Bladder cancer organoids as a functional system to model different disease stages and therapy response

Martina Minoli, Thomas Cantore, Daniel Hanhart, Mirjam Kiener, Tarcisio Fedrizzi, Federico La Manna, Sofia Karkampouna, Panagiotis Chouvardas, Vera Genitsch, Antonio Rodriguez-Calero, Eva Compérat, Irena Klima, Paola Gasperini, Bernhard Kiss, Roland Seiler, Francesca Demichelis, George N. Thalmann and Marianna Kruithof- de Julio ()
Additional contact information
Martina Minoli: University of Bern
Thomas Cantore: University of Trento
Daniel Hanhart: University of Bern
Mirjam Kiener: University of Bern
Tarcisio Fedrizzi: University of Trento
Federico La Manna: University of Bern
Sofia Karkampouna: University of Bern
Panagiotis Chouvardas: University of Bern
Vera Genitsch: University of Bern
Antonio Rodriguez-Calero: University of Bern
Eva Compérat: Medical University Vienna
Irena Klima: University of Bern
Paola Gasperini: University of Trento
Bernhard Kiss: University of Bern
Roland Seiler: Hospital Center Biel
Francesca Demichelis: University of Trento
George N. Thalmann: University of Bern
Marianna Kruithof- de Julio: University of Bern

Nature Communications, 2023, vol. 14, issue 1, 1-16

Abstract: Abstract Bladder Cancer (BLCa) inter-patient heterogeneity is the primary cause of treatment failure, suggesting that patients could benefit from a more personalized treatment approach. Patient-derived organoids (PDOs) have been successfully used as a functional model for predicting drug response in different cancers. In our study, we establish PDO cultures from different BLCa stages and grades. PDOs preserve the histological and molecular heterogeneity of the parental tumors, including their multiclonal genetic landscapes, and consistently share key genetic alterations, mirroring tumor evolution in longitudinal sampling. Our drug screening pipeline is implemented using PDOs, testing standard-of-care and FDA-approved compounds for other tumors. Integrative analysis of drug response profiles with matched PDO genomic analysis is used to determine enrichment thresholds for candidate markers of therapy response and resistance. Finally, by assessing the clinical history of longitudinally sampled cases, we can determine whether the disease clonal evolution matched with drug response.

Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-023-37696-2 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:14:y:2023:i:1:d:10.1038_s41467-023-37696-2

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

DOI: 10.1038/s41467-023-37696-2

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 ().