Predicting responses to cancer immunotherapy
Background
Immune therapies based on ‘checkpoint inhibitors’ have provided new hope for patients with previously incurable melanomas and lung cancers. Checkpoint inhibitors are drugs that enable the patient’s immune cells to eliminate cancers by blocking immune ‘off switches’. Up to 40% of patients with advanced melanoma and over 20% of patients with advanced lung cancer can be cured with these new treatments. But they don’t work for some patients. Since they are costly and can produce life-threatening side effects, a way of predicting which patients will benefit from treatment is urgently needed.
The research
Professor Fazekas is using technology called mass cytometry to develop a blood test that will predict which patients will respond to immune checkpoint therapy. Her team has identified a large number of different types of immune cells in the blood of cancer patients and previously found that the numbers of many immune cell types were abnormal in patients who later failed to respond. This research will test how accurately each patient’s pattern of abnormal immune cells, or ‘immune signature’, predicts their response to therapy. The team will also try to identify additional signatures for patients with other types of cancer and link these with responses to immune therapies or combinations of immune therapies and chemotherapies.
The impact
Professor Fazekas’ work has potentially major benefits for patient care because it brings the concept of personalised medicine much closer to reality. It should allow informed decisions about which patients should undergo immune checkpoint therapies, reducing the burden of side effects for those that won’t respond and allowing these costly new treatments to be given to only those patients who stand to benefit. Since many different types of immune therapies are in the pipeline for other cancers, her work also has potential to significantly improve cancer care more broadly in future.