C-G.C.12: OncoSpat: A Harmonized Pan-Cancer Spatial Transcriptomics Atlas for Quality-Aware Exploration of Tumor Architecture
Spatial transcriptomics is becoming an important approach for studying cancer tissue architecture, heterogeneity, and microenvironment organization. However, publicly available cancer datasets remain highly heterogeneous in terms of preprocessing, metadata quality, format variability, which limits their usability and complicates large-scale, cross-study analyses. To address this, we are developing OncoSpat, a pan-cancer spatial transcriptomics atlas and interactive portal that currently integrates 1,229 spatial samples from manually curated public Visium and related in situ-capturing transcriptomics datasets across human and mouse tumors. OncoSpat is designed as a harmonized and continuously expandable atlas framework rather than a static collection. At this stage, the project focuses on building a standardized sample registry with unified metadata, source information, spatial asset availability, and sample readiness information. In parallel, we are establishing a harmonization workflow for consistent sample formatting, gene identifier standardization, and quality-aware spatial preprocessing. Planned analysis layers include spot-level quality control, spatial graph diagnostics, tissue fragmentation, hotspot-based region definition, and co-localization analysis, with the broader aim of identifying conserved spatial phenotypes and recurring niches across cancers. We are also developing a scalable Python-based benchmarking module to evaluate data transformation strategies for Visium spatial transcriptomics at cohort scale. The framework is further being organized as a modular workflow system with checkpointed analysis stages, explicit quality control review steps, and structured tracking of datasets and outputs. The first public release is planned for next year, with continued expansion through integration of newly published public cancer spatial datasets.
Co-authors: Geoffroy Andrieux, Sajib Chakraborty, Andreas Tsouris, Melanie Boerries
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