Head and Neck Early Relapse Detection (HERD) Study
Research type
Research Study
Full title
A prospective cohort study of patients with radically treated newly diagnosed locally advanced HPV negative head and neck cancer to develop and validate a multimodal signature to risk-stratify for recurrence
IRAS ID
277885
Contact name
Martin Forster
Contact email
Sponsor organisation
University College London
Clinicaltrials.gov Identifier
Clinicaltrials.gov Identifier
Z6364106/2021/05/188 cancer research, Data Protection Number; 143627, Sponsors Edge Reference
Duration of Study in the UK
4 years, 3 months, 27 days
Research summary
Head and neck cancer is the 8th most common cancer in the UK. Doctors aim to cure the patient with either surgery and/or radiotherapy and chemotherapy as initial treatment. Despite this treatment, the cancer recurs within the first year in about 3 in 10 patients, and patients remain under surveillance after initial treatment to detect whether the cancer has returned. There is no standardised programme for surveillance, and practices vary widely leading to inconsistencies.
Existing surveillance programmes are unable to adapt to the different risks of cancer recurrence in individual patients, because of a lack of understanding of the factors that influence the risk of recurrence in different patients. Our study will gather information from scans, blood, stool and saliva samples from 200 patients with localised head and neck cancer. The patients recruited into the study would be at a higher risk of recurrence based on factors related to the tumour (e.g. size, involvement of lymph nodes). Samples will be taken before, during their initial treatment (either chemo-radiotherapy, surgery or a combination of both). By using novel imaging and laboratory techniques, we then aim to extract key information, such as changes in the stiffness of tumour, distribution and types of immune cells, genetic material, and bacteria and their influence on the immune cells, in response to their initial treatment. We will then use a complex model to integrate these individual variables and link them in order to predict how well patients will respond to treatment, and likelihood for cancer recurrence.
A successful predictive model may benefit future patients by generating a more personalised surveillance programme, with more intensive assessments for patients at higher risk, we may detect recurrence earlier, and patients at higher risk may benefit from extra treatment to reduce the chance of recurrence.REC name
London - Bromley Research Ethics Committee
REC reference
21/PR/1581
Date of REC Opinion
8 Dec 2021
REC opinion
Favourable Opinion