Molecular Cell Biology

Genome-Wide Interrogation of Human CancersIdentifies EGLN1 Dependency in Clear CellOvarian CancersColles Price1,2,3, Stanley Gill1,2, Zandra V. Ho1, Shawn M. Davidson1,4, Erin Merkel1,James M. McFarland1, Lisa Leung1, Andrew Tang1, Maria Kost-Alimova1,Aviad Tsherniak1, Oliver Jonas1,4, Francisca Vazquez1,2, and William C. Hahn1,2,3,5

Abstract

Wehypothesized that candidate dependencies forwhich thereare small molecules that are either approved or in advanceddevelopment for a nononcology indication may representpotential therapeutic targets. To test this hypothesis, we per-formed genome-scale loss-of-function screens in hundreds ofcancer cell lines. We found that knockout of EGLN1, whichencodes prolyl hydroxylase domain-containing protein 2(PHD2), reduced theproliferationof a subsetof clear cell ovariancancer cell lines in vitro. EGLN1-dependent cells exhibited sen-sitivity to the pan-EGLN inhibitor FG-4592. The response to FG-

4592 was reversed by deletion of HIF1A, demonstrating thatEGLN1dependencywas related to negative regulation ofHIF1A.We also found that ovarian clear cell tumors susceptible to bothgenetic and pharmacologic inhibition of EGLN1 required intactHIF1A. Collectively, these observations identify EGLN1 as acancer target with therapeutic potential.

Significance: These findings reveal a differential dependencyof clear cellovariancancersonEGLN1, thus identifyingEGLN1asa potential therapeutic target in clear cell ovarian cancer patients.

IntroductionThe development of genome-scale methods to interrogate the

function of genes now provides a path to systematically identifygenes that are essential for cell survival. Several studies using RNAiand more recently CRISPR-Cas9 to suppress or delete genes haveidentified cell-essential genes in human cancer cell lines (1–8).Compared with similar experiments in yeast (9, 10), these studieshave identified a larger number of essential genes in humancells (7, 10–12).

We and others have used similar approaches to identify genesthat are essential for subsets of cancer cell lines (7, 10–13). Anumber of screens involving a modest number of cell lines haveidentified several cancer dependencies related to oncogenicmuta-tion (12) or genes that become required in the setting of loss of aparalog (11, 13). In recent work, we and others have expandedsuch efforts to hundreds of cancer cell lines and defined severaltypes of dependencies that occur in subsets of cancers (2, 5, 7,12, 13). Such efforts will eventually lead to a comprehensive mapof cancer dependencies.

A direct downstream sensor of oxygen tension is the family ofprolyl hydroxylases called EGLN (Egl-9 family hypoxia-induciblefactor 1). When oxygen is present, EGLN, also known as hypoxia-inducible factor prolyl hydroxylase (HIF-PH) and prolyl hydrox-ylase domain-containing protein (PHD), hydroxylates HIF1,which results in a binding site for a ubiquitin ligase complex thatincludes VHL (von Hippel-Lindau tumor suppressor), leading toHIF1 degradation (14–17). This leads to HIF1 degradation byforming a binding site for a ubiquitin ligase complex that includesVHL (14–17). Among the three EGLN family members, EGLN1 isgenerally regarded as the primaryHIF1hydroxylase, while EGLN2and EGLN3 regulate HIF1 under specific conditions (15–17).With decreased concentrations of oxygen, EGLN-mediatedhydroxylation of HIF1 is reduced, which leads to increased levelsof HIF1 (17). When HIF1 is increased under low oxygen condi-tions, the a subunit heterodimerizes with the b subunit andtranslocates to the nucleus to activate genes that promote glucoseuptake, glycolysis, and decreases oxidative phosphorylation (17).

We initiated an effort to identify differential dependencies byinterrogating the data derived from screening a large number ofcell lines with genome-scale RNAi and CRISPR-Cas9 libraries.From a list of nominated strong preferential dependencies, weidentified EGLN1 as a dependency preferentially required for theviability of a subset of cancer cell lines.

Materials and MethodsAnalysis of dependency data

We used gene dependency data from Project Achilles includingdata from the screening of 501 cancer cell lines by RNAi (�94kshRNAs,�5 shRNAs/gene) and 436 cancer cell lineswithCRISPR-Cas9 (�70k sgRNAs, �4 sgRNAs/gene; refs. 1–5). EGLN1 wasoriginally identified as an interesting target to pursue using theDEMETER six sigma dependencies (2). Further analyses were

1Broad Institute of Harvard and MIT, Cambridge, Massachusetts. 2Department ofMedical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts. 3Har-vard Medical School, Boston, Massachusetts. 4Koch Institute for IntegrativeCancer Research, Massachusetts Institute of Technology, Cambridge, Massa-chusetts. 5Department of Medicine, Brigham and Women's Hospital, Boston,Massachusetts.

Note: Supplementary data for this article are available at Cancer ResearchOnline (http://cancerres.aacrjournals.org/).

Corresponding Author: William C. Hahn, Dana-Farber Cancer Institute, Dana1538, 450 Brookline Avenue, Boston, MA 02215. Phone: 617-632-2641; Fax: 617-632-4005; E-mail: william_hahn@dfci.harvard.edu

doi: 10.1158/0008-5472.CAN-18-2674

�2019 American Association for Cancer Research.

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performed using CERES CRISPR/Cas9 dependency data (7, 18–22) and DEMETER2 RNAi (20, 21). All significant findings aresummarized in the Supplementary Tables.

Identifying features associated with EGLN1 dependencyWe used multiple approaches to find relationships between

EGLN1 dependency and mRNA expression, copy number, muta-tion, or dependency data [see Cancer Cell Line Encyclopedia(CCLE) Omics Data section for details]. To identify genes whosemutation was associated with EGLN1 dependency, we used two-sample t tests to measure the mean difference of dependencybetween mutant and WT cell lines for each gene and type ofmutation (damaging or missense). Only mutations present in atleast 5 cell lines were included in the analysis. To identify othercontinuous-valued (expression, copy number, dependency) fea-tures associated with EGLN1 dependency, we used the R packageLimma. Linear model coefficients were used to assess the strengthof association between each feature and EGLN1 dependency, Pvalues were determined using empirical Bayes moderated t-sta-tistics (analyzing each data type separately). Note that we alsoused this method to identify top dependencies associated withHIF1A expression in Supplementary Fig. S1M.

CCLE omics dataWe used the following CCLE (22) datasets available for down-

load at https://depmap.org/portal/download/all/.mRNA Expression: CCLE_DepMap_18Q2_RNAseq_RPKM_

20180502.gctCopy number: public_18Q2_gene_cn_v2.csvDamaging/nondamaging mutations: CCLE_DepMap_18Q2_

maf_20180502.txt (genes are classified as having a "damagingmutation" if isDeleterious is "TRUE" for any of its mutations).

Identifying gene sets associated with EGLN1 dependencyUsing the GenePattern software platform developed by the

Broad Institute, we ran single-sample Gene Set Enrichment Anal-ysis (ssGSEA) on ovarian mRNA expression to calculate theactivity level of each HALLMARK gene set in each ovarian cellline (using theHallmark collection from theMolecular SignaturesDatabase). We then found gene sets whose ssGSEA values wereassociated with EGLN1 dependency using linear model analysisas mentioned above.

Lineage enrichment analysisUsing a one-sided Fisher exact test, we measured the positive

enrichment of EGLN1-dependent lines in a given lineage com-pared with all other lineages. We defined CRISPR EGLN1 depen-dencies as cell lines with at least a 50% probability of beingdependent, leveraging the CERES probabilities of dependencythat were published alongside the CERES effect scores (19).Lineages represented by fewer than 5 lines were filtered out andfalse discovery rates were calculated to correct for multiplehypothesis testing.

Cell culture conditionsAll cell lines were provided from stocks curated by the

Cancer Dependency Map project at the Broad Institute andoriginally obtained from the CCLE (www.broadinstitute.org/ccle) unless otherwise indicated. All cell lines were finger-printed using one of two genotyping platforms, Sequenom orFluidigm. Mycoplasma testing was performed when cell lines

were obtained from CCLE. Cell lines expressing pLX_311-cas9were generated by the Project Achilles for use in the genome-wide pooled CRISPR screening data used to inform this project.ES2, OVTOKO, TOV112D, HEYA8, OVISE were cultured inRPMI1640 þ 10% FBS. OVK18 was cultured in MEM þ 10%FBS. COV434 was cultured in DMEM þ 10% FBS þ 2 mmol/Lglutamine. TOV21G was cultured in MCDB105:Medium199(1:1) þ 15% FBS. JHOC5 was cultured in DMEM:F12 (1:1) þ10% FBS þ 0.1 mmol/L NEAA. All media were supplementedwith penicillin–streptomycin.

Vectors and constructsAll vectors andplasmids used for these experiments, where they

were obtained and sequences for all single-guide RNAs (sgRNA)designed are listed in Supplementary Table S1.

Arrayed lentivirus production293T cells were seeded in 6-well plates at 1.5 � 106 per well

(2-mL volume) 24 hours pretransfection. Transfection was per-formed using TransIT-LTI Transfection Reagent (Mirus). 8.25 mLof LT1 was diluted in 75 mL of Opti-MEM (Corning) for eachwell and incubated at room temperature for 5 minutes. Thediluted LTI mixture was added to a mixture of 1,250 ng PsPAX2,250 ng VSVG, and 1,250 ng lentivector DNA suspended inOpti-MEM and incubated at room temperature for 20–30 min-utes. The transfection mixture was then added to the plate ofcells, spun at 1,000� g for 30minutes, and incubated at standardcell culture conditions (37�C, 5%CO2) for 6–8hours.Mediawerethen changed to high serum media (DMEM þ 30% FBS) andincubated until harvest after 48–72hours. Viral supernatantswerestored at �80�C until use in the experiments.

ImmunoblotsCells were infected with lentiviruses expressing sgRNAs tar-

geting EGLN1 and selected with puromycin at a concentrationof 4 mg/mL for 48–72 hours or until all uninfected cells weredead. Whole-cell lysates were prepared using RIPA buffer(Sigma-Aldrich) supplemented with EDTA-free Protease Inhib-itor Cocktail (Roche), 1 mmol/L sodium orthovanadate (NEB),and 5 mmol/L sodium fluoride (NEB). Protein levels werequantified using the Pierce BCA assay kit (Thermo FisherScientific). Protein lysates were run on 4%–12% Bis-Tris Pre-Cast gels (Thermo Fisher Scientific) and proteins transferred toa polyvinylidene difluoride membrane using the iBlot 2 system(Thermo Fisher Scientific). HIF1a levels were detected using amouse monoclonal anti-HIF1a antibody at 1:1,000 dilution(BD Biosciences, catalog no. #610958) and a LI-COR–compat-ible anti-mouse IR secondary antibody (LI-COR, catalog no.#926-68020) at 1:5,000 dilution. EGLN1 levels were detectedusing a rabbit monoclonal EGLN1 antibody (Cell SignalingTechnology, catalog no. #3293) at 1:1,000 and a LICOR-compatible anti-rabbit IR secondary antibody (#926-32211)at 1:5,000 dilution. VHL levels were detected using a rabbitmonoclonal VHL antibody (Cell Signaling Technology, catalogno. #685475) and a LI-COR–compatible anti-rabbit IR second-ary antibody. GAPDH levels were detected using a rabbitmonoclonal GAPDH antibody (Cell Signaling Technology,14C10 #2118) at 1:1,000 and the same LI-COR–compatibleanti-rabbit IR secondary antibody at 1:5,000 dilution.

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EGLN1 and VHL inhibitorsEGLN1 inhibitors IOX2 (catalog no. S2919), roxadustat FG-

4592 (catalog no. S1007), Molidustat Bay-85-3934 (catalog no.S8138), FG-2216 (catalog no. S7979), and DaprodustatGSK1278863 (catalog no. S8171) were obtained from SelleckChemicals (www.selleckchem.com). The VHL inhibitor VH298was obtained from Tocris Bioscience (catalog no. 6156). Com-pounds were dissolved in DMSO per manufacturer's instructionsto an initial stock concentration of 10 mmol/L or formulated in50% PEG for microdevice experiments.

Luciferase competition assayInfections to make cell lines expressing open reading frames

(ORF) for Firefly luciferase 2P (gateway cloned into pLX_313from Promega pGL4.11[FLuc2P]) and Renilla luciferase(pLX_313) were performed by centrifuging freshly seeded platescontaining cells with lentiviral particles and 4 mg/mL polybrenefor 2 hours at 2,000 rpm. Cell lines expressing pLX_311-cas9 wereinfected with pLX_313-FireflyLuc2P and parental cell lines wereinfected with pLX_313-RenillaLuc. Firefly- and Renilla-expressingcells weremixed 1:1 inwells of a 96-well plate containing 4mg/mLpolybrene. After mixing the cell population, we added sgRNAlentivirus. Both firefly- and Renilla-expressing cells were infectedwith the sgRNA, but only the firefly expressing cells have Cas9.Freshly seeded plates were spun at 2,000 rpm for 30 minutes andincubated overnight at standard cell culture conditions. Thefollowing day, infected cells were selected using 4 mg/mL puro-mycin for 48hours. Every 3–4days following the selection period,the plate is split into two new plates. One plate is read 24 hourslater using reagents from a Dual-Glo Luciferase Assay Kit(Promega). The other plate continues until the next time point.Luciferase signal was quantified using an Envision Plate Reader.Data was expressed as the fold change of the ratio of Fireflyluciferase signal to Renilla luciferase signal normalized to thesignal ratio postselection from an infection with the emptysgRNA delivery vector (xPR_003) and averaged across six bio-logical replicates. Results are representative of two independentexperiments.

CellTiter-Glo viability assaysWe performed CellTiter-Glo (CTG) viability assays per the

manufacturer's instructions. Briefly,we seeded5,000–10,000 cellsper well in a 96-well plate then treated with either DMSO orEGLN1 inhibitor in doses indicated. After 3–6 days treatment(retreated at day 4), we added CTG compound and measuredviability. Data presented are the average of three technical repli-cates. Data are representative of three independent experiments.

Proliferation assaysNon-Cas9–expressing cells were plated on a 12-well or 6-well

plate in triplicate with varying concentrations of FG-4592. Every3–4 days cells were trypsinized, counted using Nexcelom Cell-ometer, and reseeded at equal densities with the same concentra-tions of FG-4592, IOX2, BAY85-3934, or VH298. Data presentedare the average of three technical replicate wells when available,excluding wells where there are no live cells to passage. Data arerepresentative of two independent experiments.

Flow cytometry analysisFlow cytometry was performed at the Broad Institute

Flow Cytometry core using CFSE [5(6)-Carboxyfluorescein

N-hydroxysuccinimidyl ester] Cell Labeling Kit (Abcamab113853) to label dividing cells with CFSE and analyzed onFL-1 Excitation(max) 492nm, Emission(max) 517nm.Apoptosiswas detected using PE Annexin V Apoptosis Detection Kit 1(BD Biosciences 559763). Dual labeling Annexin V with7-Amino-Actinomycin (7-AAD) allowed quantification of apo-ptosis. Data are presented as the average of two technical repli-cates and is representative of three independent experiments.Data were analyzed using FlowJo data analysis.

Hypoxia response elementThehypoxia response element (HRE)was designed on the basis

of previous publications (18, 19) andwas clonedwith a luciferasereporter. The construct was then stably transfected to EGLN1-dependent and independent cell lines. After transfection, cellswere treated with EGLN1 inhibitor for up to 6 days and luciferaseactivity was measured as a readout of HIF1A activity.

Colony formation assay and dose–response curvesCells were plated at a low density (250–1,000 cells/well) with

varying concentrations of FG-4592 (Selleck) on 24-well plates.Media and drug were refreshed every 3–4 days for 10 days. Cellswere fixed on the plate with 10% neutral buffered formalin(Thermo Fisher Scientific) and stained with 0.1% crystal violetin 10% ethanol. After plates were washed and dried, dye wasextracted using 10% acetic acid. Absorbance of extracted dyesamples at 595 nm was quantified using a Molecular DevicesSpectraMax M5 Plate Reader. Data presented are normalized tothe DMSO-treated sample. Data are representative of three inde-pendent experiments.

Animal modelsFemale AthymicNu/Nu mice (8–10 weeks old) were used and

obtained from Jackson Laboratories and had access to food andwater in appropriate housing conditions. All animal proceduresperformed at the Broad Institute were approved by the Institu-tional Animal Care and Use Committee and according to insti-tutional regulations.

In vivo transplantationCells were infected with lentivirus expression control sgChr2-1

or sgEGLN1-9. After 24 hours of selection, cells were subcutane-ously implanted into recipient mice (athymicNu/Nu, Jackson Lab-oratories) in 50% Matrigel at a concentration of 250,000 cells(ES2) or 400,000 cells (OVISE). Mice were implanted withsgChr2-1 and sgEGLN1-9 at opposite side flanks to control forany variability between mice. Following the detection of tumors,tumor size was quantified at least once per week using a calipermeasuring tumor dimensions. Mice were euthanized at the end ofthe experiment. Tumors were harvested from the euthanizedmiceand dissociated in cell culture and further saved for RNA, protein,or frozen cell storage.

RT-PCRES2-formed tumors harvested from implanted cells were iso-

lated from total tumor. Total RNA was isolated using QIAGENmiRNeasy Mini Kits. Reverse transcription for RNA samples wasperformed using Thermo Fisher Superscript III First-Strand Syn-thesis System (Thermo Fisher Scientific; #18080-051). RT-PCRwas performed on the QuantStudio 6 Flex (Applied Biosystems)using SYBR Green Master Mix available from Thermo Fisher

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Figure 1.

Identification of EGLN1 as a preferential cancer cell dependency. A, Identification of EGLN1 dependency in RNAi data from Project Achilles. From the initialapproximately 17k genes tested, we found 762 were strong (Six Sigma) dependencies using DEMETER scores. From these dependencies, we found 153 werecurrently druggable, while 15 of them had compounds in clinical trials. We identified EGLN1 as one of these 15 clinically druggable dependencies. B, Identificationof cancer cells dependent on EGLN1 using CRISPR-Cas9 data from Project Achilles. Histogram shows the distribution of EGLN1 CERES dependencies (x-axis)across 436 cancer cell lines screened with CRISPR. The left tail shows that a subset of lines are preferentially dependent on EGLN1. C, Concordance between RNAiand CRISPR-Cas9 datasets. EGLN1 DEMETER2 scores are graphed against EGLN1 CERES scores (CRISPR, x-axis) for the 243 cell lines screened in both datasets.The correlation between the datasets was strong and highly significant. Pearson¼ 0.512; n¼ 243; P < 10�21. D, Volcano plot showing cancer dependenciesassociated with EGLN1 dependency graphed as P value (�log10, y-axis) against effect size (x-axis). Red, other members of the EGLN1 pathway. E, EGLN1 and VHLare the strongest correlated dependencies within the EGLN1 pathway while EGLN1 and HIF1AN are the second strongest correlated dependencies. P values wereadjusted using the Benjamini and Hochberg FDRmethod. FDR < 0.05 (�), 0.01 (��), 0.001 (���). F, Cell lines that express low levels of HIF1A (y-axis) are notdependent on EGLN1 (x-axis).

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Scientific (catalog no. #4367659) with probes against EGLN1deletion site in triplicate. Comparative Ct was chosen for analysisand was normalized against RPS17 as an internal control.

RNA sequencingRNA sequencing was performing following 5-day treatment of

EGLN1-dependent and independent cell lines in triplicate witheither EGLN1 inhibitor or DMSO (control). Cell lines also hadEGLN1KOorChr2-1KO(control) andwere allowed to grow for 7days before cell pellets were collected and RNAwas isolated usingQiagen TRIzol (Qiazol) RNA extraction kit. Purified RNA wasprovided to RNA sequencing core at the Broad Institute andsequencing was performed on the Illumina HiSeq 2000 or HiSeq25000 instruments with coverage of 100 million paired 101nucleotides-long reads per sample. RNA reads were aligned usingTopHat version 1.4 and Gene RPKM values were calculated usingGTEx project pipeline.

Microdevice manufacturing and implantationDevices were manufactured as described previously (23).

Briefly, microscale devices at diameter 820 mm and length 3mm were manufactured and pure powder compounds wereformulated in 50% PEG. Mice were subcutaneously deliveredovarian cancer cells and tumors were allowed to grow to 1 cm2.Mice were anesthetized during implantation using 1% isoflur-ane. A 19-guage spinal biopsy needle (Angiotech) was insertedinto the tumor and served as the location for the device to beimplanted.

IHCIHC was done on 4-mm serial sections from formalin-fixed,

paraffin-embedded (FFPE) tumor tissues. The samples weredeparaffinized, rehydrated, and pretreated for antigen retrievalby microwave treatment. Immunostaining was done using cellproliferation marker Ki67 and cleaved caspase-3 at the KochInstitute Microscopy Core Facility.

Statistical analysisAll experiments presented here are shown either as the average

of at least three samples repeated twice and analyzed either withstandard Student t test or ANOVA as appropriate. Computationaltests such as GSEA and The Cancer Genome Atlas statisticalanalysis were performed using computational software toolsdeveloped at the Broad Institute andDana-Farber Cancer Institute(Boston, MA).

ResultsIdentificationof EGLN1as adruggable preferential dependency

Genes that are essential for cell viability in a context-specificmanner, in contrast to pan-essential genes, represent potentialcancer dependencies. To identify such genes, we have performedgenome-scale loss-of-function screens using RNAi and CRISPR-Cas9 technologies inhundreds of human cancer cell lines (2, 3, 5).Our earlier analysis of the data derived from screening 501humancancer cell lines with RNAi had identified 762 genes that wereessential for the proliferation/survival of a subset of cell lines at alevel of 6 SDs from the mean dependency score (2, 3, 5); astringent metric to find such differential dependencies. Of these762 genes, we found that 153 were classified as druggable basedonprevious annotations (Fig. 1A; Supplementary Table S2; ref. 2).

Among the druggable genes, 15 were targets of molecules that areeither approved or in clinical trials. As expected, most of thesecompounds have been developed for oncology indications, pro-viding proof of concept of using this approach in identifyingcancer targets. In addition, we found one gene, EGLN1, forwhich small-molecule inhibitors are in phase II and III clinicaltrials to treat anemia in patients with chronic kidney disease(NCT03263091, NCT03303066, clinicaltrials.gov). We selectedthis target for further investigation as a candidate novel oncologytherapeutic target.

To validate EGLN1 dependency with an orthogonal technologyto RNAi, we analyzed data derived from screening 436 cell linesusing a genome-scale CRISPR-Cas9 library (7, 18). We found thatEGLN1 scored as a preferential dependency both inCRISPR and inRNAi datasets (Fig. 1B; Supplementary Fig. S1A–S1C; refs. 18–22). Indeed, the concordance between EGLN1 dependency in celllines screened by CRISPR and RNAi was highly significant(Fig. 1C; Pearson correlation 0.512; P < 10�17).

Because EGLN1 is one of three family members, we queriedwhether the other family members, EGLN2 and EGLN3, were alsodependencies in any of the cell lines. We found that among theEGLN familymembers,EGLN1was the strongest preferential depen-dency in both CRISPR and RNAi datasets (Supplementary Fig. S1A–1C). Furthermore, we found that there were few cell lines dependenton EGLN1 that were also dependent on EGLN2 or EGLN3.

We and others have previously shown that we could usecodependencies derived from the Achilles datasets to identifygenes that have similar function (2, 11–13, 24, 25). Here, weapplied this method to gain insight into the molecular basis ofEGLN1 dependency. Specifically, we built linear models toidentify codependency relationships between EGLN1 and everyother gene. We found that VHL was the strongest and mostsignificantly associated dependency in the CRISPR-Cas9screens, while HIF1AN were among the top hits in bothCRISPR-Cas9 and RNAi and HIF1A was one of the strongestnegatively associated hits (Fig. 1D; Supplementary Fig. S1D).These observations suggest that EGLN1 dependency is relatedto its canonical function in the HIF pathway. To further inves-tigate this association with members of the HIF pathway, wecalculated the correlations between dependency profiles ofevery pair of genes in the pathway (VHL, HIF1AN, HIF1A,HIF2A, EGLN1, EGLN2, and EGLN3). We found the strongestcorrelation within the pathway existed between EGLN1 depen-dency and VHL dependency followed by EGLN1 dependencyand HIF1AN (hypoxia inducible factor 1 alpha subunit inhib-itor) dependency in CRISPR datasets (Fig. 1E).

To understand why some cell lines are more dependent onEGLN1 than others, we next searched for genomic features,including gene expression, copy number alterations, and muta-tions that related to EGLN1 dependency estimated from CRISPRand RNAi data (Supplementary Fig. S1E–S1L; SupplementaryTables S3 and S4). We found that higher levels of HIF1A ex-pression were among the most significantly associated geneexpression features. In addition, in the reverse analysis, EGLN1dependency was the top correlated dependency with HIF1Aexpression (Supplementary Fig. S1M). In particular, we foundthat cell lines that express lower levels of HIF1A were not depen-dent on EGLN1, suggesting that high levels of expressionmight berequired for a dependency (Fig. 1F). Finally, whenwe interrogatedwhich gene sets correlatedwith EGLN1 dependency, we identifiedhypoxia-related gene sets among the top correlated gene sets

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Figure 2.

EGLN1 dependency is enriched in clear cell ovarian cancer and melanoma and associated with high HIF1A levels.A,Ovarian andmelanoma cancer cell lines areenriched for EGLN1 dependencies in CRISPR screens. The percentage of EGLN1-dependent lines is graphed for each lineage. Red, significantly enriched lineagesfound by lineage enrichment analysis. Note that lung lines are also significant, but are negatively enriched for EGLN1 dependencies. B, Distribution of EGLN1CERES scores (y-axis) in ovarian cancer cell lines. Thirteen of 33 cell lines screened in CRISPR cell lines have a greater than 50% probability of being dependent onEGLN1. �CAOV3 is not identified as an EGLN1-dependency, despite having a lower CERES effect score than EGLN1-dependent line EFO27, because the screenquality and other cell line–specific differences are reflected in the CERES probabilities of dependency. C, CRISPR EGLN1 dependencies are enriched in clear cellovarian cancer. A two-sample t test of clear cells versus all other ovarian subtypes revealed that clear cells are significantly enriched for EGLN1 dependencies (P <0.05). D, EGLN1-dependent cell lines (n¼ 13) express significantly more HIF1A (y-axis) than non-EGLN1–dependent lines (n¼ 20) in CRISPR. Two-sample t test,P < 0.01. E, Clear cell ovarian cancer cell lines have high HIF1A expression compared with all other ovarian lines. Two-sample t test, P < 0.05. F, ssGSEA (singlesample Gene Set Enrichment Analysis) revealed that EGLN1 dependency is associated with HIF1A-related pathways. Pearson correlations of EGLN1 dependencywith each profile (y-axis) were calculated, and z-score–normalized profiles are shown.

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(Supplementary Fig. S1N and S1O). Taken together, all theseobservations are consistent with the hypothesis that EGLN1regulation of HIF1A is the downstream event responsible for thedependency.

EGLN1 dependency is enriched in ovarian clear cell carcinomaTo investigate whether EGLN1 dependency was enriched in

specific cancer types, we performed a lineage enrichment anal-ysis using Fisher exact test. We found that melanoma and

Figure 3.

EGLN1 deletion inhibits proliferation in EGLN1-dependent cell lines. A, Effects of EGLN1 deletion on HIF1A levels. Whole-cell lysates of ES2-Cas9 with theindicated sgRNAs. Equal amounts of protein were analyzed by immunoblotting with GAPDH, included as a marker of equal loading. Deletion of EGLN1 leads toincreased HIF1A phosphorylation. � , sgRNAs used for subsequent experiments. B, EGLN1 deletion reduces proliferation in EGLN1-dependent cells. Cells wereserially passaged every 3–4 days. Results are representative of three independent experiments and data are graphed as mean� SD of three replicates. � , P <0.05. C, EGLN1 deletion does not affect proliferation in EGLN1-independent cells. Cells were serially passaged every 3–4 days. Results are representative of threeindependent experiments and data are graphed as mean� SD of three replicates. D, VHL deletion reduces proliferation in EGLN1-dependent cells. Results arerepresentative of two independent experiments and data are graphed as mean� SD of three replicates. � , P < 0.05. E, Luciferase competition assay. Renilla-expressing and Fireflyþ Cas9-expressing cells were mixed at 1:1 and then sgRNAs were expressed. Ratio of Renilla/Firefly luciferase was quantified, and cellswere serially passaged every 3–4 days. EGLN1 KO cells (red) were compared with negative controls (blue) and positive controls (black). EGLN1-deleted cellsdependent on EGLN1 were outcompeted by EGLN1WT cells over time in a proliferation assay, whereas EGLN1-deleted cells insensitive to EGLN1 were not.Results are representative of two independent experiments and data are graphed as mean� SD of three technical replicates. Empty vector, empty vector withno sgRNA; Chr2 Intragenic, sgRNA targeting intergenic region of chromosome 2; LacZ-1, sgRNA targeting LacZ gene; PolR2D-1, sgRNA targeting RNApolymerase 2 subunit D; PolR1C-1, sgRNA targeting RNA polymerase 1 subunit C.

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Figure 4.

Inhibiting EGLN1 increases HIF1A expression and reduces proliferation in EGLN1-dependent cells. A, Immunoblot showing that pharmaceutical inhibition (FG-4592) of EGLN1 increases HIF1A levels in a dose-dependent manner. B, EGLN1 inhibitor increases HIF1A activity measured by using a HRE fused to a luciferasereporter. Luciferase activity was measured 48 hours posttreatment with increasing dose of FG-4592. Data are representative of two independent experiments. C,EGLN1 inhibitor reduces viability selectively in EGLN1-dependent cells. Cells were treated for 48 hours with increasing dose of FG-4592 (x-axis). Cell viability (y-axis) was measured using CellTiter-Glo. The data shown are representative of three independent experiments. D, EGLN1 inhibitor reduces long-term proliferationin EGLN1-dependent cells. Results are representative of three independent experiments and data are graphed as mean� SD of three replicates. E, EGLN1inhibitor does not affect long-term proliferation of EGLN1-insensitive cells. Results are representative of three independent experiments and data are graphed asmean� SD of three replicates. F, EGLN1 inhibition reduces colony formation in a dose-dependent manner in EGLN1-dependent cells. Colonies were quantified asabsorbance (y-axis) over drug concentration (x-axis) over 10 days of treatment normalized to DMSO. Results are representative of three independentexperiments.G, EGLN1 inhibition has no significant effect on colony formation in EGLN1-insensitive cells. Colonies were quantified as absorbance (y-axis) overdrug concentration (x-axis) over 10 days of treatment normalized to DMSO. Results are representative of three independent experiments. H, VHL inhibitionreduces proliferation of EGLN1-dependent cells. Results are representative of three independent experiments, and data are graphed as mean� SD of threereplicates. � , P < 0.05. I, VHL inhibition does not affect proliferation of EGLN1-insensitive cells. Results are representative of two independent experiments, anddata are graphed as mean� SD of biological triplicates.

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ovarian cell lines were significantly enriched for EGLN1 depen-dencies in the CRISPR-Cas9 dataset (Supplementary Fig. S2A,FDR < 0.1). Specifically, among the ovarian cancer cell lines, wefound that 13 of 33 (39%) ovarian cancer cell lines weredependent on EGLN1 (Fig. 2A and B). Similarly, we foundthese cell lines were also dependent on EGLN1 in RNAi datasetand cell lines in both datasets show strongly correlated EGLN1dependencies (Supplementary Fig. S2C). Ovarian cancer repre-sents a heterogeneous disease comprised of four major differentsubtypes (26–36). To determine whether EGLN1 dependencywas enriched in a specific subtype, we grouped the ovariancancer cell lines by historical pathologic, molecular, and his-tologic subtypes (Supplementary Table S5; adapted fromrefs. 31, 34, 36). We found that EGLN1 dependency wassignificantly enriched in ovarian clear cell carcinoma linescompared with every other ovarian subtype (Fig. 2C, P <0.05; Supplementary Fig. S2D, P < 0.05).

We next evaluated whether the association of EGLN1 depen-dency with high expression levels of HIF1A remained signifi-cant in ovarian cell lines. We found that EGLN1-dependentovarian cancer cells express HIF1A at significantly higher levelsthan non-EGLN1–dependent cells in the CRISPR dataset(Fig. 2D, P < 0.005). We found a similar relationship betweenEGLN1 dependency scores and HIF1A expression in RNAidataset (Supplementary Fig. S2E). In particular, when subdi-vided by histologic subtypes, we found that ovarian clear celllines showed the highest group expression of HIF1A in CRISPR(Fig. 2E, P < 0.05) and RNAi datasets (Supplementary Fig. S2E).These results are consistent with previous observations thatHIF1A is overexpressed/activated in clear cell compared withother ovarian cancer subtypes (37, 38).

Higher levels of HIF1A and a hypoxia gene set were correlatedwith EGLN1 dependency within the ovarian cell lines after per-forming ssGSEA (Fig. 2F; Supplementary Fig. S2F), indicating thatin ovarian cell lines EGLN1 dependency is also related to itsknown function in the HIF1A pathway.

Validation of EGLN1 dependencyTo validate and characterize the observed EGLN1 dependency,

we designed a series of sgRNAs to identify sgRNAs that effectivelydelete EGLN1. We selected an EGLN1-dependent cancer cell line,ES2 (Fig. 3A), and an EGLN1-independent cell line, TOV112D(Supplementary Fig. S3A), from the panel of cell lines screened inProject Achilles.We chose three sgRNAs (EGLN1-1, EGLN1-8, andEGLN1-9) for subsequent EGLN1 knockout experiments. Wefound that deleting EGLN1 led to an increase in HIF1A andsubsequently phosphorylatedHIF1A (39, 40).We concluded thatthese selected EGLN1 sgRNAs effectively targeted EGLN1.

To assess the consequences of EGLN1 inhibition, wemeasuredcell proliferation. We found that expression of the negativecontrol sgChr2-1 (sgRNA designed to target an intergenic regionon Chromosome 2) did not affect cell proliferation. In contrast,depletion of EGLN1 using sgRNA EGLN1-9 led to a significant(P < 0.05) decrease in cell proliferation in the EGLN1-dependentcell line ES2 (Fig. 3B), but did not affect proliferation of theEGLN1-independent cell line, TOV112D (Fig. 3C).

We hypothesized that EGLN1-dependent cells would also bedependent on VHL. To test this, we generated VHL-knockoutcells using VHL-specific sgRNAs and confirmed loss of VHL byimmunoblotting (Supplementary Fig. S3B). We found thatVHL-deficient cells showed a significant (P < 0.05) decrease inpopulation doublings (Fig. 3D).

To assess the effects of depleting EGLN1 in a longer termassay, we performed a competition experiment in which weassessed whether cells with EGLN1 KO would be depleted overtime when directly compared with cells expressing a controlsgRNA (Fig. 3E). Specifically, we introduced different sgRNAs inRenilla or FireflyþCas9–expressing cells then determined theconsequences of expressing each sgRNA by assessing Firefly/Renilla ratio over time. We performed the competition assay inthree EGLN1-dependent cell lines (ES2, OVTOKO, and OVISE)and two cell lines not dependent on EGLN1 (TOV112D andHEYA8). As a positive control for depletion of the cell popu-lation, we used sgRNAs targeting the RNA polymerase II subunitD (sgPOLR2D) and RNA polymerase I subunit C (sgPOLR1C).As negative controls, we used sgChr2-1 and sgLacZ. We found inES2, OVTOKO, and OVISE (EGLN1-dependent) that cellsexpressing an EGLN1-targeting sgRNA were depleted over timeat a rate similar to cells expressing sgRNAs targeting POlR2D orPOLR1C (Fig. 3E, top). Furthermore, when we performed thissame experiment in EGLN1-independent cell lines, we found nodifference between cells expressing the negative control sgRNAor sgRNA targeting EGLN1 (Fig. 3E, bottom). These observa-tions confirm that depletion of EGLN1 leads to decreased cellproliferation in a subset of ovarian cancer cell lines.

Pharmacologic inhibition of EGLN1 or VHL inhibitsproliferation in a subset of cell lines

EGLN1 is a prolyl hydroxylase, and several PHD inhibitors havebeen described (41, 42), which prevent the degradation ofHIF1A.Specifically, compound FG-4592, also known as Roxadustat, iscurrently being evaluated for the treatment of anemia in dialysis-dependent chronic kidney disease (CKD) and nondialysis-dependent CKD (42–45). We found that exposure of both ES2and TOV112D to FG-4592 and the Bayer compound Molidustat(Bay 85-3934) led to increased HIF1A levels and phosphorylated

Figure 5.Depletion of HIF1A rescues cells from EGLN1 dependency. A, RNA sequencing performed on ES2 and OVISE wild-type cells compared with ES2 and OVISE EGLN1knockout cells. GSEA of RNA sequencing data shows that HIF1A-related pathways are affected in EGLN1 KO cells. B, Immunoblot showing efficiency of controlsgRNAs (sgChr2-1) or HIF1A deletion with sgRNAs (sgHIF1A-5, sgHIF1A-6, sgHIF1A-7) 48 hours posttreatment with control (DMSO) or EGLN1 inhibitor (FG-4592).C, EGLN1-dependent ES2 cells are resistant to EGLN1 inhibition after HIF1A knockout. DMSO treatment (top) has no effect on control or HIF1A-knockout cells.Increasing treatment with FG-4592 (10 mmol/L, middle; 20 mmol/L, bottom) reduces proliferation of control cells (blue), but not HIF1A-knockout. Results arerepresentative of three independent experiments and data are graphed as mean� SD of three replicates. � , P < 0.05. D, EGLN1-dependent OVISE cells areresistant to EGLN1 inhibition after HIF1A knockout. Similar to ES2 cells, DMSO treatment (top) has no effect on control or HIF1A-knockout cells. Increasingtreatment with FG-4592 (10 mmol/L, middle; 20 mmol/L, bottom) reduces proliferation of control cells (blue). Results are representative of two independentexperiments and data are graphed as mean� SD of biological triplicates. � , P < 0.05. E, EGLN1-dependent ES2 cells are resistant to VHL1 inhibition after HIF1Aknockout. Treatment with 100 mmol/L of VH298 significantly reduces proliferation of control cells (orange) compared with HIF1A knockout cells. Results arerepresentative of two independent experiments and data are graphed as mean� SD of biological triplicates. �, P < 0.05.

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HIF1A in a concentration-dependent manner (Fig. 4A; Supple-mentary Fig. S4A–S4C). This accumulation was observed at 5–10 mmol/L and peaked at 20–40 mmol/L, consistent with thereported effective concentrations of both compounds (43–47).These concentrations (5–40 mmol/L) were used for all subse-quent experiments to test the effect of pharmacologic inhibitionof EGLN1 in EGLN1-dependent cells. In addition, we also testedHIF1A activity using a HRE fused to a luciferase reporter(Fig. 4B; ref. 48). Following treatment with FG-4592, we foundthat HIF1A activity was increased in both EGLN1-dependent celllines and cell lines that are not EGLN1-dependent. However,when we compared HIF1A activity between both cell lines, wefound that the EGLN1-dependent cell line exhibited higher HIF1Aactivity (Fig. 4B). This finding suggests that pharmacologic inhibi-tionofEGLN1 inEGLN1-dependent cell line induceshigheractivityof HIF1A than the cell line not dependent on EGLN1. We alsofound that FG-4592 reduced viability in ES2 over 48 hours whilehad no effect on TOV112D via CellTiter-Glo assay in a dose-dependent manner (Fig. 4C). To confirm these observations, wetested several other EGLN1 inhibitors FG-2216 (Fibrogen) andDaprodustat (GSK1278863 GlaxoSmithKline). We found that theEGLN1-dependent cell line ES2 showed sensitivity to all of theseEGLN1 inhibitors (Supplementary Fig. S4D). These observationsconfirm that inhibition of EGLN1 activity recapitulates the effectsobserved by deleting EGLN1.

To investigate whether the observed decrease in cell numberwas due to apoptosis or cell-cycle arrest, we treated cells for 3 dayswith FG-4592 and then labeled them with proliferation agentCFSE (Invitrogen) and analyzed proliferation by flow cytometry.We found that higher doses of FG-4592 reduced proliferationof ES2, but did not have a similar effect on TOV112D (Sup-plementary Fig. S4E). Furthermore, we found a slight increasein apoptosis following three days of EGLN1 inhibition suggest-ing an initial cytotoxic effect (Supplementary Fig. S4F). Thus,we found that short-term pharmacologic inhibition of EGLN1results in decreased proliferation. To evaluate the effect of FG-4592 on long-term proliferation, we carried out treatment ofsensitive and resistant cells for up to 30 days. We foundtreatment with FG-4592 significantly reduced long-term pro-liferation in sensitive cell lines (Fig. 4D; Supplementary Fig.S4G and S4H, P < 0.01), but not insensitive cell lines (Fig. 4E).Together, these observations show that EGLN1 inhibitionreduces cell proliferation.

To further understand EGLN1 dependency, we tested the effectof EGLN1 inhibition in a low-cell–density colony formationassay. We found that EGLN1-dependent cell lines were sensitiveto EGLN1 inhibition (Fig. 4F; Supplementary Fig. S4I) in a dose-dependent measurement. When we focused on cell lines that donot exhibit dependence on EGLN1, we found that increasingconcentrations of FG-4592 did not have the same effect(Fig. 4G; Supplementary Fig. S4I). Together, these observationssuggest that these cell lines are dependent on EGLN1 for colony-forming cell viability assays.

We hypothesized that EGLN1 sensitivity to small-moleculeinhibition would persist in the context of hypoxia. To test thishypothesis, we performed colony-forming assays in the context ofnormal culture conditions (normoxia) or in a hypoxic incubatorat 5% oxygen (hypoxia). We found that EGLN1-dependent cellswere sensitive to EGLN1 small-molecule inhibitor FG-4592 in adose-dependent manner in hypoxia and normoxia (Supplemen-tary Fig. S4J). We did not observe an increased sensitivity to

EGLN1 inhibition in 5% oxygen with the EGLN1-insensitive cellline TOV112D, suggesting that EGLN1dependency persists underreduced oxygen concentrations in vitro.

Several inhibitors developed for clinical trials have the ability totarget EGLN2 and EGLN3. To confirm that the effect that weobserved was related to EGLN1 inhibition, we used the moreselective EGLN1 inhibitor IOX2. We found in a short-term via-bility assay that IOX2-mediated inhibition of EGLN1 resembledFG-4592 inhibition of EGLN1 (Supplementary Fig. S5A and S5B).Furthermore, when we looked at colony growth, we foundEGLN1-dependent cell lines exhibited reduced dose-dependentcolony growth compared with cell lines that were not dependenton EGLN1 (Supplementary Fig. S5C).Whenwe assessed the effectof IOX2 on long-termproliferation, we found that EGLN1-depen-dent cell lines showed decreased proliferation over time (Sup-plementary Fig. S5D–S5F). These findings suggest that the effectsobserved with FG-4592 and other EGLN clinical inhibitors arepredominantly through inhibition of EGLN1, not EGLN2 orEGLN3.

We also hypothesized that pharmacologic inhibition of VHLshould mimic pharmacologic inhibition of EGLN1. We usedVH298 (Tocris), a novel chemical probe that is reported toblock the interaction of VHL and HIF1A downstream of hydrox-ylation of HIF1A by EGLN1 (42), leading to HIF1A stabiliza-tion (Supplementary Fig. S5G) and activation of HIF targetgenes. We found that treatment with VH298 substantiallydecreased cell proliferation in ES2 (Fig. 4H) and slightlydecreased proliferation in TOV112D (Fig. 4I). We observedthis effect in several other EGLN1-dependent cell lines (Sup-plementary Fig. S5H and S5I). These findings suggested thatEGLN1-dependent cells were more sensitive to VHL inhibitionthan EGLN1-independent cells.

Deletion of HIF1A rescues EGLN1 and VHL dependencyOn the basis of our observation that EGLN1 dependency

was associated with higher expression of HIF1A, we hypothe-sized that depleting EGLN1 would affect HIF1A targets andpathways. We therefore performed RNA sequencing in theEGLN1-dependent cell lines ES2 and OVISE with and withoutEGLN1. We found that among the genes that were differentiallyexpressed (Supplementary Table S6; Supplementary Fig. S6A),several genes act downstream of the EGLN1–HIF1A pathway.We next performed GSEA to identify significant changes inthe EGLN1 knockout population. Within the top scoring path-ways, we found enrichment in hypoxia and HIF1A-relatedpathways (Fig. 5A; Supplementary Fig. S6B). Together, theseobservations suggest that loss of EGLN1 strongly affects HIF1A-related pathways.

As HIF1A pathways were significantly affected by EGLN1inhibition,wehypothesized that knockoutofHIF1Awould rescueEGLN1dependency.We designed several sgRNAs targetingHIF1Aand selected three that efficiently depleted HIF1A in EGLN1-sensitive cell line ES2 (Fig. 5B). We found that deletion of HIF1Afailed to affect the proliferation of EGLN1-insensitive cell lines.However, we found that depletion of HIF1A rescued EGLN1sensitivity in ES2 (Fig. 5C) and OVISE (Fig. 5D) to FG-4592(P < 0.05). These observations suggest that the observed depen-dence of EGLN1 requires HIF1A.

Because HIF1A knockout rescued EGLN1 dependency, wehypothesized that HIF1A knockout may rescue VHL dependency.When compared with DMSO-treated control, we found that

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knockout of HIF1A rescued cell proliferation blocked by VHLinhibitor VH298 (Fig. 5E, P < 0.05), suggesting that VHL depen-dency functioned through the regulation of HIF1A.

Genetic andpharmacologic inhibitionof EGLN1 reduces tumorgrowth

We then tested whether depletion of EGLN1 affected tumorgrowth in vivo. Specifically, we used cells expressing sgRNAsagainst EGLN1 or Chr2-1 (negative control) and subcutaneouslyimplanted these into recipient mice (Fig. 6A). We implantedcontrol and knockout cells intooppositeflanks of the samemouseto control for any tumor variation between mice. We found thatEGLN1 knockout significantly reduced tumor size over time inES2 (Fig. 6B). Furthermore, we found thatOVISE tumor cells withdeleted EGLN1 grew slower than OVISE tumors in which EGLN1was expressed (Fig. 6C).Whenwe looked at the RNAexpression ofa subset of the tumors that had grown in ES2 cells with inhibitedEGLN1,we founda correlationwithEGLN1 expression and tumor

size (Fig. 6D), suggesting that inactivation of the EGLN1 genesignificantly inhibits tumor growth.

To determine whether pharmacologic inhibition of EGLN1also inhibited tumor growth,weused an implantablemicrodeviceto directly deliver multiple compounds to different parts ofthe tumor (23). By inserting this microdevice directly into thetumor, we assessed the effect of FG-4592, and VH298 in tumorsin vivo. Specifically, we implanted ES2 cells with intact HIF1A(ES2) or HIF1A knockout (ES2 HIF1A KO) in immunodeficientmice andallowed the tumors to grow to1 cm2before implanting amicrodevice containing the EGLN1 inhibitor FG-4592 and VHLinhibitor VH298. Following implantation, we collected thetumors after 48 hours and sectioned and stained the tumorsfor proliferation with an antibody against Ki-67, a cellularmarker for proliferating cells, or cleaved caspase-3, a cellularmarker for apoptosis. As expected, local delivery of PEG (poly-ethylene glycol, the vehicle used for drug formulation) had noeffect on cell proliferation or apoptosis, demonstrated by clear

Figure 6.

EGLN1 deletion or inhibition reduces tumor formationinduced by EGLN1-dependent cells. A, Schematic of tumorformation experiments. Each recipient mouse receivescontrol (sgChr2-1) and EGLN1-KO cells. B, EGLN1 knockoutimpairs tumor formation induced by ES2 cells. Control andEGLN1 KO ES2 cells (100,000) were injectedsubcutaneously in parallel flanks and tumor size wasmeasured. Data are represented as mean� SD (n¼ 15)and represent two experimental repeats. � , P < 0.01.C, EGLN1 knockout impairs tumor formation induced byOVISE cells. Control and EGLN1 KOOVISE cells (250,000)were injected subcutaneously in parallel flanks and tumorsize was measured. Data are represented as mean� SD(n¼ 6). �, P < 0.05. D, EGLN1 expression levels in theknockout cells correlate with tumor size. RNAwasharvested from tumors and expression was quantifiedusing qRT-PCR. Data are represented as the average offour biological replicates and represent two experimentalrepeats. E,Microdevice delivery of EGLN1 inhibitors andVHL inhibitors to ovarian tumor with HIF1A expressionreduces proliferation. Microdevices were loaded with PEG-formulated compounds and implanted into 1-cm2 tumorsformed from ES2 or from ES2 with HIF1A KO and grown to1 cm2. At 48 hours postmicrodevice implantation, tumorswere harvested and serially sectioned and stained. Thecontrol for drug formulation (PEG control) has no effect onthe proliferation of ES2 tumor cells with or without HIF1A(top). Doxorubicin treatment reduces tumor cellproliferation (top middle). Inhibition of EGLN1 withFG-4592 blocks proliferation of tumor cells, whileknockout of HIF1A rescues EGLN1 inhibition (bottommiddle). Inhibition of VHL with VH298 blocks proliferationof tumor cells while knockout of HIF1A rescues VHLinhibition. Figure is representative of three independentexperiments. Arrow, area where drug is released.Black line, 1 mmol/L.

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immunostaining of Ki-67 (Fig. 6E; Supplementary Fig. S7, top) inboth HIF1A WT or HIF1A KO tumors. We found that addition ofdoxorubicin reduced proliferation and increases apoptosis inboth HIF1A WT and HIF1A KO cells (Fig. 6E; SupplementaryFig. S7, top middle). Consistent with our previous in vitro data(Fig. 4), we found that pharmacologic inhibitionof EGLN1 causesa dramatic reduction in proliferating cells and increased apoptosisin the region of tumor exposed to the localmicrodevice delivery ofFG-4592 (Fig. 6E; Supplementary Fig. S7, bottom middle), whileknockout of HIF1A exhibited no change in proliferation orapoptosis upon local delivery of FG-4592. Similarly, we foundaddition of VH298 resulted in the reduction of proliferating cellsand increased apoptosis, which was also dependent on intactHIF1A (Fig. 6E; Supplementary Fig. S7, bottom). These datacollectively demonstrate that genetic and pharmacologic pertur-bation of EGLN1 reduces tumor burden, cell proliferation, andincrease apoptosis in vivo and that this effect requires HIF1A.

DiscussionUnder normal conditions, EGLN1 functions to hydroxylate

HIF1A and target it for ubiquitination and subsequent degrada-tion by VHL (Fig. 7A). Here we show that in a subset of clear cellovarian cancer and melanomas, genetic knockout or pharmaco-logic inhibition of EGLN1 stabilizes HIF1A and inhibits prolif-eration (Fig. 7B). Similarly, genetic knockout or pharmacologicinhibition of VHL prevents HIF1A ubiquitination and degrada-tion that results in HIF1A stabilization and, subsequently, adecrease in proliferation (Fig. 7C). Deletion of HIF1A rescuesboth EGLN1 dependency and VHL dependency (Fig. 7D). Phar-macologic inhibition of EGLN1 recapitulated the effects of geneticsuppression of EGLN1, both in vitro and in vivo, suggesting thatEGLN1 inhibition is a potential therapeutic strategy in thesetumors.

As expected, genetic deletion of EGLN1 led to HIF1A accumu-lation (Fig. 3A), which in turn led to a decrease in proliferation(Fig. 3B) and reduced fitness in our competition assay comparedwith EGLN1 functional cells (Fig. 3E). After pharmaceuticalinhibition of EGLN1,we observed similar accumulation ofHIF1Aand increased HIF1A activity (Fig. 4A, B). While EGLN1 has beendescribed both as an oncogene and a tumor suppressor (39, 45,49–53), a specific dependencyof a subset of cancers onEGLN1hasnot been reported. Jokilehto and colleagues (39) found thatEGLN1 expression was strongly associated with highly prolifer-ative head and neck squamous cell carcinomas (HNSCC). Fur-thermore, Klotzsche-von Ameln and colleagues found EGLN1inhibition led to a decrease in tumor growth using mouse lungcancer cell line LLC andosterocarcoma line LM8 (51). Conversely,Chan and colleagues found that low PHD2 expression in colo-rectal cancers correlated with poorer survival, and experiments incell line models showed that low PHD2 expression correlatedwith increased tumor vasculature (52). Bordoli and colleaguesobserved decreased EGLN1 was associated with increased tumorgrowth through increased VEGF, Amphiregulin, and IL8 (53).Thus, EGLN1 is another of a growing number of genes that can actboth as an oncogene and tumor suppressor gene depending onthe context (54).

Our findings suggest that increased HIF1A decreases thefitness of EGLN1-dependent cells. Specifically, we found thatinactivation of EGLN1, using either genetic or pharmacologicmeans, resulted in a pronounced decrease in proliferation. We

observed that this decrease in proliferation coincided withstabilization of HIF1A. Thus, we hypothesized that HIF1A losscould rescue EGLN1-mediated death. Indeed, we found that

Figure 7.

Model of EGLN1 and VHL dependency. A, Normal conditions. EGLN1 in thepresence of oxygen hydroxylates HIF1A at residues P402 and P564.Hydroxylated HIF1A is then targeted for ubiquitination and subsequentdegradation by VHL. B, EGLN1 KO. After EGLN1 KO, HIF1A is nonhydroxylatedand therefore not degraded by VHL. Stabilized HIF1A translocates to thenucleus and activates downstream effectors, resulting in a decrease in cellviability and increased cell death. C, VHL KO. Following KO of VHL, HIF1A ishydroxylated by EGLN1, but cannot be ubiquitinated and degraded by VHL.Stabilized HIF1A translocates to the nucleus and activates downstreameffectors, resulting in a decrease in cell viability and increased cell death.D, HIF1A KO. HIF1A KO rescues cell proliferation and cell death seen in EGLN1and VHL KO cells.

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EGLN1-dependent cell lines were rendered insensitive toEGLN1 inhibition by loss of HIF1A (Fig. 5C and D). Together,these observations demonstrate that EGLN1 dependencyrequires HIF1A expression.

We found a large number of cell lines dependent on VHL(Supplementary Fig. S1A). We also found that cell lines depen-dent on EGLN1 were also dependent on VHL (Figs. 3D and4H; Supplementary Fig. S5H and S5I). While all the EGLN1-dependent cell lines were also dependent on VHL in the CRISPRdataset (Fig. 1D and E), we were surprised to observe VHL scoredas an essential gene (Supplementary Fig. S1A). As VHL is down-stream of EGLN1 and directly responsible for the ubiquitinationand degradation of HIF1A (16, 17, 40–42), one possible hypoth-esis for this observation is that loss of VHL can drive stabilizationand accumulation of HIF1A in cell lines. We found that depen-dency on VHL in EGLN1-dependent cell lines was dependent onfunctional HIF1A. Similar to our previous observations withEGLN1dependency, knockout ofHIF1A rescues VHL dependency(Fig. 5E). This finding suggests VHL also functions as a cancerdependency in EGLN1-dependent cells through negative regula-tion of HIF1A.

Previous reports found that overexpression of HIF1A has beenassociated with multiple tumor types, and tumors in hypoxia areknown to require HIF1A to survive (14–17). However, otherreports have also implicated HIF1A as a negative regulator ofproliferation (49–51). There are a several hypotheses that couldexplain these observations. First, the observed effects of activatingHIF1A may be tumor type–specific. In our initial analysis, wefound significant enrichment in of EGLN1 dependency in mel-anoma and clear cell ovarian cancer. Indeed, these clear cellovarian cancer cell lines died when HIF1A expression wasstabilized.

Alternatively, it was possible that the observed difference insensitivity involved whether the cells experienced hypoxia. How-ever, we found under conditions of 5% oxygen that EGLN1-dependent cells were still susceptible to EGLN1 inhibition. Fur-thermore, when we examined in vivo growth of tumors underpresumably hypoxic conditions, we found that genetic knockoutand small-molecule inhibition of EGLN1 reduced tumor growthand suppressed proliferation. These findings suggest that cell linedependence on EGLN1 is independent of hypoxia.

The standard therapeutic procedure to clear cell ovarian cancerremains surgical cytoreduction and subsequent chemotherapyusing platinum agents and taxane (55). Clear cell ovarian cancers,compared with other histologic ovarian subtypes, are resistant tochemotherapy (55). Thus, targeted therapy has been proposed,and the need for potential targets and therapy is pressing. EGLN1inhibitors are in clinical trials for anemia in patients withCKD (43–45). Our observations suggest that these inhibitors

may also be useful in EGLN1-dependent cancers. Our data suggestthat cancers with low HIF1A expression would not respond toEGLN1 therapy, while a subset of patients with high HIF1Aexpression would respond. This association was stronger in mel-anoma and extremely strong in clear cell ovarian cancer cells.Previous work has shown that patients with clear cell ovariancancer have higher expression of HIF1A (37, 38). Thus, thesepatients would be the ideal patient population to assess theefficacy of EGLN1-targeted therapy in cancers.

Disclosure of Potential Conflicts of InterestA. Tsherniak is a consultant/advisory board member for Tango Thera-

peutics. O. Jonas reports receiving a commercial research grant from Novartisand is a consultant/advisory board member for Kibur Medical. W.C. Hahnreports receiving a commercial research grant from Deerfield, has ownershipinterest (including stock, patents, etc.) in KSQ Therapeutics, is a consultant/advisory board member for Thermo Fisher Scientific, KSQ Therapeutics,AjuIB, MPM, and Parexel. No potential conflicts of interest were disclosed bythe other authors.

Authors' ContributionsConception and design: C. Price, F. Vazquez, W.C. HahnDevelopment ofmethodology:C. Price, S. Gill, S.M.Davidson,M. Kost-Alimova,F. VazquezAcquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): C. Price, S. Gill, S.M. Davidson, E. Merkel,L. Leung, M. Kost-Alimova, W.C. HahnAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): C. Price, S. Gill, Z.V. Ho, S.M. Davidson, E. Merkel,J.M. McFarland, M. Kost-Alimova, A. Tsherniak, O. Jonas, F. Vazquez,W.C. HahnWriting, review, and/or revision of the manuscript: C. Price, S. Gill, Z.V. Ho,S.M. Davidson, J.M. McFarland, A. Tsherniak, O. Jonas, F. Vazquez, W.C. HahnAdministrative, technical, or material support (i.e., reporting or organizingdata, constructing databases): C. PriceStudy supervision: C. Price, F. Vazquez, W.C. HahnOther (illustration and visualization of figures): A. Tang

AcknowledgmentsWewould like to thank Nancy Dumont (Broad Institute) for her suggestions

and feedback on all in vivo experiments and William G. Kaelin Jr and his lab formaterials provided and advice on experiments. This work was supported by theNIH/National Cancer Institute U01 CA176058 (toW.C. Hahn), theH.L. SynderFoundation (to W.C. Hahn), National Cancer Institute Training Grant 5T32CA009361-35 (to C. Price), and the Carlos Slim Foundation inMexico throughthe Slim Initiative for Genomic Medicine (to F. Vazquez).

The costs of publication of this articlewere defrayed inpart by the payment ofpage charges. This article must therefore be hereby marked advertisement inaccordance with 18 U.S.C. Section 1734 solely to indicate this fact.

ReceivedAugust 28, 2018; revised January 18, 2019; acceptedMarch 14, 2019;published first March 21, 2019.

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2019;79:2564-2579. Published OnlineFirst March 21, 2019.Cancer Res   Colles Price, Stanley Gill, Zandra V. Ho, et al.   Dependency in Clear Cell Ovarian CancersGenome-Wide Interrogation of Human Cancers Identifies EGLN1

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