Fully-funded PhD Studentship: Innate-adaptive immune interactions in cancer evolution

Supervisor: Dr Tim Halim

Course start date: 1st October 2027

Project details

For further information about the research group, please visit our website at

The immune system is capable of detecting cancer, which forms the basis of immunotherapy. While immunotherapy has revolutionised cancer treatment in recent years, it remains unclear how exactly the immue system detects and responds to pre-cancereous lesions. Nevertheless, it is known that the activatin (or deactivation) of anti-cancer immune cells involve many different cell-types that communicate dynamically in specific tissue niches.

Our laboratory has an interest in resolving the interactions between tissue-resident immune cells, with the overall goal of identifying key mechanisms that can be targeted therapeutically at different stages of cancer. We found that recently discovered, tissue-resident, group 2 innate lymphoid cells (ILC2) can play key roles in coordinating different types of immune and non-immune cells that play critical roles in tumourigenesis (i.e. Tregs, Dendritic Cells, cancer associated fibroblasts, etc.). The project will start by asking if ILC2, or other tissue-resident innate immune cells, play an important role during tumour evolution.

The PhD project will build on ongoing work in different cancer types (pancreatic, breast, ovarian and lung), where we have already developed mouse models and human tissue processing pipelines. The candidate will learn and use cutting edge immunological methods (high parameter flow cytometry, multiplex imaging, single cell and spatial transcriptomics) in conjuction with intravital imaging and proximity labelling tools to define the dynamic interaction of immune cells in cancer. The candidate will need to master both complex 'wet lab' techniques, as well as learn how to perform robust in silico analysis of data (training will be provided by senior lab members, collaborators, or experts in the Institute core facilities). The candidate will generate hypotheses from these large descriptive datasets, which will then be tested rigorously using in vitro assays, in vivo, and ultimately in patient derived material. The overall ambition is to uncover novel mechanistic insights about immune regulation in early cancer, and to design interventions that halt or delay tumour progression.

References/further reading

Yip T et al. Science 2026 (DOI: /science.aea5113)

Stockis J et al. Science Immunology 2024 (DOI: /sciimmunol.adl1903)

Schuijs MJ et al. Nature Immunology 2020

Preferred skills/knowledge

• Cell biology / Immunology laboratory experience, such as: Flow cytometry and FACS, immunofluorescence microscopy/imaging, cell culture, tumour cell killing assays, tissue processing, etc.
• Molecular biology laboratory experience, such as: DNA/RNA extraction, PCR, cloning, virus production, ELISA, etc.
• Computational biology laboratory experience, such as: bulk- singl cell- or spatial-transcriptomic data analysis, spatial biology analysis using HALO, Imaris, etc.
• Murine or human tissue work, such as: tissue processing, tumour or immune-related in vivo models, surgical skills, transgenic animal models, UK PIL training, etc.
• Knowledge: background in cancer biology, immunology, cell biology, etc. Training and interest in bioinformatics, computational biology.

References

We would appreciate it if you could ask your referees to submit their references as soon as possible upon request, despite the longer University deadline for references. They will receive a request once you have completed the References section of your application.

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.

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