An Oncogenomics-Based in Vivo Rnai Screen Identifies Tumor
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Please cite this article in press as: Zender et al., An Oncogenomics-Based In Vivo RNAi Screen Identifies Tumor Suppressors in Liver Cancer, Cell (2008), doi:10.1016/j.cell.2008.09.061 An Oncogenomics-Based In Vivo RNAi Screen Identifies Tumor Suppressors in Liver Cancer Lars Zender,1,6,7 Wen Xue,1,7 Johannes Zuber,1 Camile P. Semighini,1 Alexander Krasnitz,1 Beicong Ma,1 Peggy Zender,1 Stefan Kubicka,3 John M. Luk,4 Peter Schirmacher,5 W. Richard McCombie,1 Michael Wigler,1 James Hicks,1 Gregory J. Hannon,1,2 Scott Powers,1 and Scott W. Lowe1,2,* 1Cold Spring Harbor Laboratory 2Howard Hughes Medical Institute Cold Spring Harbor, New York 11724, USA 3Department of Gastroenterology and Hepatology, Medical School Hannover, 30625 Hannover, Germany 4Department of Surgery, University of Hong Kong, Hong Kong, China 5Institute of Pathology, University Hospital Heidelberg, 69120 Heidelberg, Germany 6Present address: Helmholtz Centre for Infection Research (HZI), 38124 Braunschweig, Germany and Department of Gastroenterology, Hepatology and Endocrinology, Medical School Hannover, 30625 Hannover, Germany 7These authors contributed equally to this work *Correspondence: [email protected] DOI 10.1016/j.cell.2008.09.061 SUMMARY of their action, new treatment strategies have been established. It follows that a further understanding of cancer genetics will im- Cancers are highly heterogeneous and contain many prove cancer diagnosis, prognosis, and therapy. passenger and driver mutations. To functionally Recent technological advances have greatly increased the identify tumor suppressor genes relevant to human resolution and depth at which cancer cell genomes can be ex- cancer, we compiled pools of short hairpin RNAs amined, making it possible to envision the cataloging of every (shRNAs) targeting the mouse orthologs of genes re- gene whose mutation or alteration occurs in human tumors (Vel- currently deleted in a series of human hepatocellular culescu, 2008). For example, regions of copy number alteration can be identified by high-resolution array-based comparative carcinomas and tested their ability to promote tumor- genomic hybridization (CGH); in many cases, regions of chromo- igenesis in a mosaic mouse model. In contrast to ran- somal amplification harbor oncogenes, whereas deleted regions domly selected shRNA pools, many deletion-specific harbor tumor suppressor genes (Chin and Gray, 2008). In addi- pools accelerated hepatocarcinogenesis in mice. tion, somatic point mutations potentially selected for during tu- Through further analysis, we identified and validated mor evolution can be identified by high-throughput sequencing 13 tumor suppressor genes, 12 of which had not been (Wood et al., 2007; Greenman et al., 2007). However, owing to linked to cancer before. One gene, XPO4, encodes the inherent genomic instability of cancer cells, gene linkage, a nuclear export protein whose substrate, EIF5A2, is and spontaneous mutagenesis, cancers also contain somatically amplified in human tumors, is required for prolifera- acquired ‘‘passenger’’ mutations that may not confer a selective tion of XPO4-deficient tumor cells, and promotes he- advantage to the developing tumor. Moreover, some genes are patocellular carcinoma in mice. Our results establish haploinsufficient tumor suppressors, such that loss of even one allele can promote tumorigenesis—even without a corre- the feasibility of in vivo RNAi screens and illustrate sponding mutation in the remaining wild-type allele—making it how combining cancer genomics, RNA interference, difficult to pinpoint relevant tumor suppressors in large deletions. and mosaic mouse models can facilitate the func- Therefore, candidate genes identified through genomic ap- tional annotation of the cancer genome. proaches require functional validation before they are useful for clinical applications. INTRODUCTION Functional characterization of cancer genes is often tedious, and it is not always obvious which assays will reveal the putative Diversity and complexity are hallmarks of cancer genomes. Even oncogenic activity of relatively uncharacterized genes. More- tumors arising from the same cell type or tissue harbor a range of over, although cell culture systems are tractable, in vitro models genetic lesions that facilitate their uncontrolled expansion and do not recapitulate all features of the tumor microenvironment eventual metastasis. As a consequence, the behavior of individ- and so do not survey all relevant gene activities. Currently, ual tumors—how they progress and ultimately respond to ther- a ‘‘gold-standard’’ approach for studying candidate oncogenes apy—is heterogeneous and unpredictable. To date, many can- and tumor suppressors involves the production of transgenic cer genes have been identified, and through characterization and knockout mice that contain germline alterations in the Cell 135, 1–13, November 28, 2008 ª2008 Elsevier Inc. 1 Please cite this article in press as: Zender et al., An Oncogenomics-Based In Vivo RNAi Screen Identifies Tumor Suppressors in Liver Cancer, Cell (2008), doi:10.1016/j.cell.2008.09.061 candidate oncogenic lesion (Van Dyke and Jacks, 2002). These oligonucleotide microarray analysis (ROMA), a high-resolution strains have proven invaluable for validating cancer genes and array-based CGH platform (Lucito et al., 2003). Raw data were create powerful models for subsequent studies. Nevertheless, converted into segmented profiles (Hicks et al., 2006), and seg- their generation and analysis is time consuming and expensive. ments that showed significant decrease from the ground state To facilitate a more rapid and cost-effective analysis of cancer were identified (Figure 1A). We then computationally estimated gene action in vivo, we developed a ‘‘mosaic’’ mouse model of genetic events so that a homozygous deletion within a heterozy- hepatocellular carcinoma (Zender et al., 2006), a common but gous deletion would be scored as two deletion events rather than understudied cancer for which there are few treatment options one (A.K., J.H., and M.W., unpublished data) and plotted the (Lee and Thorgeirsson, 2006; Teufel et al., 2007). In our mouse resulting deletion event frequency across the entire genome model, hepatocellular carcinomas (HCCs) with different onco- (Figure 1B). Among the many deletions detected, only a fraction genic lesions can be rapidly produced by genetic manipulation were less then 5MB. We hypothesized that these focal deletions of cultured embryonic liver progenitor cells (hepatoblasts) fol- were most likely to be enriched for tumor suppressor genes. lowed by their retransplantation into the livers of recipient mice To develop an initial gene list for further studies, we identified (Zender et al., 2006; Zender et al., 2005). We have previously all of the genes embedded in recurrent focal deletions or in used this model to characterize the gene products contained unique focal deletions whose gene content was also contained in the 11q22 amplicon observed in human tumors and showed in broader deletions that were recurrent. On the basis of these that both YAP1 and cIAP1 cooperate to promote tumorigenesis criteria, we identified 58 deletions ranging in size from 98 kb to in particular genetic contexts (Zender et al., 2006). 2.6 Mb, containing 1 to 46 genes, respectively (see, for example, To further accelerate the study of cancer genes in vivo, our Figure 1C). Of the 362 annotated genes identified in total (Table laboratory has adapted stable RNA interference (RNAi) technol- S1 available online, see red circles in Figure 1B), we were able to ogy to downregulate tumor suppressor genes in mice (Hemann bioinformatically identify 301 mouse orthologs. We next ob- et al., 2003). We utilize microRNA-based short hairpin RNAs tained all 631 of the mir30-based shRNAs from the Cold Spring (shRNAmir, hereafter referred to as shRNAs) that are potent trig- Harbor Laboratory RNAi CODEX library. Thus, on average, gers of the RNAi machinery and can efficiently suppress gene each deleted gene was represented by approximately 2 murine expression when expressed from a single genomic copy (Dickins shRNAs (see workflow in Figure S1A). et al., 2005; Silva et al., 2005). We previously used this technol- ogy in our mosaic mouse model of hepatocellular carcinoma to Constructing an In Vivo RNAi Screen show that stable knockdown of the p53 tumor suppressor by We recently developed a ‘‘mosaic’’ mouse model in which liver RNAi can mimic p53 gene loss in vivo (Zender et al., 2005) and carcinomas can be rapidly produced by genetic manipulation that regulated RNAi can reversibly modulate endogenous p53 of liver progenitor cells followed by their retransplantation into expression to implicate the role of p53 loss in tumor maintenance recipient mice (Zender et al., 2006). Since systemic delivery of (Xue et al., 2007). We also used similar approaches to rapidly RNAi currently does not enable efficient and stable knockdown validate Deleted in Liver Cancer 1 (DLC1) as a potent tumor sup- of genes in tissues, we decided to introduce pools of shRNAs pressor gene (Xue et al., 2008). into premalignant progenitor cells and select for those that pro- The goal of this study was to integrate cancer genomics, RNAi mote tumor formation after transplantation. We previously gen- technology, and mouse models to rapidly discover and validate erated immortalized lines of embryonic hepatocytes lacking cancer genes. Our approach was based on the premise that ge- p53 and overexpressing Myc that were not tumorigenic in vivo nomic deletions occurring