Researchers have received £800,000 in funding to investigate why oestrogen receptor-positive (ER-positive) breast cancer, the most common form of the disease, can recur years after initial treatment. ER-positive breast cancers account for approximately 80% of breast cancer cases, affecting around 44,000 individuals annually in the UK.
While many patients remain cancer-free following treatment, some experience late recurrence, when dormant cancer cells that have survived treatment become active again. This reactivation often leads to metastatic breast cancer, which is typically incurable and presents a major clinical challenge.
The funding, awarded by the charity Breast Cancer Now, supports two research projects aimed at understanding the mechanisms underlying cancer cell dormancy and recurrence. Dr. Simon Vincent, chief scientific officer at Breast Cancer Now, emphasized the impact of uncertainty on survivors, stating that fear of recurrence can be overwhelming and that the goal is to improve quality of life by reducing this anxiety through better scientific understanding.
The first project, a collaboration between University College London (UCL) and The Institute of Cancer Research (ICR), focuses on how ER-positive cancer cells enter and maintain a dormant state. Employing artificial intelligence (AI), researchers will analyze molecular data derived from cancer cells during treatment to map the changes that facilitate dormancy. By examining interactions between cancer cells and their microenvironment—including immune cells, blood vessels, and connective tissue—the team aims to identify which cancers are most likely to return. Professor Maria Secrier of UCL, leading this project, noted that the challenge lies not in data scarcity but in lacking the advanced tools to interpret existing data innovatively.
The second project, based at The ICR, will study tumour samples from patients monitored for over a decade post-surgery. Utilizing advanced imaging and molecular techniques, researchers will investigate tumour architecture, cell organization, and cellular interactions to pinpoint residual cancer cells that survive treatment. AI will be applied to detect early patterns—present within two weeks of diagnosis—that might indicate treatment resistance or a higher risk of relapse. Dr. Maggie Cheang, part of the ICR team, highlighted the potential of combining detailed imaging, molecular data, and clinical information with AI to reveal insights that are otherwise difficult to discern, with the aim of enabling earlier identification of relapse risk and more precise treatments.
Dr. Vincent expressed optimism that these projects could lead to tools that better identify patients at risk of recurrence, allowing for more personalized follow-up care and ultimately reducing mortality from breast cancer.