Abstract
In recent years, large numbers of putative disease biomarkers have been identified. Combinations of protein biomarkers have been proposed to overcome the lack of single, magic-bullet identifiers of disease conditions. The number of biomarkers in a panel must be kept small to avoid the combinatorial explosion that requires very large, uneconomical sample cohorts for validation. Recent results on high sensitivity blood-based diagnostic proteomics (Godovac-Zimmermann, J et al., J. Proteome Res. 2006) suggest that the keys to identifying useful panels include judicious application of physiological knowledge to choose appropriate combinations of local, tissue/disease markers and global, systemic markers and to use very high sensitivity protein detection. Biomarkers that show non-Gaussian landscapes reminiscent of Rene Thom's multiple, stable-state landscapes seem to have the greatest predictive value for breast cancer (Godovac-Zimmermann, J. et al., J. Proteome Res. 2006).
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