Exosomal Proteins as Diagnostic Biomarkers in Lung Cancer
Exosomes have been suggested as promising biomarkers in NSCLC because they contain proteins from their originating cells and are readily available in plasma. In this study, we explored the potential of exosome protein profiling in diagnosing lung cancers of all stages and various histological subtyp...
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Published in | Journal of thoracic oncology Vol. 11; no. 10; pp. 1701 - 1710 |
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Main Authors | , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
Elsevier Inc
01.10.2016
Copyright by the International Association for the Study of Lung Cancer |
Subjects | |
Online Access | Get full text |
ISSN | 1556-0864 1556-1380 1556-1380 |
DOI | 10.1016/j.jtho.2016.05.034 |
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Abstract | Exosomes have been suggested as promising biomarkers in NSCLC because they contain proteins from their originating cells and are readily available in plasma. In this study, we explored the potential of exosome protein profiling in diagnosing lung cancers of all stages and various histological subtypes in patients.
Plasma was isolated from 581 patients (431 with lung cancer and 150 controls). The extracellular vesicle array was used to phenotype exosomes. The extracellular vesicle array contained 49 antibodies for capturing exosomes. Subsequently, a cocktail of biotin-conjugated CD9, CD81, and CD63 antibodies was used to detect and visualize captured exosomes. Multimarker models were made by combining two or more markers. The optimal multimarker model was evaluated by area under the curve (AUC) and random forests analysis.
The markers CD151, CD171, and tetraspanin 8 were the strongest separators of patients with cancer of all histological subtypes versus patients without cancer (CD151: AUC = 0.68, p = 0.0002; CD171: AUC = 0.60, p = 0.0002; and TSPAN8: AUC = 0.60, p = 0.0002). The multimarker models with the largest AUC in the cohort of patients with all lung cancer histological subtypes and in the cohort of patients with adenocarcinoma only covered 10 markers (all cancer: AUC = 0.74 [95% confidence interval: 0.70–0.80]; adenocarcinoma only: AUC = 0.76 [95% confidence interval: 0.70–0.83]). In squamous cell cancer and SCLC, multimarker models did not exceed CD151 as an individual marker in separating patients with cancer from controls.
We have demonstrated exosome protein profiling to be a promising diagnostic tool in lung cancer independently of stage and histological subtype. Multimarker models could make a fair separation of patients, demonstrating the perspectives of exosome protein profiling as a biomarker. |
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AbstractList | INTRODUCTION:Exosomes have been suggested as promising biomarkers in NSCLC because they contain proteins from their originating cells and are readily available in plasma. In this study, we explored the potential of exosome protein profiling in diagnosing lung cancers of all stages and various histological subtypes in patients.
METHODS:Plasma was isolated from 581 patients (431 with lung cancer and 150 controls). The extracellular vesicle array was used to phenotype exosomes. The extracellular vesicle array contained 49 antibodies for capturing exosomes. Subsequently, a cocktail of biotin-conjugated CD9, CD81, and CD63 antibodies was used to detect and visualize captured exosomes. Multimarker models were made by combining two or more markers. The optimal multimarker model was evaluated by area under the curve (AUC) and random forests analysis.
RESULTS:The markers CD151, CD171, and tetraspanin 8 were the strongest separators of patients with cancer of all histological subtypes versus patients without cancer (CD151AUC = 0.68, p = 0.0002; CD171AUC = 0.60, p = 0.0002; and TSPAN8AUC = 0.60, p = 0.0002). The multimarker models with the largest AUC in the cohort of patients with all lung cancer histological subtypes and in the cohort of patients with adenocarcinoma only covered 10 markers (all cancerAUC = 0.74 [95% confidence interval0.70–0.80]; adenocarcinoma onlyAUC = 0.76 [95% confidence interval0.70–0.83]). In squamous cell cancer and SCLC, multimarker models did not exceed CD151 as an individual marker in separating patients with cancer from controls.
CONCLUSION:We have demonstrated exosome protein profiling to be a promising diagnostic tool in lung cancer independently of stage and histological subtype. Multimarker models could make a fair separation of patients, demonstrating the perspectives of exosome protein profiling as a biomarker. Exosomes have been suggested as promising biomarkers in NSCLC because they contain proteins from their originating cells and are readily available in plasma. In this study, we explored the potential of exosome protein profiling in diagnosing lung cancers of all stages and various histological subtypes in patients.INTRODUCTIONExosomes have been suggested as promising biomarkers in NSCLC because they contain proteins from their originating cells and are readily available in plasma. In this study, we explored the potential of exosome protein profiling in diagnosing lung cancers of all stages and various histological subtypes in patients.Plasma was isolated from 581 patients (431 with lung cancer and 150 controls). The extracellular vesicle array was used to phenotype exosomes. The extracellular vesicle array contained 49 antibodies for capturing exosomes. Subsequently, a cocktail of biotin-conjugated CD9, CD81, and CD63 antibodies was used to detect and visualize captured exosomes. Multimarker models were made by combining two or more markers. The optimal multimarker model was evaluated by area under the curve (AUC) and random forests analysis.METHODSPlasma was isolated from 581 patients (431 with lung cancer and 150 controls). The extracellular vesicle array was used to phenotype exosomes. The extracellular vesicle array contained 49 antibodies for capturing exosomes. Subsequently, a cocktail of biotin-conjugated CD9, CD81, and CD63 antibodies was used to detect and visualize captured exosomes. Multimarker models were made by combining two or more markers. The optimal multimarker model was evaluated by area under the curve (AUC) and random forests analysis.The markers CD151, CD171, and tetraspanin 8 were the strongest separators of patients with cancer of all histological subtypes versus patients without cancer (CD151: AUC = 0.68, p = 0.0002; CD171: AUC = 0.60, p = 0.0002; and TSPAN8: AUC = 0.60, p = 0.0002). The multimarker models with the largest AUC in the cohort of patients with all lung cancer histological subtypes and in the cohort of patients with adenocarcinoma only covered 10 markers (all cancer: AUC = 0.74 [95% confidence interval: 0.70-0.80]; adenocarcinoma only: AUC = 0.76 [95% confidence interval: 0.70-0.83]). In squamous cell cancer and SCLC, multimarker models did not exceed CD151 as an individual marker in separating patients with cancer from controls.RESULTSThe markers CD151, CD171, and tetraspanin 8 were the strongest separators of patients with cancer of all histological subtypes versus patients without cancer (CD151: AUC = 0.68, p = 0.0002; CD171: AUC = 0.60, p = 0.0002; and TSPAN8: AUC = 0.60, p = 0.0002). The multimarker models with the largest AUC in the cohort of patients with all lung cancer histological subtypes and in the cohort of patients with adenocarcinoma only covered 10 markers (all cancer: AUC = 0.74 [95% confidence interval: 0.70-0.80]; adenocarcinoma only: AUC = 0.76 [95% confidence interval: 0.70-0.83]). In squamous cell cancer and SCLC, multimarker models did not exceed CD151 as an individual marker in separating patients with cancer from controls.We have demonstrated exosome protein profiling to be a promising diagnostic tool in lung cancer independently of stage and histological subtype. Multimarker models could make a fair separation of patients, demonstrating the perspectives of exosome protein profiling as a biomarker.CONCLUSIONWe have demonstrated exosome protein profiling to be a promising diagnostic tool in lung cancer independently of stage and histological subtype. Multimarker models could make a fair separation of patients, demonstrating the perspectives of exosome protein profiling as a biomarker. Exosomes have been suggested as promising biomarkers in NSCLC because they contain proteins from their originating cells and are readily available in plasma. In this study, we explored the potential of exosome protein profiling in diagnosing lung cancers of all stages and various histological subtypes in patients. Plasma was isolated from 581 patients (431 with lung cancer and 150 controls). The extracellular vesicle array was used to phenotype exosomes. The extracellular vesicle array contained 49 antibodies for capturing exosomes. Subsequently, a cocktail of biotin-conjugated CD9, CD81, and CD63 antibodies was used to detect and visualize captured exosomes. Multimarker models were made by combining two or more markers. The optimal multimarker model was evaluated by area under the curve (AUC) and random forests analysis. The markers CD151, CD171, and tetraspanin 8 were the strongest separators of patients with cancer of all histological subtypes versus patients without cancer (CD151: AUC = 0.68, p = 0.0002; CD171: AUC = 0.60, p = 0.0002; and TSPAN8: AUC = 0.60, p = 0.0002). The multimarker models with the largest AUC in the cohort of patients with all lung cancer histological subtypes and in the cohort of patients with adenocarcinoma only covered 10 markers (all cancer: AUC = 0.74 [95% confidence interval: 0.70-0.80]; adenocarcinoma only: AUC = 0.76 [95% confidence interval: 0.70-0.83]). In squamous cell cancer and SCLC, multimarker models did not exceed CD151 as an individual marker in separating patients with cancer from controls. We have demonstrated exosome protein profiling to be a promising diagnostic tool in lung cancer independently of stage and histological subtype. Multimarker models could make a fair separation of patients, demonstrating the perspectives of exosome protein profiling as a biomarker. |
Author | Sorensen, Boe Sandahl Rasmussen, Torben Riis Jørgensen, Malene Møller Meldgaard, Peter Jakobsen, Kristine Raaby Bæk, Rikke Folkersen, Birgitte Holst Varming, Kim Sandfeld-Paulsen, Birgitte |
AuthorAffiliation | aDepartment of Clinical Biochemistry, Aarhus University Hospital, Aarhus, Denmark bDepartment of Biomedicine, Aarhus University, Aarhus, Denmark cDepartment of Immunology, Aalborg University Hospital, Aalborg, Denmark dDepartment of Pulmonary Medicine, Aarhus University Hospital, Aarhus, Denmark eDepartment of Oncology, Aarhus University Hospital, Aarhus, Denmark |
AuthorAffiliation_xml | – name: aDepartment of Clinical Biochemistry, Aarhus University Hospital, Aarhus, Denmark bDepartment of Biomedicine, Aarhus University, Aarhus, Denmark cDepartment of Immunology, Aalborg University Hospital, Aalborg, Denmark dDepartment of Pulmonary Medicine, Aarhus University Hospital, Aarhus, Denmark eDepartment of Oncology, Aarhus University Hospital, Aarhus, Denmark |
Author_xml | – sequence: 1 givenname: Birgitte surname: Sandfeld-Paulsen fullname: Sandfeld-Paulsen, Birgitte email: birgne@rm.dk organization: Department of Clinical Biochemistry, Aarhus University Hospital, Aarhus, Denmark – sequence: 2 givenname: Kristine Raaby surname: Jakobsen fullname: Jakobsen, Kristine Raaby organization: Department of Clinical Biochemistry, Aarhus University Hospital, Aarhus, Denmark – sequence: 3 givenname: Rikke surname: Bæk fullname: Bæk, Rikke organization: Department of Immunology, Aalborg University Hospital, Aalborg, Denmark – sequence: 4 givenname: Birgitte Holst surname: Folkersen fullname: Folkersen, Birgitte Holst organization: Department of Pulmonary Medicine, Aarhus University Hospital, Aarhus, Denmark – sequence: 5 givenname: Torben Riis surname: Rasmussen fullname: Rasmussen, Torben Riis organization: Department of Pulmonary Medicine, Aarhus University Hospital, Aarhus, Denmark – sequence: 6 givenname: Peter surname: Meldgaard fullname: Meldgaard, Peter organization: Department of Oncology, Aarhus University Hospital, Aarhus, Denmark – sequence: 7 givenname: Kim surname: Varming fullname: Varming, Kim organization: Department of Immunology, Aalborg University Hospital, Aalborg, Denmark – sequence: 8 givenname: Malene Møller surname: Jørgensen fullname: Jørgensen, Malene Møller organization: Department of Immunology, Aalborg University Hospital, Aalborg, Denmark – sequence: 9 givenname: Boe Sandahl surname: Sorensen fullname: Sorensen, Boe Sandahl organization: Department of Clinical Biochemistry, Aarhus University Hospital, Aarhus, Denmark |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/27343445$$D View this record in MEDLINE/PubMed |
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Keywords | Diagnostic EV array Exosomes Lung cancer |
Language | English |
License | This article is made available under the Elsevier license. Copyright © 2016 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved. |
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Snippet | Exosomes have been suggested as promising biomarkers in NSCLC because they contain proteins from their originating cells and are readily available in plasma.... INTRODUCTION:Exosomes have been suggested as promising biomarkers in NSCLC because they contain proteins from their originating cells and are readily available... |
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SubjectTerms | Adult Aged Aged, 80 and over Biomarkers, Tumor - metabolism Cohort Studies Diagnostic EV array Exosomes Female Humans Lung cancer Lung Neoplasms - diagnosis Lung Neoplasms - pathology Male Middle Aged Prospective Studies Proteins - genetics |
Title | Exosomal Proteins as Diagnostic Biomarkers in Lung Cancer |
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