Development of an Open-Source Preclinical Imaging Informatics Platform for Cancer Research

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ABSTRACT Preclinical imaging is widely used in cancer research to devise novel tumor detection strategies, assess tumor burden and physiology/biology, as well as to validate novel therapeutic strategies and predictive and biomarkers of response to therapy. More recently, the use of patient-derived tumor xenografts (PDX) and genetically engineered mouse models (GEMMs) has ushered an era of co-clinical trials where preclinical studies can inform clinical trials, thus potentially bridging the translational gap in cancer research. However, differences in the deployment of the instruments, differences in imaging formats and protocols, differences in animal models, and variability in analytic pipelines among other factors result in non-tractable data and poor reproducibility. Importantly, current databases are not compatible with complexity and growing demands in preclinical cancer imaging which include big data needs and collection of metadata/annotation to support NCI’s precision medicine initiative. Thus, there is an unmet need to develop a unifying imaging informatics and workflow management platform to support cancer research, which will ultimately support the premise of translational precision medicine. We propose to develop an open-source preclinical imaging informatics platform—Preclinical Imaging XNAT- enabled Informatics (PIXI)—to manage the workflow of preclinical imaging laboratories, harmonize imaging databases, and enable deployment of analytic and computational pipelines in preclinical imaging. PIXI will be based on XNAT as the underlying informatics architecture. XNAT is used by over 200 academic institutions and industry entities as the backbone for data management across a wide range of imaging applications in clinical research, and thus offers a robust platform for the development and deployment of PIXI. Through this effort, we will 1) develop the PIXI database and server to capture preclinical imaging associated data, metadata, and preclinical imaging workflow and experiments; 2) develop the PIXI “point-of-service” interface, notebook capabilities, and software development kit (SDK); and 3) develop the PIXI container-based application (“App”) environment to implement portable analytic pipelines. Overall, the development of a preclinical imaging informatics platform is expected to have a profound impact on the management of preclinical imaging in cancer research which will ultimately support translational precision medicine.