Functional Genomic Strategies To Identify Oncogenes In Breast Cancer

During the process of tumorigenesis and tumor progression cancer cells acquire an array of genetic alterations. The theory of oncogene addiction proposes that a small portion of these alterations generate driver oncogenes that are critical for maintaining the transformed phenotype. Strong support for this theory comes from the identification and subsequent therapeutic targeting of individual driver oncogenes that has resulted in significant clinical responses. However, in solid tumors that contain multiple driver oncogenes, these responses are not durable and it is therefore likely that multiple oncogenes will need to be identified and targeted in these tumors in order to reverse the transformed phenotype and effect durable clinical responses. Identifying multiple driver oncogenes among the large number of mutated/amplified genes that are present in most solid tumors is a nontrivial task that will require novel approaches. In this study, we have developed an approach that combines exome sequencing, array comparative genomic hybridization, expression analysis and genome-wide, RNAi-based functional screening to identify driver oncogenes that are functioning in individual cell lines derived from human tumors. In a pilot study using this approach we have shown that of the 583 amplified genes in the SUM225 breast cancer cell line only 45 are hits in the RNAi-based functional screen. Similarly, of the 806 genes that are mutated in this cell line only 26 are hits in the RNAi-based functional screen. Interestingly, the SMPD3 gene that was both amplified and a hit in the functional screen also contained a point mutation and this point mutation was present in another HER2 positive breast cancer cell line, SUM190. The identification of SMPD3 as a likely driver oncogene in the SUM225 and SUM190 cell lines highlights the power of an approach that combines sequence and copy number data with functional screening to identify driver oncogenes. Future analysis will incorporate expression data to further refine this approach. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5125. doi:1538-7445.AM2012-5125