MRC DTP in Precision Medicine

Biomarkers for Predicting Radiotherapy Side Effects to Improve Lung Cancer Patient Outcomes

Supervisors

Dr Ximena Raffo-Iraolagoitia, School of Cancer Sciences, University of Glasgow
Prof Leo Carlin, School of Cancer Sciences, University of Glasgow
Prof Kevin Blyth, School of Cancer Sciences, University of Glasgow
Dr Vanessa Smer-Barreto, School of Informatics & Institute of Genetics and Cancer, University of Edinburgh

Summary

Lung cancer is the leading cause of cancer-related death worldwide, and for many patients, radical radiotherapy is the only curative treatment. A complication of radiotherapy is radiation-induced lung injury (RILI), which can severely affect quality of life and, in some cases, be life-threatening. Currently, there are no reliable biomarkers to identify patients at highest risk, representing a barrier to improving patients’ outcomes.

This PhD project will explore how the tumour immune microenvironment influences susceptibility to RILI, with the goal of developing a predictive biomarker test. The student will integrate multi-omics datasets, including whole-exome and bulk RNA sequencing, with clinical and demographic data using bioinformatics and machine learning approaches. Candidate biomarkers will be validated using multiplexed tissue imaging of patient samples and investigated in preclinical lung cancer models. Real-world data from Public Health Scotland will be leveraged to assess clinical impact and support translation into practice.

By combining cancer immunology, computational biology, and data science, this project offers a unique interdisciplinary training environment. The work has the potential to develop a clinically applicable test to identify patients at risk of RILI, enabling personalised treatment, supporting novel neo/adjuvant interventions, reducing treatment-related toxicity, improving patient outcomes, and advancing precision oncology in lung cancer.