INSIGHT: Spatially resolved survival modelling from routine histology crosslinked with molecular profiling reveals prognostic epithelial-immune axes in stage II/III colorectal cancer
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| Autores principales: | , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| author | Keller, Piotr Eastwood, Mark Hu, Zedong Selten, Aimée Awan, Ruqayya Rasschaert, Gertjan Verbandt, Sara Popovici, Vlad Piessevaux, Hubert Morris, Hayley T Tsantoulis, Petros McKee, Thomas Alexander D'Hoore, André Schraepen, Cédric Sagaert, Xavier De Hertogh, Gert Tejpar, Sabine Minhas, Fayyaz |
| author_facet | Keller, Piotr Eastwood, Mark Hu, Zedong Selten, Aimée Awan, Ruqayya Rasschaert, Gertjan Verbandt, Sara Popovici, Vlad Piessevaux, Hubert Morris, Hayley T Tsantoulis, Petros McKee, Thomas Alexander D'Hoore, André Schraepen, Cédric Sagaert, Xavier De Hertogh, Gert Tejpar, Sabine Minhas, Fayyaz |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_22262 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | INSIGHT: Spatially resolved survival modelling from routine histology crosslinked with molecular profiling reveals prognostic epithelial-immune axes in stage II/III colorectal cancer Keller, Piotr Eastwood, Mark Hu, Zedong Selten, Aimée Awan, Ruqayya Rasschaert, Gertjan Verbandt, Sara Popovici, Vlad Piessevaux, Hubert Morris, Hayley T Tsantoulis, Petros McKee, Thomas Alexander D'Hoore, André Schraepen, Cédric Sagaert, Xavier De Hertogh, Gert Tejpar, Sabine Minhas, Fayyaz Quantitative Methods Machine Learning 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. |
| title | INSIGHT: Spatially resolved survival modelling from routine histology crosslinked with molecular profiling reveals prognostic epithelial-immune axes in stage II/III colorectal cancer |
| topic | Quantitative Methods Machine Learning |
| url | https://arxiv.org/abs/2512.22262 |