MICRO-WESTERN ARRAY METHODOLOGY FOR ASSESSMENT OF PREANALYTICAL VARIABILITY IN BI


Year of Award:
2014
Award Type:
R21
Project Number:
CA177467
RFA Number:
RFA-CA-13-003
Technology Track:
Biospecimen Science Technologies
PI/Project Leader:
WHITE, KEVIN P.
Other PI or Project Leader:
LANGERMAN, ALEXANDER JAY
Institution:
UNIVERSITY OF CHICAGO
Biospecimens are an invaluable resource to the biomedical community. The objective of this proposal is to employ a combination of micro-western arrays and reverse phase protein arrays to dramatically increase the throughput of antibody validation as well as the comprehensiveness of proteins that can be examined in biospecimens. We will use this platform to examine the relationship of about 500 protein abundances and modification states with sources of in- and ex-vivo peri-operative sources of preanalytical variability. We will record standard sources of per-operative variability during the normal course of surgical resection of head-and- neck tumors and relate these variables to differential protein expression and modification. In parallel, we will measure changes in protein expression and modification following defined times of ex-vivo ischemia to mimic a standard source of ex-vivo peri-operative pre-analytical variability. In summary, we will develop standard operating procedures for surgical biospecimen removal and test the efficacy of a platform employing micro- western arrays and reverse phase protein arrays for measuring changes in the expression of about 500 cell signaling proteins in approximately 140 biospecimens subjected either to standard perioperative handling variables or to defined and controlled sources of preanalytical variability. Following surrogate variable analysis to control for known biological covariates (such as age, sec, etc.), we will use linear mixed effects modeling to identify baseline protein levels associated with preanalytical variability. Two-step, cubic regression will be used to identify proteins with significant ex-vivo temporal expression changes following resection. Our model will be used to establish metrics of tissue quality and to identify protein signatures indicative of biospecimen quality.