Lung Cancer Diagnosis from Proteomic Analysis of Preinvasive Lesions

Early detection may help improve survival from lung cancer. In this study, our goal was to derive and validate a signature from the proteomic analysis of bronchial lesions that could predict the diagnosis of lung cancer. Using previously published studies of bronchial tissues, we selected a signatur...

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Published inCancer research (Chicago, Ill.) Vol. 71; no. 8; pp. 3009 - 3017
Main Authors JAMSHEDUR RAHMAN, S. M, GONZALEZ, Adriana L, PUTNAM, Joe B, MILLER, York E, FRANKLIN, Wilbur A, BLOT, William J, CARBONE, David P, SHYR, Yu, CAPRIOLI, Richard M, MASSION, Pierre P, MING LI, SEELE, Erin H, ZIMMERMAN, Lisa J, ZHANG, Xueqiong J, MANIER, M. Lisa, OLSON, Sandra J, SHAH, Ronak N, MILLER, Alison N
Format Journal Article
LanguageEnglish
Published Philadelphia, PA American Association for Cancer Research 15.04.2011
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Abstract Early detection may help improve survival from lung cancer. In this study, our goal was to derive and validate a signature from the proteomic analysis of bronchial lesions that could predict the diagnosis of lung cancer. Using previously published studies of bronchial tissues, we selected a signature of nine matrix-assisted laser desorption ionization mass spectrometry (MALDI MS) mass-to-charge ratio features to build a prediction model diagnostic of lung cancer. The model was based on MALDI MS signal intensity (MALDI score) from bronchial tissue specimens from our 2005 published cohort of 51 patients. The performance of the prediction model in identifying lung cancer was tested in an independent cohort of bronchial specimens from 60 patients. The probability of having lung cancer based on the proteomic analysis of the bronchial specimens was characterized by an area under the receiver operating characteristic curve of 0.77 (95% CI 0.66-0.88) in this validation cohort. Eight of the nine features were identified and validated by Western blotting and immunohistochemistry. These results show that proteomic analysis of endobronchial lesions may facilitate the diagnosis of lung cancer and the monitoring of high-risk individuals for lung cancer in surveillance and chemoprevention trials.
AbstractList Early detection may help improve survival from lung cancer. In this study, our goal was to derive and validate a signature from the proteomic analysis of bronchial lesions that could predict the diagnosis of lung cancer. Using previously published studies of bronchial tissues, we selected a signature of nine matrix-assisted laser desorption ionization mass spectrometry (MALDI MS) mass-to-charge ratio features to build a prediction model diagnostic of lung cancer. The model was based on MALDI MS signal intensity (MALDI score) from bronchial tissue specimens from our 2005 published cohort of 51 patients. The performance of the prediction model in identifying lung cancer was tested in an independent cohort of bronchial specimens from 60 patients. The probability of having lung cancer based on the proteomic analysis of the bronchial specimens was characterized by an area under the receiver operating characteristic curve of 0.77 (95% CI 0.66-0.88) in this validation cohort. Eight of the nine features were identified and validated by Western blotting and immunohistochemistry. These results show that proteomic analysis of endobronchial lesions may facilitate the diagnosis of lung cancer and the monitoring of high-risk individuals for lung cancer in surveillance and chemoprevention trials.
Early detection may help improve survival from lung cancer. In this study our goal was to derive and validate a signature from the proteomic analysis of bronchial lesions that could predict the diagnosis of lung cancer. Using previously published studies of bronchial tissues we selected a signature of 9 matrix-assisted laser desorption ionization mass spectrometry (MALDI MS) mass to charge ratio features to build a prediction model diagnostic of lung cancer. The model was based on MALDI MS signal intensity (MALDI score) from bronchial tissue specimens from our 2005 published cohort of 51 patients. The performance of the prediction model in identifying lung cancer was tested in an independent cohort of bronchial specimens from 60 patients. The probability of having lung cancer based on the proteomic analysis of the bronchial specimens was characterized by an area under the receiver operating characteristic curve of 0.77 (95% CI 0.66 to 0.88) in this validation cohort. Eight of the 9 features were identified and validated by Western blotting and immunohistochemistry. These results demonstrate that proteomic analysis of endobronchial lesions may facilitate the diagnosis of lung cancer and the monitoring of high risk individuals for lung cancer in surveillance and chemoprevention trials.
Early detection may help improve survival from lung cancer. In this study, our goal was to derive and validate a signature from the proteomic analysis of bronchial lesions that could predict the diagnosis of lung cancer. Using previously published studies of bronchial tissues, we selected a signature of nine matrix-assisted laser desorption ionization mass spectrometry (MALDI MS) mass-to-charge ratio features to build a prediction model diagnostic of lung cancer. The model was based on MALDI MS signal intensity (MALDI score) from bronchial tissue specimens from our 2005 published cohort of 51 patients. The performance of the prediction model in identifying lung cancer was tested in an independent cohort of bronchial specimens from 60 patients. The probability of having lung cancer based on the proteomic analysis of the bronchial specimens was characterized by an area under the receiver operating characteristic curve of 0.77 (95% CI 0.66–0.88) in this validation cohort. Eight of the nine features were identified and validated by Western blotting and immunohistochemistry. These results show that proteomic analysis of endobronchial lesions may facilitate the diagnosis of lung cancer and the monitoring of high-risk individuals for lung cancer in surveillance and chemoprevention trials. Cancer Res; 71(8); 3009–17. ©2011 AACR.
Author SHAH, Ronak N
PUTNAM, Joe B
ZHANG, Xueqiong J
MILLER, York E
MANIER, M. Lisa
OLSON, Sandra J
SHYR, Yu
MASSION, Pierre P
FRANKLIN, Wilbur A
ZIMMERMAN, Lisa J
SEELE, Erin H
GONZALEZ, Adriana L
JAMSHEDUR RAHMAN, S. M
BLOT, William J
MING LI
CAPRIOLI, Richard M
MILLER, Alison N
CARBONE, David P
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Issue 8
Keywords Lung disease
Premalignant lesion
Respiratory disease
Analysis
Lung cancer
Proteomics
Bronchus disease
Malignant tumor
Diagnosis
Bronchopulmonary
Cancer
Language English
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Snippet Early detection may help improve survival from lung cancer. In this study, our goal was to derive and validate a signature from the proteomic analysis of...
Early detection may help improve survival from lung cancer. In this study our goal was to derive and validate a signature from the proteomic analysis of...
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SubjectTerms Aged
Antineoplastic agents
Biological and medical sciences
Blotting, Western
Early Detection of Cancer - methods
Female
Humans
Immunohistochemistry
Lung Neoplasms - diagnosis
Lung Neoplasms - metabolism
Male
Medical sciences
Middle Aged
Neoplasm Proteins - analysis
Neoplasm Proteins - metabolism
Pharmacology. Drug treatments
Pneumology
Proteomics - methods
Reproducibility of Results
Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization - methods
Tumors
Tumors of the respiratory system and mediastinum
Title Lung Cancer Diagnosis from Proteomic Analysis of Preinvasive Lesions
URI https://www.ncbi.nlm.nih.gov/pubmed/21487035
https://search.proquest.com/docview/869804025
https://pubmed.ncbi.nlm.nih.gov/PMC3110721
Volume 71
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