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Proteome activity landscapes of tumor cell lines determine drug responses

Author

Listed:
  • Martin Frejno

    (Technical University of Munich)

  • Chen Meng

    (Technical University of Munich
    Technical University of Munich)

  • Benjamin Ruprecht

    (Technical University of Munich
    Center for Integrated Protein Science Munich (CIPSM)
    Chemical Biology, Merck Research Laboratories)

  • Thomas Oellerich

    (Hematology/Oncology, Goethe-University
    Lymphoid Malignancies Branch, National Cancer Institute, National Institutes of Health
    German Cancer Consortium (DKTK))

  • Sebastian Scheich

    (Hematology/Oncology, Goethe-University)

  • Karin Kleigrewe

    (Technical University of Munich)

  • Enken Drecoll

    (Technical University of Munich)

  • Patroklos Samaras

    (Technical University of Munich)

  • Alexander Hogrebe

    (Technical University of Munich)

  • Dominic Helm

    (Technical University of Munich)

  • Julia Mergner

    (Technical University of Munich)

  • Jana Zecha

    (Technical University of Munich)

  • Stephanie Heinzlmeir

    (Technical University of Munich)

  • Mathias Wilhelm

    (Technical University of Munich)

  • Julia Dorn

    (Technical University of Munich)

  • Hans-Michael Kvasnicka

    (Goethe University)

  • Hubert Serve

    (Hematology/Oncology, Goethe-University
    German Cancer Consortium (DKTK))

  • Wilko Weichert

    (German Cancer Consortium (DKTK)
    Technical University of Munich)

  • Bernhard Kuster

    (Technical University of Munich
    Technical University of Munich
    Center for Integrated Protein Science Munich (CIPSM)
    Lymphoid Malignancies Branch, National Cancer Institute, National Institutes of Health)

Abstract

Integrated analysis of genomes, transcriptomes, proteomes and drug responses of cancer cell lines (CCLs) is an emerging approach to uncover molecular mechanisms of drug action. We extend this paradigm to measuring proteome activity landscapes by acquiring and integrating quantitative data for 10,000 proteins and 55,000 phosphorylation sites (p-sites) from 125 CCLs. These data are used to contextualize proteins and p-sites and predict drug sensitivity. For example, we find that Progesterone Receptor (PGR) phosphorylation is associated with sensitivity to drugs modulating estrogen signaling such as Raloxifene. We also demonstrate that Adenylate kinase isoenzyme 1 (AK1) inactivates antimetabolites like Cytarabine. Consequently, high AK1 levels correlate with poor survival of Cytarabine-treated acute myeloid leukemia patients, qualifying AK1 as a patient stratification marker and possibly as a drug target. We provide an interactive web application termed ATLANTiC (http://atlantic.proteomics.wzw.tum.de), which enables the community to explore the thousands of novel functional associations generated by this work.

Suggested Citation

  • Martin Frejno & Chen Meng & Benjamin Ruprecht & Thomas Oellerich & Sebastian Scheich & Karin Kleigrewe & Enken Drecoll & Patroklos Samaras & Alexander Hogrebe & Dominic Helm & Julia Mergner & Jana Zec, 2020. "Proteome activity landscapes of tumor cell lines determine drug responses," Nature Communications, Nature, vol. 11(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-17336-9
    DOI: 10.1038/s41467-020-17336-9
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    Cited by:

    1. Merve Öztürk & Anja Freiwald & Jasmin Cartano & Ramona Schmitt & Mario Dejung & Katja Luck & Bassem Al-Sady & Sigurd Braun & Michal Levin & Falk Butter, 2022. "Proteome effects of genome-wide single gene perturbations," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    2. Mirjam Bentum & Bertram Klinger & Anja Sieber & Sheyda Naghiloo & Henrik Zauber & Nadine Lehmann & Mohamed Haji & Sylvia Niquet & Philipp Mertins & Nils Blüthgen & Matthias Selbach, 2025. "Spike-in enhanced phosphoproteomics uncovers synergistic signaling responses to MEK inhibition in colon cancer cells," Nature Communications, Nature, vol. 16(1), pages 1-16, December.
    3. Valentina Cordo’ & Mariska T. Meijer & Rico Hagelaar & Richard R. Goeij-de Haas & Vera M. Poort & Alex A. Henneman & Sander R. Piersma & Thang V. Pham & Koichi Oshima & Adolfo A. Ferrando & Guido J. R, 2022. "Phosphoproteomic profiling of T cell acute lymphoblastic leukemia reveals targetable kinases and combination treatment strategies," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    4. Florian P. Bayer & Manuel Gander & Bernhard Kuster & Matthew The, 2023. "CurveCurator: a recalibrated F-statistic to assess, classify, and explore significance of dose–response curves," Nature Communications, Nature, vol. 14(1), pages 1-11, December.

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