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Predicting response to enzalutamide and abiraterone in metastatic prostate cancer using whole-omics machine learning

Author

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  • Anouk C. Jong

    (Erasmus MC Cancer Institute)

  • Alexandra Danyi

    (University Medical Center Utrecht)

  • Job Riet

    (Erasmus MC Cancer Institute)

  • Ronald Wit

    (Erasmus MC Cancer Institute)

  • Martin Sjöström

    (University of California)

  • Felix Feng

    (University of California)

  • Jeroen Ridder

    (University Medical Center Utrecht)

  • Martijn P. Lolkema

    (Erasmus MC Cancer Institute)

Abstract

Response to androgen receptor signaling inhibitors (ARSI) varies widely in metastatic castration resistant prostate cancer (mCRPC). To improve treatment guidance, biomarkers are needed. We use whole-genomics (WGS; n = 155) with matching whole-transcriptomics (WTS; n = 113) from biopsies of ARSI-treated mCRPC patients for unbiased discovery of biomarkers and development of machine learning-based prediction models. Tumor mutational burden (q

Suggested Citation

  • Anouk C. Jong & Alexandra Danyi & Job Riet & Ronald Wit & Martin Sjöström & Felix Feng & Jeroen Ridder & Martijn P. Lolkema, 2023. "Predicting response to enzalutamide and abiraterone in metastatic prostate cancer using whole-omics machine learning," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37647-x
    DOI: 10.1038/s41467-023-37647-x
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    References listed on IDEAS

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    2. Peter Priestley & Jonathan Baber & Martijn P. Lolkema & Neeltje Steeghs & Ewart Bruijn & Charles Shale & Korneel Duyvesteyn & Susan Haidari & Arne Hoeck & Wendy Onstenk & Paul Roepman & Mircea Voda & , 2019. "Pan-cancer whole-genome analyses of metastatic solid tumours," Nature, Nature, vol. 575(7781), pages 210-216, November.
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