Clonal Dynamics and Relapse Risk Revealed by High-Sensitivity FLT3-Internal Tandem Duplication Detection in Acute Myeloid Leukemia.

The ability to detect low-level disease is key to our understanding of clonal heterogeneity in acute myeloid leukemia (AML) and residual disease that elude conventional assays and seed relapse. We developed a high-sensitivity next-generation sequencing (HS-NGS) clinical assay, able to reliably detect low levels (1 × 10-5) of FLT3-ITD, a frequent, therapeutically targetable and prognostically relevant mutation in AML. By applying this assay to 289 longitudinal samples from 62 patients at initial diagnosis and/or clinical follow-up (mean follow-up of 22 months), we reveal the frequent occurrence of FLT3-ITD subclones at diagnosis and demonstrate a significantly decreased relapse risk when FLT3-ITD is cleared after induction or thereafter. We perform pairwise sequencing of diagnosis and relapse samples from 23 patients to uncover more detailed patterns of FLT3-ITD clonal evolution at relapse than is detectable by less-sensitive assays. Finally, we show that rising ITD level during consecutive biopsies is a harbinger of impending relapse. Our findings corroborate the emerging clinical utility of high-sensitivity FLT3-ITD testing and expands our understanding of clonal dynamics in FLT3-ITD-positive AML.

Deep Convolutional Neural Networks Implementation for the Analysis of Urine Culture.

BACKGROUND: Urine culture images collected using bacteriology automation are currently interpreted by technologists during routine standard-of-care workflows. Machine learning may be able to improve the harmonization of and assist with these interpretations. METHODS: A deep learning model, BacterioSight, was developed, trained, and tested on standard BD-Kiestra images of routine blood agar urine cultures from 2 different medical centers. RESULTS: BacterioSight displayed performance on par with standard-of-care-trained technologist interpretations. BacterioSight accuracy ranged from 97% when compared to standard-of-care (single technologist) and reached 100% when compared to a consensus reached by a group of technologists (gold standard in this study). Variability in image interpretation by trained technologists was identified and annotation "fuzziness" was quantified and found to correlate with reduced confidence in BacterioSight interpretation. Intra-testing (training and testing performed within the same institution) performed well giving Area Under the Curve (AUC) ≥0.98 for negative and positive plates, whereas, cross-testing on images (trained on one institution's images and tested on images from another institution) showed decreased performance with AUC ≥0.90 for negative and positive plates. CONCLUSIONS: Our study provides a roadmap on how BacterioSight or similar deep learning prototypes may be implemented to screen for microbial growth, flag difficult cases for multi-personnel review, or auto-verify a subset of cultures with high confidence. In addition, our results highlight image interpretation variability by trained technologist within an institution and globally across institutions. We propose a model in which deep learning can enhance patient care by identifying inherent sample annotation variability and improving personnel training.

High-Throughput Adaptable SARS-CoV-2 Screening for Rapid Identification of Dominant and Emerging Regional Variants.

OBJECTIVES: Emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant strains can be associated with increased transmissibility, more severe disease, and reduced effectiveness of treatments. To improve the availability of regional variant surveillance, we describe a variant genotyping system that is rapid, accurate, adaptable, and able to detect new low-level variants built with existing hospital infrastructure. METHODS: We used a tiered high-throughput SARS-CoV-2 screening program to characterize variants in a supraregional health system over 76 days. Combining targeted reverse transcription-polymerase chain reaction (RT-PCR) and selective sequencing, we screened SARS-CoV-2 reactive samples from all hospitals within our health care system for genotyping dominant and emerging variants. RESULTS: The median turnaround for genotyping was 2 days using the high-throughput RT-PCR-based screen, allowing us to rapidly characterize the emerging Delta variant. In our population, the Delta variant is associated with a lower cycle threshold value, lower age at infection, and increased vaccine-breakthrough cases. Detection of low-level and potentially emerging variants highlights the utility of a tiered approach. CONCLUSIONS: These findings underscore the need for fast, low-cost, high-throughput monitoring of regional viral sequences as the pandemic unfolds and the emergence of SARS-CoV-2 variants increases. Combining RT-PCR-based screening with selective sequencing allows for rapid genotyping of variants and dynamic system improvement.

Specificity of SARS-CoV-2 Real-Time PCR Improved by Deep Learning Analysis.

Real-time PCR (RT-PCR) is widely used to diagnose human pathogens. RT-PCR data are traditionally analyzed by estimating the threshold cycle (CT ) at which the fluorescence signal produced by emission of a probe crosses a baseline level. Current models used to estimate the CT value are based on approximations that do not adequately account for the stochastic variations of the fluorescence signal that is detected during RT-PCR. Less common deviations become more apparent as the sample size increases, as is the case in the current SARS-CoV-2 pandemic. In this work, we employ a method independent of CT value to interpret RT-PCR data. In this novel approach, we built and trained a deep learning model, qPCRdeepNet, to analyze the fluorescent readings obtained during RT-PCR. We describe how this model can be deployed as a quality assurance tool to monitor result interpretation in real time. The model's performance with the TaqPath COVID19 Combo Kit assay, widely used for SARS-CoV-2 detection, is described. This model can be applied broadly for the primary interpretation of RT-PCR assays and potentially replace the CT interpretive paradigm.

Helicobacter pylori Mutations Detected by Next-Generation Sequencing in Formalin-Fixed, Paraffin-Embedded Gastric Biopsy Specimens Are Associated with Treatment Failure.

Helicobacter pylori antibiotic resistance is widespread and increasing worldwide. Routine detection of H. pylori mutations that invoke antimicrobial resistance may be a useful approach to guide antimicrobial therapy and possibly avert treatment failure. In this study, formalin-fixed, paraffin-embedded (FFPE) gastric biopsy specimens from a cohort of individuals from northern Ohio in the United States were examined using a next-generation sequencing (NGS) assay to detect H. pylori mutations that are known to confer resistance to clarithromycin, levofloxacin, and tetracycline. From January 2016 to January 2017, 133 H. pylori-infected gastric biopsy specimens were identified histologically and subsequently analyzed by NGS to detect mutations in gyrA, 23S rRNA, and 16S rRNA genes. The method successfully detected H. pylori in 126 of 133 cases (95% sensitivity). Mutations conferring resistance were present in 92 cases (73%), including 63 cases with one mutation (50%) and 29 cases with mutations in multiple genes (23%). Treatment outcomes were available in 58 cases. Sixteen of the 58 cases failed therapy (28%). Therapy failure correlated with the number of mutated genes: no failure in cases with no mutations (0/15), 19% (5/27) failure in cases with one gene mutation, and 69% (11/16) failure in cases with more than one mutated gene. Common 23S rRNA mutations (A2142G or A2413G) were present in 88% (14/16) of failed cases as opposed to in only 10% (4/42) of eradicated cases (P < 0.001). This NGS assay can be used on remnant specimens collected during standard-of-care testing to detect mutations that correlate with increased risk of treatment failure. A prospective study is needed to determine if the risk of treatment failure can be decreased by using this assay to guide antibiotic therapy.

Comparison of cytocentrifugation supernatant fluid and formalin-fixed paraffin-embedded tissue for targeted next-generation sequencing.

BACKGROUND: The emergence of less invasive procedures coupled with the growth of molecular testing have created a need for clinical laboratories to optimize workflows to enable tissue preservation and ancillary testing. In the preparation of formalin-fixed paraffin-embedded cell blocks (FFPE CBs), there is a cytocentrifugation step for cell pellet extraction that results in postcentrifugation supernatant fluid (SN). This SN, which in most routine workflows is discarded, has been suggested to contain adequate cellular material for molecular testing. In the current study, the authors describe the use of DNA and RNA extracted from SN for the detection of clinically relevant biomarkers by next-generation sequencing (NGS). METHODS: After cell pellet removal, cytocentrifugation SN from 30 endobronchial fine-needle aspiration rinses that were positive for malignancy on FFPE CB were collected. DNA and RNA were extracted from the SN and tested using an in-house NGS Solid Tumor Focus Assay. The NGS results were compared with findings from corresponding FFPE samples. RESULTS: Testing was successful in all 30 samples. There was 100% concordance between variants observed in the SN and corresponding FFPE specimens, which included 50 single-nucleotide variants, 9 copy number amplifications, 3 structural variants, and 2 indels. Furthermore, there was excellent correlation (correlation coefficient, 0.93) between the variant allele frequency of mutations observed in SN compared with that noted in corresponding FFPE CBs. CONCLUSIONS: Cytocentrifugation SN is a valuable source for NGS, is comparable to FFPE that preserves tissue for other ancillary testing, and can reduce the failure rate of testing that may result from insufficient material being available in the CB.

Characterization of minority HIV-1 drug resistant variants in the United Kingdom following the verification of a deep sequencing-based HIV-1 genotyping and tropism assay.

BACKGROUND: The widespread global access to antiretroviral drugs has led to considerable reductions in morbidity and mortality but, unfortunately, the risk of virologic failure increases with the emergence, and potential transmission, of drug resistant viruses. Detecting and quantifying HIV-1 drug resistance has therefore become the standard of care when designing new antiretroviral regimens. The sensitivity of Sanger sequencing-based HIV-1 genotypic assays is limited by its inability to identify minority members of the quasispecies, i.e., it only detects variants present above ~ 20% of the viral population, thus, failing to detect minority variants below this threshold. It is clear that deep sequencing-based HIV-1 genotyping assays are an important step change towards accurately monitoring HIV-infected individuals. METHODS: We implemented and verified a clinically validated HIV-1 genotyping assay based on deep sequencing (DEEPGEN™) in two clinical laboratories in the United Kingdom: St. George's University Hospitals Healthcare NHS Foundation Trust (London) and at NHS Lothian (Edinburgh), to characterize minority HIV-1 variants in 109 plasma samples from ART-naïve or -experienced individuals. RESULTS: Although subtype B HIV-1 strains were highly prevalent (44%, 48/109), most individuals were infected with non-B subtype viruses (i.e., A1, A2, C, D, F1, G, CRF02_AG, and CRF01_AE). DEEPGEN™ was able to accurately detect drug resistance-associated mutations not identified using standard Sanger sequencing-based tests, which correlated significantly with patient's antiretroviral treatment histories. A higher proportion of minority PI-, NRTI-, and NNRTI-resistance mutations was detected in NHS Lothian patients compared to individuals from St. George's, mainly M46I/L and I50 V (associated with PIs), D67 N, K65R, L74I, M184 V/I, and K219Q (NRTIs), and L100I (NNRTIs). Interestingly, we observed an inverse correlation between intra-patient HIV-1 diversity and CD4+ T cell counts in the NHS Lothian patients. CONCLUSIONS: This is the first study evaluating the transition, training, and implementation of DEEPGEN™ between three clinical laboratories in two different countries. More importantly, we were able to characterize the HIV-1 drug resistance profile (including minority variants), coreceptor tropism, subtyping, and intra-patient viral diversity in patients from the United Kingdom, providing a rigorous foundation for basing clinical decisions on highly sensitive and cost-effective deep sequencing-based HIV-1 genotyping assays in the country.

Impaired human immunodeficiency virus type 1 replicative fitness in atypical viremic non-progressor individuals.

BACKGROUND: Progression rates from initial HIV-1 infection to advanced AIDS vary significantly among infected individuals. A distinct subgroup of HIV-1-infected individuals-termed viremic non-progressors (VNP) or controllers-do not seem to progress to AIDS, maintaining high CD4+ T cell counts despite high levels of viremia for many years. Several studies have evaluated multiple host factors, including immune activation, trying to elucidate the atypical HIV-1 disease progression in these patients; however, limited work has been done to characterize viral factors in viremic controllers. METHODS: We analyzed HIV-1 isolates from three VNP individuals and compared the replicative fitness, near full-length HIV-1 genomes and intra-patient HIV-1 genetic diversity with viruses from three typical (TP) and one rapid (RP) progressor individuals. RESULTS: Viremic non-progressors and typical patients were infected for >10 years (range 10-17 years), with a mean CD4+ T-cell count of 472 cells/mm3 (442-529) and 400 cells/mm3 (126-789), respectively. VNP individuals had a less marked decline in CD4+ cells (mean -0.56, range -0.4 to -0.7 CD4+/month) than TP patients (mean -10.3, -8.2 to -13.1 CD4+/month). Interestingly, VNP individuals carried viruses with impaired replicative fitness, compared to HIV-1 isolates from the TP and RP patients (p < 0.05, 95% CI). Although analyses of the near full-length HIV-1 genomes showed no clear patterns of single-nucleotide polymorphisms (SNP) that could explain the decrease in replicative fitness, both the number of SNPs and HIV-1 population diversity correlated inversely with the replication capacity of the viruses (r = -0.956 and r = -0.878, p < 0.01, respectively). CONCLUSION: It is likely that complex multifactorial parameters govern HIV-1 disease progression in each individual, starting with the infecting virus (phenotype, load, and quasispecies diversity) and the intrinsic ability of the host to respond to the infection. Here we analyzed a subset of viremic controller patients and demonstrated that similar to the phenomenon observed in patients with a discordant response to antiretroviral therapy (i.e., high CD4+ cell counts with detectable plasma HIV-1 RNA load), reduced viral replicative fitness seems to be linked to slow disease progression in these antiretroviral-naïve individuals.