Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unannotated pathology slides

Adalberto Claudio Quiros, Nicolas Coudray, Anna Yeaton, Xinyu Yang, Bojing Liu, Hortense Le, Luis Chiriboga, Afreen Karimkhan, Navneet Narula, David A. Moore, Christopher Y. Park, Harvey Pass, Andre L. Moreira, John Quesne (), Aristotelis Tsirigos () and Ke Yuan ()
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Adalberto Claudio Quiros: University of Glasgow
Nicolas Coudray: NYU Grossman School of Medicine
Anna Yeaton: NYU Grossman School of Medicine
Xinyu Yang: University of Glasgow
Bojing Liu: NYU Grossman School of Medicine
Hortense Le: NYU Grossman School of Medicine
Luis Chiriboga: NYU Grossman School of Medicine
Afreen Karimkhan: NYU Grossman School of Medicine
Navneet Narula: NYU Grossman School of Medicine
David A. Moore: University College London Hospital
Christopher Y. Park: NYU Grossman School of Medicine
Harvey Pass: NYU Grossman School of Medicine
Andre L. Moreira: NYU Grossman School of Medicine
John Quesne: University of Glasgow
Aristotelis Tsirigos: NYU Grossman School of Medicine
Ke Yuan: University of Glasgow

Nature Communications, 2024, vol. 15, issue 1, 1-24

Abstract: Abstract Cancer diagnosis and management depend upon the extraction of complex information from microscopy images by pathologists, which requires time-consuming expert interpretation prone to human bias. Supervised deep learning approaches have proven powerful, but are inherently limited by the cost and quality of annotations used for training. Therefore, we present Histomorphological Phenotype Learning, a self-supervised methodology requiring no labels and operating via the automatic discovery of discriminatory features in image tiles. Tiles are grouped into morphologically similar clusters which constitute an atlas of histomorphological phenotypes (HP-Atlas), revealing trajectories from benign to malignant tissue via inflammatory and reactive phenotypes. These clusters have distinct features which can be identified using orthogonal methods, linking histologic, molecular and clinical phenotypes. Applied to lung cancer, we show that they align closely with patient survival, with histopathologically recognised tumor types and growth patterns, and with transcriptomic measures of immunophenotype. These properties are maintained in a multi-cancer study.

Date: 2024
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DOI: 10.1038/s41467-024-48666-7

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