Scientists at Yale Cancer Center have found that patients with breast cancer and high levels of insulin in the blood may be responsive to metabolism-targeting treatments, which in turn may improve the effectiveness of subsequent chemotherapy treatments. The findings were published today in Communications Biology.
The incidence of breast cancer is projected to increase more than 50% between 2011 and 2030. This forecast is partially attributable to rising rates of obesity, which accelerate the incidence and progression of breast cancer in postmenopausal women. At the same time, metabolism-targeting therapies such as the diabetes drug metformin have gained increasing popularity in breast cancer treatment, with mixed outcomes in clinical trials.
Using a new class of diabetes drug, SGLT2 inhibitors, Yale researchers studied the SGLT2 inhibitor dapagliflozin as an alternative to metformin. The research was done in animal models to determine the responsiveness to dapagliflozin in addition to chemotherapy in breast tumors. Their findings were mutation-specific. The study revealed that dapagliflozin improves the effectiveness of chemotherapy in slowing breast tumor growth in models with breast cancer driven by mutations upstream of the insulin signaling pathway. Models with breast cancer driven by mutations downstream of the insulin pathway or in pathways with other driver mutations were not improved with the combined therapy.
“Our data supports the development of insulin-lowering approaches to breast cancer treatment in hyperinsulinemic patients,” said Rachel Perry, PhD, Assistant Professor of Medicine (Endocrinology) and Cellular and Molecular Physiology at Yale Cancer Center and lead author on the paper. “The next step will be to move this research into early clinical trials at Smilow Cancer Hospital, which should position SGLT2 inhibitors as an attractive target to fill this niche.”
Funding for the study was provided by the Lionheart Foundation.
The following Yale authors contributed to this study: Ngozi D. Akingbesote, Aaron Norman, Wanling Zhu, Alexandra A. Halberstam, Xinyi Zhang, Julia Foldi, and Maryam B. Lustberg.