NCSA, University of Illinois at Urbana-Champaign, Champaign, Illinois.
TECNUN School of Engineering, University of Navarra, Navarra, Spain.
Cancer Res. 2023 Apr 14;83(8):1361-1380. doi: 10.1158/0008-5472.CAN-22-1910.
Survival rates of patients with metastatic castration-resistant prostate cancer (mCRPC) are low due to lack of response or acquired resistance to available therapies, such as abiraterone (Abi). A better understanding of the underlying molecular mechanisms is needed to identify effective targets to overcome resistance. Given the complexity of the transcriptional dynamics in cells, differential gene expression analysis of bulk transcriptomics data cannot provide sufficient detailed insights into resistance mechanisms. Incorporating network structures could overcome this limitation to provide a global and functional perspective of Abi resistance in mCRPC. Here, we developed TraRe, a computational method using sparse Bayesian models to examine phenotypically driven transcriptional mechanistic differences at three distinct levels: transcriptional networks, specific regulons, and individual transcription factors (TF). TraRe was applied to transcriptomic data from 46 patients with mCRPC with Abi-response clinical data and uncovered abrogated immune response transcriptional modules that showed strong differential regulation in Abi-responsive compared with Abi-resistant patients. These modules were replicated in an independent mCRPC study. Furthermore, key rewiring predictions and their associated TFs were experimentally validated in two prostate cancer cell lines with different Abi-resistance features. Among them, ELK3, MXD1, and MYB played a differential role in cell survival in Abi-sensitive and Abi-resistant cells. Moreover, ELK3 regulated cell migration capacity, which could have a direct impact on mCRPC. Collectively, these findings shed light on the underlying transcriptional mechanisms driving Abi response, demonstrating that TraRe is a promising tool for generating novel hypotheses based on identified transcriptional network disruptions.
The computational method TraRe built on Bayesian machine learning models for investigating transcriptional network structures shows that disruption of ELK3, MXD1, and MYB signaling cascades impacts abiraterone resistance in prostate cancer.
转移性去势抵抗性前列腺癌（mCRPC）患者的生存率很低，这是由于缺乏对现有疗法（如阿比特龙（Abi））的反应或获得性耐药。为了确定有效的耐药克服靶点，需要更好地了解潜在的分子机制。鉴于细胞转录动力学的复杂性，批量转录组数据分析中的差异基因表达分析无法为耐药机制提供足够详细的见解。纳入网络结构可以克服这一限制，为 mCRPC 中 Abi 耐药提供全局和功能视角。在这里，我们开发了 TraRe，这是一种使用稀疏贝叶斯模型的计算方法，用于检查三个不同水平的表型驱动转录机制差异：转录网络、特定调节子和单个转录因子（TF）。TraRe 应用于 46 名 mCRPC 患者的转录组数据，这些患者具有 Abi 反应的临床数据，并发现了被废除的免疫反应转录模块，这些模块在 Abi 反应性患者中与 Abi 耐药性患者相比表现出强烈的差异调节。这些模块在另一个 mCRPC 研究中得到了复制。此外，关键的重布线预测及其相关 TF 在具有不同 Abi 耐药特征的两种前列腺癌细胞系中进行了实验验证。其中，ELK3、MXD1 和 MYB 在 Abi 敏感和 Abi 耐药细胞中的细胞存活中发挥了不同的作用。此外，ELK3 调节细胞迁移能力，这可能对 mCRPC 有直接影响。总的来说，这些发现揭示了驱动 Abi 反应的潜在转录机制，表明 TraRe 是一种基于鉴定的转录网络中断生成新假设的有前途的工具。
基于贝叶斯机器学习模型构建的用于研究转录网络结构的计算方法 TraRe 表明，ELK3、MXD1 和 MYB 信号级联的破坏会影响前列腺癌对阿比特龙的耐药性。
