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Proteomic analysis of the urothelial cancer landscape

Author

Listed:
  • Franz F. Dressler

    (and Berlin Institute of Health
    Berlin Institute of Health at Charité - Universitätsmedizin Berlin
    University Medical Center Schleswig-Holstein)

  • Falk Diedrichs

    (and Berlin Institute of Health)

  • Deema Sabtan

    (and Berlin Institute of Health)

  • Sofie Hinrichs

    (University Medical Center Schleswig-Holstein)

  • Christoph Krisp

    (University Medical Center Hamburg-Eppendorf)

  • Timo Gemoll

    (University Medical Center Schleswig-Holstein)

  • Martin Hennig

    (University Hospital Schleswig-Holstein, Campus Lübeck)

  • Paulina Mackedanz

    (University Medical Center Schleswig-Holstein)

  • Mareile Schlotfeldt

    (University Medical Center Schleswig-Holstein)

  • Hannah Voß

    (University Medical Center Hamburg-Eppendorf)

  • Anne Offermann

    (University Medical Center Schleswig-Holstein)

  • Jutta Kirfel

    (University Medical Center Schleswig-Holstein)

  • Marie C. Roesch

    (University Hospital Schleswig-Holstein, Campus Lübeck)

  • Julian P. Struck

    (University Hospital Schleswig-Holstein, Campus Lübeck
    Brandenburg Medical School Theodor Fontane)

  • Mario W. Kramer

    (University Hospital Schleswig-Holstein, Campus Lübeck)

  • Axel S. Merseburger

    (University Hospital Schleswig-Holstein, Campus Lübeck)

  • Christian Gratzke

    (University of Freiburg)

  • Dominik S. Schoeb

    (University of Freiburg)

  • Arkadiusz Miernik

    (University of Freiburg)

  • Hartmut Schlüter

    (University Medical Center Hamburg-Eppendorf)

  • Ulrich Wetterauer

    (University of Freiburg
    Danube Private University)

  • Roman Zubarev

    (Karolinska Institutet
    The National Medical Research Center for Endocrinology
    I.M. Sechenov First Moscow State Medical University)

  • Sven Perner

    (University Medical Center Schleswig-Holstein
    Leibniz Lung Center
    Center for Precision Oncology)

  • Philipp Wolf

    (University of Freiburg)

  • Ákos Végvári

    (Karolinska Institutet)

Abstract

Urothelial bladder cancer (UC) has a wide tumor biological spectrum with challenging prognostic stratification and relevant therapy-associated morbidity. Most molecular classifications relate only indirectly to the therapeutically relevant protein level. We improve the pre-analytics of clinical samples for proteome analyses and characterize a cohort of 434 samples with 242 tumors and 192 paired normal mucosae covering the full range of UC. We evaluate sample-wise tumor specificity and rank biomarkers by target relevance. We identify robust proteomic subtypes with prognostic information independent from histopathological groups. In silico drug prediction suggests efficacy of several compounds hitherto not in clinical use. Both in silico and in vitro data indicate predictive value of the proteomic clusters for these drugs. We underline that proteomics is relevant for personalized oncology and provide abundance and tumor specificity data for a large part of the UC proteome ( www.cancerproteins.org ).

Suggested Citation

  • Franz F. Dressler & Falk Diedrichs & Deema Sabtan & Sofie Hinrichs & Christoph Krisp & Timo Gemoll & Martin Hennig & Paulina Mackedanz & Mareile Schlotfeldt & Hannah Voß & Anne Offermann & Jutta Kirfe, 2024. "Proteomic analysis of the urothelial cancer landscape," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-48096-5
    DOI: 10.1038/s41467-024-48096-5
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    References listed on IDEAS

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    1. Jordi Barretina & Giordano Caponigro & Nicolas Stransky & Kavitha Venkatesan & Adam A. Margolin & Sungjoon Kim & Christopher J.Wilson & Joseph Lehár & Gregory V. Kryukov & Dmitriy Sonkin & Anupama Red, 2012. "Addendum: The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity," Nature, Nature, vol. 492(7428), pages 290-290, December.
    2. Jordi Barretina & Giordano Caponigro & Nicolas Stransky & Kavitha Venkatesan & Adam A. Margolin & Sungjoon Kim & Christopher J. Wilson & Joseph Lehár & Gregory V. Kryukov & Dmitriy Sonkin & Anupama Re, 2012. "The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity," Nature, Nature, vol. 483(7391), pages 603-607, March.
    3. Michael Schubert & Bertram Klinger & Martina Klünemann & Anja Sieber & Florian Uhlitz & Sascha Sauer & Mathew J. Garnett & Nils Blüthgen & Julio Saez-Rodriguez, 2018. "Perturbation-response genes reveal signaling footprints in cancer gene expression," Nature Communications, Nature, vol. 9(1), pages 1-11, December.
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