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SFI Centre for Research Training in Genomic Data Science: Molecular characterisation of tumour-immune-stromal interactions to identify therapeutic options in colorectal cancer

   School of Medicine, Dentistry & Biomedical Sciences

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  Dr Philip Dunne, Dr Aideen Ryan  No more applications being accepted  Competition Funded PhD Project (UK Students Only)

About the Project

Start date: 1 September 2022

Solid tumours are complex ecosystems, dictated by the inter-dependence of distinct tissue compartments (tumour epithelium, immune system, stroma, and matrix). These compartments co-evolve, supporting tumour evolution, malignant progression, metastatic dissemination, and the adaptive response to therapy. Of all human diseases, cancer has one of the worst rates for converting insight from preclinical research models into clinically useful treatments. The genomic sequencing of hard-to-treat cancers, such as metastatic CRC, has yielded fewer clinically druggable mutations than expected. Therefore despite small successes, there have been many expensive failures in this space.

Our lab (https://dunne-lab.com/) is focussed on improving outcomes for patients with CRC, by increasing understanding of the biology underpinning aggressive traits in tumours and converting that new knowledge into new treatment options. The group employs a wide range of laboratory techniques, both "wet-lab" and "dry-lab", by combining in vivo and in vitro molecular biology, in situ molecular pathology and in silico translational bioinformatics.

Aims: Our group is looking to recruit a PhD candidate, with experience of working with molecular datasets, with an interest in performing translational bioinformatics analyses to understand the nuanced and complex interactions between cancer cells and their microenvironment for major emerging clinical tumour subtypes. This work will involve assessment of the temporal evolution of these tumours within the immune and stromal environment, and alignment of findings to clinical response data (including surgery, chemotherapy, radiation) in our human cancer data collections.

In this study we aim to:

  • Utilise existing transcriptional cancer dataset to comprehensively characterise critical signalling cascades that contribute to cancer progression
  • Perform in silico analyses to identify potential therapeutic vulnerabilities in clinically-relevant patient subtypes
  • Work in partnership with our inter-disciplinary team to identify the mechanism of action from therapeutic interventions using in vitro and in vivo experiments

In order to more efficiently treat patients, we must first improve our understanding of disease. This project will utilise our existing (and future) data and bioinformatics pipelines to identify, characterise and validate new treatment options based on new mechanistic insights into cancer.

As our understanding of cancer biology improves, the focus of cancer therapeutics is shifting: from predominantly targeting the proliferating tumour epithelium to harnessing the power of the tumour microenvironment over cancer cell fate. This provides an opportunity to develop a new generation of treatments, but further clinical progress will be delayed or limited without detailed interrogation of the molecular signalling cascades that underpin disease progression in colorectal cancer.


  • Generate a dual-species “deep phenotyping” system that will combine existing molecular and histological data for human tumours and mouse colon cancer models, to enable selection of the most appropriate genetically engineered mouse models for pre-clinical testing. Protocols are established within the group already and the student will begin to refine these for their project.
  • Identify and comprehensively characterise the spatial and temporal responses induced following innate surveillance activation in stroma-rich colon cancer, through assessment of inter-compartmental crosstalk within the primary tumour and metastatic niche. Again, using established protocols as a starting point, the student will be supported to develop these further for newer high-dimensionality data.

Queen’s University Belfast is a partner of the SFI CRT in Genomics Data Science, which was formed in 2019. Over seven years of its operation, the Centre will train 100 PhD students in data analytical and computational skills that will help power the implementation of genomics on the island of Ireland. The Centre is built on a national cohort-based model of advanced training. At the outset of their training programme, each student cohort undertakes an intensive, semester-long series of training courses. Participation in the SFI CRT in Genomics Data Science includes a mandatory 3-month residential training programme for the students at NUI Galway, held in Semester 1 (start of September to mid-December) of Year 1 of the PhD programme. Through their experience of working and living together on this phase of the training programme, student cohorts form tight knit groups, enabling them to work collectively on the data science challenges they will encounter during their research. For more information on the SFI CRT in Genomics Data Science and the residential training programme see: https://genomicsdatascience.ie/

Students would benefit from having some experience with bioinformatics / computational biology tools and analyses, although training in these areas will be provided in the residential training programme at the start of the project.


You must hold or expect to get an upper second class honours degree from a university in the UK or Ireland, or qualifications and experience considered by the University as equivalent to that standard. Candidates who already hold a doctoral degree, or who have registered on a PhD for one year (or part-time equivalent) or not eligible.

English Language

Candidates applying from countries where the first language is not English should produce evidence of their competence through a qualification such as IELTS or TOEFL score.

The minimum recommended score for the School of Medicine, Dentistry and Biomedical Science is:

·        IELTS score of 6.0 with not less than 5.5 in each of the four component elements of listening, reading, speaking and writing taken within the last 2 years;

·        TOEFL score of 80+ (internet basted test), taken within the last 2 years, with minimum component scores of; Listening 17, Reading 18, Speaking 20, Writing 17);

·        A valid Certificate of Proficiency in English grade A or B;

·        A valid Certificate of Advanced English grade A; or

·        A first or upper second class honours degree from a university based in the UK, Republic of Ireland or other suitably quality assured location in a country deemed by the UK Border Agency to be majority English speaking.

For a list of English Language qualifications also accepted by the School and University please see the following link:


INTO Queen’s English Language Courses offers both pre-sessional and in-sessional courses in English for academic purposes and study skills. Courses vary in length and full information can be obtained at: https://www.qub.ac.uk/International/International-students/Applying/University-Preparation-Courses/INTOEnglishlanguagecoursesatQueens/

Funding Notes

Funded by the Department for the Economy (DfE). For UK domiciled students the value of an award includes the cost of approved tuition fees and maintenance support (Fees £4,596, Stipend £16,062 for 2022/23). To be considered eligible you must have been ordinarily resident in the UK for the full 3 year period prior to the start of the studentship and you must be ordinarily resident in Northern Ireland on the first day of the start of the studentship. For further information about eligibility criteria please refer to the DfE Postgraduate Studentship Terms and Conditions 2021-22 at https://go.qub.ac.uk/dfeterms


Further funding eligibility: ROI (and EU applicants with pre-settled/settled) status may be eligible for funding if 3 year UK residency met.
Please visit the School of Medicine, Dentistry and Biomedical Sciences, The Patrick G Johnston Centre for Cancer Research, website for further details about the Centre:

Please click on the 'Apply Now' button to apply.
When applying, please choose 'MEDICINE' as your subject area/School
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