The project:

Colorectal carcinoma (CRC) is the second deadliest cancer worldwide, with a 5-year overall survival rate below 10%. CRC is classified into four consensus molecular subtypes (CMS1–4). While immune checkpoint immunotherapy is effective for CMS1 patients, innovative therapies are urgently needed to improve outcomes for CMS2–CMS4 CRC patients since current treatments primarily rely on front-line combination chemotherapies. Recently, we identified carboxylesterase 1 (CES1) as an NF-?B-regulated lipase critical for linking lipid metabolism to energy stress adaptation in aggressive CRC. CES1 is overexpressed in CMS2 and CMS4 tumours, correlating with poorer clinical outcomes, making it a promising therapeutic target.

Although data underscore the importance of the CES1-dependent metabolic mechanism in CRC cells, they do not exclude additional roles of CES1 in the symbiosis between tumour microenvironment (TME) cells and cancer cells that fuel tumour progression.

This project aims to elucidate the role of CES1 in TME interactions, cancer progression, and therapy response in CRC. By developing and evaluating artificial neural networks, including deep learning models, integrated with bioinformatics tools, the final goal is to extend these findings to other solid cancers, such as ovarian cancer.

By leveraging single-cell RNA sequencing and spatial transcriptomics data from CRC patients and other cancers, integrated with functional pathways and ligand-receptor interaction data, the project will:

1. develop high-resolution cellular maps to unravel how distinct subpopulations collaborate to shape tissue phenotypes;
2. analyse the produced cell-cell interaction networks to define CES1’s role in tumorigenesis and immune evasion;
3. extend this approach to ovarian and other solid cancers to uncover shared mechanisms driving tumour progression.

No prior knowledge of biology is required. The student will work within a dynamic, interdisciplinary research environment, collaborating with immunologists, bioinformaticians, and data scientists. They will gain expertise and preparation for careers at the intersection of computational and biomedical sciences.

Candidate requirements:

Applicants must hold/achieve a minimum of a merit at master’s degree level (or international equivalent) in a science, mathematics or engineering discipline. Applicants without a master's qualification may be considered on an exceptional basis, provided they hold a first-class undergraduate degree. Please note, acceptance will also depend on evidence of readiness to pursue a research degree.

If English is not your first language, you need to meet this profile level: Profile E

Further information about English language requirements and profile levels .

Contacts:

For questions about the research topic, please contact the project supervisor.

For questions about eligibility and the application process please contact Engineering Postgraduate Research Admissions [email protected]

How to apply:

Prior to submitting an online application, you will need to contact the project supervisor to discuss.

Online applications are made at http://www.bris.ac.uk/pg-howtoapply . Please select Engineering Mathematics (PhD) on the Programme Choice page. You will be prompted to enter details of any studentship you would like to be considered for in the Funding and Research Details sections of the form.