Interactome Mapping: Using Protein Microarray Technology to Reconstruct Diverse Protein Networks

Uzoma, Ijeoma; Zhu, Heng

This study represents a new paradigm in biomarker identification using protein microarrays for three reasons. First, a manageable number of candidate biomarkers can be rapidly identified at low cost because fewer expensive protein microarrays of high-content are needed in the first phase of this two-phase strategy. Second, by using small arrays comprised of selected candidate proteins, the validation step can be rapidly carried out with a much larger cohort at low cost. This validation step is extremely important for avoiding the overfitting problem associated with statistical analysis in biomarker or classifier identification, especially when dealing with a small cohort (e.g., <40). Overfitting is a problem in which a statistical model describes random error or noise instead of the underlying relationship. It generally occurs in biomarker identification when the system is excessively complex, such as having too many individual-to-individual variations relative to the number of samples used. As a result, biomarkers that have been overfit generally have poor predictive performance. Therefore, testing an additional, larger cohort in a doubleblind design is an effective way to rule out overfit biomarkers. Third, the authors developed ELISA-based assays to examine the performance of the validated biomarkers with additional samples. These newly identified biomarkers could serve as a translational step toward clinical practice.