Investigating genetic and circadian rhythm effects on radiotherapy side effects for breast cancer

About the Project

Project:

Radiotherapy is an effective form of cancer therapy that targets high energy irradiation to the tumour volume, resulting in destruction of cancer cells and improved survival. The treatment is well tolerated in most patients, but a minority suffer from life-long side effects that reduce quality of life. The side effects include fibrosis and atrophy of the breast, which results in a poor cosmetic outcome and pain. Some patients develop arm lymphodema, which is uncomfortable and reduces arm function. These adverse reactions develop some years after treatment but last for the rest of the patient’s life, which with improved survival is now often for decades.

Work by us and others have found some predictors that raise risk of side effects in individual patients. These predictive factors include genetic variation (Jandu 2023, Naderi 2023), radiation dose distribution (Jaikuna 2023), interaction with chemotherapy agents and circadian rhythm (Webb 2022). In the latter work, we have discovered an interaction between genotype and time of treatment (Johnson 2019), such that some patients would have reduced toxicity if treated in the morning, and others with afternoon treatment (Webb 2022).

Depending upon experience, the student working on this project can carry out several genetic analyses and subsequently use the resulting knowledge to build statistical models:

1. A Meta-GWAS of four breast radiotherapy cohorts (REQUITE, RAPPER, DBCG & CANTO, total 6500 patients), which will be the largest genetic study in the field to date.
2. Apply results of multi-trait GWAS conducted across fibrotic diseases including Idiopathic Pulmonary Fibrosis to radiotherapy side effects driven by fibrosis
3. Extend the study of circadian rhythm effects in radiotherapy beyond the current candidate gene strategy to a genome-wide analysis

As the mechanism of the circadian (time-of-day) effect on radiotherapy is unknown, the student could perform laboratory assays such as radiobiology tests for DNA damage, repair and apoptosis. These involve techniques including cell culture, flow cytometry and immunohistochemistry.

Prediction models will be built using machine learning methods. These models will include dosiomic and radiomic parameters already computed from the REQUITE and CANTO patients’ radiotherapy planning CT scans by industrial partner Therapanacea. Genomic and radiologic factors will be complemented by clinical information on co-morbidities and cancer treatment.

Training opportunities:

The project is fundamentally inter-disciplinary, including radiation oncology, genetics, statistics, computing methods and radiology. Through the project the student will learn skills in all these areas, and benefit from interactions in each area through our wide network of collaborators across Europe and North America.

Depending upon the background of the student they will need training in one or more of the disciplines involved. If the student has a biology/genetic background the training will be in machine learning and statistics. For computing or mathematics students training will be in oncology and genetics. This may involve paid online or in person courses.

Outputs:

The student will contribute to producing a clinically useful predictive test for which patients will suffer from radiotherapy side effects. We expect this clinical test to be incorporated rapidly into clinical trials, allowing the student to see their work progress from bench to bedside.

The work will be published in leading research journals in the field. The student will present their work at radiotherapy (e.g. Radiogenomics Consortium & ESTRO) and genetics conferences (e.g. ESHG or ASHG).

Enquiries

Project Enquiries to cjt14@le.ac.uk

To apply please refer to

https://le.ac.uk/study/research-degrees/research-subjects/genetics