Protein biomarkers have enormous potential in the diagnosis, prognosis and treatment of cancer. Realization of the high therapeutic and financial potential of cancer biomarkers demands new technologies for speedy and cost-effective production of high-quality biomarkers and facile adjustments for measuring newly-produced biomarkers. Liquid chromatography- stable isotope dilution-multiple reaction monitoring mass spectrometry (LC-SID-MRM MS) of signature peptides of candidate biomarkers is an emerging technology well suited to this field. This application will establish the feasibility of transforming this mass spectrometry method to break through the sample-throughput bottleneck. The new technology is termed Ultrathroughput Multiple Reaction Monitoring (UMRM) MS and is an integration of LC-SID-MRM MS and peptide derivatization.
Two specific aims will be pursued: (1) identification of suitable peptide derivatizations for UMRM measurements, and (2) validation of derivatizations for UMRM analysis of prostate-specific antigen in 108 serum samples in a single experiment. The fundamental novelty of the new technology rests on the novel transformation of LC-SID-MRM MS to produce unprecedented sample throughput. It has three unique advantages: the throughput advantage, the flexibility and immediate-impact advantage, and the economic advantage. The new technology can be implemented with inexpensive reagents and commercial mass spectrometers for focused quantitation of cancer biomarker candidates in many phases of a biomarker pipeline. The drastically increased sample throughput of the new technology will in particular impact the verification and validation of biomarker candidates, which require targeted quantitation of hundreds to thousands of patient-control samples. Thus, the UMRM technology will significantly accelerate new cancer biomarker generation. The new technology also has potential in advancing quantitative biology of cancer.

Public Health Relevance

This project examines the feasibility of transforming he multiplexing potential to the sample- throughput potential of the emerging peptide multiple reaction monitoring mass spectrometry for cancer biomarker development. The new technology, capable of one-experiment quantitation of large numbers of samples, will significantly impact the large-scale validation of cancer biomarkers which uses hundreds and thousands of patient-control samples.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Exploratory/Developmental Grants (R21)
Project #
1R21CA155536-01
Application #
8034965
Study Section
Special Emphasis Panel (ZCA1-SRLB-R (O1))
Program Officer
Kagan, Jacob
Project Start
2011-08-09
Project End
2013-07-31
Budget Start
2011-08-09
Budget End
2012-07-31
Support Year
1
Fiscal Year
2011
Total Cost
$184,459
Indirect Cost
Name
University of Connecticut
Department
Chemistry
Type
Schools of Arts and Sciences
DUNS #
614209054
City
Storrs-Mansfield
State
CT
Country
United States
Zip Code
06269
Li, Song; Diego-Limpin, Pamela A; Bajrami, Bekim et al. (2017) Scaling Proteome-Wide Reactions of Activity-Based Probes. Anal Chem 89:6295-6299
Farrokhi, Vahid; Bajrami, Bekim; Nemati, Reza et al. (2015) Development of structural marker peptides for cystic fibrosis transmembrane conductance regulator in cell plasma membrane by reversed-footprinting mass spectrometry. Anal Chem 87:8603-7
Castillo, Mary Joan; McShane, Adam J; Cai, Min et al. (2015) Nonisotopic reagents for a cost-effective increase in sample throughput of targeted quantitative proteomics. Anal Chem 87:9209-16
McShane, Adam J; Bajrami, Bekim; Ramos, Alex A et al. (2014) Targeted proteomic quantitation of the absolute expression and turnover of cystic fibrosis transmembrane conductance regulator in the apical plasma membrane. J Proteome Res 13:4676-85
McShane, Adam J; Shen, Yuanyuan; Castillo, Mary Joan et al. (2014) Peptide dimethylation: fragmentation control via distancing the dimethylamino group. J Am Soc Mass Spectrom 25:1694-704
Castillo, Mary Joan; Reynolds, Kristy J; Gomes, Alexander et al. (2014) Quantitative protein analysis using enzymatic [ยน?O]water labeling. Curr Protoc Protein Sci 76:23.4.1-9