title: Looking for Thom's biomarkers with proteomics.
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).
authors.forename: Andrzej K
authors.initials: AK
authors.lastname: Drukier
authors.forename: Ivan
authors.initials: I
authors.lastname: Grigoriev
authors.forename: Larry R
authors.initials: LR
authors.lastname: Brown
authors.forename: John E
authors.initials: JE
authors.lastname: Tomaszewski
authors.forename: Richard
authors.initials: R
authors.lastname: Sainsbury
authors.forename: Jasminka
authors.initials: J
authors.lastname: Godovac-Zimmermann
mesh_terms: D015415:Biomarkers / Q000032:analysis*; D004194:Disease; D006801:Humans; D007091:Image Processing, Computer-Assisted; D040901:Proteomics / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D015415:Biomarkers