Cargando…
Early Detection of Malignant Pleural Mesothelioma in Asbestos-Exposed Individuals with a Noninvasive Proteomics-Based Surveillance Tool
BACKGROUND: Malignant pleural mesothelioma (MM) is an aggressive, asbestos-related pulmonary cancer that is increasing in incidence. Because diagnosis is difficult and the disease is relatively rare, most patients present at a clinically advanced stage where possibility of cure is minimal. To improv...
Autores principales: | , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2012
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3463527/ https://www.ncbi.nlm.nih.gov/pubmed/23056237 http://dx.doi.org/10.1371/journal.pone.0046091 |
_version_ | 1782245299583975424 |
---|---|
author | Ostroff, Rachel M. Mehan, Michael R. Stewart, Alex Ayers, Deborah Brody, Edward N. Williams, Stephen A. Levin, Stephen Black, Brad Harbut, Michael Carbone, Michele Goparaju, Chandra Pass, Harvey I. |
author_facet | Ostroff, Rachel M. Mehan, Michael R. Stewart, Alex Ayers, Deborah Brody, Edward N. Williams, Stephen A. Levin, Stephen Black, Brad Harbut, Michael Carbone, Michele Goparaju, Chandra Pass, Harvey I. |
author_sort | Ostroff, Rachel M. |
collection | PubMed |
description | BACKGROUND: Malignant pleural mesothelioma (MM) is an aggressive, asbestos-related pulmonary cancer that is increasing in incidence. Because diagnosis is difficult and the disease is relatively rare, most patients present at a clinically advanced stage where possibility of cure is minimal. To improve surveillance and detection of MM in the high-risk population, we completed a series of clinical studies to develop a noninvasive test for early detection. METHODOLOGY/PRINCIPAL FINDINGS: We conducted multi-center case-control studies in serum from 117 MM cases and 142 asbestos-exposed control individuals. Biomarker discovery, verification, and validation were performed using SOMAmer proteomic technology, which simultaneously measures over 1000 proteins in unfractionated biologic samples. Using univariate and multivariate approaches we discovered 64 candidate protein biomarkers and derived a 13-marker random forest classifier with an AUC of 0.99±0.01 in training, 0.98±0.04 in independent blinded verification and 0.95±0.04 in blinded validation studies. Sensitivity and specificity at our pre-specified decision threshold were 97%/92% in training and 90%/95% in blinded verification. This classifier accuracy was maintained in a second blinded validation set with a sensitivity/specificity of 90%/89% and combined accuracy of 92%. Sensitivity correlated with pathologic stage; 77% of Stage I, 93% of Stage II, 96% of Stage III and 96% of Stage IV cases were detected. An alternative decision threshold in the validation study yielding 98% specificity would still detect 60% of MM cases. In a paired sample set the classifier AUC of 0.99 and 91%/94% sensitivity/specificity was superior to that of mesothelin with an AUC of 0.82 and 66%/88% sensitivity/specificity. The candidate biomarker panel consists of both inflammatory and proliferative proteins, processes strongly associated with asbestos-induced malignancy. SIGNIFICANCE: The SOMAmer biomarker panel discovered and validated in these studies provides a solid foundation for surveillance and diagnosis of MM in those at highest risk for this disease. |
format | Online Article Text |
id | pubmed-3463527 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-34635272012-10-09 Early Detection of Malignant Pleural Mesothelioma in Asbestos-Exposed Individuals with a Noninvasive Proteomics-Based Surveillance Tool Ostroff, Rachel M. Mehan, Michael R. Stewart, Alex Ayers, Deborah Brody, Edward N. Williams, Stephen A. Levin, Stephen Black, Brad Harbut, Michael Carbone, Michele Goparaju, Chandra Pass, Harvey I. PLoS One Research Article BACKGROUND: Malignant pleural mesothelioma (MM) is an aggressive, asbestos-related pulmonary cancer that is increasing in incidence. Because diagnosis is difficult and the disease is relatively rare, most patients present at a clinically advanced stage where possibility of cure is minimal. To improve surveillance and detection of MM in the high-risk population, we completed a series of clinical studies to develop a noninvasive test for early detection. METHODOLOGY/PRINCIPAL FINDINGS: We conducted multi-center case-control studies in serum from 117 MM cases and 142 asbestos-exposed control individuals. Biomarker discovery, verification, and validation were performed using SOMAmer proteomic technology, which simultaneously measures over 1000 proteins in unfractionated biologic samples. Using univariate and multivariate approaches we discovered 64 candidate protein biomarkers and derived a 13-marker random forest classifier with an AUC of 0.99±0.01 in training, 0.98±0.04 in independent blinded verification and 0.95±0.04 in blinded validation studies. Sensitivity and specificity at our pre-specified decision threshold were 97%/92% in training and 90%/95% in blinded verification. This classifier accuracy was maintained in a second blinded validation set with a sensitivity/specificity of 90%/89% and combined accuracy of 92%. Sensitivity correlated with pathologic stage; 77% of Stage I, 93% of Stage II, 96% of Stage III and 96% of Stage IV cases were detected. An alternative decision threshold in the validation study yielding 98% specificity would still detect 60% of MM cases. In a paired sample set the classifier AUC of 0.99 and 91%/94% sensitivity/specificity was superior to that of mesothelin with an AUC of 0.82 and 66%/88% sensitivity/specificity. The candidate biomarker panel consists of both inflammatory and proliferative proteins, processes strongly associated with asbestos-induced malignancy. SIGNIFICANCE: The SOMAmer biomarker panel discovered and validated in these studies provides a solid foundation for surveillance and diagnosis of MM in those at highest risk for this disease. Public Library of Science 2012-10-03 /pmc/articles/PMC3463527/ /pubmed/23056237 http://dx.doi.org/10.1371/journal.pone.0046091 Text en © 2012 Ostroff et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Ostroff, Rachel M. Mehan, Michael R. Stewart, Alex Ayers, Deborah Brody, Edward N. Williams, Stephen A. Levin, Stephen Black, Brad Harbut, Michael Carbone, Michele Goparaju, Chandra Pass, Harvey I. Early Detection of Malignant Pleural Mesothelioma in Asbestos-Exposed Individuals with a Noninvasive Proteomics-Based Surveillance Tool |
title | Early Detection of Malignant Pleural Mesothelioma in Asbestos-Exposed Individuals with a Noninvasive Proteomics-Based Surveillance Tool |
title_full | Early Detection of Malignant Pleural Mesothelioma in Asbestos-Exposed Individuals with a Noninvasive Proteomics-Based Surveillance Tool |
title_fullStr | Early Detection of Malignant Pleural Mesothelioma in Asbestos-Exposed Individuals with a Noninvasive Proteomics-Based Surveillance Tool |
title_full_unstemmed | Early Detection of Malignant Pleural Mesothelioma in Asbestos-Exposed Individuals with a Noninvasive Proteomics-Based Surveillance Tool |
title_short | Early Detection of Malignant Pleural Mesothelioma in Asbestos-Exposed Individuals with a Noninvasive Proteomics-Based Surveillance Tool |
title_sort | early detection of malignant pleural mesothelioma in asbestos-exposed individuals with a noninvasive proteomics-based surveillance tool |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3463527/ https://www.ncbi.nlm.nih.gov/pubmed/23056237 http://dx.doi.org/10.1371/journal.pone.0046091 |
work_keys_str_mv | AT ostroffrachelm earlydetectionofmalignantpleuralmesotheliomainasbestosexposedindividualswithanoninvasiveproteomicsbasedsurveillancetool AT mehanmichaelr earlydetectionofmalignantpleuralmesotheliomainasbestosexposedindividualswithanoninvasiveproteomicsbasedsurveillancetool AT stewartalex earlydetectionofmalignantpleuralmesotheliomainasbestosexposedindividualswithanoninvasiveproteomicsbasedsurveillancetool AT ayersdeborah earlydetectionofmalignantpleuralmesotheliomainasbestosexposedindividualswithanoninvasiveproteomicsbasedsurveillancetool AT brodyedwardn earlydetectionofmalignantpleuralmesotheliomainasbestosexposedindividualswithanoninvasiveproteomicsbasedsurveillancetool AT williamsstephena earlydetectionofmalignantpleuralmesotheliomainasbestosexposedindividualswithanoninvasiveproteomicsbasedsurveillancetool AT levinstephen earlydetectionofmalignantpleuralmesotheliomainasbestosexposedindividualswithanoninvasiveproteomicsbasedsurveillancetool AT blackbrad earlydetectionofmalignantpleuralmesotheliomainasbestosexposedindividualswithanoninvasiveproteomicsbasedsurveillancetool AT harbutmichael earlydetectionofmalignantpleuralmesotheliomainasbestosexposedindividualswithanoninvasiveproteomicsbasedsurveillancetool AT carbonemichele earlydetectionofmalignantpleuralmesotheliomainasbestosexposedindividualswithanoninvasiveproteomicsbasedsurveillancetool AT goparajuchandra earlydetectionofmalignantpleuralmesotheliomainasbestosexposedindividualswithanoninvasiveproteomicsbasedsurveillancetool AT passharveyi earlydetectionofmalignantpleuralmesotheliomainasbestosexposedindividualswithanoninvasiveproteomicsbasedsurveillancetool |