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Standardization and harmonization of distributed multi-center proteotype analysis supporting precision medicine studies
Cancer has no borders: Generation and analysis of molecular data across multiple centers worldwide is necessary to gain statistically significant clinical insights for the benefit of patients. Here we conceived and standardized a proteotype data generation and analysis workflow enabling distributed...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7568553/ https://www.ncbi.nlm.nih.gov/pubmed/33067419 http://dx.doi.org/10.1038/s41467-020-18904-9 |
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author | Xuan, Yue Bateman, Nicholas W. Gallien, Sebastien Goetze, Sandra Zhou, Yue Navarro, Pedro Hu, Mo Parikh, Niyati Hood, Brian L. Conrads, Kelly A. Loosse, Christina Kitata, Reta Birhanu Piersma, Sander R. Chiasserini, Davide Zhu, Hongwen Hou, Guixue Tahir, Muhammad Macklin, Andrew Khoo, Amanda Sun, Xiuxuan Crossett, Ben Sickmann, Albert Chen, Yu-Ju Jimenez, Connie R. Zhou, Hu Liu, Siqi Larsen, Martin R. Kislinger, Thomas Chen, Zhinan Parker, Benjamin L. Cordwell, Stuart J. Wollscheid, Bernd Conrads, Thomas P. |
author_facet | Xuan, Yue Bateman, Nicholas W. Gallien, Sebastien Goetze, Sandra Zhou, Yue Navarro, Pedro Hu, Mo Parikh, Niyati Hood, Brian L. Conrads, Kelly A. Loosse, Christina Kitata, Reta Birhanu Piersma, Sander R. Chiasserini, Davide Zhu, Hongwen Hou, Guixue Tahir, Muhammad Macklin, Andrew Khoo, Amanda Sun, Xiuxuan Crossett, Ben Sickmann, Albert Chen, Yu-Ju Jimenez, Connie R. Zhou, Hu Liu, Siqi Larsen, Martin R. Kislinger, Thomas Chen, Zhinan Parker, Benjamin L. Cordwell, Stuart J. Wollscheid, Bernd Conrads, Thomas P. |
author_sort | Xuan, Yue |
collection | PubMed |
description | Cancer has no borders: Generation and analysis of molecular data across multiple centers worldwide is necessary to gain statistically significant clinical insights for the benefit of patients. Here we conceived and standardized a proteotype data generation and analysis workflow enabling distributed data generation and evaluated the quantitative data generated across laboratories of the international Cancer Moonshot consortium. Using harmonized mass spectrometry (MS) instrument platforms and standardized data acquisition procedures, we demonstrate robust, sensitive, and reproducible data generation across eleven international sites on seven consecutive days in a 24/7 operation mode. The data presented from the high-resolution MS1-based quantitative data-independent acquisition (HRMS1-DIA) workflow shows that coordinated proteotype data acquisition is feasible from clinical specimens using such standardized strategies. This work paves the way for the distributed multi-omic digitization of large clinical specimen cohorts across multiple sites as a prerequisite for turning molecular precision medicine into reality. |
format | Online Article Text |
id | pubmed-7568553 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-75685532020-10-21 Standardization and harmonization of distributed multi-center proteotype analysis supporting precision medicine studies Xuan, Yue Bateman, Nicholas W. Gallien, Sebastien Goetze, Sandra Zhou, Yue Navarro, Pedro Hu, Mo Parikh, Niyati Hood, Brian L. Conrads, Kelly A. Loosse, Christina Kitata, Reta Birhanu Piersma, Sander R. Chiasserini, Davide Zhu, Hongwen Hou, Guixue Tahir, Muhammad Macklin, Andrew Khoo, Amanda Sun, Xiuxuan Crossett, Ben Sickmann, Albert Chen, Yu-Ju Jimenez, Connie R. Zhou, Hu Liu, Siqi Larsen, Martin R. Kislinger, Thomas Chen, Zhinan Parker, Benjamin L. Cordwell, Stuart J. Wollscheid, Bernd Conrads, Thomas P. Nat Commun Article Cancer has no borders: Generation and analysis of molecular data across multiple centers worldwide is necessary to gain statistically significant clinical insights for the benefit of patients. Here we conceived and standardized a proteotype data generation and analysis workflow enabling distributed data generation and evaluated the quantitative data generated across laboratories of the international Cancer Moonshot consortium. Using harmonized mass spectrometry (MS) instrument platforms and standardized data acquisition procedures, we demonstrate robust, sensitive, and reproducible data generation across eleven international sites on seven consecutive days in a 24/7 operation mode. The data presented from the high-resolution MS1-based quantitative data-independent acquisition (HRMS1-DIA) workflow shows that coordinated proteotype data acquisition is feasible from clinical specimens using such standardized strategies. This work paves the way for the distributed multi-omic digitization of large clinical specimen cohorts across multiple sites as a prerequisite for turning molecular precision medicine into reality. Nature Publishing Group UK 2020-10-16 /pmc/articles/PMC7568553/ /pubmed/33067419 http://dx.doi.org/10.1038/s41467-020-18904-9 Text en © The Author(s) 2020 Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Xuan, Yue Bateman, Nicholas W. Gallien, Sebastien Goetze, Sandra Zhou, Yue Navarro, Pedro Hu, Mo Parikh, Niyati Hood, Brian L. Conrads, Kelly A. Loosse, Christina Kitata, Reta Birhanu Piersma, Sander R. Chiasserini, Davide Zhu, Hongwen Hou, Guixue Tahir, Muhammad Macklin, Andrew Khoo, Amanda Sun, Xiuxuan Crossett, Ben Sickmann, Albert Chen, Yu-Ju Jimenez, Connie R. Zhou, Hu Liu, Siqi Larsen, Martin R. Kislinger, Thomas Chen, Zhinan Parker, Benjamin L. Cordwell, Stuart J. Wollscheid, Bernd Conrads, Thomas P. Standardization and harmonization of distributed multi-center proteotype analysis supporting precision medicine studies |
title | Standardization and harmonization of distributed multi-center proteotype analysis supporting precision medicine studies |
title_full | Standardization and harmonization of distributed multi-center proteotype analysis supporting precision medicine studies |
title_fullStr | Standardization and harmonization of distributed multi-center proteotype analysis supporting precision medicine studies |
title_full_unstemmed | Standardization and harmonization of distributed multi-center proteotype analysis supporting precision medicine studies |
title_short | Standardization and harmonization of distributed multi-center proteotype analysis supporting precision medicine studies |
title_sort | standardization and harmonization of distributed multi-center proteotype analysis supporting precision medicine studies |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7568553/ https://www.ncbi.nlm.nih.gov/pubmed/33067419 http://dx.doi.org/10.1038/s41467-020-18904-9 |
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