There is increasing appreciation that the immune system plays critical roles not only in the traditional domains of infection and inflammation but also in many areas of biology, including tumorigenesis, metabolism, and even neurobiology. However, one of the major barriers for understanding human immunological mechanisms is that immune assays have not been reproducibly characterized for a sufficiently large and diverse healthy human cohort. Here, we present the 10,000 Immunomes Project (10KIP), a framework for growing a diverse human immunology reference, from ImmPort, a publicly available resource of subject-level immunology data. Although some measurement types are sparse in the presently deposited ImmPort database, the extant data allow for a diversity of robust comparisons. Using 10KIP, we describe variations in serum cytokines and leukocytes by age, race, and sex; define a baseline cell-cytokine network; and describe immunologic changes in pregnancy. All data in the resource are available for visualization and download at http://10kimmunomes.org/.

Immunology researchers are beginning to explore the possibilities of reproducibility, reuse and secondary analyses of immunology data. Open-access datasets are being applied in the validation of the methods used in the original studies, leveraging studies for meta-analysis, or generating new hypotheses. To promote these goals, the ImmPort data repository was created for the broader research community to explore the wide spectrum of clinical and basic research data and associated findings. The ImmPort ecosystem consists of four components-Private Data, Shared Data, Data Analysis, and Resources-for data archiving, dissemination, analyses, and reuse. To date, more than 300 studies have been made freely available through the Shared Data portal (www.immport.org/immport-open), which allows research data to be repurposed to accelerate the translation of new insights into discoveries.

Patient-derived tumor xenograft (PDX) mouse models have emerged as an important oncology research platform to study tumor evolution, mechanisms of drug response and resistance, and tailoring chemotherapeutic approaches for individual patients. The lack of robust standards for reporting on PDX models has hampered the ability of researchers to find relevant PDX models and associated data. Here we present the PDX models minimal information standard (PDX-MI) for reporting on the generation, quality assurance, and use of PDX models. PDX-MI defines the minimal information for describing the clinical attributes of a patient's tumor, the processes of implantation and passaging of tumors in a host mouse strain, quality assurance methods, and the use of PDX models in cancer research. Adherence to PDX-MI standards will facilitate accurate search results for oncology models and their associated data across distributed repository databases and promote reproducibility in research studies using these models. Cancer Res; 77(21); e62-66. ©2017 AACR.