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.
Nature communications. 2023 Apr 08*** epublish ***
Anouk C de Jong, Alexandra Danyi, Job van Riet, Ronald de Wit, Martin Sjöström, Felix Feng, Jeroen de Ridder, Martijn P Lolkema
Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, the Netherlands., Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, the Netherlands., Department of Radiation Oncology, University of California, San Francisco, CA, USA., Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, the Netherlands. .