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用机器学习算法评估的瘤微环境的预后意义在手术切除的非小细胞肺癌中

Yukihiro Terada^1,2, Mitsuhiro Isaka¹, Akira Ono³

¹Division of Thoracic Surgery, Shizuoka Cancer Center, Shizuoka, Japan.

Cancer reports (Hoboken, N.J.)

|October 30, 2023

概括

在非小细胞肺癌 (NSCLC) 中评估免疫微环境因素,如CD8+T细胞透和PD-L1表达,可以预测患者的生存率. 对这些因素的定量分析有助于识别针对性治疗的高风险患者.

科学领域:

在瘤学瘤学.
免疫学免疫学免疫学
计算病理学计算病理学

背景情况:

缺乏用于评估非小细胞肺癌 (NSCLC) 免疫微环境 (IME) 的既定方法.
在NSCLC中IME因子的预后意义仍然不清楚.

研究的目的:

开发和应用一种方法来评估NSCLC中的IME因素.
评估IME因子的预后价值,包括CD8+瘤透淋巴细胞 (TILs) 和编程死亡配体1 (PD-L1) 表达.

主要方法:

在全幻灯片成像 (WSI) 上使用机器学习算法量化CD8+TIL密度,分叉盒蛋白P3+ (Foxp3+) TIL密度和PD-L1瘤比例得分 (TPS).
评估了表皮,肌层和组合区的IME因子.
根据TIL密度或PD-L1 TPS对165名NSCLC患者进行了二切切分析 (2014年9月至2015年9月注册),以比较临床结果.

主要成果:

高CD8+TIL密度在上皮质,肌层和联合区间与改善的无病生存率 (DFS) 相相关.
在上皮层中高的PD-L1 TPS与更好的DFS有关.
多变量分析确定了CD8+TIL密度 (组合) 和PD-L1TPS (表皮) 作为独立的预后因素 (HR=0.43,P=.001;HR=0.49,P=.005).

结论:

对IME因子的定量评估,包括CD8+TIL密度和PD-L1TPS,与NSCLC患者的生存率有关.
这种方法可以识别患有复发高风险的患者,他们可能从辅助治疗中受益.
在WSI中评估组合CD8+TIL密度为预后评估提供了一种简单,节省时间的方法.

关键词:

免疫微观环境免疫微环境机器学习是机器学习.非小细胞肺癌的肺癌.预后预后预后整个幻灯片成像成像技术

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The Tumor Microenvironment

The Tumor Microenvironment

Every normal cell or tissue is embedded in a complex local environment called stroma, consisting of different cell types, a basal membrane, and blood vessels. As normal cells mutate and develop into cancer cells, their local environment also changes to allow cancer progression. The tumor microenvironment (TME) consists of a complex cellular matrix of stromal cells and the developing tumor. The cross-talk between cancer cells and surrounding stromal cells is critical to disrupt normal tissue...