Breast cancer patients are often faced with a difficult decision at the end of their primary treatment: Should they get systemic adjuvant therapy, such as the anti-estrogen drug tamoxifen? Such therapies lower the risk that the cancer will come back, but they also carry the risk of potentially serious side effects.
What would be helpful is for physicians to have a way to predict which patients have the best prognosis and might not need adjuvant therapy. Now, researchers from the Lawrence Berkeley National Laboratory may have a solution, according to a study recently published in Oncotarget.
The research team analyzed clinical patient data and large genomic datasets of normal and tumor breast tissues — identifying 381 genes associated with the relapse-free survival of breast cancer patients. With further analysis, they were able to develop a scoring system based on a 12-gene signature that predicts breast cancer survival. Patients with a low score were more likely to live longer.
Senior author Antoine Snijders, PhD, a research scientist at Berkeley Lab, explained in a recent news release:
“Distinguishing patients with good prognosis could potentially spare them the toxic side effects associated with adjuvant therapy. Determining prognosis involves a range of other clinical factors, including tumor size and grade, the degree to which the cancer has spread, and the age and race of the patient. Our scoring system was predictive of survival independent of these other variables.”
The study showed that their 12-gene signature was effective at predicting patient survival for two specific subtypes of breast cancer — luminal-A and HER2 — but it wasn’t effective for other subtypes.
In addition, the researchers identified seven genes as potential tumor suppressors that could be targeted when developing new breast cancer therapies. They hope that their work will help doctors and patients make more informed treatment decisions, as well as help others develop better breast cancer drugs.
This is a reposting of my Scope blog story, courtesy of Stanford School of Medicine.