Researchers develop a new integrated approach to locate "drivers" of genetic cancer

Researchers develop a new integrated approach to locate "drivers" of genetic cancer -

researchers UNC Lineberger Comprehensive Cancer Center have developed a new integrated approach to identify the "drivers" genetic cancer, uncovering eight genes that may be viable for the treatment of breast cancer target.

The study, published online August 24 in Nature Genetics , was written by Michael Gatza, PhD, lead author and post doctoral research associate; With Silva, graduate student; Joel Parker, Ph.D., director of bioinformatics, UNC Lineberger; Cheng Fan, research associate; and lead author Chuck Perou, PhD, professor of genetics and pathology.

These researchers studied a variety of cancer causing routes, genetic alterations step by step in which normal cells transition in cancer cells, including the way that regulates cancer cell growth. A high growth rate of cells, also known as cell proliferation, is known to be associated with poor prognosis for patients with breast cancer.

Analysis of multiple types of genomic data, researchers UNC Lineberger have identified eight genes that were amplified in the genomic DNA, and necessary for the proliferation of breast cancer cells luminal, which is the subtype most common breast cancer.

"With this new approach to computing, we were able to leverage the rich data resources that exist and identify a number of new potential drug targets for a specific subset of patients breast cancer. This is an important step on the road towards a more personalized medicine, "said Peru.

In fact, one of the identified genes - CPT1A - is already a target for drug development in lymphoma and could be tested for patients with breast cancer. CPT1A targeting drugs have been shown to inhibit the growth of human cancer cell line in vitro and in mouse models of lymphoma.

This analytical approach used to better understand the factors cancers includes a comprehensive and integrated analysis of multiple data types, including gene expression data, somatic mutations, number of DNA copies, and a set of functional genomic data.

although the study focused on identifying the genetic drivers for breast cancer, the approach could easily be applied to other types of tumors as well. Lead author Mike Gatza added: "While we were able to identify drivers for breast cancer, this approach can and will be applied to other types of tumors in the future"

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