|Funding for:||UK Students, EU Students, International Students|
|Funding amount:||The studentship is open to both home and overseas applicants and will cover both the cost of tuition fee and a yearly stipend (at UKRI rate) over the course of the PhD programme.|
|Placed On:||30th March 2023|
|Closes:||16th April 2023|
Topological Data Analysis for Predictive Spatial Signatures of Cancer Progression and Treatment Response
Industrial Partner: AstraZeneca
Industrial Supervisor: Arthur Lewis
Duration of the PhD: 4 years
Cancer remains a major killer, still rarely curable in advanced stages. Recent experiments revealed a high complexity in terms of a huge number of perturbed molecules (e.g. genes, proteins or metabolites), and also a high disturbance of the spatial patterning of the different cell types in a tumour. Mathematical methods typically focus on characterising one of these complexities. In this project, we will advance methods to reveal the interplay of spatial and high-dimensional molecular signatures through novel mathematical methods based on topological data analysis, network science and spatial statistics.
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 a new workflow 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.
The project will combine methods from topological data analysis, spatial statistics and bioinformatics. Specifically, they will advance approaches based on persistent homology, pair correlation functions, network analysis, pathway enrichment analysis, clustering analysis, entropy measures and community detection algorithms to combine information from different data types (cell types, transcriptomics, metabolomics, proteomics, lipidomics etc) to obtain a more holistic view of the spatial patterning of cells in tumours.
Dr Fabian Spill, email@example.com, Mathematical Biology, School of Mathematics;
Co-Supervisor: Prof. Iain Styles, firstname.lastname@example.org, School of Computer Science.
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