title: Mining the proteome: the application of tandem mass spectrometry to endocrine cancer research.
abstract: Tandem mass spectrometry (MS/MS) permits the detection of femtomolar quantities of protein from a wide variety of tissue sources. As endocrine cancers are frequently aetiologically complex, they are particularly amenable to mass spectrometry. The most widely studied aspect is the search for novel reliable biomarkers that would allow cancers to be diagnosed earlier and distinguished from benign tumours. MS/MS allows for the rapid analysis of blood and urine in addition to tumour tissue, and in this regard it has been applied on research involving thyroid, pancreatic, adrenal and ovarian cancers with varying degrees of success, as well as additional organ sites including breast and lung. The description of an individual cancer proteome potentially allows for personalized management of each patient, avoiding unnecessary therapies and targeting treatments to those which will have the most effect. The application of MS/MS to interaction proteomics is a field that has generated recent novel targets for chemotherapy. However, the technology involved in MS/MS has a number of drawbacks that at present prevent its widespread use in translational cancer research, including a poor reproducibility of results, in part due to the large amount of data generated and the inability to accurately differentiate true from false-positive results. Further, the current cost of running MS/MS restricts the number of times the experiments can be repeated, contributing to the lack of significance and concordance between studies. Despite these problems, however, MS/MS is emerging as a front line tool in endocrine cancer research and it is likely that this will continue over the next decade.
authors.affiliation: School of Clinical and Experimental Medicine, Institute for Biomedical Research and School of Cancer Sciences, University of Birmingham, Birmingham B15 2TT, UK.
authors.forename: Neil
authors.initials: N
authors.lastname: Sharma
authors.forename: Ashley
authors.initials: A
authors.lastname: Martin
authors.forename: Christopher J
authors.initials: CJ
authors.lastname: McCabe
mesh_terms: D035843:Biomedical Research / Q000379:methods*; D002277:Carcinoma / Q000378:metabolism*; D057225:Data Mining / Q000379:methods; D030562:Databases, Protein; D004701:Endocrine Gland Neoplasms / Q000378:metabolism*; D006801:Humans; D008954:Models, Biological; D009363:Neoplasm Proteins / Q000032:analysis / Q000378:metabolism; D020543:Proteome / Q000032:analysis*; D053719:Tandem Mass Spectrometry / Q000379:methods / Q000706:statistics & numerical data
chemical_list: D009363:Neoplasm Proteins; D020543:Proteome