Data analysis from several cancer research centers suggests new opportunities in breast cancer treatment and pathways.
Data analysis from several cancer research centers suggests new opportunities in breast cancer treatment and pathways. Genetic changes in breast cancer cells interfere with pathways critical to their growth and survival, and this analysis of breast cancer cell function reveals potential new uses for existing drugs and new drug combinations, along with new druggable targets.
Researchers from New York University (NYU) Langone Medical Center, NYU’s Laura and Isaac Perlmutter Cancer Center, and the Princess Margaret Cancer Centre in Toronto, worked together to analyze large data sets surrounding these genetic changes to discover new possibilities in the therapeutic treatment for breast and other cancer types.
First published in the journal Cell, this analysis of breast cancer cell function identified potential drug therapies in other cancer types and also highlighted how cancer cells resist treatment. By combining genetic analyses of many breast cancer cell types using new statistical methods, and comparing with databases of molecular signatures and the effects of anticancer drugs, the research team made these discoveries.
Using a new statistical model, the research team was able to identify several previously known genes that are essential for specific breast cancer subtypes: HER2, estrogen receptor (ER), and HER3.
The relationship between HER3 and other breast cancer tumor markers is still not entirely understood. A 2005 study suggests that HER3 and the progesterone receptor (PR) may have an inverse relationship. For example, in the study of 402 patients diagnosed with ER-positive breast cancer who were treated with tamoxifen, there was a statistically significant inverse relationship between HER3 expression and expression of PR (P = .001).
The current study performed short hairpin RNA (shRNA) screens on 77 breast cancer cell lines. The research team then applied their statistical technique, the si/shRNA Mixed-Effect Model (siMEM), to score the results of the cell line genetic knockdown studies, identifying genes most important to cancer growth. They also compared the results against information in large databases on cancer genetics, protein interactions, and genetic changes seen in cancer cells when treated with therapies.
Targets that may be sensitive to new drugs or drug combinations identified for triple-negative breast cancer (TNBC) were signaling proteins linked by past studies to brain tumors (EFNB3 and EPHA4), proteins that regulate cell growth pathways (MAP2K4, MAPK13), and a protein known to drive inflammation (interleukin 32).The data also suggested for additional study dozens of new, potential drug combinations for the treatment of breast cancer subtypes, including RAF/MEK and CDK4 inhibitors, EGFR inhibitors, bromodomain and extra terminal domain (BET)-inhibitors with epirubicin and vinorelbine, and PLK1 inhibitors with AKT inhibitors. As part of the BET family, the BRD4 gene is essential to the survival of most luminal/HER2-positive cancer cells, as well as a subset of TNBC cells.
“Very few patients today get a whole genome sequence analysis done on their cancer cells, and the few that do typically receive little medical benefit from the results,” said Benjamin Neel, MD, PhD, Professor of Medicine at the Perlmutter Cancer Center, in a NYU press release. “The ultimate goal of researchers worldwide is to finally understand each cancer cell’s wiring diagram well enough to clarify both the molecular targets against which therapeutics should be developed and the patient groups most likely to respond to any treatment.”
Further investigation will be necessary to determine the efficacy for these new uses for existing drugs, new targets for drug discovery, and new drug combinations--but this appears to be promising for these aggressive breast cancer types.