NCRI Conference Abstracts
Poster Session B ...Breast cancer

B56 

An in vitro/vivo shRNA screen to identify novel breast cancer metastasis suppressor genes

Nirupa Murugaesu, Damian Johnson, Darren Burgess, Christopher Lord, Alan Ashworth, Clare Isacke

Breakthrough Breast Cancer Research Centre, The Institute of Cancer Research, London, UK

Background

The majority of deaths from breast cancer are due to metastatic disease. Metastasis suppressor genes (MSGs) can act at various stages of the metastatic cascade i.e. in the inhibition of tumour cell motility and invasion at the primary site, tumour cells arrest and extravasation at the secondary site and/or survival and growth at the secondary site. The aim of this project is to identify and characterise breast cancer MSGs using a two part shRNA in vitro and in vivo screen.

Method

4T1 mouse mammary tumour cells have been infected with the Cancer 1000 shRNA library containing 2,200 individual shRNAs targeting the Breast Cancer 1000 mouse gene set. Screen 1 is an in vitro Transwell invasion assay across Matrigel coated filters. Screen 2 is an in vivo lung metastasis assay using subpools of the library. Enrichment of PCR amplified shRNAs will be assessed by next generation sequencing using the Illumina Genome Analyser II (GAII) platform.

Results

Both screens have been optimized and the integrity of the library validated by next generation sequencing. The invasion screen is currently being analysed and the in vivo screen is underway. Enriched shRNAs in either screen will be validated using independent shRNAs, in other cell lines and in an in vivo spontaneous metastasis assay (inoculation of infected 4T1-Luc cells into the mammary fat pad and IVIS monitoring for lung and liver metastasis). Putative MSGs will be ectopically expressed and tested for their ability to suppress invasion and/or metastatic colonization. Finally, candidate genes will be analysed in clinical samples.

Conclusion

The results of the 4T1 screen will enable us to develop future screens using more complex libraries in other model systems. Together these approaches will provide a fuller picture of the metastatic process and identify key pathways for therapeutic intervention.