Effective quality management practices in routine clinical next-generation sequencing

AbstractBackground:Molecular technologies have allowed laboratories to detect and establish the profiles of human cancers by identifying a variety of somatic variants. In order to improve personalized patient care, we have established a next-generation sequencing (NGS) test to screen for somatic variants in primary or advanced cancers. In this study, we describe the laboratory quality management program for NGS testing, and also provide an overview of the somatic variants identified in over 1000 patient samples as well as their implications in clinical practice.Methods:Over the past one-and-a-half years, our laboratory received a total of 1028 formalin-fixed, paraffin-embedded (FFPE) tumor tissues, which consisted of non-small-cell lung carcinomas (NSCLCs), colon adenocarcinomas, glioma/glioblastomas, melanomas, breast carcinomas, and other tumor types. During this time period, we implemented a series of quality control (QC) checks that included (1) pre-DNA extraction, (2) DNA quantification, (3) DNA quality, (4) library quantification, (5) post-emulsification PCR, and (6) post-sequencing metrics. At least 10 ng of genomic DNA (gDNA) were used to prepare barcoded libraries using the AmpliSeq CHPv2. Samples were multiplexed and sequenced on Ion Torrent 318 chips using the Ion PGM System. Variants were identified using the Variant Caller Plugin, and annotation and functional predictions were performed using the Golden Helix SVS.Results:A total of 1005 samples passed QC1–3, and following additional library preparation QC checkpoints, 877 samples were sequenced. Samples were classified into two categories: wild-type (127) and positive for somatic variants (750). Somatic variants were classified into clinically actionable (60%) and non-actionable (40%).Conclusions:The use of NGS in routine clinical laboratory practice allowed for the detection of tumor profiles that are essential for the selection of targeted therapies and identification of applicable clinical trials, contributing to the improvement of personalized patient care in oncology.

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P 956. Next-Generation Sequencing in Patient Care—Arrived or Rejected in Rare Diseases?

10.1055/s-0038-1676019 ◽

2018 ◽

Author(s):

Angela Abicht ◽

Teresa Neuhann ◽

Stefanie Balg ◽

Daniela Gonzalez-Fassreiner ◽

Verena Steinke-Lange ◽

...

Keyword(s):

Next Generation Sequencing ◽

Patient Care ◽

Rare Diseases ◽

Next Generation ◽

Generation Sequencing

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Next-generation sequencing as a clinical laboratory tool for describing different microbiotas: an urgent need for future paediatric practice

Archives of Disease in Childhood ◽

10.1136/archdischild-2021-321683 ◽

2021 ◽

pp. archdischild-2021-321683

Author(s):

Richard Hansen ◽

Mona Bajaj-Elliott ◽

Georgina L Hold ◽

Konstantinos Gerasimidis ◽

Tariq H Iqbal ◽

...

Keyword(s):

Next Generation Sequencing ◽

Clinical Laboratory ◽

Next Generation ◽

Paediatric Practice ◽

Laboratory Tool ◽

Generation Sequencing

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Implementation of Cloud based Next Generation Sequencing data analysis in a clinical laboratory

BMC Research Notes ◽

10.1186/1756-0500-7-314 ◽

2014 ◽

Vol 7 (1) ◽

pp. 314 ◽

Cited By ~ 15

Author(s):

Getiria Onsongo ◽

Jesse Erdmann ◽

Michael D Spears ◽

John Chilton ◽

Kenneth B Beckman ◽

...

Keyword(s):

Data Analysis ◽

Next Generation Sequencing ◽

Clinical Laboratory ◽

Next Generation Sequencing Data ◽

Next Generation ◽

Sequencing Data ◽

Generation Sequencing ◽

Sequencing Data Analysis

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Using Machine Learning to Identify True Somatic Variants from Next-Generation Sequencing

Clinical Chemistry ◽

10.1373/clinchem.2019.308213 ◽

2019 ◽

Vol 66 (1) ◽

pp. 239-246 ◽

Cited By ~ 1

Author(s):

Chao Wu ◽

Xiaonan Zhao ◽

Mark Welsh ◽

Kellianne Costello ◽

Kajia Cao ◽

...

Keyword(s):

Machine Learning ◽

Next Generation Sequencing ◽

Clinical Laboratory ◽

Next Generation ◽

Single Nucleotide Variants ◽

Test Set ◽

Clinical Laboratories ◽

Bona Fide ◽

Validation Set ◽

Generation Sequencing

Abstract BACKGROUND Molecular profiling has become essential for tumor risk stratification and treatment selection. However, cancer genome complexity and technical artifacts make identification of real variants a challenge. Currently, clinical laboratories rely on manual screening, which is costly, subjective, and not scalable. We present a machine learning–based method to distinguish artifacts from bona fide single-nucleotide variants (SNVs) detected by next-generation sequencing from nonformalin-fixed paraffin-embedded tumor specimens. METHODS A cohort of 11278 SNVs identified through clinical sequencing of tumor specimens was collected and divided into training, validation, and test sets. Each SNV was manually inspected and labeled as either real or artifact as part of clinical laboratory workflow. A 3-class (real, artifact, and uncertain) model was developed on the training set, fine-tuned with the validation set, and then evaluated on the test set. Prediction intervals reflecting the certainty of the classifications were derived during the process to label “uncertain” variants. RESULTS The optimized classifier demonstrated 100% specificity and 97% sensitivity over 5587 SNVs of the test set. Overall, 1252 of 1341 true-positive variants were identified as real, 4143 of 4246 false-positive calls were deemed artifacts, whereas only 192 (3.4%) SNVs were labeled as “uncertain,” with zero misclassification between the true positives and artifacts in the test set. CONCLUSIONS We presented a computational classifier to identify variant artifacts detected from tumor sequencing. Overall, 96.6% of the SNVs received definitive labels and thus were exempt from manual review. This framework could improve quality and efficiency of the variant review process in clinical laboratories.

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Practical Guidance to Implementing Quality Management Systems in Public Health Laboratories Performing Next-Generation Sequencing: Personnel, Equipment, and Process Management (Phase 1)

Journal of Clinical Microbiology ◽

10.1128/jcm.00261-19 ◽

2019 ◽

Vol 57 (8) ◽

Cited By ~ 3

Author(s):

Rebecca J. Hutchins ◽

Kristy L. Phan ◽

Adeeba Saboor ◽

Joseph D. Miller ◽

Atis Muehlenbachs

Keyword(s):

Public Health ◽

Next Generation Sequencing ◽

Quality Management ◽

Process Management ◽

Quality Management System ◽

Phase 1 ◽

Quality System ◽

Clinical Diagnostics ◽

Next Generation ◽

Generation Sequencing

ABSTRACT Quality standards as part of an effective quality management system (QMS) are the cornerstone for generating high-quality test results. Next-generation sequencing (NGS) has the potential to improve both clinical diagnostics and public health surveillance efforts in multiple areas, including infectious diseases. However, the laboratories adopting NGS methods face significant challenges due to the complex and modular process design. This document summarizes the first phase of quality system guidance developed by the Centers for Disease Control and Prevention (CDC) NGS Quality Workgroup. The quality system essentials of personnel, equipment, and process management (quality control and validation) were prioritized based on a risk assessment using information gathered from participating CDC laboratories. Here, we present a prioritized QMS framework, including procedures and documentation tools, to assist laboratory implementation and maintenance of quality practices for NGS workflows.

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Digital PCR—An Emerging Technology with Broad Applications in Microbiology

Clinical Chemistry ◽

10.1373/clinchem.2019.304048 ◽

2019 ◽

Vol 66 (1) ◽

pp. 117-123 ◽

Cited By ~ 11

Author(s):

Stephen J Salipante ◽

Keith R Jerome

Keyword(s):

Next Generation Sequencing ◽

Quantitative Pcr ◽

Clinical Microbiology ◽

Clinical Laboratory ◽

Digital Pcr ◽

Emerging Technology ◽

Next Generation ◽

Powerful Technique ◽

Advantages And Disadvantages ◽

Generation Sequencing

Abstract BACKGROUND The PCR and its variant, quantitative PCR (qPCR), have revolutionized the practice of clinical microbiology. Continued advancements in PCR have led to a new derivative, digital PCR (dPCR), which promises to address certain limitations inherent to qPCR. CONTENT Here we highlight the important technical differences between qPCR and dPCR, and the potential advantages and disadvantages of each. We then review specific situations in which dPCR has been implemented in clinical microbiology and the results of such applications. Finally, we attempt to place dPCR in the context of other emerging technologies relevant to the clinical laboratory, including next-generation sequencing. SUMMARY dPCR offers certain clear advantages over traditional qPCR, but these are to some degree offset by limitations of the technology, at least as currently practiced. Laboratories considering implementation of dPCR should carefully weigh the potential advantages and disadvantages of this powerful technique for each specific application planned.

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From Clinical Standards to Translating Next-Generation Sequencing Research into Patient Care Improvement for Hepatobiliary and Pancreatic Cancers

International Journal of Molecular Sciences ◽

10.3390/ijms18010180 ◽

2017 ◽

Vol 18 (1) ◽

pp. 180 ◽

Cited By ~ 9

Author(s):

Ioannis Kyrochristos ◽

Georgios Glantzounis ◽

Demosthenes Ziogas ◽

Ioannis Gizas ◽

Dimitrios Schizas ◽

...

Keyword(s):

Next Generation Sequencing ◽

Patient Care ◽

Next Generation ◽

Clinical Standards ◽

Pancreatic Cancers ◽

Care Improvement ◽

Generation Sequencing

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Validation of a next generation sequencing panel for detection of hotspot cancer mutations in a clinical laboratory

Pathology - Research and Practice ◽

10.1016/j.prp.2016.11.016 ◽

2017 ◽

Vol 213 (2) ◽

pp. 98-105 ◽

Cited By ~ 5

Author(s):

Reza Shahsiah ◽

Jenefer DeKoning ◽

Saeed Samie ◽

Seyed Ziaeddin Latifzadeh ◽

Zahra Mehdizadeh Kashi

Keyword(s):

Next Generation Sequencing ◽

Clinical Laboratory ◽

Next Generation ◽

Cancer Mutations ◽

Generation Sequencing

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Using a Next Generation Sequencing Panel to Discover the Obscure Causes of Hereditary Hemolytic Anemias

Blood ◽

10.1182/blood.v128.22.2433.2433 ◽

2016 ◽

Vol 128 (22) ◽

pp. 2433-2433 ◽

Cited By ~ 1

Author(s):

Archana M Agarwal ◽

N. Scott Reading ◽

Kimberly Frizzell ◽

Wei Shen ◽

Shelly Sorrells ◽

...

Keyword(s):

Next Generation Sequencing ◽

Hemolytic Anemia ◽

Clinical Laboratory ◽

Single Gene ◽

Cytoskeletal Proteins ◽

Next Generation ◽

Pathogenic Variants ◽

Late Morbidity ◽

Next Generation Sequencing Ngs ◽

Generation Sequencing

Abstract Hereditary hemolytic anemias are a heterogeneous group of disorders with consequences ranging from non-anemic hemolysis to severe life-threatening anemia. However, the late morbidity in patients without transfusions is often underappreciated because of erythropoietic compensatory stimulation inducing hematopoiesis by erythroferrone/hepcidin axis. Principal causes of hereditary hemolytic anemias are germline mutations of red cell cytoskeleton (e.g. hereditary spherocytosis and elliptocytosis/pyropoikilocytosis) or enzyme deficiencies (e.g. Glucose 6 phosphate dehydrogenase deficiency and pyruvate kinase deficiency). Routine morphological and biochemical analysis may be inconclusive and misleading particularly in transfusion-dependent infants and children. Molecular studies have not been extensively used to diagnose these disorders due to the complex genetic nature of these disorders, and multi-gene disorders. In these cases, patients may undergo multiple rounds of single gene testing, which can be very costly and time consuming. The advent of next generation sequencing (NGS) methods in the clinical laboratory has made diagnosing complex genetic disorders feasible. Our diagnostic panel includes 28 genes encoding cytoskeletal proteins and enzymes, and covers the complete coding region, splice site junctions, and, where appropriate, deep intronic or regulatory regions. Targeted gene capture and library construction for next-generation sequencing (NGS) was performed using Sure Select kit (Agilent Technologies, Santa Clara, USA). Prior to sequencing on the Illumina Next Seq, (Illumina Inc) instrument, indexed samples are quantified using qPCR and then pooled. Samples were sequenced using 2x150 paired end sequencing. We now report the first 68 patients evaluated using our NGS panel. The age of the patients ranged from newborn to 62 years. These patients presented with symptoms ranging from mild lifelong anemia to severe hemolytic anemia with extreme hyperbilirubinemia. Genetic variants were classified using the American College of Medical Genetics (ACMG) guidelines. We identified pathogenic variants in 11 patients and likely pathogenic variants in 12 others, the majority of these were novel. Many variants with unknown significance were also identified that could potentially contribute to disease. The most commonly mutated genes were SPTB and SPTA1, encoding spectrin subunits. Some complex interactions were uncovered i.e. SPTA1 mutations along with alpha LELY leading to hereditary pyropoikilocytosis; Spectrin variants along with Gilbert syndrome causing severe hyperbilirubinemia in neonates; and Spectrin variants in combination with PKLR and G6PD variants. Our results demonstrate that many patients with hemolytic anemia harbor complex combinations of known and novel mutations in RBC cytoskeleton/enzyme genes, but their clinical significance is further augmented by polymorphisms of UGT1A1 gene contributing to severe neonatal hyperbilirubinemia and its consequences. To conclude, next-generation sequencing provides a cost-effective and relatively rapid approach to molecular diagnosis, especially in instances where traditional testing failed. We have used this technology successfully to determine the molecular causes of hemolytic anemia in many cases with no prior family history. Disclosures Yaish: Octapharma: Other: Study investigator.

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