整合计算病理学和多转录组学来描述肺腺癌异质性和预后建模
Zerong Li1, Wenmei Qiao2, Siming Yu3,4
1Department of Pharmacy, The Second People's Hospital of Shenzhen, The First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, P. R. China.
International journal of surgery (London, England)
|June 6, 2025
概括
这项研究将肺腺癌 (LUAD) 的基因组不稳定性 (高拷贝数变异) 与特定的成像特征和不良预后联系起来. 这些发现为LUAD患者的结果和向治疗提供了潜在的生物标志物.
科学领域:
- 在瘤学瘤学.
- 计算病理学计算病理学
- 基因组学就是基因组学.
背景情况:
- 肺腺癌 (LUAD) 是一种异质的非小细胞肺癌 (NSCLC).
- 传统的组织病理学至关重要,但将计算病理学与多组学相结合,可以更深入地了解瘤微环境 (TME).
- 在LUAD中,病理特征和基因组不稳定性之间的联系尚未得到充分理解.
研究的目的:
- 研究LUAD病理特征与基因组不稳定性之间的关系.
- 在LUAD中识别成像生物标志物用于预后和治疗向.
- 开发一个多维的框架,整合计算病理学和多omics,用于LUAD的表征.
主要方法:
- 分析了来自TCGA-LUAD的全幻灯片图像 (WSI) 和多omics数据.
- 深度学习 (ResNet-50) 和CellProfiler被用于图像特征提取.
- 推断出拷贝数变异 (CNV),加权基因共同表达网络分析 (hdWGCNA) 确定了调控模块.
主要成果:
- 较高的CNV LUAD细胞显示增加了干性,糖解,MYC信号和免疫逃避.
- 192个成像特征,包括11个病理特征和181个深度学习特征,与CNV负担相关.
- 使用这些特征的机器学习模型预测了生存率,识别了具有较低免疫透率和免疫治疗反应的高风险患者.
结论:
- 一个整合计算病理学和多组学的框架标志着LUAD异质性.
- 与CNV相关的成像特征和分子调节剂可以作为预后生物标志物.
- 临床实用性需要前性验证;研究结果表明转化应用的潜力.
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