Title: Genomic and digital pathology approaches to elucidate cancer dormancy
Abstract: Therapy resistance in cancer is often driven by a subpopulation of cells that are temporarily arrested in a non-proliferative, quiescent or ‘dormant’ state, which is difficult to capture and whose mutational drivers remain largely unknown. I will describe the methodology that we developed to uniquely and robustly identify this state from transcriptomic signals and characterised its prevalence and genomic constraints in solid primary tumours. We show dormancy preferentially emerges in the context of more stable, less mutated genomes which maintain TP53 integrity and lack the hallmarks of DNA damage repair deficiency, while presenting increased APOBEC mutagenesis. We employ an ensemble elastic net regression model to uncover novel genomic dependencies of this process, including the amplification of a centrosomal protein as a driver of dormancy impairment. We also further refine our methodology to quantify dormancy signals in single cell datasets and link it with resistance to various therapies. Finally, we employ deep learning and graph-based approaches on digital pathology slides to show tumour dormancy is detectable within the cancer tissue, and describe its broader spatial distribution and interactions with other cells in the microenvironment.