Dr. Robert Clarke discusses research on endocrine resistance in breast cancer in a new video posted by the Congressionally Directed Medical Research Programs (CDMRP). Clarke’s team is supported by a grant from Department of Defense Breast Cancer Research Program (BCRP). The CDMRP strive to support research opportunities that encourage innovation and ingenuity in the biomedical sciences in response to the expressed needs of our stakeholders -- Congress, the American Public, and the military.
Cancer Systems Biology researchers from Virginia Tech and the Lombardi Comprehensive Cancer Center at Georgetown University Medical Center have developed a model that may eventually help explain and overcome drug resistance in breast cancer treatment.
Virginia Tech electrical engineering professors William Baumann, Joseph Wang, and Jason Xuan, and biology professor John Tyson, have been using mathematical modeling and machine learning approaches to understand the causes of drug resistance in breast cancer treatment.
Recently, Lombardi researchers Robert Clarke, Katherine Cook, and Ayesha Shajahan-Haq found that a key chaperone protein known as BIP or GRP78, which helps fold newly synthesized proteins, was over-expressed in a resistant breast cancer cell line compared to a sensitive cancer cell line. Reducing the expression of BIP in the resistant cell line caused the cells to become sensitive to drugs, while enhancing its expression in the sensitive cell line caused the cells to become resistant to drugs.
Understanding this intriguing experimental result is complicated by the interaction of three complex cellular subsystems: the Unfolded Protein Response, autophagy (self-eating), and apoptosis (programmed death).
The Virginia Tech team mathematically modeled each of the three subsystems using nonlinear ordinary differential equations to capture their basic behavior, then combined the subsystems as depicted in the figure above. While much is unknown about some of the interactions, the model was able to recapitulate the experimental results. The model is being used to suggest new experiments to improve the model and understanding of drug resistance. Ultimately, the model can be used to find combinations of drugs to kill the resistant cancer cells, according to Dr. Baumann.
While it is well known that family history is an important factor in breast cancer risk, in many cases an increased risk of developing breast cancer is not due to genetic mutations that are passed down to future generations. Lombardi researchers led by Dr. Sonia de Assis in Dr. Leena Hilakivi-Clarke's laboratory have found that in rats, exposure of a pregnant mother to estrogenic compounds can result in increased risk of cancer in daughters, granddaughters, and even great granddaughters.
To understand how this increased risk is transmitted without genetic mutation, Dr. Joseph Wang, professor of electrical engineering at Virginia Tech, used statistical machine-learning techniques to analyze changes in the methylation status of the DNA of descendants with increased risk. DNA methylation is a key process in normal development, allowing cells with the same genome to perform different functions by using methylation to turn some genes on and some genes off.
Dr. Wang’s group found that the descendants with increased risk had several hundred common DNA regions that were methylated differently than a control group, providing a possible mechanism for how breast cancer risk can be transmitted without genetic mutations. Ultimately, it may be possible to undo this harmful methylation and decrease the risk of breast cancer.