Dynamic prostate cancer transcriptome analysis delineates the trajectory to disease progression

Marco Bolis (), Daniela Bossi, Arianna Vallerga, Valentina Ceserani, Manuela Cavalli, Daniela Impellizzieri, Laura Di Rito, Eugenio Zoni, Simone Mosole, Angela Rita Elia, Andrea Rinaldi, Ricardo Pereira Mestre, Eugenia D’Antonio, Matteo Ferrari, Flavio Stoffel, Fernando Jermini, Silke Gillessen, Lukas Bubendorf, Peter Schraml, Arianna Calcinotto, Eva Corey, Holger Moch, Martin Spahn, George Thalmann, Marianna Kruithof- de Julio, Mark A. Rubin and Jean-Philippe P. Theurillat ()
Additional contact information
Marco Bolis: USI
Daniela Bossi: USI
Arianna Vallerga: USI
Valentina Ceserani: USI
Manuela Cavalli: USI
Daniela Impellizzieri: USI
Laura Di Rito: Istituto di Richerche Farmacologiche ‘Mario Negri’ IRCCS
Eugenio Zoni: University of Bern
Simone Mosole: USI
Angela Rita Elia: USI
Andrea Rinaldi: USI
Ricardo Pereira Mestre: Oncology Institute of Southern Switzerland
Eugenia D’Antonio: Oncology Institute of Southern Switzerland
Matteo Ferrari: Ente Ospedaliero Cantonale
Flavio Stoffel: Ente Ospedaliero Cantonale
Fernando Jermini: Ente Ospedaliero Cantonale
Silke Gillessen: Oncology Institute of Southern Switzerland
Lukas Bubendorf: University Hospital Basel
Peter Schraml: University Hospital Zurich
Arianna Calcinotto: USI
Eva Corey: University of Washington
Holger Moch: University Hospital Zurich
Martin Spahn: Prostate Center Bern
George Thalmann: University of Bern
Marianna Kruithof- de Julio: University of Bern
Mark A. Rubin: University of Bern
Jean-Philippe P. Theurillat: USI

Nature Communications, 2021, vol. 12, issue 1, 1-15

Abstract: Abstract Comprehensive genomic studies have delineated key driver mutations linked to disease progression for most cancers. However, corresponding transcriptional changes remain largely elusive because of the bias associated with cross-study analysis. Here, we overcome these hurdles and generate a comprehensive prostate cancer transcriptome atlas that describes the roadmap to tumor progression in a qualitative and quantitative manner. Most cancers follow a uniform trajectory characterized by upregulation of polycomb-repressive-complex-2, G2-M checkpoints, and M2 macrophage polarization. Using patient-derived xenograft models, we functionally validate our observations and add single-cell resolution. Thereby, we show that tumor progression occurs through transcriptional adaption rather than a selection of pre-existing cancer cell clusters. Moreover, we determine at the single-cell level how inhibition of EZH2 - the top upregulated gene along the trajectory – reverts tumor progression and macrophage polarization. Finally, a user-friendly web-resource is provided enabling the investigation of dynamic transcriptional perturbations linked to disease progression.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26840-5

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DOI: 10.1038/s41467-021-26840-5

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