Abstract
Many cancers are characterised by the presence of a rare subpopulation of quiescent stem-like cells which are a source of heterogeneity, phenotypic therapy resistance, relapse and further disease evolution. These cells are able to evade cytotoxic chemotherapy, which selectively targets proliferating cells, thus representing a major therapeutic challenge. The mechanisms controlling quiescence remain largely unknown, and transcriptomic analysis of quiescent cells has yielded only limited insights. Proteomic analysis of rare cancer stem cells requires the development of a low-input methodology in order to capture global protein changes and differentially expressed post-translational modifications. To identify and isolate rare quiescent cells, we generated and validated a fluorescent label-retaining in vitro model using patient-derived glioblastoma cell lines and used somatic mouse models with the same inducible reporter. Using these model systems, we have demonstrated the presence of a pre-existing quiescent population and shown that these cells exhibit self-renewal capacity and increased therapy resistance. We have developed a robust proteomic workflow for analysing rare FACS-sorted cells, using multiplexed isobaric labelling and phosphopeptide enrichment. This method enables the processing of protein loads down to the low-microgram/sub-microgram level to identify signalling networks that govern quiescence. Our findings indicate that the quiescent proteome is conserved across models of glioblastoma, regardless of driver mutations. Network modelling with causal integration of multi-omic data has led to the identification of potential mechanisms of resistance and targetable vulnerabilities. Functional assays indicate PML as a potential therapeutic target for overcoming quiescence mediated therapy resistance in glioblastoma. We propose extracellular interferon signalling initiates upregulation of PML leading to suppression of mTORC1-mediated translation and ultimately entry to cellular quiescence. By bridging the gap in knowledge between transcriptomic regulation and protein-driven phenotypes, we aim to contribute to the understanding of phenotypic therapy resistance and provide new strategies for modulating quiescence.