Nicholas Turner, MD, PhD, FRCP, FMedSci

Improving prediction of the risk of recurrence of ER-positive breast cancer.

Impact

Estrogen receptor-(ER) positive breast cancer is the most common subtype of breast cancer, affecting approximately 70 percent of all people diagnosed. Many hormone therapies have been developed to target the ER in cancer cells and are given for localized, early-stage cancers along with surgery and often radiation. With these treatments, most patients are cured but some will recur. The patients who are at risk of recurrence despite hormone therapies and surgery, are often offered chemotherapy to improve the chances of cure. Tests such as the Oncotype Dx™, Prosigna™, and EndoPredict™ that can predict the risk of recurrence measure gene expression in cancer as a whole—usually from a sample of thousands to millions of cancer cells—to determine whether the cancer is more aggressive (high risk) or less aggressive cancer (low risk). The main limitations of these tests are that the specimens contain complex mixtures of different cells including tumor, immune, and many other non-cancer cells. This means that information is missing about how neighboring cells communicate with each other, as immune and normal cells in the cancer can influence how a tumor responds to treatment. The tests will also miss small areas of cancer hiding among otherwise normal cells. Some cancers recur even though the test predicts low risk, and many patients with high-risk test results don’t have a recurrence. Dr. Turner aims to improve the prediction of risk of recurrence and seeks to better understand the biology of what causes a cancer to recur.

Progress Thus Far

Dr. Turner and his team have made progress in developing and testing spatial cell technologies to gain information about the complex nature of tumors. They have successfully conducted pilot experiments on tumor and tissue specimens. Early results show clear spatial differences in cancer cells within tumors. The team has also measured both gene and protein expression at the single-cell level, including optimizing techniques to assess ER protein expression alongside spatial gene expression. These pilot studies have established the foundation for a full-scale study over the coming year.

What’s next

Over the next year, Dr. Turner and his team will complete data collection on more than 1,000 breast tumors, generating both gene and protein information at the single-cell level. This will create an unprecedented dataset of millions of cells and billions of data points, offering powerful insights into the factors that influence cancer recurrence. The research will involve analyzing gene expression for 6,000 genes and profiling 64 key tumor and immune-related proteins, alongside single-cell measurements of ER protein. Each cell will be classified by type (i.e., as a tumor, immune, or stromal cell) and state (i.e., activating or suppressing immune function), and the team will map how different cells interact. The team will collaborate with experts in mathematics and computation to improve methods for linking genes to cells, refining cell classification, and building models that predict patient risk.