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Functional genomic approaches to breast cancer

Background

One of the major remaining deficits in our understanding of the human genome is that information regarding gene function is available for only one-quarter of the approximately 30,000 genes. Many of these hitherto anonymous genes are potential targets for the development of new anti-cancer drugs. It is therefore important to functionally annotate the tens of thousands of genes for which this information is currently lacking. My laboratory has developed functional genetic approaches to obtain information regarding gene function using high-throughput screens in mammalian cells. We have developed both gain-of-function genetic screens (using retroviral cDNA expression libraries) and loss-of-function genetic screens (using vector-based RNA interference libraries) to carry out large-scale genetic screens in mammalian cells. We focus on the central growth-regulatory pathways that are most frequently deregulated in cancer.

Methods

We have designed a mammalian expression vector (pSUPER), which directs the synthesis of short hairpin transcripts (shRNAs) that are processed intracellularly into siRNA-like molecules. This vector mediates persistent inhibition of gene expression in a highly specific fashion. We have used this vector to stably suppress expression of individual members of several cancer-relevant gene families.

Results

We used a retroviral derivative of the pSUPER siRNA vector to generate a large collection of siRNA vectors that each target a single gene for suppression. In total, we constructed a set of 23,742 siRNA vectors that together target 7914 human genes for suppression by RNA interference. Furthermore, we developed a very efficient way to identify biologically active shRNA vectors in a large population of vectors, a technology that we named 'siRNA bar code screening'. We will present two applications of this technology to study major questions in breast cancer. First, we have used the RNAi library to identify genes whose suppression causes resistance to anti-hormonal therapy (tamoxifen resistance). In addition, we have used RNAi technology to ask how clinical resistance to the Her2/neu/ErbB2-targeted therapeutic Herceptin can arise.

Conclusion

RNA interference is a powerful technology to identify genes that are causally involved in disease processes. Application of this technology to breast cancer may greatly expedite the development of novel diagnostics and therapeutics for the treatment of this disease.

References

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Bernards, R. Functional genomic approaches to breast cancer. Breast Cancer Res 7 (Suppl 2), S.41 (2005). https://doi.org/10.1186/bcr1084

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