AUTOMATED GLYCO-ANALYSIS OF CANCER RELATED PROTEINS


Year of Award:
2005
Award Type:
R21
Project Number:
CA116070
RFA Number:
RFA-CA-05-002
Technology Track:
Molecular & Cellular Analysis Technologies
PI/Project Leader:
PANNELL, LEWIS KENNETH
Other PI or Project Leader:
N/A
Institution:
UNIVERSITY OF SOUTH ALABAMA
The quote that 'aberrant glycosylation is the hallmark of cancer cells' is reflected in numerous reports in the literature documenting changes in glycosylation on specific membrane proteins in cancer cells relative to normal cells. These changes have been shown to be involved in the release of cancer cells into the extracellular matrix and in the formation of metastasis. Glycosylated proteins represent a huge, almost untapped source of biomarkers, considering the wealth of evidence documenting their significance in cancer. Unique glycoforms could be used for diagnostic purposes, to target drugs at cancer cells, and for the development of immunotherapy. Despite the evolution of new mass spectrometry based methods for protein analysis, few of these involve the determination of post-translational modifications, especially glycosylation. As routine methods (e.g., MS/MS based sequencing methods) yield little light on glycosylated peptides, this proteomics research facility has established a new approach to automatically identify glycan structures on pure proteins from commercial or recombinant sources. It involves the acquisition of molecular weight only spectra and the detection of the glycosylation patterns using accurately determined mass gaps between the various glycoforms. The presence of multiple glycoforms is used to enhance the analysis rather than to confuse it. The approach has been shown to be reliable and extremely fast (taking less than one second) at identifying and characterizing such sites, including in proteins with highly complex glycosylation patterns. The aim of this proposal is to prove its utility in cancer where changes in glycosylation changes are interlaced with the progression of the disease. It will concentrate on both the cell surface proteins and those secreted from cells. Data will be compared to previously published reports where available. The glycans on previously uncharacterized proteins will be established and validated against the best hand interpreted results. The long-term aim is to make glycoanalyses routine to all cancer investigators, and the software integral to the approach will be made publicly available on a www site. This will represent the first step, with the glyco-analysis of full proteomes being an ultimate objective.