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Purpose: The volume of data produced by the biomedical research community continues to grow rapidly. Efficient collection, curation, and sharing of these data are expected to enable researchers to synthesize larger datasets and apply novel methods to discover new patterns for diagnosis, treatment, and care of disease. Consequently, data platforms that support these functionalities have become an important instrument for the biomedical research community. The National Cancer Institute’s (NCI’s) Cancer Research Data Commons (CRDC) is a data platform for the cancer research community that provides cloud-based, secure storage and analytic tools for cancer data, including genomics, proteomics, imaging, and clinical trial data. CRDC has been funded by multiple sources, including the Beau Biden Cancer Moonshot program. Achieving long-term sustainability of the CRDC program is the focus of our study. Methods: We applied a Financial Operations (FinOps) framework to map all costs to functionalities provided by the CRDC, including processes such as data intake, storage, compute, user support, and project management. We established a comprehensive financial baseline for the operations of the CRDC, identified opportunities for optimization across multiple dimensions (people, processes, technology), projected future costs based on trends for cancer data, and evaluated sustainability under defined funding scenarios. Results: We identified 15 recommendations for optimization of the CRDC, including automation and centralization of core services (e.g., intake, curation, indexing), optimization of storage (e.g., compression, archiving), and harmonization of common services across the CRDC components (e.g., common architecture, governance). If implemented, we could demonstrate that these recommendations offer a path to long-term sustainability of the CRDC program under a variety of funding scenarios. Conclusion: Data platforms such as the CRDC will see rapid increases in operational costs due to the growing data volumes and demand from researchers. Optimizing governance and operational frameworks will result in efficiency gains that can ensure long-term sustainability. Taking these steps will be critical to provide the necessary infrastructure to advance the field of data-driven biomedical research. Citation Format: Juergen A. Klenk, Angela Maggio, Bhavani S. Singh, Erin N. Byrne, Erika Kim, Tanja M. Davidsen. A path to sustainability for the National Cancer Institute's Cancer Research Data Commons. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6582.

Inversion of chromosome 16 (inv(16)) generates a fusion gene CBFB-MYH11, which is a driver mutation for acute myeloid leukemia (AML). Gene expression profiling suggests that Gata2, a hematopoietic transcription factor, is a top upregulated genes in preleukemic Cbfb-MYH11 knockin mice and is expressed in human inv(16) AML. On the other hand, we have also identified recurrent monoallelic deletions of GATA2 in relapsed human CBF-AML patients. To clarify the role of Gata2 in leukemogenesis by Cbfb-MYH11, we generated conditional Cbfb-MYH11 knockin mice with Gata2 heterozygous knockout. Gata2 heterozygous knockout reduced abnormal myeloid progenitors, which are capable of inducing leukemia in the Cbfb-MYH11 mice. Consequently, Cbfb-MYH11 mice with Gata2 heterozygous knockout developed leukemia with longer latencies than those with intact Gata2. Interestingly, leukemic cells with Gata2 heterozygous knockout gained higher number of mutations and showed more aggressive phenotype in both primary and transplanted mice. Moreover, leukemic cells with Gata2 heterozygous knockout showed higher repopulating capacity in competitive transplantation experiments. In summary, reduction of Gata2 activity affects mutational dynamics of leukemia with delayed leukemia onset in Cbfb-MYH11 knockin mice, but paradoxically results in a more aggressive leukemia phenotype, which may be correlated with leukemia relapse or poor prognosis in human patients.

The objective of the National Cancer Institutes' Proteomic Data Commons (PDC) is to make cancer-related proteomic datasets accessible to the public. The PDC provides the cancer research community with a unified data repository that enables data sharing across cancer proteomic studies and also enables multi-omic integration in support of precision medicine. As a domain-specific repository within the Cancer Research Data Commons (CRDC), the vision for the PDC is to provide researchers the ability to find and analyze proteomic data across a wide variety of tumor types. Currently, the PDC houses data, supported by a large collection of metadata attributes, for nearly 40 datasets from over 12 cancer types produced by several large-scale cancer research programs, each with cohort sizes greater than 100 patients. The PDC facilitates the analysis of proteomic, genomic, and imaging data derived from the same tumor. Most of the datasets in the PDC also have corresponding genomic and imaging data available in the Genomic Data Commons and The Cancer Imaging Archive respectively. Researchers can discover which genomic variants are detectable at the protein-level or better understand associations between gene expression, copy number variation, and protein abundance. The resource is currently available to the public in beta phase (https://pdc.esacinc.com) and will be officially launched on the cancer.gov domain in March 2020. The PDC data portal is supported by a robust and extensible data model and provides user-friendly exploration, visualization and data analysis. This allows researchers to search for and visualize expression of proteins (through their mapped genes) across all studies, analyze protein abundance for all cases in a study through heatmaps, build and explore pan-cancer cohorts using highly curated, clinical metadata, and comprehensively view a study without needing to download the data. The PDC provides quick access to mapping of peptide identities and quantities on the human genome as well as protein databases containing patient/tumor-specific variants and novel splicing events. It also enables fast, accurate, and convenient proteomic validation of novel genomic alterations through the PepQuery algorithm. Through a highly versatile application programming interface (API), PDC allows users to interact with data programmatically and facilitates integration with data from other resources in their scripts for multi-omic analysis. Big data interoperability is critical for progress in precision medicine. PDC is designed to interoperate with other resources including the CRDC nodes, allowing users to analyze PDC data with the tools and pipelines available on the NCI cloud resources. It further allows users to use their own tools to co-analyze genomic and proteomic data available from a common sample on Amazon Web Services (AWS) platform or on a local system. The presentation will provide an overview of the PDC and it's available datasets, as well as a discussion of how it facilitates multi-omic data analyses. Citation Format: Ratna Rajesh Thangudu, Paul A. Rudnick, Michael Holck, Deepak Singhal, Michael J. MacCoss, Nathan J. Edwards, Karen A. Ketchum, Christopher R. Kinsinger, Erika Kim, Anand Basu. Proteomic Data Commons: A resource for proteogenomic analysis [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr LB-242.