Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, the Netherlands.
Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, the Netherlands.
Nat Commun. 2023 Apr 8;14(1):1968. doi: 10.1038/s41467-023-37647-x.
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.
雄激素受体信号抑制剂（ARSI）在转移性去势抵抗性前列腺癌（mCRPC）中的反应差异很大。为了改善治疗指导，需要生物标志物。我们使用全基因组学（WGS；n=155）和匹配的全转录组学（WTS；n=113）来自接受 ARSI 治疗的 mCRPC 患者的活检，用于无偏发现生物标志物和开发基于机器学习的预测模型。肿瘤突变负担（q<0.001）、结构变异（q<0.05）、串联重复（q<0.05）和缺失（q<0.05）在反应差的患者中富集，同时伴有独特的转录组表达谱。在我们的内部和外部 mCRPC 队列中，使用 ARSI 验证各种预测治疗持续时间的分类模型，揭示了两个表现最佳的模型，基于将先前的治疗信息与四个联合富集的基因组标记或整体转录组谱相结合。总之，结合基因组、转录组和临床数据的预测模型可以预测 mCRPC 患者对 ARSI 的反应，并且通过进一步优化和前瞻性验证，可以改善治疗指导。
