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使用随机森林来发现瘤微环境中不同距离的细胞相互作用的预测能力

Jeremy VanderDoes¹, Claire Marceaux^2,3, Kenta Yokote²

¹Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Canada.

PLoS computational biology

|June 14, 2024

概括

分析瘤微环境 (TMEs) 揭示了重要的空间细胞相互作用,用于预测癌症患者的结果和治疗成功. 这种方法识别了关键关系,有助于个性化癌症治疗的发展.

科学领域:

在瘤学瘤学.
计算生物学计算生物学
生物信息学是一种生物信息学.

背景情况:

瘤微环境 (TMEs) 通过细胞组成和空间安排,提供有关癌症的关键信息.
了解TME变异及其对患者生存和免疫治疗反应的预测能力是一个关键的研究领域.
在TME中识别特定的细胞相互作用是具有挑战性的,但对于预测建模至关重要.

研究的目的:

开发和验证一种新的计算方法来分析TME内部的空间关系.
根据TME特征预测患者的结果,包括生存和治疗成功.
为了确定TME中细胞之间的特定,预测性空间相互作用.

主要方法:

使用空间K函数总结空间关系.
应用功能数据分析和随机森林模型来分析空间数据.
利用来自三阴性乳腺癌和肺癌患者的多重离子束成像数据.

主要成果:

拟议的方法有效地识别了TME内的重要空间相互作用.
该方法在模拟实验中证明了对患者结果的预测能力.
该方法在识别显著相互作用时成功控制了错误发现率.
该方法应用于来自乳腺和肺癌患者的真实世界TME数据.

结论:

开发的方法为剖析TME复杂性提供了一个强大的框架.
对TME的空间分析可以对癌症预后和治疗反应产生重要的预测见解.
公开提供的R包"funkycells"有助于应用这些方法.

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The Tumor Microenvironment

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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...