Resistance to CDK2 Inhibitors Is Associated with Selection of Polyploid Cells in CCNE1 Amplified Ovarian Cancer

Author Manuscript Published OnlineFirst on September 4, 2013; DOI: 10.1158/1078-0432.CCR-13-1337 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Resistance to CDK2 inhibitors is associated with selection of polyploid cells in CCNE1 amplified ovarian cancer

AUTHORS AND AFFILIATIONS Dariush Etemadmoghadam1,2,3, George Au-Yeung1,4, Meaghan Wall5, Chris Mitchell1, Maya Kansara1, Elizabeth Loehrer1, Crisoula Batzios5, Joshy George1,4, Sarah Ftouni1, Barbara A Weir6,7, Scott Carter7, Irma Gresshoff3,8, Linda Mileshkin1,2,9, Danny Rischin1,2,9, William C Hahn6,7, Paul M Waring3,8, Gad Getz7, Carleen Cullinane1,10, Lynda J Campbell5 and David Bowtell1,2,4

1Peter MacCallum Cancer Centre, East Melbourne, Victoria, Australia; 2Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, Victoria, Australia; 3Department of Pathology, University of Melbourne, Parkville, Victoria, Australia; 4Department of Biochemistry and Molecular Biology, University of Melbourne, Parkville, Victoria, Australia; 5Victorian Cancer Cytogenetics Service, St Vincent's Hospital, Melbourne, Victoria, Australia; 6Dana-Farber Cancer Institute, Boston, Massachusetts, USA; 7The Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA; 8Centre for Translational Pathology, University of Melbourne, Parkville, Victoria, Australia; 9Department of Medicine, University of Melbourne, Parkville, Victoria, Australia; 10Translational Research Program, Peter MacCallum Cancer Centre, East Melbourne, Victoria, Australia.

RUNNING TITLE Resistance to CDK2 inhibitors in CCNE1 amplified cancer

KEYWORDS Ovarian Cancer, Resistance, Cyclin E1, CDK inhibitors

CORRESPONDING AUTHOR Professor David DL Bowtell Peter MacCallum Cancer Centre [email protected]

Page 1 of 22

Downloaded from clincancerres.aacrjournals.org on September 26, 2021. © 2013 American Association for Cancer Research. Author Manuscript Published OnlineFirst on September 4, 2013; DOI: 10.1158/1078-0432.CCR-13-1337 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

DISCLOSURE OF POTENTIAL CONFLICTS OF INTEREST The authors declare that they have no competing financial interests in relation to the work described in this manuscript.

TRANSLATIONAL RELEVANCE Cyclin E1 (CCNE1) is amplified in various tumor types including high-grade serous ovarian cancer where it is associated with poor clinical outcome. We demonstrate that suppression of the Cyclin E1 partner kinase, CDK2, induces apoptosis in a CCNE1 amplicon-dependent manner. Little is known of mechanisms of resistance to CDK inhibitors. We therefore generated cells with reduced sensitivity to CDK2 inhibitors and identified two bypass mechanisms, one involving CDK2 upregulation and another associated with the selection of pre-existing polyploid cells from a heterogeneous parental population. Using primary tumor data, we show for the first time that polyploidy is a common and specific feature of CCNE1 amplified cancers. These findings validate CDK2 as a novel therapeutic target in CCNE1 amplified tumors and pre-emptively identify mechanisms of resistance that may influence clinical response.

WORD COUNT: 4,882

TOTAL NUMBER OF FIGURES AND/OR TABLES: 6

ABSTRACT Purpose: Amplification of Cyclin E1 (CCNE1) is associated with poor outcome in breast, lung and other solid cancers, and is the most prominent structural variant associated with primary treatment failure in high-grade serous ovarian cancer (HGSC). We have previously shown that CCNE1 amplified tumors show amplicon-dependent sensitivity to CCNE1 suppression. Here, we explore targeting CDK2 as a novel therapeutic strategy in CCNE1 amplified cancers and mechanisms of resistance.

Experimental Design: We examined the effect of CDK2 suppression using RNA interference and small molecule inhibitors in SK-OV-3, OVCAR-4, and OVCAR-3 ovarian cancer cell lines. To identify mechanisms of resistance, we derived multiple, independent resistant sub-lines of OVCAR-3 to CDK2 inhibitors. Resistant cells were extensively characterized by gene expression and copy number analysis, FACS profiling

Page 2 of 22

and conventional karyotyping. Additionally, we explored the relationship between CCNE1 amplification and polyploidy using data from primary tumors.

Results: We validate CDK2 as a therapeutic target by demonstrating selective sensitivity to suppression, either by gene knockdown or using small molecule inhibitors. In addition, we identified two resistance mechanisms, one involving up-regulation of CDK2 and another novel mechanism involving selection of polyploid cells from the pre-treatment tumor population. Our analysis of genomic data shows that polyploidy is a feature of cancer genomes with CCNE1 amplification.

Conclusions: These findings suggest that CyclinE1/CDK2 is an important therapeutic target in HGSC, but that resistance to CDK2 inhibitors may emerge due to upregulation of CDK2 target protein and pre-existing cellular polyploidy.

INTRODUCTION

Deregulation of the cell cycle is a hallmark of cancer and is therefore an attractive therapeutic target (1, 2). Despite this, the clinical utility of cell cycle inhibitors has been disappointing to date. In contrast to the development of other targeted agents in cancer, surprisingly few trials of cell cycle inhibitors have involved selection of patients based on molecular features (1). Identifying predictive biomarkers and patient subsets that are most likely to benefit from cell cycle inhibitors is important to the clinical development of these agents.

High-grade serous ovarian cancer (HGSC) is the most common subtype of epithelial ovarian cancer (3). Recent studies have identified a high frequency of TP53 mutations, BRCA dysfunction and clinically relevant gene expression subtypes (3). In addition, genomic instability and wide-spread copy number changes appear to be a mechanism of tumor evolution and may also influence treatment response. For example, genomic amplification of 19q12 incorporating Cyclin E1 (CCNE1) in ~20% of HGSC is associated with poor overall survival (4) and primary treatment failure (5).

Cyclin E1 forms a complex with CDK2 to regulate G1/S transition by phosphorylation of downstream targets including the tumor suppressor RB1. Deregulation of the cell cycle in tumors is thought to induce a hyper-proliferative phenotype, leading to genomic

Page 3 of 22

instability and driving malignant transformation (6). Recent functional studies in vitro have shown ‘oncogene addiction’ to maintained CCNE1 expression when amplified, evidenced by amplicon-dependent attenuation of cell viability, clonogenic survival, induced G1 arrest and increased apoptosis after siRNA-mediated knockdown (7, 8). Therapeutically, CCNE1 function may most readily be targeted via its partner kinase CDK2. Currently, there are more than 20 small molecule CDK inhibitors in clinical trials for various cancer types (1, 2). These compounds are generally classified as pan-CDK or highly selective inhibitors and act by inducing cell cycle arrest and apoptosis via inhibition of cell cycle kinases (Cdk1,2,4,6) and/or transcriptional Cdks (Cdk7,8,9) (9).

We aimed to determine whether ovarian tumor cells with CCNE1 gene amplification are selectively sensitive to inhibition of CDK2 by gene knockdown or with small molecule inhibitors. We also explored potential mechanisms of resistance to CDK inhibition to pre- empt the likely emergence in patients.

MATERIALS AND METHODS Cell lines Ovarian cell lines were obtained from the National Cancer Institute Repository (NCI) and fingerprinted using short tandem repeat (STR) markers to confirm identity against the Cancer Genome Project database (Wellcome Trust Sanger Institute). Primer sequences for six short tandem repeat (STR) markers (CSF1PO, TPOX, THO1, vWA, D16S539, D7S820, D5S818) and analysis have been previously described (10).

Gene suppression studies Methods and transfection conditions for siRNA studies have been previously described (7). Microarray data from shRNA experiments was obtained from the Integrative Genomics Portal and analyzed using the GENE-E software (11). Cell line copy number data was obtained from the Cancer Cell Line Encyclopedia (12).

Inhibitors and drug sensitivity assays PHA-533533 was obtained from Pfizer (New York, NY) and dinaciclib from Merck

(Whitehouse Station, NJ). Cells were maintained at 37ºC and 5% CO2 in RPMI 1640 containing 10% (v/v) FCS, 50 U.mL-1 penicillin and 50ug.mL-1 streptomycin. Drug sensitivity was assessed using a 72 hour viability assay (MTS) and a 7 day clonogenic

Page 4 of 22

survival assay. For the viability assay, 5,000 cells were seeded in 96-well plates and allowed to attach over night prior to the addition of drug at various concentrations. After 72 hours of drug incubation, cell viability was determined using the CellTiter 96 Aqueous Non-Radioactive Cell Proliferation Assay (Promega, Madison, WI). For clonogenic survival assays, single cells were seeded at low density in 6-well plates, allowed to attach overnight prior to the addition of drug. After 7 days of growth in drug, cell colonies were washed, fixed and stained with 20% (v/v) methanol and 0.1% (w/v) crystal violet. Cells were rinsed in water, air-dried, digitally scanned and discrete colonies (>50 cells per colony) counted using MetaMorph (Molecular Devices, Sunnyvale, CA). IC50 dose was approximated by fitting a four-parameter dose-response curve (Hill equation) using Prism 5 (GraphPad Software, La Jolla, CA).

Molecular methods Cell line DNA was extracted using a DNeasy Kit (Qiagen, Valencia, CA) for quantitative- PCR (qPCR) of CCNE1 DNA copy number status as described previously (13) or for SNP microarray analysis (below). Total RNA was extracted from cell pellets using the mirVana RNA Isolation Kit (Life Technologies) for gene expression profiling or reverse transcribed using M-MLV (Promega) prior to SYBR green RT-PCR. Experimental details for gene expression analysis, including primer sequences, have been described elsewhere (13).

Western blot Whole cell protein lysates were boiled, resolved by SDS-PAGE using 12.5% (w/v) acrylamide gels and then transferred to PVDF membranes. Blots were blocked in 5% (w/v) non-fat milk powder in PBS-T (0.1% Tween 20 in PBS) and probed overnight at 4º C in primary antibody against Cyclin E1 (clone HE12) (Santa Cruz Biotechnology, Santa Cruz, CA), CDK2 (clone D-12) (Santa Cruz), p-Rb (Ser 807/811) (Cell Signaling, Danvers, MA) or the p89 PARP1 caspase cleavage fragment (Cell Signaling or Promega, Madison, WI). Membranes were washed in PBS-T and incubated with peroxidase-conjugate secondary antibody for 1hr at room temperature, washed and developed by chemiluminescence before being exposed to radiographic film. Blots were re-probed with an antibody against α-tubulin to assess protein loading.

Generation of cell lines resistant to CDK inhibitors

Page 5 of 22

Sub-confluent cells in 6-well plates were treated with PHA-533533 or dinaciclib at the IC50 dose (4 µM and 10 nM respectively based on a 72hr cytotoxicity assays) for two 72 hour periods (media removed and fresh drug added) after which surviving cells were allowed to re-populate the culture for 96 hrs. The process was repeated once and remaining cells were cultured in the presence of drug for three additional passages. Selected cells (passage 4) were then maintained either in the presence or absence of drug to monitor the change in drug sensitivity over time. Cell pellets were collected on dry ice and stored at -80ºC prior to nucleic acid extraction.

Proliferation assays To determine proliferation rate, 50,000 cells were seeded in multiple wells of 6-well plates. Cells were collected from triplicate wells and counted using a Countess automated cell counter (Invitrogen) every 24 hrs for four days.

SNP and gene expression microarrays Illumina OmniExpress microarrays were run as a service from AGRF (Australian Genome Research Facility) according to the manufacturer’s instructions. Data was extracted using Illumina’s GenomeStudio v2010.3 with Genotyping module 1.8.4 software, with the default Illumina settings and Illumina HumanOmniExpress 12v1_H

manifest cluster file. Normalised Log2 R ratios (Log2 ratio of observed normalized signal intensity to expected intensity) were segmented using circular binary segmentation (CBS) (14) and the regions of copy number change per gene estimated using the mean segment value. Regions of gain or amplification where defined as those where the mean

segment log2 R ratio value was greater than 0.3; losses were defined as regions of log2 R less than -0.3. Data visualization was performed using Partek Genomics Suite 6.6 (Partek Inc., St Louis, MO). Affymetrix Gene ST 1.0 microarrays were performed and data normalized using the GCRMA method available in the R package as described previously (15). Molecular profiles of stable and selected cells were compared to that of the parental cells and differentially expressed genes identified using empirical Bayes methods available in the R-package limma (16). A gene was selected as differentially expressed if the false discovery rate (FDR) was less than 5%. Pathway analysis was conducted in either GeneGo (Thomson Reuters) or GSEA (Broad Institute). Complete copy number and gene expression microarray data is available from the Gene Expression Omnibus (GEO) (Accession ID GSE48921). Methods for the analysis of copy

Page 6 of 22

number data obtained from The Cancer Genome Atlas (TCGA) is described elsewhere (17).

Cell cycle analysis All viable and dead cells were collected 48 hrs after drug treatment for death assessment and cell cycle analysis by flow cytometry. For characterization of cell ploidy, viable cells were collected from sub-confluent cultures. Collected cells were fixed in ethanol and stained with propidium iodide (PI) as described previously (7). Up to 10,000 single cell events were recorded using a FACS Canto II flow cytometer (BD Biosciences). Cell cycle profiles and percentage of cells in each cell cycle phase for populations of each ploidy were modeled using Modfit LT (Beckman Coulter, Brea, CA).

Cell sorting by ploidy Approximately 2 x 106 OVCAR-3 parental cells were collected, resuspended as a single cell suspension and filtered through a 70 µM filter to eliminate clumps and aggregates. Cells were then stained with the live-cell DNA-selective Vybrant DyeCycle Violet stain (Life Technologies), incubated at 37ºC for 30 minutes and Hypotriploid (G1 sub- population peak) and Hyperpentaploid (G2 sub-population peak) cells sorted by flow cytometery using the Aria II system (BD Biosciences). Sorted cells were expanded, then re-sorted, to further enrich for each population. Purity of established cultures were assessed by PI staining as described above.

Karyotyping Cells were treated with colcemid (0.2ug/mL) for 30 minutes, harvested, incubated in 0.075M hypotonic KCl at 37°C for 30 minutes, fixed in methanol:acetic acid (3:1), dropped onto glass slides and G-banded with trypsin and Leishman stain according to standard cytogenetic techniques.

Immunohistochemistry (IHC) Sections from formalin-fixed paraffin embedded (FFPE) tissue blocks were cut to 4 μm, dried at 60°C for 30 min and stained with Cyclin E1 specific clone HE12 on a Ventana BenchMark ULTRA immunostainer (Ventana Medical Systems, Tucson, USA). The Ventana staining protocol using the OptiView DAB IHC Detection Kit (Catalogue Number 760-700) included pretreatment with cell conditioner 1 (pH 8.5) for 64 minutes, followed

Page 7 of 22

by incubation with diluted HE12 antibody (Santa Cruz Biotechnology, Inc.) at 36°C for 12 minutes. Antibody incubation was followed by counterstaining with hematoxylin II and bluing reagent for 4 minutes each. Subsequently, slides were removed from the immunostainer, washed in water with a drop of dishwashing detergent, and mounted. No chromogen was detected when primary antibody Cyclin E (HE12) was omitted.

RESULTS CCNE1 amplified tumor cells require CDK2 for survival We selected tumor cell lines that either had no CCNE1 copy number change (SK-OV-3), low-level gain (OVCAR-4) or high-level amplification (OVCAR-3) based on our previous analysis of 19q12 copy number (7). Copy number of CCNE1 was strongly associated with gene expression in these lines, but CDK2 expression was unrelated to CCNE1 status (Supplementary Figure S1A). We have previously reported an amplicon- dependent decrease in cell viability after siRNA-mediated knockdown of CCNE1 (7). Consistent with this observation, we found that both CCNE1-gained and amplified lines showed selective sensitivity to siRNA mediated CDK2 knockdown (Figure 1A), validated at the RNA (Supplementary Figure S1B) and protein level (Figure 1B). The effect was less pronounced in short term survival assays, where only the CCNE1 amplified OVCAR-3 cell line showed specific sensitivity to CCNE1 or CDK2 knockdown (Supplementary Figure S1C).

To validate our findings in a larger and diverse set of tumor cell types, we made use of data from a genome-wide shRNA screen of 102 cancer cell lines with known copy number status (12), including a high proportion of epithelial ovarian cancer (n = 25) (11). Cells were infected with a pool of 54,020 shRNAs targeting 11,194 genes and grown for at least 16 doublings. The abundance of shRNA sequence relative to a reference pool was measured by microarray (11) to identify genes essential for survival.

Consistent with the siRNA data, we found a statistically significant depletion of shRNAs against CCNE1 and CDK2 in CCNE1 amplified cell lines across multiple tumor types

(Figure 1C). Of the lines assayed, 23 had a copy number gain involving CCNE1 (log2 ratio > 0.3), including 11 ovarian cancer lines. The remaining 79 lines without CCNE1 amplification included 14 ovarian cancers, providing a comparison group (Supplementary Figure S2A). As each gene was targeted with multiple independent

Page 8 of 22

shRNA, depletion of individual genes was estimated by considering the median normalized value of all shRNAs (n = 12 for CDK2; n = 4 for CCNE1). A statistically significant dependence on CDK2 but not CCNE1 was observed when restricting our analysis to ovarian tumor cell lines alone (Figure 1C), in part due to the reduced sample size and the larger number of individual shRNAs that targeted CDK2 compared to CCNE1 (Figure 1D). For example, of the four shRNAs specific for CCNE1, only shRNA3 strongly discriminated tumors by amplification status (Figure 1D and Supplementary Figure S2B).

Cyclin E1 primarily interacts with CDK2 but can also activate CDK1 and CDK3 (6), however we found that only depletion of CDK2 shRNAs was significantly associated with reduced survival in CCNE1 amplified cells (Supplementary Table S1). shRNAs targeting CDK6 were enriched in CCNE1 amplified cells, suggesting that CCNE1 amplification protects cells from inhibition of CDK6. Alternatively, CDK6 expression may be essential in CCNE1 non-amplified cells and therefore associated with shRNA depletion.

CCNE1 amplified cells are sensitive to CDK2 small molecule inhibitors Two small molecule CDK inhibitors, PHA-533533 (18) and dinaciclib (19), were obtained to examine the relative sensitivity in cell lines by CCNE1 amplification status. These compounds were selected as they show high specificity against CDK2, however both also have in vitro activity against other kinases. PHA-533533 inhibits CDK2/A, CDK2/E, CDK5/p25, CDK1/B, GSK3B (glycogen synthase kinase 3 beta) and CDK4/D (IC50 values of 37, 55, 65, 208, 732 nM and >10 µM respectively) (18), while the more potent inhibitor, dinaciclib, targets CDK2/E, CDK5/p35, CDK1/B and CDK9/T (IC50 values of 1, 1, 3 and 4 nM respectively) (1, 19). We observed CCNE1 amplicon dependent sensitivity to both PHA-533533 (Figure 2A) and dinaciclib (Figure 2B) in clonogenic survival assays. Differential effects were less apparent in short-term viability assays (Supplementary Figure S3), with only OVCAR-3 cells showing heightened sensitivity to dinaciclib. These results are consistent with our gene suppression experiments, where the most pronounced effects of CCNE1 and CDK2 inhibition were seen in 7-day siRNA clonogenic (Figure 1A) and long-term shRNA culture experiments (Figure 1C and 1D). Treatment with either inhibitor resulted in decreased phosphorylation of the downstream target Rb at CDK-specific serine Ser 807/811 and initiation of apoptosis, indicated by the presence of PARP cleavage products 24 hours after treatment (Figure 2C and 2D).

Page 9 of 22

Consistent with the survival data, the strongest effects were observed in the OVCAR-3 CCNE1 amplified cell line.

Resistance to CDK2 inhibition generated in vitro is stable and associated with cross- resistance Resistance to single agent molecularly targeted therapies is a common clinical problem (20), yet little is known of resistance mechanisms to CDK inhibitors (1). We therefore investigated resistance after extended exposure of OVCAR-3 CCNE1 amplified cells to CDK2 inhibitors, deriving five independent cell lines that were resistant to PHA-533533 (OVCAR3-533533-R1, -R3, -R5, -R6 and -R7). Cells were pulse treated with drug at the IC50 concentration (4 µM) followed by recovery in media as outlined in Figure 3A. After the selection process, the average IC50 values in a 72 hours cytotoxicity assay shifted from ~4 to 8 µM (average 2.1 fold increase in IC50 value, p<0.001; Figure 3B) and from 0.46 µM to 2.9 µM in clonogenic survival assays for the R1 cell line (6.3 fold increase in IC50 value, Supplementary Figure S4A). Although more pronounced in clonogenic survival assays, the level of resistance generated was modest. We therefore also attempted to generate resistant lines after continued drug exposure (without media recovery steps). However, we found this method to be less reproducible and did not result in a higher level of resistance (data not shown). Similarly, we were unable to derive stable resistant cell lines after treatment with escalating drug doses (up to 10 µM), possibly due to an increase in off-target effects at higher concentrations.

A resistant line to dinaciclib was similarly derived (Supplementary Figure S4B). Resistance was surprisingly stable in both the 533533-R1 and Dinaciclib-RD1 cell lines and we observed little attenuation of resistance for up to 40 passages in the absence of inhibitor (Figure 3C and Supplementary Figure S4C). Microsatellite fingerprinting of long- term cell cultures confirmed that resistant cells were derived from the parental population, and not outgrowth of contaminating cells (see Methods). As PHA-533533 is most selective for CDK2 (21), our subsequent analyses focused mainly on lines made resistant to this drug.

We next investigated whether resistance in the 533533-R1 cells also altered sensitivity to dinaciclib and other cytotoxic agents (Figure 3D). We observed decreased sensitivity to dinaciclib (p<0.01) and cisplatin (p<0.01). No cross-resistance was observed with

Page 10 of 22

doxorubicin (p = 0.378). As both PHA-533533 and doxorubicin are substrates for the p- glycoprotein drug efflux pump (M Ciomei, pers. comm.) the lack of cross-resistance suggests that upregulation of p-glycoprotein is unlikely to account for PHA-533533 resistance in R1 cells. Increased proliferation rates observed in resistant cells, suggests altered drug sensitivity was not due to a reduction in growth (Supplementary Figure S4D).

We characterized Rb-phosphorylation and induction of PARP cleavage in resistant and parental lines following PHA-533533 exposure. The degree of Rb de-phosphorylation following drug treatment was comparable in R1 and parental lines, and marginally attenuated in the R6 cell line, suggesting resistance was unlikely to be due to decreased CDK2 signaling via Rb (Figure 3E). By contrast, the appearance of PARP cleavage products by western blot and increased cell death as determined by FACS (Supplementary Figure S5), was only apparent at higher drug doses in resistant lines, suggesting that the induction of apoptosis was impaired downstream of Rb regulation. The observation of cross-resistance to cisplatin further supports a generalized mechanism of resistance, possibly associated with increased pro-survival signaling.

Resistance is associated with CDK2 up-regulation rather than mutation We examined potential genetic mechanisms that may confer resistance in the established cell lines, focusing initially on CCNE1 copy number and CDK2 mutation status. All resistant cell lines showed identical microarray copy number profiles at CCNE1, suggesting resistance was not associated with a change in copy number status (Figure 4A). Additionally, no mutations in CDK2 were identified after complete exon sequencing in the parental and all five resistant cell lines (Supplementary Methods).

To broaden our genomic analysis, we compared gene expression profiles of parental and resistant cells collected immediately after drug selection or after culture in media alone (collection points T1 and T2, Figure 3A and 3C). In a genome-wide analysis, we found drug exposure resulted in substantial changes in gene expression with relative few changes observed in the stable resistant compared to parental cells cultured in the absence of drug (Figure 4B and 4C and Supplementary Data). Pathway analysis revealed significant enrichment of genes involved with AKT signaling, cell cycle and DNA damage response in selected cells (Supplementary Table S2).

Page 11 of 22

Looking specifically at CCNE1, we observed transient up-regulation by exposure to PHA533533, but this was not apparent in the stably resistant lines maintained in the absence of drug (Figure 4D). In contrast, CDK2 mRNA was up-regulated in the stable resistant lines (Figure 4D). Increased CDK2 protein was most apparent in the R6 line (Supplementary Figure S6A). We failed to observe an alteration in the gene expression of other PHA-533533 targets including CDK1, CDK5, CDK4, or GSK3B (18) (Supplementary Figure S6B), suggesting that drug resistance was not associated with up-regulation of these targets.

We also looked specifically for changes in gene copy number by SNP microarray. We identified 26 genes that were gained in at least four out of five resistant lines compared to parental cells, and 136 new deletions (Supplementary Table S3). Gene set enrichment analysis (GSEA) of significantly altered positional gene sets (C1 gene set) identified 14q32 as the most significantly amplified region, incorporating 10 genes including AKT1 (Supplementary Table S4). Increased AKT1 copy number is consistent with AKT1 pathway upregulation in the cells cultured in the presence of drug (Supplementary Table S2), although does not appear to result in maintained gene up- regulation in stable cells. The most significant regions of loss were localized to 13q12, incorporating BRCA2, and 22q13, including RBX1 (Supplementary Tables S3 and S4). Loss of RBX1 is intriguing, given its involvement with ubiquitin-mediated degradation of Cyclin E1 (22) suggesting a possible mechanism of pathway deregulation. Further functional analysis is required to validate the functional significance of these and other identified changes.

Increased DNA ploidy is associated with resistance to CDK2 inhibition We performed FACS analysis to characterize the cell cycle effects of inhibitors and noted a substantial shift in the DNA content of resistant cell lines (Figure 5A and Supplementary Figure S7A). Modeling of FACS data suggested the presence of two distinct populations in the parental line that were diploid or pseudo-diploid, and another with approximately double the DNA content that was possibly tetraploid or near- tetraploid. By contrast, the near-tetraploid population appeared to dominate the R1, R3, R5 and R7 cells (Figure 5B). FACS analysis of OVCAR-3 cells that were selected for resistance to dinaciclib (RD1) also showed profound enrichment of the near-tetraploid

Page 12 of 22

population (Supplementary Figure S7B). Interestingly, the FACS profile of the R6 cell line, which has the highest levels of CDK2 expression, more closely resembled the parental line.

Analysis of FACS data is complicated by overlapping cell cycle profiles of multiple populations, we therefore performed conventional karyotyping of the parental, R1 and R6 cell lines (Supplementary Table S5), with the relative frequency of karyotypes estimated across 50 metaphases (Supplementary Figure S8A). Karyotyping confirmed the presence of two populations in parental OVCAR-3 cells, one that was hypotriploid (62-68 chromosomes) and a second that was hyperpentaploid (118-128 chromosomes) (Figure 5C and Supplementary Figure S8B). Shared structural rearrangements between populations suggest that hyperpentaploid cells are likely to have originated after duplication of the hypotriploid genome (Supplementary Table S5). By contrast, the R1 cell line consisted almost entirely of hyperpentaploid cells (Figure 5D, Supplementary Figure S8C). As noted in the FACS analysis, the R6 line contained both hypotriploid and hyperpentaploid cells (Supplementary Figure S8D). These findings suggested that cells with an increased DNA content had a selective advantage in the presence of CDK2 inhibitors. Up-regulation of CDK2 in the R6 cell line, and a karyotype that more closely resembled the parental line, was consistent with a different mechanism of resistance.

To determine whether hyperpentaploid cells pre-existing in the parental population show intrinsic resistance to CDK2 inhibitors, we used flow cytometry to isolate live cells from

each population. Hypotriploid cells in G1 (AG1) or hyperpentaploid cells in G2/M (BG2) were collected and expanded in culture (Figure 5E). FACS analysis of selected cells estimate an enrichment of greater than ~90% purity of each population (Figure 5F) and remained stable throughout the course of our experiments. Dose response assays showed that hyperpentaploid cells had partial intrinsic resistance to the PHA-533533 inhibitor compared to the hypotriploid population, with the unsorted (parental) cells showing intermediate sensitivity (Figure 5G). We did not see an increased resistance to cisplatin (data not shown), suggesting the reduced sensitivity of hyperpentaploid cells to PHA-533533 is specific.

Primary tumors with CCNE1 gene amplification are associated with polyploidy

Page 13 of 22

Cancer genomes of high DNA ploidy are thought to arise as a result of discrete whole genome doubling (WGD) events, followed by further focal loss of chromosomal material, resulting in variations in absolute DNA ploidy values (23). Using allele specific copy number data derived from SNP microarrays, it is possible to assess genome doubling events and DNA ploidy bioinformatically, and we took this approach to study the relationship with CCNE1 copy number in existing data from TCGA (23).

We found that CCNE1 gain or high-level amplification was significantly associated with an increased proportion of tumors with evidence of WGD compared to unamplified tumors (χ2 p<0.0001, Figure 6A). Furthermore, the number of tumors with increased DNA ploidy (>2) was higher in patients with CCNE1 copy number amplification (χ2 p<0.0001, Figure 6B and Supplementary Figure S9A). To determine whether the association with WGD was specific to CCNE1 amplification and not a generalized increase in copy number events, we assessed the proportion of genome amplified segments in each tumor subset (17). Interestingly, tumors with >1 WGD events had fewer regions of copy number amplification, suggesting a specific association with CCNE1 (Figure 6C). Moreover, tumors with increased CCNE1 copy number did not show a higher proportion of total amplification events (Figure 6D). Tumors that showed no evidence of WGD (Supplementary Figure S9B) or CCNE1 amplification (Supplementary Figure S9C) had a higher number of deletions, consistent with previous reports (23). Taken together these findings suggest that high ploidy genomes are a common property of tumors with CCNE1 amplification. Genome-doubled HGSC samples have previously been reported to have a greater increase of cancer recurrence (23). Consistent with these findings, we found that samples without CCNE1 amplification and no WGD had improved overall survival over patients with at least one WGD event (Figure 6E). In contrast, CCNE1 amplified tumours, irrespective of WGD status, showed the shortest overall survival. Using IHC to interrogate primary tumour samples known to have CCNE1 gene amplification, we observed intense nuclear staining of Cyclin E1, and identified some positively stained cells that had giant nuclei consistent with increased ploidy (Figure 6F).

DISCUSSION Tumors with amplification of the CCNE1 gene are associated with poor clinical outcome in HGSC, and we (7) and others (8) have previously demonstrated the essentiality of

Page 14 of 22

maintained CCNE1 overexpression in these tumors. Here we show a dependency on CDK2, the partner protein of Cyclin E1, in ovarian and other tumor types with amplification of the 19q12 locus. The essentiality of CCNE1 and CDK2 in ovarian tumors with 19q12 amplification is consistent with a recent report in breast cancer (24).

Expression of other 19q12 genes may also contribute to the oncogenic effect of amplification. Suggested targets include the pro-survival protein URI1 (7, 25), and CCNE1, POP4, PLEKHF1 and TSHZ3 in breast cancer (24). Furthermore, genes elsewhere in the genome such as TPX2, that we have shown to be frequently co- amplified with CCNE1 (7), may function in essential co-operational networks.

To date, no clinical trials have used CCNE1 copy number status to inform treatment decisions, and our data suggests that CDK inhibitors may be an effective treatment strategy in HGSC. The utility of CCNE1 copy number status as a predictive tool should be explored in a clinical setting. As with other molecularly targeted therapeutics, we also demonstrated that resistance to CDK inhibition can emerge despite initial sensitivity. Resistance to molecularly targeted therapies can arise through mutation or amplification of target proteins or deregulation of other signaling pathway components (20). The level of resistance we observed in cell lines after prolonged drug exposure was modest and may relate to both inhibitors targeting multiple CDKs or compensation by other CDKs. We were, however, able to identify two possible mechanisms of resistance to CDK2 inhibitors; one involving up-regulation of CDK2 protein, consistent with previous studies with other CDK inhibitors (1), and a second novel mechanism of resistance to CDK inhibitors associated with selection of polyploid cells.

Resistance to CDK inhibitors through naturally occurring mutations in target genes has not been described to date. We found no evidence of CCNE1 copy number change or CDK2 mutation in resistant cell lines derived after extended exposure to PHA-533533. Recent studies in Xenopus demonstrate that engineering of compound mutations in the kinase domain of Cdk2 can achieve resistance to CDK inhibitors (26). However, the requirement for multiple residue changes may limit the likelihood of emergence of resistance by mutation in vivo.

Page 15 of 22

Previous studies have shown an association between tetraploidization of tumor cells to DNA-damaging agents (27) and targeted agents (28). In our study, rather than inducing an increase in DNA ploidy leading to drug resistance, treatment with CDK2 inhibition appeared to select for pre-existing polyploid cells. Defective apoptotic pathways, facilitating the survival of polyploid cells, may also influence their sensitivity to cytotoxic and targeted agents. Recently, tetraploid cells have been shown to have an increased sensitivity to Aurora B inhibition (29), presenting a potential therapeutic approach for these tumors. However, we were unable to demonstrate increased sensitivity of the 533533-R1 line or FACS-sorted hyperpentaploid cells to an Aurora B specific inhibitor (data not shown).

Although flow cytometry-sorted hyperpentaploid cells had increased resistance to CDK2 inhibition, this was not to the extent of cells selected in the presence of drug, suggesting that high DNA ploidy does not fully account for the resistance observed in drug-exposed cell lines. Indeed, increased genomic stability in polyploid cells may facilitate the accumulation of further genomic changes. SNP-based copy number analysis and cell karyotyping revealed structural and copy number changes that may contribute to increased resistance, including increased copy number of AKT1. Activation of the AKT pathway may promote DNA repair and cell survival and has been associated with resistance to chemo- and radiotherapy previously (30). The lack of resistance to cisplatin suggests that the reduced sensitivity of sorted hyperpentaploid cells to PHA-533533 is not due to the selection of cells with a generalized attenuation of apoptotic responses to cytotoxic agents.

Consistent with our in vitro data, we found a clear association between CCNE1 copy number increase and high DNA ploidy in primary tumors. The association was specific to CCNE1 gene amplification and not an increased number of amplification events overall. Our analysis of primary tumor data is consistent with previous in vitro studies showing that constitutive overexpression of CCNE1 does not increase the overall number of gene amplification events, but does increase the frequency of polyploid tumor cells (31). Expression of the hyperactive low-molecular weight (LMW) isoform of CCNE1 has also been shown to lead to failed cytokinesis and polyploidy in breast tumor cells (32).

Page 16 of 22

Our findings demonstrate that while therapeutic strategies designed to inhibit CDK2 function may prove useful in the treatment of CCNE1 amplified tumors, resistance related to a propensity for increased ploidy in these tumors is likely to emerge.

ACKNOWLEDGMENTS The authors wish to acknowledge assistance from Elaine Sanij, Sophie Kostakidis and Viki Milovac in conducting cell sorting experiments by flow cytometry.

GRANT SUPPORT This study was funded by a National Health and Medical Research Council (NHMRC) project grant (APP 1042358).

AUTHOR’S CONTRIBUTIONS Conception and design: DE, DB Development of methodology: DE Acquisition of data: DE, GA, MW, CM, EL, CB, SF, IG Analysis and interpretation of data: DE, GA, MW, MK, CB, JG, BAW, SC, PMW, GG, LJC, DB Writing, review and/or revision of the manuscript: DE, GA, MW, BAW, LM, DR, WCH, CC, LJC, DB Study supervision: DB

Page 17 of 22

REFERENCES

1. Stone A, Sutherland RL, Musgrove EA. Inhibitors of cell cycle kinases: recent advances and future prospects as cancer therapeutics. Crit Rev Oncog 2012; 17:175-98. 2. Krystof V, Uldrijan S. Cyclin-dependent kinase inhibitors as anticancer drugs. Curr Drug Targets 2010; 11:291-302. 3. Vaughan S, Coward JI, Bast RC, Berchuck A, Berek JS, Brenton JD, et al. Rethinking ovarian cancer: recommendations for improving outcomes. Nat Rev Cancer 2011; 11:719-25. 4. Mayr D, Kanitz V, Anderegg B, Luthardt B, Engel J, Löhrs U, et al. Analysis of Gene Amplification and Prognostic Markers in Ovarian Cancer Using Comparative Genomic Hybridization for Microarrays and Immunohistochemical Analysis for Tissue Microarrays. American Journal of Clinical Pathology 2006; 126:101-9. 5. Etemadmoghadam D, Defazio A, Beroukhim R, Mermel C, George J, Getz G, et al. Integrated genome-wide DNA copy number and expression analysis identifies distinct mechanisms of primary chemoresistance in ovarian carcinomas. Clin Cancer Res 2009; 15:1417- 27. 6. Caldon CE, Musgrove EA. Distinct and redundant functions of cyclin E1 and cyclin E2 in development and cancer. Cell Div 2010; 5:2. 7. Etemadmoghadam D, George J, Cowin PA, Cullinane C, Kansara M, Group AOCS, et al. Amplicon-Dependent CCNE1 Expression Is Critical for Clonogenic Survival after Cisplatin Treatment and Is Correlated with 20q11 Gain in Ovarian Cancer. PLoS ONE 2010; 5:e15498. 8. Nakayama N, Nakayama K, Shamima Y, Ishikawa M, Katagiri A, Iida K, et al. Gene amplificationCCNE1is related to poor survival and potential therapeutic target in ovarian cancer. Cancer 2010:NA-NA. 9. Węsierska-Gądek J, Maurer M, Zulehner N, Komina O. Whether to target single or multiple CDKs for therapy? That is the question. J Cell Physiol 2011; 226:341-9. 10. Masibay A, Mozer TJ, Sprecher C. Promega Corporation reveals primer sequences in its testing kits. J Forensic Sci 2000; 45:1360-2. 11. Cheung HW, Cowley GS, Weir BA, Boehm JS, Rusin S, Scott JA, et al. Systematic investigation of genetic vulnerabilities across cancer cell lines reveals lineage-specific dependencies in ovarian cancer. Proc Natl Acad Sci USA 2011. 12. Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 2012; 483:603-7. 13. Etemadmoghadam D, deFazio A, Beroukhim R, Mermel C, George J, Getz G, et al. Integrated genome-wide DNA copy number and expression analysis identifies distinct mechanisms of primary chemoresistance in ovarian carcinomas. Clin Cancer Res 2009; 15:1417- 27. 14. Olshen AB, Venkatraman ES, Lucito R, Wigler M. Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics 2004; 5:557-72. 15. Cowin PA, George J, Fereday S, Loehrer E, Van Loo P, Cullinane C, et al. LRP1B Deletion in High-Grade Serous Ovarian Cancers Is Associated with Acquired Chemotherapy Resistance to Liposomal Doxorubicin. Cancer Research 2012; 72:4060-73. 16. Smyth GK. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 2004; 3:Article3. 17. George J, Alsop K, Etemadmoghadam D, Hondow H, Mikeska T, Dobrovic A, et al. Non- equivalent gene expression and copy number alterations in high-grade serous ovarian cancers with BRCA1 and BRCA2 mutations. Clin Cancer Res 2013. 18. Pevarello P, Brasca MG, Orsini P, Traquandi G, Longo A, Nesi M, et al. 3-Aminopyrazole inhibitors of CDK2/cyclin A as antitumor agents. 2. Lead optimization. J Med Chem 2005; 48:2944-56. 19. Parry D, Guzi T, Shanahan F, Davis N, Prabhavalkar D, Wiswell D, et al. Dinaciclib (SCH 727965), a novel and potent cyclin-dependent kinase inhibitor. Mol Cancer Ther 2010; 9:2344-53. 20. Lackner MR, Wilson TR, Settleman J. Mechanisms of acquired resistance to targeted cancer therapies. Future Oncol 2012; 8:999-1014.

Page 18 of 22

21. Deans AJ, Khanna KK, McNees CJ, Mercurio C, Heierhorst J, McArthur GA. Cyclin- dependent kinase 2 functions in normal DNA repair and is a therapeutic target in BRCA1-deficient cancers. Cancer Research 2006; 66:8219-26. 22. Akli S, Keyomarsi K. Cyclin E and its low molecular weight forms in human cancer and as targets for cancer therapy. Cancer Biol Ther 2003; 2:S38-47. 23. Carter SL, Cibulskis K, Helman E, McKenna A, Shen H, Zack T, et al. Absolute quantification of somatic DNA alterations in human cancer. Nat Biotechnol 2012; 30:413-21. 24. Natrajan R, Mackay A, Wilkerson PM, Lambros MB, Wetterskog D, Arnedos M, et al. Functional characterization of the 19q12 amplicon in grade III breast cancers. Breast cancer research : BCR 2012; 14:R53. 25. Davis SJ, Sheppard KE, Pearson RB, Campbell IG, Gorringe KL, Simpson KJ. Functional Analysis of Genes in Regions Commonly Amplified in High-Grade Serous and Endometrioid Ovarian Cancer. Clinical Cancer Research 2013:1-12. 26. Echalier A, Cot E, Camasses A, Hodimont E, Hoh F, Jay P, et al. An integrated chemical biology approach provides insight into Cdk2 functional redundancy and inhibitor sensitivity. Chemistry & Biology 2012; 19:1028-40. 27. Castedo M, Coquelle A, Vitale I, Vivet S, Mouhamad S, Viaud S, et al. Selective resistance of tetraploid cancer cells against DNA damage-induced apoptosis. Ann N Y Acad Sci 2006; 1090:35-49. 28. Shen H, Moran DM, Maki CG. Transient nutlin-3a treatment promotes endoreduplication and the generation of therapy-resistant tetraploid cells. Cancer Research 2008; 68:8260-8. 29. Marxer M, Foucar CE, Man WY, Chen Y, Ma HT, Poon RYC. Tetraploidization increases sensitivity to Aurora B kinase inhibition. Cell Cycle 2012; 11:2567-77. 30. Xu N, Lao Y, Zhang Y, Gillespie DA. Akt: a double-edged sword in cell proliferation and genome stability. Journal of Oncology 2012; 2012:951724. 31. Spruck CH, Won KA, Reed SI. Deregulated cyclin E induces chromosome instability. Nature 1999; 401:297-300. 32. Bagheri-Yarmand R, Biernacka A, Hunt KK, Keyomarsi K. Low molecular weight cyclin E overexpression shortens mitosis, leading to chromosome missegregation and centrosome amplification. Cancer Research 2010; 70:5074-84.

Page 19 of 22

FIGURE LEGENDS

Figure 1 A) Clonogenic survival after transfection with CCNE1 and CDK2 siRNAs in SK-OV-3 (CCNE1 unamplified), OVCAR-4 (CCNE1 gained) and OVCAR-3 (CCNE1 amplified) ovarian cell lines. Average percentage of discrete colonies formed after 7 days relative to no siRNA controls shown (n = 3). Statistical significance (T-test) calculated by comparison to non-silencing (NS) siRNA in the same cell line. B) CCNE1 and CDK2 protein level after gene knockdown assessed by western blot. C) Boxplots of median shRNA abundance in 102 tumor cell lines, and a subset of 25 ovarian cell lines, stratified by CCNE1 copy number status. Data includes multiple shRNA hairpins targeting CCNE1 (n = 4) and CDK2 (n = 12). Depletion of shRNA within a group suggests requirement for maintained expression of its target gene. D) Microarray sample cluster showing relative abundance of individual shRNA hairpins against CCNE1 and CDK2 in ovarian cell lines.

Figure 2 A) PHA-533533 and B) Dinaciclib dose-response analysis of clonogenic survival in ovarian tumor cell lines. Bar graphs indicate IC50 values derived from dose-response curves plotted as the average percentage of discrete colonies formed compared to untreated controls (n = 3). C) Western blot showing decrease in phosphorylated-Rb (Ser 807/811) and appearance of PARP cleavage products with increasing concentration of PHA-533533 and D) dinaciclib after 24 hours of drug exposure. Error bars indicate SEM. *p-value <0.05, **p-value <0.01, ***p-value <0.001.

Figure 3 A) Experimental schematic for deriving OVCAR-3 cell lines resistant to PHA-533533. B) PHA-533533 IC50 values determined using a 72 hr MTS proliferation assay for the parental cell line and resistant cell lines (n = 5) immediately after drug selection (passage 1) and after maintained growth in drug (passage 4). Dose-response curves of individual 533533-R1 resistant cell line passages shows average normalized absorbance from triplicate wells. C) IC50 values for 533533-R1 over time. Cells were cultured in the presence (solid line) or absence of PHA-533533 (dashed line). The IC50 value for parental cells is indicated by the dotted line. D) Average IC50 values for the parental and 533533-R1 cell line against dinaciclib, cisplatin and doxorubicin determined

Page 20 of 22

using a 72 hr MTS proliferation assay (n = 3). E) Western blot of phosphorylated-Rb (Ser 807/811) and PARP cleavage products in OVCAR-3 parental, 533533-R1 and –R6 resistant cell lines, 24 hours after treatment with PHA-533533.

Figure 4 A) SNP microarray copy number profiles across chromosome 19 for OVCAR-3 parental and five PHA-533533 resistant cell lines. Peak amplification at 19q12 incorporating CCNE1 indicated. B) Gene expression heatmap of parental and resistant cell lines after drug selection (selected) and after maintained growth in media (stable). Samples clustered by 1142 unique genes differentially expressed between each pair-wise comparison (FDR-corrected p < 0.05). C) Venn diagram depicts number of significantly differentially expressed genes (FDR-corrected p < 0.05) identified in each pair-wise comparison. D) Dot plot of CCNE1 and CDK2 microarray gene expression in parental (n = 4) and resistant cell lines maintained in inhibitor (selected) or in media (stable) (n = 5). Error bars indicate SEM. *p-value <0.05, **p-value <0.01, ***p-value <0.001.

Figure 5 A) Cell cycle profile of PI stained cells analyzed by flow cytometry. Parental (P) and the resistant cell line 533533-R6 (R6) consist of two dominant populations. G1 and G2/M

peaks of the hypotriploid population (AG1 and AG2) and the hyperpentaploid population

(BG1 and BG2) are labeled. The 533533-R1 (R1) cell line is comprised of the hyperpentaploid population only. B) Proportion of hypotriploid and hyperpentaploid cell populations estimated by analysis of Parental and Resistant cell line FACS traces. C) Representative hypotriploid and hyperpentaploid karyotypes of the OVCAR-3 parental cell line. D) Representative karyotype for the hyperpentaploid 533533-R1 cell line. E) Profile of unsorted PI stained OVCAR-3 cells and hypotriploid and hyperpentaploid populations collected after sorting by flow cytometry. G1 and G2 peaks of the

hypotriploid population (AG1 and AG2) and the hyperpentaploid population (BG1 and BG2) are labeled. F) Proportion of hypotriploid and hyperpentaploid cells present in sorted cells estimated by analysis of FACS traces. G) Dose-response analysis of unsorted (parental) and flow-sorted OVCAR-3 cell line sensitivity to PHA-533533 using a 72 hr MTS assay. Dose-response curves for each cell line shows average normalized absorbance to untreated cells at increasing drug doses (n = 3). Bar graphs show average derived IC50 values. Error bars are SEM. T-test *p-value <0.05.

Page 21 of 22

Figure 6 A) Frequency of whole genome doubling (WGD) events and B) 2N or >2N DNA ploidy states inferred from SNP microarray data of TCGA primary ovarian tumors using

ABSOLUTE (23). Samples are stratified by CCNE1 log2 copy number ratio; >0.3 for

gains and >0.8 for high-level amplifications. C) Fraction of genome amplified (log2 copy number ratio >0.8) for tumor samples stratified by number of WGD events or D) CCNE1 copy number status. T-test **p-value <0.01, ***p-value <0.001. E) Kaplan-Meir curves

showing overall survival of patients (n = 397) stratified by CCNE1 amplification (log2 copy number ratio >0.8) and number of WGD events. Log-rank p-value reported. F) Immunohistochemical staining at 200X magnification of Cyclin E1 in a HGSC sample with CCNE1 gene amplification. Nuclear staining of tumor cells identified some cells with giant nuclei (arrows).

Page 22 of 22

A C D Various Lines

B Ovarian

Downloaded from clincancerres.aacrjournals.org on September 26, 2021. © 2013 American Association for Cancer Research. Figure 1 Author Manuscript Published OnlineFirst on September 4, 2013; DOI: 10.1158/1078-0432.CCR-13-1337 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

A C PHA-533533

B D Dinaciclib

Downloaded from clincancerres.aacrjournals.org on September 26, 2021. © 2013 AmericanFigure Association 2 for Cancer Research. Author Manuscript Published OnlineFirst on September 4, 2013; DOI: 10.1158/1078-0432.CCR-13-1337 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

A Inhibitor Inhibitor Maintain in Inhibitor Media Inhibitor Selected Cells Parental Line 4 µM 4 µM Selected Cells Selected Cells OVCAR-3 72 72 Expand (Passage 1) (Passage 4) Stable Resistant T1 hrs hrs survivors Media Cell Line T2

repeat once B C PHA-533533 T1 T2 Inhibitor Media

533533-R1 533533-R1

Passage ~12 months

D E Dinaciclib Cisplatin Doxorubicin

Downloaded from clincancerres.aacrjournals.org on September 26, 2021. © 2013 American Association for Cancer Research. Figure 3 Author Manuscript Published OnlineFirst on September 4, 2013; DOI: 10.1158/1078-0432.CCR-13-1337 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

A B C

Parental vs Selected

573

Selected 347 6 Parental vs 0 vs Stable 213 2 1 Stable

Downloaded from clincancerres.aacrjournals.org on September 26, 2021. © 2013 American Association for Cancer Research. Figure 4 Author Manuscript Published OnlineFirst on September 4, 2013; DOI: 10.1158/1078-0432.CCR-13-1337 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

A B

C D Parental Hypotriploid Parental Hyperpentaploid 533533-R1 Hyperpentaploid

E F

sort

Downloaded from clincancerres.aacrjournals.org on September 26, 2021. © 2013 American Association for Cancer Research. Figure 5 Author Manuscript Published OnlineFirst on September 4, 2013; DOI: 10.1158/1078-0432.CCR-13-1337 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

A B

Genome Doublings Ploidy Bin 80 100 0 2N 1 80 >2N 60 >1 60 40 40

20 20 Frequency of WGD (%) WGD of Frequency

0 (%) Frequency Bin Ploidy 0 Unamp Gain Amp Unamp Gain Amp CCNE1 Status CCNE1 Status C D *** ** 0.15 0.15

0.10 0.10

0.05 0.05 ANOVA ANOVA p <0.01 p = 0.608 0.00 0.00 Fraction GenomeAmplified Fraction Fraction GenomeAmplified Fraction 0 1 >1 Unamp Gain Amp Genome Doublings CCNE1 E F

CCNE1 ; WGD 100 non-amplified ; 0 80 non-amplified ; 1 amplified ; 0 60 amplified ; 1 40

Percent Survival Percent 20 p < 0.03 0 0 2 4 6 8 10 100µm Overall Survival (years)

Downloaded from clincancerres.aacrjournals.org on September 26, 2021. © 2013 American Association for Cancer Research. Figure 6 Author Manuscript Published OnlineFirst on September 4, 2013; DOI: 10.1158/1078-0432.CCR-13-1337 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Resistance to CDK2 inhibitors is associated with selection of polyploid cells in CCNE1 amplified ovarian cancer

Dariush Etemadmoghadam, George Au-Yeung, Meaghan Wall, et al.

Clin Cancer Res Published OnlineFirst September 4, 2013.

Updated version Access the most recent version of this article at: doi:10.1158/1078-0432.CCR-13-1337

Supplementary Access the most recent supplemental material at: Material http://clincancerres.aacrjournals.org/content/suppl/2013/09/04/1078-0432.CCR-13-1337.DC1

Author Author manuscripts have been peer reviewed and accepted for publication but have not yet been Manuscript edited.

E-mail alerts Sign up to receive free email-alerts related to this article or journal.

Reprints and To order reprints of this article or to subscribe to the journal, contact the AACR Publications Subscriptions Department at [email protected].

Permissions To request permission to re-use all or part of this article, use this link http://clincancerres.aacrjournals.org/content/early/2013/08/31/1078-0432.CCR-13-1337. Click on "Request Permissions" which will take you to the Copyright Clearance Center's (CCC) Rightslink site.