Cancer genetics-guided discovery of serum biomarker signatures for diagnosis and prognosis of prostate cancer
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 conte...
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| Veröffentlicht in: | Proceedings of the National Academy of Sciences - PNAS 2011-02, Vol.108 (8), p.3342-3347 |
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| creator | Cima, Igor Schiess, Ralph Wild, Peter Kaelin, Martin Schüffler, Peter Lange, Vinzenz Picotti, Paola Ossola, Reto Templeton, Arnoud Schubert, Olga Fuchs, Thomas Leippold, Thomas Wyler, Stephen Zehetner, Jens Jochum, Wolfram Buhmann, Joachim Cerny, Thomas Moch, Holger Gillessen, Silke Aebersold, Ruedi Krek, Wilhelm |
| description | 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. |
| doi_str_mv | 10.1073/pnas.1013699108 |
| format | Article |
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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.</description><identifier>ISSN: 0027-8424</identifier><identifier>EISSN: 1091-6490</identifier><identifier>DOI: 10.1073/pnas.1013699108</identifier><identifier>PMID: 21300890</identifier><language>eng</language><publisher>United States: National Academy of Sciences</publisher><subject>Animal models ; Animals ; Bioinformatics ; Biological markers ; Biological Sciences ; Biomarkers ; Biomarkers, Tumor - blood ; Cancer ; Computational Biology ; Computer applications ; Datasets ; Gene Silencing ; Genetics ; Glycoproteins ; Glycoproteins - blood ; Humans ; Integration ; Learning algorithms ; Male ; Medical diagnosis ; Medical prognosis ; Methods ; Mice ; Mutation ; Prognosis ; Prostate ; Prostate cancer ; Prostatic hyperplasia ; Prostatic Neoplasms - diagnosis ; Proteomics ; Proteomics - methods ; PTEN Phosphohydrolase - analysis ; PTEN Phosphohydrolase - genetics ; PTEN protein ; Regression analysis ; Signatures ; Tissues ; Tumor suppressor genes ; Tumors</subject><ispartof>Proceedings of the National Academy of Sciences - PNAS, 2011-02, Vol.108 (8), p.3342-3347</ispartof><rights>Copyright National Academy of Sciences Feb 22, 2011</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c586t-8826ed27cf89ee34c067a7b75edd702951d3b9fbed32b3ea77ca7cacadb473373</citedby><cites>FETCH-LOGICAL-c586t-8826ed27cf89ee34c067a7b75edd702951d3b9fbed32b3ea77ca7cacadb473373</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Uhttp://www.pnas.org/content/108/8.cover.gif</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/41060929$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/41060929$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>230,315,729,782,786,805,887,27933,27934,53800,53802,58026,58259</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/21300890$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Cima, Igor</creatorcontrib><creatorcontrib>Schiess, Ralph</creatorcontrib><creatorcontrib>Wild, Peter</creatorcontrib><creatorcontrib>Kaelin, Martin</creatorcontrib><creatorcontrib>Schüffler, Peter</creatorcontrib><creatorcontrib>Lange, Vinzenz</creatorcontrib><creatorcontrib>Picotti, Paola</creatorcontrib><creatorcontrib>Ossola, Reto</creatorcontrib><creatorcontrib>Templeton, Arnoud</creatorcontrib><creatorcontrib>Schubert, Olga</creatorcontrib><creatorcontrib>Fuchs, Thomas</creatorcontrib><creatorcontrib>Leippold, Thomas</creatorcontrib><creatorcontrib>Wyler, Stephen</creatorcontrib><creatorcontrib>Zehetner, Jens</creatorcontrib><creatorcontrib>Jochum, Wolfram</creatorcontrib><creatorcontrib>Buhmann, Joachim</creatorcontrib><creatorcontrib>Cerny, Thomas</creatorcontrib><creatorcontrib>Moch, Holger</creatorcontrib><creatorcontrib>Gillessen, Silke</creatorcontrib><creatorcontrib>Aebersold, Ruedi</creatorcontrib><creatorcontrib>Krek, Wilhelm</creatorcontrib><title>Cancer genetics-guided discovery of serum biomarker signatures for diagnosis and prognosis of prostate cancer</title><title>Proceedings of the National Academy of Sciences - PNAS</title><addtitle>Proc Natl Acad Sci U S A</addtitle><description>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.</description><subject>Animal models</subject><subject>Animals</subject><subject>Bioinformatics</subject><subject>Biological markers</subject><subject>Biological Sciences</subject><subject>Biomarkers</subject><subject>Biomarkers, Tumor - blood</subject><subject>Cancer</subject><subject>Computational Biology</subject><subject>Computer applications</subject><subject>Datasets</subject><subject>Gene Silencing</subject><subject>Genetics</subject><subject>Glycoproteins</subject><subject>Glycoproteins - blood</subject><subject>Humans</subject><subject>Integration</subject><subject>Learning algorithms</subject><subject>Male</subject><subject>Medical diagnosis</subject><subject>Medical prognosis</subject><subject>Methods</subject><subject>Mice</subject><subject>Mutation</subject><subject>Prognosis</subject><subject>Prostate</subject><subject>Prostate cancer</subject><subject>Prostatic hyperplasia</subject><subject>Prostatic Neoplasms - diagnosis</subject><subject>Proteomics</subject><subject>Proteomics - methods</subject><subject>PTEN Phosphohydrolase - analysis</subject><subject>PTEN Phosphohydrolase - genetics</subject><subject>PTEN protein</subject><subject>Regression analysis</subject><subject>Signatures</subject><subject>Tissues</subject><subject>Tumor suppressor genes</subject><subject>Tumors</subject><issn>0027-8424</issn><issn>1091-6490</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkU1v1DAQhiMEokvhzAmIuHAKHX8kti-V0IovqRIH6Nly7EnIsokXO6nUf88su2wLFyRL9mieeWc8b1E8Z_CWgRIXu8llejHRGMNAPyhWDAyrGmngYbEC4KrSksuz4knOGwAwtYbHxRlnAkAbWBXj2k0eU9njhPPgc9UvQ8BQhiH7eIPptoxdmTEtY9kOcXTpB8F56Cc3Lwlz2cVErOunmIdcuimUuxSPEVVSkGc3Y-l_t3laPOrcNuOz431eXH94_239qbr68vHz-t1V5WvdzJXWvMHAle-0QRTSQ6OcalWNISjgpmZBtKZrMQjeCnRKeUfHu9BKJYQS58XlQXe3tCMGj9Oc3Nbu0kA_uLXRDfbvzDR8t328sQKkFHVNAm-OAin-XDDPdqSF4HbrJoxLtqbRrBG10P8ldV1z4OQHka__ITdxSRPtgSChQAohCbo4QJ42lxN2p6EZ2L3ldm-5vbOcKl7e_-uJ_-PxPWBfeSenrbbUkhPw4gBs8hzTiZAMGjDcUP7VId-5aF2fhmyvv3KaAJiRTU3EL1J4x3s</recordid><startdate>20110222</startdate><enddate>20110222</enddate><creator>Cima, Igor</creator><creator>Schiess, Ralph</creator><creator>Wild, Peter</creator><creator>Kaelin, Martin</creator><creator>Schüffler, Peter</creator><creator>Lange, Vinzenz</creator><creator>Picotti, Paola</creator><creator>Ossola, Reto</creator><creator>Templeton, Arnoud</creator><creator>Schubert, Olga</creator><creator>Fuchs, Thomas</creator><creator>Leippold, Thomas</creator><creator>Wyler, Stephen</creator><creator>Zehetner, Jens</creator><creator>Jochum, Wolfram</creator><creator>Buhmann, Joachim</creator><creator>Cerny, Thomas</creator><creator>Moch, Holger</creator><creator>Gillessen, Silke</creator><creator>Aebersold, Ruedi</creator><creator>Krek, Wilhelm</creator><general>National Academy of Sciences</general><general>National Acad Sciences</general><scope>FBQ</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QG</scope><scope>7QL</scope><scope>7QP</scope><scope>7QR</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TK</scope><scope>7TM</scope><scope>7TO</scope><scope>7U9</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>H94</scope><scope>M7N</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20110222</creationdate><title>Cancer genetics-guided discovery of serum biomarker signatures for diagnosis and prognosis of prostate cancer</title><author>Cima, Igor ; Schiess, Ralph ; Wild, Peter ; Kaelin, Martin ; Schüffler, Peter ; Lange, Vinzenz ; Picotti, Paola ; Ossola, Reto ; Templeton, Arnoud ; Schubert, Olga ; Fuchs, Thomas ; Leippold, Thomas ; Wyler, Stephen ; Zehetner, Jens ; Jochum, Wolfram ; Buhmann, Joachim ; Cerny, Thomas ; Moch, Holger ; Gillessen, Silke ; Aebersold, Ruedi ; Krek, Wilhelm</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c586t-8826ed27cf89ee34c067a7b75edd702951d3b9fbed32b3ea77ca7cacadb473373</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Animal models</topic><topic>Animals</topic><topic>Bioinformatics</topic><topic>Biological markers</topic><topic>Biological Sciences</topic><topic>Biomarkers</topic><topic>Biomarkers, Tumor - 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PNAS</jtitle><addtitle>Proc Natl Acad Sci U S A</addtitle><date>2011-02-22</date><risdate>2011</risdate><volume>108</volume><issue>8</issue><spage>3342</spage><epage>3347</epage><pages>3342-3347</pages><issn>0027-8424</issn><eissn>1091-6490</eissn><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. 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| subjects | Animal models Animals Bioinformatics Biological markers Biological Sciences Biomarkers Biomarkers, Tumor - blood Cancer Computational Biology Computer applications Datasets Gene Silencing Genetics Glycoproteins Glycoproteins - blood Humans Integration Learning algorithms Male Medical diagnosis Medical prognosis Methods Mice Mutation Prognosis Prostate Prostate cancer Prostatic hyperplasia Prostatic Neoplasms - diagnosis Proteomics Proteomics - methods PTEN Phosphohydrolase - analysis PTEN Phosphohydrolase - genetics PTEN protein Regression analysis Signatures Tissues Tumor suppressor genes Tumors |
| title | Cancer genetics-guided discovery of serum biomarker signatures for diagnosis and prognosis of prostate cancer |
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