权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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

癌症进展和治疗反应的预测空间特征的拓扑分析

基本信息

批准号：
2882735
负责人：
金额：
--
依托单位：
University of Birmingham
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2023
资助国家：
英国
起止时间：
2023 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2882735
关键词：
Topological Analysis Predictive Spatial Signatures

项目摘要

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.

长期以来，人们主要通过癌症的基因改变来研究它。然而，我们现在知道，其他类型的细胞，如免疫细胞，严重影响肿瘤的进展。此外，免疫系统可以通过免疫疗法重新编程，这正在彻底改变我们的癌症治疗方法。最初对免疫细胞在癌症中的作用的研究仅仅是计算肿瘤内的某些免疫细胞，如t细胞或巨噬细胞。检查大量的多路荧光数据集，可以同时看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。他们将把空间方法与整合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，获得包含空间和分子异质性的肿瘤整体视图。该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志物，这将支持优化的个性化治疗。