Detection Of Genomic Variants Research Articles

CIEVaD: A Lightweight Workflow Collection for the Rapid and On-Demand Deployment of End-to-End Testing for Genomic Variant Detection.

The identification of genomic variants has become a routine task in the age of genome sequencing. In particular, small genomic variants of a single or few nucleotides are routinely investigated for their impact on an organism's phenotype. Hence, the precise and robust detection of the variants' exact genomic locations and changes in nucleotide composition is vital in many biological applications. Although a plethora of methods exist for the many key steps of variant detection, thoroughly testing the detection process and evaluating its results is still a cumbersome procedure. In this work, we present a collection of easy-to-apply and highly modifiable workflows to facilitate the generation of synthetic test data, as well as to evaluate the accordance of a user-provided set of variants with the test data. The workflows are implemented in Nextflow and are open-source and freely available on Github under the GPL-3.0 license.

Abstract PO4-14-11: Findings of clinically significant variants (Tier IA) with OmniSeq INSIGHT ® in a breast cancer cohort of 987 patients

Abstract Background: There were an estimated 2.3 million new breast cancer cases worldwide in 2020, with breast cancer now the most diagnosed type of cancer representing 25% of cancer cases and 17% of cancer deaths (1). The GLOBOCAN Cancer Tomorrow prediction tool estimates that incidence will increase by > 45% by 2040 (2). Optimal treatment for breast cancer is increasingly dependent upon knowing a patient’s somatic and germline genomic alteration status. To highlight the importance of these genomic alterations, we provide an overview of the genomic findings in 987 consecutive breast cancer patients tested in the course of routine clinical care. Methods: Comprehensive Genomic and Immune Profiling (CGIP) was performed on 987 qualified breast cancer samples at a CAP/CLIA and NYS CLEP certified reference laboratory with the OmniSeq INSIGHT ® test (3). OmniSeq INSIGHT ® is a next generation sequencing-based laboratory developed test for the detection of genomic variants, signatures, HLA Class I genotypes, and immune gene expression in formalin-fixed paraffin-embedded (FFPE) tumor tissue. DNA is sequenced to detect small variants in the full exonic coding region of 523 genes, copy number alterations in 59 genes (gains and losses), as well as analysis of microsatellite instability (MSI) and tumor mutational burden (TMB). RNA is sequenced to detect fusions and splice variants in 55 genes, in addition to mRNA expression in 64 immune genes. The resultant information, along with PD-L1 protein expression by immunohistochemistry (IHC), is intended for use by qualified health care professionals in accordance with professional guidelines in oncology for management of patients with solid neoplasms. Tier IA variants include variants with strong clinical significance as per AMP–ASCO–CAP recommendations (4). Results: There were 384 Tier IA variants detected in 370 of 987 breast cancer patients (~37.5%) with 251 in PIK3CA (65.3%), 60 in ERBB2 (15.6%), 46 in BRCA2 (11.9%), 19 in BRCA1 (4.9%), 4 in NTRK3 (1.0%), 1 in NTRK1 (0.26%) and 1 in PALB2 (0.26%) including copy number variations (CNV), gene fusion and single nucleotide variants (SNV). At the individual gene level for Tier IA variants, PIK3CA had 251 SNVs detected, ERBB2 had 55 CNVs, 1 gene fusion and 4 SNVs detected, BRCA2 had 39 SNVs and 8 CNVs detected, BRCA1 had 19 SNVs, NTRK3 had 4 gene fusions, NTRK1 had 1 gene fusion and PALB2 had 1 SNV detected. At the variant class level, there were 63 CNVs, 6 gene fusions and 315 SNVs observed. Conclusions: CGIP for breast cancer patients identified one or more clinically significant Tier IA genomic alterations that directs targeted therapy in ~ 37.5% of patients in a cohort of real world patients tested during the standard course of clinical care. This highlights the need for comprehensive genomic testing in breast cancer patients to drive therapeutic decision making.

Evaluation of 10 Different Pipelines for Bacterial Single-Nucleotide Variant Detection

Abstract Bacterial genome sequencing is a powerful technique for studying the genetic diversity and evolution of microbial populations. However, the detection of genomic variants from sequencing data is challenging due to the presence of contamination, sequencing errors and multiple strains within the same species. Several bioinformatics tools have been developed to address these issues, but their performance and accuracy have not been systematically evaluated. In this study, we compared 10 variant detection pipelines using 18 simulated and 17 real datasets of high-throughput sequences from a bundle of representative bacteria. We assessed the sensitivity of each pipeline under different conditions of coverage, simulation and strain diversity. We also demonstrated the application of these tools to identify consistent mutations in a 30-time repeated sequencing dataset of Staphylococcus hominis. We found that HaplotypeCaller, but not Mutect2, from the GATK tool set showed the best performance in terms of accuracy and robustness. CFSAN and Snippy performed not as well in several simulated and real sequencing datasets. Our results provided a comprehensive benchmark and guidance for choosing the optimal variant detection pipeline for high-throughput bacterial genome sequencing data.

Whole genome sequencing improves the discrimination between Mycobacterium bovis strains on the southern border of Kruger National Park, South Africa

BackgroundMycobacterium bovis forms part of the Mycobacterium tuberculosis complex and has an extensive host range and zoonotic potential. Various genotyping methods (e.g., spoligotyping) have been used to describe the molecular epidemiology of M. bovis. Advances in whole genome sequencing (WGS) have increased resolution to enable detection of genomic variants to the level of single nucleotide polymorphisms. This is especially relevant to One Health research on tuberculosis which benefits by being able to use WGS to identify epidemiologically linked cases, especially recent transmission. The use of WGS in molecular epidemiology has been extensively used in humans and cattle but is limited in wildlife. This approach appears to overcome the limitations of conventional genotyping methods due to lack of genetic diversity in M. bovis. MethodsThis pilot study investigated the spoligotype and WGS of M. bovis isolates (n = 7) from wildlife in Marloth Park (MP) and compared these with WGS data from other South African M. bovis isolates. In addition, the greater resolution of WGS was used to explore the phylogenetic relatedness of M. bovis isolates in neighbouring wildlife populations. ResultsThe phylogenetic analyses showed the closest relatives to the seven isolates from MP were isolates from wildlife in Kruger National Park (KNP), which shares a border with MP. However, WGS data indicated that the KNP and MP isolates formed two distinct clades, even though they had similar spoligotypes and identical in silico genetic regions of difference profiles. ConclusionsMycobacterium bovis isolates from MP were hypothesized to be directly linked to KNP wildlife, based on spoligotyping. However, WGS indicated more complex epidemiology. The presence of two distinct clades which were genetically distinct (SNP distance of 19–47) and suggested multiple transmission events. Therefore, WGS provided new insight into the molecular epidemiology of the M. bovis isolates from MP and their relationship to isolates from KNP. This approach will facilitate greater understanding of M. bovis transmission at wildlife-livestock-human interfaces and advances One Health research on tuberculosis, especially across different host species.

Detection of genomic variations and selection signatures in Wagyu using whole-genome sequencing data.

Wagyu is recognized for producing marbled beef with high nutritional value and flavor. Reportedly, Wagyu has been widely used to improve the meat quality of local breeds around the world. However, studies on the genetic mechanism of meat quality in Wagyu at the whole-genome level are rarely reported. Here, whole-genome sequencing data of 11 Wagyu and 115 other individuals were used to explore the genomic variations and genes under selection pressure in Wagyu. A total of 31 349 non-synonymous variants and 53 102 synonymous variants were identified in Wagyu. The population structure analysis showed that Wagyu had the closest genetic relationship with Mishima-Ushi cattle and was apparently separated from other cattle breeds. Then, composite likelihood ratio (CLR), integrated haplotype score, fixation index and cross-population composite likelihood ratio (XP-CLR) tests were performed to identify the candidate genes under positive selection in Wagyu. In total, 770 regions containing 312 genes were identified by at least three methods. Among them, 97 regions containing 27 genes were detected by all four methods. We specifically illustrate a list of interesting genes, including LRP2BP, GAA, CACNG6, CXADR, GPCPD1, KLF2, KLF13, SOX5, MYBPC1, SLC25A10, ATP8A1 and MYH15, which are associated with lipid metabolism, fat deposition, muscle development, bone development, feed intake and growth traits in Wagyu. This is the first study to explore the genomic variations and selection signatures of Wagyu at the whole-genome level. These results will provide significant help to beef cattle improvement and breeding.

Mapinsights: deep exploration of quality issues and error profiles in high-throughput sequence data.

High-throughput sequencing (HTS) has revolutionized science by enabling super-fast detection of genomic variants at base-pair resolution. Consequently, it poses the challenging problem of identification of technical artifacts, i.e. hidden non-random error patterns. Understanding the properties of sequencing artifacts holds the key in separating true variants from false positives. Here, we develop Mapinsights, a toolkit that performs quality control (QC) analysis of sequence alignment files, capable of detecting outliers based on sequencing artifacts of HTS data at a deeper resolution compared with existing methods. Mapinsights performs a cluster analysis based on novel and existing QC features derived from the sequence alignment for outlier detection. We applied Mapinsights on community standard open-source datasets and identified various quality issues including technical errors related to sequencing cycles, sequencing chemistry, sequencing libraries and across various orthogonal sequencing platforms. Mapinsights also enables identification of anomalies related to sequencing depth. A logistic regression-based model built on the features of Mapinsights shows high accuracy in detecting 'low-confidence' variant sites. Quantitative estimates and probabilistic arguments provided by Mapinsights can be utilized in identifying errors, bias and outlier samples, and also aid in improving the authenticity of variant calls.

Mobocertinib efficacy in patients with NSCLC and EGFR exon 20 insertion mutations (ex20ins) identified by next-generation sequencing (NGS) of circulating tumor DNA (ctDNA).

9082 Background: EGFR ex20ins are distinct targetable driver mutations present in ~2% of all NSCLC. While most EGFR TKIs have limited effectiveness, mobocertinib is the first oral therapy approved for this unmet need. EGFR ex20ins are heterogeneous ( > 60 variants), and PCR testing detects a limited number of variants, leading to undetected disease in ~50% of cases, whereas NGS can identify all such mutations. Testing plasma ctDNA is a noninvasive approach for detection of genomic variants, such as EGFR ex20ins. Methods: In the mobocertinib phase 1/2 study (NCT02716116), patients were enrolled based on EGFR ex20ins detected by various local tissue or liquid clinical trial assays (CTAs). Baseline plasma samples were collected and processed to extract cell-free DNA and were evaluated for EGFR ex20ins status using FoundationOne Liquid CDX (F1LCDX), an NGS-based plasma ctDNA test. Concordance between F1LCDX and the CTAs was demonstrated by testing patient samples from the phase 1/2 trial and paired tissue and plasma samples from commercially acquired and stage-matched patients with NSCLC (N = 305). Efficacy analyses by assay method were performed in EGFR ex20ins–positive platinum-pretreated patients (PPP; n = 114 [FDA approved population]; data cutoff: 1 Nov 2021) in the phase 1/2 trial. Results: Concordance between F1LCDX and CTAs was demonstrated with samples from the CTA-positive (n = 159) and CTA-negative populations (n = 87) tested by F1LCDX where tissue and plasma were assessable; the point estimate of positive percentage agreement (95% CI) was 68.6% (61.0–75.3), and the point estimate of negative percentage agreement was 100% (95.8–100). F1LCDX detected 34 individual EGFR ex20ins variant types. In the response evaluable population, patients who tested EGFR ex20ins positive by F1LCDX (n = 55) demonstrated confirmed ORR (34.5%) and disease control rate (74.5%), which was comparable to those observed among all PPP in the phase 1/2 trial (confirmed ORR: 28%; confirmed disease control rate: 78%). Conclusions: Mobocertinib has shown efficacy in PPP with EGFR ex20ins NSCLC. F1LCDX effectively identified patients with EGFR ex20ins who may benefit from mobocertinib, providing an additional noninvasive diagnostic option to guide treatment. Clinical trial information: NCT02716116 .

Analytical performance assessment of ASPYRE-Lung: A new assay for rapid detection of actionable variants from tissue and plasma in NSCLC patients.

e15038 Background: We developed ASPYRE technology to make detection of genomic variants in oncology patients more accessible. Although ASPYRE is not traditional PCR, it can be performed using standard qPCR instruments on nucleic acid extracted from FFPE tissue or plasma and is therefore easily adoptable by clinical laboratories. Using ASPYRE, we designed a targeted panel called ASPYRE-LungTM consisting of 114 variants in 11 genes ( ALK, BRAF, EGFR, ERBB2, KRAS, RET, ROS1, MET & NTRK1/2/3) to inform the clinical management of patients with NSCLC. We have previously published general assay descriptions of ASPYRE-Lung (Silva et al., 2021, Scientific Reports and Gray et al., 2022, BMC Medical Genomics). Here we extend our prior studies with a detailed analytic performance assessment across all variants using precisely controlled synthetic samples. We determined assay-wide performance using samples with low variant allele fractions (VAFs) near the limit of detection (LoD) of the assay and compared results across 2 separate laboratory settings. Methods: Nine independent batches of reagents were split between 2 laboratories. Contrived test samples were prepared with known VAFs (DNA variants) or copy numbers (RNA variants). Each sample contained a single variant with VAFs of 0.1-0.5% for DNA variants, and 6-150 copies for RNA variants. Multiple replicates were performed at both testing sites using 20ng DNA/6ng RNA. Data was processed using an internally designed algorithm and analyzed using a machine learning classifier which determined the presence or absence of each individual variant. Results: Study data resulted in a total of 133,083 variant calls from 2,319 samples. For each variant at each VAF or copy number, the detection rate was calculated, and the results for each variant were used to calculate an LoD95 using probit regression. Target assay specifications and the assay-wide analytical performance data are shown in the table below. Conclusions: The data show that ASPYRE-Lung meets or exceeds target specifications for the detection of single nucleotide variants (SNVs), insertions/deletions (indels), and gene fusions. Estimates of LoD for key variants fall within target specifications, and assay-wide performance demonstrates highly sensitive detection of each class of variant. Performance was similar at the two laboratory sites further supporting the clinical potential of the assay. Moreover, the assay only requires 20ng DNA and as little as 6ng RNA, depending on sample type, so it is highly suitable for use with typical NSCLC patient samples such as needle core biopsies or plasma where available nucleic acid may be limited. This data supports further clinical development of ASPYRE-Lung. [Table: see text]

Surveillance for feline herpesvirus type 1 mutation and development of resistance in cats treated with antiviral medications.

Feline herpesvirus type 1 (FHV-1) commonly causes ocular surface disease in cats and is treated with antiviral medications targeting viral DNA polymerase (UL30/42). Herein, we describe a method to assess the FHV-1 genome for mutation development and to assess the functional impact of mutations, if present. Fourteen shelter-housed domestic cats with FHV-1 ocular surface disease were assigned to one of four treatment groups: placebo (n = 3), cidofovir 0.5% ophthalmic solution (n = 3), famciclovir oral solution (n = 5), or ganciclovir 0.15% ophthalmic solution (n = 3). Swabs were collected before (day 1) and after (day 8) 1 week of twice-daily treatments to isolate viable FHV-1. Viral DNA was extracted for sequencing using Illumina MiSeq with subsequent genomic variant detection between paired day 1 and day 8 isolates. Plaque reduction assay was performed on paired isolates demonstrating non-synonymous variants. A total of 171 synonymous and 3 non-synonymous variants were identified in day 8 isolates. No variants were detected in viral UL23, UL30, or UL42 genes. Variant totals were not statistically different in animals receiving antiviral or placebo (p = 0.4997). A day 8 isolate from each antiviral treatment group contained a single non-synonymous variant in ICP4 (transcriptional regulator). These 3 isolates demonstrated no evidence of functional antiviral resistance when IC50 was assessed. Most (10/14 pairs) day 1 and 8 viral isolate pairs from the same host animal were near-identical. While functional variants were not detected in this small sample, these techniques can be replicated to assess FHV-1 isolates suspected of having developed resistance to antiviral medications.

FHH Quick App Review: How Can a Quality Review Process Assist Primary Care Providers in Choosing a Family Health History App for Patient Care?

Family health history (FHH) is a data type serving risk assessment, diagnosis, research, and preventive health. Despite technological leaps in genomic variant detection, FHH remains the most accessible, least expensive, and most practical assessment tool for assessing risks attributable to genetic inheritance. The purpose of this manuscript is to outline a process to assist primary care professionals in choosing FHH digital tools for patient care based on the new ISO/TS 82304-2 Technical Specification (TS), which is a recently developed method to determine eHealth app quality. With a focus on eHealth in primary care, we applied the quality label concept to FHH, and how a primary care physician can quickly review the quality and reliability of an FHH app. Based on our review of the ISO TS’s 81 questions, we compiled a list of 25 questions that are recommended to be more succinct as an initial review. We call this process the FHH Quick App Review. Our ‘informative-only’ 25 questions do not produce a quality score, but a guide to complete an initial review of FHH apps. Most of the questions are straight from the ISO TS, some are modified or de novo. We believe the 25 questions are not only relevant to FHH app reviews but could also serve to aid app development and clinical implementation.

A single center experience of prenatal parent-fetus trio exome sequencing for pregnancies with congenital anomalies.

To examine the diagnostic yield of trio exome sequencing in fetuses with multiple structural defects with no pathogenic findings in cytogenetic and microarray analyses. We recruited 51 fetuses with two or more defects, non-immune fetal hydrops or fetal akinesia deformation syndrome|or fetal akinesia deformation sequence (FADS). Trio exome sequencing was performed on DNA from chorionic villi samples and parental blood. Detection of genomic variation and prioritization of clinically relevant variants was performed according to in-house standard operating procedures. Median maternal and gestational age was 32.0years and 21.0weeks, respectively. Forty-three (84.3%) fetuses had two or more affected organ systems. The remaining fetuses had isolated fetal hydrops or FADS. In total, the exome analysis established the genetic cause for the clinical abnormalities in 22 (43.1%, 95% CI 29.4%-57.8%) pregnancies. In fetuses with multiple defects, hydrops or FADS and normal standard genetic results, trio exome sequencing has the potential to identify genetic anomalies in more than 40% of cases.

Accurate genomic variant detection in single cells with primary template-directed amplification.

Improvements in whole genome amplification (WGA) would enable new types of basic and applied biomedical research, including studies of intratissue genetic diversity that require more accurate single-cell genotyping. Here, we present primary template-directed amplification (PTA), an isothermal WGA method that reproducibly captures >95% of the genomes of single cells in a more uniform and accurate manner than existing approaches, resulting in significantly improved variant calling sensitivity and precision. To illustrate the types of studies that are enabled by PTA, we developed direct measurement of environmental mutagenicity (DMEM), a tool for mapping genome-wide interactions of mutagens with single living human cells at base-pair resolution. In addition, we utilized PTA for genome-wide off-target indel and structural variant detection in cells that had undergone CRISPR-mediated genome editing, establishing the feasibility for performing single-cell evaluations of biopsies from edited tissues. The improved precision and accuracy of variant detection with PTA overcomes the current limitations of accurate WGA, which is the major obstacle to studying genetic diversity and evolution at cellular resolution.

Integrative genomics of the mammalian alveolar macrophage response to intracellular mycobacteria

BackgroundBovine TB (bTB), caused by infection with Mycobacterium bovis, is a major endemic disease affecting global cattle production. The key innate immune cell that first encounters the pathogen is the alveolar macrophage, previously shown to be substantially reprogrammed during intracellular infection by the pathogen. Here we use differential expression, and correlation- and interaction-based network approaches to analyse the host response to infection with M. bovis at the transcriptome level to identify core infection response pathways and gene modules. These outputs were then integrated with genome-wide association study (GWAS) data sets to enhance detection of genomic variants for susceptibility/resistance to M. bovis infection.ResultsThe host gene expression data consisted of RNA-seq data from bovine alveolar macrophages (bAM) infected with M. bovis at 24 and 48 h post-infection (hpi) compared to non-infected control bAM. These RNA-seq data were analysed using three distinct computational pipelines to produce six separate gene sets: 1) DE genes filtered using stringent fold-change and P-value thresholds (DEG-24: 378 genes, DEG-48: 390 genes); 2) genes obtained from expression correlation networks (CON-24: 460 genes, CON-48: 416 genes); and 3) genes obtained from differential expression networks (DEN-24: 339 genes, DEN-48: 495 genes). These six gene sets were integrated with three bTB breed GWAS data sets by employing a new genomics data integration tool—gwinteR. Using GWAS summary statistics, this methodology enabled detection of 36, 102 and 921 prioritised SNPs for Charolais, Limousin and Holstein-Friesian, respectively.ConclusionsThe results from the three parallel analyses showed that the three computational approaches could identify genes significantly enriched for SNPs associated with susceptibility/resistance to M. bovis infection. Results indicate distinct and significant overlap in SNP discovery, demonstrating that network-based integration of biologically relevant transcriptomics data can leverage substantial additional information from GWAS data sets. These analyses also demonstrated significant differences among breeds, with the Holstein-Friesian breed GWAS proving most useful for prioritising SNPS through data integration. Because the functional genomics data were generated using bAM from this population, this suggests that the genomic architecture of bTB resilience traits may be more breed-specific than previously assumed.

Bioinformatic strategies for the analysis of genomic aberrations detected by targeted NGS panels with clinical application.

Molecular profiling of tumor samples has acquired importance in cancer research, but currently also plays an important role in the clinical management of cancer patients. Rapid identification of genomic aberrations improves diagnosis, prognosis and effective therapy selection. This can be attributed mainly to the development of next-generation sequencing (NGS) methods, especially targeted DNA panels. Such panels enable a relatively inexpensive and rapid analysis of various aberrations with clinical impact specific to particular diagnoses. In this review, we discuss the experimental approaches and bioinformatic strategies available for the development of an NGS panel for a reliable analysis of selected biomarkers. Compliance with defined analytical steps is crucial to ensure accurate and reproducible results. In addition, a careful validation procedure has to be performed before the application of NGS targeted assays in routine clinical practice. With more focus on bioinformatics, we emphasize the need for thorough pipeline validation and management in relation to the particular experimental setting as an integral part of the NGS method establishment. A robust and reproducible bioinformatic analysis running on powerful machines is essential for proper detection of genomic variants in clinical settings since distinguishing between experimental noise and real biological variants is fundamental. This review summarizes state-of-the-art bioinformatic solutions for careful detection of the SNV/Indels and CNVs for targeted sequencing resulting in translation of sequencing data into clinically relevant information. Finally, we share our experience with the development of a custom targeted NGS panel for an integrated analysis of biomarkers in lymphoproliferative disorders.

Status and prospects of genome-wide association studies in plants.

Genome-wide association studies (GWAS) have developed into a powerful and ubiquitous tool for the investigation of complex traits. In large part, this was fueled by advances in genomic technology, enabling us to examine genome-wide genetic variants across diverse genetic materials. The development of the mixed model framework for GWAS dramatically reduced the number of false positives compared with naïve methods. Building on this foundation, many methods have since been developed to increase computational speed or improve statistical power in GWAS. These methods have allowed the detection of genomic variants associated with either traditional agronomic phenotypes or biochemical and molecular phenotypes. In turn, these associations enable applications in gene cloning and in accelerated crop breeding through marker assisted selection or genetic engineering. Current topics of investigation include rare-variant analysis, synthetic associations, optimizing the choice of GWAS model, and utilizing GWAS results to advance knowledge of biological processes. Ongoing research in these areas will facilitate further advances in GWAS methods and their applications.

Comprehensive fundamental somatic variant calling and quality management strategies for human cancer genomes.

Next-generation sequencing (NGS) technology has revolutionised human cancer research, particularly via detection of genomic variants with its ultra-high-throughput sequencing and increasing affordability. However, the inundation of rich cancer genomics data has resulted in significant challenges in its exploration and translation into biological insights. One of the difficulties in cancer genome sequencing is software selection. Currently, multiple tools are widely used to process NGS data in four stages: raw sequence data pre-processing and quality control (QC), sequence alignment, variant calling and annotation and visualisation. However, the differences between these NGS tools, including their installation, merits, drawbacks and application, have not been fully appreciated. Therefore, a systematic review of the functionality and performance of NGS tools is required to provide cancer researchers with guidance on software and strategy selection. Another challenge is the multidimensional QC of sequencing data because QC can not only report varied sequence data characteristics but also reveal deviations in diverse features and is essential for a meaningful and successful study. However, monitoring of QC metrics in specific steps including alignment and variant calling is neglected in certain pipelines such as the 'Best Practices Workflows' in GATK. In this review, we investigated the most widely used software for the fundamental analysis and QC of cancer genome sequencing data and provided instructions for selecting the most appropriate software and pipelines to ensure precise and efficient conclusions. We further discussed the prospects and new research directions for cancer genomics.

Utility of clinical comprehensive genomic characterization for diagnostic categorization in patients presenting with hypocellular bone marrow failure syndromes.

Bone marrow failure (BMF) related to hypoplasia of hematopoietic elements in the bone marrow is a heterogeneous clinical entity with a broad differential diagnosis including both inherited and acquired causes. Accurate diagnostic categorization is critical to optimal patient care and detection of genomic variants in these patients may provide this important diagnostic and prognostic information. We performed real-time, accredited (ISO15189) comprehensive genomic characterization including targeted sequencing and whole exome sequencing in 115 patients with BMF syndromes (median age 24 years, range: 3 months - 81 years). In patients with clinical diagnoses of inherited BMF syndromes, acquired BMF syndromes or clinically unclassifiable BMF we detected variants in 52% (12 of 23), 53% (25 of 47) and 56% (25 of 45) respectively. Genomic characterization resulted in a change of diagnosis in 30 of 115 (26%) including the identification of germline causes for 3 of 47 and 16 of 45 cases with pre-test diagnoses of acquired and clinically unclassifiable BMF respectively. The observed clinical impact of accurate diagnostic categorization included choice to perform allogeneic stem cell transplantation, disease-specific targeted treatments, identification of at-risk family members and influence of sibling allogeneic stem cell donor choice. Multiple novel pathogenic variants and copy number changes were identified in our cohort including in TERT, FANCA, RPS7 and SAMD9. Whole exome sequence analysis facilitated the identification of variants in two genes not typically associated with a primary clinical manifestation of BMF but also demonstrated reduced sensitivity for detecting low level acquired variants. In conclusion, genomic characterization can improve diagnostic categorization of patients presenting with hypoplastic BMF syndromes and should be routinely performed in this group of patients.

VEGF-A and ICAM-1 Gene Polymorphisms as Predictors of Clinical Outcome to First-Line Bevacizumab-Based Treatment in Metastatic Colorectal Cancer.

Bevacizumab is used to treat metastatic colorectal cancer (mCRC). However, there are still no available predictors of clinical outcomes. We investigated selected single nucleotide polymorphisms (SNPs) in the genes involved in VEGF-dependent and -independent angiogenesis pathways and other major intracellular signaling pathways involved in the pathogenesis of mCRC as an attempt to find predictors of clinical outcome. Forty-six patients treated with first-line bevacizumab-based chemotherapy were included in this study with a 5 year follow up. Genomic DNA was isolated from whole blood for the analysis of VEGF-A (rs2010963, 1570360, rs699947), ICAM-1 (rs5498, rs1799969) SNPs and from tumor tissue for the detection of genomic variants in KRAS, NRAS, BRAF genes. PCR and next generation sequencing were used for the analysis. The endpoints of the study were progression-free survival (PFS) and overall survival (OS). The VEGF-A rs699947 A/A allele was associated with increased PFS (p = 0.006) and OS (p = 0.043). The ICAM-1 rs1799969 G/A allele was associated with prolonged OS (p = 0.036). Finally, BRAF wild type was associated with increased OS (p = 0.027). We identified VEGF-A and ICAM-1 variants in angiogenesis and other major intracellular signaling pathways, such as BRAF, that can predict clinical outcome upon bevacizumab administration. These identified biomarkers could be used to select patients with mCRC who may achieve long-term responses and benefit from bevacizumab-based therapies.

Variant analysis pipeline for accurate detection of genomic variants from transcriptome sequencing data.

The wealth of information deliverable from transcriptome sequencing (RNA-seq) is significant, however current applications for variant detection still remain a challenge due to the complexity of the transcriptome. Given the ability of RNA-seq to reveal active regions of the genome, detection of RNA-seq SNPs can prove valuable in understanding the phenotypic diversity between populations. Thus, we present a novel computational workflow named VAP (Variant Analysis Pipeline) that takes advantage of multiple RNA-seq splice aware aligners to call SNPs in non-human models using RNA-seq data only. We applied VAP to RNA-seq from a highly inbred chicken line and achieved high accuracy when compared with the matching whole genome sequencing (WGS) data. Over 65% of WGS coding variants were identified from RNA-seq. Further, our results discovered SNPs resulting from post transcriptional modifications, such as RNA editing, which may reveal potentially functional variation that would have otherwise been missed in genomic data. Even with the limitation in detecting variants in expressed regions only, our method proves to be a reliable alternative for SNP identification using RNA-seq data. The source code and user manuals are available at https://modupeore.github.io/VAP/.

Prenatal Diagnosis of Fetuses With Increased Nuchal Translucency by Genome Sequencing Analysis.

Background: Increased nuchal translucency (NT) is an important biomarker associated with increased risk of fetal structural anomalies. It is known to be contributed by a wide range of genetic etiologies from single-nucleotide variants to those affecting millions of base pairs. Currently, prenatal diagnosis is routinely performed by karyotyping and chromosomal microarray analysis (CMA); however, both of them have limited resolution. The diversity of the genetic etiologies warrants an integrated assay such as genome sequencing (GS) for comprehensive detection of genomic variants. Herein, we aim to evaluate the feasibility of applying GS in prenatal diagnosis for the fetuses with increased NT. Methods: We retrospectively applied GS (> 30-fold) for fetuses with increased NT (≥3.5 mm) who underwent routine prenatal diagnosis. Detection of single-nucleotide variants, copy number variants, and structural rearrangements was performed simultaneously, and the results were integrated for interpretation in accordance with the guidelines of the American College of Medical Genetics and Genomics. Pathogenic or likely pathogenic (P/LP) variants were selected for validation and parental confirmation, when available. Results: Overall, 50 fetuses were enrolled, including 34 cases with isolated increased NT and 16 cases with other fetal structural malformations. Routine CMA and karyotyping reported eight P/LP CNVs, yielding a diagnostic rate of 16.0% (8/50). In comparison, GS provided a twofold increase in diagnostic yield (32.0%, 16/50), including one mosaic turner syndrome, eight cases with microdeletions/microduplications, and seven cases with P/LP point mutations. Moreover, GS identified two cryptic insertions and two inversions. Follow-up study further demonstrated the potential pathogenicity of an apparently balanced insertion that disrupted an OMIM autosomal dominant disease-causing gene at the insertion site. Conclusions: Our study demonstrates that applying GS in fetuses with increased NT can comprehensively detect and delineate the various genomic variants that are causative to the diseases. Importantly, prenatal diagnosis by GS doubled the diagnostic yield compared with routine protocols. Given a comparable turnaround time and less DNA required, our study provides strong evidence to facilitate GS in prenatal diagnosis, particularly in fetuses with increased NT.