Multicenter self-supervised computational pathology identifies prognostic histomorphological phenotypes in colorectal cancer
bioRxiv (Cold Spring Harbor Laboratory)
Abstract
Abstract H&E whole-slide images capture prognostic information encoded in tumor morphology and the surrounding microenvironment, but these signals remain difficult to extract and interpret at scale. Here, we developed a self-supervised computational pathology framework to predict disease-free survival in colorectal cancer and link model-derived risk to interpretable histomorphology and spatial tumor biology. Using a multicenter developmental cohort spanning colorectal adenomas and invasive colorectal cancer, we trained HPL-PanColon, a self-supervised representation model, to extract tile-level embeddings and identify recurrent histomorphological phenotype clusters across the adenoma-carcinoma spectrum. Compared with general-purpose pathology foundation models, HPL-PanColon yielded representations with reduced institution- and dataset-specific batch effects. We then applied HPL-PanColon to a global survival cohort of 1,024 colorectal cancer patients in a leave-one-institution-out framework, using tile embeddings to train an attention-based survival model and derive the Colon Histomorphology Prognostic Score (CHiPS). CHiPS stratified patients by disease-free survival and provided complementary prognostic information to a UICC TNM-informed clinicopathological model, increasing the c-index from 0.683 to 0.706. Integrating model attention with phenotype assignments traced CHiPS-associated risk to pathologist-recognizable tissue patterns, with high-risk regions enriched for desmoplastic, stromal, and fibroinflammatory morphologies and low-risk regions reflecting tumor-rich epithelial glandular patterns. Spatial transcriptomic analysis further linked high-risk morphologies to fibroblastic, perivascular, myofibroblastic, and immune-reactive tumor microenvironment programs, while low-risk morphologies mapped to epithelial and tumor-enriched regions. These findings establish a scalable framework for interpretable histology-based prognosis and spatial biological discovery in colorectal cancer.
Authors 9
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Leiden University Medical Center
Affiliation as printed
Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands
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Affiliation as printed
Department of Pathology, New York University Grossman School of Medicine, New York, NY, USA
Division of Precision Medicine, Department of Medicine, Grossman School of Medicine, New York University, New York, New York
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Affiliation as printed
Applied Bioinformatics Laboratories, New York University Grossman School of Medicine, New York, NY, USA
Division of Precision Medicine, Department of Medicine, Grossman School of Medicine, New York University, New York, New York
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Affiliation as printed
Department of Pathology, New York University Grossman School of Medicine, New York, NY, USA
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Affiliation as printed
Department of Pathology, New York University Grossman School of Medicine, New York, NY, USA
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Leiden University Medical Center
Affiliation as printed
Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands
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Affiliation as printed
Department of Pathology, New York University Grossman School of Medicine, New York, NY, USA
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Leiden University Medical Center
Affiliation as printed
Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands
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Aristotelis Tsirigos corresponding
Affiliation as printed
Applied Bioinformatics Laboratories, New York University Grossman School of Medicine, New York, NY, USA
Department of Pathology, New York University Grossman School of Medicine, New York, NY, USA
Division of Precision Medicine, Department of Medicine, Grossman School of Medicine, New York University, New York, New York
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