Immunotherapy drugs that unleash the immune system against tumours work well in some cancers, but in breast cancer they help only a minority of patients, and doctors cannot predict who. This project will map the spatial arrangement of immune cells and tumour cells in hundreds of breast cancer tissue samples taken before, during, and after immunotherapy treatment. Using a technique called imaging mass cytometry, the researchers will track 44 different proteins at subcellular resolution to identify recurring patterns of cell organisation—what they call multicellular configurations—and see how those patterns change in patients who respond versus those who do not. The goal is to find a reliable biomarker that distinguishes responders from non-responders before treatment begins. Because the high-dimensional imaging used in the lab cannot be replicated in routine hospital pathology, the team will also develop machine-learning tools that can translate these complex spatial features onto standard stained tissue slides. If successful, this work could lead to a new clinical discipline of augmented pathology, where routine diagnostic slides reveal immune response patterns that currently remain invisible, sparing non-responders from ineffective immunotherapy and its side effects.
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Immunotherapy has revolutionised cancer treatment, but its role in breast cancer is unclear. For breast cancer patients to benefit, we must understand why some respond whereas others don't, and identify a pragmatic biomarker to distinguish between them. Immunotherapy depends on spatial organisation of the tumour microenvironment (TME) because it targets T cell interactions, but the principles of TME organisation are poorly understood. How immunotherapy remodels this structure during treatment is also unknown but may explain why responses differ. To understand immunotherapy response in breast cancer, and to uncover a reliable discriminatory biomarker, I propose to dissect multicellular TME structure in situ by highly multiplexed imaging of tissues. Imaging mass cytometry (IMC) uses time-of-flight mass spectrometry to localise the expression of 44 proteins at subcellular resolution in tissues. Using IMC, we will analyse thousands of samples from hundreds of breast cancer patients recruited to randomised trials of immunotherapy where longitudinal samples (at baseline, on-treatment, and post-treatment) have been collected. Using automated image analysis, graph theory and spatial statistics we will identify multicellular configurations that recur across tumours and chart how these evolve under therapy in responders versus non-responders. Findings arising from these analyses are disconnected from routine clinical pathology however, because equivalent assays are not possible in that setting, frustrating translation. We will bridge this gap by using the large digital pathology resource accrued for these trials to develop novel machine-learning tools to transfer features learned in high-dimensional space to routine digital pathology stains. Together, this programme will elucidate the pathologic basis of immunotherapy response and take first steps toward a new clinical discipline of augmented pathology.
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