PROJECT SUMMARY/ABSTRACT Multiplex immunofluorescence (mIF) microscopy and accompanying automated image analysis is a widely used technique which allows for the assessment and visualization of the tumor immune microenvironment (TIME). Generally, mIF data has been used to simply examine the presence and abundance of immune cells in cancer patients; however, this aggregate measure assumes uniform patterns of immune cells and overlooks spatial heterogeneity. The spatial contexture of the TIME has not been adequately explored, in part due to the lack of available analytical approaches and tools. Therefore, the goal of this research is to develop novel statistical methods and software for the downstream analysis of mIF data. We will apply these methods to the study of epithelial ovarian cancer (EOC), the deadliest gynecologic malignancy in the US, to develop an immunoscore predictive of survival among EOC patients. Aim 1 is focused on developing spatial statistical approaches for the analysis of mIF data which leverages the spatial architecture of the TIME. Aim 2 will develop Bayesian models for the analysis of mIF data which accounts for the zero-inflated and over-dispersed nature of the immune count data for determination of immune subtypes. Lastly, Aim 3 will characterize the immune landscape of ovarian tumors and develop an immunoscore (Oimmuno) predictive of survival using existing mIF data from ~2,500 diverse EOC patients enrolled in established epidemiological studies. We will validate the Oimmuno by generating targeted mIF data in two independent cohorts, each with more than 1,200 EOC patients. In summary, this proposal will develop and test novel statistical methods for the analysis of mIF data that incorporates the spatial heterogeneity of the TIME, in addition to abundance measures of immune cells, producing freely available software that can be used in the study of EOC or adapted for use in other cancer types. The derivation of methods to quantify the spatial contexture of immune cells has important applications as such biomarker discovery for predictors of outcomes and therapeutic efficacy in cancer patients, ultimately reducing cancer mortality.