Author Interviews, Breast Cancer, Genetic Research, NYU / 16.01.2016
Functional Genomics Identifies Genes Essential To Breast Cancer Cell Survival
More on Breast Cancer Research on MedicalResearch.com
MedicalResearch.com Interview with:
Dr. Benjamin Neel MD PhD
Professor, Department of Medicine
Director Perlmutter Cancer Center
NYU Langone Medical Center
Medical Research: What is the background for this study? What are the main findings?
Dr. Neel: Over the past 10 years, there have been major advances in cancer genomics--i.e., defining what changes in genes are found in different types of cancer cells. Sometimes, such studies have resulted in the identification of new drug targets, such as EGF receptor mutations or EML-ALK translocations in lung cancer, RAF mutations in melanoma and hairy cell leukemia, and KIT or PDGFR mutations in GIST. More often, though, either the genetic changes that genomic studies reveal are difficult to target by conventional small molecule drugs or we dont know which of the many mutations found in a given tumor are critical to its proliferation/survival.
"Functional genomics" is a parallel approach to tumor genomics, that aims to use large scale screening technology to identify which genes are essential to cancer cell survival/proliferation. This approach can reveal which genetic changes in cancer cells "drive" the cancer--but it also can find genes on which the cancer becomes dependent because of the other "driver" genes. One major approach to functional genomics uses short hairpin RNAs (a type of RNAinterference/RNAi) to "knock down" the expression of each gene in a cell. Scientists can generate a "library" of designer virus particles, each of which expresses a different hairpin that can "knockdown" a different gene. A large population of tumor cells is then infected with the virus, and scientists use gene sequencing or array based approaches to see which shRNAs become depleted from the starting population of shRNAs; this type of screen is called a "dropout screen".
Earlier studies, including by our group, performed dropout screens on smaller numbers of cancer cell lines. Yet because these screens involved only a few cell lines, they could not represent the large number of sub-types knownt to occur in, for example, breast cancer. Our study, by using 77 breast cancer lines, has adequate power to survey the landscape of breast cancer. Furthermore, by obtaining parallel genomic information, as well as some information on the breast cancer cell "proteome" (the proteins in these cells), we can couple genomic analysis with functional genomics. In addition, we had drug response information for a large number of these lines, and so were able to make some predictions for drugs that might prove additive for breast cancer therapy.
The result is a large number of potential new targets linked to genetic information, as well as new insights into how the different sub-types of breast cancer "rewire" their respective signaling diagrams compared with normal cells.
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