Abstract
Digital analysis of pathology whole-slide images is fast becoming a game changer in cancer diagnosis and treatment. Specifically, deep learning methods have shown great potential to support pathology analysis, with recent studies identifying molecular traits that were not previously recognized on pathology H&E whole-slide images. Simultaneous to these developments, it is becoming increasingly evident that tumor heterogeneity is an important determinant of cancer prognosis and susceptibility to treatment, and should therefore play a role in the evolving practices of matching treatment protocols to patients. State of the art diagnostic procedures, however, do not provide scalable methods for characterizing and/or quantifying tumor heterogeneity, certainly not in a spatial context. In this paper, we present a scalable approach that accurately and automatically spatially resolves mRNA and miRNA expression levels on pathology whole-slide images. This is the first demonstration of this type of inference from H&E images. We use this method to produce tumor molecular cartographies and to characterize certain aspects of tumor spatial transcriptomics. Specifically, we develop a heterogeneity index (HTI), derived from the molecular cartographies. Applying our methods to breast and lung cancer slides, we show a significant statistical link between heterogeneity and survival. Our results highlight the value of automated analysis of pathology whole slide images. Our methods potentially open a new approach to investigating tumor heterogeneity and other spatial molecular properties and their link to clinical characteristics, including treatment susceptibility and survival.
Competing Interest Statement
The authors have declared no competing interest.
