Integrating NGS-derived mutational profiling in the diagnosis of multiple lung adenocarcinomas
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...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-01-01
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| Series: | Cancer Treatment and Research Communications |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468294221001805 |
| _version_ | 1818871939384475648 |
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| author | Nicole Ezer Hangjun Wang Andrea Gomez Corredor Pierre Olivier Fiset Ayesha Baig Léon C. van Kempen George Chong Marianne S.M. Issac Richard Fraser Alan Spatz Jean-Baptiste Riviere Philippe Broët Jonathan Spicer Sophie Camilleri-Broët, MD, PhD |
| author_facet | Nicole Ezer Hangjun Wang Andrea Gomez Corredor Pierre Olivier Fiset Ayesha Baig Léon C. van Kempen George Chong Marianne S.M. Issac Richard Fraser Alan Spatz Jean-Baptiste Riviere Philippe Broët Jonathan Spicer Sophie Camilleri-Broët, MD, PhD |
| author_sort | Nicole Ezer |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-19T12:30:53Z |
| format | Article |
| id | doaj.art-dc04ed82ecd74740b72116c2e0a8e691 |
| institution | Directory Open Access Journal |
| issn | 2468-2942 |
| language | English |
| last_indexed | 2024-12-19T12:30:53Z |
| publishDate | 2021-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Cancer Treatment and Research Communications |
| spelling | doaj.art-dc04ed82ecd74740b72116c2e0a8e6912022-12-21T20:21:24ZengElsevierCancer Treatment and Research Communications2468-29422021-01-0129100484Integrating NGS-derived mutational profiling in the diagnosis of multiple lung adenocarcinomasNicole Ezer0Hangjun Wang1Andrea Gomez Corredor2Pierre Olivier Fiset3Ayesha Baig4Léon C. van Kempen5George Chong6Marianne S.M. Issac7Richard Fraser8Alan Spatz9Jean-Baptiste Riviere10Philippe Broët11Jonathan Spicer12Sophie Camilleri-Broët, MD, PhD13Department 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, CanadaDivision 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, CanadaOPTILAB-MUHC & Department of Laboratory Medicine, 1001 Decarie Blvd., Montreal, QC, Canada; Division of Molecular Genetics, McGill University Health Center, 1001 Decarie Blvd., Montreal, QC, CanadaDivision of Pathology, McGill University Health Center, 1001 Decarie Blvd., Montreal, QC, Canada; OPTILAB-MUHC & Department of Laboratory Medicine, 1001 Decarie Blvd., Montreal, QC, CanadaDivision of Pathology, McGill University Health Center, 1001 Decarie Blvd., Montreal, QC, Canada; OPTILAB-MUHC & Department of Laboratory Medicine, 1001 Decarie Blvd., Montreal, QC, CanadaOPTILAB-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, NetherlandsOPTILAB-MUHC & Department of Laboratory Medicine, 1001 Decarie Blvd., Montreal, QC, Canada; Division of Molecular Genetics, McGill University Health Center, 1001 Decarie Blvd., Montreal, QC, CanadaResearch 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, EgyptDivision of Pathology, McGill University Health Center, 1001 Decarie Blvd., Montreal, QC, Canada; OPTILAB-MUHC & Department of Laboratory Medicine, 1001 Decarie Blvd., Montreal, QC, CanadaDivision 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, CanadaOPTILAB-MUHC & Department of Laboratory Medicine, 1001 Decarie Blvd., Montreal, QC, Canada; Division of Molecular Genetics, McGill University Health Center, 1001 Decarie Blvd., Montreal, QC, CanadaUMR 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, CanadaDivision of Thoracic and Upper GI Surgery, McGill University Health Center, 1650 Cedar Avenue Montreal, H3G 1A4, Montreal, QC, CanadaDivision of Pathology, McGill University Health Center, 1001 Decarie Blvd., Montreal, QC, Canada; OPTILAB-MUHC & Department of Laboratory Medicine, 1001 Decarie Blvd., Montreal, QC, Canada; Corresponding author at: Division of Pathology, McGill University Health Center, 1001 Decarie Blvd., Montreal, Quebec, H4A 3J1 Canada.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.http://www.sciencedirect.com/science/article/pii/S2468294221001805Multiple primary lung cancersIntrapulmonary metastasesNGSComprehensive histologic assessmentProbabilistic model |
| spellingShingle | Nicole Ezer Hangjun Wang Andrea Gomez Corredor Pierre Olivier Fiset Ayesha Baig Léon C. van Kempen George Chong Marianne S.M. Issac Richard Fraser Alan Spatz Jean-Baptiste Riviere Philippe Broët Jonathan Spicer Sophie Camilleri-Broët, MD, PhD Integrating NGS-derived mutational profiling in the diagnosis of multiple lung adenocarcinomas Cancer Treatment and Research Communications Multiple primary lung cancers Intrapulmonary metastases NGS Comprehensive histologic assessment Probabilistic model |
| title | Integrating NGS-derived mutational profiling in the diagnosis of multiple lung adenocarcinomas |
| title_full | Integrating NGS-derived mutational profiling in the diagnosis of multiple lung adenocarcinomas |
| title_fullStr | Integrating NGS-derived mutational profiling in the diagnosis of multiple lung adenocarcinomas |
| title_full_unstemmed | Integrating NGS-derived mutational profiling in the diagnosis of multiple lung adenocarcinomas |
| title_short | Integrating NGS-derived mutational profiling in the diagnosis of multiple lung adenocarcinomas |
| title_sort | integrating ngs derived mutational profiling in the diagnosis of multiple lung adenocarcinomas |
| topic | Multiple primary lung cancers Intrapulmonary metastases NGS Comprehensive histologic assessment Probabilistic model |
| url | http://www.sciencedirect.com/science/article/pii/S2468294221001805 |
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