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 inJournal of thoracic oncology Vol. 11; no. 10; pp. 1701 - 1710
Main Authors Sandfeld-Paulsen, Birgitte, Jakobsen, Kristine Raaby, Bæk, Rikke, Folkersen, Birgitte Holst, Rasmussen, Torben Riis, Meldgaard, Peter, Varming, Kim, Jørgensen, Malene Møller, Sorensen, Boe Sandahl
Format Journal Article
LanguageEnglish
Published United States Elsevier Inc 01.10.2016
Copyright by the International Association for the Study of Lung Cancer
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Online AccessGet full text
ISSN1556-0864
1556-1380
1556-1380
DOI10.1016/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.
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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ContentType Journal Article
Copyright 2016 International Association for the Study of Lung Cancer
Copyright © 2016 by the International Association for the Study of Lung Cancer
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Keywords Diagnostic
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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
URI https://dx.doi.org/10.1016/j.jtho.2016.05.034
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https://www.ncbi.nlm.nih.gov/pubmed/27343445
https://www.proquest.com/docview/1823031277
Volume 11
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