整合基因组和病理特征,以提高先进NSCLC的预后精度
Yingjie Han1,2,3, Junxun Ma3, Zhefeng Liu3
1Medical School of Chinese PLA, Beijing, China.
NPJ precision oncology
|August 2, 2025
概括
预测晚期非小细胞肺癌 (NSCLC) 的结果具有挑战性. 一个新的 Prognostic Multimodal Classifier for Progression (PMCP) 集成了基因组和成像数据,以准确预测无进展和整体存活率.
科学领域:
- 在瘤学瘤学.
- 基因组学就是基因组学.
- 医疗成像医学成像
背景情况:
- 与化疗 (ICT) 结合的免疫疗法是高级非小细胞肺癌 (NSCLC) 的标准.
- 可靠的预后生物标志物用于预测NSCLC治疗反应和患者结果仍然是一个临床挑战.
- 准确的预后对于指导高级NSCLC个性化治疗策略至关重要.
研究的目的:
- 为接受一线ICT的高级NSCLC患者开发和验证一种多式预后模型.
- 整合基因组数据和基因病理图像特征,以更好地预测无进展生存 (PFS) 和整体生存 (OS).
- 确定新的多式联络生物标志物,可以指导先进NSCLC的临床决策.
主要方法:
- 来自162名先进NSCLC患者的瘤样本的下一代测序,接受一线ICT治疗.
- 深度学习分析病理图像以识别和分类细胞类型.
- 开发一个可预测的多模式分类器 (PMCP) 的进展,整合基因组和成像数据.
- 验证PMCP模型用于预测PFS和OS,并计算PFS的AUC.
主要成果:
- 建立了一个基于基因组的模型来预测瘤进展风险.
- 深度学习显著提高了使用病理图像的PFS和OS预测的准确性.
- 开发的进展预测多模式分类器 (PMCP) 准确预测PFS和OS.
- PMCP1亚型的特征是上皮细胞的比例较高,表明进展风险较低.
- 预测PFS的曲线下的面积 (AUC) 达到0.807.
结论:
- 整合基因组和病理学数据为先进的NSCLC提供了优越的预后准确性,而不是单一模式的方法.
- PMCP模型展示了多式联络生物标志物在完善预测和指导个性化ICT战略方面的潜力.
- 这种方法有望改善临床决策和优化高级NSCLC患者的治疗结果.
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