Why have so few proteomic biomarkers "survived" validation? (Sample size and independent validation considerations)

Proteomics

Volumn 14, Issue 13-14, 2014, Pages 1587-1592

  Hernández, Belinda ^a   Parnell, Andrew ^a   Pennington, Stephen R ^a  

^a UNIVERSITY COLLEGE DUBLIN   (Ireland)

Author keywords

Bioinformatics; Biomarker panels; Cross validation; Proteomic discovery; Random forest; Sample size

Indexed keywords

BIOLOGICAL MARKER;

ACCURACY; ANALYSIS OF VARIANCE; ANALYTIC METHOD; AREA UNDER THE CURVE; ARTICLE; BIOINFORMATICS; BIOMETRY; CLASSIFICATION ALGORITHM; FILTRATION; LIQUID CHROMATOGRAPHY; LIQUID CHROMATOGRAPHY TANDEM MASS SPECTROMETRY; MACHINE LEARNING; PREDICTIVE VALUE; PRIORITY JOURNAL; PROTEOMICS; RANDOM FOREST; SAMPLE SIZE; SIMULATION; TANDEM MASS SPECTROMETRY; VALIDATION PROCESS; ALGORITHM; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; DECISION TREE; HUMAN; MASS SPECTROMETRY; PROCEDURES; STATISTICAL MODEL; VALIDATION STUDY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BIOLOGICAL MARKERS; CHROMATOGRAPHY, LIQUID; COMPUTER SIMULATION; DECISION TREES; HUMANS; MASS SPECTROMETRY; MODELS, STATISTICAL; PROTEOMICS; SAMPLE SIZE; VALIDATION STUDIES AS TOPIC;

EID: 84904022083     PISSN: 16159853     EISSN: 16159861     Source Type: Journal    
DOI: 10.1002/pmic.201300377     Document Type: Article

References (22)

1. Diamandis, E. P., The failure of protein cancer biomarkers to reach the clinic: why, and what can be done to address the problem? BMC Med. 2012, 10, 87.
2. Oon, S., Pennington, S., Fitzpatrick, J., Watson, R. W. G., Biomarker research in prostate cancer-towards utility, not futility. Nat. Rev. Urol. 2011, 8, 131-138.
3. Rifai, N., Gillette, M. A., Carr, S. A., Protein biomarker discovery and validation: the long and uncertain path to clinical utility. 2006, 24, 971-983.
4. Kaiser, J., Clinical medicine. Biomarker tests need closer scrutiny, IOM concludes. Science 2012, 335, 1554.
5. Hilario, M., Kalousis, A., Pellegrini, C., Müller, M., Processing and classification of protein mass spectra. Mass Spectrom. Rev. 2006, 25, 409-449.
6. Tibshirani, R., Hastie, T., Narasimhan, B., Soltys, S. et al., Sample classification from protein mass spectrometry, by "peak probability contrasts". Bioinformatics 2004, 20, 3034-3044.
7. Robin, X., Turck, N., Hainard, A., Lisacek, F., Sanchez, J., Bioinformatics for protein biomarker panel classification: what is needed to bring biomarker panels into in vitro diagnostics? Expert Rev. Proteomics 2009, 6, 675-679.
8. Ambroise, C., McLachlan, G. J., Selection bias in gene extraction on the basis of microarray gene-expression data. Proc. Natl. Acad. Sci. U S A 2002, 99, 6562-6566.
9. Wright, M. E., Han, D. K., Aebersold, R., Mass spectrometry-based expression profiling of clinical prostate cancer. Mol. Cell. Proteomics 2005, 4, 545-554.
10. Surinova, S., Schiess, R., Hüttenhain, R., Cerciello, F. et al., On the development of plasma protein biomarkers. J. Proteome Res. 2011, 10, 5-16.
11. Podwojski, K., Eisenacher, M., Kohl, M., Turewicz, M. et al., Peek a peak: a glance at statistics for quantitative label-free proteomics. Proteomics 2010, 7, 249-261.
12. Sampson, D. L., Parker, T. J., Upton, Z., Hurst, C. P., A comparison of methods for classifying clinical samples based on proteomics data: a case study for statistical and machine learning approaches. PLoS One 2011, 6, e24973.
13. Wu, B., Abbott, T., Fishman, D., McMurray, W. et al., Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data. Bioinformatics 2003, 19, 1636-1643.
14. Chen, B., Sheridan, R., Hornak, V., Voigt, J. H., Comparison of random forest and Pipeline Pilot Naïve Bayes in prospective QSAR predictions. J. Chem. Inf. Model. 2012, 52, 792-803.
15. Caruana, R., Karampatziakis, N., Yessenalina, A., An empirical evaluation of supervised learning in high dimensions. Proceedings of the 25th International Conference on Machine Learning (ICML '08), Helsinki. 2008, pp. 96-103.
16. Breiman, L., Random forests. Mach. Learn. 2001, 45, 5-32.
17. Böhm, D., Keller, K., Wehrwein, N., Lebrecht, A. et al., Serum proteome profiling of primary breast cancer indicates a specific biomarker profile. Oncol. Rep. 2011, 1051-1056.
18. Long, L., Li, R., Li, Y., Hu, C., Li, Z., Pattern-based diagnosis and screening of differentially expressed serum proteins for rheumatoid arthritis by proteomic fingerprinting. Rheumatol. Int. 2011, 31, 1069-1074.
19. Chen, F., Xue, J., Zhou, L., Wu, S., Chen, Z., Identification of serum biomarkers of hepatocarcinoma through liquid chromatography/mass spectrometry-based metabonomic method. Anal. Bioanal. Chem. 2011, 401, 1899-904.
20. Lausser, L., Müssel, C., Maucher, M., Kestler, H. a., Measuring and visualizing the stability of biomarker selection techniques. Comput. Stat. 2011, 28, 51-65.
21. Jelizarow, M., Guillemot, V., Tenenhaus, A., Strimmer, K., Boulesteix, A.-L., Over-optimism in bioinformatics: an illustration. Bioinformatics 2010, 26, 1990-1998.
22. Strobl, C., Boulesteix, A.-L., Kneib, T., Augustin, T., Zeileis, A., Conditional variable importance for random forests. BMC Bioinformatics 2008, 9, 307.