Combined experimental and statistical strategy for mass spectrometry based serum protein profiling for diagnosis of breast cancer: a case-control study

Anne Kjærgaard Callesen, Werner Vach, Per E Jørgensen, Søren Cold, Qihua Tan, René Depont Christensen, Ole Mogensen, Torben Kruse, Jonna Skov Madsen, Ole Nørregaard Jensen

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Serum protein profiling by mass spectrometry is a promising method for early detection of cancer. We have implemented a combined strategy based on matrix-assisted laser desorption ionization mass spectrometry (MALDI MS) and statistical data analysis for serum protein profiling and applied it in a well-described breast cancer case-control study. A rigorous sample collection protocol ensured high quality specimen and reduced bias from preanalytical factors. Preoperative serum samples obtained from 48 breast cancer patients and 28 controls were used to generate MALDI MS protein profiles. A total of nine mass spectrometric protein profiles were obtained for each serum sample. A total of 533 common peaks were defined and represented a 'reference protein profile'. Among these 533 common peaks, we identified 72 peaks exhibiting statistically significant intensity differences ( p < 0.01) between cases and controls. A diagnostic rule based on these 72 mass values was constructed and exhibited a cross-validated sensitivity and specificity of approximately 85% for the detection of breast cancer. With this method, it was possible to distinguish early stage cancers from controls without major loss of sensitivity and specificity. We conclude that optimized serum sample handling and mass spectrometry data acquisition strategies in combination with statistical analysis provide a viable platform for serum protein profiling in cancer diagnosis.
Original languageEnglish
JournalJournal of Proteome Research
Volume7
Issue number4
Pages (from-to)1419-1426
Number of pages7
ISSN1535-3893
DOIs
Publication statusPublished - 1. Apr 2008

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Mass spectrometry
Case-Control Studies
Blood Proteins
Matrix-Assisted Laser Desorption-Ionization Mass Spectrometry
Serum
Ionization
Desorption
Statistical Data Interpretation
Proteins
Early Detection of Cancer
Lasers
Neoplasms
Data acquisition
Statistical methods

Cite this

@article{61e0cb4037a011dda26c000ea68e967b,
title = "Combined experimental and statistical strategy for mass spectrometry based serum protein profiling for diagnosis of breast cancer: a case-control study",
abstract = "Serum protein profiling by mass spectrometry is a promising method for early detection of cancer. We have implemented a combined strategy based on matrix-assisted laser desorption ionization mass spectrometry (MALDI MS) and statistical data analysis for serum protein profiling and applied it in a well-described breast cancer case-control study. A rigorous sample collection protocol ensured high quality specimen and reduced bias from preanalytical factors. Preoperative serum samples obtained from 48 breast cancer patients and 28 controls were used to generate MALDI MS protein profiles. A total of nine mass spectrometric protein profiles were obtained for each serum sample. A total of 533 common peaks were defined and represented a 'reference protein profile'. Among these 533 common peaks, we identified 72 peaks exhibiting statistically significant intensity differences ( p < 0.01) between cases and controls. A diagnostic rule based on these 72 mass values was constructed and exhibited a cross-validated sensitivity and specificity of approximately 85{\%} for the detection of breast cancer. With this method, it was possible to distinguish early stage cancers from controls without major loss of sensitivity and specificity. We conclude that optimized serum sample handling and mass spectrometry data acquisition strategies in combination with statistical analysis provide a viable platform for serum protein profiling in cancer diagnosis.",
author = "Callesen, {Anne Kj{\ae}rgaard} and Werner Vach and J{\o}rgensen, {Per E} and S{\o}ren Cold and Qihua Tan and {Depont Christensen}, Ren{\'e} and Ole Mogensen and Torben Kruse and Madsen, {Jonna Skov} and Jensen, {Ole N{\o}rregaard}",
year = "2008",
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Combined experimental and statistical strategy for mass spectrometry based serum protein profiling for diagnosis of breast cancer : a case-control study. / Callesen, Anne Kjærgaard; Vach, Werner; Jørgensen, Per E; Cold, Søren; Tan, Qihua; Depont Christensen, René; Mogensen, Ole; Kruse, Torben; Madsen, Jonna Skov; Jensen, Ole Nørregaard.

In: Journal of Proteome Research, Vol. 7, No. 4, 01.04.2008, p. 1419-1426.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Combined experimental and statistical strategy for mass spectrometry based serum protein profiling for diagnosis of breast cancer

T2 - a case-control study

AU - Callesen, Anne Kjærgaard

AU - Vach, Werner

AU - Jørgensen, Per E

AU - Cold, Søren

AU - Tan, Qihua

AU - Depont Christensen, René

AU - Mogensen, Ole

AU - Kruse, Torben

AU - Madsen, Jonna Skov

AU - Jensen, Ole Nørregaard

PY - 2008/4/1

Y1 - 2008/4/1

N2 - Serum protein profiling by mass spectrometry is a promising method for early detection of cancer. We have implemented a combined strategy based on matrix-assisted laser desorption ionization mass spectrometry (MALDI MS) and statistical data analysis for serum protein profiling and applied it in a well-described breast cancer case-control study. A rigorous sample collection protocol ensured high quality specimen and reduced bias from preanalytical factors. Preoperative serum samples obtained from 48 breast cancer patients and 28 controls were used to generate MALDI MS protein profiles. A total of nine mass spectrometric protein profiles were obtained for each serum sample. A total of 533 common peaks were defined and represented a 'reference protein profile'. Among these 533 common peaks, we identified 72 peaks exhibiting statistically significant intensity differences ( p < 0.01) between cases and controls. A diagnostic rule based on these 72 mass values was constructed and exhibited a cross-validated sensitivity and specificity of approximately 85% for the detection of breast cancer. With this method, it was possible to distinguish early stage cancers from controls without major loss of sensitivity and specificity. We conclude that optimized serum sample handling and mass spectrometry data acquisition strategies in combination with statistical analysis provide a viable platform for serum protein profiling in cancer diagnosis.

AB - Serum protein profiling by mass spectrometry is a promising method for early detection of cancer. We have implemented a combined strategy based on matrix-assisted laser desorption ionization mass spectrometry (MALDI MS) and statistical data analysis for serum protein profiling and applied it in a well-described breast cancer case-control study. A rigorous sample collection protocol ensured high quality specimen and reduced bias from preanalytical factors. Preoperative serum samples obtained from 48 breast cancer patients and 28 controls were used to generate MALDI MS protein profiles. A total of nine mass spectrometric protein profiles were obtained for each serum sample. A total of 533 common peaks were defined and represented a 'reference protein profile'. Among these 533 common peaks, we identified 72 peaks exhibiting statistically significant intensity differences ( p < 0.01) between cases and controls. A diagnostic rule based on these 72 mass values was constructed and exhibited a cross-validated sensitivity and specificity of approximately 85% for the detection of breast cancer. With this method, it was possible to distinguish early stage cancers from controls without major loss of sensitivity and specificity. We conclude that optimized serum sample handling and mass spectrometry data acquisition strategies in combination with statistical analysis provide a viable platform for serum protein profiling in cancer diagnosis.

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