Researchers from the Geisel School of Medicine at Dartmouth will present a scientific poster on Wednesday, April 9, 2014 at the American Association of Cancer Researchers conference in San Diego, CA. Their study suggests that manipulation of drug dosage and schedules may improve anti-tumor effects of PI3K-inhibitors to target breast cancer tumors. These findings have implications for the optimal strategy to use such drugs in patients, and lay the groundwork for future development of anti-cancer therapeutics.
Sixty-five percent of all breast cancers are estrogen receptor positive (ER+), with tumors that can stop growing or die when treated with drugs that block estrogen signaling. Eighty percent of these ER+ breast cancers have mutations on the PI3K pathway, which regulates cell growth. Drugs that target the PI3K pathway have shown promise for the treatment of anti-estrogen-resistant breast cancers in early clinical trials.
The Dartmouth researchers noted that in clinical studies, novel drugs are often delivered in escalating doses until toxicities are observed in patients; this approach doesn't provide information on target inhibition in tumors. Current PI3K inhibitor treatment regimens incompletely and temporarily inhibit the pathway in cancers, and are often accompanied by adverse effects in patients.
"We wanted to see if short-term, complete blocking of PI3K would have a greater impact on tumors, and also reduce the adverse effects," said Todd Miller, principal investigator on the study and assistant professor of Pharmacology and Toxicology at Geisel. "Our findings indicate that optimization of rational drug doses and schedules early in the clinical development process may improve anti-tumor effects in larger, later-phase trials."
The study suggests that short-term, complete inhibition of PI3K would have a greater anti-tumor effect than chronic, partial inhibition. The researchers will build on this study to test the anti-tumor efficacy of different treatment regimens of anti-estrogen and PI3K inhibition in different models of ER+/HER2- breast cancer.