Despite enormous progress in the understanding, early diagnosis and treatment, solid tumors still account for a quarter of deaths. Solid tumors arise from somatic cells through the accumulation of molecular alterations that eventually lead to their uncontrolled proliferation, invasion of healthy tissues and ultimately, distant metastases. In current clinical practice, the best treatment is chosen by assessing clinical presentation, histological tumor type and molecular characteristics, such as oncogenic mutations and, in some cases, gene expression profiles. The evaluation of histomorphological tumor properties in combination with molecular profiles guides risk-adjusted, personalized therapies that aim to optimize outcome for individual patients. However, diagnostic molecular profiling currently mostly relies on techniques performed on bulk tissue without providing any spatial information about the observed molecular tumor properties. While this can already make the interpretation of mutational profiles challenging, spatially resolved profiling becomes indispensable for the molecular analysis of the tumor microenvironment composed of multiple different cell types whose complex interactions influence therapy response. In this context, single-cell sequencing techniques may offer a unique opportunity to offer both high spatial and molecular resolution in the context of complex tumor histology.
Recently, the fruitful interdisciplinary collaboration between clinical, technological, and computational researchers has cultivated rapid progress in the field of digital pathology. AI-based models provide support to diagnostic pathology, for instance by automating tumor identification and tissue classification and cell detection from tumor histology images. Current computational models, however, are limited in their ability to predict tumor molecular characteristics due to the scarcity of paired imaging and molecular training data. Development of spatial transcriptomics assays aim to fill this gap by enabling unbiased, transcriptome-wide profiling of mRNA expression in intact tissue sections. Thus, they represent an ideal source of such paired morphological and molecular data with unprecedented resolution for training the next generation of digital pathology algorithms.
The project aims to push forward the field of digital pathology by predicting gene expression in tissue space from histomorphology alone. To achieve that, we will train deep learning models on the high-resolution gene expression maps provided by spatial transcriptomics co-registered with the corresponding histomorphology images. Advances in explainable AI approaches will be leveraged to reveal which morphological features and areas are exploited by such models to predict gene expression. This would be vital to allow for a transparent decision process, crucial for medical applications, and would also deepen our understanding of tumor biology by correlating tissue and cellular composition/histomorphology of the tumor and its microenvironment with function. We anticipate such a computational approach to have a positive impact on clinical practices by facilitating the prediction of molecular properties from routine diagnostic H&E images and thus to complement or even partially replace molecular testing.