Computational protein biomarker prediction: a case study for prostate cancer.
BACKGROUND
Recent technological advances in mass spectrometry pose challenges in computational mathematics and statistics to process the mass spectral data into predictive models with clinical and biological significance. We discuss several classification-based approaches to finding protein biomarker candidates using protein profiles obtained via mass spectrometry, and we assess their statistical significance. Our overall goal is to implicate peaks that have a high likelihood of being biologically linked to a given disease state, and thus to narrow the search for biomarker candidates.


RESULTS
Thorough cross-validation studies and randomization tests are performed on a prostate cancer dataset with over 300 patients, obtained at the Eastern Virginia Medical School using SELDI-TOF mass spectrometry. We obtain average classification accuracies of 87% on a four-group classification problem using a two-stage linear SVM-based procedure and just 13 peaks, with other methods performing comparably.


CONCLUSIONS
Modern feature selection and classification methods are powerful techniques for both the identification of biomarker candidates and the related problem of building predictive models from protein mass spectrometric profiles. Cross-validation and randomization are essential tools that must be performed carefully in order not to bias the results unfairly. However, only a biological validation and identification of the underlying proteins will ultimately confirm the actual value and power of any computational predictions.
Wagner
Michael
M
Cincinnati Children's Hospital Research Foundation and Department of Biomedical Engineering, University of Cincinnati, Cincinnati, OH 45229, USA. mwagner@cchmc.org
Naik
Dayanand N
DN
Pothen
Alex
A
Kasukurti
Srinivas
S
Devineni
Raghu Ram
RR
Adam
Bao-Ling
BL
Semmes
O John
OJ
Wright
George L
GL
D014408:Biomarkers, Tumor / Q000096:biosynthesis / Q000145:classification*; D019295:Computational Biology / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D008297:Male; D009363:Neoplasm Proteins / Q000096:biosynthesis / Q000145:classification*; D011237:Predictive Value of Tests; D011471:Prostatic Neoplasms / Q000737:chemistry* / Q000175:diagnosis / Q000235:genetics; D011897:Random Allocation; D015203:Reproducibility of Results; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods / Q000706:statistics & numerical data
D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins