Diagnosis of early relapse in ovarian cancer using serum proteomic profiling.

Surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry data has been increasingly analyzed for identifying biomarkers to help early detection of the disease. Ovarian cancer commonly recurs at the rate of 75% within a few months or several years later after standard treatment. Since recurrent ovarian cancer is relatively difficult to be diagnosed and small tumors generally respond better to treatment, new methods for the detection of early relapse in ovarian cancer are urgently needed. Here, we propose a new algorithm SVM-MB/RFE (SVM-Markov Blanket/Recursive Feature Elimination) based on SVM-RFE, which identifies biomarkers for predicting the early recurrence of ovarian cancer. In this approach, we first apply t-test for feature pruning and then binning using 5-fold cross validation. Finally, 58 peaks are obtained from 27,000 of the raw data. Such dramatically reduced features relax the computational burden in the next step of our algorithm. We compare the performance of three feature selection algorithms and demonstrate that SVM-MB/RFE outperforms other methods.

[1]  J. Glimm,et al.  Detection of cancer-specific markers amid massive mass spectral data , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[2]  Marshall W. Bern,et al.  Automatic Quality Assessment of Peptide Tandem Mass Spectra , 2004, ISMB/ECCB.

[3]  Richard M. Karp,et al.  CLIFF: clustering of high-dimensional microarray data via iterative feature filtering using normalized cuts , 2001, ISMB.

[4]  G. Li,et al.  An integrated approach utilizing artificial neural networks and SELDI mass spectrometry for the classification of human tumours and rapid identification of potential biomarkers , 2002, Bioinform..

[5]  Bernard De Baets,et al.  Feature subset selection for splice site prediction , 2002, ECCB.

[6]  A. Vlahou,et al.  Diagnosis of Ovarian Cancer Using Decision Tree Classification of Mass Spectral Data , 2003, Journal of biomedicine & biotechnology.

[7]  Bruce Randall Donald,et al.  Probabilistic Disease Classification of Expression-Dependent Proteomic Data from Mass Spectrometry of Human Serum , 2003, J. Comput. Biol..

[8]  B. Schölkopf,et al.  Advances in kernel methods: support vector learning , 1999 .

[9]  D. Chan,et al.  Proteomics and bioinformatics approaches for identification of serum biomarkers to detect breast cancer. , 2002, Clinical chemistry.

[10]  Fabian Model,et al.  Feature selection for DNA methylation based cancer classification , 2001, ISMB.

[11]  Thorsten Joachims,et al.  Making large scale SVM learning practical , 1998 .

[12]  Hua Lin,et al.  Quantifying reproducibility for differential proteomics: noise analysis for protein liquid chromatography-mass spectrometry of human serum , 2004, Bioinform..

[13]  Jason Weston,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.

[14]  David Ward,et al.  Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data , 2003, Bioinform..

[15]  P. Schellhammer,et al.  Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men. , 2002, Cancer research.

[16]  Emanuel F Petricoin,et al.  Serum proteomics in cancer diagnosis and management. , 2004, Annual review of medicine.

[17]  Y. Yasui,et al.  An Automated Peak Identification/Calibration Procedure for High-Dimensional Protein Measures From Mass Spectrometers , 2003, Journal of biomedicine & biotechnology.

[18]  Paul Terry,et al.  Application of the GA/KNN method to SELDI proteomics data , 2004, Bioinform..

[19]  S Hanash,et al.  Proteomics in early detection of cancer. , 2001, Clinical chemistry.

[20]  Min Zhan,et al.  A data review and re-assessment of ovarian cancer serum proteomic profiling , 2003, BMC Bioinformatics.

[21]  Daphne Koller,et al.  Toward Optimal Feature Selection , 1996, ICML.

[22]  Pär Stattin,et al.  Correspondence re: B-L. Adam et al., Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men. Cancer Res., 62: 3609-3614, 2002. , 2003, Cancer research.

[23]  Nello Cristianini,et al.  Support vector machine classification and validation of cancer tissue samples using microarray expression data , 2000, Bioinform..

[24]  E. Petricoin,et al.  Use of proteomic patterns in serum to identify ovarian cancer , 2002, The Lancet.

[25]  Jagath C. Rajapakse,et al.  SVM-RFE peak selection for cancer classification with mass spectrometry data , 2005, APBC.

[26]  E. Petricoin,et al.  Early detection: Proteomic applications for the early detection of cancer , 2003, Nature Reviews Cancer.

[27]  P. Schellhammer,et al.  Boosted decision tree analysis of surface-enhanced laser desorption/ionization mass spectral serum profiles discriminates prostate cancer from noncancer patients. , 2002, Clinical chemistry.