The Cross-Training Core (CTC) for the U54 ROBIN OligoMET Center will consist of experienced faculty members of the Division of Computational and Systems Biology (CSP) in the Department of Pathology and Laboratory Medicine of Weill Cornell Medical College (WCM). The team at WCM will work in close collaboration with teams at the other Institutions, and a history of collaboration already exists between the CTC team members and the investigators leading the Research Projects and the other Research Cores, which will further ensure the successful progression of the Center. The CTC activities will be centered around the following purposes: Aim 1) To provide a unified analytical framework across the U54 ROBIN OligoMET Center to foster cross-project data integration and comparison. Imaging, omics, and radiomics data are found in a variety of forms (e.g., different platforms, file formats, etc.), and training people on how to manage each of these instances is challenging and inefficient. We will have unified analytical framework within the U54 ROBIN OligoMET Center where data is aggregated and standardized, greatly decreases the training complexity and increases reproducibility which results in a more fluid training process. Aim 2) To provide educational support and training across the U54 ROBIN OligoMET Center. Advances in genomics, transcriptomics, metabolomics, and radiomics technologies have led to exponential rises in both production and availability of multimodal data. In light of these rapid evolutions, disseminating the latest bioinformatics methods within the U54 Center – and the broader biomedical community – is a challenge of paramount importance. The CTC will address such challenge by creating an open educational platform that will provide a rich interactive learning environment, leveraging a cloud-based framework to collaboratively create and share tutorials and learning experiences. To this end, the CTC will build upon a 10- year experience in the computational genomics and data science domains and training biologists and clinicians in computational methods. Aim 3) To develop novel analytical approaches for the comprehensive characterization of oligometastatic prostate cancer (PCa) via integrated analyses of multimodal big data. Omics and radiomics technologies, multiparametric in situ imaging, and spatially-resolved molecular and image analyses are rapidly evolving fields. Therefore, continually evolving technologies, software, algorithms, and analytical methods are efforts of essence. PCa investigations across the U54 ROBIN OligoMET Center encompass a multitude of these domains, hence it is of paramount importance that a versatile and innovative portfolio of approaches is developed to fully support the ongoing and future research. The U54 ROBIN OligoMET Center will therefore provide an ideal platform for such cross-disciplinary training, and the CTC will support such crucial endeavor through developing and disseminating ad-hoc training modules across the whole U54 ROBIN OligoMET Center and the other ROBIN Centers.