AI-guided functional modelling of tumour-immune interactions to discover therapeutic vulnerabilities

Closing date: 15/03/2027

MB-PhD Studentship: AI-guided functional modelling of tumour-immune interactions to discover therapeutic vulnerabilities

Lead Supervisors: Professor Samra Turajlic
Lead Supervisors: Professor Magnus Ratray, Dr Jon Lim, Dr Kevin Litchfield

Applications Deadline: Monday 15th March 2027
Interviews: Week commencing 3rd May 2027
Start date: September 2027

Project Keywords: Artificial intelligence; Tumour immunology; Patient-derived models
Research Opportunity: MB-PhD Studentship

Project Outline

Cancer immunotherapy can produce durable responses, but many patients do not benefit and we still cannot reliably identify which tumour-immune interactions represent effective therapeutic targets. Artificial intelligence (AI) can integrate complex genomic, transcriptomic and immunological data to prioritise new vulnerabilities, but predictions require functional validation in biologically relevant human models.

This PhD will create an integrated computational and experimental platform in which AI-generated predictions are tested directly in patient-derived cancer models. Building on Cancer Dynamics 2 expertise and the MANIFEST (Multiomic ANalysis of Immunotherapy Features Evidencing Success and Toxicity) consortium, the student will initially focus on renal cell carcinoma and/or melanoma. The student will use multimodal machine-learning approaches, informed by the MIDAS framework, to integrate tumour molecular features with immune-cell biology and functional evidence and prioritise candidate tumour-immune dependencies associated with response or resistance.

MANIFEST provides a complementary tumour-type-agnostic clinical framework for deep multiomic profiling of patients receiving immunotherapy, enabling selected hypotheses and signatures to be evaluated in clinically annotated data, subject to data access and governance. Selected candidates will then be perturbed in patient-derived tumour models that preserve key features of the tumour microenvironment. Effects on tumour and immune states will be measured using immune phenotyping, cytokine profiling, imaging and targeted molecular analyses. Experimental responses will be compared with computational predictions to determine why predictions succeed or fail and whether functional evidence improves subsequent prioritisation.

The project therefore creates a translational cycle: predict → perturb → measure → clinically contextualise → learn. The student will receive interdisciplinary training spanning cancer biology, immunology, patient-derived models, multiomics, data science and machine learning, with the long-term goal of identifying therapeutic vulnerabilities that are computationally plausible, experimentally validated and clinically relevant.

Applications for this project are now open. Please complete your application on The University of Manchester Postgraduate Application Portal.

About Prof. Samra Turajlić (project Lead Supervisor)

Samra is the Director of Cancer Research UK Manchester Institute, a Consultant Medical Oncologist at the Christie and also leads the Cancer Dynamics research group. They apply evolutionary principles to predict the trajectory of individual cancers by studying the forces that shape cancer evolution with the aim of using this knowledge to improve patient outcomes.

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