3D extension of spatial patterning analysis of cellular ensembles (SPACE) to extract parameters for dynamic tumor modeling

Investigating the cellular interactions within three-dimensional (3D) tumor microenvironments is essential for understanding tumor growth and progression. Building upon the success of our previous computational framework, SPACE, designed for spatial analysis of high-plex tissue images, we are developing 3D-SPACE. This innovation extends spatial pattern analysis to 3D volumetric tissue images. Volumetric images more accurately capture nuances of cellular arrangements and tissue structures, offering a more realistic representation of the tumor microenvironment.

Applying 3D-SPACE to analyze full 3D tumor datasets has the potential to reveal crucial parameters for understanding tumor outcomes. This approach not only elucidates previously undiscovered spatial patterns but also allows for the quantification of parameters such as contact probabilities between different cell types or the killing efficiencies of key effector cells in situ. These spatially-derived parameters can then be used in predictive equations that model and forecast tumor growth based on cellular interactions.

Using a 3D tumor model and 3D-SPACE, we can quantify cell interactions and develop predictive equations for tumor growth dynamics, with the aim of deriving a predictive reproduction number (R₀) for tumor growth. Working towards a predictive R₀ for tumor growth offers a valuable approach for predicting tumor growth outcomes.

This work was supported in part by the Intramural Research Program of NIAID, NIH.