通过瘤转录组进行合成致死性介导的精确瘤学
Joo Sang Lee1, Nishanth Ulhas Nair2, Gal Dinstag3
1Cancer Data Science Lab, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA; Next Generation Medicine Lab, Department of Artificial Intelligence & Department of Precision Medicine, School of Medicine, Sungkyunkwan University, Suwon 16419, Republic of Korea; Department of Digital Health & Health Sciences and Technology, Samsung Advanced Institute for Health Sciences & Technology, Samsung Medical Center, Sungkyunkwan University, Seoul 06351, Republic of Korea.
一个新的精确瘤框架SELECT使用瘤转录组数据来预测癌症治疗反应. 这种方法在35个临床试验中显示出80%的准确性, 扩大了个性化癌症治疗选择.
科学领域:
- 癌症学
- 基因组学
- 计算生物学
背景情况:
- 精确瘤学目前专注于向癌症驱动基因中的突变.
- 探索瘤转录组为癌症患者治疗提供了新的途径.
- 瘤中的基因相互作用越来越被认为对治疗反应至关重要.
研究的目的:
- 通过转录组引入SELECT (合成致死性和救援介导精确瘤学),这是预测癌症治疗反应的新框架.
- 利用瘤转录组数据和基因相互作用来加强治疗选择.
- 在不同类型的癌症和临床试验中验证SELECT框架.
主要方法:
- 开发SELECT框架,整合遗传相互作用和转录组数据.
- 用SELECT分析35个已发表的针对性和免疫治疗临床试验的数据.
- 在10种不同类型的癌症中测试SELECT的预测性能.
- 使用多臂WINTHER试验数据进行验证.
主要成果:
- 在分析的临床试验中,SELECT在80%的试验中表现出预测能力.
- 该框架成功预测了WINTHER试验中的患者反应.
- 预测签名和分析代码被生成并公开.
结论:
- 使用瘤转录组数据,SELECT提供了一种可靠的方法来预测患者对癌症治疗的反应.
- 该框架通过结合遗传相互作用来提高精确瘤学.
- 随着SELECT的向公众开放,支持针对个性化癌症治疗的进一步研究和前性临床研究.
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