Identification of Molecular Vulnerabilities in Human Multiple Myeloma Cells by RNA Interference Lethality Screening of the Druggable Genome

Although significant progress has been made in the treatment of patients with multiple myeloma over the last decade, current therapies are not curative. Many of the drugs currently in use for the treatment of multiple myeloma are associated with toxicities that include marrow suppression, infection, neuropathy, and risk of secondary malignancy. Therefore, new therapeutic strategies are required that more effectively or selectively target malignant plasma cells.

End-stage multiple myeloma disease often emerges from a clone that is minimally responsive to conventional therapy and/or which has extramedullary growth characteristics. To better define potential drug targets in late-stage myeloma disease, we report here a portrait of critically vulnerable genes in extramedullary KMS11 myeloma tumor cells, generated functionally from a high-throughput siRNA Achilles heel screen. Following confirmatory screening, screening-identified survival genes are examined for their expression in primary myeloma and for functional vulnerability in other myeloma and non-myeloma cells to generate a snapshot of potential “druggable” molecular vulnerabilities of end-stage myeloma cells. Notably, RNA interference (RNAi) screening has previously been used to identify IRF4 addiction in multiple myeloma (1), although no additional molecular vulnerabilities were verified in that study. Less than 10% of all genes have previously been examined in any depth for their vulnerability in myeloma cells, and with this study, core molecular vulnerabilities in multiple myeloma cells are identified and ranked from a genome-scale perspective.

Human myeloma cell lines and A549 cells and 293 cells were maintained in RPMI-1640 or Dulbecco's Modified Eagle's Medium, supplemented with 10% fetal calf serum and antibiotics. Prior to use, cell line identity was verified by PCR assay for copy number variation fingerprints. The Human Druggable Genome siRNA Set V2 and all 640 siRNA oligos for confirmation studies were purchased from Qiagen. Lipofectamine 2000 and RNAiMAX were purchased from Invitrogen. CellTiter-Glo assay kit was from Promega.

Transfection conditions for human myeloma or epithelial cell lines were individually optimized using commercially available cationic lipids, as described (2). Effective transfection efficiencies were determined by comparing viability after transfecting (i) a universally lethal positive control siRNA against ubiquitin B (UBB) or (ii) negative control siRNA including intentionally nontargeted siRNA and siRNA against GFP. Viability was determined at 96 hours by CellTiter-Glo luminescence or by MTT assay. The best reagent and transfection conditions were those that produced the least reduction in cell viability with negative controls and greatest reduction with lethal UBB siRNA. Optimized high-efficiency reverse transfection conditions were separately derived for KMS11, 8226, JJN3, A549, and 293 cells using 96- and 384-well plates.

Approximately 17,000 siRNAs targeting approximately one third of the human genome (6,722 genes, selected for potential “druggability”) with 2 oligos per gene and including staggered replicate-positive and -negative control siRNA were preprinted on 384-well plates, in a single siRNA per well format. A primary large-scale screening experiment was carried out in duplicate on KMS11 cells. KMS11 was selected for high-throughput studies as it showed the greatest siRNA transfection efficiency during optimization studies (from a panel of 16 human myeloma cell lines) and because this tumor line is characterized by “high-risk” t(4;14) and t(14;16) chromosomal translocations. Plates preloaded with siRNAs were thawed at room temperature and 20 μL of diluted Lipofectamine 2000 solution was added to each well. After 30 minutes, 1,500 cells in 20 μL of medium were added to each well. Final reagent concentrations were 16 nmol/L siRNA and 0.16 μL per well for Lipofectamine 2000. Plates were incubated at 37°C. Cell viability was determined at 96 hours by CellTiter-Glo luminescence assay read on an Analyst GT plate reader (Molecular Devices). Notably, in contrast to synthetic lethal targets that we have previously identified in myeloma (3), whose vulnerability was evident only in the setting of concurrent proteasome inhibitor therapy, molecular targets were identified here on the basis of direct vulnerability in multiple myeloma cells.

For siRNA hit selection, B-score normalization was applied to the primary screen raw luminescence values as described (4, 5) to eliminate plate-to-plate variability and well position effects. B-scores were normalized to the SD of the data. Reproducibility of siRNA results was assessed by linear regression comparison of B-scores from duplicate full-scale screening assays. The rates of false-positive off-target RNAi effects were determined by analysis of the degree of internal concordancy of results from multiple tests per gene, for multiple genes, using multiple result concordancy analysis (MRCA; Tiedemann, manuscript in preparation). From MRCA analysis, the false discovery rate (FDR) of candidate survival genes associated with one or more lethal siRNAs were determined as a function of the intensity of siRNA test results. A second method to determine the FDR of candidate survival genes was also used, using as its basis the enrichment of expressed genes among candidate survival genes. Genes were designated as expressed in screening cells using Affymetrix gene expression profiling (GEP) data, generated as described (6) and available within the Mayo Clinic Cell Line data set on the Myeloma Genomics Portal (7), when one or more Affymetrix probe set representing the gene was flagged with a “present” or “marginal” call using Affymetrix Microarray Suite Present/Absent/Marginal detection. The FDR of candidate survival genes was then calculated as follows:

where G represents all screened genes, P represents expressed (or present) genes, and A represents absent genes. Short-listed candidate survival genes with known FDRs were advanced to secondary confirmation screening.

To exclude the possibility of siRNA with biologic off-target effects contributing to our observations, we conducted secondary confirmation studies in duplicate using 4 independent siRNAs per gene. Assays conditions were similar to those described for primary high-throughput screening but were scaled to 96-well plate format. As rescreened genes were enriched for gene hits, Z-score rather than B-score normalization was used for confirmatory studies and was calculated as the difference in viability outcome between test siRNA and the mean of nontargeted negative control siRNA, normalized to the distribution (SD) of negative control siRNA replicates, as described (5).

Reproducibility of confirmatory siRNA results was again assessed by linear regression comparison of Z-scores from duplicate studies. KMS11 myeloma survival genes were ranked by MRCA FDR and cropped to an average FDR of 5%, eliminating false survival genes; and then reranked for maximum effect on viability (at a standardized per gene FDR = 0.3).

To assess the interactions between identified myeloma survival genes, pathway/network analysis was carried out using Ingenuity Pathway Analysis (IPA). Survival genes were overlaid onto a global molecular network developed from information contained in the Ingenuity Pathways Knowledge Base (IPKB). All interactions are supported by at least one reference from the literature or from canonical information stored in the IPKB. IPA P values were used to rank networks, reflecting the likelihood that the survival genes were concentrated within networks by biologic influences and not by random chance alone. The several high-confidence myeloma survival gene networks depicted were simplified to reflect the direct interactions of identified survival genes.

Plasma cells from bone marrow samples from patients with multiple myeloma, monoclonal gamopathy of uncertain significance (MGUS), or from normal donors (normal plasma cells, NPC) were collected and processed for gene expression as described (8); CEL files are deposited on the Gene Expression Omnibus (GEO accession no. GSE 6477 and GSE 5900). Expression data from primary human somatic tissues were obtained from the Genomics Institute of the Novartis Research Foundation, SymAtlas (ref. 9; GEO data set accession GSE1133). Gene expression data were generated in all cases using Hg_U133A_2 microarrays (Affymetrix). Data were analyzed using GeneSpring (Agilent Technologies). To compare the expression of genes in primary multiple myeloma samples versus other grouped human somatic tissues, one-way Welch ANOVA analysis was carried out (parametric ranking, with no assumption of equal variance) using Benjamini–Hochberg multiple testing correction (FDR = 0.05). Histogram data are shown normalized per chip to the median signal of expressed genes within each sample (genes with present detection call and/or raw signal >200).

To further assess the specificity of myeloma survival genes, 8226, JJN3, A549, and 293 cells were tested with survival gene siRNA. Experimental transfection conditions, in 96-well plates, were as follows: 8226 cells (0.5 × 10⁴ cells per well) were transfected with 50 nmol/L siRNA using 0.5 μL per well RNAiMax (Invitrogen); JJN3 (10⁴ cells per well) were transfected with 26 nmol/L siRNA using 0.6 μL per well RNAiMax (Invitrogen); A549 (8 × 10³ per well) were transfected with 40 nmol/L siRNA using 0.8 μL per well Lipofectamine 2000; and 293 cells (8.5 × 10³ per well) were transfected with 30 nmol/L siRNA using 0.25 μL per well Lipofectamine 2000. The siRNAs were prepared in OptiMEM or serum-free antibiotic-free RPMI and aliquoted to wells; transfection reagent was prepared in identical media, allowed to equilibrate at room temperature for 4 minutes, and then added to preplated siRNA solution; siRNA and transfection reagent were allowed to complex for 15 minutes at room temperature; cells were then added and the samples were incubated at 37°C. Experiments were carried out in duplicate and viability was determined at 96 hours. Z-score normalization was applied.

To identify important survival genes for multiple myeloma tumor cells, we conducted a high-throughput RNAi lethality screen in KMS11 human multiple myeloma cells. In a primary screen, we systematically transfected cells in parallel with 13,444 unique siRNAs targeting 6,722 potentially druggable targets, in duplicate, using a single siRNA per well format. The siRNAs were transfected at low concentration to minimize off-target effects, using conditions that resulted in transfection of more than 90% cells and less than 5% background cytotoxicity. After 96 hours, viability was measured by ATP-dependent luminescence (Fig. 1A and B; Supplementary Table S1). Assay reliability was confirmed with approximately 4,000 replicate controls which included as a positive control, siRNA targeting poly-ubiquitin (UBB) and, as a negative control, nontargeted siRNA. Genes silenced by one or more siRNA that induced loss of KMS11 viability were implicated as candidate survival genes. Lethal siRNA results were highly reproducible, with significant correlation between duplicate full-scale screening assays (r² = 0.82; Fig. 1C). Approximately 7.1% of siRNA induced a loss of viability exceeding 3 SDs from the median result, compared with a predicted rate because of chance of approximately 0.5% siRNA (Fig. 1D).

To verify that our results contained true-positive gene hits, we evaluated across the entire screen the extent to which concordant results were obtained from siRNA targeting the same gene, taking account of all gene-matched siRNA pairs. Notably, the “lethal hit” rate among second siRNA whose paired first siRNA was lethal, significantly and consistently exceeded the overall “lethal hit rate” of all siRNA, particularly among siRNAs that were substantially lethal (Fig. 1D). Therefore, from this analysis, we concluded that the primary screen data were enriched with true gene hits associated with concordant siRNA results exceeding the rate expected because of chance or off-target effects. We next estimated the FDR for candidate survival genes (Fig. 1E and F), from the relative rates of concordant and discordant lethal siRNA results, as a function of the magnitude of siRNA-induced viability loss. Candidate survival genes with 2 independent lethal siRNA results had an estimated false discovery of approximately 30% if both siRNAs yielded B-scores <−4; but only 15% if both siRNAs had B-scores <−6.

To corroborate these FDR estimates and to minimize false discovery, we further estimated the FDRs of candidate survival genes using a second independent method on the basis of the enrichment for expressed genes among siRNA-identified survival genes, relative to all screened genes (Fig. 1E and F); these second false discovery estimates closely matched the estimates determined from our analysis of siRNA result concordancy. Therefore, using these analyses, candidate myeloma survival genes from primary screening could be identified with tight control of FDR.

Candidate myeloma survival genes from primary screening were next advanced to a secondary confirmation siRNA study in which each gene was retested with 4 unique siRNAs to eliminate false-positive gene hits. To capture as many genuine myeloma survival genes as possible, we cast a fairly wide net and retested 160 candidate survival genes, including 134 genes associated on primary screening with 2 of 2 inhibitory siRNA with B-scores <−2 (with estimated preconfirmation false discovery calculated to be 55%) and 26 genes with a single inhibitory siRNA with B-score <−8 (with estimated preconfirmation false discovery ∼85%; Fig. 2). From the estimates of FDR from our primary screen results, we expected to verify approximately 64 true survival genes on secondary testing. Significantly, of the 160 genes advanced to confirmation testing, 71 were repeatedly lethal on silencing with 2 to 4 of 4 (2–4/4) independent siRNAs, closely matching the predicted numbers of true positives. To further minimize reporting of false-positive results and taking account of the magnitude of each gene's effect on viability, we subsequently focused on the 57 top-ranked genes with an overall FDR of only 5% (95% true-positive lethal hits). The 4 independent siRNA results for each of these 57 genes are shown in Fig. 2A. Notably, confirmation testing of these and other candidate genes with 640 siRNA and controls was highly reproducible (r² = 0.94; Fig. 2B) and was strikingly enriched for concordant lethal siRNA results clustered around individual genes, vastly exceeding the expected rate because of chance (Fig. 2C) and the rates observed in primary screening (Fig. 1D), providing high confidence in our gene selection process. The FDR determination for each of the top 36 confirmed survival genes, as a function of the number of lethal siRNA and the extent of their effect on viability, is shown in Fig. 2D. The top survival genes identified in KMS11 myeloma cells are listed in Table 1, ranked according to their effect on tumor cell viability.

To assess the specificity and portability of the most potent KMS11 vulnerabilities, the effects of targeting these genes in other myeloma and non-myeloma cells was examined (Fig. 3). Most nonredundant survival genes identified in KMS11 were also vulnerable in 8226 and JJN3 myeloma cells (Fig. 3A and B). However, many of the genes were also vulnerable in 293 and A549 cells (Fig. 3C and D), consistent with a role for many of the genes in essential “house-keeping” functions.

Several genes including proteasome subunits, MCL1, RRM1, USP8, TNK2, WBSCR22, LEPROT, SF3A1, SNRPA1, SNW1, CKAP5, XPO1, IK, KIF11, and TUBGCP6 seemed comparatively more vulnerable, on average, in myeloma cells than in non-myeloma cells, each with 4 of 4 siRNAs per gene (Fig. 3E), suggesting the potential for a therapeutic window upon inhibition of one of more of these genes. Perhaps the most striking differential vulnerabilities between myeloma and non-myeloma cells on functional testing by gene silencing were observed with the proteasome, MCL1, RRM1, USP8, and CKAP5. From the cell types tested, the relative cytotoxicity of proteasome silencing was only 2- to 3-fold greater in myeloma cells than in other cells; greater differential cytotoxicity was observed with MCL1 and RRM1. CKAP5 siRNA proved as lethal as PSM siRNA in myeloma cells, with similar differential cytotoxicity (Figs. 2A and 3).

To further assess survival genes for myeloma-selective vulnerability, we next assessed their expression across an atlas of human primary tissues and within human primary plasma cells from normal individuals (NPC) or from patients with monoclonal gammopathy or multiple myeloma. Several of the multiple myeloma survival genes, including MCL1, CDK11, TNK2, and WBSCR22, showed differential expression in plasma cells (Fig. 4). MCL1, CDK, and WBSCR22 are all upregulated (>10-fold) in expression in NPC and primary multiple myeloma, compared with most human primary somatic tissues, whereas TNK2 expression is largely restricted to nervous tissues, skeletal muscle, and testis but is semi-ubiquitous in NPC and primary multiple myeloma.

Using high-throughput siRNA screening of myeloma cells, we have identified a panel of 57 essential multiple myeloma survival genes. Strikingly, from a genome-scale perspective, and from functional testing in myeloma and non-myeloma cells, antiapoptosis Bcl2-family member, MCL1, and 26S proteasome subunits are among the most important and selective myeloma survival genes identified. The vulnerability of these molecular entities in primary myeloma tumors has previously been established (10–16) and their unprejudiced appearance within our top-ranked results provides biologic validation of our high-throughput screening approach for the identification of molecular vulnerabilities in myeloma.

Conspicuously, a number of the most lethal myeloma cell vulnerabilities identified cluster within RNA transcription and processing pathways (Fig. 5A). U2 spliceosome components SF3A1 and SNRPA1, and pre-mRNA processing factor 8 homolog (PRPF8), are among the deadliest molecular vulnerabilities in our study. SNW1, a transcription coactivator that binds vitamin D and retinoid receptors to enhance ligand-mediated gene expression, is also highly cytotoxic when silenced. Similar, although lesser, viability effects were observed with silencing of transcription cofactors MED15 and MED14. Cofactor MED14, like SNW1, interacts with the vitamin D receptor (17). MED15 is located within a 45-gene region deleted in DiGeorge syndrome, whose manifestations include hypogammaglobulinemia. Both WBSCR22, a putative H3K9 histone methyl transferase (18), and RSF1, which functions in remodeling and spacing, were also found to be nonredundant in contributing to viability of KMS11 and 8226 multiple myeloma tumor cells. From gene expression analyses, WBSCR22 seems upregulated in both primary plasma cells and primary multiple myeloma tumor cells and on functional testing, loss of WBSCR22 seems >2-fold more detrimental to multiple myeloma cells than to A549 or 293 cells (Fig. 3E), suggesting an expanded role in plasma cell biology.

Significant clustering of KMS11 multiple myeloma survival genes within protein homeostasis pathways was also identified (Fig. 5B). As expected, silencing of global protein degradation machinery including subunits of the 20S proteasome and of the 19S regulatory particle, and of UBB (encoding poly-ubiquitin), was markedly cytotoxic (Fig. 2A). Similarly, silencing of protein synthesis factors including translation initiation factors EIF4A3 and EIF3C and ribosomal proteins RPL27 and 38 also resulted in short-term cytotoxicity. However, while silencing of protein synthesis factors was equally inhibitory to 293 and A549 non-myeloma cells, relative to myeloma cells, genetic silencing of proteasome subunits induced approximately 2- to 3-fold greater toxicity in myeloma cells than non-myeloma cells. This comparative sensitivity of multiple myeloma cells to genetic silencing of protein degradation is consistent with the clinical observation that small molecule proteasome inhibitors show their greatest efficacy in the treatment of multiple myeloma.

Several selective regulators of protein degradation were identified in this study as myeloma survival genes. These included targeted ubiquitination factors TRIM21, TRIM68, USP8, USP36, CUL9, and UBQLNL. TRIM21 encodes an E3 ubiquitin ligase (also known as Sjorgrens Syndrome Antigen 1, SSA/Ro) that ubiquitinates and promote the proteasomal degradation of CDK inhibitor p27 (CDKN1B; ref. 19) and unfolded IgG (20), abrogating the cell-cycle checkpoint and alleviating estrogen receptor stress. TRIM21 was lethal in 2 of 3 myeloma lines tested, though also seemed important for 293 viability. TRIM68 encodes the related E3 ligase (SSA/Ro-related protein) that ubiquitinates histone lysine acetylase KAT5 (21) and targets it for degradation; notably, as specific histone acetylation can cause tumor suppression, expression of TRIM68 in multiple myeloma cells may function to minimize suppressive histone acetylation events. Like TRIM21 and TRIM68, the KMS11 multiple myeloma survival gene Cullin, CUL9, targets a tumor suppressor: CUL9 has been reported to interact directly with and ubiquitinates p53, sequestering it in the cytoplasm and blocking its proapoptotic function (22, 23).

The specific deubiquitinases, USP8 and USP36, are also identified here as myeloma survival genes. Silencing of either is at least mildly or moderately inhibitory to all 3 multiple myeloma tumor lines tested (Figs. 2A and 3A). USP8 has been reported to deubiquitinate progrowth factors CDC25 (24) and GRB2 (25), preventing their proteasomal degradation. Notably, GRB2 is an essential mediator of IL6-JAK2 signaling in myeloma that contributes to Ras activation (26–28), whereas CDC25 family phosphatases are required for cell-cycle G₁–S and G₂–M phase transitions. Notably, in normal cells, CDC25 is ubiquitinated and degraded in response to chromosomal abnormalities to induce cell-cycle arrest (29); however, this tumor suppressor response to chromosomal abnormalities may conceivably be abrogated in tumor cells via overriding USP8 deubiquitinase activity.

Although silencing of specific E3 ligases, deubiquitinases, or cullins was found to be inhibitory to multiple myeloma cells and may theoretically provide a more selective means of targeting tumor cells in vivo than global proteasome inhibition, silencing of these individual branches of the ubiquitination machinery seemed substantially less cytotoxic in multiple myeloma tumor cells in short-term assays than silencing of universal protein degradation machinery such as the 26S proteasome or poly-ubiquitin itself (Figs. 2A and 3), raising concerns for the efficacy of an overly selective targeted approach.

Among cell division genes PLK1, WEE1, XPO1, AURKB, NUF2, CDK11A, CDK11B, KIF11, CKAP5, and TUBGCP6 seem most vulnerable within KMS11 multiple myeloma cells. The majority are also vulnerable within other myeloma and non-myeloma cells as well. PLK1 (via activation of phosphatase CDC25) and WEE1/XPO1 both regulate the phospho-activation status of CDC2/CCNB and entry into mitosis. In turn, KIF11 is activated by CDC2/CCNB (Fig. 5C). Although many of these cell-cycle genes are likely to be broadly essential to many tissues, some selective vulnerability of myeloma cells is noted on functional testing to inhibition of microtubule, centrosome, or spindle elements TUBGCP6, CKAP5, and KIF11 (Fig. 3E).

Our results highlight aspects of nucleotide metabolism that are indispensable for short-term myeloma cell viability, most notably ribonucleotide reductase M1, RRM1, and thiopurine S-methyltransferase TMPT. Although the M2 subunit of ribonucleotide reductase is the target of hydroxyurea and is vulnerable in other hematologic and solid malignancies, only the M1 subunit is identified as vulnerable in multiple myeloma cells here. Comparative functional vulnerability testing (Fig. 3E) suggests that RRM1 is substantially more important (∼5-fold) to the survival of multiple myeloma cells than for non-myeloma 293 or A549 cells.

One of the top-ranked survival genes required for KMS11, 8226, and JJN3 multiple myeloma cells is IK cytokine, downregulator of HLA II (Table 1). Surprisingly, its function is largely unknown. IK has been reported to localize to within the nucleus, block IFN effects (30, 31), and associate directly with histone acetylase KAT5 (ref. 32; a target of the survival E3 ligase TRIM68), suggesting a role in gene expression.

Overall, our study provides a genome-scale view of indispensable survival genes in multiple myeloma cells, derived from high-throughput siRNA screening of 6,722 genes. Determination of the mechanisms by which myeloma-selective survival genes influence plasma cell fate will enhance the understanding of oncogenic signaling in this disease. From the perspective of this large-scale study, MCL1 ranks as one of the most lethal targets identified in KMS11 and 8226 multiple myeloma cells, revealing the extreme extent to which some myeloma tumor cells rely upon this single molecular counterweight to apoptosis signaling. From our comparative genetic screening for functional vulnerability (Fig. 3) and from gene expression analyses of short-listed multiple myeloma survival genes (Fig. 4), MCL1 ranks first as a potential therapeutic target, being both essential in multiple myeloma cells and redundant in other cell types tested here. Others have also reported that multiple myeloma tumor cells are universally dependent on MCL1, although vary in sensitivity to a Bcl2 inhibitor, ABT-737 (33); in that study, acquired ABT-737 resistance in Bcl2-expressing multiple myeloma cells involved a shift in proapoptotic Bim sequestration from Bcl2 to MCL1, further supporting MCL1 as an effective target in multiple myeloma.

Like MCL1, ribosome reductase subunit M1, the proteasome, USP8, CKAP5, and WBSCR22 also seem attractive as potential therapeutic targets, by virtue of selective vulnerability in multiple myeloma cells. Although proteasome inhibitors are already in the clinic, the exploration of small-molecule inhibitors of MCL1 and of other vulnerable targets for the treatment of patients with refractory aggressive multiple myeloma is urgently desired.

No potential conflicts of interest were disclosed.

The views expressed do not necessarily reflect those of the OMOHLTC.

This work was financially supported by Mayo Clinic (A.K. Stewart and R.E. Tiedemann), Princess Margaret Hospital Foundation, NIH grants RO1CA129009-03, RO1CA133115-02, 5P5OCA100707-08 “SPORE in Myeloma,” Multiple Myeloma Research Foundation (MMRF) fellowship (R.E. Tiedemann), MMRF senior research grant (A.K. Stewart), American Society of Hematology Scholar award (R.E. Tiedemann). This research was funded in part by the Ontario Ministry of Health and Long-Term Care.

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