Genomic triggers of cellular dormancy in cancer and therapeutic opportunities

Cellular dormancy has often been observed in cancer as one of the contributing factors of resistance to chemotherapy, radiotherapy or other treatments that target cycling cells. During cancer development and after treatment, a small population of cells emerges that are malignant but do not proliferate and instead are temporarily blocked in the G0 cell cycle phase, thereby becoming 'dormant' or 'quiescent'. This allows them to evade treatments such as chemotherapy and to eventually start dividing again so as to enable metastatic dissemination. The molecular changes that enable the transition to this cellular state during cancer are largely unknown. We hypothesize that tumours contain distinct levels of quiescent cells and this determines their ability to overcome therapies targeting cycling cells. The aim of this PhD project is to identify the genomic triggers underlying quiescence across multiple cancer tissues and to develop a predictive model of response to chemo- and radiotherapy based on this information. The PhD work will build upon a preliminary framework that the student has developed in oesophageal cancer during her rotation, which is already highlighting several mutational processes associated with cancer quiescence. To achieve the goals of the project, we will evaluate the quiescent potential of primary tumours from bulk RNA-sequencing data based on carefully curated and computationally derived quiescence markers. Our ability to quantify the quiescence potential will also be tested using publicly available single cell datasets. Subsequently, we will search for genomic features associated with the quiescent potential in distinct cancer tissues using data integration and machine learning approaches. Mutational mechanisms that enable cell state transitions to dormancy will be explored in depth and modelled using bulk and single cell data. Linked epigenetic modifications facilitating cellular plasticity will also be identified. This analysis will highlight potential biomarkers predictive of responses to chemo- or radiotherapy, thereby allowing us to build a classifier that distinguishes patients at high risk of resistance or relapse upon these treatments. The findings will be validated in collaboration with labs from Imperial College and the University of Cambridge.