Major Cause of Drug-resistance in Prostate Cancer Patients Identified
Mutations in the SPOP gene (depicted on the left) can be used to guide administration of anti-cancer drugs (labeled treatment A through D below) in patients with prostate cancer. Credit: Mayo Clinic
Mayo Clinic researchers have identified a new cause of treatment resistance in prostate cancer. Their discovery also suggests ways to improve prostate cancer therapy. The findings appear in Nature Medicine.
In the publication, the authors explain the role of mutations within the SPOP gene on the development of resistance to one class of drugs. SPOP mutations are the most frequent genetic changes seen in primary prostate cancer. These mutations play a central role in the development of resistance to drugs called BET-inhibitors.
BET, bromodomain and extra-terminal domain, inhibitors are drugs that prevent the action of BET proteins. These proteins help guide the abnormal growth of cancer cells.
As a therapy, BET-inhibitors are promising, but drug resistance often develops, says Haojie Huang, Ph.D., senior author and a molecular biologist within Mayo Clinic’s Center for Biomedical Discovery. Prostate cancer is among the most diagnosed malignancies in the United States. It is also the third leading cause of cancer death in American men, according to the American Cancer Society. Because of this, says Dr. Huang, improving treatments for prostate cancer is an important public health goal.
In the publication, the authors report SPOP mutations stabilize BET proteins against the action of BET-inhibitors. By this action, the mutations also promote cancer cell proliferation, invasion and survival.
“These findings have important implications for prostate cancer treatment, because SPOP mutation or elevated BET protein expression can now be used as biomarkers to improve outcome of BET inhibitor-oriented therapy of prostate cancer with SPOP mutation or BET protein overexpression,” says Dr. Huang.
Mutations in the SPOP gene can then be used to guide administration of anti-cancer drugs in patients with prostate cancer:
Computer scientists at Carnegie Mellon University say neural networks and supervised machine learning techniques can efficiently characterize cells that have been studied using single cell RNA-sequencing (scRNA-seq). This finding could help researchers identify new cell subtypes and differentiate between healthy and diseased cells.