ArXiv Preprint
Tumour heterogeneity in breast cancer poses challenges in predicting outcome
and response to therapy. Spatial transcriptomics technologies may address these
challenges, as they provide a wealth of information about gene expression at
the cell level, but they are expensive, hindering their use in large-scale
clinical oncology studies. Predicting gene expression from hematoxylin and
eosin stained histology images provides a more affordable alternative for such
studies. Here we present BrST-Net, a deep learning framework for predicting
gene expression from histopathology images using spatial transcriptomics data.
Using this framework, we trained and evaluated 10 state-of-the-art deep
learning models without utilizing pretrained weights for the prediction of 250
genes. To enhance the generalisation performance of the main network, we
introduce an auxiliary network into the framework. Our methodology outperforms
previous studies, with 237 genes identified with positive correlation,
including 24 genes with a median correlation coefficient greater than 0.50.
This is a notable improvement over previous studies, which could predict only
102 genes with positive correlation, with the highest correlation values
ranging from 0.29 to 0.34.
Md Mamunur Rahaman, Ewan K. A. Millar, Erik Meijering
2023-03-17
