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J Proteome Res. 2008 Apr;7(4):1529-41. doi: 10.1021/pr700836q. Epub 2008 Feb 27.

Evaluation of data-dependent versus targeted shotgun proteomic approaches for monitoring transcription factor expression in breast cancer.

Author information

1
Program in Proteomics and Bioinformatics, Banting and Best Department of Medical Research, Terrence Donnelly Centre for Cellular and Biomolecular Research (CCBR), and Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.

Abstract

In breast cancer, there is a significant degree of molecular diversity among tumors. Multiple perturbations in signal transduction pathways impinge on transcriptional networks that in turn dictate malignant transformation and metastatic progression. Detailed knowledge of the sequence-specific transcription factors that become activated or repressed within a tumor and comparison of their relative levels of expression in cancer versus normal tissue should therefore provide insight into disease mechanisms, improving patient stratification and facilitating personalized treatment. While high-throughput tandem mass spectrometry methods for global proteome profiling have been developed, existing approaches have limited sensitivity and are often unable to detect low-abundance transcription factors in a complex biological specimen like a biopsy or tumor cell extract. To this end, we have undertaken a systematic comparative evaluation of three MS/MS methods for the ability to detect reference transcription factors spiked in known amounts into a cell-free breast cancer nuclear extract: Data-Dependent Acquisition (DDA), wherein precursor ion intensity dictates selection for fragmentation; Targeted Peptide Monitoring (TPM), a directed approach using successive isolation and fragmentation of predefined m/ z ratios; and Multiple Reaction Monitoring (MRM), in which specific precursor ion to product ion transitions are selectively monitored. Through a series of controlled, parallel benchmarking experiments, we have determined the relative figures-of-merit of each approach, and have established that prior knowledge of signature proteotypic peptides markedly improves overall detection sensitivity, reliability, and quantification.

PMID:
18311902
DOI:
10.1021/pr700836q
[Indexed for MEDLINE]

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