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Dynamic prostate cancer transcriptome analysis delineates the trajectory to disease progression

Author

Listed:
  • Marco Bolis

    (USI
    Istituto di Richerche Farmacologiche ‘Mario Negri’ IRCCS
    Swiss Institute of Bioinformatics)

  • Daniela Bossi

    (USI)

  • Arianna Vallerga

    (USI
    Swiss Institute of Bioinformatics)

  • 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
    University of Southern Switzerland (USI))

  • 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
    Bern University Hospital)

  • Marianna Kruithof-de Julio

    (University of Bern
    Bern University Hospital)

  • Mark A. Rubin

    (University of Bern
    University of Bern and Inselspital)

  • Jean-Philippe P. Theurillat

    (USI)

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.

Suggested Citation

  • Marco Bolis & Daniela Bossi & Arianna Vallerga & Valentina Ceserani & Manuela Cavalli & Daniela Impellizzieri & Laura Di Rito & Eugenio Zoni & Simone Mosole & Angela Rita Elia & Andrea Rinaldi & Ricar, 2021. "Dynamic prostate cancer transcriptome analysis delineates the trajectory to disease progression," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26840-5
    DOI: 10.1038/s41467-021-26840-5
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    1. Alessia Cacciatore & Dheeraj Shinde & Carola Musumeci & Giada Sandrini & Luca Guarrera & Domenico Albino & Gianluca Civenni & Elisa Storelli & Simone Mosole & Elisa Federici & Alessio Fusina & Marta I, 2024. "Epigenome-wide impact of MAT2A sustains the androgen-indifferent state and confers synthetic vulnerability in ERG fusion-positive prostate cancer," Nature Communications, Nature, vol. 15(1), pages 1-25, December.

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