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

Anouk C. Jong, Alexandra Danyi, Job Riet, Ronald Wit, Martin Sjöström, Felix Feng, Jeroen Ridder and Martijn P. Lolkema ()
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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

Nature Communications, 2023, vol. 14, issue 1, 1-19

Abstract: 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

Date: 2023
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DOI: 10.1038/s41467-023-37647-x

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