Predicting clinical response to breast cancer therapy – the immune signature approach
Background
Breast cancer is the most common cancer in women and is still a major cause of cancer death despite significant improvements in treatment over the past 30 years. Those improvements include the introduction of adjuvant chemotherapy following primary surgery, and specific therapies directed to factors expressed by some but not all breast cancers, including estrogen and progesterone receptors, and the Her2 molecule. A minority of cancers, termed triple negative, have neither hormone receptors nor Her2, and treatment options are very limited if chemotherapy proves ineffective.
For several other cancers, most notably melanoma and lung cancer, a novel type of therapy introduced over the past 10 years has provided new options for previously incurable diseases. This treatment, checkpoint inhibitor therapy, changes the way the patient’s own immune system reacts to their cancer, allowing their natural anti-cancer defences to control the tumour. This type of immune attack is very long lived, and in those patients who respond well to therapy, it can generate stable cures. However, only a minority of patients respond (40% in melanoma, 20% in lung cancer).
Unfortunately, breast cancer is one of the cancers where the response rate is so low that single agent checkpoint therapy is generally not used. Some recent trials of checkpoint therapy combined with chemotherapy have produced promising results in advanced triple negative breast cancer, but more work is needed.
About the Project
Previous research by Professor Fazekas, funded by a 2018-2020 Cancer Council NSW Grant provided unique insight into why breast cancer is so resistant to checkpoint therapy. Her team developed a blood “immune signature” test which robustly predicts which melanoma and lung cancer patients will derive no benefit from checkpoint therapies.
In this project Professor Fazekas and team will analyse pre- and post-therapy blood samples from breast cancer patients and use machine learning statistical techniques to identify the immune signatures that can predict patient response to a particular therapy with the greatest accuracy. This project will not only reveal which components within the immune system are crucial predictors of therapeutic response in breast cancer, providing clinicians with new laboratory tests aiding therapeutic choice for each of their patients, but will also open up the possibility of using new immunomodulatory drugs to render breast cancer patients responsive to checkpoint therapy.
Impact
This project will identify new blood signatures that can provide guidance for choice of therapy in breast cancer patients. Publication of our findings on how breast cancer controls patients’ immune systems will stimulate an immediate increase in research into how these findings could be incorporated into new treatments. Results will be developed as prototype clinical tests (as described above). Trials at different centres will be carried out to test the predictive accuracy of the tests.
The team has now looked for signatures in over 10 different diseases and therapies and never failed to identify a signature – they are much more widespread than might generally be assumed. This will be an important step on the way to realising the full potential of precision medicine in treating cancers.