对多发性骨髓瘤进行个性化动态风险评估和治疗选择
Carl Murie1, Serdar Turkarslan1, Anoop P Patel2
1Institute for Systems Biology, Seattle, WA, USA.
British journal of cancer
|April 1, 2025
概括
这项研究引入了多发性骨髓瘤 (MM) 的新风险模型,该模型使用转录程序来比现有方法更准确地预测患者的结果和治疗反应. 这种个性化的方法有助于在整个疾病轨迹的个性化治疗决策.
科学领域:
- 基因组学就是基因组学.
- 系统生物学 系统生物学
- 计算生物学 计算生物学
背景情况:
- 准确的风险分层对于多发性骨髓瘤 (MM) 的个性化治疗至关重要.
- 细胞遗传异常显著影响MM疾病的进展.
- 现有的风险分层方法需要改进,以实现个性化的MM护理.
研究的目的:
- 制定一个统一的框架,用于在MM中对细胞遗传亚型的特定风险分类.
- 用机器学习方法预测MM患者的治疗反应.
- 改进整个MM疾病轨迹的个性化风险评估.
主要方法:
- 利用SYstems遗传网络分析 (SYGNAL) 框架对881名MM患者的多omics数据进行了分析.
- 产生了与MM进展相关的转录程序的mmSYGNAL网络.
- 应用机器学习到mmSYGNAL程序活动档案,用于风险和治疗预测.
主要成果:
- 在各种MM患者队列中,mmSYGNAL风险模型在预测无进展生存 (PFS) 方面超过了细胞遗传学,ISS和多基因小组.
- 治疗应答预测在体外显示与药物疗效有显著一致.
- 该模型确定了MM患者的潜在药物匹配,包括复发性耐药性疾病患者.
结论:
- 转录程序活动在MM中提供了卓越的预后和预测评估.
- 这一框架使得MM患者的风险分层和治疗选择更加准确,个性化.
- 这些发现支持使用转录网络分析来进行个性化的MM管理.
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