開發以密度為基礎的空間功能分析框架： 解析 HER2 陽性乳癌中的功能區域與腫瘤休眠異質性

Abstract

空間轉錄體技術可在保留組織結構下分析基因表現，有助於探討細胞空間分布與腫瘤微環境。然而如何解析空間中生物功能狀態分布仍具挑戰性。現有分析方法多延用自單細胞RNA定序流程，主要著重於單一基因表現或空間變異基因，即使結合 GSEA 或 GSVA 等路徑分析，在面臨基因表現訊號微弱或分布不均時，仍難以完整捕捉真實生物功能狀態的連續性空間變化，也缺乏在組織空間中明確辨識功能富集區域的能力。為克服上述限制，本研究提出一套以密度為基礎之分析框架 KDESH（KDE-based Spatial Hotspot Detection），用以辨識具有空間聚集特徵之生物功能路徑。此方法結合路徑活性評分、核密度估計（kernel density estimation）及置換檢定（permutation-based statistical testing），建立具統計基礎的空間功能分布模型。相較於傳統方法，KDESH不需依賴預先劃分之空間區域，而能捕捉功能途徑在空間上的連續性變化，並進一步辨識局部功能活性顯著富集的空間熱點（spatial hotspots）。此外，本方法亦可量化不同生物途徑之間的空間共定位（co-localization）。將KDESH應用於HER2 陽性乳癌空間轉錄體資料，並以腫瘤休眠作為具體的生物學探討案例。分析發現多數生物功能途徑在腫瘤組織中呈現顯著空間聚集現象，是腫瘤微環境的一項普遍特徵。另外，具有休眠特徵之腫瘤上皮細胞亦呈現顯著空間聚集 (p < 0.05)，進一步分析發現，腫瘤休眠並非單一的細胞狀態，而是呈現空間與功能異質性。根據所處微環境條件，休眠細胞可區分為缺氧相關休眠熱點（hypoxia-associated dormancy hotspots, HDH）與常氧相關休眠熱點（normoxia-associated dormancy hotspots, NDH），代表不同的功能狀態與潛在調控機制。本研究建立的 KDESH 框架，可系統性解析腫瘤組織中的功能空間分布，並量化不同生物路徑之間的空間關聯，有助於對腫瘤微環境空間結構的理解，亦突顯微環境所驅動之功能異質性在癌症生物學中的重要性，並可作為未來空間腫瘤生物學研究與微環境導向治療策略發展之基礎。

Spatial transcriptomics can analyze gene expression while preserving tissue structure, which helps in exploring the spatial distribution of cells and the tumor microenvironment. However, analyzing the spatial distribution of biological functional states remains challenging. Current analytical methods mostly follow the single-cell RNA sequencing pipeline, mainly focusing on single-gene expression or spatially variable genes. Even when combined with pathway analysis such as GSEA or GSVA, they still struggle to fully capture the continuous spatial changes in true biological functional states when faced with weak or unevenly distributed gene expression signals, and cannot clearly identify functionally enriched regions in tissue. To address these limitations, we introduce a density-based analytical framework, KDESH. This framework combines pathway activity scoring, kernel density estimation, and permutation-based statistical testing to establish a statistically based spatial functional distribution model. Compared to traditional approaches, KDESH does not depend on predefined spatial regions but can capture continuous spatial changes in functional pathways and further identify spatial hotspots where local functional activity is significantly enriched. Furthermore, this framework can also quantify the spatial co-localization between different biological pathways. We applied KDESH to HER2-positive breast cancer spatial transcriptome data, using tumor dormancy as a case study. This analysis revealed that most biological functional pathways exhibit significant spatial aggregation in tumor tissues, indicating that such spatial functional organization is a common feature of the tumor microenvironment. Additionally, dormancy-like cells showed significant spatial aggregation (p < 0.05), indicating that these cells tend to accumulate in specific microenvironmental niches. Further analysis suggested that tumor dormancy is not a single cellular state, but rather exhibits spatial and functional heterogeneity. Based on their microenvironment, they can be classified into hypoxia-associated dormancy hotspots (HDH) and normoxia-associated dormancy hotspots (NDH), representing different functional states and potential regulatory mechanisms. Overall, the KDESH framework provides a novel approach to explore the spatial functional distribution within tumor tissues and to quantify the spatial relationships between different biological pathways. This deepens our understanding of the spatial architecture of the tumor microenvironment, highlights the importance of microenvironment-driven functional heterogeneity in cancer biology, and can be used as a foundation for future spatial tumor biology research and the development of microenvironment- guided therapy strategies.