Synergia-Breast Cancer Database
Research type
Research Database
IRAS ID
329142
Contact name
Jean Abraham
Contact email
Research summary
Synergia-Breast Cancer
REC name
South West - Central Bristol Research Ethics Committee
REC reference
23/SW/0109
Date of REC Opinion
9 Oct 2023
REC opinion
Favourable Opinion
Data collection arrangements
Data has and will continue to be collected as part of the Partner clinical Trial and Personalised Breast Cancer Program. Participants have given consent for the collection of data and it’s use in future research.
All data will be de-identified and include (not an exhaustive list):
• Clinical Data – that is collected through the course of a clinical trial or study. The nature of clinical research data is that is consistent and of high quality.
• Sequencing data – through participation in this trial /study, samples have been subjected to genome and transcriptome sequencing.
• Digital pathology – pathology slides of diagnostic and surgical specimens are scanned to create a digital image.
• Radiology images – Images from radiography examinations from participants at diagnosis and through treatment, including mammograms, ultrasound and MRI.
• Translational research samples – Information about samples that were collected as a part of the trial /study and their availability.
• Data derived from samples – research on translational and pathology samples including circulating tumour DNA and looking at gene and protein expression at a single cell and spatial level.Research programme
Critical questions that people with breast cancer ask during their treatment are: “How will I know if I’m responding to treatment?”; “How will I know if my disease will come back?”. To answer these questions, we need to use a more joined up approach by bringing together all the information and data generated about each individual patient. Specific information from each type of investigation, such as, genetic tests or mammograms or pathology specimens can give us clues as to why some is likely to respond to treatment or to go on and get metastatic disease. However, currently we look at all this information in a disconnected and siloed manner. This database will be by researchers at the University of Cambridge to develop specific tools that help predict whether someone might respond to treatment or be at risk of their disease spreading. The research team will select key factors they believe influence prediction of response to treatment or risk of metastatic spread in different breast cancer types. The factors will then be carefully combined using special mathematical models and tests called machine learning and artificial intelligence. We will then understand what factors are truly important for these predictions.
Research database title
Synergia-Breast Cancer
Establishment organisation
Cambridge University Hospitals and University of Cambridge
Establishment organisation address
Box 277, Addenbrookes Hospital
Hills Road
Cambridge
CB20QQ