Large-scale breast cancer study could provide novel drug targets and combinations
Study of breast cancer cells suggests new approaches to drug discovery and cancer treatment.
Researchers from New York University (NY, USA) and Princess Margaret Cancer Centre (Montreal, Canada) have conducted the largest analysis of breast cancer cell function to date, suggesting dozens of novel uses for existing drugs, new drug combinations and new targets for drug discovery.
The authors believe that their results will be used by laboratories worldwide to identify new drug candidates for other forms of cancer, as well as to identify the means by which cancer cells resist treatment. Lead study author Benjamin Neel explained: "This study represents the largest survey yet of how the genetic changes in breast cancer cells interfere with pathways critical to their growth and survival, pathways that might be targeted by combinations of new or existing drugs."
The researchers combined genetic analyses of breast cancer cell types, novel statistical methods and comparisons with databases of molecular signatures and the effects of anti-cancer drugs. Neel elaborated, "Our new statistical approach represents an improvement on earlier methods that were unable to link the webs of genetic changes in cancer cells to the complex functions on which they most depend."
Previously, research teams have conducted large-scale genomic studies aiming to identify the genetic changes that contribute to breast cancer. These have been successful in identifying which genetic changes are found in types and subtypes of cancer, but less successful in determining which of these changes are needed for cancerous cell proliferation and survival. The current study performed shRNA screens on 77 breast cancer cell lines, representing the many sub-types of the disease. These shRNA ‘dropout screens’ shut down each gene in a cancer cell one by one to determine which are most critical to its survival.
The researchers then applied their novel statistical technique, dubbed si/shRNA Mixed-Effect Model, to score the results and identify candidate genes most vital to cancer growth. They compared their results against information in large databases on drug efficacy, cancer genetics and protein interactions. These combined methods identified a number of candidate genes previously not known to play a role in breast cancer survival, and discovered clusters of genes required in cells either sensitive or resistant to 90 anti-cancer drugs.
"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," concluded Neel. "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."
Sources:Marcotte, R., Sayad, A., Brown, K. R., et al (2016). Functional Genomic Landscape of Human Breast Cancer Drivers, Vulnerabilities, and Resistance. Cell, 164(1), 293–309.