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机器学习用于基于遗传学的分类和预测未知的原发性癌症的治疗反应

Intae Moon^1,2, Jaclyn LoPiccolo³, Sylvan C Baca^3,4

¹Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.

Nature medicine

|August 7, 2023

概括

一个新的机器学习工具OncoNPC准确地预测了未知初级癌症 (CUP) 的癌症原发点. 这种人工智能驱动的方法可以识别不同的CUP子组,并改善治疗决策,从而改善患者的治疗结果.

科学领域:

在瘤学瘤学.
生物信息学是一种生物信息学.
计算生物学计算生物学

背景情况:

未知原发性癌症 (CUP) 占所有癌症的3-5%.
由于CUP缺乏确定的向疗法,导致患者预后不佳.
准确的初级瘤部位识别对于有效的癌症治疗至关重要.

研究的目的:

开发和验证OncoNPC,用于预测CUP病例中原发性癌症类型的机器学习分类器.
根据基因组和临床数据确定不同的CUP子组.
评估OncoNPC在指导治疗决策和改善患者存活率方面的临床实用性.

主要方法:

在22种癌症类型的36,445种瘤的目标下一代测序 (NGS) 数据上接受了OncoNPC的培训.
在持有瘤样本上验证了OncoNPC的性能,达到0.942.9的加权F1得分.
应用OncoNPC到达达纳-法伯癌症研究所的971个CUP瘤.

主要成果:

在OncoNPC中,在65.2%的保留样本和41.2%的CUP瘤中,获得了高可信度预测.
识别了具有差异多基因生殖线风险和生存结果的CUP子组.
接受与OncoNPC预测一致的治疗的患者的生存率显著改善 (HR=0.348).
在CUP患者中,OncoNPC增加了基因组指导疗法的潜力2.2倍.

结论:

作为管理CUP的临床决策支持工具,OncoNPC显示出显著的潜力.
该分类器提供了不同的CUP子组的证据,使更个性化的治疗策略成为可能.
通过促进向治疗和优化息护理决策,OncoNPC可以改善患者的治疗结果.

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