scverse Conference 2024

Gastric cancer is a highly heterogeneous tumor, not only in its clinical and biological behavior but also histologically. Numerous classification attempts have been made in the past, but these have relied on histological analysis and bulk sequencing, potentially failing to fully capture the complexity of this disease. Therefore, we collected single-cell level spatial transcriptomics (scST) data from large number of surgical cases. This gastric cancer atlas is unprecedented in scale for scST analysis, incorporating genomic mutation data and H&E-stained images, enabling more comprehensive analysis.
We used a deep neural network model to extract morphological features from pathological images and quantitatively integrated the three modalities of scST, genomic mutation information, and pathological images. This allowed for a comprehensive interpretation of how tumor cells with specific gene variation affect surrounding cells, form niches, and how these are reflected in histological variation. Using this atlas, we identified characteristic niches associated with drug response and elucidated the interactions between stromal cells and tumor cells as well as distinctive histology.
This study provides a new standard for detailed tumor characterization and potentially paves the way for advanced precision medicine in gastric cancer.