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Proteomic analysis of the urothelial cancer landscape

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Proteomic analysis of the urothelial cancer landscape. / Dressler, Franz F; Diedrichs, Falk; Sabtan, Deema; Hinrichs, Sofie; Krisp, Christoph; Gemoll, Timo; Hennig, Martin; Mackedanz, Paulina; Schlotfeldt, Mareile; Voß, Hannah; Offermann, Anne; Kirfel, Jutta; Roesch, Marie C; Struck, Julian P; Kramer, Mario W; Merseburger, Axel S; Gratzke, Christian; Schoeb, Dominik S; Miernik, Arkadiusz; Schlüter, Hartmut; Wetterauer, Ulrich; Zubarev, Roman; Perner, Sven; Wolf, Philipp; Végvári, Ákos.

in: NAT COMMUN, Jahrgang 15, Nr. 1, 27.05.2024, S. 4513.

Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung

Harvard

Dressler, FF, Diedrichs, F, Sabtan, D, Hinrichs, S, Krisp, C, Gemoll, T, Hennig, M, Mackedanz, P, Schlotfeldt, M, Voß, H, Offermann, A, Kirfel, J, Roesch, MC, Struck, JP, Kramer, MW, Merseburger, AS, Gratzke, C, Schoeb, DS, Miernik, A, Schlüter, H, Wetterauer, U, Zubarev, R, Perner, S, Wolf, P & Végvári, Á 2024, 'Proteomic analysis of the urothelial cancer landscape', NAT COMMUN, Jg. 15, Nr. 1, S. 4513. https://doi.org/10.1038/s41467-024-48096-5

APA

Dressler, F. F., Diedrichs, F., Sabtan, D., Hinrichs, S., Krisp, C., Gemoll, T., Hennig, M., Mackedanz, P., Schlotfeldt, M., Voß, H., Offermann, A., Kirfel, J., Roesch, M. C., Struck, J. P., Kramer, M. W., Merseburger, A. S., Gratzke, C., Schoeb, D. S., Miernik, A., ... Végvári, Á. (2024). Proteomic analysis of the urothelial cancer landscape. NAT COMMUN, 15(1), 4513. https://doi.org/10.1038/s41467-024-48096-5

Vancouver

Dressler FF, Diedrichs F, Sabtan D, Hinrichs S, Krisp C, Gemoll T et al. Proteomic analysis of the urothelial cancer landscape. NAT COMMUN. 2024 Mai 27;15(1):4513. https://doi.org/10.1038/s41467-024-48096-5

Bibtex

@article{7b466154613a4ccb8e13807f1c15af7e,
title = "Proteomic analysis of the urothelial cancer landscape",
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 ).",
keywords = "Humans, Proteomics/methods, Urinary Bladder Neoplasms/metabolism, Biomarkers, Tumor/metabolism, Proteome/metabolism, Female, Male, Urothelium/pathology, Aged, Prognosis, Middle Aged, Aged, 80 and over",
author = "Dressler, {Franz F} and Falk Diedrichs and Deema Sabtan and Sofie Hinrichs and Christoph Krisp and Timo Gemoll and Martin Hennig and Paulina Mackedanz and Mareile Schlotfeldt and Hannah Vo{\ss} and Anne Offermann and Jutta Kirfel and Roesch, {Marie C} and Struck, {Julian P} and Kramer, {Mario W} and Merseburger, {Axel S} and Christian Gratzke and Schoeb, {Dominik S} and Arkadiusz Miernik and Hartmut Schl{\"u}ter and Ulrich Wetterauer and Roman Zubarev and Sven Perner and Philipp Wolf and {\'A}kos V{\'e}gv{\'a}ri",
note = "{\textcopyright} 2024. The Author(s).",
year = "2024",
month = may,
day = "27",
doi = "10.1038/s41467-024-48096-5",
language = "English",
volume = "15",
pages = "4513",
journal = "NAT COMMUN",
issn = "2041-1723",
publisher = "NATURE PUBLISHING GROUP",
number = "1",

}

RIS

TY - JOUR

T1 - Proteomic analysis of the urothelial cancer landscape

AU - Dressler, Franz F

AU - Diedrichs, Falk

AU - Sabtan, Deema

AU - Hinrichs, Sofie

AU - Krisp, Christoph

AU - Gemoll, Timo

AU - Hennig, Martin

AU - Mackedanz, Paulina

AU - Schlotfeldt, Mareile

AU - Voß, Hannah

AU - Offermann, Anne

AU - Kirfel, Jutta

AU - Roesch, Marie C

AU - Struck, Julian P

AU - Kramer, Mario W

AU - Merseburger, Axel S

AU - Gratzke, Christian

AU - Schoeb, Dominik S

AU - Miernik, Arkadiusz

AU - Schlüter, Hartmut

AU - Wetterauer, Ulrich

AU - Zubarev, Roman

AU - Perner, Sven

AU - Wolf, Philipp

AU - Végvári, Ákos

N1 - © 2024. The Author(s).

PY - 2024/5/27

Y1 - 2024/5/27

N2 - 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 ).

AB - 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 ).

KW - Humans

KW - Proteomics/methods

KW - Urinary Bladder Neoplasms/metabolism

KW - Biomarkers, Tumor/metabolism

KW - Proteome/metabolism

KW - Female

KW - Male

KW - Urothelium/pathology

KW - Aged

KW - Prognosis

KW - Middle Aged

KW - Aged, 80 and over

U2 - 10.1038/s41467-024-48096-5

DO - 10.1038/s41467-024-48096-5

M3 - SCORING: Journal article

C2 - 38802361

VL - 15

SP - 4513

JO - NAT COMMUN

JF - NAT COMMUN

SN - 2041-1723

IS - 1

ER -