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Cancer genetics-guided discovery of serum biomarker signatures for diagnosis and prognosis of prostate cancer

Cima, Igor and Schiess, Ralph and Wild, Peter and Kaelin, Martin and Schüffler, Peter and Lange, Vinzenz and Picotti, Paola and Ossola, Reto and Templeton, Arnoud and Schubert, Olga and Fuchs, Thomas and Leippold, Thomas and Wyler, Stephen and Zehetner, Jens and Jochum, Wolfram and Buhmann, Joachim and Cerny, Thomas and Moch, Holger and Gillessen, Silke and Aebersold, Ruedi and Krek, Wilhelm. (2011) Cancer genetics-guided discovery of serum biomarker signatures for diagnosis and prognosis of prostate cancer. Proceedings of the National Academy of Sciences of the United States of America, Vol. 108, H. 8. pp. 3342-3347.

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Official URL: http://edoc.unibas.ch/dok/A6004755

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Abstract

A key barrier to the realization of personalized medicine for cancer is the identification of biomarkers. Here we describe a two-stage strategy for the discovery of serum biomarker signatures corresponding to specific cancer-causing mutations and its application to prostate cancer (PCa) in the context of the commonly occurring phosphatase and tensin homolog (PTEN) tumor-suppressor gene inactivation. In the first stage of our approach, we identified 775 N-linked glycoproteins from sera and prostate tissue of wild-type and Pten-null mice. Using label-free quantitative proteomics, we showed that Pten inactivation leads to measurable perturbations in the murine prostate and serum glycoproteome. Following bioinformatic prioritization, in a second stage we applied targeted proteomics to detect and quantify 39 human ortholog candidate biomarkers in the sera of PCa patients and control individuals. The resulting proteomic profiles were analyzed by machine learning to build predictive regression models for tissue PTEN status and diagnosis and grading of PCa. Our approach suggests a general path to rational cancer biomarker discovery and initial validation guided by cancer genetics and based on the integration of experimental mouse models, proteomics-based technologies, and computational modeling.
Faculties and Departments:03 Faculty of Medicine > Bereich Operative Fächer (Klinik) > Innere Organe > Urologie USB (Bachmann)
03 Faculty of Medicine > Departement Klinische Forschung > Bereich Operative Fächer (Klinik) > Innere Organe > Urologie USB (Bachmann)
UniBasel Contributors:Wyler, Stephen
Item Type:Article, refereed
Article Subtype:Research Article
Bibsysno:Link to catalogue
Publisher:National Academy of Sciences
ISSN:0027-8424
Note:Publication type according to Uni Basel Research Database: Journal article
Identification Number:
Last Modified:27 Mar 2014 13:13
Deposited On:27 Mar 2014 13:13

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