PROFILE SERUM PROTEINS BY GLYCOPEPTIDE CAPTURE AND LC-MS


Year of Award:
2005
Award Type:
R21
Project Number:
CA114852
RFA Number:
RFA-CA-05-002
Technology Track:
Molecular & Cellular Analysis Technologies
PI/Project Leader:
ZHANG, HUI
Other PI or Project Leader:
N/A
Institution:
INSTITUTE FOR SYSTEMS BIOLOGY
Cancers develop over a period of several years and they are characterized by molecular changes prior to invasion and metastasis. Development of a technology that enables screening of cancer from body fluids could permit cancer detection at early and treatable stages. It is expected that the composition of the serum proteome contains valuable information about the state of the human body in health and disease, and that this information can be extracted via quantitative proteomic measurements. Suitable proteomic techniques need to be sensitive, reproducible and robust, to detect potential biomarkers below the level of highly expressed proteins, to generate data sets that are comparable between experiments and laboratories, and have high throughput to support studies with sufficient statistical power. In this proposal, we will develop a method for high throughput quantitative analysis of serum proteins. It consists of the selective isolation of the peptides that are N-linked glycosylated in the intact protein using solid-phase extraction of glycopeptides (SPEG) on a robotic workstation, the analysis of these now de-glycosylated peptides by liquid chromatography mass spectrometry (LC-MS), and the comparative analysis of the resulting patterns. By focusing selectively on a few formerly N-linked glycopeptides per serum protein, the complexity of the analyte sample is significantly reduced, and the sensitivity, reproducibility, and throughput of serum proteome analysis are increased compared with the analysis of total tryptic peptides from unfractionated samples. We will explore the feasibility to identify cancer-specific serum proteins in the background of normal variation using a carcinogen-induced skin cancer mouse model. The specific aims are: 1) To develop chemistries and protocols for an automatic robotic system to isolate N-linked glycopeptides from serum in a high throughput and highly reproducible fashion; 2) To develop efficient and reproducible procedures for LC-MS analyses, and sequence identification of discriminatory peptides by tandem mass spectrometry; 3) To explore the feasibility of this method for the identification of distinctive serum peptides specific to cancer-bearing mice in the background of normal variations. If successful, the proposed research could subsequently be used for profiling human serum samples from cancer patients and normal individuals to identify the cancer-associated proteins in serum. The identified biomarkers will open a new paradigm for performing screening and detection of human cancer at early stage and for clinical therapeutic management.