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

Author

Listed:
  • 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
    King’s College London)

  • 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
    Karolinska Institutet)

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.

Suggested Citation

  • María Bueno Álvez & Fredrik Edfors & Kalle Feilitzen & Martin Zwahlen & Adil Mardinoglu & Per-Henrik Edqvist & Tobias Sjöblom & Emma Lundin & Natallia Rameika & Gunilla Enblad & Henrik Lindman & Marti, 2023. "Next generation pan-cancer blood proteome profiling using proximity extension assay," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-39765-y
    DOI: 10.1038/s41467-023-39765-y
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    References listed on IDEAS

    as
    1. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    2. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    3. Rehan Akbani & Patrick Kwok Shing Ng & Henrica M. J. Werner & Maria Shahmoradgoli & Fan Zhang & Zhenlin Ju & Wenbin Liu & Ji-Yeon Yang & Kosuke Yoshihara & Jun Li & Shiyun Ling & Elena G. Seviour & Pr, 2014. "A pan-cancer proteomic perspective on The Cancer Genome Atlas," Nature Communications, Nature, vol. 5(1), pages 1-15, September.
    Full references (including those not matched with items on IDEAS)

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