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

Anouk C. de Jong, Alexandra Danyi, Job van Riet, Ronald de Wit, Martin Sjöström, Felix Feng, Jeroen de Ridder, Martijn P. Lolkema*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)
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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 < 0.001), structural variants (q < 0.05), tandem duplications (q < 0.05) and deletions (q < 0.05) are enriched in poor responders, coupled with distinct transcriptomic expression profiles. Validating various classification models predicting treatment duration with ARSI on our internal and external mCRPC cohort reveals two best-performing models, based on the combination of prior treatment information with either the four combined enriched genomic markers or with overall transcriptomic profiles. In conclusion, predictive models combining genomic, transcriptomic, and clinical data can predict response to ARSI in mCRPC patients and, with additional optimization and prospective validation, could improve treatment guidance.

Original languageEnglish
Article number1968
Pages (from-to)1968
Number of pages1
JournalNature Communications
Volume14
Issue number1
DOIs
Publication statusPublished - Dec 2023

Bibliographical note

Funding Information:
This research was financially supported with an unrestricted grant by Johnson & Johnson (ML; 212082PCR3014) and Astellas Pharma (ML; Lolkema/NL-72-RG-11). In addition, we would like to acknowledge the Erasmus MC Cancer Computational Biology Center (CCBC) and Hartwig Medical Foundation (HMF) for sharing their expertise and computational resources.

Funding Information:
This research was financially supported with an unrestricted grant by Johnson & Johnson (ML; 212082PCR3014) and Astellas Pharma (ML; Lolkema/NL-72-RG-11). In addition, we would like to acknowledge the Erasmus MC Cancer Computational Biology Center (CCBC) and Hartwig Medical Foundation (HMF) for sharing their expertise and computational resources.

Publisher Copyright:
© 2023, The Author(s).

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