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

DOI
https://doi.org/10.1038/s41467-024-48096-5
Journal volume & issue
Vol. 15, no. 1
pp. 1 – 19

Abstract

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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 ).