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Use of MS-GUIDE for identification of protein biomarkers for risk stratification of patients with prostate cancer

BACKGROUND: Non-invasive liquid biopsies could complement current pathological nomograms for risk stratification of prostate cancer patients. Development and testing of potential liquid biopsy markers is time, resource, and cost-intensive. For most protein targets, no antibodies or ELISAs for effici...

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Autores principales: Goetze, Sandra, Schüffler, Peter, Athanasiou, Alcibiade, Koetemann, Anika, Poyet, Cedric, Fankhauser, Christian Daniel, Wild, Peter J., Schiess, Ralph, Wollscheid, Bernd
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044739/
https://www.ncbi.nlm.nih.gov/pubmed/35477343
http://dx.doi.org/10.1186/s12014-022-09349-x
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author Goetze, Sandra
Schüffler, Peter
Athanasiou, Alcibiade
Koetemann, Anika
Poyet, Cedric
Fankhauser, Christian Daniel
Wild, Peter J.
Schiess, Ralph
Wollscheid, Bernd
author_facet Goetze, Sandra
Schüffler, Peter
Athanasiou, Alcibiade
Koetemann, Anika
Poyet, Cedric
Fankhauser, Christian Daniel
Wild, Peter J.
Schiess, Ralph
Wollscheid, Bernd
author_sort Goetze, Sandra
collection PubMed
description BACKGROUND: Non-invasive liquid biopsies could complement current pathological nomograms for risk stratification of prostate cancer patients. Development and testing of potential liquid biopsy markers is time, resource, and cost-intensive. For most protein targets, no antibodies or ELISAs for efficient clinical cohort pre-evaluation are currently available. We reasoned that mass spectrometry-based prescreening would enable the cost-effective and rational preselection of candidates for subsequent clinical-grade ELISA development. METHODS: Using Mass Spectrometry-GUided Immunoassay DEvelopment (MS-GUIDE), we screened 48 literature-derived biomarker candidates for their potential utility in risk stratification scoring of prostate cancer patients. Parallel reaction monitoring was used to evaluate these 48 potential protein markers in a highly multiplexed fashion in a medium-sized patient cohort of 78 patients with ground-truth prostatectomy and clinical follow-up information. Clinical-grade ELISAs were then developed for two of these candidate proteins and used for significance testing in a larger, independent patient cohort of 263 patients. RESULTS: Machine learning-based analysis of the parallel reaction monitoring data of the liquid biopsies prequalified fibronectin and vitronectin as candidate biomarkers. We evaluated their predictive value for prostate cancer biochemical recurrence scoring in an independent validation cohort of 263 prostate cancer patients using clinical-grade ELISAs. The results of our prostate cancer risk stratification test were statistically significantly 10% better than results of the current gold standards PSA alone, PSA plus prostatectomy biopsy Gleason score, or the National Comprehensive Cancer Network score in prediction of recurrence. CONCLUSION: Using MS-GUIDE we identified fibronectin and vitronectin as candidate biomarkers for prostate cancer risk stratification. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12014-022-09349-x.
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spelling pubmed-90447392022-04-28 Use of MS-GUIDE for identification of protein biomarkers for risk stratification of patients with prostate cancer Goetze, Sandra Schüffler, Peter Athanasiou, Alcibiade Koetemann, Anika Poyet, Cedric Fankhauser, Christian Daniel Wild, Peter J. Schiess, Ralph Wollscheid, Bernd Clin Proteomics Research BACKGROUND: Non-invasive liquid biopsies could complement current pathological nomograms for risk stratification of prostate cancer patients. Development and testing of potential liquid biopsy markers is time, resource, and cost-intensive. For most protein targets, no antibodies or ELISAs for efficient clinical cohort pre-evaluation are currently available. We reasoned that mass spectrometry-based prescreening would enable the cost-effective and rational preselection of candidates for subsequent clinical-grade ELISA development. METHODS: Using Mass Spectrometry-GUided Immunoassay DEvelopment (MS-GUIDE), we screened 48 literature-derived biomarker candidates for their potential utility in risk stratification scoring of prostate cancer patients. Parallel reaction monitoring was used to evaluate these 48 potential protein markers in a highly multiplexed fashion in a medium-sized patient cohort of 78 patients with ground-truth prostatectomy and clinical follow-up information. Clinical-grade ELISAs were then developed for two of these candidate proteins and used for significance testing in a larger, independent patient cohort of 263 patients. RESULTS: Machine learning-based analysis of the parallel reaction monitoring data of the liquid biopsies prequalified fibronectin and vitronectin as candidate biomarkers. We evaluated their predictive value for prostate cancer biochemical recurrence scoring in an independent validation cohort of 263 prostate cancer patients using clinical-grade ELISAs. The results of our prostate cancer risk stratification test were statistically significantly 10% better than results of the current gold standards PSA alone, PSA plus prostatectomy biopsy Gleason score, or the National Comprehensive Cancer Network score in prediction of recurrence. CONCLUSION: Using MS-GUIDE we identified fibronectin and vitronectin as candidate biomarkers for prostate cancer risk stratification. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12014-022-09349-x. BioMed Central 2022-04-27 /pmc/articles/PMC9044739/ /pubmed/35477343 http://dx.doi.org/10.1186/s12014-022-09349-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Goetze, Sandra
Schüffler, Peter
Athanasiou, Alcibiade
Koetemann, Anika
Poyet, Cedric
Fankhauser, Christian Daniel
Wild, Peter J.
Schiess, Ralph
Wollscheid, Bernd
Use of MS-GUIDE for identification of protein biomarkers for risk stratification of patients with prostate cancer
title Use of MS-GUIDE for identification of protein biomarkers for risk stratification of patients with prostate cancer
title_full Use of MS-GUIDE for identification of protein biomarkers for risk stratification of patients with prostate cancer
title_fullStr Use of MS-GUIDE for identification of protein biomarkers for risk stratification of patients with prostate cancer
title_full_unstemmed Use of MS-GUIDE for identification of protein biomarkers for risk stratification of patients with prostate cancer
title_short Use of MS-GUIDE for identification of protein biomarkers for risk stratification of patients with prostate cancer
title_sort use of ms-guide for identification of protein biomarkers for risk stratification of patients with prostate cancer
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044739/
https://www.ncbi.nlm.nih.gov/pubmed/35477343
http://dx.doi.org/10.1186/s12014-022-09349-x
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