Profiling the effects of short time-course cold ischemia on tumor protein phosphorylation using a Bayesian approach.

Phosphorylated proteins provide insight into tumor etiology and are used as diagnostic, prognostic, and therapeutic markers of complex diseases. However, pre-analytic variations, such as freezing delay after biopsy acquisition, often occur in real hospital settings and potentially lead to inaccurate results. The objective of this work is to develop statistical methodology to assess the stability of phosphorylated proteins under short-time cold ischemia. We consider a hierarchical model to determine if phosphorylation abundance of a protein at a particular phosphorylation site remains constant or not during cold ischemia. When phosphorylation levels vary across time, we estimate the direction of the changes in each protein based on the maximum overall posterior probability and on the pairwise posterior probabilities, respectively. We analyze a dataset of ovarian tumor tissues that suffered cold-ischemia shock before the proteomic profiling. Gajadhar et al. (2015) applied independent clusterings for each patient because of the high heterogeneity across patients, while our proposed model shares information allowing conclusions for the entire sample population. Using the proposed model, 15 out of 32 proteins show significant changes during 1-hour cold ischemia. Through simulation studies, we conclude that our proposed methodology has a higher accuracy for detecting changes compared to an order restricted inference method. Our approach provides inference on the stability of these phosphorylated proteins, which is valuable when using these proteins as biomarkers for a disease.