Systematic searches for plasma proteins that are biological indicators, or biomarkers, for cancer are underway. The difficulties caused by the complexity of biological-fluid proteomes and tissue proteomes (which contribute proteins to plasma) and by the extensive heterogeneity among diseases, subjects and levels of sample procurement are gradually being overcome. This is being achieved through rigorous experimental design and in-depth quantitative studies. The expected outcome is the development of panels of biomarkers that will allow early detection of cancer and prediction of the probable response to therapy. Achieving these objectives requires high-quality specimens with well-matched controls, reagent resources, and an efficient process to confirm discoveries through independent validation studies.

BackgroundThe complexity and heterogeneity of the human plasma proteome have presented significant challenges in the identification of protein changes associated with tumor development. Refined genetically engineered mouse (GEM) models of human cancer have been shown to faithfully recapitulate the molecular, biological, and clinical features of human disease. Here, we sought to exploit the merits of a well-characterized GEM model of pancreatic cancer to determine whether proteomics technologies allow identification of protein changes associated with tumor development and whether such changes are relevant to human pancreatic cancer.Methods and FindingsPlasma was sampled from mice at early and advanced stages of tumor development and from matched controls. Using a proteomic approach based on extensive protein fractionation, we confidently identified 1,442 proteins that were distributed across seven orders of magnitude of abundance in plasma. Analysis of proteins chosen on the basis of increased levels in plasma from tumor-bearing mice and corroborating protein or RNA expression in tissue documented concordance in the blood from 30 newly diagnosed patients with pancreatic cancer relative to 30 control specimens. A panel of five proteins selected on the basis of their increased level at an early stage of tumor development in the mouse was tested in a blinded study in 26 humans from the CARET (Carotene and Retinol Efficacy Trial) cohort. The panel discriminated pancreatic cancer cases from matched controls in blood specimens obtained between 7 and 13 mo prior to the development of symptoms and clinical diagnosis of pancreatic cancer.ConclusionsOur findings indicate that GEM models of cancer, in combination with in-depth proteomic analysis, provide a useful strategy to identify candidate markers applicable to human cancer with potential utility for early detection.

In-depth analysis of the serum and plasma proteomes by mass spectrometry is challenged by the vast dynamic range of protein abundance and substantial complexity. There is merit in reducing complexity through fractionation to facilitate mass spectrometry analysis of low-abundance proteins. However, fractionation reduces throughput and has the potential of diluting individual proteins or inducing their loss. Here, we have investigated the contribution of extensive fractionation of intact proteins to depth of analysis. Pooled serum depleted of abundant proteins was fractionated by an orthogonal two-dimensional system consisting of anion-exchange and reversed-phase chromatography. The resulting protein fractions were aliquotted; one aliquot was analyzed by shotgun LC-MS/MS, and another was further resolved into protein bands in a third dimension using SDS-PAGE. Individual gel bands were excised and subjected to in situ digestion and mass spectrometry. We demonstrate that increased fractionation results in increased depth of analysis based on total number of proteins identified in serum and based on representation in individual fractions of specific proteins identified in gel bands following a third-dimension SDS gel analysis. An intact protein analysis system (IPAS) based on a two-dimensional plasma fractionation schema was implemented that resulted in identification of 1662 proteins with high confidence with representation of protein isoforms that differed in their chromatographic mobility. Further increase in depth of analysis was accomplished by repeat analysis of aliquots from the same set of two-dimensional fractions resulting in overall identification of 2254 proteins. We conclude that substantial depth of analysis of proteins from milliliter quantities of serum or plasma and detection of isoforms are achieved with depletion of abundant proteins followed by two-dimensional protein fractionation and MS analysis of individual fractions.

The open-source Computational Proteomics Analysis System (CPAS) contains an entire data analysis and management pipeline for Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS) proteomics, including experiment annotation, protein database searching and sequence management, and mining LC-MS/MS peptide and protein identifications. CPAS architecture and features, such as a general experiment annotation component, installation software, and data security management, make it useful for collaborative projects across geographical locations and for proteomics laboratories without substantial computational support.