Standardization and harmonization of distributed multi-center proteotype analysis supporting precision medicine studies

Yue Xuan; Nicholas W. Bateman; Sebastien Gallien; Sandra Goetze; Yue Zhou; Pedro Navarro; Mo Hu; Niyati Parikh; Brian L. Hood; Kelly A. Conrads; Christina Loosse; Reta Birhanu Kitata; Sander R. Piersma; Davide Chiasserini; Hongwen Zhu; Guixue Hou; Muhammad Tahir; Andrew Macklin; Amanda Khoo; Xiuxuan Sun; Ben Crossett; Albert Sickmann; Yu Ju Chen; Connie R. Jimenez; Hu Zhou; Siqi Liu; Martin R. Larsen; Thomas Kislinger; Zhinan Chen; Benjamin L. Parker; Stuart J. Cordwell; Bernd Wollscheid; Thomas P. Conrads

doi:10.1038/s41467-020-18904-9

Standardization and harmonization of distributed multi-center proteotype analysis supporting precision medicine studies

Yue Xuan^*, Nicholas W. Bateman, Sebastien Gallien, Sandra Goetze, Yue Zhou, Pedro Navarro, Mo Hu, Niyati Parikh, Brian L. Hood, Kelly A. Conrads, Christina Loosse, Reta Birhanu Kitata, Sander R. Piersma, Davide Chiasserini, Hongwen Zhu, Guixue Hou, Muhammad Tahir, Andrew Macklin, Amanda Khoo, Xiuxuan SunBen Crossett, Albert Sickmann, Yu Ju Chen, Connie R. Jimenez, Hu Zhou, Siqi Liu, Martin R. Larsen, Thomas Kislinger, Zhinan Chen, Benjamin L. Parker, Stuart J. Cordwell, Bernd Wollscheid^*, Thomas P. Conrads^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

51 Scopus citations

Abstract

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.

Original language	English
Article number	5248
Journal	Nature Communications
Volume	11
Issue number	1
DOIs	https://doi.org/10.1038/s41467-020-18904-9
State	Published - 1 Dec 2020
Externally published	Yes

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Xuan, Y., Bateman, N. W., Gallien, S., Goetze, S., Zhou, Y., Navarro, P., Hu, M., Parikh, N., Hood, B. L., Conrads, K. A., Loosse, C., Kitata, R. B., Piersma, S. R., Chiasserini, D., Zhu, H., Hou, G., Tahir, M., Macklin, A., Khoo, A., ... Conrads, T. P. (2020). Standardization and harmonization of distributed multi-center proteotype analysis supporting precision medicine studies. Nature Communications, 11(1), Article 5248. https://doi.org/10.1038/s41467-020-18904-9

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title = "Standardization and harmonization of distributed multi-center proteotype analysis supporting precision medicine studies",

abstract = "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.",

author = "Yue Xuan and Bateman, {Nicholas W.} and Sebastien Gallien and Sandra Goetze and Yue Zhou and Pedro Navarro and Mo Hu and Niyati Parikh and Hood, {Brian L.} and Conrads, {Kelly A.} and Christina Loosse and Kitata, {Reta Birhanu} and Piersma, {Sander R.} and Davide Chiasserini and Hongwen Zhu and Guixue Hou and Muhammad Tahir and Andrew Macklin and Amanda Khoo and Xiuxuan Sun and Ben Crossett and Albert Sickmann and Chen, {Yu Ju} and Jimenez, {Connie R.} and Hu Zhou and Siqi Liu and Larsen, {Martin R.} and Thomas Kislinger and Zhinan Chen and Parker, {Benjamin L.} and Cordwell, {Stuart J.} and Bernd Wollscheid and Conrads, {Thomas P.}",

note = "Publisher Copyright: {\textcopyright} 2020, The Author(s).",

year = "2020",

month = dec,

day = "1",

doi = "10.1038/s41467-020-18904-9",

language = "English",

volume = "11",

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Xuan, Y, Bateman, NW, Gallien, S, Goetze, S, Zhou, Y, Navarro, P, Hu, M, Parikh, N, Hood, BL, Conrads, KA, Loosse, C, Kitata, RB, Piersma, SR, Chiasserini, D, Zhu, H, Hou, G, Tahir, M, Macklin, A, Khoo, A, Sun, X, Crossett, B, Sickmann, A, Chen, YJ, Jimenez, CR, Zhou, H, Liu, S, Larsen, MR, Kislinger, T, Chen, Z, Parker, BL, Cordwell, SJ, Wollscheid, B & Conrads, TP 2020, 'Standardization and harmonization of distributed multi-center proteotype analysis supporting precision medicine studies', Nature Communications, vol. 11, no. 1, 5248. https://doi.org/10.1038/s41467-020-18904-9

TY - JOUR

T1 - Standardization and harmonization of distributed multi-center proteotype analysis supporting precision medicine studies

AU - Xuan, Yue

AU - Bateman, Nicholas W.

AU - Gallien, Sebastien

AU - Goetze, Sandra

AU - Zhou, Yue

AU - Navarro, Pedro

AU - Hu, Mo

AU - Parikh, Niyati

AU - Hood, Brian L.

AU - Conrads, Kelly A.

AU - Loosse, Christina

AU - Kitata, Reta Birhanu

AU - Piersma, Sander R.

AU - Chiasserini, Davide

AU - Zhu, Hongwen

AU - Hou, Guixue

AU - Tahir, Muhammad

AU - Macklin, Andrew

AU - Khoo, Amanda

AU - Sun, Xiuxuan

AU - Crossett, Ben

AU - Sickmann, Albert

AU - Chen, Yu Ju

AU - Jimenez, Connie R.

AU - Zhou, Hu

AU - Liu, Siqi

AU - Larsen, Martin R.

AU - Kislinger, Thomas

AU - Chen, Zhinan

AU - Parker, Benjamin L.

AU - Cordwell, Stuart J.

AU - Wollscheid, Bernd

AU - Conrads, Thomas P.

PY - 2020/12/1

Y1 - 2020/12/1

N2 - 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.

AB - 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.

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U2 - 10.1038/s41467-020-18904-9

DO - 10.1038/s41467-020-18904-9

M3 - Article

C2 - 33067419

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SN - 2041-1723

VL - 11

JO - Nature Communications

JF - Nature Communications

IS - 1

M1 - 5248

ER -

Standardization and harmonization of distributed multi-center proteotype analysis supporting precision medicine studies

Abstract

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