一种灵活的系统分析流程，用于阐明肿瘤微环境中与细胞表型和患者层面特征相关的空间关系

We developed a computational pipeline to quantify and analyze the neighborhood profiles of individual cells in multiplexed immunofluorescence images. The pipeline characterizes spatial co-localization patterns within the tumor microenvironment and applies interpretable supervised machine learning models, specifically orthogonal partial least squares analysis (OPLS), to identify spatial relationships predictive of cell states and clinical phenotypes.

We applied this framework to a previously published non-small cell lung cancer (NSCLC) cohort across four applications. At the cellular level, we identified neighborhood features associated with lymphocyte activation states. At the tumor-immune interface, we demonstrated that the immune cell composition surrounding major histocompatibility complex class I-expressing (MHC I + ) tumor cells could distinguish adenocarcinoma from squamous cell carcinoma. At the patient level, spatial features predicted tumor grade. DISCUSSION: By integrating cell-segmented imaging data with interpretable modeling, our pipeline reveals key spatial determinants of tumor biology. These findings generate testable mechanistic hypotheses about intercellular interactions and support the development of spatially informed prognostic and therapeutic strategies.