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

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-tr...

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Veröffentlicht in:	Nature communications 2023-04, Vol.14 (1), p.1968-1968, Article 1968
Hauptverfasser:	de Jong, Anouk C., Danyi, Alexandra, van Riet, Job, de Wit, Ronald, Sjöström, Martin, Feng, Felix, de Ridder, Jeroen, Lolkema, Martijn P.
Format:	Artikel
Sprache:	eng
Schlagworte:	38 45 45/23 45/91 631/114/1305 631/1647/514/1949 631/208/514/1948 692/4028/67/589/466 692/4028/67/69 Androgen receptors Androgens Androstenes - therapeutic use Biomarkers Biomarkers, Tumor - genetics Biopsy Castration Genomics Humanities and Social Sciences Humans Inhibitors Learning algorithms Machine learning Male Metastases Metastasis multidisciplinary Nitriles - therapeutic use Optimization Patients Phenylthiohydantoin - therapeutic use Prediction models Prostate cancer Prostatic Neoplasms, Castration-Resistant - drug therapy Prostatic Neoplasms, Castration-Resistant - genetics Prostatic Neoplasms, Castration-Resistant - pathology Receptors Science Science (multidisciplinary) Signaling Transcriptomics Treatment Outcome
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Zusammenfassung:	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
ISSN:	2041-1723 2041-1723
DOI:	10.1038/s41467-023-37647-x