PRIMAVO: Interactive exploration of cancer patient precision immune monitoring data in clinical trials

项目总结

PROJECT SUMMARY Cancer immunotherapies have dramatically changed clinical practice following the approval of more than a dozen new drugs and/or indications in the past few years1–4. However, only a minority of patients derives clinical benefit, while some develop off-target effects. To identify novel biomarkers of disease course and response, as well as potential off-target responses to immunotherapy in cancer patients, the NCI’s Cancer Moonshot Initiative (CMI) is supporting a growing number of cancer immunotherapy trials. Our team is at the forefront of the CMI efforts with two grants: 1) a U24 on high-dimensional immune monitoring of NCI-supported immunotherapy trials as part of the Cancer Therapy Evaluation Program (CTEP); and 2) a U01 on characterizing and predicting a toxic side-effect of immunotherapy, colitis, in immune checkpoint blockade- treated cancer patients, as part of the Immuno-Oncology Translational Network (IOTN). However, with the rapid development of new and improved technologies, these studies produce diverse data types of ever- expanding size and complexity. Visualizing, exploring, and communicating results from the computational analysis of these disparate high-dimensional immune monitoring assays is imperative but challenging, especially because many visualization tools are either built only for in-house utilization or typically specialize in only one or perhaps a few data types. At the same time, cross-assay or cross-participant data interpretation is the only way to identify relevant immune biomarkers that impact clinical trial design and clinical cancer care. There is thus an unmet need for easy-to-use interactive tools for integrative exploration of cancer patient immune monitoring data by biologists and clinicians. This proposal aims to address this need through the development of a user-friendly, integrative web-based visualization tool, Precision Immunology Monitoring Analysis and Visualization Online (PRIMAVO), that will enable researchers of all computational skill levels to visually analyze and interactively explore immune monitoring assay results that belong to a cancer immunotherapy trial, at multiple levels of granularity. The PRIMAVO tool will scale with current and future data growth and complexity to enable fast and effective research towards identifying associations between different data types, participants, and clinical variables. We hypothesize that developing (i) cohort-level; and (ii) participant-level visualizations that integrate views across multiple immune data types, participants, cohorts and time points will provide a deep, comprehensive view of events; and that (iii) the content and features of PRIMAVO will be greatly enhanced by engaging with the user community during its development. The results from this highly integrated effort will have important clinical and public health implications by accelerating our understanding of the immune responses of cancer patients to immunotherapies and significantly impact the rate at which immune biomarkers from clinical trials will be identified, interpreted, verified and translated into clinically actionable findings. Overall, PRIMAVO will be a highly valuable tool for the research community.