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Comparative Oncogenomics Identifies PSMB4 and SHMT2 As Potential Cancer Driver Genes

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Comparative Oncogenomics Identifies PSMB4 and SHMT2 As Potential Cancer Driver Genes Published OnlineFirst April 22, 2014; DOI: 10.1158/0008-5472.CAN-13-2683 Cancer Therapeutics, Targets, and Chemical Biology Research Comparative Oncogenomics Identifies PSMB4 and SHMT2 as Potential Cancer Driver Genes Genee Y. Lee1, Peter M. Haverty3,LiLi3, Noelyn M. Kljavin2, Richard Bourgon3, James Lee1, Howard Stern4, Zora Modrusan2, Somasekar Seshagiri2, Zemin Zhang3, David Davis1, David Stokoe1, Jeffrey Settleman1, Frederic J. de Sauvage5, and Richard M. Neve1 Abstract Cancer genomes maintain a complex array of somatic alterations required for maintenance and progression of the disease, posing a challenge to identify driver genes among this genetic disorder. Toward this end, we mapped regions of recurrent amplification in a large collection (n ¼ 392) of primary human cancers and selected 620 genes whose expression is elevated in tumors. An RNAi loss-of-function screen targeting these genes across a panel of 32 cancer cell lines identified potential driver genes. Subsequent functional assays identified SHMT2, a key enzyme in the serine/glycine synthesis pathway, as necessary for tumor cell survival but insufficient for transformation. The 26S proteasomal subunit, PSMB4, was identified as the first proteasomal subunit with oncogenic properties promoting cancer cell survival and tumor growth in vivo. Elevated expression of SHMT2 and PSMB4 was found to be associated with poor prognosis in human cancer, supporting the development of molecular therapies targeting these genes or components of their pathways. Cancer Res; 74(11); 1–13. Ó2014 AACR. Introduction suggests that individual amplicons contain multiple genes that Genome-wide documentation of tumor gene copy number, collectively alter processes important for tumor growth and gene expression, and somatic mutations has become standard survival (7) and that there may be equivalent cooperativity fi practice, driven by remarkable technologic advances over the between genes in separate but coampli ed genomic regions last decade (1, 2). By elucidating the "omic" landscape of tumor (8, 9). The distinct challenge is to separate passenger genes fi cells, we are only now realizing the vast complexity of the from driver genes (10). In the quest to nd new driver genes diseases that are classed as cancer (3). Assigning function to and, hence, potential therapeutic targets, a number of groups any gene in this morass of observed changes can be a challenge, have used either a reductionist approach by identifying a and years of collective study have begun to hone in on core region of copy-number gain/loss of clinical interest and testing functionalities regulating specific hallmarks of cancer (4). the functionality of each gene within that region (11, 12), or a However, if the long-term goal of diagnostic-driven, individu- holistic approach using RNA interference library screens in a fi alized therapy is to be realized, we need to begin to understand single cell line to nd driver genes (13, 14). how these genetic changes sustain tumor growth, progression, The primary goal of this study was to develop a genome-wide and survival. Oncogenes and tumor suppressors are the clas- oncogenomic screening strategy to identify candidate onco- sical drivers of oncogenesis, but it is still an open question as to genic driver genes. Our approach was to identify recurrent which genes are the drivers in any single tumor when as much regions of copy-number gain and increased expression across as 10% to 15% of the genome can be altered by recurrent copy- multiple solid tumor types, with the hypothesis that these number abnormalities (5). The task of finding "driver genes" is genes represent fundamental processes for sustaining solid further hampered by the fact that the majority of copy-number tumor growth and survival. We selected 620 genes from fi abnormalities are low-level gains, often encompassing large commonly ampli ed regions whose expression is elevated in regions of the genome, which contrasts with high-level, focal tumor versus normal and determined the effect on cell viability copy-number gains as exemplified by HER2 (6). Evidence also by RNAi targeting in 32 cancer cell lines. Here, we report the findings of this study that represents one of the most com- prehensive functional oncogenomic screens to date. Authors' Affiliations: Departments of 1Discovery Oncology, 2Molecular Biology, 3Bioinformatics, 4Pathology, and 5Molecular Oncology, Genen- tech Inc., South San Francisco, California Materials and Methods Note: Supplementary data for this article are available at Cancer Research Cell lines Online (http://cancerres.aacrjournals.org/). See Supplementary Table S7 for full list of cell lines, cell line fi Corresponding Author: Richard M. Neve, Genentech, 1 DNA Way, South short tandem repeat (STR) pro les, growth conditions, and San Francisco, CA 94080. Phone: 650-467-4452; Fax: 650-742-5179; transfection conditions used. All cell lines are tested for E-mail: [email protected] mycoplasma, cross contamination, and genetically finger- doi: 10.1158/0008-5472.CAN-13-2683 printed when new stocks are generated to ensure quality and Ó2014 American Association for Cancer Research. confirm ancestry. www.aacrjournals.org OF1 Downloaded from cancerres.aacrjournals.org on October 1, 2021. © 2014 American Association for Cancer Research. Published OnlineFirst April 22, 2014; DOI: 10.1158/0008-5472.CAN-13-2683 Lee et al. Cell line fingerprinting: SNP fingerprinting. Single- RNA Extraction Kit (Qiagen). DNA was then hybridized to the nucleotide polymorphism (SNP) genotypes are performed each Agilent Human Genome CGH Microarray Kit 244A and geno- time new stocks are expanded for cryopreservation. Cell line mic copy number was calculated by comparing the intensity identity is verified by high-throughput SNP genotyping using ratio between individual tumors and the averaged normal DNA Fluidigm multiplexed assays. SNPs were selected on the basis of content. minor allele frequency and presence on commercial genotyp- ing platforms. SNP profiles are compared with SNP calls from RNAi screen available internal and external data (when available) to deter- Cells were reverse lipid transfected with pools of four mine or confirm ancestry. In cases in which data are unavail- siRNAs; siGENOME siRNAs were used in the primary screen able or cell line ancestry is questionable, DNA or cell lines are and ON-TARGETplus siRNA was used in the secondary screen repurchased to perform profiling to confirm cell line ancestry. and all follow-up studies (Dharmacon). siRNA pools were used SNPs. The following SNPs were used: rs11746396, at 50 nmol/L and singles at 25 nmol/L. Pooled siRNAs targeting rs16928965, rs2172614, rs10050093, rs10828176, rs16888998, PLK1 and TOX transfection reagent were used as positive rs16999576, rs1912640, rs2355988, rs3125842, rs10018359, controls at 25 nmol/L. Lipids used were Dharmafects 1, 2, 3, rs10410468, rs10834627, rs11083145, rs11100847, rs11638893, and 4 (Dharmacon) and Lipofectamine RNAiMAX (Invitrogen). rs12537, rs1956898, rs2069492, rs10740186, rs12486048, See Supplementary Table S7 for transfection conditions for rs13032222, rs1635191, rs17174920, rs2590442, rs2714679, each cell line. siRNA–lipid complexes were formed for 1 hour, rs2928432, rs2999156, rs10461909, rs11180435, rs1784232, then cells were seeded on top of complexes. After 5 days, cell rs3783412, rs10885378, rs1726254, rs2391691, rs3739422, viability was assayed using CellTiter-Glo (Promega) and lumi- rs10108245, rs1425916, rs1325922, rs1709795, rs1934395, nescence readout was performed using Envision plate reader rs2280916, rs2563263, rs10755578, rs1529192, rs2927899, (PerkinElmer). Gene selection criteria: Genes were selected rs2848745, and rs10977980. from the screen by one of three criteria: (i) correlation of RNAi STR profiling. STR profiles are determined for each line response with total RNA expression (expression, value >0.3 using the Promega PowerPlex 16 System. This is performed Pearson correlation); (ii) correlation of RNAi response with once and compared with external STR profiles of cell lines copy-number [comparative genomic hybridization (CGH), val- (when available) to determine cell line ancestry. ue >0.3 Pearson correlation]; (iii) genes that affected cell Loci analyzed. Detection of sixteen loci (fifteen STR loci viability across the panel of cell lines regardless of copy number and Amelogenin for gender identification), including D3S1358, or gene expression (multiple). Genes were classified as a TH01, D21S11, D18S51, Penta E, D5S818, D13S317, D7S820, multiple hit if six or more cell lines had an RNAi response of D16S539, CSF1PO, Penta D, AMEL, vWA, D8S1179, and TPOX. greater than 0.65 (65% cell death). Cloning Gene expression analysis Full-length open reading frame clones were obtained from Patient tissue samples with the appropriate Institutional Invitrogen and Genentech. C-terminus FLAG tags were added by Review Board approval and patient-informed consent were PCR then tagged proteins were subcloned into pLPCX retroviral obtained from commercial sources (Supplementary Table S1). expression vector (Clontech). NIH/3T3 and MCF10A stable cell The human tissue samples used in the study were deidentified lines were generated by infection with retroviral particles expres- (double-coded) before their use, and, hence, the study using sing FLAG-tagged constructs and selection with puromycin. See these samples is not considered human subject research under Supplementary Table S7 for a full list of clones and primers used. the US Department of Human and
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