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Transcriptional Profiling of Endobronchial Ultrasound-Guided Lymph Node Samples Aids Diagnosis of Mediastinal Lymphadenopathy

BACKGROUND: Endobronchial ultrasound (EBUS)-guided biopsy is the mainstay for investigation of mediastinal lymphadenopathy for laboratory diagnosis of malignancy, sarcoidosis, or TB. However, improved methods for discriminating between TB and sarcoidosis and excluding malignancy are still needed. We...

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Autores principales: Tomlinson, Gillian S., Thomas, Niclas, Chain, Benjamin M., Best, Katharine, Simpson, Nandi, Hardavella, Georgia, Brown, James, Bhowmik, Angshu, Navani, Neal, Janes, Samuel M., Miller, Robert F., Noursadeghi, Mahdad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American College of Chest Physicians 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4740456/
https://www.ncbi.nlm.nih.gov/pubmed/26270185
http://dx.doi.org/10.1378/chest.15-0647
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author Tomlinson, Gillian S.
Thomas, Niclas
Chain, Benjamin M.
Best, Katharine
Simpson, Nandi
Hardavella, Georgia
Brown, James
Bhowmik, Angshu
Navani, Neal
Janes, Samuel M.
Miller, Robert F.
Noursadeghi, Mahdad
author_facet Tomlinson, Gillian S.
Thomas, Niclas
Chain, Benjamin M.
Best, Katharine
Simpson, Nandi
Hardavella, Georgia
Brown, James
Bhowmik, Angshu
Navani, Neal
Janes, Samuel M.
Miller, Robert F.
Noursadeghi, Mahdad
author_sort Tomlinson, Gillian S.
collection PubMed
description BACKGROUND: Endobronchial ultrasound (EBUS)-guided biopsy is the mainstay for investigation of mediastinal lymphadenopathy for laboratory diagnosis of malignancy, sarcoidosis, or TB. However, improved methods for discriminating between TB and sarcoidosis and excluding malignancy are still needed. We sought to evaluate the role of genomewide transcriptional profiling to aid diagnostic processes in this setting. METHODS: Mediastinal lymph node samples from 88 individuals were obtained by EBUS-guided aspiration for investigation of mediastinal lymphadenopathy and subjected to transcriptional profiling in addition to conventional laboratory assessments. Computational strategies were used to evaluate the potential for using the transcriptome to distinguish between diagnostic categories. RESULTS: Molecular signatures associated with granulomas or neoplastic and metastatic processes were clearly discernible in granulomatous and malignant lymph node samples, respectively. Support vector machine (SVM) learning using differentially expressed genes showed excellent sensitivity and specificity profiles in receiver operating characteristic curve analysis with area under curve values > 0.9 for discriminating between granulomatous and nongranulomatous disease, TB and sarcoidosis, and between cancer and reactive lymphadenopathy. A two-step decision tree using SVM to distinguish granulomatous and nongranulomatous disease, then between TB and sarcoidosis in granulomatous cases, and between cancer and reactive lymphadenopathy in nongranulomatous cases, achieved > 90% specificity for each diagnosis and afforded greater sensitivity than existing tests to detect TB and cancer. In some diagnostically ambiguous cases, computational classification predicted granulomatous disease or cancer before pathologic abnormalities were evident. CONCLUSIONS: Machine learning analysis of transcriptional profiling in mediastinal lymphadenopathy may significantly improve the clinical utility of EBUS-guided biopsies.
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spelling pubmed-47404562016-02-29 Transcriptional Profiling of Endobronchial Ultrasound-Guided Lymph Node Samples Aids Diagnosis of Mediastinal Lymphadenopathy Tomlinson, Gillian S. Thomas, Niclas Chain, Benjamin M. Best, Katharine Simpson, Nandi Hardavella, Georgia Brown, James Bhowmik, Angshu Navani, Neal Janes, Samuel M. Miller, Robert F. Noursadeghi, Mahdad Chest Original Research: Pulmonary Procedures BACKGROUND: Endobronchial ultrasound (EBUS)-guided biopsy is the mainstay for investigation of mediastinal lymphadenopathy for laboratory diagnosis of malignancy, sarcoidosis, or TB. However, improved methods for discriminating between TB and sarcoidosis and excluding malignancy are still needed. We sought to evaluate the role of genomewide transcriptional profiling to aid diagnostic processes in this setting. METHODS: Mediastinal lymph node samples from 88 individuals were obtained by EBUS-guided aspiration for investigation of mediastinal lymphadenopathy and subjected to transcriptional profiling in addition to conventional laboratory assessments. Computational strategies were used to evaluate the potential for using the transcriptome to distinguish between diagnostic categories. RESULTS: Molecular signatures associated with granulomas or neoplastic and metastatic processes were clearly discernible in granulomatous and malignant lymph node samples, respectively. Support vector machine (SVM) learning using differentially expressed genes showed excellent sensitivity and specificity profiles in receiver operating characteristic curve analysis with area under curve values > 0.9 for discriminating between granulomatous and nongranulomatous disease, TB and sarcoidosis, and between cancer and reactive lymphadenopathy. A two-step decision tree using SVM to distinguish granulomatous and nongranulomatous disease, then between TB and sarcoidosis in granulomatous cases, and between cancer and reactive lymphadenopathy in nongranulomatous cases, achieved > 90% specificity for each diagnosis and afforded greater sensitivity than existing tests to detect TB and cancer. In some diagnostically ambiguous cases, computational classification predicted granulomatous disease or cancer before pathologic abnormalities were evident. CONCLUSIONS: Machine learning analysis of transcriptional profiling in mediastinal lymphadenopathy may significantly improve the clinical utility of EBUS-guided biopsies. American College of Chest Physicians 2016-02 2016-01-12 /pmc/articles/PMC4740456/ /pubmed/26270185 http://dx.doi.org/10.1378/chest.15-0647 Text en © 2016 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Original Research: Pulmonary Procedures
Tomlinson, Gillian S.
Thomas, Niclas
Chain, Benjamin M.
Best, Katharine
Simpson, Nandi
Hardavella, Georgia
Brown, James
Bhowmik, Angshu
Navani, Neal
Janes, Samuel M.
Miller, Robert F.
Noursadeghi, Mahdad
Transcriptional Profiling of Endobronchial Ultrasound-Guided Lymph Node Samples Aids Diagnosis of Mediastinal Lymphadenopathy
title Transcriptional Profiling of Endobronchial Ultrasound-Guided Lymph Node Samples Aids Diagnosis of Mediastinal Lymphadenopathy
title_full Transcriptional Profiling of Endobronchial Ultrasound-Guided Lymph Node Samples Aids Diagnosis of Mediastinal Lymphadenopathy
title_fullStr Transcriptional Profiling of Endobronchial Ultrasound-Guided Lymph Node Samples Aids Diagnosis of Mediastinal Lymphadenopathy
title_full_unstemmed Transcriptional Profiling of Endobronchial Ultrasound-Guided Lymph Node Samples Aids Diagnosis of Mediastinal Lymphadenopathy
title_short Transcriptional Profiling of Endobronchial Ultrasound-Guided Lymph Node Samples Aids Diagnosis of Mediastinal Lymphadenopathy
title_sort transcriptional profiling of endobronchial ultrasound-guided lymph node samples aids diagnosis of mediastinal lymphadenopathy
topic Original Research: Pulmonary Procedures
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4740456/
https://www.ncbi.nlm.nih.gov/pubmed/26270185
http://dx.doi.org/10.1378/chest.15-0647
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