Systems biology successes and areas for opportunity in prostate cancer.

IF 4.6
Endocrine-related cancer Pub Date : 2025-08-20 Print Date: 2025-08-01 DOI:10.1530/ERC-25-0067
Michael V Orman, Laura S Graham, Scott D Cramer, James C Costello
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Abstract

Systems biology approaches have been applied to prostate cancer to model how individual cellular and molecular components interact to influence cancer development, progression, and treatment responses. The integration of multi-omic experimental data with computational models has provided insights into the molecular characteristics of prostate cancer and emerging treatment strategies that have the potential to improve patient outcomes. Here, we highlight recent advancements that have emerged from systems modeling in prostate cancer. These include descriptions of the molecular landscape of prostate cancer and how genomic alterations inform computational models of disease progression, how evolutionary processes give rise to mechanisms of therapeutic resistance, and the development of innovative treatment strategies such as adaptive therapy. We also highlight current challenges in prostate cancer that can be addressed through systems biology approaches. These include tumor heterogeneity, poor immunotherapy response, a paucity of experimental model systems, and the ongoing translation of computational models for clinical decision making. Leveraging systems biology approaches has the potential to lead to a better understanding of the disease and better patient outcomes in the treatment of prostate cancer.

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系统生物学的成功和前列腺癌的机会领域。
系统生物学方法已应用于前列腺癌,以模拟个体细胞和分子成分如何相互作用,影响癌症的发展,进展和治疗反应。多组学实验数据与计算模型的整合为前列腺癌的分子特征和新兴的治疗策略提供了见解,这些策略有可能改善患者的预后。在这里,我们重点介绍了前列腺癌系统建模的最新进展。这些研究包括前列腺癌的分子结构描述,基因组改变如何为疾病进展的计算模型提供信息,进化过程如何产生治疗耐药性机制,以及适应性治疗等创新治疗策略的发展。我们还强调了当前可以通过系统生物学方法解决的前列腺癌挑战。这些因素包括肿瘤异质性、免疫治疗反应差、实验模型系统的缺乏以及用于临床决策的计算模型的持续转化。利用系统生物学方法有可能导致更好地了解疾病和更好的治疗前列腺癌患者的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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