Next generation pan-cancer blood proteome profiling using proximity extension assay

María Bueno Álvez, Fredrik Edfors, Kalle Feilitzen, Martin Zwahlen, Adil Mardinoglu, Per-Henrik Edqvist, Tobias Sjöblom, Emma Lundin, Natallia Rameika, Gunilla Enblad, Henrik Lindman, Martin Höglund, Göran Hesselager, Karin Stålberg, Malin Enblad, Oscar E. Simonson, Michael Häggman, Tomas Axelsson, Mikael Åberg, Jessica Nordlund, Wen Zhong, Max Karlsson, Ulf Gyllensten, Fredrik Ponten, Linn Fagerberg and Mathias Uhlén ()
Additional contact information
María Bueno Álvez: KTH Royal Institute of Technology
Fredrik Edfors: KTH Royal Institute of Technology
Kalle Feilitzen: KTH Royal Institute of Technology
Martin Zwahlen: KTH Royal Institute of Technology
Adil Mardinoglu: KTH Royal Institute of Technology
Per-Henrik Edqvist: Uppsala University
Tobias Sjöblom: Uppsala University
Emma Lundin: Uppsala University
Natallia Rameika: Uppsala University
Gunilla Enblad: Uppsala University
Henrik Lindman: Uppsala University
Martin Höglund: Uppsala University
Göran Hesselager: Uppsala University
Karin Stålberg: Uppsala University
Malin Enblad: Uppsala University
Oscar E. Simonson: Uppsala University
Michael Häggman: Uppsala University
Tomas Axelsson: Uppsala University
Mikael Åberg: Uppsala University
Jessica Nordlund: Uppsala University
Wen Zhong: Linköping University
Max Karlsson: KTH Royal Institute of Technology
Ulf Gyllensten: Uppsala University
Fredrik Ponten: Uppsala University
Linn Fagerberg: KTH Royal Institute of Technology
Mathias Uhlén: KTH Royal Institute of Technology

Nature Communications, 2023, vol. 14, issue 1, 1-13

Abstract: Abstract A comprehensive characterization of blood proteome profiles in cancer patients can contribute to a better understanding of the disease etiology, resulting in earlier diagnosis, risk stratification and better monitoring of the different cancer subtypes. Here, we describe the use of next generation protein profiling to explore the proteome signature in blood across patients representing many of the major cancer types. Plasma profiles of 1463 proteins from more than 1400 cancer patients are measured in minute amounts of blood collected at the time of diagnosis and before treatment. An open access Disease Blood Atlas resource allows the exploration of the individual protein profiles in blood collected from the individual cancer patients. We also present studies in which classification models based on machine learning have been used for the identification of a set of proteins associated with each of the analyzed cancers. The implication for cancer precision medicine of next generation plasma profiling is discussed.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-39765-y

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DOI: 10.1038/s41467-023-39765-y

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