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.oncogene ͉ pooled library ͉ RNAi ͉ screen ͉ shRNA A lthough human cancers harbor hundreds of genetic alterations, only a subset of these alterations is likely to impact tumor initiation or maintenance. Furthermore, genes that are not altered at the genomic level may play essential roles in tumor development. Thus, to identify genes with important roles in cancer, systematic functional assessment of genes for their contribution to specific cancer phenotypes is complementary to structural characterization of the cancer genome. Integrating both structural and functional approaches will provide insight into therapeutic targets for treating cancer.The recent development of RNAi libraries targeting the human and mouse genomes has enabled systematic genetic studies in mammalian cells by using arrayed and pooled screens (1-8). However, scaling up the application of this methodology to identify all essential genes across a diverse range of human cancers requires an integrated experimental and computational approach that is efficient, robust, and economical. Here, we describe the development and application of genome-scale high-throughput methods using our lentiviral RNAi library to systematically assess cancer gene function and to integrate structural and functional approaches in the study of cancer.

Warfarin is an effective, commonly prescribed anticoagulant used to treat and prevent thrombotic events. Because of historically high rates of drug-associated adverse events, warfarin remains underprescribed.

Background: Alternative splicing is a mechanism for increasing protein diversity by excluding or including exons during post-transcriptional processing. Alternatively spliced proteins are particularly relevant in oncology since they may contribute to the etiology of cancer, provide selective drug targets, or serve as a marker set for cancer diagnosis. While conventional identification of splice variants generally targets individual genes, we present here a new exon-centric array (GeneChip Human Exon 1.0 ST) that allows genome-wide identification of differential splice variation, and concurrently provides a flexible and inclusive analysis of gene expression.

We measured daily gene expression in heads of control and period mutant Drosophila by using oligonucleotide microarrays. In control flies, 72 genes showed diurnal rhythms in light-dark cycles; 22 of these also oscillated in free-running conditions. The period gene significantly influenced the expression levels of over 600 nonoscillating transcripts. Expression levels of several hundred genes also differed significantly between control flies kept in light-dark versus constant darkness but differed minimally between per 01 flies kept in the same two conditions. Thus, the period-dependent circadian clock regulates only a limited set of rhythmically expressed transcripts. Unexpectedly, period regulates basal and light-regulated gene expression to a very broad extent.F orward genetic screens in Drosophila melanogaster have identified at least eight genes [period (per), timeless (tim), cycle (cyc), clock (Clk), vrille (vri), doubletime, cryptochrome (cry), and shaggy] necessary for the normal functioning of the circadian time-keeping system. Null mutations in most of these genes render flies behaviorally arrhythmic in constant conditions, but they otherwise have minimal morphologic phenotype (1). A model for the mechanism by which specific gene products give rise to a stable clock mechanism has been formulated over the past 10 years (2, 3). These clock genes appear to function in a time-delayed transcription-translation feedback loop. A rhythmically expressed subset of the core clock genes (per, tim, and Clk) and a nonrhythmically expressed core clock gene (cyc) are thought to function as the state variables of the oscillator mechanism (4). This model predicts that these core clock genes also should influence the rhythmic expression of ''output'' genes important in regulating physiologic and biologic processes controlled by the circadian clock (5).Previous screens for such clock-controlled output genes have yielded varying estimates of their abundance and character in different organisms. An insertional reporter screen in the photosynthetic prokaryote Synechococcus suggested that most genes in this organism are transcribed in circadian fashion (6). Using microarray analysis, Harmer et al. identified 453 genes undergoing rhythmic expression under constant conditions in the plant Arabidopsis thaliana (7), representing Ϸ6% of the expressed genome. In Drosophila, analysis of 280 expressed sequence tags from the fly head revealed 20 diurnally varying transcripts, the majority of which were extremely rare, long messages of unclear physiologic function (8). The full extent of circadian gene expression is not known in any organism. The recent availability of an oligonucleotide-based microarray containing probes for nearly all known and predicted Drosophila genes allows estimation of the number of clock-controlled genes in the fly. Here we describe results of measuring circadian gene expression in control and period mutant flies in both light-dark (LD) and freerunning conditions. While this article was in preparation, three ot...

DNA target sites for a "multivalent" 11-zinc-finger CCTC-binding factor (CTCF) are unusually long (ϳ50 base pairs) and remarkably different. In conjunction with the thyroid receptor (TR), CTCF binding to the lysozyme gene transcriptional silencer mediates the thyroid hormone response element (TRE)-dependent transcriptional repression. We tested whether other TREs, which in addition to the presence of a TR binding site require neighboring sequences for transcriptional function, might also contain a previously unrecognized binding site(s) for CTCF. One such candidate DNA region, previously isolated by Bigler and Eisenman (Bigler, J., and Eisenman, R. N. (1995) EMBO J. 14, 5710 -5723), is the TRE-containing genomic element 144. We have identified a new CTCF target sequence that is adjacent to the TR binding site within the 144 fragment. Comparison of CTCF recognition nucleotides in the lysozyme silencer and in the 144 sequences revealed both similarities and differences. Several C-terminal CTCF zinc fingers contribute differently to binding each of these sequences. Mutations that eliminate CTCF binding impair 144-mediated negative transcriptional regulation. Thus, the 144 element provides an additional example of a functionally significant composite "TRE plus CTCF binding site" regulatory element suggesting an important role for CTCF in cooperation with the steroid/ thyroid superfamily of nuclear receptors to mediate TRE-dependent transcriptional repression.The CTCF 1 transcription factor harbors an evolutionarily conserved 11-zinc-finger (ZF) DNA binding domain and recognizes unusually long (ϳ50 bp) and remarkably different DNA target sequences in avian and mammalian c-myc promoters (1-3). This multiple sequence specificity of CTCF is achieved through its ability to employ different groups of individual ZFs to recognize highly divergent sequences, and we have described CTCF as a multivalent factor (3, 4).This divergence of DNA target sequences recognized by CTCF makes it difficult to predict CTCF binding sites by sequence homology. For instance the AT-rich CTCF binding site within the F1 sequence of the S-2.4 lysozyme transcriptional silencer, which is required for optimal regulation by the thyroid hormone and/or retinoic acid receptors (5, 6), has little similarity to the GC-rich CTCF binding sequences in the promoters of avian and mammalian c-myc genes. Indeed CTCF utilizes different combinations of ZFs to bind the lysozyme F1 target sequences compared with the c-myc promoter targets (4). Moreover, CTCF also binds to a functionally important region of the amyloid ␤-protein precursor (APP) gene promoter (7,8), and this site harbors no CTCC repeats and demonstrates no overall homology to the CTCF target sequences in either the c-myc promoter or the F1 lysozyme silencer element (3, 4,9). CTCF binding to the F1 sequence of the S-2.4 lysozyme transcriptional silencer is of particular interest because in conjunction with the thyroid hormone receptor (TR) or v-ERBA it leads to a strong synergistic repression in transie...

ACE inhibition after MI inhibits cardiac hypertrophy, preserves cardiac function, and attenuates changes in myocardial gene expression. Gene expression profiling reveals, however, that some elements of the pathophysiology may be unaffected by the treatment and be targets for new therapies.

GPR56 is an orphan G protein -coupled receptor, mutations of which have recently been associated with bilateral frontoparietal polymicrogyria, a rare neurologic disease that has implications in brain development. However, no phenotype beyond central nervous system has yet been described for the GPR56-null mutations despite abundant GPR56 expression in many non -central nervous system adult tissues. In the present study, we show that higher GPR56 expression is correlated with the cellular transformation phenotypes of several cancer tissues compared with their normal counterparts, implying a potential oncogenic function. RNA interference -mediated GPR56 silencing results in apoptosis induction and reduced anchorage-independent growth of cancer cells via increased anoikis, whereas cDNA overexpression resulted in increased foci formation in mouse fibroblast NIH3T3 cell line. When GPR56 silencing was induced in vivo in several xenograft tumor models, significant tumor responses (including regression) were observed, suggesting the potential of targeting GPR56 in the development of tumor therapies. The expression profiling of GPR56-silenced A2058 melanoma cell line revealed several genes whose expression was affected by GPR56 silencing, particularly those in the integrin-mediated signaling and cell adhesion pathways. The potential role of GPR56 in cancer cell adhesion was further confirmed by the observation that GPR56 silencing also reduced cell adhesion to the extracellular matrix, which is consistent with the observed increase in anoikis and reduction in anchorage-independent growth phenotypes. The oncogenic potential and apparent absence of physiologic defects in adult human tissues lacking GPR56, as well as the targetable nature of G protein -coupled receptor by small molecule or antibody, make GPR56 an attractive drug target for the development of cancer therapies.

We have developed an algorithm for inferring the degree of similarity between genes by using the graph-based structure of Gene Ontology (GO). We applied this knowledge-based similarity metric to a clique-finding algorithm for detecting sets of related genes with biological classifications. We also combined it with an expression-based distance metric to produce a co-cluster analysis, which accentuates genes with both similar expression profiles and similar biological characteristics and identifies gene clusters that are more stable and biologically meaningful. These algorithms are demonstrated in the analysis of MPRO cell differentiation time series experiments.