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.
Bibliographical noteAcknowledgements The authors thank Aurelien de Reynies for advice on CMS calling in FFPE blocks, Claire Butler and Michael Youdell for excellent managing in S:CORT and the MRC Clinical Trials Unit who provided the clinical data from the FOCUS trial with permission from the FOCUS trial steering group. We would further like to thank Indica Labs for providing the HALO software.
The S:CORT consortium is a Medical Research Council stratified medicine consortium jointly funded by the MRC and CRUK. This work was further supported by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre. Computation used the Oxford Biomedical Research Computing (BMRC) facility, a joint development between the Wellcome Centre for Human Genetics and the Big Data Institute supported by Health Data Research UK and the NIHR Oxford Biomedical Research Centre. Jens Rittscher is supported through the EPSRC funded Seebibyte programme (EP/M013774/1). Viktor H Koelzer gratefully acknowledges funding by the Swiss National Science Foundation (P2SKP3_168322/1 and P2SKP3_168322/2), and the Promedica Foundation F-87701-41-01. The authors thank Aurelien de Reynies for advice on CMS calling in FFPE blocks, Claire Butler and Michael Youdell for excellent managing in S:CORT and the MRC Clinical Trials Unit who provided the clinical data from the FOCUS trial with permission from the FOCUS trial steering group. We would further like to thank Indica Labs for providing the HALOTM software. The results published or shown here based in part upon data generated by the TCGA Research Network established by the NCI and NHGRI. Information about TCGA and the investigators and institutions who constitute the TCGA research network can be found at http://cancergenome.nih.gov. We would specifically like to thank all patients who have consented to take part in S:CORT and TCGA. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.