Gastric cancer is a major cause of global cancer mortality. To explore geospatial interactions in gastric tumors, we integrated 2,138 spatial transcriptomic regions of interest with 152,423 single-cell expression profiles across 226 cancer samples from 121 patients. We observed pervasive expression-based intratumor heterogeneity, recapitulating tumor progression through spatially localized and functionally ordered subgroups associated with specific immune microenvironments, checkpoint profiles, and genetic drivers (SOX9). Phylogenetic analysis revealed two separate evolutionary trajectories (branched evolution and internal diaspora evolution) associated with distinct molecular subtypes, clinical prognoses, and stromal neighborhoods, including VWF+ ACKR1+ endothelial cells. Spatial analysis of tumor–stroma interfaces across multiple gastric cancers highlighted new ecosystem states not attributable to mere tumor/stroma admixture, landmarked by increased GREM1 expression. Our results provide insights into how the cellular ecosystems of individual gastric cancers are sculpted by tumor-intrinsic and extrinsic selective pressures, culminating in individualized patient-specific cancer cartographies.

Significance:

Integration of spatial transcriptomic (GeoMx Digital Spatial Profiler) and single-cell RNA sequencing data from multiple gastric cancers identifies spatially resolved expression-based intratumoral heterogeneity, associated with distinct immune microenvironments. We uncovered two separate evolutionary trajectories associated with specific molecular subtypes, clinical prognoses, stromal neighborhoods, and genetic drivers. Tumor–stroma interfaces emerged as a unique state of tumor ecology.

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©2025 The Authors; Published by the American Association for Cancer Research
2025
American Association for Cancer Research
This open access article is distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License .
Attribution-NonCommercial-NoDerivatives

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Supplementary data

Supplementary Table 1-8

(1) Supplementary Table 1 shows GeoMx DSP ROI and scRNA-seq characteristics of samples profiled; (2) Supplementary Table 2 shows clinical characteristics of GeoMx DSP ROI and scRNA-seq samples profiled; (3) Supplementary Table 3A shows consistency between before and after batch effect removal on top enriched pathways in various ROI categories, 3B shows consistency between before and after batch effect removal on G1 and G2 labels on Tumor ROIs; (4) Supplementary Table 4 shows Silhouette scores of using different number of clusters in Tumor ROI hierarchical clustering; (5) Supplementary Table 5 shows detailed information of GeoMx ROIs in the TMA cohort; (6) Supplementary Table 6 shows top DEGs for endothelial cell subtypes (Endo1-Endo3); (7) Supplementary Table 7 shows top DEGs for macrophage cell subtypes (TAM1-TAM6); (8) Supplementary Table 8 shows top DEGs for fibroblast cell subtypes (Fib1-Fib4).

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Supplementary Figure 1-9

(1) Supplementary Figure 1 shows morphologies of different ROI categories and their representative genes, pathways and similarities with paired scRNA-seq data. (2) Supplementary Figure 2 shows validation of findings from the discovery GeoMx DSP cohort in the whole-section GeoMx DSP cohort and also the TMA GeoMx DSP cohort. (3) Supplementary Figure 3 shows experiments validating that intratumor G1/G2 subregion expression is tumor-intrinsic using multiplexed IHC, Stereoseq spatial transcriptomics, and in vitro Stereoseq drug treatment profiling. (4) Supplementary Figure 4 shows expression of individual immune checkpoints, chemokines, cytokines, molecular signatures (immune exhaustion, intracellular signaling) and emerging therapeutic targets in G1 and G2 subregions in the discovery and validation cohort. (5) Supplementary Figure 5 shows experiments validating that G2 RNA-ITH subregions have higher CNV burdens than G1 subregions using signature scores, CNV calling from scRNA-seq, and validation using WES and Stereoseq data. (6) Supplementary Figure 6 shows phylogenetic analysis and trajectory analysis of scRNA-seq tumor subgroups using various methods. (7) Supplementary Figure 7 shows survival analysis and multi-variate analysis validating that internal diaspora GC is associated with worse prognosis in various cohorts. (8) Supplementary Figure 8 shows functional validation experiments for the potential G2 markers SOX9, AGR2 and TSPAN8 using CellOracle, RNAi and CRISPR knock out assays. (9) Supplementary Figure 9 shows typical pathways, markers and cell types for tumor-stroma interface ROIs.

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