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EMBO Mol Med. 2015 Sep;7(9):1153-65. doi: 10.15252/emmm.201404874.

Non-invasive prognostic protein biomarker signatures associated with colorectal cancer.

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Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacký University, Olomouc, Czech Republic.
Department of Statistics, Purdue University, West Lafayette, IN, USA.
Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.
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
Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland Faculty of Science, University of Zurich, Zurich, Switzerland


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.


colorectal cancer; prognostic protein biomarker; targeted proteomics

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