Proteomic Biomarkers Prognostic for Diabetic Wound Healing - PROJECT SUMMARY / ABSTRACT Impaired wound healing is an alarming problem in diabetes, attributed to the development of over 750,000 diabetic foot ulcerations (DFU) and 70,000 lower extremity amputations per year in the USA. DFU is a heterogeneous disease with a variable clinical course and there is an urgent, unmet need to identify subjects who are at higher risk of an impaired wound healing. Our preliminary collaborative study of the circulating proteome points to the interesting candidate biomarker proteins prognostic of the DFU course. In our pilot, study we have identified proteins associated with the prospective wound healing outcome belonging to such classes as adhesion molecules, carboxypeptidases, and inflammatory proteins among others. Pathway network analyses seeded with these proteins revealed β catenin and cellular Myc (c-Myc) among the most connected nodes. Interestingly, Wnt/β catenin pathway and transcription factor, c-Myc have been extensively implicated to play a role in the DFU including work of the Diabetic Foot Consortium (DFC) investigators. The DFU course is heterogeneous and multi-factorial, thus we hypothesize that multi-biomarker panel will offer the most optimal prognostication. Biological processes reflected by protein levels are often connected, thus we hypothesize that by employing parallal biostatistical and machine learning approach will offer us tools to build a robust signature. This proposal has the three aims: Aim 1 (R61 phase) will focus on refining candidate protein exemplars for a prospective wound healing among subjects with diabetes. We will perform semi-targeted measurements of our candidate proteins (identified in our preliminary study) in subjects of the DFC cohort followed for a 3-month disease course. We will employ biostatistical tools supported by machine learning methods, adequate for correlated data to finally determine key protein exemplars. In Aim 2, (R61 phase), we will develop a focused, proteomics biomarker signature comprising of a targeted quantitative panel of 10-12 proteins. For that purpose, we will perform an extensive analytical validation and subsequently we will build a focused and quantitative multi- biomarker panel. Finally, in Aim 3 (R33 phase) will initiate determination of the clinical utility of our prognostic biomarker signature for the DFU course. To this end, we will perform targeted biomarker measurements in the DFC cohort. We will employ rigorous biostatistical metrics to evaluate the biomarker signature’s performance (integrated discrimination ability, Akake criterion among others). Advancements in this project will identify and initially determine a value of the biomarker signature prognostic for the prospective diabetic wound healing course. These efforts will aid in refining a target population with a phenotype of interest by providing objective quantifiable metrics that can enhance clinical trial enrollment criteria for Phase 2/3 studies.
