Using random forests to uncover the predictive power of distance-varying cell interactions in tumor microenvironments.

ABSTRACT: Tumor microenvironments (TMEs) contain vast amounts of information on patient's cancer through their cellular composition and the spatial distribution of tumor cells and immune cell populations. Exploring variations in TMEs between patient groups, as well as determining the extent to which this information can predict outcomes such as patient survival or treatment success with emerging immunotherapies, is of great interest. Moreover, in the face of a large number of cell interactions to consider, we often wish to identify specific interactions that are useful in making such predictions. We present an approach to achieve these goals based on summarizing spatial relationships in the TME using spatial K functions, and then applying functional data analysis and random forest models to both pre

SUBMITTER: VanderDoes J 

PROVIDER: S-EPMC11210873 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature