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Abstract:
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The tumor microenvironment (TME), which characterizes the tumor and its surroundings, plays a critical role in understanding cancer development and progression. Recent advances in imaging techniques enable researchers to study spatial structure of the TME at a single-cell level. Investigating spatial patterns and interactions of cell subtypes within the TME provides useful insights into how cells with different biological purposes behave, which may impact a subject's outcomes. We utilize the K-function to explore inter-cell dependence as a function of distances. Using techniques from functional data analysis, we introduce an approach to model the association between the summary spatial functions and subject-level outcomes, while controlling for other scalar predictors. We leverage the additive functional Cox regression model to study the nonlinear impact of spatial interaction between tumor and stromal cells on overall survival in patients with lung cancer, using multiplex immunohistochemistry (mIHC) data. The approach is further validated using a publicly available Multiplexed Ion beam Imaging (MIBI) triple-negative breast cancer dataset.
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