The current management of colorectal cancer (CRC) would greatly benefit from non-invasive prognostic biomarkers indicative of clinicopathological tumor characteristics. Here, we employed targeted proteomic profiling of 80 glycoprotein biomarker candidates across plasma samples of a well-annotated patient cohort with comprehensive CRC characteristics. Clinical data included 8-year overall survival, tumor staging, histological grading, regional localization, and molecular tumor characteristics. The acquired quantitative proteomic dataset was subjected to the development of biomarker signatures predicting prognostic clinical endpoints. Protein candidates were selected into the signatures based on significance testing and a stepwise protein selection, each within 10-fold cross-validation. A six-protein biomarker signature of patient outcome could predict survival beyond clinical stage and was able to stratify patients into groups of better and worse prognosis. We further evaluated the performance of the signature on the mRNA level and assessed its prognostic value in the context of previously published transcriptional signatures. Additional signatures predicting regional tumor localization and disease dissemination were also identified. The integration of rich clinical data, quantitative proteomic technologies, and tailored computational modeling facilitated the characterization of these signatures in patient circulation. These findings highlight the value of a simultaneous assessment of important prognostic disease characteristics within a single measurement.

Department of Biology Institute of Molecular Systems Biology ETH Zurich Zurich Switzerland

Department of Biology Institute of Molecular Systems Biology ETH Zurich Zurich Switzerland Faculty of Science University of Zurich Zurich Switzerland

Department of Statistics Purdue University West Lafayette IN USA

Department of Statistics Purdue University West Lafayette IN USA Department of Computer Science Purdue University West Lafayette IN USA College of Science and College of Computer and Information Science Northeastern University Boston MA USA

Division of Clinical Epidemiology and Aging Research German Cancer Research Center Heidelberg Germany

Institute of Molecular and Translational Medicine Faculty of Medicine and Dentistry Palacký University Olomouc Czech Republic

EMBO Mol Med. 7:1166. PubMed

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Prediction of colorectal cancer diagnosis based on circulating plasma proteins