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Title: Predicting radiotherapy toxicity in patients treated with radical radiotherapy using predictive assays and circadian rhythm
Author: Johnson, Kerstie Anne
ISNI:       0000 0004 6495 046X
Awarding Body: University of Leicester
Current Institution: University of Leicester
Date of Award: 2018
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Radiotherapy is a fundamental cancer treatment and plays a pivotal role in the improving outcomes for the disease. Depending on the cancer site and organ at risk rates of moderate to severe acute toxicity range between 15 to 30 percent and rates of late toxicity between 5 and 15 percent. If a patient’s individual risk of radiotherapy toxicity could be predicted then their treatment could be tailored appropriately. In this thesis two cohorts have been used to analyse predictive measures for acute and late radiotherapy toxicity: the REQUITE cohort (prospective international observational study of breast, prostate and lung cancer patients) and the LeND cohort (retrospective local study of breast cancer patients). Three main areas have been examined to establish whether they can be used to predict for radiotherapy reactions. In the prostate and lung patients associations between clinical and treatment variables and acute toxicity were reviewed. The second area was predictive assays: DNA damage and repair were assessed using the comet and γ-H2AX assays and apoptosis in lymphocytes using the RILA (radiation induced lymphocyte apoptosis assay). Finally the effect of circadian rhythm and its underlying genetics on radiotherapy toxicity were assessed. Many of the variables were significantly associated with increased toxicity on univariate analysis. Three were significantly associated with toxicity on multivariate analysis. Acute toxicity in prostate patients was associated with intended duration of hormones (p=0.05) and V50 bladder (p=0.01)). Morning radiotherapy was associated with increased overall bivariate STAT score (p=0.03) in the LeND volunteers. The results of this study indicate clinical and genetic variables and the use of predictive assays can be utilised to create more personalised radiotherapy treatments that strive for better cancer and quality of life outcomes for patients.
Supervisor: Talbot, Christopher ; Symonds, Paul ; Jones, George Don Sponsor: Not available
Qualification Name: Thesis (M.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available