The spatial composition and cellular heterogeneity of the tumor microenvironment plays a critical role in cancer development and progression. High-definition pathology imaging of tumor biopsies provide a high-resolution view of the spatial organization of different types of cells. This allows for systematic assessment of intra- and inter-patient spatial cellular interactions and heterogeneity by integrating accompanying patient-level genomics data. However, joint modeling across tumor biopsies presents unique challenges due to non-conformability (lack of a common spatial domain across biopsies) as well as high-dimensionality. To address this problem, we propose the Dual random effect and main effect selection model for Spatially structured regression model (DreameSpase). DreameSpase employs a Bayesian variable selection framework that facilitates the assessment of spatial heterogeneity with respect to covariates both within (through fixed effects) and between spaces (through spatial random effects) for non-conformable spatial domains. We demonstrate the efficacy of DreameSpase via simulations and integrative analyses of pathology imaging and gene expression data obtained from $335$ melanoma biopsies. Our findings confirm several existing relationships, e.g. neutrophil genes being associated with both inter- and intra-patient spatial heterogeneity, as well as discovering novel associations. We also provide freely available and computationally efficient software for implementing DreameSpase.

翻译：肿瘤微环境的空间组成与细胞异质性在癌症发生与进展中起着关键作用。肿瘤活检的高清病理成像为不同类型细胞的空间组织结构提供了高分辨率视图。通过整合患者层面的基因组学数据，这允许系统评估患者内部及患者间的空间细胞相互作用与异质性。然而，跨肿瘤活检的联合建模面临独特挑战，包括非共形性（活检间缺乏共同空间域）以及高维性。为解决此问题，我们提出了面向空间结构化回归模型的双重随机效应与主效应选择模型（DreameSpase）。DreameSpase采用贝叶斯变量选择框架，能够针对非共形空间域，评估协变量在空间内部（通过固定效应）与空间之间（通过空间随机效应）的空间异质性。我们通过模拟实验以及对$335$例黑色素瘤活检获取的病理成像与基因表达数据的整合分析，验证了DreameSpase的有效性。我们的研究结果证实了若干已知关联（例如中性粒细胞基因与患者间及患者内空间异质性均相关），并发现了新的关联。我们还提供了免费开源、计算高效的软件以实现DreameSpase。