When I began working in cancer genomics, the goal seemed clear: catalogue mutations, define subtypes, and link them to outcomes. We were building increasingly detailed snapshots of tumours, and for a long time that worked. But as datasets have grown in scale and complexity, a different picture has started to emerge – one that computation is helping to bring into focus: tumours don’t sit still.
Across genomics, single-cell and spatial data, what we see is not a stable system but one in constant flux. Cells shift between phenotypic states, adapt to their environment, and respond to pressures like therapy in often reversible ways. Yet many of our models and assumptions remain based on static categories. This mismatch is becoming a central challenge in cancer research.
Rethinking cell identity in cancer
Single-cell technologies were expected to define the “parts list” of a tumour. Instead, they have shown how blurry those parts are. Rather than discrete cell types, we often observe continuous spectra of cell states.
Take the epithelial-to-mesenchymal transition (EMT), a process where epithelial cells become more mobile and invasive. Instead of distinct categories, EMT unfolds along a continuum of intermediate phenotypes. In our own computational modelling of these transitions, we found that cells rarely commit fully, instead occupying hybrid states that are highly context-dependent, often transient and notably difficult to predict.
This suggests that focusing on what a cell is at a single time point may be less informative than understanding what states it can access, and under what conditions. This is where computation becomes essential. The trajectories cancer cells follow are not directly observable; they must be inferred from high-dimensional data. By modelling how cells move through state space, we shift from asking not just where they are, but where they might go next – a subtle, but important step towards prediction.
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