What should a synthetic TME model need to predict therapeutic response?

Synthetic tumour microenvironment (TME) models could help bridge the gap between simplified in-vitro assays and heterogeneous patient tumours. This project will explore what a useful, practical synthetic TME should reproduce for cancer-therapeutics discovery.

Initial questions:

• Which components are essential for a minimum viable model: tumour cells, stromal cells, immune cells, extracellular matrix, perfusion, gradients, or hypoxia?
• Which therapeutic questions benefit most from synthetic TME models: immune-cell engagement, resistance mechanisms, combination therapy, toxicity, or patient stratification?
• Which readouts should define translational value: tumour-cell killing, immune-state changes, spatial organisation, biomarker response, or concordance with clinical outcomes?
• How should models be benchmarked against patient samples, organoids, animal models, and clinical data?

The goal is not to reproduce every aspect of a tumour. It is to identify the smallest model that is fit for a defined discovery decision.