Predicting cancer evolution in breast cancer (version 1)
Research type
Research Study
Full title
Using machine learning to predict subclone evolution and response to chemotherapy in secondary breast cancer
IRAS ID
310553
Contact name
David Mark Davies
Contact email
Sponsor organisation
Swansea Bay University health Board
Duration of Study in the UK
1 years, 2 months, 30 days
Research summary
This research will apply artificial intelligence (AI) approaches to develop models, using genomic and clinical data, which predict cancer evolution and response to chemotherapy in secondary breast cancer.
The response to chemotherapy is very variable. For some people treatment works very well, for others, treatment has little or no effect. Being able to predict who will respond would allow treatment to be targeted to those most likely to benefit. Those unlikely to respond could be offered alternative treatments and spared unnecessary toxicities.
Much of a cancer’s behaviour is driven by mutations in genes controlling cell functions. The patterns of mutation vary from one area of cancer to another and change over time. As a result, genetically distinct subpopulations of cancer cells (subclones) arise, which vary in their sensitivity to chemotherapy. Our approach to capture the genetic profiles of subclones is to analyze circulating tumour DNA (ctDNA). This is DNA shed from the cancer into the bloodstream and which can be extracted from a blood sample.
We will develop predictive models of cancer evolution using machine learning, a branch of AI.
Participants will be asked to provide two additional blood samples for ctDNA analysis, one before chemotherapy starts and a second, either after the chemotherapy stops working or when the study ends, if the chemotherapy is still working at that point. Otherwise, their treatment plan will be unaltered.
Participants will be recruited in cancer centres across Wales. The study in funded by Health and Care Research Wales.REC name
South Central - Hampshire A Research Ethics Committee
REC reference
23/SC/0164
Date of REC Opinion
16 May 2023
REC opinion
Favourable Opinion