  Molecular phenotyping by gene expression profiling is common in contemporary
cancer research and in molecular diagnostics. However, molecular profiling
remains costly and resource intense to implement, and is just starting to be
introduced into clinical diagnostics. Molecular changes, including genetic
alterations and gene expression changes, occuring in tumors cause morphological
changes in tissue, which can be observed on the microscopic level. The
relationship between morphological patterns and some of the molecular
phenotypes can be exploited to predict molecular phenotypes directly from
routine haematoxylin and eosin (H&E) stained whole slide images (WSIs) using
deep convolutional neural networks (CNNs). In this study, we propose a new,
computationally efficient approach for disease specific modelling of
relationships between morphology and gene expression, and we conducted the
first transcriptome-wide analysis in prostate cancer, using CNNs to predict
bulk RNA-sequencing estimates from WSIs of H&E stained tissue. The work is
based on the TCGA PRAD study and includes both WSIs and RNA-seq data for 370
patients. Out of 15586 protein coding and sufficiently frequently expressed
transcripts, 6618 had predicted expression significantly associated with
RNA-seq estimates (FDR-adjusted p-value < 1*10-4) in a cross-validation. 5419
(81.9%) of these were subsequently validated in a held-out test set. We also
demonstrate the ability to predict a prostate cancer specific cell cycle
progression score directly from WSIs. These findings suggest that contemporary
computer vision models offer an inexpensive and scalable solution for
prediction of gene expression phenotypes directly from WSIs, providing
opportunity for cost-effective large-scale research studies and molecular
diagnostics.
