Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Expert Opin Ther Targets. 2023 Jul-Dec;27(12):1195-1206. doi: 10.1080/14728222.2023.2293757. Epub 2023 Dec 30.
The extensive heterogeneity of prostate cancer (PCa) and multilayered complexity of progression to castration-resistant prostate cancer (CRPC) have contributed to the challenges of accurately monitoring advanced disease. Profiling of the tumor microenvironment with large-scale transcriptomic studies have identified gene signatures that predict biochemical recurrence, lymph node invasion, metastases, and development of therapeutic resistance through critical determinants driving CRPC.
This review encompasses understanding of the role of different molecular determinants of PCa progression to lethal disease including the phenotypic dynamic of cell plasticity, EMT-MET interconversion, and signaling-pathways driving PCa cells to advance and metastasize. The value of liquid biopsies encompassing circulating tumor cells and extracellular vesicles to detect disease progression and emergence of therapeutic resistance in patients progressing to lethal disease is discussed. Relevant literature was added from PubMed portal.
Despite progress in the tumor-targeted therapeutics and biomarker discovery, distant metastasis and therapeutic resistance remain the major cause of mortality in patients with advanced CRPC. No single signature can encompass the tremendous phenotypic and genomic heterogeneity of PCa, but rather multi-threaded omics-derived and phenotypic markers tailored and validated into a multimodal signature.
前列腺癌（PCa）具有广泛的异质性，向去势抵抗性前列腺癌（CRPC）进展的复杂性也呈多层次，这导致准确监测晚期疾病面临挑战。通过大规模转录组学研究对肿瘤微环境进行分析，确定了基因特征，这些特征通过推动 CRPC 的关键决定因素预测生化复发、淋巴结浸润、转移和治疗耐药性的发展。
本综述包括理解不同分子决定因素在 PCa 进展为致命疾病中的作用，包括细胞可塑性、EMT-MET 转换以及推动 PCa 细胞进展和转移的信号通路的表型动态。讨论了液体活检在检测进展为致命疾病的患者疾病进展和出现治疗耐药性方面的价值，其中包括循环肿瘤细胞和细胞外囊泡。
尽管在肿瘤靶向治疗和生物标志物发现方面取得了进展，但远处转移和治疗耐药性仍然是晚期 CRPC 患者死亡的主要原因。没有单一的特征可以涵盖 PCa 巨大的表型和基因组异质性，而是需要将多线程组学衍生和表型标志物进行定制和验证，形成一个多模态的特征。
