Abstract 235TiP
Background
Hamlet.rt (Heuristics, Algorithms and Machine Learning: Evaluation & Testing in Radiation Therapy) is a multi-centre study exploring the role of machine learning in predicting response and toxicity in patients receiving radical radiotherapy for non-small cell lung cancer (NSCLC), Head and Neck Cancer (H&N Cancer) and Prostate cancer. Hamlet.rt Trans is a translational sub-study that leverages the infrastructure of Cancer Research UK RadNet Cambridge to evaluate multiple liquid biomarkers of radiation response. This study aims to investigate biomarkers of treatment response in patients with lung and head and neck cancers and markers of treatment toxicity in patients with prostate cancer.
Trial design
HAMLET.trans is a translational biosampling study of patients undergoing radical radiotherapy for NSCLC, H&N cancer or prostate cancer. Venous samples for ctDNA analysis and senescence-related profiling are collected together with dried blood spot samples before chemotherapy and radiotherapy at multiple time points during radiotherapy and post-treatment. Urine is collected from patients with prostate cancer. Patients are completing toxicity questionnaires during and after radiotherapy. Delivery dose per fraction is assessed based on imaging just before radiotherapy delivery. The trial aims to recruit at least 20 patients per arm. Patient samples are analysed for tumor-specific mutation, methylation profiling of cfDNA as well as cytokine assays to establish markers or response and toxicity and identify optimal sample timings for future studies. Blood spots are analysed for ctDNA tumour content to establish a less invasive way of frequent sampling on therapy. The results of HAMLET.trans will inform ongoing translational work.
Clinical trial identification
NCT04060706.
Editorial acknowledgement
Legal entity responsible for the study
Cambridge Cancer Trials Centre.
Funding
Cancer Research UK and the Medical Research Council UK.
Disclosure
All authors have declared no conflicts of interest.
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