Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning

Author:

Sirinukunwattana Korsuk,Domingo EnricORCID,Richman Susan D,Redmond Keara L,Blake Andrew,Verrill Clare,Leedham Simon J,Chatzipli Aikaterini,Hardy Claire,Whalley Celina M,Wu Chieh-hsi,Beggs Andrew DORCID,McDermott Ultan,Dunne Philip DORCID,Meade Angela,Walker Steven M,Murray Graeme I,Samuel Leslie,Seymour Matthew,Tomlinson IanORCID,Quirke Phil,Maughan TimothyORCID,Rittscher JensORCID,Koelzer Viktor HORCID

Abstract

Objective Complex phenotypes captured on histological slides represent the biological processes at play in individual cancers, but the link to underlying molecular classification has not been clarified or systematised. In colorectal cancer (CRC), histological grading is a poor predictor of disease progression, and consensus molecular subtypes (CMSs) cannot be distinguished without gene expression profiling. We hypothesise that image analysis is a cost-effective tool to associate complex features of tissue organisation with molecular and outcome data and to resolve unclassifiable or heterogeneous cases. In this study, we present an image-based approach to predict CRC CMS from standard H&E sections using deep learning. Design Training and evaluation of a neural network were performed using a total of n=1206 tissue sections with comprehensive multi-omic data from three independent datasets (training on FOCUS trial, n=278 patients; test on rectal cancer biopsies, GRAMPIAN cohort, n=144 patients; and The Cancer Genome Atlas (TCGA), n=430 patients). Ground truth CMS calls were ascertained by matching random forest and single sample predictions from CMS classifier. Results Image-based CMS (imCMS) accurately classified slides in unseen datasets from TCGA (n=431 slides, AUC)=0.84) and rectal cancer biopsies (n=265 slides, AUC=0.85). imCMS spatially resolved intratumoural heterogeneity and provided secondary calls correlating with bioinformatic prediction from molecular data. imCMS classified samples previously unclassifiable by RNA expression profiling, reproduced the expected correlations with genomic and epigenetic alterations and showed similar prognostic associations as transcriptomic CMS. Conclusion This study shows that a prediction of RNA expression classifiers can be made from H&E images, opening the door to simple, cheap and reliable biological stratification within routine workflows.

Funder

National Institute for Health Research (NIHR) Oxford Biomedical Research Centre

Engineering and Physical Sciences Research Council

Medical Research Council UK

Cancer Research UK

Promedica Foundation

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

Publisher

BMJ

Subject

Gastroenterology

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