The All of Us Research Program is a historic effort to collect and study data from one million or more people living in the United States. The goal of the program is better health for all of us. Our mission is to accelerate health research and medical breakthroughs, enabling individualized prevention, treatment, and care for all of us. This mission is carried out through three connected focus areas that are supported and made possible by a team that maintains a culture built around the program's core values.

The goal of the Dependency Map (DepMap) portal is to empower the research community to make discoveries related to cancer vulnerabilities by providing open access to key cancer dependencies analytical and visualization tools.

The Cancer Epidemiology Descriptive Cohort Database (CEDCD) contains descriptive information about cohort studies that follow groups of persons over time for cancer incidence, mortality, and other health outcomes. The CEDCD is a searchable database that contains general study information (e.g., eligibility criteria and size), the type of data collected at baseline, cancer sites, number of participants diagnosed with cancer, and biospecimen information. All data included in this database are aggregated for each cohort; there are no individual level data. The goal of the CEDCD is to facilitate collaboration and highlight the opportunities for research within existing cohort studies.

The Cancer Genome Characterization Initiative (CGCI) uses molecular characterization to uncover distinct features of rare cancers. Current projects perform comprehensive molecular cataloging of HIV+ and other rare adult and pediatric cancers. The CGCI Data Matrix is a high-level catalog of data generated by the Initiative, with links to the sites where the controlled access data are hosted. The projects described on CGCI pages include Burkitt Lymphoma, HIV+ Tumor Molecular Characterization Project, Medulloblastoma-Complete, and Non-Hodgkin Lymphoma-Complete.

The Cancer Genomics Cloud (CGC), powered by Seven Bridges and funded by the NCI, is a flexible cloud platform that enables analysis, storage, and computation of large cancer datasets. The CGC provides a user-friendly portal to access and analyze cancer data where it lives. With the CGC, any user with an account can easily access petabytes of cancer data, share it, analyze and use the computational power of the cloud without having to learn how to program and get familiar with several different data portals.

The Cancer Research Institute (CRI) iAtlas is an interactive web platform and a set of analytic tools for studying interactions between tumors and the immune microenvironment. These tools allow researchers to explore associations between a variety of genomic characterizations of immune response, clinical phenotypes, germline genetics, and response to immunotherapy. Immune checkpoint inhibitor analysis modules allow for interactive exploration of the relationship between possible biomarkers of immune response and the outcome of response to checkpoint blockade, by direct comparison and using multivariable statistical models. Underlying these modules is a harmonization of primary sequencing data from 12 immuno-oncology trials with genomics data and matched clinical data available in the public domain. iAtlas also allows researchers to identify how tumor-intrinsic alterations, including mutations, copy-number alterations, and neoantigens relate to the immune microenvironment as evidenced in cancer genomic studies.

Patient-derived cancer models have become an essential tool in both cancer research, drug development and preclinical studies. Each model type - PDX, organoid and cell line - offers unique advantages and is better suited for specific research areas. Researchers, clinicians, bioinformaticians and analytical tool developers face the challenge of navigating a complex landscape to find suitable models and associated data across multiple commercial and academic resources without the benefit of shared data standards or interoperable data. CancerModels.Org aims to solve this problem by providing harmonized and integrated model attributes to support consistent searching across the originating resources.

The cBioPortal for Cancer Genomics is a resource for interactive exploration of multidimensional cancer genomics data sets. The goal of cBioPortal is to significantly lower the barriers between complex genomic data and cancer researchers by providing rapid, intuitive, and high-quality access to molecular profiles and clinical attributes from large-scale cancer genomics projects, and therefore to empower researchers to translate these rich data sets into biologic insights and clinical applications.

The Central Brain Tumor Registry of the United States (CBTRUS) is a not-for-profit research corporation, recognized by the international research community as the premier resource for annual histology-specific statistical information for all primary brain and other CNS tumors in the United States. The CBTRUS analytic database has been developed by compiling data from central (state) cancer registries, the District of Columbia, and Puerto Rico that include information on both malignant and non-malignant primary brain and other CNS tumors.

NCI's Childhood Cancer Data Initiative (CCDI) is building a community centered around childhood cancer care and research data. Through enhanced data sharing, we can improve our understanding of cancer biology to improve preventive measures, treatment, quality of life, and survivorship, as well as ensure that researchers learn from every child with cancer. While childhood cancers represent the leading cause of death in children over the age of 1, they are collectively rare, comprising about 1%-3% of cancers diagnosed annually in the United States. Information on diagnosis, treatment, and outcomes is often stored at the hospital or institution where a child is treated, making it difficult to answer scientific questions about childhood cancer. Sharing clinical care and research data generated by children's hospitals, clinics, or networks broadly with the community can help us learn faster and on a scale much larger than any single institution caring for children can learn on its own.