Abstract A23: An integrated platform for the functional annotation of the cancer genome
Authors: Jesse S. Boehm, Glenn Cowley, Barbara Weir, Jordi Barretina, Biao Luo, Hiu Wing Cheung, Scott Rusin, Justine Scott, Alan Derr, Aviad Tsherniak, Shuba Gopalakrishnan, Pablo Tamayo, David Barbie, Xiaoping Yang, Bruno Piqani, Kourosh Salehi-Ashtiani, David Hill, Marc Vidal, Matthew Meyerson, Levi Garraway, David Root, William C. Hahn
Journal: Cancer Research (2009)

Abstract The systematic characterization of mutations in cancer genomes through efforts such as The Cancer Genome Atlas will lead to a comprehensive list of alterations associated with particular cancers. A powerful complementary approach is to comprehensively 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. The recent development of an efficient, robust approach to perform genome-scale pooled shRNA screens now permits the highly parallel identification of essential genes in cancer cells in a cost-effective manner. We have initiated a project to identify essential genes in 300 cancer cell lines representing a diverse range of lineages and genotypes. In each screen the abundance of 55,000 shRNA constructs targeting 11,000 genes is monitored in quadruplicate at the completion of 16 population doublings via hybridization of half-hairpin barcodes to a custom Affymetrix microarray. We have developed multiple complementary approaches for the analysis of this screening data at the shRNA level and at the gene level. shRNA level analytical tools include signal to noise and fold depletion metrics to identify individual shRNA constructs whose abundance at the completion of the experiment discriminates two classes of cell lines (e.g., KRASmut vs. KRASwt). Gene level analytical tools include RIGER, a gene-set enrichment analysis (GSEA)-based non-parametric algorithm which treats the 5 shRNA constructs targeting a given gene as a set and assesses bias of each gene-shRNA set as showing evidence of depletion during the experiment. Using these tools, we have begun to systematically identify known and novel anti-cancer drug targets via the integration of these functional screening results with corresponding structural cancer genomic data derived from both the screened cell lines and from known alterations in tumor samples. To facilitate this analysis, each of the screened cell lines has undergone comprehensive molecular characterization (DNA copy number, RNA expression, OncoMap high-throughput mutation profiling) to identify the genomic alterations harbored in its genome. Our preliminary data suggests that this integrated approach is efficient at pinpointing molecular targets that not only include genes altered in cancer genomes but additionally include genes exhibiting a synthetic lethal relationship with an oncogenic driver mutation (e.g., KRAS).We are validating candidate molecular targets using both loss-of-function and gain-of-function secondary screens. To facilitate these gain-of-function screens, we are creating a library of human open reading frames (ORFs) by sequencing and transferring the Human ORFeome collection, developed by the Center for Cancer Systems Biology at the Dana-Farber Cancer Institute, from Gateway Entry vectors into lentiviral expression vectors. This integrated platform for the unbiased, systematic functional annotation of the cancer genome represents an opportunity to identify molecular targets at genome-scale. Citation Information: Cancer Res 2009;69(23 Suppl):A23.