AstroPath Integration Resource Core

SUMMARY The AstroPath-Genomics Core will be a hub to disseminate powerful molecular pathology, informatics, big data science, and genomics methods tailored to accomplish the goals of the Prostate TBEL Program. It will provide critical support for ALL three projects. The key strategy is to leverage existing infrastructure, equipment and services at the SKCCC, while also providing specialized approaches and capabilities that are highly tailored to support the activities in the TBEL Program. This model will allow efficient and cost-effective harnessing of these powerful technologies through the dedicated effort and expertise of the current AstroPath-Genomics Core personnel through the close interaction with all Projects and Cores here, without having to establish the equipment, administrative infrastructure, biospecimen SOPs, and laboratory informatics management systems (LIMS), from scratch. This centralization of the molecular pathology, informatics and big data science, and mathematical/computational integration methods will yield cost-effective strategies that avoid duplicating effort in each component of the TBEL Program. The Core will carry out its work in three major Aims. Aim 1 will deploy advanced digital pathology tools for multi-analyte immunohistochemistry, immunofluorescence, and in situ hybridization approaches and image analysis for deep characterization of the microenvironmental transitions from inflammation to neoplasia. Specific technologies include multi-spectral multi-analyte imaging, Chromogenic iterative multiplex-IHC(ChIM-IHC), and multiplex ACD in situ hybridization. Aim 2 will utilize bulk, single cell, and spatial genomics approaches to measure the genomic and epigenomic alterations to the epithelial and stromal compartments of PIA, PIN, and cancer lesions. Specific technologies include, genome wide and ultra-deep targeted DNA methylation analysis, multi-modal single nucleus ATAC- and coupled RNA- seq (snATAC-/snRNA-seq), and spatial transcriptomics. Integration across the multi-modal data streams will be possible through implementation of the innovative Bayesian non-negative matrix factorization and transfer learning approaches through the CoGAPS and ProjectR framework. Aim 3 will implement and extend the AstroPath framework for computational analysis of high dimensional single cell and bulk genomics/epigenomics, multi-analyte digital pathology, and advanced imaging data. To accomplish these goals, the core leaders have assembled an impressive team of experts to support the TBEL Center, with complementary expertise in big data science/informatics, molecular pathology, genomics technologies, immunopathology, computational/systems biology and applied mathematics.