Published OnlineFirst December 6, 2011; DOI: 10.1158/0008-5472.CAN-11-2781
Cancer Therapeutics, Targets, and Chemical Biology Research
Identification of Molecular Vulnerabilities in Human Multiple Myeloma Cells by RNA Interference Lethality Screening of the Druggable Genome
Rodger E. Tiedemann1, Yuan Xao Zhu2, Jessica Schmidt2, Chang Xin Shi2, Chris Sereduk3, Hongwei Yin3, Spyro Mousses3, and A. Keith Stewart2
Abstract Despite recent advances in targeted treatments for multiple myeloma, optimal molecular therapeutic targets have yet to be identified. To functionally identify critical molecular targets, we conducted a genome-scale lethality study in multiple myeloma cells using siRNAs. We validated the top 160 lethal hits with four siRNAs per gene in three multiple myeloma cell lines and two non-myeloma cell lines, cataloging a total of 57 potent multiple myeloma survival genes. We identified the Bcl2 family member MCL1 and several 26S proteasome subunits among the most important and selective multiple myeloma survival genes. These results provided biologic validation of our screening strategy. Other essential targets included genes involved in RNA splicing, ubiquitina- tion, transcription, translation, and mitosis. Several of the multiple myeloma survival genes, especially MCL1, TNK2, CDK11, and WBSCR22, exhibited differential expression in primary plasma cells compared with other human primary somatic tissues. Overall, the most striking differential functional vulnerabilities between multiple myeloma and non–multiple myeloma cells were found to occur within the 20S proteasome subunits, MCL1, RRM1, USP8, and CKAP5. We propose that these genes should be investigated further as potential therapeutic targets in multiple myeloma. Cancer Res; 72(3); 757–68. 2011 AACR.
Introduction survival genes are examined for their expression in primary myeloma and for functional vulnerability in other myeloma Although significant progress has been made in the treat- and non-myeloma cells to generate a snapshot of potential ment of patients with multiple myeloma over the last decade, "druggable" molecular vulnerabilities of end-stage myeloma current therapies are not curative. Many of the drugs currently cells. Notably, RNA interference (RNAi) screening has previ- in use for the treatment of multiple myeloma are associated ously been used to identify IRF4 addiction in multiple myeloma with toxicities that include marrow suppression, infection, (1), although no additional molecular vulnerabilities were neuropathy, and risk of secondary malignancy. Therefore, new verified in that study. Less than 10% of all genes have previously therapeutic strategies are required that more effectively or been examined in any depth for their vulnerability in myeloma selectively target malignant plasma cells. cells, and with this study, core molecular vulnerabilities in End-stage multiple myeloma disease often emerges from a multiple myeloma cells are identified and ranked from a clone that is minimally responsive to conventional therapy genome-scale perspective. and/or which has extramedullary growth characteristics. To better define potential drug targets in late-stage myeloma Materials and Methods disease, we report here a portrait of critically vulnerable genes in extramedullary KMS11 myeloma tumor cells, generated Cell lines, siRNA, and transfection reagents functionally from a high-throughput siRNA Achilles heel Human myeloma cell lines and A549 cells and 293 cells were screen. Following confirmatory screening, screening-identified maintained in RPMI-1640 or Dulbecco's Modified Eagle's Medium, supplemented with 10% fetal calf serum and anti- biotics. Prior to use, cell line identity was verified by PCR assay Authors' Affiliations: 1Princess Margaret Hospital, Ontario Cancer Insti- for copy number variation fingerprints. The Human Druggable tute and the University of Toronto, Toronto, Ontario, Canada; 2Division of fi Hematology-Oncology, Mayo Clinic, Scottsdale; and 3Translational Geno- Genome siRNA Set V2 and all 640 siRNA oligos for con rma- mics Institute, Phoenix, Arizona tion studies were purchased from Qiagen. Lipofectamine 2000 Note: Supplementary data for this article are available at Cancer Research and RNAiMAX were purchased from Invitrogen. CellTiter-Glo Online (http://cancerres.aacrjournals.org/). assay kit was from Promega. Corresponding Author: Rodger E. Tiedemann, Princess Margaret Hos- pital, 610 University Ave, Toronto, Ontario, Canada M5G 2M9. Phone: 416- siRNA transfection optimization and assay development 581-8451; Fax: 416-946-6546; E-mail: [email protected] Transfection conditions for human myeloma or epithelial doi: 10.1158/0008-5472.CAN-11-2781 cell lines were individually optimized using commercially 2011 American Association for Cancer Research. available cationic lipids, as described (2). Effective transfection
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Tiedemann et al.
efficiencies were determined by comparing viability after within the Mayo Clinic Cell Line data set on the Myeloma transfecting (i) a universally lethal positive control siRNA Genomics Portal (7), when one or more Affymetrix probe set against ubiquitin B (UBB) or (ii) negative control siRNA representing the gene was flagged with a "present" or "mar- including intentionally nontargeted siRNA and siRNA against ginal" call using Affymetrix Microarray Suite Present/Absent/ GFP. Viability was determined at 96 hours by CellTiter-Glo Marginal detection. The FDR of candidate survival genes was luminescence or by MTT assay. The best reagent and trans- then calculated as follows: fection conditions were those that produced the least reduc- "# tion in cell viability with negative controls and greatest reduc- P P hits G P þA P þA tion with lethal UBB siRNA. Optimized high-efficiency reverse hits hits G G FDR ¼ 1 P FDR GEP method G randomly selected genes transfection conditions were separately derived for KMS11, 1 P þA G G 8226, JJN3, A549, and 293 cells using 96- and 384-well plates. where G represents all screened genes, P represents High-throughput siRNA screening expressed (or present) genes, and A represents absent genes. Approximately 17,000 siRNAs targeting approximately one Short-listed candidate survival genes with known FDRs were third of the human genome (6,722 genes, selected for potential advanced to secondary confirmation screening. "druggability") with 2 oligos per gene and including staggered replicate-positive and -negative control siRNA were preprinted Confirmation of KMS11 myeloma survival genes on 384-well plates, in a single siRNA per well format. A primary To exclude the possibility of siRNA with biologic off-target large-scale screening experiment was carried out in duplicate effects contributing to our observations, we conducted sec- on KMS11 cells. KMS11 was selected for high-throughput ondary confirmation studies in duplicate using 4 independent studies as it showed the greatest siRNA transfection efficiency siRNAs per gene. Assays conditions were similar to those during optimization studies (from a panel of 16 human mye- described for primary high-throughput screening but were loma cell lines) and because this tumor line is characterized by scaled to 96-well plate format. As rescreened genes were "high-risk" t(4;14) and t(14;16) chromosomal translocations. enriched for gene hits, Z-score rather than B-score normali- Plates preloaded with siRNAs were thawed at room temper- zation was used for confirmatory studies and was calculated as ature and 20 mL of diluted Lipofectamine 2000 solution was the difference in viability outcome between test siRNA and the added to each well. After 30 minutes, 1,500 cells in 20 mLof mean of nontargeted negative control siRNA, normalized to medium were added to each well. Final reagent concentrations the distribution (SD) of negative control siRNA replicates, as were 16 nmol/L siRNA and 0.16 mL per well for Lipofectamine described (5). 2000. Plates were incubated at 37 C. Cell viability was deter- Reproducibility of confirmatory siRNA results was again mined at 96 hours by CellTiter-Glo luminescence assay read on assessed by linear regression comparison of Z-scores from an Analyst GT plate reader (Molecular Devices). Notably, in duplicate studies. KMS11 myeloma survival genes were ranked contrast to synthetic lethal targets that we have previously by MRCA FDR and cropped to an average FDR of 5%, elim- identified in myeloma (3), whose vulnerability was evident only inating false survival genes; and then reranked for maximum in the setting of concurrent proteasome inhibitor therapy, effect on viability (at a standardized per gene FDR ¼ 0.3). molecular targets were identified here on the basis of direct vulnerability in multiple myeloma cells. Ingenuity Pathway Analysis To assess the interactions between identified myeloma Hit selection from primary screening survival genes, pathway/network analysis was carried out For siRNA hit selection, B-score normalization was applied using Ingenuity Pathway Analysis (IPA). Survival genes were to the primary screen raw luminescence values as described overlaid onto a global molecular network developed from (4, 5) to eliminate plate-to-plate variability and well position information contained in the Ingenuity Pathways Knowledge effects. B-scores were normalized to the SD of the data. Base (IPKB). All interactions are supported by at least one Reproducibility of siRNA results was assessed by linear regres- reference from the literature or from canonical information sion comparison of B-scores from duplicate full-scale screen- stored in the IPKB. IPA P values were used to rank networks, ing assays. The rates of false-positive off-target RNAi effects reflecting the likelihood that the survival genes were concen- were determined by analysis of the degree of internal con- trated within networks by biologic influences and not by cordancy of results from multiple tests per gene, for multiple random chance alone. The several high-confidence myeloma genes, using multiple result concordancy analysis (MRCA; survival gene networks depicted were simplified to reflect the Tiedemann, manuscript in preparation). From MRCA analysis, direct interactions of identified survival genes. the false discovery rate (FDR) of candidate survival genes associated with one or more lethal siRNAs were determined Gene expression of KMS11 myeloma survival genes in as a function of the intensity of siRNA test results. A second primary multiple myeloma tumors and normal primary method to determine the FDR of candidate survival genes was human tissues also used, using as its basis the enrichment of expressed genes Plasma cells from bone marrow samples from patients with among candidate survival genes. Genes were designated as multiple myeloma, monoclonal gamopathy of uncertain sig- expressed in screening cells using Affymetrix gene expression nificance (MGUS), or from normal donors (normal plasma profiling (GEP) data, generated as described (6) and available cells, NPC) were collected and processed for gene expression as
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Molecular Vulnerabilities in Multiple Myeloma
described (8); CEL files are deposited on the Gene Expression poly-ubiquitin (UBB) and, as a negative control, nontargeted Omnibus (GEO accession no. GSE 6477 and GSE 5900). Expres- siRNA. Genes silenced by one or more siRNA that induced loss sion data from primary human somatic tissues were obtained of KMS11 viability were implicated as candidate survival genes. from the Genomics Institute of the Novartis Research Foun- Lethal siRNA results were highly reproducible, with significant dation, SymAtlas (ref. 9; GEO data set accession GSE1133). correlation between duplicate full-scale screening assays (r2 ¼ Gene expression data were generated in all cases using 0.82; Fig. 1C). Approximately 7.1% of siRNA induced a loss of Hg_U133A_2 microarrays (Affymetrix). Data were analyzed viability exceeding 3 SDs from the median result, compared using GeneSpring (Agilent Technologies). To compare the with a predicted rate because of chance of approximately 0.5% expression of genes in primary multiple myeloma samples siRNA (Fig. 1D). versus other grouped human somatic tissues, one-way Welch To verify that our results contained true-positive gene hits, ANOVA analysis was carried out (parametric ranking, with no we evaluated across the entire screen the extent to which assumption of equal variance) using Benjamini–Hochberg concordant results were obtained from siRNA targeting the multiple testing correction (FDR ¼ 0.05). Histogram data are same gene, taking account of all gene-matched siRNA pairs. shown normalized per chip to the median signal of expressed Notably, the "lethal hit" rate among second siRNA whose paired genes within each sample (genes with present detection call first siRNA was lethal, significantly and consistently exceeded and/or raw signal >200). the overall "lethal hit rate" of all siRNA, particularly among siRNAs that were substantially lethal (Fig. 1D). Therefore, from Retesting of survival genes in 8226, JJN3, A549, and 293 this analysis, we concluded that the primary screen data were cells enriched with true gene hits associated with concordant siRNA To further assess the specificity of myeloma survival genes, results exceeding the rate expected because of chance or off- 8226, JJN3, A549, and 293 cells were tested with survival gene target effects. We next estimated the FDR for candidate siRNA. Experimental transfection conditions, in 96-well plates, survival genes (Fig. 1E and F), from the relative rates of were as follows: 8226 cells (0.5 104 cells per well) were concordant and discordant lethal siRNA results, as a function transfected with 50 nmol/L siRNA using 0.5 mL per well of the magnitude of siRNA-induced viability loss. Candidate RNAiMax (Invitrogen); JJN3 (104 cells per well) were trans- survival genes with 2 independent lethal siRNA results had an fected with 26 nmol/L siRNA using 0.6 mL per well RNAiMax estimated false discovery of approximately 30% if both siRNAs (Invitrogen); A549 (8 103 per well) were transfected with 40 yielded B-scores <