Nature Communications (May 2024)
Proteomic analysis of the urothelial cancer landscape
- Franz F. Dressler,
- Falk Diedrichs,
- Deema Sabtan,
- Sofie Hinrichs,
- Christoph Krisp,
- Timo Gemoll,
- Martin Hennig,
- Paulina Mackedanz,
- Mareile Schlotfeldt,
- Hannah Voß,
- Anne Offermann,
- Jutta Kirfel,
- Marie C. Roesch,
- Julian P. Struck,
- Mario W. Kramer,
- Axel S. Merseburger,
- Christian Gratzke,
- Dominik S. Schoeb,
- Arkadiusz Miernik,
- Hartmut Schlüter,
- Ulrich Wetterauer,
- Roman Zubarev,
- Sven Perner,
- Philipp Wolf,
- Ákos Végvári
Affiliations
- Franz F. Dressler
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health
- Falk Diedrichs
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health
- Deema Sabtan
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health
- Sofie Hinrichs
- Institute of Pathology, University Medical Center Schleswig-Holstein
- Christoph Krisp
- Section Mass Spectrometry and Proteomics, Campus Forschung N27 00.008, University Medical Center Hamburg-Eppendorf
- Timo Gemoll
- Section for Translational Surgical Oncology and Biobanking, Department of Surgery, University Medical Center Schleswig-Holstein
- Martin Hennig
- Department of Urology, University Hospital Schleswig-Holstein, Campus Lübeck
- Paulina Mackedanz
- Institute of Pathology, University Medical Center Schleswig-Holstein
- Mareile Schlotfeldt
- Institute of Pathology, University Medical Center Schleswig-Holstein
- Hannah Voß
- Section Mass Spectrometry and Proteomics, Campus Forschung N27 00.008, University Medical Center Hamburg-Eppendorf
- Anne Offermann
- Institute of Pathology, University Medical Center Schleswig-Holstein
- Jutta Kirfel
- Institute of Pathology, University Medical Center Schleswig-Holstein
- Marie C. Roesch
- Department of Urology, University Hospital Schleswig-Holstein, Campus Lübeck
- Julian P. Struck
- Department of Urology, University Hospital Schleswig-Holstein, Campus Lübeck
- Mario W. Kramer
- Department of Urology, University Hospital Schleswig-Holstein, Campus Lübeck
- Axel S. Merseburger
- Department of Urology, University Hospital Schleswig-Holstein, Campus Lübeck
- Christian Gratzke
- Department of Urology, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg
- Dominik S. Schoeb
- Department of Urology, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg
- Arkadiusz Miernik
- Department of Urology, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg
- Hartmut Schlüter
- Section Mass Spectrometry and Proteomics, Campus Forschung N27 00.008, University Medical Center Hamburg-Eppendorf
- Ulrich Wetterauer
- Department of Urology, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg
- Roman Zubarev
- Division of Chemistry I, Department of Medical Biochemistry and Biophysics, Karolinska Institutet
- Sven Perner
- Institute of Pathology, University Medical Center Schleswig-Holstein
- Philipp Wolf
- Department of Urology, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg
- Ákos Végvári
- Division of Chemistry I, Department of Medical Biochemistry and Biophysics, Karolinska Institutet
- DOI
- https://doi.org/10.1038/s41467-024-48096-5
- Journal volume & issue
-
Vol. 15,
no. 1
pp. 1 – 19
Abstract
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 ).
