Date Published: June 10, 2008
Publisher: Public Library of Science
Author(s): Vitor M Faca, Kenneth S Song, Hong Wang, Qing Zhang, Alexei L Krasnoselsky, Lisa F Newcomb, Ruben R Plentz, Sushma Gurumurthy, Mark S Redston, Sharon J Pitteri, Sandra R Pereira-Faca, Renee C Ireton, Hiroyuki Katayama, Veronika Glukhova, Douglas Phanstiel, Dean E Brenner, Michelle A Anderson, David Misek, Nathalie Scholler, Nicole D Urban, Matt J Barnett, Cim Edelstein, Gary E Goodman, Mark D Thornquist, Martin W McIntosh, Ronald A DePinho, Nabeel Bardeesy, Samir M Hanash, Steven Narod
Abstract: 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.
Partial Text: A major goal of the cancer biomarker field is the development of noninvasive tests that allow early cancer detection. Blood constituents, notably plasma proteins, reflect diverse physiologic or pathologic states. The ease with which this compartment can be sampled makes it a logical choice for screening applications to detect cancer at an early stage. However, the vast dynamic range of protein abundance in plasma and the likely occurrence of tumor-derived proteins in the lower range of protein abundance represent major challenges in the application of proteomic-based strategies for cancer biomarker identification [1,2]. Recent experience in comprehensive profiling of plasma proteins indicates that low-abundance proteins may be identified with high confidence following extensive plasma fractionation and with the use of high-resolution mass spectrometry [3,4].
Our findings here indicate that plasma proteomic analysis of GEM models of cancer provide a useful strategy to identify candidate markers applicable to human cancer with potential utility for early detection. This is very relevant, since there is a compelling need to develop blood-based markers that allow early cancer detection, classify tumors to direct therapy, and monitor disease progression, regression, or recurrence. Early detection is particularly relevant to pancreatic adenocarcinoma, which is the fourth leading cause of cancer death in the United States and with a 5-y survival rate of only 3%. Because of limitations in diagnostic methods and a lack of specific symptoms at an early stage, the disease is often diagnosed at late stages. In contrast, early stage disease is associated with prolonged survival following surgical resection of the tumor . Therefore, improvement in means to detect pancreatic cancer early would be expected to impact outcome.