? 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. ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA126219-03
Application #
7484093
Study Section
Special Emphasis Panel (ZCA1-SRRB-9 (O1))
Program Officer
Rodriguez, Henry
Project Start
2006-09-27
Project End
2009-06-30
Budget Start
2008-09-01
Budget End
2009-06-30
Support Year
3
Fiscal Year
2008
Total Cost
$92,368
Indirect Cost
Name
Massachusetts Institute of Technology
Department
Type
Organized Research Units
DUNS #
001425594
City
Cambridge
State
MA
Country
United States
Zip Code
02139
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Fruhwirth, Rudolf; Mani, D R; Pyne, Saumyadipta (2011) Clustering with position-specific constraints on variance: applying redescending M-estimators to label-free LC-MS data analysis. BMC Bioinformatics 12:358
Addona, Terri A; Shi, Xu; Keshishian, Hasmik et al. (2011) A pipeline that integrates the discovery and verification of plasma protein biomarkers reveals candidate markers for cardiovascular disease. Nat Biotechnol 29:635-43
Abbatiello, Susan E; Mani, D R; Keshishian, Hasmik et al. (2010) Automated detection of inaccurate and imprecise transitions in peptide quantification by multiple reaction monitoring mass spectrometry. Clin Chem 56:291-305
Fusaro, Vincent A; Mani, D R; Mesirov, Jill P et al. (2009) Prediction of high-responding peptides for targeted protein assays by mass spectrometry. Nat Biotechnol 27:190-8
Jaffe, Jacob D; Mani, D R; Leptos, Kyriacos C et al. (2006) PEPPeR, a platform for experimental proteomic pattern recognition. Mol Cell Proteomics 5:1927-41