Reproducibility of Differential Proteomic Technologies in CPTAC Fractionated Xenografts

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• 作者：David L. Tabb ; Xia Wang ; Steven A. Carr ; Karl R. Clauser ; Philipp Mertins ; Matthew C. Chambers ; Jerry D. Holman ; Jing Wang ; Bing Zhang ; Lisa J. Zimmerman ; Xian Chen ; Harsha P. Gunawardena ; Sherri R. Davies ; Matthew J. C. Ellis ; Shunqiang Li ; R. Reid Townsend ; Emily S. Boja ; Karen A. Ketchum ; Christopher R. Kinsinger ; Mehdi Mesri ; Henry Rodriguez ; Tao Liu ; Sangtae Kim ; Jason E. McDermott ; Samuel H. Payne ; Vladislav A. Petyuk ; Karin D. Rodland ; Richard D. Smith ; Feng Yang ; Daniel W. Chan ; Bai Zhang ; Hui Zhang ; Zhen Zhang ; Jian-Ying Zhou ; Daniel C. Liebler
• 刊名：Journal of Proteome Research
• 出版年：2016
• 出版时间：March 4, 2016
• 年：2016
• 卷：15
• 期：3
• 页码：691-706
• 全文大小：846K
• ISSN：1535-3907

文摘

The NCI Clinical Proteomic Tumor Analysis Consortium (CPTAC) employed a pair of reference xenograft proteomes for initial platform validation and ongoing quality control of its data collection for The Cancer Genome Atlas (TCGA) tumors. These two xenografts, representing basal and luminal-B human breast cancer, were fractionated and analyzed on six mass spectrometers in a total of 46 replicates divided between iTRAQ and label-free technologies, spanning a total of 1095 LC–MS/MS experiments. These data represent a unique opportunity to evaluate the stability of proteomic differentiation by mass spectrometry over many months of time for individual instruments or across instruments running dissimilar workflows. We evaluated iTRAQ reporter ions, label-free spectral counts, and label-free extracted ion chromatograms as strategies for data interpretation (source code is available from http://homepages.uc.edu/~wang2x7/Research.htm). From these assessments, we found that differential genes from a single replicate were confirmed by other replicates on the same instrument from 61 to 93% of the time. When comparing across different instruments and quantitative technologies, using multiple replicates, differential genes were reproduced by other data sets from 67 to 99% of the time. Projecting gene differences to biological pathways and networks increased the degree of similarity. These overlaps send an encouraging message about the maturity of technologies for proteomic differentiation.