Dataset Information

Proteomic Analysis of the Urothelial Cancer Landscape

ABSTRACT: Urothelial cancer is a challenging disease with a wide tumor-biological spec-trum. Most molecular classification attempts are transcriptome-based and re-late only indirectly to the therapeutically relevant protein level. We improve preanalytical preparation of clinical samples for proteome analyses and char-acterize a comprehensive cohort of 434 samples with 242 tumors and 192 paired normal mucosae. We evaluate sample-wise tumor specificity and rank biomarkers by target relevance. We identify five tumor clusters that add prog-nostic information independent from histopathological groups. In silico drug prediction suggests efficacy of several compounds hitherto not in clinical use. Both, in silico as well as in vitro data, indicate predictive value of the proteo-mic clusters for these drugs. Comparative validation on external transcriptom-ic data confirms prognostic relevance and contextualizes proteomic and tran-scriptomic classifications. A real-world diagnostic approach based on im-munohistochemistry further validates our proteomic data but underlines the necessity of omics scale molecular analyses for personalized oncology.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Solid Cancer Cell

SUBMITTER: Hannah Voß

LAB HEAD: Franz F. Dressler

PROVIDER: PXD041733 | Pride | 2024-06-16

REPOSITORIES: Pride

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Proteomic analysis of the urothelial cancer landscape.

Dressler Franz F FF Diedrichs Falk F Sabtan Deema D Hinrichs Sofie S Krisp Christoph C Gemoll Timo T Hennig Martin M Mackedanz Paulina P Schlotfeldt Mareile M Voß Hannah H Offermann Anne A Kirfel Jutta J Roesch Marie C MC Struck Julian P JP Kramer Mario W MW Merseburger Axel S AS Gratzke Christian C Schoeb Dominik S DS Miernik Arkadiusz A Schlüter Hartmut H Wetterauer Ulrich U Zubarev Roman R Perner Sven S Wolf Philipp P Végvári Ákos Á

Nature communications 20240527 1

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 bioma ...[more]

PMID: 38802361

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