About our research
We apply computational approaches to understand cancer biology and improve molecular biomarkers for precision oncology. We work primarily in breast cancer, while also using large pan-cancer datasets to identify biological principles that are shared across tumour types. A central goal of our research is to determine when molecular information can add clinically meaningful information beyond established pathological and clinical markers.
Our current research focuses on three interconnected areas:
- Cancer biology with a cell cycle focus: using bulk and single-cell transcriptomics to investigate tumour heterogeneity, cell-cycle activity and their relationships with tumour biology and patient outcome.
- The tumour microenvironment: investigating the composition and spatial organisation of immune and stromal cells, and how interactions between tumour and non-tumour cells relate to prognosis and treatment response.
- AI, digital pathology and biomarkers: integrating histopathology images with genomic, transcriptomic and clinical data to develop computational biomarkers and explore what molecular characteristics can be inferred directly from routinely collected tumour tissue.
Across these areas, we combine large clinical cohorts with genomics, transcriptomics, computational pathology and machine learning. Our long-term aim is to translate complex molecular data into robust biomarkers that improve patient stratification and support more personalised cancer care.