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

长期以来，癌症主要是通过其遗传改变来研究的。然而，我们现在知道，其他细胞类型，如免疫细胞，对肿瘤的进展有重要影响。此外，免疫系统可以通过免疫疗法进行重新编程，这正在彻底改变我们的癌症治疗手段。免疫细胞在癌症中的作用的最初研究只是计算肿瘤内的某些免疫细胞，如T细胞或巨噬细胞。通过检查大量的多重荧光数据集，可以同时观察到许多不同的细胞类型，并揭示这些免疫细胞在肿瘤中以复杂的模式出现。同样，空间质谱数据揭示了肿瘤内分子特征的高度异质性。在这个项目中，博士生将开发基于拓扑数据分析的新方法，以揭示表征肿瘤进展不同阶段的空间特征，并预测对癌症治疗的反应。他们将联合收割机空间方法与整合不同数据类型的方法相结合，以利用来自肿瘤的特殊多组学数据，获得结合空间和分子异质性的肿瘤的整体视图。学生将根据肿瘤微环境的空间，多组学和拓扑特征建立新的生物标志物，这些特征将支持优化的个性化治疗。

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.