The project:

With over 1.5 million deaths annually, non-small cell lung cancer (NSCLC) remains one of the leading causes of cancer-related mortality worldwide. The NSCLC tumour microenvironment (TME) is a dynamic, complex ecosystem where cancer cells interact with non-cancerous cells and stromal components, significantly influencing tumour progression and therapy response. Understanding the mechanisms underlying this complexity is crucial for identifying potential therapeutic targets.

Recent advancements in single-cell RNA sequencing allow us to profile individual cell transcriptomes, revealing distinct cell subpopulation phenotypes within specific tissues. Additionally, cutting-edge spatial transcriptomics techniques enable precise quantification of gene expression patterns within the spatial context of tissues. While each of these technologies alone may not fully capture the cancer complexity, their integration offers unprecedented opportunities to decipher the biology of its heterogeneous ecosystem. One approach to address this challenge is to leverage Artificial Intelligence (AI) and its capacity to manage large, complex datasets to uncover hidden biological features.

This project aims to harness AI to analyse diverse, publicly available multi-omics datasets of NSCLC alongside clinical patient data, with the goal of revealing cellular dynamics within the TME and identifying key genes influencing cancer progression and therapy response.

Our approach will involve developing and evaluating deep learning and machine learning models, combined with bioinformatics tools, to: 1) integrate single-cell and spatial transcriptomics data for high-resolution mapping of cellular subpopulations and spatial structures; 2) create and analyse networks of intercellular cross-talk among cell subpopulations, including immune niches, to identify factors impacting tumour progression and development; and 3) link these communication networks to clinical data to identify sources of perturbation affecting therapeutic response.

The student will join a vibrant, international research environment, collaborating with experts across multiple fields, including immunology. They will gain valuable experience in bioinformatics, computer science, statistics, and genomics.

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.