My work sits at the interface of cancer cell biology and computational method development, centred on one question: what governs the switch between dormant and proliferative cell states, and how is that switch organised in space within a tumour.
Cancer cells can pause in a reversible, non-dividing G0 state that survives chemotherapy and later reactivates to drive relapse. I study the regulatory logic of entry into, maintenance of and exit from quiescence, and how proteostasis remodelling helps cells sustain this arrested state under stress.
Bulk profiling averages away the heterogeneity that often determines treatment response. I use single-cell RNA sequencing to resolve individual cell states and lineage trajectories in tumours and their microenvironment, from wound infection models to breast cancer atlases.
Where a cell sits within a tissue shapes what it becomes. I develop analytical frameworks, including EnrichMap, for spatially informed enrichment and niche detection, so gene expression can be interpreted in its tissue context rather than in isolation.
Quiescent and proliferative tumour cells don’t exist in isolation: they organise into niches alongside immune and stromal cells. I map how this compartmentalisation forms and how it relates to immune evasion in breast, brain and liver metastases.
Much of my current work uses breast cancer as a model system for studying tumour cell plasticity, building single-cell and spatial atlases, including a dedicated G0 breast cancer atlas, to track how tumours balance growth against dormancy.
Behind each biological question sits a pipeline: statistical models, workflows and reproducible code for processing large-scale sequencing data in R and Python, built to be reused rather than rewritten for every new project.
Existing tools weren’t built with spatial and dormancy questions in mind, so I build new ones. Open-source packages such as EnrichMap aim to give other researchers spatially aware statistical methods they can drop straight into their own analyses.