Recommendations for biomarker identification and qualification in clinical proteomics.
Mischak H., Allmaier G., Apweiler R., Attwood T., Baumann M., Benigni A., Bennett SE., Bischoff R., Bongcam-Rudloff E., Capasso G., Coon JJ., D'Haese P., Dominiczak AF., Dakna M., Dihazi H., Ehrich JH., Fernandez-Llama P., Fliser D., Frokiaer J., Garin J., Girolami M., Hancock WS., Haubitz M., Hochstrasser D., Holman RR., Ioannidis JPA., Jankowski J., Julian BA., Klein JB., Kolch W., Luider T., Massy Z., Mattes WB., Molina F., Monsarrat B., Novak J., Peter K., Rossing P., Sánchez-Carbayo M., Schanstra JP., Semmes OJ., Spasovski G., Theodorescu D., Thongboonkerd V., Vanholder R., Veenstra TD., Weissinger E., Yamamoto T., Vlahou A.
Clinical proteomics has yielded some early positive results-the identification of potential disease biomarkers-indicating the promise for this analytical approach to improve the current state of the art in clinical practice. However, the inability to verify some candidate molecules in subsequent studies has led to skepticism among many clinicians and regulatory bodies, and it has become evident that commonly encountered shortcomings in fundamental aspects of experimental design mainly during biomarker discovery must be addressed in order to provide robust data. In this Perspective, we assert that successful studies generally use suitable statistical approaches for biomarker definition and confirm results in independent test sets; in addition, we describe a brief set of practical and feasible recommendations that we have developed for investigators to properly identify and qualify proteomic biomarkers, which could also be used as reporting requirements. Such recommendations should help put proteomic biomarker discovery on the solid ground needed for turning the old promise into a new reality.