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.
Resources from the same session
294P - Prognostic significance and evolution of HER2 zero, HER2 low and HER2 positive in breast cancer after neoadjuvant treatment
Presenter: Tong Wei
Session: Poster session 02
295P - ESR1, PGR, ERBB2, MKI67 single gene analysis in neoadjuvant-treated early breast cancer patients
Presenter: Rebekks Spiller
Session: Poster session 02
296P - Identification of metabolism-related therapeutic targets to improve response to neoadjuvant chemotherapy in early breast cancers
Presenter: Françoise Derouane
Session: Poster session 02
297P - Prognostic and predictive impact of NOTCH1 in early breast cancer
Presenter: Julia Engel
Session: Poster session 02
298P - Association of luminal-androgen receptor (LAR) subtype with low HER2 in triple-negative breast cancer
Presenter: Lee Min Ji
Session: Poster session 02
299P - Single-cell transcriptomic analysis reveals specific luminal and T cell subpopulations associated with response to neoadjuvant therapy in early-stage breast cancer
Presenter: Xiaoxiao Wang
Session: Poster session 02
300P - Correlation of PD-L1 protein and mRNA expression and their prognostic impact in triple-negative breast cancer
Presenter: Kathleen Schüler
Session: Poster session 02
301P - Epigenetic modifications of IL-17 gene in patients with early breast cancer and healthy controls
Presenter: Ljubica Radmilovic Varga
Session: Poster session 02
302P - Clinicopathological features and outcomes of pregnancy associated breast cancer: Case control study -single institution experience
Presenter: Nashwa Kordy
Session: Poster session 02
303P - Differential prognostic role of PDGFRA alterations in breast cancer subtypes
Presenter: Panagiotis Vlachostergios
Session: Poster session 02