PatientProfiler:从蛋白质基因组数据构建患者特定的信号模型
Veronica Lombardi1, Lorenzo Di Rocco2, Eleonora Meo3
1Department of Biology and Biotechnologies 'Charles Darwin', Sapienza University of Rome, Laboratory affiliated to Istituto Pasteur Italia-Fondazione Cenci Bolognetti, 00185, Rome, Italy.
Molecular systems biology
|October 10, 2025
概括
PatientProfiler集成蛋白质基因组数据和因果网络,为个别患者建模癌症信号通路. 这种方法可以识别患者子组和新的预后生物标志物,从而推进个性化癌症治疗.
科学领域:
- 系统瘤学 系统瘤学
- 计算生物学是一种计算生物学.
- 癌症基因组学 癌症基因组学
背景情况:
- 了解患者特异性的癌细胞重编程对于诊断和治疗至关重要.
- 多种瘤瘤特征在临床环境中越来越多地使用.
研究的目的:
- 开发一个计算工作流程,PatientProfiler,用于生成特定患者的信号传导机制模型.
- 整合蛋白质基因组数据与因果相互作用网络,用于机械模型.
- 识别基于网络的预后生物标志物.
主要方法:
- 开发了PatientProfiler,这是一个用于多原子数据分析和标准化的工作流.
- 集成的蛋白质基因组数据与精选的因果相互作用网络.
- 在CPTAC门户网站上对122个未经治疗的乳腺癌活检进行了基准测试.
主要成果:
- 生成了针对患者的机械模型,重复了瘤原体信号通路.
- 确定了七个患者子组,具有明显的转录基因特征和预后值.
- 突出了诸如MYC-CDK4/6轴和NF-kappaB炎症程序等机械驱动因素.
结论:
- PatientProfiler提供了一个可概括的框架,用于将队列级多组数据转化为可解释的机制模型.
- 该工具可以应用于各种癌症类型和复杂疾病.
- 能够更深入地了解癌症异质性和个性化治疗策略.
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