基于组织病理学几何的空间-分子信息分析：PD-1免疫组织化学中细胞分布与位置依赖性表达状态的KL散度分解

中文摘要 AI 辅助

空间生物学使得分子表达能够与组织中细胞的位置一起分析。然而，通常难以区分细胞是否优先在组织病理学边界附近聚集，以及其分子表达状态是否随该位置而变化。为建立信息科学框架，我们将边界距离定义为R，将二元分子表达状态定义为G，并比较观测到的联合分布P(R,G)与Q(R)P(G)，其中Q(R)是由组织几何结构决定的零分布。由此产生的Kullback-Leibler散度分解为D_R（量化细胞空间分布相对于几何零分布的偏差）和I(R;G)（衡量分子表达状态对边界距离的依赖强度）。使用从PD-1 IHC图像重建的几何结构进行的理想化模拟和半合成验证证实，这两种效应可以分别生成并由相应项恢复。插件估计器表现出正有限样本偏差，支持基于置换的推断。随后，我们将该框架应用于来自人类蛋白质图谱的五张公开PD-1肺癌IHC图像。在汇总分析中，I(R;G)=0.0302 nats，并且在分层图像内标签置换检验中显著（p=0.00040）。该框架提供了一种定量方法，用以区分细胞位于何处与其分子表达状态如何随组织位置变化。需要更大的队列、独立数据集、其他癌症类型和其他分子标记物来建立泛化性和临床实用性。

英文摘要

Spatial biology enables molecular expression to be analyzed together with the location of cells in tissue. However, it is often difficult to distinguish whether cells preferentially accumulate near a histopathological boundary from whether their molecular expression states vary with that location. To establish informational scientific framework, we define boundary distance as R and a binary molecular expression state as G, and compare the observed joint distribution P\left(R,G\right) with Q\left(R\right)P\left(G\right), where Q\left(R\right) is a null distribution determined by tissue geometry. The resulting Kullback-Leibler divergence decomposes into D_{R}, which quantifies deviation of the spatial distribution of cells from the geometric null, and I\left(R;G\right), which measures how strongly molecular expression state depends on boundary distance. Idealized simulations and semi-synthetic validation using geometry reconstructed from a PD-1 IHC image confirmed that these two effects can be generated separately and recovered by the corresponding terms. Plug-in estimators showed positive finite-sample bias, supporting permutation-based inference. We then applied the framework to five publicly available PD-1 lung cancer IHC images from the Human Protein Atlas. In pooled analysis, I\left(R;G\right)=0.0302 nats and was significant in a stratified within-image label-permutation test (p=0.00040). This framework provides a quantitative way to distinguish where cells are located from how their molecular expression states vary with tissue location. Larger cohorts, independent datasets, additional cancer types, and other molecular markers will be required to establish generalizability and clinical utility.

发表机构

• Graduate School of Medicine, Kyoto University（京都大学研究生院医学研究科）
• Graduate School of Medicine, Tohoku University（东北大学研究生院医学研究科）

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