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Candidate serum biomarkers for early intestinal cancer using 15N metabolic labeling and quantitative proteomics in the ApcMin/+ mouse.

ABSTRACT: Current screening procedures for colorectal cancer are imperfect and highly invasive and result in increased mortality rates due to low compliance. The goal of the experiments reported herein is to identify potential blood-based biomarkers indicative of early stage intestinal cancers using the ApcMin/+ mouse model of intestinal cancer as an experimental system. Serum proteins from tumor-bearing ApcMin/+ mice were quantitatively compared to tumor-free Apc+/+ wild-type mice via in anima metabolic labeling with 14N/15N-labeled Spirulina algae and an LTQ Orbitrap mass spectrometer. Out of 1116 total serum proteins quantified, this study identified 40 that were differentially expressed and correlated with the increase in intestinal neoplasms. A subset of these differentially expressed proteins underwent a secondary quantitative screen using selected reaction monitoring-mass spectrometry with stable isotope-labeled peptides. Using both quantitative techniques, we identified MGAM and COL1A1 as downregulated and ITIH3 and F5 as upregulated in serum. All but COL1A1 were similarly differentially expressed in the mRNA of neoplastic colonic tissues of ApcMin/+ mice compared to normal wild-type tissue. These differentially expressed proteins identified in the ApcMin/+ mouse model have provided a set of candidate biomarkers for future validation screens in humans.

SUBMITTER: Ivancic MM

PROVIDER: S-EPMC3792563 | biostudies-literature | 2013 Sep

REPOSITORIES: biostudies-literature

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Candidate serum biomarkers for early intestinal cancer using 15N metabolic labeling and quantitative proteomics in the ApcMin/+ mouse.

Ivancic Melanie M MM Huttlin Edward L EL Chen Xiaodi X Pleiman Jennifer K JK Irving Amy A AA Hegeman Adrian D AD Dove William F WF Sussman Michael R MR

Journal of proteome research 20130808 9

Current screening procedures for colorectal cancer are imperfect and highly invasive and result in increased mortality rates due to low compliance. The goal of the experiments reported herein is to identify potential blood-based biomarkers indicative of early stage intestinal cancers using the ApcMin/+ mouse model of intestinal cancer as an experimental system. Serum proteins from tumor-bearing ApcMin/+ mice were quantitatively compared to tumor-free Apc+/+ wild-type mice via in anima metabolic ...[more]

PMID: 23924158

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Project description:Comparative proteomics is a powerful analytical method for learning about the responses of biological systems to changes in growth parameters. To make confident inferences about biological responses, proteomics approaches must incorporate appropriate statistical measures of quantitative data. In the present work we applied microarray-based normalization and statistical analysis (significance testing) methods to analyze quantitative proteomics data generated from the metabolic labeling of a marine bacterium (Sphingopyxis alaskensis). Quantitative data were generated for 1,172 proteins, representing 1,736 high confidence protein identifications (54% genome coverage). To test approaches for normalization, cells were grown at a single temperature, metabolically labeled with (14)N or (15)N, and combined in different ratios to give an artificially skewed data set. Inspection of ratio versus average (MA) plots determined that a fixed value median normalization was most suitable for the data. To determine an appropriate statistical method for assessing differential abundance, a -fold change approach, Student's t test, unmoderated t test, and empirical Bayes moderated t test were applied to proteomics data from cells grown at two temperatures. Inverse metabolic labeling was used with multiple technical and biological replicates, and proteomics was performed on cells that were combined based on equal optical density of cultures (providing skewed data) or on cell extracts that were combined to give equal amounts of protein (no skew). To account for arbitrarily complex experiment-specific parameters, a linear modeling approach was used to analyze the data using the limma package in R/Bioconductor. A high quality list of statistically significant differentially abundant proteins was obtained by using lowess normalization (after inspection of MA plots) and applying the empirical Bayes moderated t test. The approach also effectively controlled for the number of false discoveries and corrected for the multiple testing problem using the Storey-Tibshirani false discovery rate (Storey, J. D., and Tibshirani, R. (2003) Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. U.S.A. 100, 9440-9445). The approach we have developed is generally applicable to quantitative proteomics analyses of diverse biological systems.

Dataset Information

Candidate serum biomarkers for early intestinal cancer using 15N metabolic labeling and quantitative proteomics in the ApcMin/+ mouse.

Publications

Candidate serum biomarkers for early intestinal cancer using 15N metabolic labeling and quantitative proteomics in the ApcMin/+ mouse.

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