在SCRUM-MONSTAR癌症-Omics生态系统:努力在精密医学的量子飞跃
Tadayoshi Hashimoto1,2, Yoshiaki Nakamura1,2, Takao Fujisawa1,3
1Translational Research Support Office, National Cancer Center Hospital East, Kashiwa, Japan.
Cancer discovery
|July 18, 2024
概括
在SCRUM-Japan MONSTAR-SCREEN项目中,使用人工智能驱动的多组学来将晚期癌症患者与临床试验相匹配. 匹配疗法显著改善了整体存活率,证明了精准医学在瘤学中的价值.
科学领域:
- 在瘤学瘤学.
- 基因组学就是基因组学.
- 人工智能的人工智能
背景情况:
- 晚期恶性瘤带来了重大的治疗挑战.
- 精准医学方法对于改善患者的治疗结果至关重要.
研究的目的:
- 评估人工智能驱动的多组学在晚期癌症患者的分子分析中的有效性.
- 评估匹配的临床试验治疗对患者存活时间的影响.
- 为药物发现建立一个行业和学术界数据共享基础设施.
主要方法:
- 在全国范围内使用人工智能驱动的多体经济学 (MONSTAR-SCREEN) 进行分子分析.
- 分析了超过24000名注册患者的数据.
- 对那些接受匹配治疗的患者和那些没有接受匹配治疗的患者的结局进行比较.
主要成果:
- 在16,144名在匹配的临床试验中注册的先进固体瘤患者中,5.0%.
- 在匹配试验中,客观应答率为29.2%,中位生存期为14.8个月.
- 接受匹配治疗的患者的整体存活时间明显延长 (HR 0.77).
结论:
- 全国范围内的分子分析有助于患者参加匹配的临床试验.
- 匹配疗法为晚期固体瘤患者提供了相当大的生存益处.
- 工业和学术界的数据共享生态系统可以促进新药发现和精准医学.
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