A Mater researcher has developed a method to predict breast cancer patient responses to a commonly prescribed breast cancer drug.
Dr Cameron Snell, an Anatomical Pathologist with Mater Pathology and a Mater researcher has developed a technique to image proteins and their interactions in cells and has used these to predict which patients are at risk of relapsing on aromatase inhibitors.
Aromatase inhibitors work by blocking the production of estrogen in postmenopausal women. This reduces the amount of estrogen available to stimulate the growth of the breast cancer cells. Approximately 80 per cent of all breast cancers are Estrogen Receptor Positive (ER+) and ER+ cancer cells grow in response to the hormone estrogen.
In Australia breast cancer is the most common cancer in women, and the second most common cancer to cause death in women. One in eight women in Australia will be diagnosed with breast cancer before they turn 85.
Currently immunohistochemistry is the only clinical biomarker that predicts the effect of anti-oestrogen therapies.
Dr Snell has developed a proximity ligation assay (a technique that extends the capabilities of immunohistochemistry) to image proteins and their interactions in cells by binding specific antibodies to those proteins to test for interactions between estrogen receptors and progesterone receptors in the breast.
The test was applied to a cohort of 229 patients with ER+ and HER2-negative breast cancer using the patient archives of Mater Pathology.
The test showed that a low frequency of interaction between the estrogen and progesterone receptors was associated with a higher risk of relapse. A subset analysis showed that low estrogen-progesterone receptor interaction could be used to predict which patients are at risk of relapsing on aromatase inhibitors, but not on tamoxifen.
“If we can effectively understand who is going to relapse on standard treatments, we may be able to offer these patients new treatments at a stage when their disease is potentially curable,” Dr Snell said.
“The tools we have at the moment are quite crude so we are looking at better ways to predict a patient’s response to treatment and to identify patients who may relapse.”