Biology subjects

Wald, A.

Publications and source records attributed to Wald, A..

3 recordsLinked to original sources

bioRxiv2018

Limited marginal utility of deep sequencing for HIV drug resistance testing in the age of integrase inhibitors

HIV drug resistance genotyping is a critical tool in the clinical management of HIV infections. Although resistance genotyping has traditionally been conducted using Sanger sequencing, next-generation sequencing (NGS) is emerging as a powerful tool due to its ability to detect lower frequency alleles. However, the value added from NGS approaches to antiviral resistance testing remains to be demonstrated. We compared the variant detection capacity of NGS versus Sanger sequencing methods for resistance genotyping of 144 drug resistance tests (105 protease-reverse transcriptase tests and 39 integrase tests) submitted to our clinical virology laboratory over a four-month period in 2016 for Sanger-based HIV drug resistance testing. NGS detected all true high frequency drug resistance mutations (>20% frequency) found by Sanger sequencing, with greater accuracy in one instance of a Sanger-detected false positive. Freely available online NGS variant callers Hydra and PASeq were superior to Sanger methods for interpretations of allele linkage and automated variant calling. NGS additionally detected low frequency mutations (1-20% frequency) associated with higher levels of drug resistance in 30/105 (29%) of protease-reverse transcriptase tests and 4/39 (10%) of integrase tests. Clinical follow-up of 69 individuals for a median of 674 days found no difference in rates of virological failure between individuals with and without low frequency mutations, although rates of virological failure were higher for individuals with drug-relevant low frequency mutations. However, all 27 individuals who experienced virological failure reported poor adherence to their drug regimen during preceding follow-up time, and all 19 who subsequently improved their adherence achieved viral suppression at later time points consistent with a lack of clinical resistance. In conclusion, in a population with low antiviral resistance emergence, NGS methods detected numerous instances of minor alleles that did not result in subsequent bona fide virological failure due to antiviral resistance.\n\nImportanceGenotypic antiviral resistance testing for HIV is an essential component of the clinical microbiology and virology laboratory. Next-generation sequencing (NGS) has emerged as a powerful tool for the detection of low frequency sequence variants (allele frequencies <20%). Whether detecting these low frequency mutations in HIV contributes to improved patient health, however, remains unclear. We compared NGS to conventional Sanger sequencing for detecting resistance mutations for 144 HIV drug resistance tests submitted to our clinical virology laboratory and detected low frequency mutations in 24% of tests. Over approximately two years of follow-up for 69 patients for which we had access to electronic health records, no patients had virological failure due to antiviral resistance. Instead, virological failure was entirely explained by medication non-adherence. While larger studies are required, we suggest that detection of low frequency variants by NGS presents limited marginal clinical utility when compared to standard of care.

epidemiology↗

bioRxiv2018

Ultrasensitive capture of human herpes simplex virus genomes directly from clinical samples reveals extraordinarily limited evolution in cell culture

Herpes simplex viruses (HSV) are difficult to sequence due to their large DNA genome, high GC content, and the presence of repeats. To date, most HSV genomes have been recovered from culture isolates, raising concern that these genomes may not accurately represent circulating clinical strains. We report the development and validation of a DNA oligonucleotide hybridization panel to recover near complete HSV genomes at abundances up to 50,000-fold lower than previously reported. Using copy number information on herpesvirus and host DNA background via quantitative PCR, we developed a protocol for pooling for cost-effective recovery of more than 50 HSV-1 or HSV-2 genomes per MiSeq run. We demonstrate the ability to recover >99% of the HSV genome at >100X coverage in 72 hours at viral loads that allow whole genome recovery from latently-infected ganglia. We also report a new computational pipeline for rapid HSV genome assembly and annotation. Using the above tools and a series of 17 HSV-1-positive clinical swabs sent to our laboratory for viral isolation, we show limited evolution of HSV-1 during viral isolation in human fibroblast cells compared to the original clinical samples. Our data indicate that previous studies using low passage clinical isolates of herpes simplex viruses are reflective of the viral sequences present in the lesion and thus can be used in phylogenetic analyses. We also detect superinfection within a single sample with unrelated HSV-1 strains recovered from separate oral lesions in an immunosuppressed patient during a 2.5-week period, illustrating the power of direct-from-specimen sequencing of HSV.\n\nImportanceHerpes simplex viruses affect more than 4 billion people across the globe, constituting a large burden of disease. Understanding global diversity of herpes simplex viruses is important for diagnostics and therapeutics as well as cure research and tracking transmission among humans. To date, most HSV genomics has been performed on culture isolates and DNA swabs with high quantities of virus. We describe the development of wet-lab and computational tools that enable the accurate sequencing of near-complete genomes of HSV-1 and HSV-2 directly from clinical specimens at abundances >50,000-fold lower than previously sequenced and at significantly reduced cost. We use these tools to profile circulating HSV-1 strains in the community and illustrate limited changes to the viral genome during the viral isolation process. These techniques enable cost-effective, rapid sequencing of HSV-1 and HSV-2 genomes that will help enable improved detection, surveillance, and control of this human pathogen.

genomics↗

bioRxiv2018

Targeted mutation detection in breast cancer using MammaSeqTM

BackgroundBreast cancer is the most common invasive cancer among women worldwide. Next-generation sequencing (NGS) has revolutionized the study of cancer across research labs around the globe, however genomic testing in clinical settings remain limited. Advances in sequencing reliability, pipeline analysis, accumulation of relevant data, and the reduction of costs are rapidly increasing the feasibility of NGS-based clinical decision making.\n\nMethodsWe report the development of MammaSeq, a breast cancer specific NGS panel, targeting 79 genes and 1369 mutations, optimized for use in primary and metastatic breast cancer. To validate the panel, 46 solid tumor and 14 plasma circulating-free cfDNA samples were sequenced to a mean depth of 2311X and 1820 X respectively. Variants were called using Ion Torrent Suite 4.0 and annotated with cravat CHASM. CNVKit was used to call copy number variants in the solid tumor cohort. The oncoKB Precision Oncology Database was used to identify clinically actionable variants. ddPCR was used to validate select cfDNA mutations.\n\nResultsIn cohorts of 46 solid tumors and 14 cfDNA samples from patients with advanced breast cancer we identified 592 and 43 protein coding mutations. Mutations per sample in the solid tumor cohort ranged from 1 to 128 (median 3) and the cfDNA cohort ranged from 0 to 26 (median 2.5). Copy number analysis in the solid tumor cohort identified 46 amplifications and 35 deletions. We identified 26 clinically actionable variants (levels 1-3) annotated by OncoKB, distributed across 20 out of 46 cases (40%), in the solid tumor cohort. Allele frequencies of ESR1 and FOXA1 mutations correlated with CA.27.29 levels in patient matched blood draws.\n\nConclusionsIn solid tumors biopsies and cfDNA, MammaSeq detects clinicaly actionable mutations (oncoKB levels 1-3) in 22/46 (48%) solid tumors and in 4/14 (29%) of cfDNA samples. MammaSeq is a targeted panel suitable for clinically actionable mutation detection in breast cancer.

cancer biology↗