|Publication Type||Journal Article|
|Year of Publication||2008|
|Authors||Luo, B, Cheung, HW, Subramanian, A, Sharifnia, T, Okamoto, M, Yang, X, Hinkle, G, Boehm, JS, Beroukhim, R, Weir, BA, Mermel, C, Barbie, DA, Awad, T, Zhou, X, Nguyen, T, Piqani, B, Li, C, Golub, TR, Meyerson, M, Hacohen, N, Hahn, WC, Lander, ES, Sabatini, DM, Root, DE|
|Journal||Proceedings of the National Academy of Sciences of the United States of America|
More complete knowledge of the molecular mechanisms underlying cancer will improve prevention, diagnosis and treatment. Efforts such as The Cancer Genome Atlas are systematically characterizing the structural basis of cancer, by identifying the genomic mutations associated with each cancer type. A powerful complementary approach is to systematically characterize the functional basis of cancer, by identifying the genes essential for growth and related phenotypes in different cancer cells. Such information would be particularly valuable for identifying potential drug targets. Here, we report the development of an efficient, robust approach to perform genome-scale pooled shRNA screens for both positive and negative selection and its application to systematically identify cell essential genes in 12 cancer cell lines. By integrating these functional data with comprehensive genetic analyses of primary human tumors, we identified known and putative oncogenes such as EGFR, KRAS, MYC, BCR-ABL, MYB, CRKL, and CDK4 that are essential for cancer cell proliferation and also altered in human cancers. We further used this approach to identify genes involved in the response of cancer cells to tumoricidal agents and found 4 genes required for the response of CML cells to imatinib treatment: PTPN1, NF1, SMARCB1, and SMARCE1, and 5 regulators of the response to FAS activation, FAS, FADD, CASP8, ARID1A and CBX1. Broad application of this highly parallel genetic screening strategy will not only facilitate the rapid identification of genes that drive the malignant state and its response to therapeutics but will also enable the discovery of genes that participate in any biological process.