Identification of single nucleotide variants using position-specific error estimation in deep sequencing data

Outlier Expression of Isoforms by Targeted or Total RNA Sequencing Identifies Clinically Significant Genomic Variants in Hematolymphoid Tumors

Recognition of aberrant gene isoforms due to DNA events can impact risk stratification and molecular classification of hematolymphoid tumors. In myelodysplastic syndromes, KMT2A partial tandem duplication (PTD) was one of the top adverse predictors in the International Prognostic Scoring System-Molecular study. In B-cell acute lymphoblastic leukemia (B-ALL), ERG isoforms have been proposed as markers of favorable-risk DUX4 rearrangements, whereas deletion-mediated IKZF1 isoforms are associated with adverse prognosis and have been extended to the high-risk IKZF1plus signature defined by codeletions, including PAX5. In this limited study, outlier expression of isoforms as markers of IKZF1 intragenic or 3' deletions, DUX4 rearrangements, or PAX5 intragenic deletions were 92.3% (48/52), 90% (9/10), or 100% (9/9) sensitive, respectively, and 98.7% (368/373), 100% (35/35), or 97.1% (102/105) specific, respectively, by targeted RNA sequencing, and 84.0% (21/25), 85.7% (6/7), or 81.8% (9/11) sensitive, respectively, and 98.2% (109/111), 98.4% (127/129), or 98.7% (78/79) specific, respectively, by total RNA sequencing. Comprehensive split-read analysis identified expressed DNA breakpoints, cryptic splice sites associated with IKZF1 3' deletions, PTD of IKZF1 exon 5 spanning N159Y in B-ALL with mutated IKZF1 N159Y, and truncated KMT2A-PTD isoforms. Outlier isoforms were also effective targeted RNA markers for PAX5 intragenic amplifications (B-ALL), KMT2A-PTD (myeloid malignant cancers), and rare NOTCH1 intragenic deletions (T-cell acute lymphoblastic leukemia). These findings support the use of outlier isoform analysis as a robust strategy for detecting clinically significant DNA events.

Liquid biopsy for children with central nervous system tumours: Clinical integration and technical considerations

Circulating cell-free DNA (cfDNA) analysis has the potential to revolutionise the care of patients with cancer and is already moving towards standard of care in some adult malignancies. Evidence for the utility of cfDNA analysis in paediatric cancer patients is also accumulating. In this review we discuss the limitations of blood-based assays in patients with brain tumours and describe the evidence supporting cerebrospinal fluid (CSF) cfDNA analysis. We make recommendations for CSF cfDNA processing to aid the standardisation and technical validation of future assays. We discuss the considerations for interpretation of cfDNA analysis and highlight promising future directions. Overall, cfDNA profiling shows great potential as an adjunct to the analysis of biopsy tissue in paediatric cancer patients, with the potential to provide a genetic molecular profile of the tumour when tissue biopsy is not feasible. However, to fully realise the potential of cfDNA analysis for children with brain tumours larger prospective studies incorporating serial CSF sampling are required.

A Retrospective Statistical Validation Approach for Panel of Normal-Based Single-Nucleotide Variant Detection in Tumor Sequencing

An important step of somatic variant calling algorithms for deep sequencing data is quantifying the errors. For targeted sequencing in which hotspot mutations are of interest, site-specific error estimation allows more accurate calling. The site-specific error rates are often estimated from a panel of normal samples, which has limited size and is subject to sampling bias and variance. We propose a novel statistical validation method for single-nucleotide variation (SNV) calling based on historical data. The validation method extracts the high-quality reads from the Binary Alignment/Map (BAM) files, finds the negative samples in the data, and builds a statistical model to call individual samples. It is particularly useful in detecting low-frequency variants that may be missed by traditional panel of normal-based SNV methods. The proposed method makes it possible to launch a simple and parallel validation pipeline for SNV calling and improve the detection limit.

SVLR: Genome Structural Variant Detection Using Long-Read Sequencing Data

Genome structural variants (SVs) have great impacts on human phenotype and diversity, and have been linked to numerous diseases. Long-read sequencing technologies arise to make it possible to find SVs of as long as 10,000 nucleotides. Thus, long read-based SV detection has been drawing attention of many recent research projects, and many tools have been developed for long reads to detect SVs recently. In this article, we present a new method, called SVLR, to detect SVs based on long-read sequencing data. Comparing with existing methods, SVLR can detect three new kinds of SVs: block replacements, block interchanges, and translocations. Although these new SVs are structurally more complicated, SVLR achieves accuracies that are comparable with those of the classic SVs. Moreover, for the classic SVs that can be detected by state-of-the-art methods (e.g., SVIM and Sniffles), our experiments demonstrate recall improvements of up to 38% without harming the precisions (i.e., >78%). We also point out three directions to further improve SV detection in the future. Source codes: https://github.com/GWYSDU/SVLR.

Rare variants discovery by extensive whole-genome sequencing of the Han Chinese population in Taiwan: Applications to cardiovascular medicine

Introduction

A population-specific genomic reference is important for research and clinical practice, yet it remains unavailable for Han Chinese (HC) in Taiwan.

Objectives

We report the first whole genome sequencing (WGS) database of HC (1000 Taiwanese genome (1KTW-WGS)) and demonstrate several applications to cardiovascular medicine.

Methods

Whole genomes of 997 HC were sequenced to at least 30X depth. A total of 20,117 relatively healthy HC individuals were genotyped using a customized Axiom GWAS array. We performed a genome-wide genotype imputation technique using IMPUTE2.

Results

We identified 26.7 million single-nucleotide variants (SNVs) and 4.2 million insertions-deletions. Of the SNVs, 16.1% were novel relative to dbSNP (build 152), and 34.2% were novel relative to gnomAD. A total of 18,450 healthy HC individuals were genotyped using a customized Genome-Wide Association Study (GWAS) array. We identified hypertension-associated variants and developed a hypertension prediction model based on the correlation between the WGS data and GWAS data (combined clinical and genetic models, AUC 0.887), and also identified 3 novel hyperlipidemia-associated variants. Each individual carried an average of 16.42 (SD = 3.72) disease-causing variants. Additionally, we established an online SCN5A (an important cardiac gene) database that can be used to explore racial differences. Finally, pharmacogenetics studies identified HC population-specific SNVs in genes (CYP2C9 and VKORC1) involved in drug metabolism and blood clotting.

Conclusion

This research demonstrates the benefits of constructing a population-specific genomic reference database for precision medicine.

Detection of genomic alterations in breast cancer with circulating tumour DNA sequencing

Analysis of circulating cell-free DNA (cfDNA) has opened new opportunities for characterizing tumour mutational landscapes with many applications in genomic-driven oncology. We developed a customized targeted cfDNA sequencing approach for breast cancer (BC) using unique molecular identifiers (UMIs) for error correction. Our assay, spanning a 284.5 kb target region, is combined with a novel freely-licensed bioinformatics pipeline that provides detection of low-frequency variants, and reliable identification of copy number variations (CNVs) directly from plasma DNA. We first evaluated our pipeline on reference samples. Then in a cohort of 35 BC patients our approach detected actionable driver and clonal variants at low variant frequency levels in cfDNA that were concordant (77%) with sequencing of primary and/or metastatic solid tumour sites. We also detected ERRB2 gene CNVs used for HER2 subtype classification with 80% precision compared to immunohistochemistry. Further, we evaluated fragmentation profiles of cfDNA in BC and observed distinct differences compared to data from healthy individuals. Our results show that the developed assay addresses the majority of tumour associated aberrations directly from plasma DNA, and thus may be used to elucidate genomic alterations in liquid biopsy studies.

Mutational Mosaics of Cell-Free DNA from Pancreatic Cyst Fluids

BACKGROUND

Pancreatic cyst fluids (PCFs) enriched in tumor-derived DNA are a potential source of new biomarkers. The study aimed to analyze germinal variants and mutational profiles of cell-free (cf)DNA shed into the cavity of pancreatic cysts.

METHODS

The study cohort consisted of 71 patients who underwent endoscopic ultrasound fine-needle aspiration of PCF. Five malignant cysts, 19 intraductal papillary mucinous neoplasms (IPMNs), 11 mucinous cystic neoplasms (MCNs), eight serous cystic neoplasms (SCNs), and 28 pseudocysts were identified. The sequencing of 409 genes included in Comprehensive Cancer Panel was performed using Ion Proton System. The mutation rate of the KRAS and GNAS canonical loci was additionally determined using digital PCR.

RESULTS

The number of mutations detected with NGS varied from 0 to 22 per gene, and genes with the most mutations were: TP53, KRAS, PIK3CA, GNAS, ADGRA2, and APC. The frequencies of the majority of mutations did not differ between non-malignant cystic neoplasms and pseudocysts. NGS detected KRAS mutations in malignant cysts (60%), IPMNs (32%), MCNs (64%), SCNs (13%), and pseudocysts (14%), with GNAS mutations in 20%, 26%, 27%, 13%, and 21% of samples, respectively. Digital PCR-based testing increased KRAS (68%) and GNAS (52%) mutations detection level in IPMNs, but not other cyst types.

CONCLUSIONS

We demonstrate relatively high rates of somatic mutations of cancer-related genes, including KRAS and GNAS, in cfDNA isolated from PCFs irrespectively of the pancreatic cyst type. Further studies on molecular mechanisms of pancreatic cysts malignant transformation in relation to their mutational profiles are required.

ABEMUS: platform-specific and data-informed detection of somatic SNVs in cfDNA

MOTIVATION

The use of liquid biopsies for cancer patients enables the non-invasive tracking of treatment response and tumor dynamics through single or serial blood drawn tests. Next-generation sequencing assays allow for the simultaneous interrogation of extended sets of somatic single-nucleotide variants (SNVs) in circulating cell-free DNA (cfDNA), a mixture of DNA molecules originating both from normal and tumor tissue cells. However, low circulating tumor DNA (ctDNA) fractions together with sequencing background noise and potential tumor heterogeneity challenge the ability to confidently call SNVs.

RESULTS

We present a computational methodology, called Adaptive Base Error Model in Ultra-deep Sequencing data (ABEMUS), which combines platform-specific genetic knowledge and empirical signal to readily detect and quantify somatic SNVs in cfDNA. We tested the capability of our method to analyze data generated using different platforms with distinct sequencing error properties and we compared ABEMUS performances with other popular SNV callers on both synthetic and real cancer patients sequencing data. Results show that ABEMUS performs better in most of the tested conditions proving its reliability in calling low variant allele frequencies somatic SNVs in low ctDNA levels plasma samples.

AVAILABILITY AND IMPLEMENTATION

ABEMUS is cross-platform and can be installed as R package. The source code is maintained on Github at http://github.com/cibiobcg/abemus, and it is also available at CRAN official R repository.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.