Integrating NGS-derived mutational profiling in the diagnosis of multiple lung adenocarcinomas.


Journal

Cancer treatment and research communications
ISSN: 2468-2942
Titre abrégé: Cancer Treat Res Commun
Pays: England
ID NLM: 101694651

Informations de publication

Date de publication:
2021
Historique:
received: 25 07 2021
revised: 24 10 2021
accepted: 25 10 2021
pubmed: 14 11 2021
medline: 11 3 2022
entrez: 13 11 2021
Statut: ppublish

Résumé

Integration of Next Generation Sequencing (NGS) information for use in distinguishing between Multiple Primary Lung Cancer and intrapulmonary metastasis was evaluated. We used a probabilistic model, comprehensive histologic assessment and NGS to classify patients. Integrating NGS data confirmed initial diagnosis (n = 41), revised the diagnosis (n = 12), while resulted in non-informative data (n = 8). Accuracy of diagnosis can be significantly improved with integration of NGS data. Distinguishing between multiple primary lung cancers (MPLC) and intrapulmonary metastases (IPM) is challenging. The goal of this study was to evaluate how Next Generation Sequencing (NGS) information may be integrated in the diagnostic strategy. Patients with multiple lung adenocarcinomas were classified using both the comprehensive histologic assessment and NGS. We computed the joint probability of each pair having independent mutations by chance (thus being classified as MPLC). These probabilities were computed using the marginal mutation rates of each mutation, and the known negative dependencies between driver genes and different gene loci. With these NGS-driven data, cases were re-classified as MPLC or IPM. We analyzed 61 patients with a total of 131 tumors. The most frequent mutation was KRAS (57.3%) which occured at a rate higher than expected (p < 0.001) in lung cancer. No mutation was detected in 25/131 tumors (19.1%). Discordant molecular findings between tumor sites were found in 46 patients (75.4%); 11 patients (18.0%) had concordant molecular findings, and 4 patients (6.6%) had concordant molecular findings at 2 of the 3 sites. After integration of the NGS data, the initial diagnosis was confirmed for 41 patients (67.2%), the diagnosis was revised for 12 patients (19.7%) or was considered as non-informative for 8 patients (13.1%). Integrating the information of NGS data may significantly improve accuracy of diagnosis and staging.

Sections du résumé

MICROABSTRACT
Integration of Next Generation Sequencing (NGS) information for use in distinguishing between Multiple Primary Lung Cancer and intrapulmonary metastasis was evaluated. We used a probabilistic model, comprehensive histologic assessment and NGS to classify patients. Integrating NGS data confirmed initial diagnosis (n = 41), revised the diagnosis (n = 12), while resulted in non-informative data (n = 8). Accuracy of diagnosis can be significantly improved with integration of NGS data.
BACKGROUND
Distinguishing between multiple primary lung cancers (MPLC) and intrapulmonary metastases (IPM) is challenging. The goal of this study was to evaluate how Next Generation Sequencing (NGS) information may be integrated in the diagnostic strategy.
PATIENTS AND METHODS
Patients with multiple lung adenocarcinomas were classified using both the comprehensive histologic assessment and NGS. We computed the joint probability of each pair having independent mutations by chance (thus being classified as MPLC). These probabilities were computed using the marginal mutation rates of each mutation, and the known negative dependencies between driver genes and different gene loci. With these NGS-driven data, cases were re-classified as MPLC or IPM.
RESULTS
We analyzed 61 patients with a total of 131 tumors. The most frequent mutation was KRAS (57.3%) which occured at a rate higher than expected (p < 0.001) in lung cancer. No mutation was detected in 25/131 tumors (19.1%). Discordant molecular findings between tumor sites were found in 46 patients (75.4%); 11 patients (18.0%) had concordant molecular findings, and 4 patients (6.6%) had concordant molecular findings at 2 of the 3 sites. After integration of the NGS data, the initial diagnosis was confirmed for 41 patients (67.2%), the diagnosis was revised for 12 patients (19.7%) or was considered as non-informative for 8 patients (13.1%).
CONCLUSION
Integrating the information of NGS data may significantly improve accuracy of diagnosis and staging.

Identifiants

pubmed: 34773797
pii: S2468-2942(21)00180-5
doi: 10.1016/j.ctarc.2021.100484
pii:
doi:

Types de publication

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

Langues

eng

Sous-ensembles de citation

IM

Pagination

100484

Informations de copyright

Copyright © 2021. Published by Elsevier Ltd.

Auteurs

Nicole Ezer (N)

Department of Medicine, Division of Respirology, McGill University Health Center, 1001 Decarie Blvd., Montreal, QC, Canada; Centre for Outcomes Research and Evaluation - Research Institute of the McGill University Health Center, Montreal, 1001 Decarie Blvd., QC, Canada.

Hangjun Wang (H)

Division of Pathology, McGill University Health Center, 1001 Decarie Blvd., Montreal, QC, Canada; OPTILAB-MUHC & Department of Laboratory Medicine, 1001 Decarie Blvd., Montreal, QC, Canada; Research Molecular Pathology Center, Lady Davis Institute, 3755 Côte Ste-Catherine Road, Montreal, QC, Canada.

Andrea Gomez Corredor (AG)

OPTILAB-MUHC & Department of Laboratory Medicine, 1001 Decarie Blvd., Montreal, QC, Canada; Division of Molecular Genetics, McGill University Health Center, 1001 Decarie Blvd., Montreal, QC, Canada.

Pierre Olivier Fiset (PO)

Division of Pathology, McGill University Health Center, 1001 Decarie Blvd., Montreal, QC, Canada; OPTILAB-MUHC & Department of Laboratory Medicine, 1001 Decarie Blvd., Montreal, QC, Canada.

Ayesha Baig (A)

Division of Pathology, McGill University Health Center, 1001 Decarie Blvd., Montreal, QC, Canada; OPTILAB-MUHC & Department of Laboratory Medicine, 1001 Decarie Blvd., Montreal, QC, Canada.

Léon C van Kempen (LC)

OPTILAB-MUHC & Department of Laboratory Medicine, 1001 Decarie Blvd., Montreal, QC, Canada; Division of Molecular Genetics, McGill University Health Center, 1001 Decarie Blvd., Montreal, QC, Canada; University Medical Center of Groningen, PO box 30.001, 9700 RB, Groningen, Netherlands.

George Chong (G)

OPTILAB-MUHC & Department of Laboratory Medicine, 1001 Decarie Blvd., Montreal, QC, Canada; Division of Molecular Genetics, McGill University Health Center, 1001 Decarie Blvd., Montreal, QC, Canada.

Marianne S M Issac (MSM)

Research Institute of the McGill University Health Centre, 1001 Decarie Blvd, Montreal, QC, Canada; Department of Clinical and Chemical Pathology, Faculty of Medicine, Cairo University, El Saray St., El Manial, Postal Code 11956, Cairo, Egypt.

Richard Fraser (R)

Division of Pathology, McGill University Health Center, 1001 Decarie Blvd., Montreal, QC, Canada; OPTILAB-MUHC & Department of Laboratory Medicine, 1001 Decarie Blvd., Montreal, QC, Canada.

Alan Spatz (A)

Division of Pathology, McGill University Health Center, 1001 Decarie Blvd., Montreal, QC, Canada; OPTILAB-MUHC & Department of Laboratory Medicine, 1001 Decarie Blvd., Montreal, QC, Canada; Research Molecular Pathology Center, Lady Davis Institute, 3755 Côte Ste-Catherine Road, Montreal, QC, Canada.

Jean-Baptiste Riviere (JB)

OPTILAB-MUHC & Department of Laboratory Medicine, 1001 Decarie Blvd., Montreal, QC, Canada; Division of Molecular Genetics, McGill University Health Center, 1001 Decarie Blvd., Montreal, QC, Canada.

Philippe Broët (P)

UMR 1018, INSERM, CESP, Paris-Saclay University, Faculty of Medicine, Paul-Brousse Hospital AP-AP, Villejuif, France; Research Center, CHU Ste-Justine, University of Montreal, 3175 Côte-Sainte-Catherine Road, H3T 1C5, Montreal, QC, Canada.

Jonathan Spicer (J)

Division of Thoracic and Upper GI Surgery, McGill University Health Center, 1650 Cedar Avenue Montreal, H3G 1A4, Montreal, QC, Canada.

Sophie Camilleri-Broët (S)

Division of Pathology, McGill University Health Center, 1001 Decarie Blvd., Montreal, QC, Canada; OPTILAB-MUHC & Department of Laboratory Medicine, 1001 Decarie Blvd., Montreal, QC, Canada. Electronic address: sophie.camilleri-broet@mcgill.ca.

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