In:
Gut, BMJ, Vol. 70, No. 3 ( 2021-03), p. 544-554
Abstract:
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.
Type of Medium:
Online Resource
ISSN:
0017-5749
,
1468-3288
DOI:
10.1136/gutjnl-2019-319866
Language:
English
Publisher:
BMJ
Publication Date:
2021
detail.hit.zdb_id:
1492637-4
