? Protein or peptide biomarkers offer great promise in early detection, monitoring and targeted treatment of cancer. Two main strategies have been employed in proteomic biomarker discovery. Identity-based methods use high quality tandem mass spectrometry and identify potential biomarkers among sequenced peptides. Pattern-based, or label-free, approaches, on the other hand, look for discriminating peak patterns in mass spectra, without regard to their identity-enabling higher throughput analysis. In spite of the potential for biomarker discovery afforded by these methods, efficient discovery of robust biomarkers has remained a significant and unfulfilled challenge. Here we propose to develop a robust, high throughput analytical platform for biomarker discovery that combines identity and pattern obtained at high resolution and high mass accuracy. A key innovation of our approach is the use of sequence identified peptides to guide the alignment of unidentified m/z peaks (both obtained in the same LC-MS experiment) and to correct for chromatographic variation. The software will employ mathematically and statistically sound algorithms to match unidentified peaks across multiple samples, integrate peptide intensities into associated protein abundance, and use advanced pattern recognition tools for differential marker selection and quantitation. Importantly, we intend to adapt and extend the algorithm to derive quantitative data from samples that have undergone additional levels of fractionation such as strong-cation exchange at the peptide level. We anticipate that the methods we develop will provide at least an order of magnitude increase in the number of peaks detected as differentially regulated and subsequently sequence identified relative to existing identity- centric biomarker discovery approaches. Successful development and validation of the proposed platform has the potential to significantly accelerate biomarker discovery efforts for cancer as well as for other diseases, both at the Broad Institute and elsewhere. Furthermore, deployment of the biomarker discovery platform as a caBIG service will provide wide access to the platform, thereby maximizing impact in the research community. ? ? ?

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
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Special Emphasis Panel (ZCA1-SRRB-9 (O1))
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Rodriguez, Henry
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Massachusetts Institute of Technology
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Mani, D R; Abbatiello, Susan E; Carr, Steven A (2012) Statistical characterization of multiple-reaction monitoring mass spectrometry (MRM-MS) assays for quantitative proteomics. BMC Bioinformatics 13 Suppl 16:S9
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