EZH2 and AKT Inhibitor Combination Shows Promise in Triple-Negative Breast Cancer
Key Insights
Researchers have identified a novel therapeutic strategy combining EZH2 (search) and AKT (search) inhibitors to target triple-negative breast cancer (search) (TNBC (search)) cells, offering a potential new treatment option.
The combination therapy induces differentiation in TNBC (search) cells, making them more susceptible to cell death through a mechanism similar to mammary gland involution.
Machine learning was used to develop a predictive model to identify patients most likely to respond to the combined treatment, enhancing the potential for clinical trial success.
Researchers from Brigham and Women’s Hospital (search) have identified a promising therapeutic strategy for triple-negative breast cancer (search) (TNBC (search)), combining EZH2 (search) and AKT (search) inhibitors to selectively target and kill TNBC cells. Published in Nature, the study highlights the potential of this combination to address the high recurrence rate and poor prognosis associated with TNBC.
Targeting TNBC with EZH2 and AKT Inhibitors
TNBC (search), an aggressive form of breast cancer, lacks estrogen and progesterone receptors and does not produce HER2 (search), making it unresponsive to hormone therapy or anti-HER2 drugs. The research team, led by Karen Cichowski, PhD, investigated a novel approach by combining EZH2 (search) inhibitors, which target epigenetic changes, with AKT (search) inhibitors, which target the PI3K pathway—a pathway altered in over 70% of TNBC cases.
The study found that the combination of EZH2 (search) and AKT (search) inhibitors induces differentiation in TNBC (search) cells, shifting them into a state resembling healthy, non-cancerous cells. This differentiation process makes the tumor cells vulnerable to cell death through a mechanism similar to mammary gland involution. According to Dr. Cichowski, this combination "hijack[s] signals that occur naturally in the body to eliminate breast cells after lactation to kill these aggressive cancer cells."
Machine Learning for Patient Selection
To optimize patient selection for this therapy, the researchers employed machine learning to develop a predictive model. This model forecasts which patients would respond best to the combined treatment based on the interaction between EZH2 (search) and AKT (search) inhibitors. This approach could be a valuable tool in identifying patients most likely to benefit from this therapy.
Overcoming Resistance Mechanisms
The study also revealed that TNBC (search) cells use epigenetic enzymes like EZH2 (search) to protect themselves from conventional treatments. By inhibiting EZH2, the researchers disrupted this protective barrier, allowing the AKT (search) inhibitors to drive the cancer cells into a more vulnerable, differentiated state, making them susceptible to programmed cell death.
Clinical Trial Potential
Based on these results, the researchers are optimistic about the potential of this therapeutic combination. They plan to expand their studies to test the effectiveness of EZH2 (search) and AKT (search) inhibitors in additional tumor types and move forward with clinical trials to evaluate the safety and efficacy of this combination treatment in TNBC (search) patients. Preclinical studies in cell cultures and mouse models demonstrated significant tumor regression, further supporting the development of clinical trials in humans.
Ryan Schoenfeld, CEO of The Mark Foundation for Cancer Research, noted that this research is especially hopeful following the recent failure of Truqap, a targeted therapy with disappointing clinical trial results. He added that it offers a new hypothesis on why combining an epigenetic inhibitor with AKT (search) could succeed where AKT inhibition plus chemotherapy failed, supported by compelling preclinical data.