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 and Ákos Végvári
Additional contact information
Franz F. Dressler: and Berlin Institute of Health
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
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
Roman Zubarev: Karolinska Institutet
Sven Perner: University Medical Center Schleswig-Holstein
Philipp Wolf: University of Freiburg
Ákos Végvári: Karolinska Institutet

Nature Communications, 2024, vol. 15, issue 1, 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 ).

Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-024-48096-5 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-48096-5

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-024-48096-5

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().