Spatial-omics-guided treatment selection
Choosing treatment from a map of where each cell type sits in the tumour, not just from a list of its mutations.
Spatial transcriptomics and multiplex imaging show that immune exclusion, stromal barriers and clonal geography predict response in ways bulk sequencing cannot; tertiary lymphoid structures, for example, predict immunotherapy benefit across tumour types. Turning these maps into a clinical assay requires standardisation, lower cost and prospective validation. Today they are correlative science inside trials, not decision tools.
How it works
Spatially resolved RNA or protein profiling quantifies the architecture of the tumour microenvironment; machine learning turns that architecture into a predictive score.
- Captures exclusion and heterogeneity that bulk assays average away
- Uses the same tissue block as routine pathology
- Predictive features already identified
- Cost and turnaround far from clinical
- No standardised platform or scoring
- Prospective validation missing
Latest papers
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