Breast Cancer study shows new potential drug targets

The current study performed shRNA screens on 77 breast cancer cell lines, a large enough sample to represent the many sub-types of this disease. The research team then applied their newly designed statistical technique, the si/shRNA Mixed-Effect Model (siMEM), to score the results of the cell-line genetic knockdown studies, identifying candidate genes most vital 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 drugs are effective or not.
The combined methods created newfound signals in the data more closely tied to impact on cancer cell traits and did a better job screening out false positives. The study identified a number of candidate genes previously unknown to play a role in breast cancer cell survival. In addition, the team found clusters of genes that were required in cells that were either sensitive or resistant to 90 anti-cancer drugs.
Among the new and potential "druggable" targets identified for triple negative breast cancer, the most deadly form of the disease, 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 and BET-inhibitors with epirubicin and vinorelbine, and PLK1 inhibitors with AKT inhibitors.
While the new method suggests pathways for further study in every breast cancer subtype, the authors chose one for additional analysis to show the potential of the work to guide the field. Further experiments validated BRD4 as a gene essential to the survival of most luminal/HER2+ cancer cells, as well as a subset of triple negative breast cancer cells.