Improving RNA Fusion Call Confidence and Reliability in Molecular Diagnostic Testing.


Journal

The Journal of molecular diagnostics : JMD
ISSN: 1943-7811
Titre abrégé: J Mol Diagn
Pays: United States
ID NLM: 100893612

Informations de publication

Date de publication:
06 2023
Historique:
received: 11 11 2022
revised: 17 02 2023
accepted: 08 03 2023
medline: 22 5 2023
pubmed: 24 3 2023
entrez: 23 3 2023
Statut: ppublish

Résumé

Next-generation sequencing is a superior method for detecting known and novel RNA fusions in formalin-fixed, paraffin-embedded tissue over fluorescence in situ hybridization and RT-PCR. However, confidence in fusion calling and true negatives may be compromised by poor RNA quality. Using a commercial panel of 507 genes and the recommended 3-million read threshold to accept results, two cases yielded false negatives while exceeding this recommendation during clinical validation. To develop a reliable quality control metric that better reflects internal sample quality and improves call confidence, gene expression across 361 patient tumor samples was evaluated to derive a set of 15 genes to serve as a proxy quality control (pQC). These 15 genes were assessed for their normalized expression using the sequencing data from each case and selected for robustness. A threshold of 11 pQC genes produced a 4.71% fail rate, selected for stringency as an acceptable level of repeated testing in the clinical setting, minimizing false-negative calls. To increase the chance that low-quality samples pass pQC, a revision to the library preparation method was also tested, with 75% of previously failed samples passing pQC on resequencing by increasing cDNA input. Taken together, a next-generation sequencing analysis quality control tool is presented that serves as a surrogate for housekeeping genes and improves confidence in fusion calls.

Identifiants

pubmed: 36958423
pii: S1525-1578(23)00055-7
doi: 10.1016/j.jmoldx.2023.03.003
pii:
doi:

Substances chimiques

RNA 63231-63-0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

320-330

Informations de copyright

Copyright © 2023 Association for Molecular Pathology and American Society for Investigative Pathology. Published by Elsevier Inc. All rights reserved.

Auteurs

Mariusz Shrestha (M)

Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada.

Sasha Blay (S)

Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada; Department of Pathology and Laboratory Medicine, Mount Sinai Hospital, Sinai Health, Toronto, Ontario, Canada.

Sydney Liang (S)

Department of Pathology and Laboratory Medicine, Mount Sinai Hospital, Sinai Health, Toronto, Ontario, Canada.

David Swanson (D)

Department of Pathology and Laboratory Medicine, Mount Sinai Hospital, Sinai Health, Toronto, Ontario, Canada.

Jordan Lerner-Ellis (J)

Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada; Department of Pathology and Laboratory Medicine, Mount Sinai Hospital, Sinai Health, Toronto, Ontario, Canada; Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Ontario, Canada.

Brendan Dickson (B)

Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada; Department of Pathology and Laboratory Medicine, Mount Sinai Hospital, Sinai Health, Toronto, Ontario, Canada; Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Ontario, Canada.

Andrew Wong (A)

Department of Pathology and Laboratory Medicine, Mount Sinai Hospital, Sinai Health, Toronto, Ontario, Canada.

George S Charames (GS)

Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada; Department of Pathology and Laboratory Medicine, Mount Sinai Hospital, Sinai Health, Toronto, Ontario, Canada; Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Ontario, Canada. Electronic address: george.charames@sinaihealth.ca.

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Classifications MeSH