Researchers have developed a new integrated approach to pinpoint the genetic drivers of cancer, uncovering eight genes that could be viable targets for breast cancer therapy.
Cancer-causing pathways are step-by-step genetic alterations through which normal cells transition into cancerous cells. This study, published in Nature Genetics (2014; doi:10.1038/ng.3073), examined a variety of known cancer-causing pathways, including the pathway that governs the growth rates of cancer cells. A high growth rate of cells, also known as cell proliferation, is recognized to be associated with poor prognosis for breast cancer patients.
The researchers, based at the University of North Carolina Lineberger Cancer Center at Chapel Hill, analyzed multiple types of genomic data. This allowed them to identify eight genes that were amplified on the genomic DNA level. These genes are necessary for cell proliferation in luminal breast cancer, which is the most common subtype of breast cancer.
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“Using this new computational approach, we were able to take advantage of the rich data resources that exist and identify a number of new potential drug targets for a specific subset of breast cancer patients. This is an important step down the road towards more personalized medicine,” said senior author Chuck Perou, PhD.
In fact, one of the genes identified, CPT1A, is already a target for drug development in lymphoma and could potentially be tested for breast cancer patients as well. Drugs targeting CPT1A have been shown to inhibit human cancer cell line growth in vitro and in mouse models of lymphoma.
This analytical approach used to better understand the drivers of cancers includes a comprehensive and integrated analysis of multiple data types including gene expression data, somatic mutations, DNA copy number, and a functional genomics data set.
While the study focused on identifying genetic drivers for breast cancer, the approach could easily be applied to other tumor types as well. Lead author and postdoctoral research associate Michael Gatza, PhD, added, “While we were able to pinpoint drivers for breast cancer, this approach can and will be applied to other tumor types in the future.”
