of cancer samples that can be analyzed is often the amount of available tumor DNA. We evaluated the performance of whole genome amplified DNA and the power to detect subclonal somatic single nucleotide variants in non-indexed pools of cancer samples using the HaloPlex technology for target enrichment and next generation sequencing.

Results

We captured a set of 1528 putative somatic single nucleotide variants and germline SNPs, which were identified by whole genome sequencing, with the HaloPlex technology and sequenced to a depth of 792–1752. We found that the allele fractions of the analyzed variants are well preserved during whole genome amplification and that capture specificity or variant calling is not affected. We detected a large majority of the known single nucleotide variants present uniquely in one sample with allele fractions as low as 0.1 in non-indexed pools of up to ten samples. We also identified and experimentally validated six novel variants in the samples included in the pools.

Conclusion

Our work demonstrates that whole genome amplified DNA can be used for target enrichment equally well as genomic DNA and that accurate variant detection is possible in non-indexed pools of cancer samples. These findings show that analysis of a large number of samples is feasible at low cost, even when only small amounts of DNA is available, and thereby significantly increases the chances of indentifying recurrent mutations in cancer samples.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-14-856) contains supplementary material, which is available to authorized users.     

Direct mutation analysis by high-throughput sequencing: from germline to low-abundant, somatic variants

DNA mutations are the source of genetic variation within populations. The majority of mutations with observable effects are deleterious. In humans mutations in the germ line can cause genetic disease. In somatic cells multiple rounds of mutations and selection lead to cancer. The study of genetic variation has progressed rapidly since the completion of the draft sequence of the human genome. Recent advances in sequencing technology, most importantly the introduction of massively parallel sequencing (MPS), have resulted in more than a hundred-fold reduction in the time and cost required for sequencing nucleic acids. These improvements have greatly expanded the use of sequencing as a practical tool for mutation analysis. While in the past the high cost of sequencing limited mutation analysis to selectable markers or small forward mutation targets assumed to be representative for the genome overall, current platforms allow whole genome sequencing for less than $5000. This has already given rise to direct estimates of germline mutation rates in multiple organisms including humans by comparing whole genome sequences between parents and offspring. Here we present a brief history of the field of mutation research, with a focus on classical tools for the measurement of mutation rates. We then review MPS, how it is currently applied and the new insight into human and animal mutation frequencies and spectra that has been obtained from whole genome sequencing. While great progress has been made, we note that the single most important limitation of current MPS approaches for mutation analysis is the inability to address low-abundance mutations that turn somatic tissues into mosaics of cells. Such mutations are at the basis of intra-tumor heterogeneity, with important implications for clinical diagnosis, and could also contribute to somatic diseases other than cancer, including aging. Some possible approaches to gain access to low-abundance mutations are discussed, with a brief overview of new sequencing platforms that are currently waiting in the wings to advance this exploding field even further.     

Targeted next-generation sequencing identifies ABCA4 mutations in Chinese families with childhood-onset and adult-onset Stargardt disease

Background: Stargardt disease (STGD) is the most common form of juvenile macular dystrophy associated with progressive central vision loss, and is agenetically and clinically heterogeneous disease. Molecular diagnosis is of great significance in aiding the clinical diagnosis, helping to determine the phenotypic severity and visual prognosis. In the present study, we determined the clinical and genetic features of seven childhood-onset and three adult-onset Chinese STGD families. We performed capture next-generation sequencing (NGS) of the probands and searched for potentially disease-causing genetic variants in previously identified retinal or macular dystrophy genes.Methods: In all, ten unrelated Chinese families were enrolled. Panel-based NGS was performed to identify potentially disease-causing genetic variants in previously identified retinal or macular dystrophy genes, including the five known STGD genes (ABCA4, PROM1, PRPH2, VMD2, and ELOVL4). Variant analysis, Sanger validation, and segregation tests were utilized to validate the disease-causing mutations in these families.Results: Using systematic data analysis with an established bioinformatics pipeline and segregation analysis, 17 pathogenic mutations in ABCA4 were identified in the 10 STGD families. Four of these mutations were novel: c.371delG, c.681T > G, c.5509C > T, and EX37del. Childhood-onset STGD was associated with severe visual loss, generalized retinal dysfunction and was due to more severe variants in ABCA4 than those found in adult-onset disease.Conclusions: We expand the existing spectrum of STGD and reveal the genotype–phenotype relationships of the ABCA4 mutations in Chinese patients. Childhood-onset STGD lies at the severe end of the spectrum of ABCA4-associated retinal phenotypes.     

Sequencing XMET genes to promote genotype-guided risk assessment and precision medicine

High-throughput next generation sequencing(NGS) is a shotgun approach applied in a parallel fashion by which the genome is fragmented and sequenced through small pieces and then analyzed either by aligning to a known reference genome or by de novo assembly without reference genome. This technology has led researchers to conduct an explosion of sequencing related projects in multidisciplinary fields of science. However, due to the limitations of sequencing-based chemistry, length of sequencing reads and the complexity of genes, it is difficult to determine the sequences of some portions of the human genome, leaving gaps in genomic data that frustrate further analysis. Particularly, some complex genes are difficult to be accurately sequenced or mapped because they contain high GC-content and/or low complexity regions, and complicated pseudogenes, such as the genes encoding xenobiotic metabolizing enzymes and transporters(XMETs). The genetic variants in XMET genes are critical to predicate interindividual variability in drug efficacy, drug safety and susceptibility to environmental toxicity. We summarized and discussed challenges, wet-lab methods, and bioinformatics algorithms in sequencing "complex" XMET genes, which may provide insightful information in the application of NGS technology for implementation in toxicogenomics and pharmacogenomics.     

Targeted Next-Generation Sequencing Revealed Novel Mutations in Chinese Ataxia Telangiectasia Patients: A Precision Medicine Perspective

Ataxia telangiectasia (AT) is an autosomal recessive disease characterized by progressive cerebellar ataxia, oculocutaneous telangiectasia and immunodeficiency due to mutations in the ATM gene. We performed targeted next-generation sequencing (NGS) on three unrelated patients and identified five disease-causing variants in three probands, including two pairs of heterozygous variants (FAT–1:c.4396C>T/p.R1466X, c.1608-2A>G; FAT–2:c.4412_4413insT/p.L1472Ffs*19, c.8824C>T/p.Q2942X) and one pair of homozygous variants (FAT–3: c.8110T>G/p.C2704G, Hom). With regard to precision medicine for rare genetic diseases, targeted NGS currently enables the rapid and cost-effective identification of causative mutations and is an updated molecular diagnostic tool that merits further optimization. This high-throughput data-based strategy would propel the development of precision diagnostic methods and establish a foundation for precision medicine.