AI-Guided Discovery of Immunotherapy Resistance Mechanisms

About the project

This PhD will investigate strategies to improve immunotherapy efficacy in cancer, integrating single-cell RNA sequencing and machine learning with experimental immunology to understand T-cell exhaustion and the tumour micro-environment. The student will identify and experimentally validate mechanisms of treatment resistance, informing the development of more effective cancer immunotherapies.

A major challenge for cancer immunotherapy is understanding why immune cells that can eliminate cancer in the laboratory often become ineffective within cancers in patients. This PhD will investigate the mechanisms underlying this loss of function and identify strategies to improve the effectiveness of immunotherapies such as chimeric antigen receptor T-cell (CAR-T) therapy.

Jointly supervised by experts in machine learning/data-driven biology and cancer immunotherapy, the project offers a distinctive opportunity to develop expertise across both computational and experimental research. The student will develop advanced skills in analysing and integrating single-cell RNA sequencing datasets from tumours to identify cellular states associated with treatment resistance. They will apply, critically evaluate and adapt machine-learning and gene-regulatory network approaches to predict interventions capable of maintaining or restoring effective anti-tumour immunity.

Crucially, these predictions will not remain in silico. The student will take discoveries from computational prediction through to experimental validation, testing the most promising candidates experimentally using cell culture, multi-colour flow cytometry, functional immunological assays and molecular biology techniques. The project will therefore provide unusually broad training spanning computational biology, machine learning, immunology and molecular biology, equipping the student to work at the increasingly important interface between data science and experimental biomedical research

The supervisory teams have strong track records in translating research towards clinical application, commercialisation and collaboration with the pharmaceutical industry, providing exposure to potential careers across academia, biotechnology, healthcare and industry.

This fully funded studentship is reserved for international students, with applications from Malaysians and Singaporeans particularly encouraged.

We are looking for a highly motivated and intellectually curious student who is excited by working across traditional disciplinary boundaries. The successful candidate should be comfortable combining computational and experimental research, and have the aptitude and motivation to develop expertise in both.

Professor Sean Lim

• Identification of new targets for antibody therapy in lymphoma
• Development of novel immunotherapy approaches for cancer
• Understanding the determinants of an effective immune response in patients with lymphoma

Supervisors

• Supervisors: Professor Owen Rackham MSc, PhD Professor

Professor Owen Rackham MSc, PhD

• Professor Owen Rackham MSc, PhD: Professor

Entry requirements

An undergraduate degree (1st or 2:1 or international equivalent) in bioinformatics/computational biological, biological sciences, immunology or related disciplines.

Fees and funding

This Studentship is funded by philanthropic donation and includes University international fees, stipend (approx. £22,500 per year to start with inflationary increase) and experimental consumables for 3 years.

How to apply

Applications should be made online at https://www.findaphd.com/phds/project/ai-guided-discovery-of-immunotherapy-resistance-mechanisms/?p199831

You should include a CV, two reference letters, and degree transcripts to date.

Contact us

Informal enquiries relating to the project or candidate suitability should be directed to Prof Sean Lim (S.H.Lim@soton.ac.uk) .

Research areas