The comprehensive and quantitative analysis of clinical proteomic samples is an outstanding challenge in biomedical research. New proteomic technologies for cancer detection are urgently needed and hold great potential for improving human health, as underscored by the improved survival rates of patients diagnosed in he early stages of cancer. To this end, we will develop computational tools aimed at increasing the effectiveness of cancer biomarker discovery from label-free MALDI-TOF (matrix-assisted laser- desorption/ionization time-of-flight) mass spectra for verification and identification. The computational algorithms and tools will result in more than an order of magnitude increase in both sensitivity and selectivity For molecular biomarker screening. Specifically, we propose: (i) to optimize signal processing resulting in at east a 4-fold enhancement of sensitivity (as measured by signal-to-noise), 2-fold gain in selectivity (resolution), and 10-fold increase in mass accuracy (Aim 1);(ii) to automate detection of ionization satellite ons followed by mass recalibration (Aim 2) resulting in tripling selectivity and mass accuracy;(iii) to deconvolve intensity distributions from satellite ions into parent protein peaks (Aim 3) resulting in tripling sensitivity for statistical detection and experimental identification of biomarkers from enhanced molecular maps (Aim 4). The increased efficiency of broad mass range screening will decrease the time and cost of the downstream identification and validation experiments. The successful completion of the studies described in this application will provide a basis for expanding these computational tools to other TOP MS platforms, and advance the endeavor of characterizing molecular basis for cancer toward better prognosis and treatment strategies.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
3R01CA126118-03S1
Application #
7923478
Study Section
Special Emphasis Panel (ZCA1-SRRB-9 (O1))
Program Officer
Rodriguez, Henry
Project Start
2006-09-29
Project End
2010-08-31
Budget Start
2009-09-01
Budget End
2010-08-31
Support Year
3
Fiscal Year
2009
Total Cost
$187,282
Indirect Cost
Name
College of William and Mary
Department
Miscellaneous
Type
Schools of Arts and Sciences
DUNS #
074762238
City
Williamsburg
State
VA
Country
United States
Zip Code
23187
Gatlin, Christine L; White, Krista Y; Tracy, Maureen B et al. (2011) Enhancement in MALDI-TOF MS analysis of the low molecular weight human serum proteome. J Mass Spectrom 46:85-9
Tracy, Maureen B; Cooke, William E; Gatlin, Christine L et al. (2011) Improved signal processing and normalization for biomarker protein detection in broad-mass-range TOF mass spectra from clinical samples. Proteomics Clin Appl 5:440-7
Kuschner, Karl W; Malyarenko, Dariya I; Cooke, William E et al. (2010) A Bayesian network approach to feature selection in mass spectrometry data. BMC Bioinformatics 11:177
Malyarenko, Dariya I; Cooke, William E; Bunai, Christine L et al. (2010) Automated assignment of ionization states in broad-mass matrix-assisted laser desorption/ionization spectra of protein mixtures. Rapid Commun Mass Spectrom 24:138-46
Tracy, Maureen B; Chen, Haijian; Weaver, Dennis M et al. (2008) Precision enhancement of MALDI-TOF MS using high resolution peak detection and label-free alignment. Proteomics 8:1530-8
Gatlin-Bunai, Christine L; Cazares, Lisa H; Cooke, William E et al. (2007) Optimization of MALDI-TOF MS detection for enhanced sensitivity of affinity-captured proteins spanning a 100 kDa mass range. J Proteome Res 6:4517-24