THE CLINICAL PROTEOMICS OF CANCER

Clinical Proteomics;Cancer Biomarkers;Quantitative Proteomics;Selected Reaction Monitoring;SRM;Early Diagnosis;Clinical Investigation

Wang, QingKinzler, Kenneth W. ;

Johns Hopkins University

关键词: Clinical Proteomics;    Cancer Biomarkers;    Quantitative Proteomics;    Selected Reaction Monitoring;    SRM;    Early Diagnosis;    Clinical Investigation;   

Others  :  https://jscholarship.library.jhu.edu/bitstream/handle/1774.2/59387/Table%204-1.%20Patient%20Sample%20Clinical%20Information.xlsx?sequence=6&isAllowed=y

瑞士|英语

来源: JOHNS HOPKINS DSpace Repository

【 摘 要 】

Cancer is the second leading cause of death in the United States, but effective screening for early detection could greatly reduce the mortality of this disease as the majority of early stage disease is curable. Although circulating cancer-specific antigens have been utilized as clinical biomarkers for monitoring disease for many decades, the currently available immunoassay based diagnostic tools have had very limited success in screening. In recent years, advancing techniques in proteomics and mass spectrometry have greatly accelerated the discovery of cancer biomarkers.Mutant proteins are the ultimate specific cancer biomarkers. In an effort to identify such cancer specific biomarkers, we developed a Selected Reaction Monitoring (SRM)-based mass spectrometry technology that quantified the levels of the WT and mutant proteins in clinically-relevant tissue samples. The technique is sensitive, allowing the detection of as little as 10 fmole, and the use of internal controls and the monitoring of multiple product ions ensure exquisite specificity. Furthermore, it does not require the development of antibodies that are mutant-specific, which can be a difficult task, especially when the target protein can be mutated at many positions. To systematically search for quantitative protein biomarkers of cancer we developed a novel iTRAQ labeling quantitative proteomics approach.More than 1200 proteins in cancer patient plasma were identified and quantified, leading to the discovery of a 318 peptide panel corresponding to 117 proteins that are significantly overexpressed in pancreatic cancer, colorectal cancer and ovarian cancer. An SRM-based technology was established to reproducibly detect and quantify the panel of the 318 peptides in cancer plasma samples. The relative abundance of the peptides in the panel was quantified using SRM in 26 healthy individuals and 44 cancer patients. A simple ranking based classifier was developed to perform the diagnosis of pancreatic cancer using this panel, and it proved to have 88% accuracy even when zero false positive rate was enforced, which is far better than the performance of CA19-9, a widely used clinical biomarker for pancreatic cancer. The above approaches either individually or in combination should greatly aid in the search and development of protein based cancer biomarkers.