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

很长一段时间以来，人们主要通过基因改变来研究癌症。然而，我们现在知道，其他类型的细胞，如免疫细胞，对肿瘤的进展起着至关重要的作用。此外，免疫系统可以通过免疫疗法重新编程，这些疗法正在彻底改变我们的癌症治疗方法。关于免疫细胞在癌症中的作用的初步研究只是计算了肿瘤中的某些免疫细胞，如T细胞或巨噬细胞。检查多路荧光的大型数据集使人们能够同时看到许多不同的细胞类型，并发现这些免疫细胞在肿瘤内以复杂的模式出现。同样，空间质量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，这位博士生将开发基于拓扑数据分析的新方法，以发现表征肿瘤进展不同阶段的空间特征，以及预测癌症治疗反应的空间特征。他们将把空间方法与集成不同数据类型的方法结合起来，利用来自肿瘤的特殊多组学数据，获得结合了空间和分子异质性的肿瘤的整体视图。学生将基于肿瘤微环境的空间、多组学和拓扑特征建立新的生物标记物，这些特征将支持优化的、个性化的治疗。

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.