Self-Supervised Learning Reveals Clinically Relevant Histomorphological Patterns for Therapeutic Strategies in Colon Cancer

bioRxiv (Cold Spring Harbor Laboratory)

Abstract

Self-supervised learning (SSL) automates the extraction and interpretation of histopathology features on unannotated hematoxylin-and-eosin-stained whole-slide images (WSIs). We trained an SSL Barlow Twins-encoder on 435 TCGA colon adenocarcinoma WSIs to extract features from small image patches. Leiden community detection then grouped tiles into histomorphological phenotype clusters (HPCs). HPC reproducibility and predictive ability for overall survival was confirmed in an independent clinical trial cohort (N=1213 WSIs). This unbiased atlas resulted in 47 HPCs displaying unique and sharing clinically significant histomorphological traits, highlighting tissue type, quantity, and architecture, especially in the context of tumor stroma. Through in-depth analysis of these HPCs, including immune landscape and gene set enrichment analysis, and association to clinical outcomes, we shed light on the factors influencing survival and responses to treatments like standard adjuvant chemotherapy and experimental therapies. Further exploration of HPCs may unveil new insights and aid decision-making and personalized treatments for colon cancer patients.

Authors 11

1. Bojing Liu

   Karolinska Institutet · New York University

   Affiliation as printed

   Applied Bioinformatics Laboratories, New York University Grossman School of Medicine, New York, New York, USA

   Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Sweden

2. Meaghan Polack Aachen Department of Surgery, Leiden University Medical Center

   Leiden University Medical Center

   Affiliation as printed

   Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands

3. Nicolas Coudray

   New York University

   Affiliation as printed

   Applied Bioinformatics Laboratories, New York University Grossman School of Medicine, New York, New York, USA

   Department of Cell Biology, New York University Grossman School of Medicine, New York, New York, USA

4. Adalberto Claudio Quiros

   University of Glasgow

   Affiliation as printed

   Department of Computing Science, University of Glasgow, Glasgow, United Kingdom

5. Theodore Sakellaropoulos

   New York University

   Affiliation as printed

   Applied Bioinformatics Laboratories, New York University Grossman School of Medicine, New York, New York, USA

6. A. Stijn L. P. Crobach Aachen Department of Pathology, Leiden University Medical Center

   Leiden University Medical Center

   Affiliation as printed

   Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands

7. J. Han van Krieken

   Radboud University Medical Center

   Affiliation as printed

   Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands

8. Ke Yuan

   University of Glasgow

   Affiliation as printed

   Department of Computing Science, University of Glasgow, Glasgow, United Kingdom

   School of Cancer Sciences, University of Glasgow, Glasgow, Scotland, UK

9. Rob A.�E.�M. Tollenaar Aachen Department of Surgery, Leiden University Medical Center

   Leiden University Medical Center

   Affiliation as printed

   Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands

10. Wilma E. Mesker Aachen Department of Surgery, Leiden University Medical Center

    Leiden University Medical Center

    Affiliation as printed

    Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands

11. Aristotelis Tsirigos corresponding

    New York University

    Affiliation as printed

    Applied Bioinformatics Laboratories, New York University Grossman School of Medicine, New York, New York, USA

    Department of Pathology, New York University Grossman School of Medicine, New York, New York, USA