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Integrated Functional, Gene Expression and Genomic Analysis for the Identification of Cancer Targets

Overview of attention for article published in PLOS ONE, April 2009
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Title
Integrated Functional, Gene Expression and Genomic Analysis for the Identification of Cancer Targets
Published in
PLOS ONE, April 2009
DOI 10.1371/journal.pone.0005120
Pubmed ID
Authors

Elizabeth Iorns, Christopher J. Lord, Anita Grigoriadis, Sarah McDonald, Kerry Fenwick, Alan MacKay, Charles A. Mein, Rachael Natrajan, Kay Savage, Narinder Tamber, Jorge S. Reis-Filho, Nicholas C. Turner, Alan Ashworth

Abstract

The majority of new drug approvals for cancer are based on existing therapeutic targets. One approach to the identification of novel targets is to perform high-throughput RNA interference (RNAi) cellular viability screens. We describe a novel approach combining RNAi screening in multiple cell lines with gene expression and genomic profiling to identify novel cancer targets. We performed parallel RNAi screens in multiple cancer cell lines to identify genes that are essential for viability in some cell lines but not others, suggesting that these genes constitute key drivers of cellular survival in specific cancer cells. This approach was verified by the identification of PIK3CA, silencing of which was selectively lethal to the MCF7 cell line, which harbours an activating oncogenic PIK3CA mutation. We combined our functional RNAi approach with gene expression and genomic analysis, allowing the identification of several novel kinases, including WEE1, that are essential for viability only in cell lines that have an elevated level of expression of this kinase. Furthermore, we identified a subset of breast tumours that highly express WEE1 suggesting that WEE1 could be a novel therapeutic target in breast cancer. In conclusion, this strategy represents a novel and effective strategy for the identification of functionally important therapeutic targets in cancer.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 103 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 4%
Netherlands 1 <1%
Germany 1 <1%
China 1 <1%
United Kingdom 1 <1%
Unknown 95 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 36 35%
Student > Ph. D. Student 18 17%
Student > Master 9 9%
Student > Bachelor 8 8%
Professor > Associate Professor 8 8%
Other 15 15%
Unknown 9 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 46 45%
Biochemistry, Genetics and Molecular Biology 22 21%
Medicine and Dentistry 17 17%
Pharmacology, Toxicology and Pharmaceutical Science 2 2%
Chemistry 2 2%
Other 3 3%
Unknown 11 11%