乳腺癌治疗反应的多组机器学习预测
Stephen-John Sammut1,2,3, Mireia Crispin-Ortuzar1, Suet-Feung Chin1
1Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK.
Nature
|December 7, 2021
概括
乳腺癌的治疗反应取决于瘤的微环境. 整合多组数据的机器学习模型可以预测治疗结果,帮助开发个性化疗法.
科学领域:
- 癌症学
- 计算生物学
- 基因组学
背景情况:
- 乳腺癌是复杂的生态系统,
- 瘤生态系统的组成影响了细胞毒性治疗的反应.
- 现有的响应预测器缺乏这种知识的整合.
研究的目的:
- 研究治疗前瘤生态系统在乳腺癌治疗反应中的作用.
- 使用多组数据开发治疗结果的预测模型.
- 与病理反应终点相关联的治疗前多组特征.
主要方法:
- 从168个治疗前乳腺瘤活检中收集了临床,数字病理,基因组和转录组资料.
- 治疗后与病理反应 (完整反应或残留疾病) 相关的多种特征.
- 开发并验证了一种多组机器学习模型.
主要成果:
- 治疗反应是由预处理的瘤生态系统调节的.
- 剩余疾病与治疗前的特征相关,如瘤突变格局,增殖和免疫透.
- 一个多组机器学习模型在预测外部队列中的病理完整反应时达到0. 87的AUC.
结论:
- 治疗反应由基线瘤生态系统特征决定.
- 数据整合和机器学习可以捕捉这些特征进行预测建模.
- 这种方法有可能在其他癌症类型中开发预测剂.
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