Loss-Of-Function Genetic Screens As a Tool to Improve the Diagnosis and Treatment of Cancer

Oncogene (2009) 28, 4409–4420 & 2009 Macmillan Publishers Limited All rights reserved 0950-9232/09 $32.00 www.nature.com/onc REVIEW Loss-of-function genetic screens as a tool to improve the diagnosis and treatment of cancer

J Mullenders and R Bernards

Division of Molecular Carcinogenesis, Centre for Biomedical Genetics and Cancer Genomics Centre, The Netherlands Cancer Institute, Amsterdam, The Netherlands

A major impediment to the effective treatment of cancer is is a member of a new class of anticancer drugs, the the molecular heterogeneity of the disease, which is also ‘targeted-therapeutics’ (Sawyers, 2004; Luo et al., 2009b). reflected in an equally diverse pattern of clinical responses These drugs are developed based on knowledge of which to therapy. Currently, only few drugs are available that genes (often kinases) are specifically altered in cancer. can be used safely and effectively to treat cancer. To Therefore, such targeted therapies are highly cancer cell- improve this situation, the development of novel and highly selective and have fewer side effects. Unfortunately, most specific targets for therapy is of utmost importance. cancer types do not have such frequently occurring Possibly even more importantly, we need better tools oncogenic driver event, making it difficult to expand on to predict which patients will respond to specific therapies. the success of this highly effective drug. Consequently, the Such drug response biomarkers will be instrumental to arsenal of targeted cancer therapeutics is small, making individualize the therapy of patients having seemingly the more conventional chemotherapy still the mainstay of similar cancers. In this study, we discuss how RNA treatment in today’s cancer clinic. In this review, we will interference-based genetic screens can be used to address discuss how loss-of-function genetic screens in mamma- these two pressing needs in the care for cancer patients. lian cells using RNA interference can contribute to the Oncogene (2009) 28, 4409–4420; doi:10.1038/onc.2009.295; identification of completely new classes of highly selective published online 21 September 2009 cancer drug targets. A second major obstacle in the treatment of cancer is Keywords: RNAi screen; biomarkers of drug response; the unpredictable response to therapy. For instance, in cancer therapy breast cancer, only 1 in 30 post-menopausal women benefit from chemotherapy (EBCTCG, 2005). Availability of biomarkers of drug responses could greatly help in the The need for new drug targets and better biomarkers development of a more personalized approach to cancer of drug response treatment, in which patients having seemingly similar cancers can be pre-selected for specific therapies based Traditionally, new cancer drugs have been identified on their predicted responses to therapy. In this study mainly through empirical approaches (Chabner and too, RNA interference-based genetic screens can be very Roberts, 2005). This has led to a generation of chemo- efficient in identifying genes that control how cells res- therapeutic agents that is relatively nonspecific in target- pond to cancer drugs. Consequently, such genes are prime ing cancer cells, yielding considerable side effects. Such candidate biomarkers to foretell drug responsiveness in conventional therapies often benefit only a minority of the clinic. Strategies to discover such biomarkers through patients, because of both intrinsic and acquired drug RNAi-based genetic screens are discussed here also. resistance. Another major factor limiting the effective treatment of cancer is the molecular heterogeneity of the Loss-of-function genetic screening tools disease. Because of this heterogeneity only few cancer types have a common ‘driver’ mutation: a genetic In recent years, an array of new high-throughput alteration that is instrumental for the malignant pheno- technologies (such as DNA sequencing, single-nucleo- type of the cancer. One such recurrent driver mutation in tide polymorphism genotyping, comparative genomic chronic myelogenous leukemia is the BCR-ABL fusion hybridization, proteomics and gene expression micro- gene, which is targeted by the drug imatinib mesylate (de arrays) have all been implemented in drug development Klein et al., 1982; Druker et al., 1996). Imatinib mesylate efforts. A drawback of most of these approaches is that the data generated are mostly correlative and therefore Correspondence: Professor R Bernards, Division of Molecular do not directly identify the driver event among the many Carcinogenesis, Centre for Biomedical Genetics and Cancer Genomics genetic alterations present in each cancer. Loss- Centre, The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX, of-function genetic screens on the other hand can also Amsterdam, The Netherlands. E-mail: [email protected] be carried out in a high throughput manner and, as a Received 7 July 2009; revised 17 August 2009; accepted 26 August 2009; consequence of the functional nature of the approach, published online 21 September 2009 leads to the identification of causal factors only. Loss-of-function genetic screens J Mullenders and R Bernards 4410 Two complementary types of functional genetic are available (for example, Moloney virus-based vectors, screens can be carried out (Brummelkamp and lentiviral vectors and adenoviral vectors (Abbas-Terki Bernards, 2003; Grimm, 2004). Gain-of-function genetic et al., 2002; Brummelkamp et al., 2002b; Michiels et al., screens involve the ectopic expression of genes. This is 2002)). Some libraries consist of shRNA vectors that often brought about by expression of collections of contain a shRNA embedded in a microRNA precursor complementary DNAs (cDNAs). Such gain-of-function for more efficient knockdown, but the beneficial effect of screens are relatively easy to perform and can be carried this has been disputed (Li et al., 2007) (Figure 1). out using heterologous cDNA collections (that is, There is no simple answer to the question which cDNAs from another species) in simple model organ- RNAi reagent is better, as each reagent has its own pros isms or by ectopic expression of homologous cDNAs in and cons. A major advantage of the use of plasmid- cells from higher organisms. For discovery of drug encoded shRNA is the possibility to perform long-term targets, however, loss-of-function screens are more cell culture experiments, such as clonogenic assays and suitable as the genetic event mimics the intended effect drug resistance assays. shRNA-based genetic screens are of the cancer drug: reduced gene product activity. In particularly useful when one searches for genes whose mammalian cells high throughput loss-of-function suppression allows cells to proliferate in the presence of screens were not feasible for a long time. This situation an anti-proliferative signal, such as a drug-induced changed in 2001, when it was discovered that RNA growth arrest or a physiological growth arrest such as interference (RNAi) can also be used in mammalian senescence. In this scenario, shRNA screens can be cells to suppress gene expression (Elbashir et al., 2001). carried out in a polyclonal format, in which a single dish As many different collections of RNAi resources have of cells is infected in culture with a large number of been created that cover the entire human and mouse shRNA vectors, after which the cells are exposed to the genomes (Brummelkamp et al., 2002a; Brummelkamp anti-proliferative signal. Cells that become resistant to and Bernards, 2003; Grimm, 2004; Moffat and Sabatini, the growth arrest through knockdown of a specific gene 2006; Iorns et al., 2007). Different reagents can be used will become positively selected. As each shRNA vector to trigger RNA interference: the original synthetic short contains a unique shRNA sequence to knockdown a duplex RNAs and vector-encoded short hairpin RNAs specific gene, this sequence can be used as a molecular (shRNAs (Brummelkamp et al., 2002a)). These two ‘bar code’ to identify the knockdown vector that RNAi reagents each come in several flavors. For conferred the growth advantage to the cell. The bar- example, apart from the chemically synthesized small coded shRNA that confers resistance to the growth interference RNAs (siRNAs), siRNAs have been made arrest signal will become more prominent in the by nuclease cleavage of long in vitro transcribed double- population over time, which is detected as an increased stranded RNAs: esiRNAs (Buchholz et al., 2006). For abundance of the bar code sequence. This approach, plasmid-derived shRNAs different viral delivery systems known as shRNA bar code screening, has been applied

Figure 1 RNA interference (RNAi) screening modalities: RNAi can be brought about by different molecules, the vector-based short hairpin RNA (shRNA) and the chemically or enzymatically generated small interference RNAs (siRNA). The choice of screening model is dependent on the species of RNAi used, shRNAs can be used in both polyclonal and single-well screens, siRNAs can only be used in single-well assays. This also limits the phenotypes that can be screened. Polyclonal assays are more applicable to screen growth phenotypes although single-well assays can be used to screen complex phenotypes.

Oncogene Loss-of-function genetic screens J Mullenders and R Bernards 4411

Figure 2 The short hairpin RNA (shRNA) bar code screening: shRNA bar code screening is a very efficient technique to identify shRNAs from a large collection of vectors that give rise to a specific phenotype. Plasmids encoding shRNAs are available in various viral vectors for high-efficiency infection of target cell lines. After sufficient numbers of cells are infected (in order to have the entire library of vectors represented) two replicate cell populations are created. One population is used as reference and left untreated, whereas the other population is treated with the stimulus of interest. After phenotypic selection has taken place the bar code identifier can be retrieved by PCR. The bar code identifier can be a separate DNA sequence in the plasmid but also an integral part of the shRNA cassette. Hybridization of the bar code identifier to a specific microarray reveals the abundance of shRNA in both the reference and experimental population. successfully to find genes whose inactivation confers features such as cell shape, DNA content, subcellular resistance to a p53 induced growth arrest (Berns et al., localization of proteins and other parameters (Wheeler 2004; Brummelkamp et al., 2006), the breast cancer drug et al., 2005; Pepperkok and Ellenberg, 2006; Rines et al., Herceptin (Berns et al., 2007) and genes that allow 2006). This combination of single-well screening and growth under non-adherent conditions (Westbrook reading of complex phenotypes is referred to as ‘high et al., 2005) (Figure 2). The screening of shRNA content screening’ (Neumann et al., 2006). The cell cycle collections by this bar coding approach to identify is one of the best-studied biological processes using shRNAs that are negatively selected in culture repre- RNAi combined with high content screening (Kittler sents a more significant technical challenge. Detecting et al., 2004; Bjorklund et al., 2006; Mukherji et al., 2006; the loss of a shRNA from a large pool is more difficult Kittler et al., 2007). The parameters examined in such because not every cell infected with a given shRNA screens include cell number, cell cycle phase, apoptosis vector has sufficient gene knockdown to provoke the (sub G1) and the total number of chromosomes desired phenotype. Obviously, only cells with sufficient (ploidy). Among the hits in these screens are many knockdown of a lethal gene are negatively selected in a genes that have been reported earlier to regulate population. In addition, there may be cells in the mammalian cell cycle, showing the validity of the population that harbor a knockdown vector without approach. High content screening also enables the having sufficient gene knockdown to provoke the lethal identification of novel morphological phenotypes (Kiger phenotype. The presence of such cells limits the signal et al., 2003; Jones et al., 2009). Single-well screens can one can obtain in this type of bar code screening also be performed using shRNA vectors (Hopkins and approach. One solution to this problem is the use of Groom, 2002; Moffat et al., 2006), but it requires relative small pools of B1000 shRNAs in negative significant automation to generate and handle individual selection assays (Ngo et al., 2006; Shaffer et al., 2008). shRNA vectors in single-well screening format. More recently, this approach was optimized as well as used successfully to screen larger shRNA libraries (Rines et al., 2006; Luo et al., 2008; Schlabach et al., 2008; Silva et al., 2008). Loss-of-function screening strategies to identify drug Small interference RNAs on the other hand are very targets suitable for high throughput single-well assays, in which every well contains a siRNA reagent that targets a single Suitable targets for cancer therapy are hard to find. transcript. An advantage of the single-well screening Most small molecule drugs today inactivate proteins by format is that more complex biological phenotypes can binding to the catalytic site of an enzyme. This mostly be screened. Such complex phenotypes can be detected restricts the development of cancer drugs to the using for instance cell sorting or high throughput products of oncogenes, not tumor suppressor genes. microscopy. Microscopic images can subsequently be Moreover, many of the well-known oncogenes (for analysed by software that can be programmed to extract example, RAS and MYC) are not considered to be

Oncogene Loss-of-function genetic screens J Mullenders and R Bernards 4412 ‘druggable’, that is, proteins whose activity can be directly place a druggable gene in a cancer-relevant readily inhibited by a low-molecular-weight compound pathway (Figure 3). As a proof of concept, we made a (Hopkins and Groom, 2002). Thus far only a small library of shRNA vectors targeting some 60 ubiquitin- number of proteins have been identified that can be specific proteases (DUBs) and searched for DUB efficiently inhibited by small molecules for cancer enzymes that control the activity of NF-kB (Brummelkamp therapy. Many of these targets belong to the family of et al., 2003; Oosterkamp et al., 2006). This led to the kinases and are either specifically (over-) expressed in identification of the familial cylindromatosis tumor cancer cells (that is, HER1 and HER2) or activated by suppressor gene CYLD in the NF-kB signaling pathway. mutation/translocation (for example, HER1 and BRAF This work suggested that tumors in cylindromatosis and BCR-ABL). Inhibitors of these targets can be very patients are caused, at least in part, by activation of efficacious in blocking cancer growth with relatively NF-kB. Indeed, in a pilot clinical study, inhibitors of mild side effects (Carter et al., 1992; Druker et al., 1996). NF-kB were found to be useful for the treatment of The identification of new drug targets with similar patients suffering from cylindromatosis (Oosterkamp cancer selectivity is urgently needed. There are four et al., 2006). approaches to finding novel drug targets through RNAi- based genetic screens: Phenotypic screens Another screening format is to search for modulators of Pathway screens a specific biological phenotype, such as apoptosis, An obvious starting point to identify novel drug targets migration, invasion, senescence and responses to cyto- is by searching for novel components of cancer-relevant kines. One of the first genome-wide screens in mamma- signaling pathways. Although many signaling pathways lian cells aimed at identifying genes whose suppression have been studied for considerable time, genetic screens allowed bypass of a p53-dependent senescence-like may identify hitherto unknown components of these growth arrest in human fibroblasts, which led to the pathways, which may serve as potential targets for identification of five novel components of the p53 therapy. Initial RNAi genetic screens in Drosophila pathway (Berns et al., 2004). A screen for bypass of using reporter gene assays established the utility of growth arrest caused by activation of p53 with specific signaling pathway screens (Baeg et al., 2005; DasGupta small molecules allowed the identification of further et al., 2005). More direct measurement of pathway components of the p53 pathway (Brummelkamp et al., activity was used to screen for modulators of ERK 2006). signaling, which was evaluated by immuno-fluorescence Oncogene expression can also induce a senescence- (Friedman and Perrimon, 2006). like proliferation arrest. Wajapeyee et al. (2008) A particularly powerful combination is to screen identified genes whose suppression confers resistance subsets consisting of families of druggable genes for to growth arrest induced by a BRAF oncogene. their ability to modulate a cancer-relevant signaling Surprisingly, the authors found that suppression of pathway. Any validated hit from such a screen would IGFBP7, which encodes a secreted protein, was required

Figure 3 Pathway screening using RNA interference (RNAi) libraries targeting druggable genes: choosing druggable genes as a starting point for RNAi screening can lead to rapid identification of a novel drug target in a cancer-relevant pathway. The first step is to select the gene family of interest. Subsequently, a targeted RNAi library of either shRNAs of siRNAs must be constructed. This library can be screened in various screening models including reporters and high-content-based assays. Small molecules targeting the hits identified from the screen can subsequently be used to perform in vivo experiments and finally clinical trials.

Oncogene Loss-of-function genetic screens J Mullenders and R Bernards 4413 for cells to proliferate in the presence of mutant BRAF. 2007; Bommi-Reddy et al., 2008; Luo et al., 2009a; Moreover, injection of recombinant IGFBP7 in mice Scholl et al., 2009) and loss of the VHL tumor carrying a BRAF mutant tumor led to inhibition of suppressor (Bommi-Reddy et al., 2008). Most of these tumor growth, providing a potential new therapeutic screens were performed using single-well assays with strategy for tumors having a mutant BRAF oncogene. only relatively small collections of RNAi reagents. Only Tumor cells often have an abrogated cell death or one study used a genome-wide shRNA library that was apoptosis pathway. Several screens have been performed screened using micro-array bar code technology (Luo to identify genes that, when knocked down, either et al., 2009a). Genes identified in the screens above suppress or stimulate cell death (Aza-Blanc et al., 2003; include Polo-like kinase 1 (PLK1), SURVIVIN and the MacKeigan et al., 2005; Hitomi et al., 2008). By STK33 kinase. Interestingly, clinical trials using small screening siRNA libraries targeting kinases and phos- molecule inhibitors of PLK1 (BI-2536) and SURVIVIN phatases MacKeigan et al. (2005) identified a surpris- (YM155) are already ongoing (Mross et al., 2008; ingly large number of genes that are involved in Satoh et al., 2009). On the basis of the results from regulating apoptosis under physiological conditions. In the activated RAS RNAi synthetic lethal screens, it will addition, a number of phosphases have been identified be interesting to monitor the specific response of to be essential for the cyotoxic effects of certain RAS mutant versus wild-type tumors in these ongoing chemotherapeutic drugs. clinical trials. Synthetic lethal screens can also be used to find genes that are specifically toxic in two closely related cancers. Synthetic lethal screens For instance, Ngo et al. (2006) screened for genes that Most targeted therapies take advantage of the fact that are essential for survival of activated B-cell-like diffuse certain oncoproteins are hyperactive in cancer cells, large B-cell lymphoma, but not for the related germinal either as a consequence of activating mutations or over- centre B-cell-like diffuse large B-cell lymphoma cells. expression. As was pointed out above, such targets are They identified the NF-kB pathway as essential only for often not ‘druggable’ or only expressed in a small survival of activated B-cell-like diffuse large B-cell fraction of the tumors, two factors that limit their lymphoma, suggesting that this cancer might benefit clinical utility. In 1997, it was proposed to exploit the from NF-kB inhibition. phenomenon of ‘synthetic lethality’ to find completely novel classes of highly cancer-selective drug targets (Hartwell et al., 1997). This strategy takes advantage of In vivo screens the notion, first observed in lower organisms, that some The identification of genes that regulate the process of genes are only lethal to cells if a second non-lethal metastasis is a very difficult and laborious task. There- mutation is also present (Tong et al., 2001). These types fore, screening in a surrogate assay for in vivo metastasis of genetic interactions are likely to be present in can be used to examine large collections of shRNAs or mammals also and therefore could be exploited for siRNAs. Hits from this screen can subsequently be novel cancer treatment strategies (Wang et al., 2004; tested in vivo, as the number of genes to be tested is Kaelin, 2005). In one of the first examples, it was shown significantly smaller. The latter approach was used to that cells with an amplified MYC oncogene are more identify the GAS1 metastasis suppressor from a complex sensitive to apoptosis induced by death receptor ligands library of shRNAs. Furthermore, when the expression (Wang et al., 2004; Rottmann et al., 2005). It is level of GAS1 in melanoma was analysed, a clear important to point out that the genes that are synthetic correlation between low GAS1 messenger RNA lethal with cancer-specific lesions are not necessarily (mRNA) and metastatic potential was identified (Gobeil mutated or over-expressed themselves in cancer. Conse- et al., 2008). quently, large-scale cancer genome re-sequencing efforts Another way to reduce the complexity of a genome- and gene expression profiling might not identify wide RNAi set for in vivo genetic screen is to use data synthetic lethality genes. As such, synthetic lethal obtained from clinical specimen. This was elegantly interactions represent a potential untapped reservoir of shown in a study by Zender et al. (2008) (Tyner et al., highly cancer-selective drug targets. 2009). Using comparative genomic hybridization pro- Finding synthetic lethal interactions in mammalian files from human liver cancer samples they identified cells is not a trivial exercise, but is greatly facilitated by some 300 genes that were frequently lost in these large-scale RNAi genetic screens. Large-scale efforts to tumors, making these genes candidate tumor suppressor identify synthetic lethal interactions were initially genes for liver cancer. On the basis of this information, examined in yeast (Pan et al., 2004, 2006). The they generated a mini shRNA targeting these 300 genes. advantage of performing screens in yeast is that, rather These shRNAs were introduced into immortalized, but than knockdown by RNAi, knockout strains can be non-oncogenic, hepatocytes that were transplanted into used. On the other hand, the increased complexity of recipient mice. In the tumors that formed in these mice mammalian signaling pathways may limit the value of shRNAs for 16 genes were selectively enriched, showing the lessons learnt from simple model organisms. To a role for these genes in hepatocyte oncogenicity. This date, synthetic lethal screens have been performed for shows that the use of selected shRNA libraries allows two specific oncogenic lesions in mammalian cells: for the very efficient identification of genes that are targeting the activated KRAS oncogene (Sarthy et al., functionally involved in cancer progression.

Oncogene Loss-of-function genetic screens J Mullenders and R Bernards 4414 A variation on this theme is to perform these types of fully with various anticancer drugs. One approach was screens in tumor cells isolated from patients. This was designed to identify genes that are essential for the shown in a recent report that described the siRNA action of the HER2 antibody trastuzumab (Herceptin) screening of tyrosine kinases in cells derived from 30 (Berns et al., 2007). A single gene was identified that patients suffering from leukemia (Tyner et al., 2009). confers resistance to trastuzumab when it was knocked In 10 out of these 30 patients, at least one kinase was down by shRNAs. This gene-encoded PTEN, the identified that was essential for tumor cell survival. inhibiting phosphatase of the PI3-kinase catalytic Furthermore, the inhibition of the JAK2 kinase by a subunit PIK3CA. When patient samples were analysed small molecule inhibitor was shown to be selectively on their activation of the PI3-kinase pathway, a clear toxic only in the tumor lines that depend on JAK2. correlation with trastuzumab response was found. Patients with an activated PI3-kinase pathway, loss of PTEN or mutation in PIK3CA, showed worse response to trastuzumab than patients that did not have these Development of predictive biomarkers of therapy efficacy mutations. This example shows that results from in vitro RNAi screens can yield biomarkers having clinical Identical treatment regimen can produce remarkably utility to predict drug responses. different responses in patients with seemingly indistin- In related studies, loss of CDK10 expression was guishable tumors at the macromolecular level. This identified as a major factor in mediating resistance to phenomenon reflects the heterogeneity that is seen when tamoxifen in breast cancer and CDK10 levels were individual tumors are studied in more detail. Under- found to be predictive of tamoxifen response in clinical standing the factors that predict whether patients will samples (Iorns et al., 2008). Similarly, ZNF423 was respond to certain treatment is very useful to create identified as a critical mediator of the response to efficient therapies. Although different techniques can be retinoic acid in neuroblastoma and RAD23B was found used to identify biomarkers of drug response, the use of to control responses to histone deacetylase inhibitors. In RNAi to find these factors has the advantage that it will all cases, the genetic screen did not only provide identify biomarkers based on their causal role in the potentially useful biomarkers of drug responses, but response to the drug. also yielded novel insights into the signaling pathways Two different types of drug response biomarkers can that mediate drug resistance (Fotheringham et al., 2009; be distinguished. First, biomarkers may foretell whether Huang et al., 2009). Such studies may therefore point at patients are resistant to a certain treatment and second, specific combination therapies that will be more power- biomarkers may predict drug sensitivity. The former ful or suggest ways to overcome therapy resistance. biomarkers have two clinical applications: they may identify patients that fail to respond to a given therapy upfront, avoiding unnecessary toxicity. Second, under- Enhancers of drug efficacy standing resistance may uncover strategies to overcome Another way of finding modulators of drug responses is resistance. The biomarkers of drug sensitivity on the to search for genes whose suppression enhances the other hand may help in identifying synergistic drug response to a given cancer drug. This is in fact a combinations. RNAi can be used for the identification variation on the theme of synthetic lethality and is also of both types of biomarkers through screening for referred to as ‘chemical synthetic lethality’. Like most modulators of drug resistance. genetic screens, this principle was also pioneered in lower organisms. For instance, testing of a set of Biomarkers of drug resistance compounds that are currently used in the clinic in a Multiple mechanisms have been suggested for the heterozygous yeast strain identified strains that show a resistance of cancer cells to conventional chemotherapy, decrease in fitness in presence of the drug (Lum et al., for example, the upregulation of drug pumps and (in)- 2004). Performing RNAi-based drug enhancer screens in activation of pathways on which the drug works mammalian cells requires some technical adaptations to (Gottesman et al., 2002; Rodriguez-Antona and Ingel- the conventional approach. To find factors that can man-Sundberg, 2006; Savage et al., 2009). However, in augment the effects of a drug, the screen should be most cases, the factors that contribute to drug resistance performed in presence of a low concentration of the drug, are not known and RNAi screens can be useful to which causes only a small decrease of cell viability. In this identify genes that modulate drug responses. way genes that significantly enhance loss of cell viability When performing a functional genetic screen to find can be identified. Most drug screens are performed in a drug resistance genes it is important to use concentra- single-well format, which allow for effective gene knock- tions of drug that are close to levels obtained in the down because of highly efficient transfection with siRNAs. clinic. In long-term shRNA-based screens, drug con- Thus, far screens have been performed aiming at the centration can be relatively low. In contrast, siRNA identification of sensitizers of well the well-known single-well screens usually require much higher concen- anticancer drugs gemcitabine, cisplatin and paclitaxel tration of drug, as the phenotype must be measured in a (MacKeigan et al., 2005; Bartz et al., 2006; Giroux et al., much shorter time frame. For drug resistance RNAi 2006; Ji et al., 2007; Swanton et al., 2007; Whitehurst screens, the use of shRNA vectors is therefore preferred. et al., 2007). Although cisplatin has been approved for Drug resistance screens have been performed success- the treatment of human malignancies since 1978, it is

Oncogene Loss-of-function genetic screens J Mullenders and R Bernards 4415 Table 1 Some noteworthy RNAi screens performed in mammalian cells in recent years Phenotype Screening method Cancer type Genes screened Hits identiﬁed Result Reference (cell line)

Apoptosis siRNA single well Cervix (HeLa) Kinases (650) 21 kinases and 12 Determinants of MacKeigan et al. sensitization and phospha- phosphatases apoptosis induced by (2005) tases (222) chemotherapy B-Raf senescence shRNA polyclonal Normal tissue Genome 17 genes IGFBP7 can block Wajapeyee et al. screen sequencing (fibroblasts) proliferation B-Raf (2008) mutant cells Bypass p53-in- shRNA polyclonal Normal tissue 8000 genes 5 genes Novel genes that are Berns et al. duced cell cycle screen sequencing (fibroblast) (mostly drug- essential for p53 (2004) arrest gable) function Cell cycle siRNA single-well Osteosarcoma Genome 1152 genes alter cell 130 genes found are Mukherji et al. microscopy (U2OS) cycle progression increased in expression (2006) in tumors Cell cycle EsiRNA single-well Cervix (HeLa) 17 000 genes 1351 genes causing One of the largest Kittler et al. microscopy cell cycle arrest/cell efforts to find genes (2007, 2004) division involved in cell cycle Cell cycle and siRNA single-well Cervix (HeLa) Genome 8 genes were Rines et al. mitosis microscopy validated in two (2008) cell lines Cell cycle in the shRNA single well Cervix (HeLa) Ubiquitin-asso- 14 new candidate USP44 is a DUB for Stegmeier et al. presence of Taxol microscopy ciated genes (800) spindle checkpoint CDC20 (2007) regulators Cisplatin/ siRNA single-well Cervix (HeLa) Genome 53 genes that Cells with a mutant Bartz et al. (2006) Gemcitabine/ cell viability sensitized cells to BRCA pathway are Paclitaxel cisplatin more sensitive to cisplatin treatment Essential genes shRNA polyclonal Diffuse large 2500 genes 15 genes are essential CARD11, MALT1 and Ngo et al. (2006) barcode screen B-cell lymphoma for proliferation BCL10 are essential for DLBCL proliferation Essential genes shRNA barcode Multiple 2500 genes 19 genes are essential IRF4 is required for Shaffer et al. myeloma for proliferation multiple myeloma (2008) proliferation Essential genes shRNA barcode Colon, breast 3000 genes, 19 genes are essential First large-scale Schlabach et al. and normal tissue kinases, for proliferation approach to find (2008) phosphatases essential genes in human cells Essential genes shRNA barcode Breast Various 166 genes that are First large-scale Silva et al. (2008) (MCF10A, essential in two cell approach to find MDA-MB-435) lines essential genes in human cells Essential genes siRNA single well Human white 100 tyrosine Several kinases in First report that shows Tyner et al. cell viability blood cells kinases different samples the use of RNAi in (2009) human tissue samples for diagnostics Essential genes shRNA polyclonal 12 cancer cell Genome 268 genes that are This screen identified Luo et al. (2008) barcode lines essential in all 12 cell regulators of imatinib lines and FAS-mediated cell death Gemcitabine siRNA single-well Pancreas 645 kinases 10 kinases sensitized Some of the identified Giroux et al. sensitization apoptosis ELISA two pancreatic kinases are potential (2006) cancer cell lines to targets for gemcitabine gemcitabine combination therapy in vivo hepatocel- shRNA in vivo liver Normal 300 genes 13 tumor new liver XPO4 suppresses tumor Zender et al. lular carcinoma cancer hepatoblast tumor suppressor growth through EIF5A (2008) genes Invasion in 3D shRNA clonogenic Melanoma Genome 22 genes GAS1 is a metastasis Gobeil et al. cell culture assay sequencing (mouse) suppressor that is (2008) deleted in human melanoma K-Ras synthetic siRNA single-well Colon (DLD-1 4000 genes Survivin inhibition is Sarthy et al. lethality cell viability isogenic mutant more toxic for mutant (2007) RAS cell lines) RAS cells K-Ras synthetic shRNA single-well Monocytic 1000 kinases and One kinase found in Identification of the Scholl et al. lethality cell viability leukemia, phosphatases a panel of cell lines STK33 kinase whose (2009) normal tissue with and without inhibition is selectively mutant RAS toxic in mutant RAS cells

Oncogene Loss-of-function genetic screens J Mullenders and R Bernards 4416 Table 1 Continued Phenotype Screening method Cancer type Genes screened Hits identiﬁed Result Reference (cell line)

K-Ras synthetic shRNA polyclonal Colon (DLD-1 Genome 370 genes identified Involvement of a Luo et al. (2009a) lethality barcode screen isogenic mutant on statistical pathway controlled by RAS cell lines) thresholds in PLK1 in mutant RAS isogenic mutant cell viability RAS cells Mitotic index shRNA single-well Lung (A549, 1000 genes 87 genes (higher One of the first Moffat and microscopy HT-29) mitotic index); 15 single-well shRNA Sabatini (2006) genes (lower mitotic screens index) NF-kB signaling shRNA single-well Osteosarcoma Deubiquitinating 1 DUB that activates The familial tumor Brummelkamp NF-kB reporter (U2OS) enzymes NF-kB signaling suppressor CYLD et al. (2003) functions through NF-kB activation Paclitaxel siRNA single-well Lung Genome 87 paclitaxel Inhibition of the Whitehurst et al. sensitization cell viability (NCI-H1155) sensitizer loci proteasome and genes (2007) involved in spindle checkpoint integrity sensitize to paclitaxel Paclitaxel shRNA single-well Lung (A549) 74 tumor sup- shRNA experiments Inhibition of the MDR1 Ji et al. (2007) sensitization cell viability pressor genes are useful to study drug transporter sensi- proteins with long tizes cells to paclitaxel half-life Paclitaxel/ siRNA single-well Colon, lung, 779 kinases and Inhibition of ceramide Swanton et al. cisplatin/ cell viability breast related proteins transport can sensitize (2007) doxorubicin/5- cells to placlitaxel FU sensitization PARP inhibitor siRNA single-well Breast (CAL51) 779 kinases and 6 ‘on-target’ hits that CDK5 is involved in Turner et al. sensitization cell viability related proteins sensitize cells to regulating DNA damage (2008) PARP inhibition response and sensitizes to PARP inhibition Resistance to shRNA polyclonal Osteosarcoma 8000 mostly Suppression of This finding opens the Fotheringham HDAC screen sequencing (U2OS) druggable genes Rad23B makes cells possibility to combine et al. (2009) inhibitors resistant to HDAC HDAC inhibitors with inhibitors proteasome inhibitors Resistance to siRNA single well Normal tissue Genome 432 genes Identification of genes Hitomi et al. necroptosis (L929 (mouse)) involved in cancer and (2008) human diseases Resistance to shRNA polyclonal Terato carcino- 15 000 genes One ‘on-target’ hit, ZNF423 is a prognostic Huang et al. retinoic acid screen sequencing ma (mouse) ZNF423 is essential marker in (2009) for retinoic acid neuroblastoma signaling Resistance to shRNA polyclonal Breast 8000 mostly Knockdown of The activation status Berns et al. trastuzumab barcode druggable genes PTEN makes cells of the PI3K kinase (2007) resistant to pathway determines the trastuzumab response to trastuzumab treatment Sensitization and siRNA single well Cervix (HeLa) 500 genes No number reported Determinants of Aza-Blanc et al. resistance to (mostly kinases) TRAIL-induced (2003) TRAIL-induced apoptosis apoptosis Soft agar Ras shRNA polyclonal Normal tissue 8000 genes 18 genes allowing Identification of REST Westbrook et al. complementation screen barcode and (HMEC) growth in soft agar as a tumor suppressor (2005) sequencing Soft agar Ras shRNA polyclonal Normal tissue 4000 genes 3 genes PITX1 is a tumor Kolfschoten et al. complementation screen sequencing (fibroblast) (mostly drug- suppressor in the RAS (2005) gable) pathway Tamoxifen siRNA single-well Breast (MCF-7) 779 kinases and 3 ‘on-target’ kinases CDK10 is a potential Iorns et al. (2008) cell viability related proteins that cause resistance biomarker of response to tamoxifen to tamoxifen VHL synthetic shRNA single-well Renal cancer 88 pre-selected 6 kinases that were CDK6 is a potential Bommi-Reddy lethality cell viability kinases that are essential in two target for therapy of et al. (2008) essential for independent VHL- VHL-deficient tumors HeLa and 293T deficient cell lines cells Wnt signaling shRNA single-well Colon (HCT116) 1000 genes, 9 genes affecting CDK8 is an oncogene in Firestein et al. reporter and cell mostly kinases both viability and colorectal cancer (2008) viability Wnt signaling

Oncogene Loss-of-function genetic screens J Mullenders and R Bernards 4417 Table 1 Continued Phenotype Screening method Cancer type Genes screened Hits identiﬁed Result Reference (cell line)

Resistance to shRNA barcode Breast (MCF-7) 8000 mostly 2 ‘on-target’ hits Knockdown of the Brummelkamp MDM2 screen druggable genes tumor suppressor 53BP1 et al. (2006) inhibitors causes resistance to MDM2 inhibition Wnt signaling siRNA single-well Cervix (HeLa) Genome 17 validated hits TCF7L2 is an inhibiting Tang et al. (2008) reporter TCF that is mutated in colon cancer

Abbreviations: NF-kB, nuclear factor-kB; RNAi, RNA interference; shRNA, short hairpin RNA; siRNA, small interfering RNA. still unclear why some tumors respond better to chances for an ‘off-target’ effects very slim. Another way treatment than others. By screening a genome-wide to validate the specificity of an RNAi generated siRNA library to identify genes that modulate cisplatin phenotype is the use of RNAi-resistant cDNAs, this efficacy, it was uncovered that cells that have an allows for the re-expression of the targeted mRNA, inactivation of both TP53 and a member of the BRCA which should lead to reversal of the phenotype network are more sensitive to cisplatin treatment. (Echeverri et al., 2006). Although this had been reported before, this example If available, one can also validate RNAi screening hits underscores the power of large-scale loss-of-function using conventional gene knockout cells. However, it is genetic screens to find enhancers of drug responses. possible that gene knockout triggers compensatory Another example of a large RNAi-based approach adaptation mechanisms that mask the phenotype of a with the objective to find genes that, when suppressed, knockout over time. Therefore, suppression of a gene by sensitize cells to paclitaxel treatment identified several RNAi may reflect more accurately the pharmacological members of the proteasome (Whitehurst et al., 2007). inhibition of a protein than a full gene knockout. It This interesting observation raises the possibility of should also be kept in mind that complete gene knockout combining treatment of taxanes with proteasome (null allele) does not necessarily yield the same phenotype inhibitors. Intriguingly, several clinical trials combing as incomplete gene suppression by RNAi. paclitaxel and the proteasome inhibitor bortezomib Finally, it must be appreciated that substantial were already performed before the results from this phenotypic differences can occur between pharmacolo- study became available (Ma et al., 2007; Cresta et al., gical inhibition of an existing protein, and the removal 2008; Jatoi et al., 2008). From these phase I/II trials it of the protein from the cell by RNAi. For example, if a was concluded that the combination therapy is well protein functions in a multi-protein complex, pharma- tolerated but unfortunately not very efficacious. cological inhibition of the target may preserve the integrity of the complex, whereas removal of the protein can cause such a complex to fall apart, removing also potential other functions of the complex. Selecting hits from RNA interference screens as candidate drug targets

Not every gene identified in an RNAi screen is a suitable Future directions for RNA interference in cancer research target for drug discovery. First, one needs to verify that the effect observed in the RNAi screen is caused by In less than a decade, RNA interference has evolved into knockdown of the intended target gene. Although RNAi a major tool for drug discovery and even a potential was initially thought to be exquisitely specific, some therapeutic agent in it’s own right (Goff, 2008; concerns have been raised after reports of so-called ‘off- Castanotto and Rossi, 2009). Large RNAi collections target’ effects (Jackson et al., 2003). The term ‘off-target have been made available to the scientific community effect’ is used to describe the nonspecific suppression of enabling hundreds of RNAi screens, ranging in size mRNAs by siRNA or shRNAs. This suppression can from only dozens of genes to entire mammalian either be through cleavage of mRNA or mRNA genomes (see Table 1 for a summary of some of the translation inhibition through miRNA-like processes. most noteworthy screens). Together, these screens have Especially this last phenomenon is difficult to predict, as assigned (additional) functions to many genes in it only requires a partial complementarity between the processes ranging from cancer, development, stem cell siRNA/shRNA and the mRNA. Several approaches can maintenance to viral replication. It should be kept in be used to validate that phenotypes produced by RNAi mind that many genes reported in these screens have not are caused by ‘on-target’ rather than ‘off-target’ been validated in additional biological assays, and mechanisms. The first approach is the use of several therefore such gene lists should be viewed with caution siRNAs/shRNAs that target different regions in a given (Goff, 2008). Of particular concern is the possible mRNA. Having several independent siRNAs/shRNAs context dependency of genetic interactions identified in that are able to produce the same phenotype makes RNAi screens. For instance, gene A may be synthetic

Oncogene Loss-of-function genetic screens J Mullenders and R Bernards 4418 lethal with the loss of gene B in lung cancer cells in cancer genomes. It will no doubt provide a deep insight which the screen was performed, but not in breast into the genes that are deregulated in cancer. However, cancer. Similarly, a gene may be involved in resistance to we will only be able to truly appreciate the meaning of a given drug in colon cancer, but not have a role in the multitude of mutations in the major signaling resistance to the same drug in prostate cancer. Under- pathways, if we also understand how these pathways standing context dependency of genetic interactions is are interconnected. We therefore need in addition to the the major challenge for the next decade and this will re-sequencing project a large-scale effort to map be a prerequisite for a more profound understanding of functional interactions between signaling pathways how patients respond to cancer drugs. Such context using the approaches described here. The technology is dependency will ultimately be governed by cross talk readily available and proof of concept has been between signaling pathways. Thus, if we want to fully delivered. All we need is a concerted funding effort to understand how cells respond to perturbations of make it happen. Anyone interested? specific signals with targeted cancer therapies, we will need to map these interactions between signaling pathways. RNAi is exquisitely well suited to identify functional interactions between signaling pathways. Conflict of interest We wholeheartedly support the massive efforts that are undertaken worldwide to re-sequence thousands of The authors declare no conflict of interest.

References

Abbas-Terki T, Blanco-Bose W, Deglon N, Pralong W, Aebischer P. Carter P, Presta L, Gorman CM, Ridgway JB, Henner D, Wong WL (2002). Lentiviral-mediated RNA interference. Hum Gene Ther 13: et al. (1992). Humanization of an anti-p185HER2 antibody for 2197–2201. human cancer therapy. Proc Natl Acad Sci USA 89: 4285–4289. Aza-Blanc P, Cooper CL, Wagner K, Batalov S, Deveraux QL, Cooke Castanotto D, Rossi JJ. (2009). The promises and pitfalls of RNA- MP. (2003). Identification of modulators of TRAIL-induced apopto- interference-based therapeutics. Nature 457: 426–433. sis via RNAi-based phenotypic screening. Mol Cell 12: 627–637. Chabner BA, Roberts Jr TG. (2005). Timeline: chemotherapy and the Baeg GH, Zhou R, Perrimon N. (2005). Genome-wide RNAi analysis war on cancer. Nat Rev Cancer 5: 65–72. of JAK/STAT signaling components in Drosophila. Genes Dev 19: Cresta S, Sessa C, Catapano CV, Gallerani E, Passalacqua D, Rinaldi A 1861–1870. et al. (2008). Phase I study of bortezomib with weekly paclitaxel in Bartz SR, Zhang Z, Burchard J, Imakura M, Martin M, Palmieri A patients with advanced solid tumours. Eur J Cancer 44: 1829–1834. et al. (2006). Small interfering RNA screens reveal enhanced DasGupta R, Kaykas A, Moon RT, Perrimon N. (2005). Functional cisplatin cytotoxicity in tumor cells having both BRCA network genomic analysis of the Wnt-wingless signaling pathway. Science and TP53 disruptions. Mol Cell Biol 26: 9377–9386. 308: 826–833. Berns K, Hijmans EM, Mullenders J, Brummelkamp TR, Velds A, de Klein A, van Kessel AG, Grosveld G, Bartram CR, Hagemeijer A, Heimerikx M et al. (2004). A large-scale RNAi screen in human cells Bootsma D et al. (1982). A cellular oncogene is translocated to the identifies new components of the p53 pathway. Nature 428: 431–437. Philadelphia chromosome in chronic myelocytic leukaemia. Nature Berns K, Horlings HM, Hennessy BT, Madiredjo M, Hijmans EM, 300: 765–767. Beelen K et al. (2007). A functional genetic approach identifies the Druker BJ, Tamura S, Buchdunger E, Ohno S, Segal GM, Fanning S PI3 K pathway as a major determinant of trastuzumab resistance in et al. (1996). Effects of a selective inhibitor of the Abl tyrosine breast cancer. Cancer Cell 12: 395–402. kinase on the growth of Bcr-Abl positive cells. Nat Med 2: 561–566. Bjorklund M, Taipale M, Varjosalo M, Saharinen J, Lahdenpera J, EBCTCG (2005). Effects of chemotherapy and hormonal therapy for Taipale J. (2006). Identification of pathways regulating cell size and early breast cancer on recurrence and 15-year survival: an overview cell-cycle progression by RNAi. Nature 439: 1009–1013. of the randomised trials. Lancet 365: 1687–1717. Bommi-Reddy A, Almeciga I, Sawyer J, Geisen C, Li W, Harlow E Echeverri CJ, Beachy PA, Baum B, Boutros M, Buchholz F, Chanda et al. (2008). Kinase requirements in human cells: III. Altered kinase SK et al. (2006). Minimizing the risk of reporting false positives in requirements in VHLÀ/À cancer cells detected in a pilot synthetic large-scale RNAi screens. Nat Methods 3: 777–779. lethal screen. Proc Natl Acad Sci USA 105: 16484–16489. Elbashir SM, Harborth J, Lendeckel W, Yalcin A, Weber K, Tuschl T. Brummelkamp TR, Bernards R. (2003). New tools for functional (2001). Duplexes of 21-nucleotide RNAs mediate RNA interference mammalian cancer genetics. Nat Rev Cancer 3: 781–789. in cultured mammalian cells. Nature 411: 494–498. Brummelkamp TR, Bernards R, Agami R. (2002a). A system for stable Firestein R, Bass AJ, Kim SY, Dunn IF, Silver SJ, Guney I et al. expression of short interfering RNAs in mammalian cells. Science (2008). CDK8 is a colorectal cancer oncogene that regulates 296: 550–553. beta-catenin activity. Nature 455: 547–551. Brummelkamp TR, Bernards R, Agami R. (2002b). Stable suppression Fotheringham S, Epping MT, Stimson L, Khan O, Wood V, of tumorigenicity by virus-mediated RNA interference. Cancer Cell Pezzella F et al. (2009). Genome-wide loss-of-function screen 2: 243–247. reveals an important role for the proteasome in HDAC inhibitor- Brummelkamp TR, Fabius AW, Mullenders J, Madiredjo M, Velds A, induced apoptosis. Cancer Cell 15: 57–66. Kerkhoven RM et al. (2006). An shRNA barcode screen provides Friedman A, Perrimon N. (2006). A functional RNAi screen for regulators insight into cancer cell vulnerability to MDM2 inhibitors. Nat Chem of receptor tyrosine kinase and ERK signalling. Nature 444: 230–234. Biol 2: 202–206. Giroux V, Iovanna J, Dagorn JC. (2006). Probing the human kinome Brummelkamp TR, Nijman SM, Dirac AM, Bernards R. (2003). Loss for kinases involved in pancreatic cancer cell survival and of the cylindromatosis tumour suppressor inhibits apoptosis by gemcitabine resistance. Faseb J 20: 1982–1991. activating NF-kappaB. Nature 424: 797–801. Gobeil S, Zhu X, Doillon CJ, Green MR. (2008). A genome-wide Buchholz F, Kittler R, Slabicki M, Theis M. (2006). Enzymatically shRNA screen identifies GAS1 as a novel melanoma metastasis prepared RNAi libraries. Nat Methods 3: 696–700. suppressor gene. Genes Dev 22: 2932–2940.

Oncogene Loss-of-function genetic screens J Mullenders and R Bernards 4419 Goff SP. (2008). Knockdown screens to knockout HIV-1. Cell 135: Luo J, Solimini NL, Elledge SJ. (2009b). Principles of cancer therapy: 417–420. oncogene and non-oncogene addiction. Cell 136: 823–837. Gottesman MM, Fojo T, Bates SE. (2002). Multidrug resistance in Ma C, Mandrekar SJ, Alberts SR, Croghan GA, Jatoi A, Reid JM cancer: role of ATP-dependent transporters. Nat Rev Cancer 2: et al. (2007). A phase I and pharmacologic study of sequences of the 48–58. proteasome inhibitor, bortezomib (PS-341, Velcade), in combina- Grimm S. (2004). The art and design of genetic screens: mammalian tion with paclitaxel and carboplatin in patients with advanced culture cells. Nat Rev Genet 5: 179–189. malignancies. Cancer Chemother Pharmacol 59: 207–215. Hartwell LH, Szankasi P, Roberts CJ, Murray AW, Friend SH. MacKeigan JP, Murphy LO, Blenis J. (2005). Sensitized RNAi screen (1997). Integrating genetic approaches into the discovery of of human kinases and phosphatases identifies new regulators of anticancer drugs. Science 278: 1064–1068. apoptosis and chemoresistance. Nat Cell Biol 7: 591–600. Hitomi J, Christofferson DE, Ng A, Yao J, Degterev A, Xavier RJ Michiels F, van Es H, van Rompaey L, Merchiers P, Francken B, et al. (2008). Identification of a molecular signaling network that Pittois K et al. (2002). Arrayed adenoviral expression libraries for regulates a cellular necrotic cell death pathway. Cell 135: 1311–1323. functional screening. Nat Biotechnol 20: 1154–1157. Hopkins AL, Groom CR. (2002). The druggable genome. Nat Rev Moffat J, Grueneberg DA, Yang X, Kim SY, Kloepfer AM, Hinkle G Drug Discov 1: 727–730. et al. (2006). A lentiviral RNAi library for human and mouse genes Huang S, Laoukili J, Epping MT, Koster J, Holzel M, Westerman BA applied to an arrayed viral high-content screen. Cell 124: 1283–1298. et al. (2009). ZNF423 is critically required for retinoic acid-induced Moffat J, Sabatini DM. (2006). Building mammalian signalling differentiation and is a marker of neuroblastoma outcome. Cancer pathways with RNAi screens. Nat Rev Mol Cell Biol 7: 177–187. Cell 15: 328–340. Mross K, Frost A, Steinbild S, Hedbom S, Rentschler J, Kaiser R et al. Iorns E, Lord CJ, Turner N, Ashworth A. (2007). Utilizing RNA (2008). Phase I dose escalation and pharmacokinetic study of BI interference to enhance cancer drug discovery. Nat Rev Drug Discov 2536, a novel Polo-like kinase 1 inhibitor, in patients with advanced 6: 556–568. solid tumors. J Clin Oncol 26: 5511–5517. Iorns E, Turner NC, Elliott R, Syed N, Garrone O, Gasco M et al. Mukherji M, Bell R, Supekova L, Wang Y, Orth AP, Batalov S et al. (2008). Identification of CDK10 as an important determinant of (2006). Genome-wide functional analysis of human cell-cycle resistance to endocrine therapy for breast cancer. Cancer Cell 13: regulators. Proc Natl Acad Sci USA 103: 14819–14824. 91–104. Neumann B, Held M, Liebel U, Erfle H, Rogers P, Pepperkok R et al. Jackson AL, Bartz SR, Schelter J, Kobayashi SV, Burchard J, Mao M (2006). High-throughput RNAi screening by time-lapse imaging of et al. (2003). Expression profiling reveals off-target gene regulation live human cells. Nat Methods 3: 385–390. by RNAi. Nat Biotechnol 21: 635–637. Ngo VN, Davis RE, Lamy L, Yu X, Zhao H, Lenz G et al. (2006). A Jatoi A, Dakhil SR, Foster NR, Ma C, Rowland Jr KM, Moore Jr DF loss-of-function RNA interference screen for molecular targets in et al. (2008). Bortezomib, paclitaxel, and carboplatin as a first-line cancer. Nature 441: 106–110. regimen for patients with metastatic esophageal, gastric, and Oosterkamp HM, Neering H, Nijman SM, Dirac AM, Mooi WJ, gastroesophageal cancer: phase II results from the North Central Bernards R et al. (2006). An evaluation of the efficacy of topical Cancer Treatment Group (N044B). J Thorac Oncol 3: 516–520. application of salicylic acid for the treatment of familial cylindro- Ji D, Deeds SL, Weinstein EJ. (2007). A screen of shRNAs targeting matosis. Br J Dermatol 155: 182–185. tumor suppressor genes to identify factors involved in A549 Pan X, Ye P, Yuan DS, Wang X, Bader JS, Boeke JD. (2006). A DNA paclitaxel sensitivity. Oncol Rep 18: 1499–1505. integrity network in the yeast Saccharomyces cerevisiae. Cell 124: Jones TR, Carpenter AE, Lamprecht MR, Moffat J, Silver SJ, Grenier 1069–1081. JK et al. (2009). Scoring diverse cellular morphologies in image- Pan X, Yuan DS, Xiang D, Wang X, Sookhai-Mahadeo S, Bader JS based screens with iterative feedback and machine learning. Proc et al. (2004). A robust toolkit for functional profiling of the yeast Natl Acad Sci USA 106: 1826–1831. genome. Mol Cell 16: 487–496. Kaelin Jr WG. (2005). The concept of synthetic lethality in the context Pepperkok R, Ellenberg J. (2006). High-throughput fluorescence of anticancer therapy. Nat Rev Cancer 5: 689–698. microscopy for systems biology. Nat Rev Mol Cell Biol 7: 690–696. Kiger AA, Baum B, Jones S, Jones MR, Coulson A, Echeverri C et al. Rines DR, Gomez-Ferreria MA, Zhou Y, DeJesus P, Grob S, Batalov (2003). A functional genomic analysis of cell morphology using S et al. (2008). Whole genome functional analysis identifies novel RNA interference. J Biol 2: 27. components required for mitotic spindle integrity in human cells. Kittler R, Pelletier L, Heninger AK, Slabicki M, Theis M, Miroslaw L Genome Biol 9: R44. et al. (2007). Genome-scale RNAi profiling of cell division in human Rines DR, Tu B, Miraglia L, Welch GL, Zhang J, Hull MV et al. tissue culture cells. Nat Cell Biol 9: 1401–1412. (2006). High-content screening of functional genomic libraries. Kittler R, Putz G, Pelletier L, Poser I, Heninger AK, Drechsel D et al. Methods Enzymol 414: 530–565. (2004). An endoribonuclease-prepared siRNA screen in human cells Rodriguez-Antona C, Ingelman-Sundberg M. (2006). Cytochrome identifies genes essential for cell division. Nature 432: 1036–1040. P450 pharmacogenetics and cancer. Oncogene 25: 1679–1691. Kolfschoten IG, van Leeuwen B, Berns K, Mullenders J, Rottmann S, Wang Y, Nasoff M, Deveraux QL, Quon KC. (2005). A Beijersbergen RL, Bernards R et al. (2005). A genetic screen TRAIL receptor-dependent synthetic lethal relationship between identifies PITX1 as a suppressor of RAS activity and tumori- MYC activation and GSK3beta/FBW7 loss of function. Proc Natl genicity. Cell 121: 849–858. Acad Sci USA 102: 15195–15200. Li L, Lin X, Khvorova A, Fesik SW, Shen Y. (2007). Defining the Sarthy AV, Morgan-Lappe SE, Zakula D, Vernetti L, Schurdak M, optimal parameters for hairpin-based knockdown constructs. RNA Packer JC et al. (2007). Survivin depletion preferentially reduces the 13: 1765–1774. survival of activated K-Ras-transformed cells. Mol Cancer Ther 6: Lum PY, Armour CD, Stepaniants SB, Cavet G, Wolf MK, Butler JS 269–276. et al. (2004). Discovering modes of action for therapeutic Satoh T, Okamoto I, Miyazaki M, Morinaga R, Tsuya A, Hasegawa compounds using a genome-wide screen of yeast heterozygotes. Y et al. (2009). Phase I study of YM155, a novel survivin Cell 116: 121–137. suppressant, in patients with advanced solid tumors. Clin Cancer Luo B, Cheung HW, Subramanian A, Sharifnia T, Okamoto M, Yang Res 15: 3872–3880. X et al. (2008). Highly parallel identification of essential genes in Savage P, Stebbing J, Bower M, Crook T. (2009). Why does cytotoxic cancer cells. Proc Natl Acad Sci USA 105: 20380–20385. chemotherapy cure only some cancers? Nat Clin Pract Oncol 6: 43–52. Luo J, Emanuele MJ, Li D, Creighton CJ, Schlabach MR, Westbrook Sawyers C. (2004). Targeted cancer therapy. Nature 432: 294–297. TF et al. (2009a). A genome-wide RNAi screen identifies multiple Schlabach MR, Luo J, Solimini NL, Hu G, Xu Q, Li MZ et al. (2008). synthetic lethal interactions with the Ras oncogene. Cell 137: Cancer proliferation gene discovery through functional genomics. 835–848. Science 319: 620–624.

Oncogene Loss-of-function genetic screens J Mullenders and R Bernards 4420 Scholl C, Frohling S, Dunn IF, Schinzel AC, Barbie DA, Kim SY et al. Turner NC, Lord CJ, Iorns E, Brough R, Swift S, Elliott R et al. (2009). Synthetic lethal interaction between oncogenic KRAS (2008). A synthetic lethal siRNA screen identifying genes mediating dependency and STK33 suppression in human cancer cells. Cell sensitivity to a PARP inhibitor. Embo J 27: 1368–1377. 137: 821–834. Tyner JW, Deininger MW, Loriaux MM, Chang BH, Gotlib JR, Willis Shaffer AL, Emre NC, Lamy L, Ngo VN, Wright G, Xiao W et al. SG et al. (2009). RNAi screen for rapid therapeutic target (2008). IRF4 addiction in multiple myeloma. Nature 454: identification in leukemia patients. Proc Natl Acad Sci USA 106: 226–231. 8695–8700. Silva JM, Marran K, Parker JS, Silva J, Golding M, Schlabach MR Wajapeyee N, Serra RW, Zhu X, Mahalingam M, Green MR. (2008). et al. (2008). Profiling essential genes in human mammary cells by Oncogenic BRAF induces senescence and apoptosis through path- multiplex RNAi screening. Science 319: 617–620. ways mediated by the secreted protein IGFBP7. Cell 132: 363–374. Stegmeier F, Rape M, Draviam VM, Nalepa G, Sowa ME, Ang XL Wang Y, Engels IH, Knee DA, Nasoff M, Deveraux QL, Quon KC. et al. (2007). Anaphase initiation is regulated by antagonistic (2004). Synthetic lethal targeting of MYC by activation of the DR5 ubiquitination and deubiquitination activities. Nature 446: death receptor pathway. Cancer Cell 5: 501–512. 876–881. Westbrook TF, Martin ES, Schlabach MR, Leng Y, Liang AC, Feng Swanton C, Marani M, Pardo O, Warne PH, Kelly G, Sahai E et al. B et al. (2005). A genetic screen for candidate tumor suppressors (2007). Regulators of mitotic arrest and ceramide metabolism are identifies REST. Cell 121: 837–848. determinants of sensitivity to paclitaxel and other chemotherapeutic Wheeler DB, Carpenter AE, Sabatini DM. (2005). Cell microarrays drugs. Cancer Cell 11: 498–512. and RNA interference chip away at gene function. Nat Genet Tang W, Dodge M, Gundapaneni D, Michnoff C, Roth M, Lum L. 37(Suppl): S25–S30. (2008). A genome-wide RNAi screen for Wnt/beta-catenin pathway Whitehurst AW, Bodemann BO, Cardenas J, Ferguson D, Girard L, components identifies unexpected roles for TCF transcription Peyton M et al. (2007). Synthetic lethal screen identification of factors in cancer. Proc Natl Acad Sci USA 105: 9697–9702. chemosensitizer loci in cancer cells. Nature 446: 815–819. Tong AH, Evangelista M, Parsons AB, Xu H, Bader GD, Page N et al. Zender L, Xue W, Zuber J, Semighini CP, Krasnitz A, Ma B et al. (2001). Systematic genetic analysis with ordered arrays of yeast (2008). An oncogenomics-based in vivo RNAi screen identifies deletion mutants. Science 294: 2364–2368. tumor suppressors in liver cancer. Cell 135: 852–864.

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