{"ID":2825512,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.22262","arxiv_id":"2512.22262","title":"INSIGHT: Spatially resolved survival modelling from routine histology crosslinked with molecular profiling reveals prognostic epithelial-immune axes in stage II/III colorectal cancer","abstract":"Routine histology contains rich prognostic information in stage II/III colorectal cancer, much of which is embedded in complex spatial tissue organisation. We present INSIGHT, a graph neural network that predicts survival directly from routine histology images. Trained and cross-validated on TCGA (n=342) and SURGEN (n=336), INSIGHT produces patient-level spatially resolved risk scores. Large independent validation showed superior prognostic performance compared with pTNM staging (C-index 0.68-0.69 vs 0.44-0.58). INSIGHT spatial risk maps recapitulated canonical prognostic histopathology and identified nuclear solidity and circularity as quantitative risk correlates. Integrating spatial risk with data-driven spatial transcriptomic signatures, spatial proteomics, bulk RNA-seq, and single-cell references revealed an epithelium-immune risk manifold capturing epithelial dedifferentiation and fetal programs, myeloid-driven stromal states including $\\mathrm{SPP1}^{+}$ macrophages and $\\mathrm{LAMP3}^{+}$ dendritic cells, and adaptive immune dysfunction. This analysis exposed patient-specific epithelial heterogeneity, stratification within MSI-High tumours, and high-risk routes of CDX2/HNF4A loss and CEACAM5/6-associated proliferative programs, highlighting coordinated therapeutic vulnerabilities.","short_abstract":"Routine histology contains rich prognostic information in stage II/III colorectal cancer, much of which is embedded in complex spatial tissue organisation. We present INSIGHT, a graph neural network that predicts survival directly from routine histology images. Trained and cross-validated on TCGA (n=342) and SURGEN (n=...","url_abs":"https://arxiv.org/abs/2512.22262","url_pdf":"https://arxiv.org/pdf/2512.22262v1","authors":"[\"Piotr Keller\",\"Mark Eastwood\",\"Zedong Hu\",\"Aimée Selten\",\"Ruqayya Awan\",\"Gertjan Rasschaert\",\"Sara Verbandt\",\"Vlad Popovici\",\"Hubert Piessevaux\",\"Hayley T Morris\",\"Petros Tsantoulis\",\"Thomas Alexander McKee\",\"André D'Hoore\",\"Cédric Schraepen\",\"Xavier Sagaert\",\"Gert De Hertogh\",\"Sabine Tejpar\",\"Fayyaz Minhas\"]","published":"2025-12-24T14:36:15Z","proceeding":"q-bio.QM","tasks":"[\"q-bio.QM\",\"cs.LG\"]","methods":"[\"Graph Neural Network\",\"Generative Adversarial Network\"]","has_code":false}