Topological Analysis for Predictive Spatial Signatures of Cancer progression and Treatment Response

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For a long time, cancer has been largely studied through its genetic alterations. However, we now know that other cell types such as immune cells critically affect tumour progression. Moreover, the immune system can be reprogrammed through immuno-therapies that are revolutionising our arsenal of cancer therapies.Initial studies of the role of immune cells in cancer simply counted certain immune cells, such as T-cells or macrophages, within the tumour. Inspecting large data sets of multiplexed fluorescent allows one to visualise many different cell types simultaneously, and reveals that these immune cells appear in complex patterns within tumours. Similarly, spatial mass spec data reveals a high level of heterogeneity of molecular signatures within a tumour.In this project, the PhD student will develop new methods based on topological data analysis to uncover spatial signatures that characterise different stages of tumour progression and that are predictive of the response to cancer therapy. They will combine spatial methods with methods to integrate different data types to utilise special multi-omics data from tumours, obtaining a holistic view of a tumour that incorporates spatial and molecular heterogeneity. The student will establish new biomarkers based on the spatial, multi-omics and topological characteristics of the tumour microenvironment that will underpin optimised, personalised treatments.