癌症复杂性知识门户：一个符合FAIR原则的资源发现工具

Enabling discovery of interoperable data and sustainable computational tools can help maximize the value of shared resources, supporting both human- and machine-driven reuse. The Cancer Complexity Knowledge Portal (CCKP), developed by Sage Bionetworks' Multi-Consortia Coordinating (MC²) Center with support from the NCI Division of Cancer Biology (DCB), serves as a public, NIH-supported, domain-specific repository that consolidates cancer research resources from multiple DCB consortia, including CSBC, PS-ON, TEC, CCBIR, MetNet, and PDMC. The portal provides a unified entry point for discovering and accessing datasets, computational tools, publications, and other outputs generated by the cancer research community. To support sharing and discoverability across diverse resources and data modalities, the CCKP employs versioned metadata models, which are maintained in a public GitHub repository and aligned with NIH Common Data Elements (CDEs). Metadata are curated through the Synapse platform, which functions as the backend repository supporting submission, review, and release via the CCKP. The portal aggregates metadata and links to original records, thereby accommodating materials stored in web-accessible archives. A unified search interface, clickable filters, and related resource links are available to help users navigate to relevant entries. The CCKP includes a number of resource-specific features to improve FAIRness: Croissant metadata is available for datasets, to support reuse in AI and ML contexts; the Cancer Complexity Toolkit provides auto-generated evaluation data, representing the sustainability and reusability of catalogued research software; visitors can access modularized educational resources via integration with the Cancer Complexity Education Program. As of late 2025, the CCKP indexes research outputs from over 160 NCI-funded cancer grants, encompassing 4,178 publications, 1,029 datasets, and 321 computational tools. Curated datasets represent key biological themes, including drug resistance or sensitivity (574), tumor microenvironment (563), metastasis (358), evolution (289), and epigenetics (103). These datasets span multiple species models, including human, mouse, and organoid systems, and data modalities such as sequencing, imaging, and spatial profiling. By surfacing a curated metadata database with intuitive search tools, the CCKP operationalizes FAIR-aligned discovery to amplify reuse of existing resources. This approach serves both experimental and computational research communities, enhancing transparency, interoperability, and secondary use of cancer data to accelerate mechanistic insights and therapeutic innovation. ChatGPT 5.1 was used for initial abstract drafting and refinement. All content was evaluated and approved by the authors.