综合性多原子测序揭示了MMTV-Myc小鼠模型模仿人类乳腺癌异质性的结果
Carson D Broeker1, Mylena M O Ortiz2, Michael S Murillo3,4
1Department of Biochemistry and Molecular Biology, Michigan State University, 567 Wilson Road, BPS Room 2120, East Lansing, MI, 48824, USA.
Breast cancer research : BCR
|October 7, 2023
概括
MMTV-Myc小鼠模型反映了人类乳腺癌的异质性,保存的遗传事件与生存率低下有关. 机器学习准确地分类小鼠亚型,有助于人类乳腺癌研究.
科学领域:
- 在瘤学瘤学.
- 基因组学就是基因组学.
- 翻译研究是翻译研究.
背景情况:
- 乳腺癌是一种复杂的疾病,具有不同的亚型和分子形状.
- 鼠标模型对于研究乳腺癌至关重要,但它们捕捉人类异质性的能力受到争议.
- 以前的研究强调了MMTV-Myc模型中的基因表达异质性,但潜在的遗传学是推测性的.
研究的目的:
- 调查乳腺癌MMTV-Myc小鼠模型中异质性的遗传基础.
- 为了确定小鼠模型中的遗传变化是否在人类乳腺癌中得到保存.
- 开发一种机器学习模型,以基于人类乳腺癌内在亚型来对小鼠模型亚型进行分类.
主要方法:
- 来自三种MMTV-Myc小鼠组织学亚型 (microacinar,状,EMT) 的瘤的全基因组测序.
- 将基因组数据与匹配的样本基因表达数据集成.
- 使用斯皮尔曼和肯德尔系数分析遗传拷贝数事件和基因表达之间的相关性.
- 在METABRIC数据上训练的监督机器学习算法的应用,以从小鼠基因表达中预测人类内在亚型.
主要成果:
- 驱动细胞信号通路的关键基因组变化在组织学亚型中得到保留.
- 在不同亚型中确定了特定突变 (KIT,RARA,SCRIB) 和通路激活 (KRAS,FGFR2).
- 在MMTV-Myc模型中发现的遗传事件与人类乳腺癌的总生存率下降有显著的相关性.
- 一个机器学习模型成功地分类了MMTV-Myc组织学亚型,将其与人类内在乳腺癌亚型联系起来.
结论:
- MMTV-Myc小鼠模型显示了与人类乳腺癌异质性相关的保存遗传变化.
- 这种模型为进一步研究人类乳腺癌亚型和结果提供了宝贵的机会.
- 开发的机器学习方法可以根据人类乳腺癌亚型对小鼠模型进行分类,从而增强翻译研究.
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