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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Cell. Author manuscript; available in PMC 2013 August 17.
Published in final edited form as:
PMCID: PMC3429351

Cancer vulnerabilities unveiled by genomic loss


Due to genome instability, most cancers exhibit loss of regions containing tumor suppressor genes and collateral loss of other genes. To identify cancer-specific vulnerabilities that are the result of copy-number losses, we performed integrated analyses of genome-wide copy-number and RNAi profiles and identified 56 genes for which gene suppression specifically inhibited the proliferation of cells harboring partial copy-number loss of that gene. These CYCLOPS (Copy-number alterations Yielding Cancer Liabilities Owing to Partial losS) genes are enriched for spliceosome, proteasome and ribosome components. One CYCLOPS gene, PSMC2, encodes an essential member of the 19S proteasome. Normal cells express excess PSMC2, which resides in a complex with PSMC1, PSMD2, and PSMD5 and acts as a reservoir protecting cells from PSMC2 suppression. Cells harboring partial PSMC2 copy-number loss lack this complex and die after PSMC2 suppression. These observations define a distinct class of cancer-specific liabilities resulting from genome instability.


Cancers arise as the result of the accumulation of somatic genetic alterations within a cell, including chromosome translocations, single base substitutions, and copy-number alterations (Stratton et al., 2009). Although a subset of these alterations (“driver events”) promote malignant transformation by activating oncogenes or inactivating tumor suppressor genes, most somatic genetic alterations are the consequence of increased genomic instability that occurs in cancer but does not contribute to tumor development (“passenger events”).

The demonstration that cancers are often dependent on specific driver oncogenes has stimulated efforts to find and exploit these targets therapeutically. For example, cancers that harbor translocations that form fusion transcripts such as BCR-ABL or EML4-ALK or mutations such as EGFR or BRAF depend on the activity of these gene products for tumor maintenance (Brose et al., 2002; Daley et al., 1990; Soda et al., 2007). Therefore, the presence of such an alteration often predicts response to drugs that inhibit the function of these proteins (Sawyers, 2005).

An alternative strategy to target cancers is to target genes that are not oncogenes, but which cancers require to accommodate cancer-specific stress (Ashworth et al., 2011; Kaelin, 2005). In comparison to normal cells, cancer cells rely inordinately on pathways that abrogate a variety of cancer related stressors that include DNA damage replication stress, proteotoxic stress, mitotic stress, metabolic stress, and oxidative stress (Solimini et al., 2007). Even though proteins within these pathways may be essential in all cells, genetic alterations may induce a state where reliance on these pathways creates a therapeutic window as a result of a cancer-specific stresses.

The proteasome, which recognizes and degrades proteins modified with a poly-ubiquitin chain (Finley, 2009), is one such target. Although proteasome function is essential to cells for basal protein turnover and degradation of unfolded proteins, multiple myeloma cells produce excessive amounts of immunoglobulin and appear to be especially dependent on effective protein turnover by the 26S proteasome. Indeed, the 20S proteasome inhibitor bortezomib is used as first-line treatment of multiple myeloma (Richardson et al., 2005).

Genomic instability may be another source of cancer specific stress. The majority of human cancers harbor copy-number alterations involving the loss or gain of broad chromosomal regions. For example, copy-number losses that target tumor suppressor genes often involve multiple neighboring genes that may not contribute to cancer development. The loss of such neighboring genes has been postulated to render cancer cells highly vulnerable to further suppression or inhibition of those genes (Frei, 1993), but until recently the tools to systematically test this hypothesis were not available. Here, we integrated both genome scale copy-number and loss of function data on a panel of 86 cancer cell lines to determine if partial copy-number loss of specific genes renders cells highly dependent on the remaining copy. We identified a class of genes, enriched for cell essential genes, most predominantly proteasome, spliceosome, and ribosome components, which render cells that harbor copy-number loss highly dependent on the expression of the remaining copy.


Integration of genome scale copy-number and gene dependency analyses identify CYCLOPS genes

By analyzing copy-number profiles from 3,131 cancers across a wide diversity of cancer types (Beroukhim et al., 2010), we found that most cancers exhibit copy-number loss affecting at least 11% of the genome and that many cancers exhibit much more extensive loss of genetic material (Fig 1A). Much of this widespread genomic disruption is due to copy-number alterations involving whole chromosomes or chromosome arms, presumably due to mechanisms that favor the generation of such large events (Fig 1B). As a consequence, most genes undergo copy-number loss in a substantial fraction of cancers (average 16.2, range 3.7–40.2%; Fig S1A). A subset of the genes affected by recurrent copy-number losses contribute to cancer development as tumor suppressor genes; however, many genes are recurrently lost due to passenger events or because of their proximity to a frequently deleted tumor suppressor gene (Fig 1C, S1B). We hypothesized that for a subset of non-driver genes, hemizygous loss may be tolerated and frequent but complete loss would lead to cell death. In some of these cases, hemizygous loss might lead to sensitivity to further suppression of the gene relative to cells that harbor two copies of these genes.

Figure 1
Identification of CYCLOPS genes

To identify genes whose loss correlated with a greater sensitivity to further gene suppression, we integrated gene dependencies and copy-number data from 86 cancer cell lines (Table S1). We analyzed gene essentiality data from Project Achilles, a dataset that scored the impact of individually expressing 54,020 shRNAs targeting 11,194 genes on the proliferation of 102 cell lines (Cheung et al., 2011). For 7,250 of these genes, multiple shRNAs had comparable effects across cell lines, suggesting their effects were due to suppression of the intended target. We used these shRNAs to construct composite “gene dependency scores” (A. Tsherniak, W.C. Hahn, J. P. Mesirov, unpublished data). We also obtained DNA copy-numbers for these same cell lines from Affymetrix SNP 6.0 array data (Barretina et al., 2012). For each gene, we first classified each cell line by whether or not it exhibited copy-numbe loss in that gene and then calculated the mean gene dependency score among cell lines in each class. We then determined the difference in mean scores between the copy-loss and copy-neutral classes and rated the significance of this difference by permuting class labels (Fig 1D). To minimize the confounding effect of lineage, all permutations maintained the initial lineage distribution within each class. We also restricted these analyses to the 5,312 genes for which each class contained at least seven cell lines. We identified 56 candidate genes with False Discovery Rate (FDR) (Benjamini and Hochberg, 1995) p-values less than 0.25 (Table 1, Table S2) and named them “CYCLOPS” genes (Copy-number alterations Yielding Cancer Liabilities Owing to Partial losS).

Table 1
Top-ranked CYCLOPS candidates and frequency of partial genomic loss in 3,131 tumors

We validated the CYCLOPS vulnerabilities using an independently generated RNAi dataset (shRNA Activity Rank Profile, or shARP) (Marcotte et al., 2012) representing the consequences of expressing 78,432 shRNAs targeting 16,056 genes on the proliferation of 72 breast, ovarian or pancreatic cancer cell lines. We applied the same analysis pipeline, constrained to the “Validation Set” of 47 cell lines for which we had copy-number data and the 6,574 genes for which at least seven cell lines were in each class (copy-loss and copy-neutral) (Table S1,S2). These genes included 3,282 of the genes that underwent full analysis in the Achilles dataset and 40 of the CYCLOPS candidates identified in that analysis. Although the lineage distribution was markedly different between the Validation and Achilles datasets (breast and pancreatic cancers made up 90% of the cell lines in the Validation set but only 15% in Project Achilles), the 40 CYCLOPS candidates identified in the Achilles analysis were also among the most significant genes in the shARP analysis (KS statistic, p=2×10−9).

Features of CYCLOPS genes

In copy-number analyses collected from 3,131 tumor samples and cancer cell lines (Beroukhim et al., 2010), each CYCLOPS candidate exhibited hemizygous loss in an average of 18.5% of samples (range 8%–33%), which was as common as for the other 5,256 genes in the analysis (average 17.7%, range 4%–34%; two tail p=0.17). In contrast, CYCLOPS genes exhibited much lower rates of homozygous deletion (p=0.02) and DNA methylation (p=0.026) (Fig 1E). This observation suggested that CYCLOPS genes are enriched for genes required for cell proliferation or survival.

We also found that CYCLOPS candidates are highly enriched among 1,336 human genes that are homologous to the set of genes found to be essential in S. cerevisiae (Zhang and Lin, 2009) (p<0.0001) and exhibit comparable rates of genetic and epigenetic alterations (Table S3). A pathway enrichment analysis showed the spliceosome, proteasome, and ribosome were the most highly enriched pathways among CYCLOPS candidates (KS statistic FDR = 1.4×10−8, 2.7×10−5, and 1.8×10−4 respectively) and in our analysis of the Validation set (FDR = 3.1×10−15; 1.5×10−12; and 2.3×10−17 respectively). Together, these observations indicate that CYCLOPS genes are a unique subset of cell essential genes for which partial but not complete suppression is compatible with cancer cell viability.

These observations lead us to hypothesize that copy-number loss might unveil vulnerabilities in CYCLOPS genes through decreased gene expression. We therefore evaluated the relation between copy-number loss and expression using integrated SNP and expression data for 16,767 and 11,118 genes respectively in two panels of samples: the Cancer Cell Line Encyclopedia (CCLE of 806 cell lines across 24 cancer types) (Barretina et al., 2012), and 429 ovarian cancers profiled by The Cancer Genome Atlas Project (Bell et al., 2011). We found that the average strength of correlation between copy-number loss and mRNA expression was significantly higher for CYCLOPS candidates than for the other genes in our analysis (CCLE r=0.39 vs. 0.26, p<0.0001; TCGA, r=0.44 vs. 0.34, p=0.0017, Fig S1C).

PSMC2 is a CYCLOPS gene

PSMC2 (Rpt1) was the highest-ranked CYCLOPS candidate in our original analysis and was also significant in the Validation dataset. PSMC2 is part of the 19S regulatory complex of the 26S proteasome, responsible for catalyzing the unfolding and translocation of substrates into the 20S proteasome (Smith et al., 2011). Either one or two 19S regulatory complexes combine with a single 20S catalytic complex to form, respectively, a singly- or doubly-capped (26S1 or 26S2) complete 26S proteasome (Finley, 2009). PSMC2 expression is essential for 19S and 26S proteasome assembly (Kaneko et al., 2009).

To minimize the possibility that other genetic alteration(s) confounded our analyses, we determined whether expression or copy-number levels of every other gene for which we had data showed significant correlations with PSMC2 sensitivity. Low PSMC2 expression (FDR<0.017) and PSMC2 copy-number loss (FDR<0.008) were the features most significantly correlated with PSMC2 sensitivity (Table S4). Conversely, among the 7,250 genes in our Achilles analysis, sensitivity to PSMC2 was the only feature that correlated with PSMC2 copy-number loss (FDR<0.25, Table S5). In particular, among all 47 other proteasome components surveyed, neither expression levels nor copy-number status significantly correlated with PSMC2 sensitivity. We also found no evidence that suppression of any of the other proteasome components inhibited the proliferation of cell lines with PSMC2 copy-number loss. The association between PSMC2 copy-number loss and PSMC2 sensitivity also remained significant when cells with PSMC2 copy-number gains were excluded from the analysis (p=0.0006).

To estimate the differential sensitivity of cell lines harboring normal copies or copy-number loss of PSMC2 to gene suppression, we compared the effects of PSMC2 suppression to that observed when we suppressed the oncogenes KRAS, PIK3CA, and BRAF. In consonance with prior studies (Weinstein and Joe, 2006), suppression of these oncogenes inhibited proliferation of cells harboring mutated and constitutively active oncogenes compared to cells expressing wild-type proto-oncogenes (p<2×10−5 in each case) (Fig 2A). However, the difference in PSMC2 dependency scores between cell lines with and without PSMC2 copy-number loss (PSMC2Loss and PSMC2Neutral, respectively) was greater than for any of these oncogenes (Fig 2A).

Figure 2
PSMC2Loss cells are sensitive to PSMC2 suppression

We confirmed the vulnerability of PSMC2Loss lines to PSMC2 suppression in a direct competition assay by comparing the proliferation rate of uninfected cells to cells that co-express GFP and either shLacZ or a PSMC2-specific shRNA (Fig S2A) in six ovarian cell lines over 21 days. The expression of shLacZ or PSMC2 shRNAs did not induce significant changes in the proliferation of PSMC2Neutral cells, including two ovarian cancers and one non-transformed Immortalized Ovarian Surface Epithelial (IOSE) cell line (Liu et al., 2004). After 21 days of culture, PSMC2 levels remained suppressed in PSMC2Neutral cells that constitutively express PSMC2 shRNA, consistent with the lack of an observed proliferation deficit (Fig S2B). In contrast, expression of PSMC2 shRNAs in PSMC2Loss cells was not compatible with long-term culture and reduced the proliferation rate by at least 50% in all three PSMC2Loss ovarian cancer cell lines within 7 days (Fig 2B).

To confirm that these observations were due to the suppression of PSMC2, we expressed an N-terminal V5-epitope tagged form of PSMC2 (hereafter referred to as V5-PSMC2) in OVCAR8, a PSMC2Loss cell line. V5-PSMC2 expression was unaffected by an shRNA that targets the 3’ UTR of endogenous PSMC2, and rescued the proliferation of OVCAR8 cells that express this shRNA (Fig 2C). These observations confirmed that partial loss of PSMC2 renders cancer cell lines highly dependent on the remaining PSMC2.

PSMC2 levels and survival in PSMC2Loss cell lines

The increased vulnerability of PSMC2Loss lines correlated with both PSMC2 copy-number loss and low mRNA expression (Table S4). Expression and copy-number of PSMC2 also correlate with each other in both the CCLE (r = 0.64) and TCGA Ovarian (r = 0.49) sample sets (Fig S3A), indicating that cancer cells that have PSMC2 copy-number loss tolerate reduced PSMC2 expression.

To explore the effects of PSMC2 loss on PSMC2 protein levels, we evaluated PSMC2 levels in IOSE cells and ten ovarian cancer cell lines, including five PSMC2Neutral and five PSMC2Loss lines. To minimize potential confounding of other genetic events affecting the 19S complex, we selected PSMC2Neutral lines that had no copy-number gains of PSMC2 and PSMC2Loss lines that had copy-number loss of no more than one other 19S regulatory complex subunit (Table S6). All five PSMC2Loss cell lines expressed lower levels of PSMC2 than any of the other cell lines (Fig 3A). In contrast, the levels of eight 19S subunits including PSMC1 (Rpt2), PSMC4 (Rpt3), PSMC6 (Rpt4), PSMC3 (Rpt5), PSMC5 (Rpt6), PSMD2 (Rpn1), PSMD1 (Rpn2), PSMD4 (Rpn10), or the 20S subunits PSMB5 (β5) and PSMA1-6 (α subunits) failed to correlate with PSMC2 copy-number (Fig S3B). Since PSMC2 is essential for cell proliferation, we concluded that PSMC2Neutral cells either require more PSMC2 or produce more than is necessary for survival. Therefore, we engineered an experimental system to manipulate the levels of PSMC2 expression in both cell types.

Figure 3
Threshold requirement for PSMC2

Specifically, we expressed a PSMC2-specific shRNA under the control of a doxycycline-regulated promoter in PSMC2Loss (Dox-shRNA-2 OVCAR8) and PSMC2Neutral (Dox-shRNA-2 A2780) cells. The addition of doxycycline led to PMSC2 suppression in both cell lines (Fig 3B). Under these conditions, A2780 cells continued to proliferate whereas OVCAR8 cells arrested in G2/M and died by apoptosis (Fig S3C). To verify that A2780 cells tolerate increased PSMC2 suppression, we varied the degree of suppression by modulating the doxycycline concentration. A 50% decrease in PSMC2 mRNA reduced the proliferation of OVCAR8 but not A2780 cells (Fig 3D), indicating that the PSMC2Neutral line, A2780, expresses more PSMC2 than is required for proliferation.

To determine the amount of PSMC2 required to maintain A2780 cell proliferation, we suppressed PSMC2 expression further by transfecting a pool of three PSMC2-specific siRNAs at varying concentrations. The proliferation of A2780 cells decreased only when PSMC2 expression was suppressed by more than 60% (Fig S3D, S3E). Using quantitative RT-PCR and immunoblotting, we estimated that untreated OVCAR8 cells express approximately 50% of the PSMC2 mRNA and protein found in A2780 cells (Fig S3F-G) and that both A2780 and OVCAR8 lose proliferative capacity at similar total levels of PSMC2 expression (Fig 3E), suggesting that they have a comparable threshold requirement for PSMC2.

To determine whether partial loss of PSMC2 affects the sensitivity of cells to suppression of other members of the 19S complex, we used an isogenic system in which Dox-shLacZ and Dox-shRNA-2 cells were cultured in doxycycline (30 ng/ml) so that shRNA-2 cells express levels of PSMC2 comparable to PSMC2Loss cells. Under these conditions, both Dox-shLacZ and Dox-shRNA-2 proliferated at comparable rates. We then suppressed the expression of either PSMC2 or PSMC5 by introducing siRNA targeting these genes at concentrations that induce a similar degree of suppression of their intended target (Fig S3H). As expected, further suppression of PSMC2 in Dox-shRNA-2 cells inhibited proliferation as compared to Dox-shLacZ cells (Fig 3F). In contrast, suppression of PSMC5 led to a comparable inhibition of cell proliferation in both Dox-shLacZ and Dox-shRNA-2 cells. Suppression of PSMC2 also did not affect the expression of other 19S components (Fig S3I). Together, these observations indicate that partial loss of PSMC2 sensitizes cells to further suppression of PSMC2 but not of other 19S proteasome components.

PSMC2Loss cells exhibit only slight alterations in proteasome content and function

The tolerance of cells for loss of PSMC2 copy-number and expression indicates that cells contain a reservoir of excess PSMC2 that is not required for proliferation. This reservoir may be maintained in an excess of fully assembled 26S proteasome or elsewhere in the cell. We analyzed proteasome assembly and content by performing Polyacrylamide Gel Electrophoresis (PAGE) on crude lysates under native (non-denaturing) conditions. Under these conditions, the 26S proteasome complex is stable and active and migrates in two distinct bands, distinguished by having either one or two 19S subunits incorporated in the formation of the 26S (Elsasser et al., 2005). Using lysates collected from IOSE, two PSMC2Neutral, and three PSMC2Loss cancer cell lines (all with comparable proliferation rates), we detected 26S1, 26S2, and 20S proteasome complexes by immunoblotting for the core 20S subunits, PSMA1-6 (Fig 4A).

Figure 4
PSMC2Loss cells lack a PSMC2 reservoir

We found that PSMC2Loss lines express only slightly less 26S proteasome (most evident in 26S2), which is not comparable to the decrease in PSMC2 in these cells (Fig 4A), and increased 20S proteasome. Similarly, comparable changes in PSMC2 expression in isogenic systems failed to substantially affect 26S proteasome content. Suppression of PSMC2 levels by 50% in the DoxshRNA-2 A2780 system led to an increase in the 20S complex but little to no change in 26S1 (Fig 4B) or 26S2 (Fig S4A) proteasome content relative to controls. Conversely, ectopic expression of PSMC2 in OVCAR8 cells led to a slight reduction in 20S levels and slight increases in 26S1 and 26S2 proteasome content (Fig 4B, S4A). The levels of other 19S proteasome units remained unchanged (Fig S4B).

Similarly, peptidase cleavage activity varied only slightly between PSMC2Neutral and PSMC2Loss lines. We observed the greatest differences in in-gel analyses of peptidase activity, which revealed less 26S2 proteasome peptidase cleavage and increased 20S peptidase activity in PSMC2Loss cells (Fig 4C). These changes were recapitulated by PSMC2 suppression in A2780 cells and reversed by ectopic PSMC2 expression in OVCAR8 cells (Fig 4D). The decrease in 26S2 activity in PSMC2Loss relative to PSMC2Neutral cells, however, was not associated with significant differences in peptidase cleavage when quantitatively assayed in whole cell lysates under conditions (in the absence of SDS) in which free 20S proteasome does not contribute activity (Kisselev and Goldberg, 2005) (p=0.39) (Fig. 4E). In this assay, proteasome-specific peptidase activity is determined by bortezomib-inhibited cleavage. We found that 97% of activity was ablated by bortezomib, suggesting that other proteases did not contribute substantially to the measured activity. Lysates from PSMC2Neutral and PSMC2Loss lines grown under conventional non-stressed conditions also exhibited qualitatively similar total levels of poly-ubiquitin (Fig S4D).

To test the acute effect of manipulating PSMC2 expression on peptidase activity, we measured peptidase activity in lysates of A2780 cells in which we suppressed PSMC2 and of OVCAR8 cells engineered to recover PSMC2 expression. Suppression of PSMC2 by 50% in A2780 cells led to a 17% reduction in total 26S specific peptidase activity, associated with reduced 26S2 activity (Fig 4F). Conversely, ectopic PSMC2 expression in OVCAR8 led to a 15% increase in peptidase activity, associated with increased 26S2 activity. The finding in both systems that modulating PSMC2 levels by up to 50% resulted in only a 17% alteration in 26S activity suggested that PSMC2 content was not the limiting component to 26S formation in PSMC2Neutral cells.

We found no increased sensitivity to bortezomib in PSMC2Loss cells and no significant correlation between the concentration of bortezomib that inhibits proliferation by 50% (IC50) and decreased expression of any of the 47 26S proteasome components, across 133 cell lines previously tested (Garnett et al., 2012) (Table S7). Suppression of PSMC2 in Dox-shRNA-2 A2780 cells or ectopic PSMC2 expression in OVCAR8 cells also did not substantially affect the bortezomib IC50 (Fig 4G–H). These observations are consistent with our prior observation that 26S proteasome function is not substantially compromised in PSMC2Loss cells.

PSMC2Neutral cells have a reservoir of PSMC2 that buffers 26S proteasome levels against PSMC2 loss

The finding that PSMC2Neutral cells have near equal 26S proteasome content to PSMC2Loss cells even though they express higher levels of PSMC2 suggests that PSMC2Neutral cells contain a separate reservoir of PSMC2 that is preferentially lost when levels are reduced. To identify this reservoir, we combined native PAGE with immunoblotting for PSMC2 across a panel of cell lines (Fig 5A). Of the multiple reactive bands identified, even after a long exposure, only one band (“ComplexPSMC2”) was present in all of the PSMC2Neutral but none of the PSMC2Loss lines. Using isogenic systems, we also found that PSMC2 suppression in Dox-shRNA-2 A2780 cells led to reduced levels of ComplexPSMC2, whereas ectopic PSMC2 expression in OVCAR8 cells led to its reappearance (Fig 5B). These results suggest that ComplexPSMC2 is a specific PSMC2 reservoir.

Figure 5
Complex PSMC2 buffers PSMC2Neutral cells against PSMC2 suppression

We hypothesized that ComplexPSMC2 serves as a “buffer” in PSMC2Neutral cells, enabling such cells to maintain 26S proteasome levels and function in the face of reduced PSMC2 expression. In this case, PSMC2 suppression should deplete ComplexPSMC2 before reducing 26S proteasome levels. To quantify the consequences of reducing PSMC2 on ComplexPSMC2 and 26S proteasome levels, we compared dilutions of lysates from Dox shRNA-2 A2780 cells propagated in the absence of doxycycline to lysate collected from these cells cultured in doxycycline (Fig 5C). In cells in which PSMC2 was suppressed, the relative loss of ComplexPSMC2 exceeded the decrease in 26S proteasome content. These observations indicate that ComplexPSMC2 was preferentially lost in A2780 cells after PSMC2 suppression. In contrast, PSMC2 suppression in OVCAR8 cells, which lack ComplexPSMC2, led to near-complete ablation of 26S proteasome levels and peptidase activity and to a qualitative increase in the amount of poly-ubiquitin (Fig 5D–F, S5A-B).

To analyze the components of ComplexPSMC2, we fractionated lysates from IOSE cells expressing either V5-GFP or V5-PSMC2 (Fig S5C) using a glycerol gradient (Fig S5D), and isolated V5-immune complexes containing either ComplexPSMC2 or 26S proteasome. ComplexPSMC2 immune complexes (collected in fractions 2–4) contained PSMC2, PSMC1 (Rpt2), PSMD2 (Rpn1), and PSMD5 (S5B) (Fig 5G), subunits of one of three complexes known to compose the base of the 19S proteasome (Funakoshi et al., 2009; Kaneko et al., 2009; Park et al., 2009; Roelofs et al., 2009; Saeki et al., 2009; Thompson et al., 2009). ComplexPSMC2 did not contain subunits of the other two complexes, PSMC3 (Rpt5), PSMC4 (Rpt3), PSMC5 (Rpt6), PSMC6 (Rpt6), or members of the 20S proteasome, PSMB5 (β5), or PSMA1-6 (α subunits) (Fig 6C). All of these proteins except PSMD5 were detected in immune complexes containing the 26S complex (from fractions 7–9). These observations indicate that the PSMC2 reservoir is a subcomplex of the 26S proteasome.

Figure 6
Tumor-penetrating nanocomplex-mediated delivery of PSMC2-specific siRNA suppresses ovarian tumor growth

The reduction of PSMC2 levels in PSMC2Loss cells inhibits orthotopic tumor growth

To explore the therapeutic potential of PSMC2 suppression in vivo, we tested the consequences of suppressing PSMC2 in ovarian xenografts. Specifically, we used a tumor-targeted nanoparticle delivery system that delivers siRNA into the cytosol of cells within the tumor parenchyma (Ren et al., 2012). We generated tumor-penetrating nanocomplexes (TPNs) consisting of PSMC2-specific siRNA noncovalently bound to tandem peptides bearing an N-terminal cell-penetrating domain, Transportan (TP), and a C-terminal tumor-specific domain, LyP-1 (CGNKRTRGC), which binds to its cognate receptor p32 (Fig 6A).

We first assessed the compatibility of cell lines with TPN-targeted siRNA delivery. OVCAR8 and A2780 cells exhibited high cell surface levels of expression of p32, whereas IOSE cells exhibited low expression (Fig S6A). In consonance with these observations, flow cytometry to quantify cytosolic delivery of fluorescently labeled siRNAs indicated substantial accumulation of siRNA in both OVCAR8 and A2780 cells (Fig 6B). A monoclonal antibody directed against p32 (mAb 60.11) substantially reduced nanocomplex uptake, whereas a control antibody had no effect on uptake. These results indicate that surface p32 expression correlates with enhanced uptake of TPNs and that TPN-mediated siRNA delivery is p32 receptor-specific.

We next used these TPNs to confirm the vulnerability of PSMC2Loss cells to PSMC2 suppression both in vitro and in vivo. We treated OVCAR8 and A2780 cells in vitro with TPNs carrying siRNAs targeting non-overlapping exons of PSMC2. In both cell types, we observed a reduction of PSMC2 protein relative to cells treated with TPNs carrying GFP siRNA (Fig S6B). This reduction was associated with a corresponding decrease in proliferation only in the OVCAR8 cells (Fig S6C). We then used these TPNs to treat mice harboring orthotopic OVCAR8 or A2780 tumors expressing firefly luciferase. We injected TPNs carrying PSMC2-siRNA (1 mg siRNA/kg body weight for 14 days) intraperitoneally every three days and monitored tumor burden non-invasively by imaging bioluminescence. We observed a reduction in tumor burden (by >75% relative to tumors treated with siGFP) only in OVCAR8 tumors (Fig 6D). A2780 and any remaining OVCAR8 tumors treated with TPN/siPSMC2 exhibited lower levels of PSMC2 but not two other members of ComplexPSMC2, PSMC1 and PSMD5 (Fig 6D, S6D).

However, TPN/siPSMC2 nanoparticles failed to decrease tumor burden of PSMC2Loss cells in which we reconstituted PSMC2 expression in vivo using orthotopic tumor xenografts derived from OVCAR8 cells expressing V5-PSMC2 (Fig 6E). This finding confirmed that the effects of TPN/siPSMC2 on tumor growth were the consequence of reduced PSMC2 expression.

Conversely, TPN/siPSMC2 nanoparticles reduced tumor growth and significantly improved survival in PSMC2Neutral cells expressing PSMC2-specific shRNAs (Fig 6F). We measured the effects of TPN/siPSMC2 nanoparticles relative to TPN/siGFP or PBS in mice with xenografts of A2780 cells engineered to express inducible PSMC2 shRNA. Among mice treated with doxycycline and TPN/siPSMC2, overall survival was 40 days and 40% survived more than 42 days, whereas all animals in the TPN/siGFP and PBS cohorts succumbed to tumors within 19 days (p=0.0013) (Fig 6F). These findings demonstrated the therapeutic efficacy of PSMC2 suppression in vivo, and support the notion that PSMC2Loss cells are sensitive to suppression of PSMC2 due to decreased basal levels of PSMC2 mRNA.


PSMC2 as a CYCLOPS gene

By integrating data derived from the genomic characterization of human tumors with systematic interrogation of essential genes in cancer cell lines, we have identified a distinct class of cancer-specific vulnerabilities associated with partial copy-number loss of essential genes. Hemizygous loss of PSMC2 in particular, and of CYCLOPS genes in general, renders cells highly dependent on the remaining allele. Although PSMC2 is frequently involved in partial copy-number loss, we did not observe homozygous deletion, consistent with the notion that PSMC2 is an essential gene. Partial copy-number loss, in contrast, did not substantially impact either proteasome function or cell proliferation.

26S proteasome components are not in stoichiometric equilibrium, and the limiting components may differ between cancer and normal cells. For example, cells often express free 20S complex, but not 19S, suggesting that 26S proteasome levels are limited by 19S regulatory complex levels (Fig 4A). The modules that make up the base of the 19S complex may be similarly imbalanced. We found that the module containing PSMC2 (Rpt1), PSMC1 (Rpt2), PSMD2 (Rpn1), and PSMD5 (S5B) was in excess in many cancer cell lines, yet became limiting to 19S formation in PSMC2Loss cells, unveiling a new sensitivity. PSMC2 levels are influenced by its sub-complex partners (Kaneko et al., 2009), suggesting that interfering with the formation of ComplexPSMC2 or with its incorporation into the 19S proteasome may be a specific approach to reduce PSMC2 levels and proliferation of PSMC2Loss cells. Indeed, when we suppressed PSMC2 in vivo, we were able to obtain more than 75% reductions in tumor burden and a doubling of overall survival.

Since the proteasome is essential in all cells, one concern is whether targeting PSMC2 would induce substantial toxicity in non-cancer cells. However, proteasome inhibition has been well tolerated in humans, with bortezomib treatment resulting in 70% reduction of proteasome-specific peptidase cleavage is well tolerated with acceptable side effect profiles (Aghajanian et al., 2002). In comparison, proliferation of PSMC2Loss cells is reduced at levels of PSMC2 suppression that result in only a 15% reduction of peptide cleavage in PSMC2Neutral cells.

More generally, our findings suggest that one consequence of genomic instability is an alteration in the stoichiometry of components of macromolecular machines including the proteasome, ribosome, and spliceosome. These observations suggest that many of these imbalances may present potential therapeutic targets in individual components or precursor complexes, and that these components, rather than the fully assembled machines, will require specific inhibition or disruption.

CYCLOPS genes as synthetic lethal targets

CYCLOPS genes represent a specific form of synthetic lethality. Several studies have investigated synthetic lethality with activation of pathways that drive cancer but that cannot themselves be easily targeted. For example, synthetic lethality is one approach to targeting inactivated tumor suppressor genes, whose functions cannot easily be reconstituted. Recent observations that breast and ovarian cancers that harbor BRCA1 or BRCA2 loss and impaired homologous recombination DNA repair pathway are highly dependent on the nucleotide excision DNA repair pathway (Bryant, 2005; Farmer, 2005) provide evidence that synthetic lethality may be clinically useful. Targeting CYCLOPS genes represents a different approach to synthetic lethality, in which the intervention is synthetic lethal with a genetic event independent of the effects that event has on the pathways that drive cancer.

Discovery of new vulnerabilities due to genomic disruption in cancer

Advances in cancer therapeutics benefit from our ability to identify vulnerabilities predicted by genomic features that are unique to cancer cells. Indeed, the inhibition of recurrent activating mutations in proto-oncogenes has led to several new cancer treatments. The cancer-specific vulnerabilities we have identified herein are the consequence of alterations in genes affected by genomic disruption that may have no consequence to the process by which the cell transformed or continues to proliferate. These genomic alterations are more frequent than most known driver alterations, occur across lineages, and could theoretically be targeted in a large number of patients.

Although individual CYCLOPS candidates such as PSMC2 will require further investigation in human subjects, the 56 candidate genes we identified may be an underestimate of the true number of potential targets. Our initial Project Achilles analysis only included 5,312 genes, and many of these genes may represent false negative results due to insufficiently effective shRNAs. The set of 86 cell lines was not large enough to enable detection of lineage-specific CYCLOPS genes. Indeed, we identified additional CYCLOPS targets in an independently generated RNAi dataset enriched in breast and pancreatic lineages in addition to validating the targets described in our more lineage-diverse dataset. Systematic evaluation of the completely annotated genome using more shRNAs for each gene and a larger group of cell lines representing many lineages is likely to uncover many more potential targets.

Besides copy-number loss, other types of genomic alteration may also unveil CYCLOPS vulnerabilities. In most cases, vulnerability to suppression of CYCLOPS genes was associated with decreased expression. Other events may also decrease expression of essential genes, including sequence variants, epigenetic modification, or chromosome translocations. Any of these mechanisms may lead to cancer-specific vulnerabilities. Further work will be necessary to explore these other classes and define the role of CYCLOPS targets in cancer therapy.

Experimental Procedures

Copy-number and methylation analysis of tumors

Copy-numbers were determined for 3,131 cancer samples as previously described (Mermel et al., 2011). Marker and gene locations were based on the hg18 genome build. The criteria used to define partial copy-number loss, homozygous deletion, and the length of each deletion are detailed in the Extended Experimental Procedures. Gene-level DNA methylation Beta-values were collected for 601 ovarian tumors from the TCGA web portal. Genes with Beta values > 0.7 were considered methylated. Genes missing data in any sample were excluded from the analysis.

CYCLOPS analysis

For each cell line, we classified each gene as intact (no copy-number loss), partial loss, or to be excluded (for genes undergoing homozygous loss or with ambiguous data) based on thresholds determined using the distribution of relative copy-numbers generated from analysis of SNP array data for that cell line (see Extended Experimental Procedures). Gene dependency scores were determined using the ATARiS algorithm (see Extended Experimental Procedures). The statistical significance mean gene dependency score differences between “intact” and “partial loss” cell lines was determined by comparing the observed data to data representing 50,000 random permutations of class labels, each maintaining the number of cell lines and lineage distribution in each class. Multiple hypotheses were corrected using the FDR framework (Benjamini and Hochberg, 1995).

26S proteasome activity

We measured excitation-emission spectra (360 nm to 430 nm) during incubation in vitro at 37°C every 30 sec for 1 h for a 100 µl solution containing 5 µl of lysate (Buffer A) in 50mM Tris-HCl pH 8.0, 40mM KCl, 5mM MgCl2, 1mM ATP, 1mM DTT, and 100µM Sucrose-LLVY-AMC (Bachem). We converted these measurements to amount of peptide cleavage using a standard curve generated from the excitation-emission spectra of AMC (Bachem). Samples were tested in triplicate with and without the addition of 1µM bortezomib. The average value of peptide cleavage in the bortezomib sample was subtracted to determine 26S proteasome activity. The reagents used in this assay and the procedure to make lysates are described in the Extended Experimental Procedures.

Native gel analysis for proteasome content or proteasome activity

10 µg of lysate (Buffer A) was loaded onto 3-8% Tris-Acetate PAGE (Invitrogen) and run in Tris-Glycine at 4°C and 60V for 17 h. Gels were transferred to nitrocellulose membranes in Tris-Glycine at 70V for 4 h for immunoblotting or in gel peptidase activity. The latter was performed by incubating with gentle agitation in 50mM Tris-HCl pH 8.0, 5mM MgCl2, 1mM ATP, 1mM DTT, and 50µM Suc-LLVY-AMC (Bachem) at 37°C for 30 min. Gels were visualized under UV transillumination. Following photography of 26S proteasome activity, gels were incubated for another 45 minutes at 37°C in the same buffer with the addition of 0.2% SDS and re-analyzed by UV transillumination to assess 20S peptidase activity.

Generation of PSMC2-specific and Control siRNA nanoparticles

The generation of TPN carrying PSMC2-siRNA (Dharmacon) and measurement of their uptake and effects on proliferation was performed as described (Ren et al., 2012). The p32-receptor specificity of cell uptake was probed by using a monoclonal antibody directed against p32 (100 µg/mL) to cells 1 h prior to the addition of TPN. More information about the reagents, chemicals and siRNA sequences can be found in the Extended Experimental Procedures.

Generation of orthotopic xenografts and TPN administration

106 OVCAR8 cells, 0.5×106 OVCAR8 cells expressing V5-PSMC2, or 0.2×106 A2780 cells expressing doxycycline-inducible shRNA against PSMC2 were implanted intraperitoneally in 4–6-week-old NCr/nude mice (Charles River). Once tumors were established and confirmed by bioluminescence imaging, animals were treated intraperitoneally with nanoparticles carrying GFP-specific siRNA (TPN/siGFP), or TPN containing PSMC2-specific siRNA (1 mg siRNA/kg/injection) every 3 d for 21 d as described (Ren et al., 2012). Mice bearing A2780 tumors expressing the doxycycline-inducible shPSMC2 were continuously fed with doxycycline-containing diet (2000 mg/kg) beginning two days after tumor cell injection. Mice were sacrificed and tumors harvested at the end of the experiment or when the tumor burden resulted in a failure to thrive according to institutional recommendations. Tumor lysates were made by homogenizing tumors using an eppendorf micropestle in RIPA buffer supplemented with protease inhibitors.


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    Partial copy-number loss renders cancer cells dependent on CYCLOPS genes
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    CYCLOPS genes are enriched for components of the proteasome including PSMC2
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    Copy-number loss of PSMC2 leads to lower expression and loss of a PSMC2 reservoir
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    Targeting PSMC2 disrupts tumor maintenance in a genotype-specific manner

Supplementary Material









We would like to thank Leslie Gaffney and Lauren Solomon for support in generating illustrations and graphics and Jesse Boehm, Scott Carter, George Demartino and members of the Goldberg and Hahn lab for helpful discussion. This work was supported in part by NIH/NCI grants RC2 CA148268 (W.C.H.), U54 CA143798 and K08 CA122833 (R.B.), T32 GM008313 (T.Z.), and U54 CA112962 (A.T., J.P.M., W.C.H.), the H.L. Snyder Medical Foundation (W.C.H.), the V Foundation (R.B.), a Conquer Cancer Foundation Young Investigator Award, and Sass Foundation Fellowship (D.N.). W.C.H. and R.B. are consultants for Novartis Pharmaceuticals.


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  • Aghajanian C, Soignet S, Dizon DS, Pien CS, Adams J, Elliott PJ, Sabbatini P, Miller V, Hensley ML, Pezzulli S, et al. A phase I trial of the novel proteasome inhibitor PS341 in advanced solid tumor malignancies. Clinical cancer research : an official journal of the American Association for Cancer Research. 2002;8:2505–2511. [PubMed]
  • Ashworth A, Lord CJ, Reis-Filho JS. Genetic interactions in cancer progression and treatment. Cell. 2011;145:30–38. [PubMed]
  • Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, Wilson CJ, Lehar J, Kryukov GV, Sonkin D, et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 2012;483:603–607. [PMC free article] [PubMed]
  • Bell D, Berchuck A, Birrer M, Chien J, Cramer DW, Dao F, Dhir R, DiSaia P, Gabra H, Glenn P, et al. Integrated genomic analyses of ovarian carcinoma. Nature. 2011;474:609–615. [PubMed]
  • Benjamini Y, Hochberg Y. Controlling the False Discovery Rate - A Practical and Powerful Approach to Multiple Testing. J R Stat Soc Ser B-Methodol. 1995;57:289–300.
  • Beroukhim R, Mermel CH, Porter D, Wei G, Raychaudhuri S, Donovan J, Barretina J, Boehm JS, Dobson J, Urashima M, et al. The landscape of somatic copy-number alteration across human cancers. Nature. 2010;463:899–905. [PMC free article] [PubMed]
  • Brose MS, Volpe P, Feldman M, Kumar M, Rishi I, Gerrero R, Einhorn E, Herlyn M, Minna J, Nicholson A, et al. BRAF and RAS mutations in human lung cancer and melanoma. Cancer Res. 2002;62:6997–7000. [PubMed]
  • Cheung HW, Cowley GS, Weir BA, Boehm JS, Rusin S, Scott JA, East A, Ali LD, Lizotte PH, Wong TC, et al. Systematic investigation of genetic vulnerabilities across cancer cell lines reveals lineage-specific dependencies in ovarian cancer. Proc Natl Acad Sci U S A. 2011;108:12372–12377. [PubMed]
  • Daley GQ, Vanetten RA, Baltimore D. Induction of Chronic Myelogenous Leukemia in Mice by the P210BCR/ABL Gene of the Philadelphia Chromosome. Science. 1990;247:824–830. [PubMed]
  • Elsasser S, Schmidt M, Finley D. Characterization of the proteasome using native gel electrophoresis. Methods in enzymology. 2005;398:353–363. [PubMed]
  • Finley D. Recognition and Processing of Ubiquitin-Protein Conjugates by the Proteasome. Annu Rev Biochem (Palo Alto: Annual Reviews) 2009:477–513. [PMC free article] [PubMed]
  • Frei E. Gene Deletion - A New Target For Cancer-Chemotherapy. Lancet. 1993;342:662–664. [PubMed]
  • Funakoshi M, Tomko RJ, Kobayashi H, Hochstrasser M. Multiple Assembly Chaperones Govern Biogenesis of the Proteasome Regulatory Particle Base. Cell. 2009;137:887–899. [PMC free article] [PubMed]
  • Garnett MJ, Edelman EJ, Heidorn SJ, Greenman CD, Dastur A, Lau KW, Greninger P, Thompson IR, Luo X, Soares J, et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature. 2012;483:570–575. [PMC free article] [PubMed]
  • Kaelin WG. The concept of synthetic lethality in the context of anticancer therapy. Nat Rev Cancer. 2005;5:689–698. [PubMed]
  • Kaneko T, Hamazaki J, Iemura S, Sasaki K, Furuyama K, Natsume T, Tanaka K, Murata S. Assembly Pathway of the Mammalian Proteasome Base Subcomplex Is Mediated by Multiple Specific Chaperones. Cell. 2009;137:914–925. [PubMed]
  • Kisselev AF, Goldberg AL. Monitoring activity and inhibition of 26S proteasomes with fluorogenic peptide substrates. Methods in enzymology. 2005;398:364–378. [PubMed]
  • Liu J, Yang G, Thompson-Lanza JA, Glassman A, Hayes K, Patterson A, Marquez RT, Auersperg N, Yu Y, Hahn WC, et al. A genetically defined model for human ovarian cancer. Cancer Res. 2004;64:1655–1663. [PubMed]
  • Marcotte R, Brown KR, Suarez F, Sayad A, Karamboulas K, Krzyzanowski PM, Sircoulomb F, Medrano M, Fedyshyn Y, Koh JLY, et al. Essential Gene Profiles in Breast, Pancreatic, and Ovarian Cancer Cells. Cancer Discov. 2012;2:172–189. [PMC free article] [PubMed]
  • Mermel CH, Schumacher SE, Hill B, Meyerson ML, Beroukhim R, Getz G. GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome biology. 2011;12:R41. [PMC free article] [PubMed]
  • Park S, Roelofs J, Kim W, Robert J, Schmidt M, Gygi SP, Finley D. Hexameric assembly of the proteasomal ATPases is templated through their C termini. Nature. 2009;459:866-U869. [PMC free article] [PubMed]
  • Ren Y, Cheung HW, von Maltzhan G, Agrawal A, Cowley GS, Weir BA, Boehm JS, Tamayo P, Karst AM, Liu JF, et al. Targeted tumor-penetrating siRNA nanocomplexes for credentialing the ovarian cancer target ID4. Science Translational Medicine. 2012;4:147ra112. [PMC free article] [PubMed]
  • Richardson PG, Sonneveld P, Schuster MW, Irwin D, Stadtmauer EA, Facon T, Harousseau JL, Ben-Yehuda D, Lonial S, Goldschmidt H, et al. Bortezomib or high-dose dexamethasone for relapsed multiple myeloma. N Engl J Med. 2005;352:2487–2498. [PubMed]
  • Roelofs J, Park S, Haas W, Tian G, McAllister FE, Huo Y, Lee BH, Zhang F, Shi YG, Gygi SP, et al. Chaperone-mediated pathway of proteasome regulatory particle assembly. Nature. 2009;459:861–865. [PMC free article] [PubMed]
  • Saeki Y, Toh-e A, Kudo T, Kawamura H, Tanaka K. Multiple Proteasome-Interacting Proteins Assist the Assembly of the Yeast 19S Regulatory Particle. Cell. 2009;137:900–913. [PubMed]
  • Sawyers CL. Making progress through molecular attacks on cancer. Cold Spring Harbor symposia on quantitative biology. 2005;70:479–482. [PubMed]
  • Smith DM, Fraga H, Reis C, Kafri G, Goldberg AL. ATP binds to proteasomal ATPases in pairs with distinct functional effects, implying an ordered reaction cycle. Cell. 2011;144:526–538. [PMC free article] [PubMed]
  • Soda M, Choi YL, Enomoto M, Takada S, Yamashita Y, Ishikawa S, Fujiwara SI, Watanabe H, Kurashina K, Hatanaka H, et al. Identification of the transforming EML4-ALK fusion gene in non-small-cell lung cancer. Nature. 2007;448:561-U563. [PubMed]
  • Solimini NL, Luo J, Elledge SJ. Non-oncogene addiction and the stress phenotype of cancer cells. Cell. 2007;130:986–988. [PubMed]
  • Stratton MR, Campbell PJ, Futreal PA. The cancer genome. Nature. 2009;458:719–724. [PMC free article] [PubMed]
  • Thompson D, Hakala K, DeMartino GN. Subcomplexes of PA700, the 19 S regulator of the 26 S proteasome, reveal relative roles of AAA subunits in 26 S proteasome assembly and activation and ATPase activity. The Journal of biological chemistry. 2009;284:24891–24903. [PMC free article] [PubMed]
  • Weinstein IB, Joe AK. Mechanisms of disease: Oncogene addiction--a rationale for molecular targeting in cancer therapy. Nature clinical practice Oncology. 2006;3:448–457. [PubMed]
  • Zhang R, Lin Y. DEG 5.0, a database of essential genes in both prokaryotes and eukaryotes. Nucleic acids research. 2009;37:D455–D458. [PMC free article] [PubMed]