Abstract 2728: The Cancer Complexity Knowledge Portal: A FAIR-aligned tool for resource discovery.

Abstract 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. Citation Format: Orion Banks, Ashley Clayton, Aditi Gopalan, Amber Nelson, Verena Chung, Amy Heiser, Jay Hodgson, Aditya Nath, Adam Hindman, Milen Nikolov, Adam Taylor, Angie Bowen, Susheel Varma, Jineta Banerjee, . The Cancer Complexity Knowledge Portal: A FAIR-aligned tool for resource discovery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2728.

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Journal
Cancer Research
Published
2026-04-03
DOI
https://doi.org/10.1158/1538-7445.am2026-2728
Primary Topic
Radiomics and Machine Learning in Medical Imaging
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article
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article

Abstract 2728: The Cancer Complexity Knowledge Portal: A FAIR-aligned tool for resource discovery.

Aditi Gopalan, Ashley Clayton, Verena Chung, Jineta Banerjee et al.
Cancer Research
Radiomics and Machine Learning in Medical Imaging
article

Abstract 2728: The Cancer Complexity Knowledge Portal: A FAIR-aligned tool for resource discovery.

Aditi Gopalan, Ashley Clayton, Verena Chung, Jineta Banerjee, Amber Nelson, Milen Nikolov, Susheel Varma, Jay Hodgson, Aditya Nath, Adam Hindman, Amy Heiser, Angie Bowen, Adam Taylor, Orion Banks
article en

Abstract

Abstract 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. Citation Format: Orion Banks, Ashley Clayton, Aditi Gopalan, Amber Nelson, Verena Chung, Amy Heiser, Jay Hodgson, Aditya Nath, Adam Hindman, Milen Nikolov, Adam Taylor, Angie Bowen, Susheel Varma, Jineta Banerjee, . The Cancer Complexity Knowledge Portal: A FAIR-aligned tool for resource discovery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2728.

Cancer ResearchVol. 86(7_Supplement)
Sage Bionetworks (US)
Quality Education
Openalex Percentile: Top 5%
Radiomics and Machine Learning in Medical Imaging
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