  Molecular phenotyping is central in cancer precision medicine, but remains
costly and standard methods only provide a tumour average profile. Microscopic
morphological patterns observable in histopathology sections from tumours are
determined by the underlying molecular phenotype and associated with clinical
factors. The relationship between morphology and molecular phenotype has a
potential to be exploited for prediction of the molecular phenotype from the
morphology visible in histopathology images.
  We report the first transcriptome-wide Expression-MOrphology (EMO) analysis
in breast cancer, where gene-specific models were optimised and validated for
prediction of mRNA expression both as a tumour average and in spatially
resolved manner. Individual deep convolutional neural networks (CNNs) were
optimised to predict the expression of 17,695 genes from hematoxylin and eosin
(HE) stained whole slide images (WSIs). Predictions for 9,334 (52.75%) genes
were significantly associated with RNA-sequencing estimates (FDR adjusted
p-value < 0.05). 1,011 of the genes were brought forward for validation, with
876 (87%) and 908 (90%) successfully replicated in internal and external test
data, respectively. Predicted spatial intra-tumour variabilities in expression
were validated in 76 genes, out of which 59 (77.6%) had a significant
association (FDR adjusted p-value < 0.05) with spatial transcriptomics
estimates. These results suggest that the proposed methodology can be applied
to predict both tumour average gene expression and intra-tumour spatial
expression directly from morphology, thus providing a scalable approach to
characterise intra-tumour heterogeneity.
