Reading the map: An invitation to the resources of the Human Tumor Atlas Network

Purpose: The Human Tumor Atlas Network (HTAN) sets out to map the cellular and molecular architecture of human tumors over space and time to advance precision oncology. The HTAN Data Coordinating Center (DCC) underpins this mission by standardizing, integrating, and distributing multimodal data; ensuring legacy and impact. Methods: The DCC developed a scalable cloud ecosystem (Synapse.org, Google BigQuery, custom data portal) for data ingestion, governance, validation, and dissemination. A community-driven process, aligned with NCI standards, produced a consistent metadata schema that ensures interoperability across rich clinical and biospecimen information and all data modalities. The HTAN data portal provides a single landing page for users of HTAN data featuring filter-based search, visualization of imaging and single-cell datasets and detailed documentation. Data are disseminated via a tiered model, with imaging and dbGaP-controlled access sequencing data available from NCI Cancer Research Data Commons General Commons and open-access processed data on Synapse.org. Current work transitions to a modular LinkML data model, adds an AI-assisted curation interface, and includes a streamlined medallion architecture in BigQuery and portal enhancements. Results: The first five years of HTAN (release v7.0) produced 334 TB of multimodal data (0.23M files) from 2,372 cases and 11,378 biospecimens, spanning >60 disease types and >25 assays. HTAN supports >3,400 unique global users a month. The most frequently accessed data includes scRNA-seq (1075 downloading users), multiplex imaging (197), and spatial transcriptomics (419). Analysis of dbGaP requests to-date (N=127) shows broad academic (75%) and industry use with research themes focused on genome instability (115 requests), immune evasion (52), and metastasis (49), often with multi-omic and AI-driven approaches. Conclusions: HTAN has established a globally utilized, harmonized foundation for spatially resolved, multimodal cancer research. This enduring platform for community-driven discovery is built on robust data management and transparent access models. The next phase deepens integration of spatial and clinical data, expanding AI-readiness, and strengthening support for reuse and reproducibility. We invite researchers to engage with this resource. ChatGPT 5, Gemini 2.5 Pro, & Claude Sonnet 4.5 were used to summarize usage metrics, conduct thematic analysis of dbGaP applications, and initial abstract drafting. All content was evaluated and approved by the authors.