{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T12:07:56Z","timestamp":1784549276414,"version":"3.55.0"},"posted":{"date-parts":[[2026,7,15]]},"group-title":"Bioinformatics","reference-count":51,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T00:00:00Z","timestamp":1784073600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,7,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>\n                  Most spatial transcriptomic analyses of solid tumors focus on individual cell states or single-sample spatial domains rather than on recurrent multicellular tissue architectures shared across patients, and typically depend on predefined cell-type annotations or compartment definitions. We developed STORM (Spatial Topology analysis of Recurrent Motifs), an unsupervised graph-attention variational autoencoder that learns recurrent spatial motifs directly from cell-level graph structure and molecular profiles without cell-type labels, manual annotation, or predefined compartments. We applied to 32 Xenium sections from 16 patients with paired early-onset (EOCRC) and average-onset (AOCRC) colorectal cancer, STORM identified 10 recurrent motifs that self-organized into tumor-parenchymal, stromal, and immune macro-compartments. Among these, the Desmoplastic Fibrotic Barrier (DFB) motif, a CAF- and ECM-rich boundary architecture, was associated with restricted CD8\n                  <jats:sup>+<\/jats:sup>\n                  T-cell geodesic access to tumor cores independently of CD8\n                  <jats:sup>+<\/jats:sup>\n                  abundance, as demonstrated by abundance-normalized neighborhood enrichment statistics and within-sample mixed-effects models. EOCRC selectively amplified this barrier\u2013exclusion architecture, exhibiting tighter tumor parenchyma, denser DFB shells, and a DFB-specific ECM activation program that yielded an age-specific prognostic signature in TCGA-COAD. Translation of motif macro-classes to H&amp;E images via a Vision Transformer classifier produced an image-derived DFB-barrier composite that predicted overall survival in advanced-stage TCGA colorectal cancer. STORM provides an annotation-free framework for discovering recurrent spatial motifs and identifies a fibroblast barrier architecture whose topological association with immune exclusion is independent of effector abundance, amplified in early-onset disease, and translatable to a deployable pathology-based prognostic biomarker.\n                <\/jats:p>","DOI":"10.64898\/2026.07.09.737584","type":"posted-content","created":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T05:25:11Z","timestamp":1784179511000},"source":"Crossref","is-referenced-by-count":0,"title":["Spatial Topology Reveals Biologically Distinct Recurrent Motifs in Colorectal Cancer"],"prefix":"10.64898","author":[{"given":"Jia","family":"Yao","sequence":"first","affiliation":[{"name":"Quantitative Biomedical Research Center, Peter O\u2019Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, Texas, 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