Rapid EGFR Mutation Detection Using the Idylla Platform: Single-Institution Experience of 1200 Cases Analyzed by an In-House Developed Pipeline and Comparison with Concurrent Next-Generation Sequencing Results.


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:
03 2021
Historique:
received: 23 06 2020
revised: 13 10 2020
accepted: 17 11 2020
pubmed: 22 12 2020
medline: 31 3 2022
entrez: 21 12 2020
Statut: ppublish

Résumé

Mutations in the epidermal growth factor receptor (EGFR) are the most common targetable alterations in lung adenocarcinoma. To facilitate rapid testing, the Idylla EGFR assay was incorporated as a screening method before next-generation sequencing (NGS). Validation and experience using an in-house developed analysis pipeline, enhanced with a manual review algorithm is described. Results are compared with corresponding NGS results. In all, 1249 samples were studied. Validation demonstrated 98.57% (69/70) concordance with the reference methods. The limit of detection varied from 2% to 5% variant allele frequency for total EGFR quantitation cycle between 20 and 23. Of the 1179 clinical cases, 23.41% were EGFR-positive by Idylla. Concurrent NGS was successfully performed on 94.9% (799/842) requests. Concordance of Idylla with NGS was 98.62% (788/799) and 98.50% (787/799) using our in-house and Idylla analysis pipelines, respectively. Discordances involved missed mutations by both assays associated with low tumor/low input. Incorporating a manual review algorithm to supplement automated calls improved accuracy from 98.62% to 99.37% and sensitivity from 94.68% to 97.58%. Overall reporting time, from receipt of material to official clinical report, ranged from 1 to 3 days. Therefore, Idylla EGFR testing enables rapid and sensitive screening without compromising subsequent comprehensive NGS, when required. Automated calling, enhanced with a manual review algorithm, reduces false-negative calls associated with low tumor/low input samples.

Identifiants

pubmed: 33346146
pii: S1525-1578(20)30584-5
doi: 10.1016/j.jmoldx.2020.11.009
pmc: PMC7919857
pii:
doi:

Substances chimiques

Biomarkers, Tumor 0
EGFR protein, human EC 2.7.10.1
ErbB Receptors EC 2.7.10.1

Types de publication

Journal Article Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

310-322

Subventions

Organisme : NCI NIH HHS
ID : P30 CA008748
Pays : United States

Informations de copyright

Copyright © 2021 Association for Molecular Pathology and American Society for Investigative Pathology. Published by Elsevier Inc. All rights reserved.

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Auteurs

Amir Momeni-Boroujeni (A)

Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York.

Paulo Salazar (P)

Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York.

Tao Zheng (T)

Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York.

Nana Mensah (N)

Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York.

Ivelise Rijo (I)

Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York.

Snjezana Dogan (S)

Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York.

JinYuan Yao (J)

Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York.

Christine Moung (C)

Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York.

Chad Vanderbilt (C)

Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York.

Jamal Benhamida (J)

Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York.

Jason Chang (J)

Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York.

William Travis (W)

Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York.

Natasha Rekhtman (N)

Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York.

Marc Ladanyi (M)

Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York.

Khedoudja Nafa (K)

Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York.

Maria E Arcila (ME)

Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York. Electronic address: arcilam@mskcc.org.

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Classifications MeSH