Translational Cancer Mechanisms and Therapy

A Novel Mechanism Driving Poor-PrognosisProstate Cancer: Overexpression of the DNARepair Gene, Ribonucleotide Reductase SmallSubunit M2 (RRM2)Ying Z. Mazzu1, Joshua Armenia2,3, Goutam Chakraborty1, Yuki Yoshikawa1,Si'Ana A. Coggins4, Subhiksha Nandakumar2, Travis A. Gerke5, Mark M. Pomerantz6,Xintao Qiu6, Huiyong Zhao7, Mohammad Atiq1, Nabeela Khan1, Kazumasa Komura8,Gwo-ShuMary Lee6, SamsonW. Fine9, Connor Bell6, Edward O'Connor6, HenryW. Long6,Matthew L. Freedman6, Baek Kim4,10, and Philip W. Kantoff1

Abstract

Purpose: Defects in genes in the DNA repair pathwayssignificantly contribute to prostate cancer progression. Wehypothesize that overexpression of DNA repair genes mayalso drive poorer outcomes in prostate cancer. The ribonucle-otide reductase small subunitM2 (RRM2) is essential for DNAsynthesis andDNA repair by producing dNTPs. It is frequentlyoverexpressed in cancers, but very little is known about itsfunction in prostate cancer.

Experimental Design: The oncogenic activity of RRM2 inprostate cancer cells was assessed by inhibiting or overexpres-sing RRM2. Themolecularmechanisms of RRM2 functionweredetermined. The clinical significance of RRM2 overexpressionwas evaluated in 11 prostate cancer clinical cohorts. The efficacyof an RRM2 inhibitor (COH29) was assessed in vitro and in vivo.Finally, the mechanism underlying the transcriptional activa-tion ofRRM2 in prostate cancer tissue and cellswas determined.

Results: Knockdown of RRM2 inhibited its oncogenicfunction, whereas overexpression of RRM2 promotedepithelial mesenchymal transition in prostate cancer cells.The prognostic value of RRM2 RNA levels in prostate cancerwas confirmed in 11 clinical cohorts. Integrating the tran-scriptomic and phosphoproteomic changes induced byRRM2 unraveled multiple oncogenic pathways downstreamof RRM2. Targeting RRM2 with COH29 showed excellentefficacy. Thirteen putative RRM2-targeting transcription fac-tors were bioinformatically identified, and FOXM1 wasvalidated to transcriptionally activate RRM2 in prostatecancer.

Conclusions: We propose that increased expression ofRRM2 is a mechanism driving poor patient outcomes inprostate cancer and that its inhibition may be of significanttherapeutic value.

IntroductionProstate cancer is the third leading cause of cancer death in

American men. Advances in genomics allow the identification of

putative drivers of cancer initiation and progression. Genomicinstability results from DNA damage induced by genotoxicinsults, dysfunction in pathways governing DNA repair, andabnormal cell-cycle control (1, 2). DNA-repair pathway defectsare prevalent in advanced prostate cancer (3, 4). Defective DNArepair is correlated with an increased sensitivity to PARP inhibi-tors, platinum chemotherapy, or immunotherapy that can beexploited for clinical benefit (5, 6). Ironically, overexpression ofDNA repair genes may also contribute to cancer progression:patients with prostate cancer with BRCA1-expressing tumorsmore likely develop lethal cancer than those with BRCA1-nonexpressing tumors (7).

The nucleotide metabolism enzyme, ribonucleotide reductase(RNR), is essential for DNA synthesis and DNA repair by pro-ducing dNTP. Abnormal dNTP levels lead to infidelity of DNAreplication, causing an increase in genomic instability (8). RNRactivity is coordinated with cell-cycle progression to maintain thefine balance between dNTP production and DNA replication (9).During the cell cycle, levels of two RNR subunits, RRM1and RRM2B, are constant but RRM2 levels fluctuate (10). As therate-limiting RNR enzyme, RRM2 levels control the cell-cycle-dependent activity of RNR. Overexpression of RRM2 is associatedwith poorer outcomes in multiple cancers (11, 12). In mice,

1Department ofMedicine,Memorial SloanKetteringCancer Center,NewYork,NewYork. 2Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center,New York, New York. 3Oncology, IMED Biotech Unit, AstraZeneca, Cambridge,United Kingdom. 4Center for Drug Discovery, Department of Pediatrics, EmoryUniversity School of Medicine, Atlanta, Georgia. 5Moffitt Cancer Center, Tampa,Florida. 6Department of Medical Oncology, Dana-Farber Cancer Institute, Boston,Massachusetts. 7Antitumor Assessment Core Facility, Memorial Sloan KetteringCancer Center, New York, New York. 8Translational Research Program andDepartment of Urology, Osaka Medical College, Osaka, Japan. 9Department ofPathology, Memorial Sloan Kettering Cancer Center, New York, New York.10Department of Pharmacy, Kyung-Hee University, Seoul, South Korea.

Note: Supplementary data for this article are available at Clinical CancerResearch Online (http://clincancerres.aacrjournals.org/).

Corresponding Author: Philip W. Kantoff, Memorial Sloan Kettering CancerCenter, 1275 York Avenue, New York, NY 10065. Phone: 212-639-5851; Fax: 929-321-5023; E-mail: kantoff@mskcc.org

Clin Cancer Res 2019;XX:XX-XX

doi: 10.1158/1078-0432.CCR-18-4046

�2019 American Association for Cancer Research.

ClinicalCancerResearch

www.aacrjournals.org OF1

Cancer Research. on December 18, 2020. © 2019 American Association forclincancerres.aacrjournals.org Downloaded from

Published OnlineFirst April 17, 2019; DOI: 10.1158/1078-0432.CCR-18-4046

overexpression of RRM2 induces the development of lung cancer.Defects in DNA mismatch repair synergistically promote RNR-induced carcinogenesis in lung neoplasms from RRM2 transgenicmice (13).

Although the oncogenic role of RRM2 has been linked topromotion of epithelial–mesenchymal transition (EMT) andangiogenesis (14, 15), there has been no comprehensive molec-ular understanding of how RRM2 regulates key downstreambiological processes and signaling to control tumor initiationand progression. In particular, very little is known about thefunction of RRM2 in prostate cancer. Here, we uncovered theoncogenic properties of RRM2 by integrating transcriptomic withphosphoproteomic analysis, and we determined the significantprognostic value of RRM2 in prostate cancer. Further, we eluci-dated the transcriptional regulation of RRM2 by FOXM1 inprostate cancer cells.

Materials and MethodsClinical cohort summary and characterization

Characteristics of patients with prostate cancer in the Physi-cians' Health Study (PHS) and Health Professionals Follow-upStudy (HPFS) cohorts are shown in Supplementary TableS1 (16, 17). mRNA expression profiling from high-density tumorareas and adjacent normal prostate tissue was reported (18).Logistic regression was used to quantify the association of RRM2expression (in quartiles) and lethal cancer (SupplementaryTable S2). The analysis was adjusted for age at cancer diagnosis(linear), calendar year of diagnosis, body mass index, currentsmoking at diagnosis (binary), and Gleason grade. An additionaleleven publicly-available prostate cancer cohorts are summarizedin Supplementary Table S3.

Cell culture and prostate cancer tissue microarrayNormal human prostate cell lines (PWR-E1, PZ-HPV-7,

RWPE-1) and human prostate cancer cell lines (LNCaP, 22Rv1,DU145, and PC-3) were purchased from ATCC. C4-2 cells wereobtained from VitroMed. E006AA cells were provided by JohnT. Isaacs (The Johns Hopkins University School of Medicine,Baltimore, MD) and the LAPC-4 cell line was provided byCharles Sawyers [Memorial Sloan Kettering Cancer Center(MSK), New York, NY]. Normal prostate cell lines were culturedin Keratinocyte Serum-Free Medium (K-SFM; Kit Cat Number:17005-042; Thermo Fisher Scientific). All other cell lines weremaintained in 10% FBS supplemented with 2 mmol/L of

L-glutamine and antibiotic at 37�C in 5% CO2. Cells wereauthenticated by human short-tandem repeat profiling at theMSK Integrated Genomics Operation Core Facility. The prostatecancer tissue microarray was purchased from US Biomax, Inc.(Catalog No. T195c).

Gene silencing and gene expressionSMARTpool siRNA were obtained from Dharmacon and trans-

fected with RNAiMAX (Invitrogen). Cells were harvested 48 or 72hours after transfection for protein andmRNA analysis. Lentiviralvectors encoding RRM2 were purchased from GeneCopoeia andtransfected with psPAX2 packaging and pMD2.G envelope plas-mid to HEK293FT cells for 2 days using Lipofectamine 3000(Invitrogen). Thereafter, prostate cancer cells were infected withviral supernatants in the presence of 8 mg/mL polybrene. Stablecells were generated using puromycin selection. A list of siRNAsand constructs is provided in Supplementary Table S4. Efficiencyof knockdown and overexpression was verified by qPCR andWestern blot analysis.

RNA analysis, RNA-Seq, and immunoblottingTotal RNA was extracted from cells and analyzed as previously

described (19). TaqMan gene expression assays (Life Technology)were used for relative gene expression (Supplementary Table S5)by qRT-PCR. Transcript levels were normalized to levels ofGAPDH transcript. RNA sequencing was performed by 50million2� 50 bp reads in theMSK Integrated Genomics Operation Core.RNA-sequencing data were analyzed by Partek Inc. The data areavailable from GEO (GSE117921-GSE117924). Proteins wereextracted by RIPA buffer and protein concentration was deter-mined by the Bradford method. Equal amounts of protein wereloaded and resolved by SDS-PAGE, then transferred to polyviny-lidene difluoridemembranes for immunoblotting. All antibodiesused are listed in Supplementary Table S4.

Phosphokinase arrays (R&D Systems) were applied accordingto the manufacturer's instructions. Briefly, protein was extractedfrom cells treated with siRNAs or inhibitors. Changes in kinaseactivity were visualized by chemiluminescence on autoradiogra-phy film and measured using ImageJ software.

Cell viability, cell cycle, apoptosis, and DNA damage analysisCells were treated with siRNAs or inhibitors. Cell viability

was assessed using CellTiter-Glo luminescent cell viability assay(Promega). At 72 hours posttransfection, cell cycle, and apoptosiswere detected using the Muse Cell Cycle Assay Kit and the MuseAnnexin V and Dead Cell Kit (EMDMillipore). DNA damage wasdetected at 48 hours posttransfection of siRNAs or at 24 hourstreatment of inhibitors in cells by using Muse multicolor DNADamage Kit (EMD Millipore).

Soft agar assays, wound healing, and invasion assaysSoft agar assays were performed in six-well tissue culture plates

by placing cells (2–5� 104/well) in 2mL of 0.3% soft-agar abovea 2-mL layer of 0.5% agar. After 2 weeks' incubation, cells werestained with 1 mg/mL MTT in medium for 1 hour. Colonieswere detected and counted using GelCount technology (OxfordOptronix Inc.).

Cells were seeded in six-well cell culture plates for 48 hours. Ascratch was made with a pipette tip in a confluent area. Triplicateswere prepared in each condition. Photographs of each scratchwere taken at 24 hours after scratching.

Translational Relevance

Despite the recognized oncogenic activity of ribonucleotidereductase small subunit M2 (RRM2) in multiple cancers, thelack of knowledge of its function in prostate cancer limits thetherapeutic application of its inhibitors. Here, we defined themechanisms of the oncogenic function of RRM2 and unra-veled its prognostic value in prostate cancer. Moreover, weidentified FOXM1 as the driver of RRM2 overexpression inprostate cancer. Importantly, we elucidated the effectivenessand molecular mechanisms of an RRM2 inhibitor, COH29.Overall, our study not only reveals RRM2 as a prognosticmarker for advancedprostate cancer, but alsoprovides supportfor prostate cancer clinical trials targeting RRM2.

Mazzu et al.

Clin Cancer Res; 2019 Clinical Cancer ResearchOF2

Cancer Research. on December 18, 2020. © 2019 American Association forclincancerres.aacrjournals.org Downloaded from

Published OnlineFirst April 17, 2019; DOI: 10.1158/1078-0432.CCR-18-4046

Matrigel invasion assays were performed in Matrigel invasionchamber (Fisher Scientific) with cells on the top of chambers inserum-free media. 10% FBS in the lower chamber was used aschemo-attractant. After indicated times, cells in the bottom cham-ber were fixed in methanol and stained with crystal violet, photo-graphed, and counted under phase-contrast microscopy.

Chromatin immunoprecipitations-Seq, ChIP-qPCR, andluciferase assays

Radical prostatectomy tissue was prepared and chromatinimmunoprecipitations (ChIP)-Seq performed as described pre-viously (20) with 6-mg antibodies to androgen receptor (AR) andH3K27Ac antibody. DNA sequencing libraries were preparedusing the ThruPLEX-FD Prep Kit (Rubicon Genomics). Librarieswere sequenced using 50-base pair reads on the Illumina platform(Illumina) at Dana-Farber Cancer Institute. Related data analysisfor ChIP-Seq and peak calling followed the same protocol asreported. The quantitation of signals on the 1-MB region ofenhancer of RRM2 was performed from four normal prostatetissue samples and four tumor samples. The data are availablefrom GEO (GSE118845).

ChIP-qPCR was performed using the ChIP-IT High SensitivityKit (Active Motif, #53040) with ChIP-grade H3K4me3, FOXM1antibodies, and native IgG, according to the manufacturer'sinstructions. DNA was analyzed via qPCR. All ChIP experimentswere completed with at least two biological replicates.

For luciferase reporter assays, siRNAs were transfected incells for 24 hours and 500 ng of RRM2 promoter reporters(GeneCopoeia) containing 0 or 3 kb promoter sequenceof human RRM2 were cotransfected with Lipofectamine 2000(Invitrogen). At 24 hours posttransfection, cells were collected forluciferase assays according to Promega's instructions.

dNTP pool assayThis assay was performed as previously described (21). Cells

were lysed with 60% cold methanol and the dry pellet wasresuspended in water. Two microliters of sample were used inthe HIV-1 RT based primer extension assay. 50 32P-endlabeledprimer ("P"; 50-GTCCCTCTTCGGGCGCCA-30) was individuallyannealed to one of four different templates (30-CAGGGA-GAAGCCCGCGGTN-50). The template:primer complex wasextended by HIV-1 RT to generate one additional nucleotideextension product ("Pþ1") for one of four dNTPs contained inthe dNTP samples extracted from the cells. In this assay, themolaramount of the Pþ1 product is equal to that of each dNTPcontained in the extracted samples, which allowed us to calculateand compare the amounts of the cellular dNTPs for the differenttreatments. The dNTP amounts were normalized to 1 � 106 cellsfor the comparisons.

Xenograft studiesNOG-SCID mice were implanted subcutaneously with C4-2

cells. After palpable tumors developed (typically 100 mm3), sixmice per group received either vehicle or COH29 (200mg/kg) byoral gavage twice a day for 3weeks. Tumorsweremeasured twice aweek using calipers. Tissues were collected for IHC staining andprotein assays. Multiple proteins were assessed by IHC usingantibodies of H2A.x (1:1000), MYC (1:100), RRM2 (1:2500),cleaved-caspase3 (1:300), prostate-specific antigen (PSA; 1:2,000;Supplementary Table S4). All animal care was in accordance with

the guidelines of the Institutional Animal Care and Use Com-mittee at MSK.

Bioinformatic analysis of clinical cohortsData for various clinical cohorts were obtained from cBioPortal

for Cancer Genomics (22) and Oncomine (23). Heatmapsand volcano plots were generated using Rv3.4.3 (https://www.R-project.org). Pathway analysis was performed using GSEA (24)and ToppGene (25).

Statistical analysisResults are reported as mean � SE. Comparisons between

groups were performed using an unpaired two-sided Student ttest or Wilcoxon test (P < 0.05 was considered significant).Disease-free survival (DFS) was examined for time since diagno-sis. Cox proportional hazard regression was performed adjustingfor clinical and demographic factors.

ResultsRRM2 functions as an oncogene in prostate cancer cells

RRM2 is highly expressed in most prostate cancer celllines tested, regardless of AR status (Supplementary Fig.S1A). Four prostate cancer cell lines (LNCaP, C4-2, 22Rv1,and E006AA) have relatively high RRM2 protein levels, where-as PC-3 cells have lower RRM2 protein expression (Supple-mentary Fig. S1A). It has been reported that RRM2 is barelyexpressed in normal prostate tissue (26). However, in threewidely used normal prostate cell lines, RRM2 was expressed ata high level similar to that in LNCaP cells (Supplementary Fig.S1B). We speculated that those immortalized prostate cell linesmay not reflect the real gene profiling of normal prostate cells.We further detected RRM2 protein expression by IHC stainingin a prostate cancer tissue microarray which included ninecases of prostate cancer adenocarcinoma and one leiomyosar-coma, plus two tissue samples from normal prostates. Com-pared with the two normal prostates that had very low levels ofRRM2, five of the nine prostate cancer tissue samples showedhigh expression of RRM2 (Supplementary Fig. S1C), indicatingthe potential oncogenic role of RRM2 in prostate cancer.

dNTP production (dATP, dCTP, and dTTP) was significantlyinhibited by knockdown of RRM2 (siRRM2; Fig. 1A). In bothLNCaP and C4-2 cells, siRRM2 induced DNA damage with sig-nificant activation of DNA damage markers, including upstreamATM and downstream H2A.X phosphorylation (Fig. 1B and C).Furthermore, siRRM2 led to cell growth inhibition, S-phase arrest,andapoptosis (Fig. 1D–F). Similarphenotypes inducedby siRRM2were also detected in 22Rv1 cells, which express AR-V7, andE006AA cells, which do not express AR (Supplementary Fig.S1D–S1H). Therefore, the tumor suppressive effects of RRM2inhibition are not related to AR status and are not cell line specific.

To further understand the oncogenic functions of RRM2, weestablished RRM2-expressing stable LNCaP and PC-3 cell lines.Overexpression of RRM2 significantly increased dNTPproductionwhereas knockdown of RRM2 decreased dNTP (Fig. 1G). Surpris-ingly, increased RRM2 expression did not affect 2D cell growth(Fig. 1H), but promoted anchorage-independent 3D colony for-mation in RRM2-overexpressing PC-3 cells (PC3-RRM2; Fig. 1I).LNCaP-RRM2 cells showed both increasing 2D cell growthand 3D colony formation (Supplementary Fig. S2A and S2B).PC3-RRM2 cells had a six-fold higher wound healing rate and

Targeting RRM2 Suppresses Poor-Prognosis Prostate Cancer

www.aacrjournals.org Clin Cancer Res; 2019 OF3

Cancer Research. on December 18, 2020. © 2019 American Association forclincancerres.aacrjournals.org Downloaded from

Published OnlineFirst April 17, 2019; DOI: 10.1158/1078-0432.CCR-18-4046

level of invasiveness than control PC-3 cells (Fig. 1J and K),indicating that RRM2 induced EMT. Mesenchymal markersincluding N-cadherin (CDH2) and vimentin (VIM) were notaffected by increased RRM2 expression, whereas the EMTmarkers

(SNAI1 and SLUG)were upregulated in PC3-RRM2 cells (Fig. 1L).Intriguingly, P-cadherin (CDH3), a cell-to-cell adhesion mole-cule, was upregulated nine-fold in PC3-RRM2 cells, whereasE-cadherin (CDH1) expression was increased about five-fold

BA

+ -++

+- -

-

C

D

C4-2

Late apoptosisEarly apoptosis

G0–G1 G2–MS

0

40

80

120 LNCaP C4-2

siNSsiRRM2

-++ +

+--

-

20

4060

80

100

siNSsiRRM2

PARP

RRM2

β-Actin0

Apo

ptot

ic (%

)

siN

Ssi

RR

M2

siN

Ssi

RR

M2

LNCaP C4-2

DNA damage profile

H2A.X Phospho.

ATM

Pho

spho

.N

eg.

Pos

. siNS

Neg

.P

os.

Neg. Pos. Neg. Pos.

LNC

aPC

4-2

siNS siRRM2

Tota

l DN

A d

amag

e (%

)

020

4060

020

40

60

DSBp-H2A.Xp-ATM

rH2A.X

RRM2

β-Actin

siN

S

siR

RM

2

siN

Ssi

RR

M2

LNCaP C4-2

G

LNCaP

E F

Day 73 50

siNS LNCaPsiRRM2 LNCaPsiNS C4-2siRRM2 C4-2

Rel

ativ

e ce

ll vi

abili

ty

02

8

12

10

46

14

Cel

l pop

ulat

ion

(%)

*** ***

***

***

******

0 hr

24 h

r

EV RRM2

20

40

60

80

EV RRM20W

ound

clo

sure

rate

(%)

***

2

4

6

8

EV RRM20 In

vasi

on a

bilit

y ***

H

EV

RR

M2

siRRM2

0.0

0.4

0.8

1.2

Rel

ativ

e dN

TP le

vels

48 72

siNS

*****

*****

*

48 72dATP dTTPdGTPdCTP

Hours 48 72 48 72

0.00.5

1.5

2.5

Rel

ativ

e dN

TP le

vels

dATP dTTPdGTPdCTP

1.0

2.0***

******** RRM2

EV

I

Day

Rel

ativ

e ce

ll vi

abili

ty

0

8

12

4

16RRM2EV

73 50 EV RRM2 Col

ony

form

atio

n ab

ility

***2

3

0

1

0.4

0.8

1.2

0.0 Inva

sion

abi

lity

siNS

siSna

i1

siRRM2

siCDH1

PC3-RRM2

**

****

*******

RRM2EV

Rel

ativ

e m

RN

A le

vels

02

810

46

RRM2CDH3

CDH2CDH1

VIMTw

ist1

SlugSna

i1ZEB1

J

MLK******

******

PC3-EV

***

***

siCDH3

**RRM2

CDH3CDH2CDH1

Snai1

RRM2-gfp

β-Actin

N

EV RRM2

siRRM2

Figure 1.

The oncogenic roles of RRM2 in prostate cancer. A, dNTP production in siRNA-transfected cells. dNTP was detected at both 48 and 72 hours after transfection ofsiRNAs in C4-2 cells. siNS, nonspecific siRNA. B, siRRM2-induced DNA damage. DNA damage marker activation was monitored in LNCaP and C4-2 cells usingMuse multicolor DNA damage kit. C,Activation of H2A.X was confirmed by immunoblotting. D and E, Analysis of cell proliferation (D) and cell cycle (E) intransfected cells. F, Apoptosis detected by Annexin V assays and immunoblots. G, dNTP production in empty vector (EV)/RRM2-overexpressing PC-3 cells(PC3-EV; PC3-RRM2). H, Cell proliferation in stable cells. I, Soft agar assays of stable PC-3 cells. The colony numbers were normalized to those in the control cells.J,Wound healing assays after the scratch done for 24 hours. K, Invasion assays after cells were plated for 48 hours. L andM, EMTmarker expression detectedby qPCR (L) and immunoblots (M) in both EV- and RRM2-expressing PC-3 cells. N, Invasion assays after multiple siRNAswere transfected in PC3-RRM2 cells.Figure values represent the mean� SE of three independent experiments. � , P < 0.05; �� , P < 0.01; ��� , P < 0.001; ���� , P < 0.0001 vs. control groups treated withempty vector (EV) or with nonspecific (siNS) siRNA.

Mazzu et al.

Clin Cancer Res; 2019 Clinical Cancer ResearchOF4

Cancer Research. on December 18, 2020. © 2019 American Association forclincancerres.aacrjournals.org Downloaded from

Published OnlineFirst April 17, 2019; DOI: 10.1158/1078-0432.CCR-18-4046

(Fig. 1L andM).CDH3hasbeenobserved in thedevelopment andprogression of multiple cancers including prostate cancer (27).CDH3 and CDH1 coregulate collective migration in breast can-cer (28). Therefore, RRM2 may promote collective migration byupregulating key regulators such as CDH1, CDH3, and SNAI1.This hypothesis was supported by showing that knockdown ofRRM2 and each of these EMT regulators significantly attenuatedRRM2-induced invasiveness in PC3-RRM2 cells (Fig. 1N; Supple-mentary Fig. S2F). In LNCaP-RRM2 cells, 2.4-fold increasedinvasiveness was detected, coupled with a five-fold upregulationof CDH2 and CDH3 (Supplementary Fig. S2C–S2E), indicatingRRM2may activate specific mediators of EMT or collective migra-tion markers during different disease states. Taken together, thisprovides strong evidence that RRM2 has oncogenic properties invitro.

RRM2 overexpression is prognostically significant in patientswith prostate cancer

Overexpression of RRM2 leads to increased genomic instabil-ity (29), contributing to cancer progression and resistanceto anticancer treatments (30). The copy number variation mea-sured by fraction of the genome altered (FGA) has been shown tobe associated with Gleason score and the development of metas-tases in patients with prostate cancer (3, 31). Here, for the firsttime, the positive correlation between RRM2 level and overallFGA was observed in both the Cancer Genome Atlas (TCGA)cohort (r ¼ 0.5; P ¼ 3.14E�15; Fig. 2A and B; ref. 3) and Taylorcohort (r¼ 0.5, P¼ 1.7E�07; Supplementary Fig. S3C; ref. 32). Inthe two advanced prostate cancer cohorts (SU2C/PCF and Kumarcohorts; refs. 4, 33), weaker but significant correlations were seen(Supplementary Fig. S3E and S3F). Correspondingly, a significant

PG Pri Met-4

-2

0

2

4

Grasso (2012)

n = 56 n = 31n = 23

********

RRM2 level (log)

0.064 108 11

FGA

sig

nal

0.1

0.40.5

0.30.2

r = 0.5 P = 3.14E−15A CB

456789

1011

76 ≥8

RR

M2

leve

l (lo

g)

Gleason score

P < 0.0001

D

Glinsky (2004)Taylor (2010) TCGA provisional (n = 499)

E

PHS/HPFS

F

HG

-2

-1

0

1

2

Taylor (2010)

n = 30 n = 131 n = 19PG Pri Met

****

********

Low RRM2High RRM2

DFS

(%) P < 0.0001

500 100 1500

20

40

60

80

100

0

20

40

60

80

100

500 100 150 500 100 150Time (months)

020

40

60

80100

P < 0.0001

75 9

5-6

2

0

4

6

RR

M2

mR

NA

leve

l

3+4 8 9-104+3Gleason grade

Ptrend < 0.01 Ptrend = 0.85

PHS/HPFS

21 3 4Quartiles of RRM2 expression

12

4

65

3

PHS/HPFS

5-6 3+4 8 9-104+3Gleason grade

OR

for R

RM

2 le

thal

dise

ase

CI (

95%

) lev

el

1 (ref.) 0.68 0.77 3.12(1.4−6.96)(0.29−1.57) (0.32−1.82)

0

-2

2

4Tumor tissue Matched normal tissue

Ptrend = 0.003Tumor tissue

RR

M2

leve

l (lo

g)

RR

M2

leve

l (lo

g)

FGACGS

RRM2BRCA2CHEK2

ATMRAD51D

12%18%14%13%10%

Genetic alteration Missense mutation Truncating mutation Germline mutation

Deep deletion Shallow deletion mRNA upregulation No alterations

0 1 106Clinical Gleason sum (CGS)Fraction genome altered (FGA)

TCGA, Cell 2015 (n = 333)

P < 0.0001

Figure 2.

Overexpression of RRM2 positively associates with poorer clinical outcomes in multiple prostate cancer clinical cohorts. A–C, The correlation of gene alterationwith the fraction of genome altered (FGA) and Gleason grades in TCGA cohort is visualized in A. The statistical quantitation is shown in B and C.D, Thecorrelation of RRM2 level with Gleason grade in tumor andmatched normal tissues in the PHS/HPFS cohorts. E and F, The correlation of RRM2 expression withtumor progression in Taylor and Grasso cohorts. RRM2 levels were analyzed in prostate gland (PG), primary (Pri), and metastasis (Met) tissue samples.G, Theassociation of RRM2 expression and the DFS in the TCGA, Taylor, and Glinsky cohorts. H, The correlation of RRM2 with the risk of lethal prostate cancer overlong-term follow-up, independent from clinical characteristics and Gleason grade, in the combined HPFS and PHS prostate cancer cohorts. The odds ratios forlethal disease were adjusted for Gleason score. ���� , P < 0.0001 vs. comparator groups.

Targeting RRM2 Suppresses Poor-Prognosis Prostate Cancer

www.aacrjournals.org Clin Cancer Res; 2019 OF5

Cancer Research. on December 18, 2020. © 2019 American Association forclincancerres.aacrjournals.org Downloaded from

Published OnlineFirst April 17, 2019; DOI: 10.1158/1078-0432.CCR-18-4046

positive correlation between RRM2 level and Gleason score wasobserved in TCGA (Fig. 2C) and Taylor cohort (SupplementaryFig. S3D). Consistent with other studies (3), alteration of DNArepair genes (BRCA2, CHEK2, ATM, and RAD51D) in the TCGAcohort was significantly correlated with high FGA (Fig. 2A; Sup-plementary Fig. S3A). Higher RRM2 levels were significantlyassociated with heterozygous loss of CHEK2 and ATM (Supple-mentary Fig. S3B), suggesting their contribution to the higher FGAand Gleason score in prostate cancer.

Besides publicly available clinical cohorts, we further vali-dated the prognostic significance of overexpression of RRM2within the HPFS (n ¼ 254) and the PHS (n ¼ 150), two cohortswith long-term follow-up for fatal outcomes. Significantlyhigher levels of RRM2 were observed with increasing Gleasonscore (Ptrend < 0.01; Fig. 2D, left) in tumor, but not in matchednormal tissue (Ptrend ¼ 0.85; Fig. 2D, right). In both Taylor andGrasso cohorts (32, 34), the levels of RRM2 were significantlyhigher in metastatic tissue than in primary and normal tissue(Fig. 2E and F). In the TCGA, Taylor, and Glinsky cohorts (3, 32,35), RRM2 levels in the highest quartile were significantlyassociated with an increased rate of biochemical recurrence(Fig. 2G).

Importantly, in HPFS/PHS cohorts, high RRM2 expression wassignificantly associated with increased risk of lethal disease (Sup-plementary Table S2). When combining the HPFS and PHScohorts, RRM2 RNA levels in the highest quartile, compared withthe lowest quartile, were associated with a 4.0 times higher risk oflethal disease (95% CI, 2.1–7.7; Supplementary Table S2). Whenadjusting for patient and tumor characteristics including centrallyreviewed Gleason grade, this association remained strong (oddsratio, 3.1; 95% CI, 1.4–7.0; Supplementary Table S2; Fig. 2H).There was no association with lethal prostate cancer for RRM2 inmatched normal tissues (Supplementary Fig. S3G). Altogether,these results suggest that increased expression of RRM2 is asso-ciated with adverse biology and poor outcomes.

Transcriptomic changes induced by regulation of RRM2unraveled the clinically relevant RRM2 signature in prostatecancer

Although the oncogenic role of RRM2 has been reported inseveral cancer types, an understanding of the comprehensivemolecular mechanisms by which it functions is lacking. Wedetected global transcriptomic changes induced by siRRM2(Supplementary Fig. S4A). The gene ontology (GO) analysisrevealed that inhibition of RRM2 repressed genes enriched insome key biological processes (e.g., cell proliferation, celldeath, protein metabolic process, and cell cycle) and oncogenicpathways (e.g., MYC pathway, PLK pathway, WNT signaling,Aurora B signaling, and FOXM1 pathway; Supplementary Fig.S4B). The gene set enrichment analysis (GSEA) further definedthe highly enriched pathways (MYC, E2F, cell cycle, p53 sig-naling, apoptosis; Fig. 3A). Targets of MYC, cell cycle, and E2Fpathways were significantly inhibited in siRRM2-treated cells(Fig. 3B). Five common genes in the p53 and apoptosis gene setpathways were validated to be upregulated by siRRM2 treat-ment (Fig. 3B).

To evaluate the clinical relevance of inhibition of RRM2,wefirstlooked at siRRM2-downregulated genes that were positively cor-relatedwith RRM2 in the Taylor cohort (named as "Down-gene"),and upregulated genes which negatively correlated with RRM2expression level (named as "Up-gene"; Fig. 3C). The heat maps

showed that the Down-gene set was activated in metastaticpatients with high RRM2 expression (Fig. 3C, left). These geneswere significantly correlated with DFS (Fig. 3C, right). The find-ings were validated in five other prostate cancer clinical cohorts,supporting the clinical relevance of the genes regulated by inhi-bition of RRM2 (Supplementary Fig. S4C).

To fully understand the oncogenic role of RRM2 in prostatecancer, we evaluated the transcriptomic changes in PC-3-RRM2cells (Supplementary Fig. S4D). GSEA analysis showed significantenrichment in EMT and angiogenesis (AGO) gene sets (Fig. 3D;refs. 36, 37),which strongly supported thephenotypes inducedbyRRM2 overexpression (Fig. 1I–N). The upregulated gene setsinduced by RRM2overexpressionmay contribute to its oncogenicproperties in prostate cancer. Following integration of RRM2-induced genes in cell lines and genes, which are positively cor-related with RRM2 upregulation in three clinical cohorts, weidentified the key 126 RRM2-regulated genes relevant to clinicaloutcome (Fig. 3E). These 126 genes were upregulated in meta-static patients in two additional clinical cohorts (SupplementaryFig. S4E).

Integration of two data sets from siRRM2RNA-Seq in C4-2 cellsand RRM2-expressing RNA-Seq in PC-3 cells provided an in-depth understanding of the RRM2-regulated molecular mechan-isms. Fifty-eight common genes were significantly enriched inoncogenic pathways (FOXM1, PLK1, MYC, and mTORC1 signal-ing) and in glycolysis and cell-cycle–related hallmark gene sets(Supplementary Fig. S4F).

RRM2 inhibitor COH29 induced molecular mechanisms oftumor suppression

Given the potential oncogenic role of RRM2, multiple RNRinhibitors have been developed for clinical application. Therecent novel RNR inhibitor COH29 was designed to target aligand pocket of RRM2 and showed low side effects (38, 39).COH29 is currently in a phase I trial to treat multiple solidtumors. Here, we assessed the effects of COH29 on prostatecancer cells. COH29 inhibited dNTP production due to inhi-bition of RRM2 activity in multiple cancer cell lines. Similarly,dose-dependent inhibition of dNTP production by COH29 wasdetected in C4-2 cells (Fig. 4A). Similar to siRRM2, COH29 ledto DNA damage (Fig. 4B and C); strong cell growth inhibitionin multiple prostate cancer cell lines (Fig. 4D); S-phase arrest(Fig. 4E; Supplementary Fig. S5A); and apoptosis (Fig. 4F;Supplementary Fig. S5B).

The COH29-induced global transcriptomic changes are dosedependent (Fig. 4G). More than 98% of the altered gene expres-sion in high (20 mmol/L) and low (10 mmol/L) doses of COH29overlapped (Supplementary Fig. S5C and S5D); therefore, onlythe RNA-Seq dataset of high-dose COH29 was applied for thesequential GO analysis. GSEA analysis of the mRNA changesinduced by COH29 exhibited similar gene set enrichment as wasseen with siRRM2 treatment, including MYC targets, E2F targets,the p53 pathway, and the apoptosis pathway (Fig. 4H). Addi-tionally, COH29 inhibited the androgen response gene set(Fig. 4H). Compared with the siRNA approach, COH29 inducedwider transcriptomic changes (Supplementary Fig. S5E). Weapplied GO analysis by using genes whose expression weresimilarly altered (219 downregulated genes; 106 upregulatedgenes) with COH29 and siRRM2 treatment. The top five inhibitedpathways were PLK, MYC activation, FOXM1, Aurora B, andtelomerase pathways, whereas p53 downstream pathway and

Mazzu et al.

Clin Cancer Res; 2019 Clinical Cancer ResearchOF6

Cancer Research. on December 18, 2020. © 2019 American Association forclincancerres.aacrjournals.org Downloaded from

Published OnlineFirst April 17, 2019; DOI: 10.1158/1078-0432.CCR-18-4046

MYC repressive pathway were activated with both approaches toinhibit RRM2 activity (Supplementary Fig. S5F). Themajor inhib-ited biological processes included cell cycle, RNA processing,response to DNA damage, and chromosome organization–relat-ed processes (Supplementary Fig. S5G). Intriguingly, besidesapoptosis, DNA damage, and cell proliferation, the processesfor signal transduction, regulation of kinase activity, and inhibi-tion of phosphorylation were activated by inhibition of RRM2(Supplementary Fig. S5G).

There are 33 genes that were inhibited by siRRM2/COH29 andpositively correlated with RRM2 upregulation in the Taylorcohort; 12 genes were upregulated with inhibition of RRM2, butnegatively correlated with RRM2 upregulation (Fig. 4I). ThisRRM2-regulated gene profile was validated in three clinicalcohorts (Fig. 4J). The 33 downregulated genes were activated inmetastatic samples, whereas the 12 upregulated geneswere down-regulated in metastatic samples (Fig. 4J). RRM2 showed onco-genic activity in colorectal and breast cancer progression (13, 14).Therefore, we applied the 33 downregulated genes in the Gaedckecolorectal cohort and Curtis breast cancer cohort (40, 41). Theexpression of the 33 genes are significantly upregulated in aggres-

sive tumor in both cohorts (Supplementary Fig. S5H and S5I).These results suggested that this gene panel can be targeted byinhibition of RRM2 in multiple cancer types in which RRM2drives aggressiveness.

Phosphoproteomic changes are induced by modulating RRM2expression in prostate cancer cells

The GO analysis of genes upon inhibition of RRM2 revealedthat signal transduction and kinase activity processes wereaffected (Supplementary Fig. S5G). Therefore, we applied thephosphokinase array in siRRM2- and COH29-treated C4-2cells (Fig. 5A; Supplementary Fig. S6A). The array detectedthe phosphorylation of 43 human kinases, which are involvedin several key oncogenic signaling processes. Both treatmentssignificantly inhibited AKT/mTOR signaling, leading to thedownstream effectors inhibition. Besides AKT/mTOR signal-ing, both treatments inhibited multiple components in STATsignaling and SFK signaling (Fig. 5A and B; Supplementary Fig.S6A and S6B). The inhibition of these signaling pathwaysinduced by COH29 was dose dependent (Fig. 5A and B). Theseresults were confirmed by Western blotting (Fig. 5C). The

B

A

D

p53 pathway

NES:3.05FDR:0.0

Apoptosis

NES:1.74FDR:0.07

MYC targets

NES:-2.78FDR:0.0

012345

BAXBTG2

FASFDXR

GADD45A

siNSsiRRM2

p53/Apoptosis pathway

0.0

1.0

0.5

1.5

MTA2A NCLPTMA

MYC targets

Rel

ativ

e m

RN

A le

vels

C

E2F targets

NES:-1.41FDR:0.15

Cell cycle

NES:-1.99FDR:0.0

1.0

0.5

1.5

CDC20CENPE

PLK1TOP2A

E2F targets/cell cycle

Up-

gene

Dow

n-ge

ne

Taylor (2010)

Pro

babi

lity

of D

FS

0.000.250.500.751.00

500 100 150Time (months)

500 100 150

P = 0.0068

Row Z-score2-2 0

RRM2Type

0.000.250.500.751.00

class_down=Highclass_down=low

class_up=Highclass_up=low

PrimaryMetastasis

Up-gene Down-gene

PC-3

NES:1.4FDR:0.2

PC-3

NES:2.18FDR:0.02

Up-gene Down-gene

(200)41

(1230)

RRM2-Upregulated

PC-3

EMT(470)39

RRM2-Upregulated

PC-3

AGO

(1230)

+++++++++++++++++++++++++++++++++++++++++++++ ++++++++

++++++ ++ ++ +

++++

+++++++++++++++++++++++++++++

+++++

P = 0.00034

E

Upregulated in PC3-RRM2

126

(1230)

(626)

Positively correlated with RRM2 in 3 cohorts

Taylor (2010)

RRM2Type

Primary Metastasis

+ +High LowTaylor (2010)

+

+

+++++

+ ++++++

+

+++++ + +

+++

+++ ++ ++++++++ +++

+++

+++ + ++

1.00

0.75

0.50

0.25

0.00500 100

DFS

pro

babi

lity

Time (months)

HR = 3.1, P = 0.004995% CI, 1.03–9.1

Figure 3.

Transcriptomic analysis unraveled the molecular mechanism of RRM2 function. A, GSEA of transcriptomic changes. The histograms show the distribution ofselect top GSEAmolecular signatures: MYC targets (MYC_up_V1_up gene set), E2F targets (hallmark_E2F_targets gene set), cell cycle (Module_54 gene set),p53 pathway (hallmark_p53_pathway), and apoptosis (hallmark apoptosis). Up-gene (upregulated genes); Down-gene (downregulated genes).B,Multipletargets of pathways were validated by qRT-PCR. C, siRRM2-regulated gene profiling and the correlation of these genes with DFS in Taylor cohort. D, Significantenrichment in EMT and Angiogenesis gene sets in RRM2-overexpressing PC-3 cells. E, RRM2-regulated 126-gene profiling and correlation with the clinicaloutcome in clinical cohorts. One hundred and twenty-six genes were revealed by overlapping 1,230 upregulated genes in PC3-RRM2 cells with 627 commongenes positively correlated with RRM2 overexpression in three prostate cancer cohorts (TCGA, Kumar, and SU2C/PCF). The correlation of these genes with DFSwas analyzed in the Taylor cohort.

Targeting RRM2 Suppresses Poor-Prognosis Prostate Cancer

www.aacrjournals.org Clin Cancer Res; 2019 OF7

Cancer Research. on December 18, 2020. © 2019 American Association forclincancerres.aacrjournals.org Downloaded from

Published OnlineFirst April 17, 2019; DOI: 10.1158/1078-0432.CCR-18-4046

activation of DNA damage markers was detected when theactivity of these kinases was inhibited. Together, multipleoncogenic signaling pathways were repressed by inhibitingRRM2.

RRM2 inhibitor COH29 has antitumor effect in vivoBecause the 126 genes induced by RRM2 are highly corre-

lated with clinical outcome, targeting them may provide ther-apeutic benefit (Fig. 3E). We applied ToppGene analysis by

A

DLNCaP

C4-2

rH2A.X

β-Actin

LNCaP C4-2

FE

Cel

l via

bilit

y (%

)

0

50

100

150

E006AA

22Rv1

2.5 20 04 0603010COH29 (μmol/L)

10 μmol/L20 μmol/L

00.20.40.60.81.01.2

Rel

ativ

e dN

TP le

vels

1.4

dATP dGTP dTTP dCTP

DNA damage profile

H2A.X Phospho.

ATM

Pho

spho

.

Neg. Pos.

Neg

.

10 μmol/L 20 μmol/L

Pos

.

Tota

l DN

A da

mag

e (%

)

0204060

0

20

40

LnC

apC

4-2

p-ATM/H2A.xp-H2A.Xp-ATM

10 20

**

**

**

COH29 0 μmol/L

0COH29 (μmol/L)

B

+-- +

C

C4-2

Late apoptosisEarly apoptosis

G0−G1 G2−MS

020406080

100120

LNCaP C4-2

20

40

60

80

100

COH29

PARP

β-Actin

0

Apo

ptot

ic c

ells

(%)

LNCaP C4-2

LNCaP

Cel

l pop

ulat

ion

(%)

+-- +COH29

p-Chk1

0 0201COH29 (μmol/L)

0 01

0 μmol/L

20

caspase-3Cleaved

0 02COH29 (μmol/L)

0 20

G

I

NES:-1.41FDR:0.0

E2F targets

NES:2.19FDR:0.014

p53 pathway

NES:-2.86FDR:0.0

Androgen response

J

H MYC targets

NES:-1.97FDR:0.007

Apoptosis

NES:-2.59FDR:0.010 200

COH29 (μmol/L)

#1 #2 #1 #2 #1 #2

33 7819761

Tayl

or c

ohor

t

Down-genesPositively correlated

siR

RM

2/C

OH

29

21 59682

Up-genes

MetastasisPrimary−2 0 2Row Z−Score

Normal

Grasso (2012) Lapointe (2004) Yu (2004)

33 d

own-

gene

s12

up-

gene

ssi

RR

M2/

CO

H29

RRM2Type

*** ***

***

*

*

*

**

******

with RRM2

Negatively correlatedwith RRM2

Figure 4.

The tumor suppressive role of COH29 (RRM2 inhibitor).A, dNTP production in COH29-treated C4-2 cells. Two doses of COH29were used in C4-2 cells, and dNTPproductionwas detected after 24 hours of treatment. B and C,Activation of DNA damagemarkers.D, Cell proliferation assay. E, Cell-cycle analysis after 48 hoursof COH29 treatment. F, COH29-induced apoptosis. G, The global mRNA changes induced by COH29 in C4-2 cells, after 48 hours of treatment. H,GSEA analysisof mRNA profiling in 20 mmol/L of COH29-treated cells. The histograms showed the distribution of select top GSEAmolecular signatures. Up-gene (COH29-induced upregulated genes); Down-gene (COH29-induced downregulated genes); NES, normalized enrichment score; FDR, false discovery rate. I and J,Identification of targeted genes by inhibition of RRM2. Genes affected by inhibition of RRM2 in cells were overlapped with genes correlated with RRM2overexpression in Taylor cohort to reveal 33 downregulated genes and 12 upregulated genes (I). These gene panels were validated in three additional prostatecancer cohorts (J). Figure values represent the mean� SE of three independent experiments. � , P < 0.05; ��, P < 0.01; ��� , P < 0.001 vs. control groups.

Mazzu et al.

Clin Cancer Res; 2019 Clinical Cancer ResearchOF8

Cancer Research. on December 18, 2020. © 2019 American Association forclincancerres.aacrjournals.org Downloaded from

Published OnlineFirst April 17, 2019; DOI: 10.1158/1078-0432.CCR-18-4046

using the 126-gene signature (25). The top three drugs thattarget the signature are lucanthone, palbociclib, and dasatinib.The numbers of genes for each drug potentially targeting thesignature are shown (Fig. 5D). We also evaluated whetherdocetaxel as the first-line treatment of prostate cancer affectsthese genes by using two profiling datasets of docetaxel treat-ment in PC-3 cells (GSE83654) and docetaxel plus androgendeprivation therapy (ADT) in patients (42). Of 126 genes, 67(from docetaxel-treated PC-3 cells) and 43 genes (from doc-etaxel þ ADT-treated patients) were downregulated (Fig. 5D,right). Compared with the other drugs mentioned, COH29inhibited the highest number of genes in the signature (104 of126 genes; Fig. 5D), due to its RRM2-specific inhibition thatwas previously reported (39).

We further assessed the antitumor activity of COH29 in vivo.After 3 weeks of treatment, COH29-treated tumor bearing micehad significantly lower tumor volume (231 mm3) and tumorweight (0.27 g) than the vehicle group (744 mm3 and 0.57 g)

(Fig. 5E and F), without significant difference in body weight ofthe mice (Supplementary Fig. S6C). The COH29-treated xeno-grafts showed increased H2A.X phosphorylation and cleavedcaspase-3, and decreased MYC and PSA (Fig. 5G and H). Wefurther detected significant inhibition of AR mRNA and proteinexpression in COH29-treated tumors (Fig. 5H; SupplementaryFig. S6D). These results are consistent with the repression ofandrogen response genes by COH29 in vitro (Fig. 4H). Inter-estingly, we did not observe inhibition of AR by siRRM2treatment (data not shown), indicating COH29-inhibited ARmay not be directly associated with inhibition of RRM2.Together, COH29 has antitumor effects in vivo in a humanprostate cancer xenograft model.

RRM2 is transcriptionally activated by FOXM1 in prostatecancer

Several studies have focused on transcriptional activation anddelayed degradation of RRM2 in cancers (29, 43–45).

A

0 μmol/L 10 μmol/L 20 μmol/L

F EDG BC A F EDG BC A F EDG BC A

10

123456789

1112131415161718

AK

T_T3

08p-

TOR

p-p7

0S6K

*p-

p70S

6Kp-

eNO

Sp-

PR

AS

40

B11

-12

C1-

2

D11

-12

D15

-16

G3-

4

AK

T_S

473

B9-

10

C11

-12

1.2

0.8

0.4

0.0

Rel

ativ

e de

nsity

AKT/mTOR signaling

Loca

tion

p-S

TAT2

pSTA

T6p-

STA

T5a/

bp-

STA

T5b

D7-

8E

7-8

F9-1

0E

9-10

1.2

0.8

0.4

0.0

COH29

0 μmol/L 10 μmol/L 20 μmol/L

p-S

rc

p-H

ckp-

FAK

p-Ye

sD

1-2

E3-

4F1

-2F5

-6

SFK signaling1.2

0.8

0.4

0.0

D

B

AKT_T308

PRAS40_T246

Src_Y419

Stat5a/bY694/Y699Chk-2_T68

rH2A.X

RRM2

β-Actin

siN

S

siR

RM

2

0 10 20

COH29 (μmol/L)STAT signaling

0

300

600

900

0 4 7 11 14 18 21

VehicleCOH29

Tum

or s

ize

(mm

)3

Treatment (days)

***

00.20.4

0.81.0

0.6

Vehicle COH29

Tum

or w

eigh

t (g) P = 0.0012

G

Vehicle COH29COH29

104Lucanthone62

Palbociclib61

Dasatinib77

126-genesignature

E

γH2A

.XC

leav

ed

MY

C

Cas

pase

3

Vehicle COH29

50 µm

PS

A

50 µm

F

Vehicle COH29H

C

COH29 104

Docetaxel

67

Docetaxel+ADT

43

126-genesignature

Toppgene analysis First-line treatment(in patients)(in PC-3 cells)

γH2A.X

AR

MYC

PSA

β-Actin

Vehicle COH29

Figure 5.

Phosphoproteomic changes induced by inhibition of RRM2 and COH29 efficacy in vivo.A and B, Phosphokinase array analysis after 24-hour COH29 treatment inC4-2 cells. The whole-cell lysates were collected for human phosphokinase array analysis. Each membrane contains kinase-specific antibodies (numberindicated). Relative phosphorylation of spots was quantified by Image J software, and the value of vehicle (0 mmol/L) was set up as "1" (B). C, Validation ofphosphokinase array by immunoblots. For siRNA-treated groups, cell lysates were collected after transfection for 48 hours. The representative blots for eachcondition are shown, and the values represent the mean� SE of two independent experiments. D, Druggable RRM2 signatures. Top three drugs from ToppGeneanalysis and COH29 are shown (left). Numbers of genes downregulated by docetaxel (in PC-3 cells) and docetaxelþ ADT (in patients) are shown (right). E and F,Antitumor effects of COH29 in vivo. Tumor volumes and weights were measured following oral administration of COH29 (200mg/kg) in established C4-2xenograft tumors (n¼ 6 per group). Values are means� SE. ��� , P < 0.001 versus vehicle mice.G and H, Regulation of key genes by COH29 in vivo. Multiple genesregulated by COH29 were assessed in xenograft tumors by IHC staining (G) or immunoblotting (H).

Targeting RRM2 Suppresses Poor-Prognosis Prostate Cancer

www.aacrjournals.org Clin Cancer Res; 2019 OF9

Cancer Research. on December 18, 2020. © 2019 American Association forclincancerres.aacrjournals.org Downloaded from

Published OnlineFirst April 17, 2019; DOI: 10.1158/1078-0432.CCR-18-4046

Amplification and mutation of RRM2 is rare in prostate cancer.Transcriptional regulation may largely contribute to the over-expression of RRM2 in prostate cancer. We applied H3K27AcChIP-Seq in prostate normal and tumor tissues. Significantlyhigher H3K27Ac binding signal was detected at the enhancer ofRRM2 (1 Mb region) in prostate cancer (n ¼ 4, P < 0.05),compared with normal prostate tissue (n¼ 4; Fig. 6A), indicatingmore activated RRM2 transcription in prostate cancer than innormal tissue.

A few transcription factors (TF) have been identified thattarget RRM2 in different cancers (e.g., E2F1, E2F3, and MYCN;refs. 43–45), but there are no studies of transcriptional regu-lation of RRM2 in prostate cancer cells. Here, we developed astrategy that combines prostate cancer clinical cohorts and TF

databases to seek potential TFs targeting RRM2. We searchedgenes that positively correlated with RRM2 expression (r > 0.5,P < 0.05) in four widely used clinical cohorts (Fig. 6B). Amongall the candidates, 13 genes are TFs. Interestingly, E2F1 andE2F3 have been demonstrated to target RRM2 in colorectalcancer and KB cells (43, 44).

A correlation matrix from the TCGA (primary tumor samples)and SU2C/PCF (metastatic samples) cohorts not only showed therelationships betweenRRM2andall potential RRM2-targeting TFsbut also showed the correlations among the TFs (SupplementaryFig. S7A). It revealed different correlation patterns for the TCGAand SU2C/PCF cohorts, indicating that disease state may con-tribute to the specificity of RRM2-targeting TFs in prostate cancer.The subset of RRM2-targeting TFs which correlate with significant

0.20.4

0.80.6

Normal tumor

RRM2

Tum

or n

orm

al

(4) (4)

H3K27Ac-ChIP Seq in tissue

E2F1E2F7E2F8

FOXM1MYBL2POU5F1SSRP1

ZNF367

E2F3E2F7E2F8

FOXM1MYBL2WHSC1ZNF165ZNF367

E2F1E2F2E2F3E2F8

FOXM1HMGB2MYBL2WHSC1ZNF367

E2F1E2F2E2F7E2F8

FOXM1HMGB2

2 cohorts 3 cohorts 4 cohorts

Search genes correlated with RRM2 expression(r > 0.5, P < 0.05)

TCGAn = 208Kumar MSKCC(Taylor)

n = 212SU2C/PCF

n = 253

1558 human transcription factors

n = 236

A

H

B C

0

0.4

0.8

1.2

% In

put

0

5

10

15

20 C4-222Rv1

FOXM1 IgG H3K4me3 IgG

ChIP with P1 primers

siNSsiFOXM1

01

2

34

5

R2-0k R2-3K

G

Luci

fera

se a

ctiv

ity

Rel

ativ

e m

RN

A le

vels

siNSsiFOXM1

*****

RRM2

β-Actin

siNS

siFOXM1

*

D E0072210100202101 10121200

MDA-MB-231

MCF7

HeLa

MCF-7

RRM2

Chr2:

1

2

3

4

**

** **

**

IP1 P2 P3

0

0.4

0.8

1.2

** FOXM1

siNS

siFOXM1

C4-222Rv1

C4-222Rv1

NT FDI-6

Rel

ativ

e m

RN

A le

vels

0

0.4

0.8

1.2

*****

**

** ***

RRM2FOXM1

MYC PSACENPF

F

0

0.4

0.8

1.2

FOXM1 IgG

****

ChIP with P2 primers

0

0.2

0.1

FOXM1 IgG

ChIP with P3 primers

PHS/HPFS

IndolentLethal

2.0 3.0 4.0 5.0FOXM1 level

2.0

3.0

4.0

RR

M2

leve

l

r = 0.4 (95% CI, 0.31−0.47)

Figure 6.

Transcriptional activation of RRM2. A, H3K27Ac ChIP-Seq in tissues. The binding signal on RRM2 enhancer (1Mb region) was quantified. B, The strategy to identifyRRM2-targeting TFs. Human TFs were selected in the genes positively correlated with RRM2 expression in prostate cancer cohorts. Yellow/orange/pink barsindicate that TFs appear in four/three/two cohorts. C, The correlation of FOXM1 and RRM2 in PHS/HPFS cohorts. D, FOXM1 binding on RRM2 promoter in cancercells. The overview of multiple ChIP-Seq datasets were extracted from Cistrome Data browser. P1–P3 primers were designed for FOXM1 ChIP-PCR. E, FOXM1 orH3K4me3-ChIP-PCR on RRM2 promoter. F, RRM2 promoter activity regulated by FOXM1. The reporters without (R2-0K) or with (R2-3K) 3kb RRM2 promotersequence were transfected in siRNA-treated 22Rv1 cells. G andH, Inhibition of RRM2 expression by siFOXM1 in 22Rv1 and C4-2 cells. I, Inhibition of FOXM1 targetsby FDI-6 (20 mmol/L) in 22Rv1 cells. Figure values represent the mean� SE of three independent experiments. � , P < 0.05; �� , P < 0.01; ��� , P < 0.001 vs. controlgroups.

Mazzu et al.

Clin Cancer Res; 2019 Clinical Cancer ResearchOF10

Cancer Research. on December 18, 2020. © 2019 American Association forclincancerres.aacrjournals.org Downloaded from

Published OnlineFirst April 17, 2019; DOI: 10.1158/1078-0432.CCR-18-4046

clinical outcome should be more meaningful and could facilitatethe development of future therapeutic strategies. Among 13 TFs,both FOXM1 and E2F8 were shared in all the cohorts (Fig. 6B).FOXM1 has been reported to be one of the major drivers inprostate cancer (46). Besides those four cohorts (Fig. 6B), thepositive correlation between FOXM1 and RRM2 was alsoobserved in PHS/HPFS cohorts (Fig. 6C). Similar to RRM2,FOXM1 level is highly correlated with DFS in the Taylor cohort(Supplementary Fig. S7B). For both FOXM1 and RRM2, higherlevels are associated with lethal disease (P < 0.0001; Supplemen-tary Fig. S7C).

To assess the transcription control of RRM2 by FOXM1, we firstidentified FOXM1 binding region to the RRM2 promoter frompublished FOXM1ChIP-Seq. In breast cancer cells andHeLa cells,there were two major FOXM1 binding peaks in the promoter ofRRM2 (Fig. 6D). Although no FOXM1 ChIP-Seq data in prostatecancer cells is available, ChIP-Seq of transcriptional activationmarks (includingH3K4me3,H3K27Ac, and POLR2A) inmultipleprostate cancer cell lines provided supporting evidence that theFOXM1-binding regions of RRM2 promoter are activated (Sup-plementary Fig. S7D). Two pairs of primers (P1 and P2) weredesigned to evaluate FOXM1 binding in prostate cancer cells,based on the ChIP-Seq data from other cells. A negative controlprimer pair (P3) located in non-FOXM1 binding region was used(Fig. 6D). ChIP-qPCR in C4-2 and 22Rv1 cells showed strongerFOXM1 binding signals in the distal P1 region of the promoterthan in the proximal P2 region, supported by significantH3K4me3 binding in the same region (Fig. 6E). More significantFOXM1 binding in 22Rv1 than in C4-2 could be due to higherexpression of FOXM1 in 22Rv1 (Fig. 6H). As a positive control, weassessed FOXM1binding at the CENPF (identified FOXM1 target)promoter in the same ChIP-qPCR. Strong FOXM1 binding at theCENPF promoter was detected in both cell lines (SupplementaryFig. S7E and S7F).

The activity of the RRM2promoter reporterwas reduced 40% insiFOXM1-treated 22Rv1 cells (Fig. 6F), due to 80% reduction ofFOXM1 expression by siFOXM1 (Supplementary Fig. S7G). Con-sistently, siFOXM1 led to approximately 50% reduction of RRM2mRNA and protein expression (Fig. 6G and H). Besides siRNAs, asmall molecule inhibitor of FOXM1 (FDI-6) repressed multipleFOXM1 targets, including RRM2,MYC, PSA, and CENPF (Fig. 6I).Altogether, FOXM1 can transcriptionally activate RRM2 expres-sion by directly binding to the promoter.

DiscussionThe well-established function of RRM2 is to maintain the

balance of dNTP pools for DNA synthesis and DNA repair.However, there has been no comprehensive mechanistic investi-gation that defines the downstream biological consequences andsignaling pathways of RRM2 in cancer, specifically in prostatecancer. This study showed for the first time the oncogenic prop-erties of RRM2 in prostate cancer. We unraveled and validated thesignificant prognostic value of RRM2 in 11 prostate cancer clinicalcohorts. Integrative analysis of transcriptomic and phosphopro-teomic changes regulated by RRM2 revealed oncogenic mechan-isms of RRM2 in prostate cancer.

Abnormal RRM2 degradation induced imbalance of the dNTPpool and genome instability (29). In our analysis, we found thatoverexpressed RRM2 is highly correlated with deletion of CHEK2or ATM, which may lead to higher FGA and Gleason score

(Supplementary Fig. S3B). Overexpression of RRM2 could berequired to compensate for the DNA repair deficiency inducedby deletion of DNA repair genes (e.g., CHEK2 or ATM). However,the correlation of RRM2 with FGA and clinical Gleason sum(CGS) is not completely dependent on the deletion of both genes,because some tumors showed high RRM2 expression withoutdeletion of CHEK2 and ATM (Fig. 2A).

Similar to what was reported in breast cancer (14), overexpres-sion of RRM2 in prostate cancer cells promoted 3D colonyformation and invasion. Unexpectedly, RRM2 activatedatypical EMT progression by upregulating E-cadherin (CDH1)and P-cadherin (CDH3). High CDH1 expressed PC-3 cellsshowed more aggressive phenotypes (47). Similarly, moderateCDH1 levels present in 4T1 breast cancer cells could promotecancer metastasis by increasing the collective migration (48). Theessential role of CDH3 in collective migration could promotetumorigenesis in prostate, colon, ovary, and breast cancers (49).Coexpression of CDH1 and CDH3 leads to the aggressive biologybehavior in breast cancer cells due to interruption of the interac-tion betweenCDH1 and catenins (48). Therefore, upregulation ofCDH1 and CDH3 by RRM2 overexpression could be one of thedriving forces of the more aggressive phenotype in PC-3-RRM2cells. Furthermore, for the first time, we demonstrated that severalTF networks (FOXM1, MYC, APC/C/CDH1, E2F targets) andoncogenic pathways (PLK1, mTORC1, Aurora B, Rho GTPasessignaling) are regulated by RRM2. All these TFs and signalingpathways have been reported to contribute to prostate cancerprogression.

Besides exploring RRM2 function, we uncovered the regula-tory mechanisms of RRM2 overexpression in prostate cancer.The fact that more transcriptional activation of RRM2 wasobserved in tumor tissue than in normal prostate tissue ledus to develop a bioinformatic strategy to identify the RRM2-targeting TFs. Multiple ChIP-Seq in prostate cancer cells showedthat the FOXM1 binding region of RRM2 promoter is highlytranscriptionally activated. Thus, the cooperation of FOXM1and other TFs regulating RRM2 is a potential avenue for futurestudy. FOXM1 was reported as a master regulator of the aggres-sive PCS1 subtype tumors (50). In our study, we identifiedFOXM1 as the key regulator of RRM2, and inhibition of RRM2effectively repressed the FOXM1 pathway (SupplementaryFig. S4F and S5F), suggesting the feedback loop betweenFOXM1 and RRM2. We believe that interruption of this regu-latory feedback loop may specifically target aggressive subtypetumors.

In summary, our study integrated the data from cell lines withclinical cohorts to reveal that overexpression of RRM2 is associ-ated with poor outcomes and is potentially a driver of aggressiveprostate cancer. Use of an RRM2-targeted inhibitor does inhibitRRM2 oncogenic activity in vivo. Furthermore, we defined theunderlying molecular mechanisms and determined the transcrip-tional activation of RRM2 by FOXM1.

Disclosure of Potential Conflicts of InterestY.Z. Mazzu has filed a patent application relevant to the work that is the

subject of this paper: U.S Provisional Patent ApplicationNo. 62/834,914; RRM2Signature as a Prognostic Marker for Prostate Cancer Survival; filed: April 16,2019; MSK Ref.: SK2019-043-01. P.W. Kantoff reports the following for the last36-month period: investment interest in Context Therapeutics, DRGT, Placon,Seer Biosciences, and Tarveda Therapeutics; company board member for Con-text Therapeutics; consultant/scientific advisory board member for BavarianNordic Immunotherapeutics, BINDBiosciences,DRGT,GEHealthcare, Janssen,

Targeting RRM2 Suppresses Poor-Prognosis Prostate Cancer

www.aacrjournals.org Clin Cancer Res; 2019 OF11

Cancer Research. on December 18, 2020. © 2019 American Association forclincancerres.aacrjournals.org Downloaded from

Published OnlineFirst April 17, 2019; DOI: 10.1158/1078-0432.CCR-18-4046

Metamark, New England Research Institutes, OncoCellMDX, Progenity, Sanofi,Seer Biosciences, Tarveda Therapeutics, and Thermo Fisher; and data safetymonitoring board member for Genentech/Roche and Merck. No potentialconflicts of interest were disclosed by the other authors.

Authors' ContributionsConception and design: Y.Z. Mazzu, P.W. KantoffDevelopment of methodology: Y.Z. Mazzu, G. Chakraborty, H. Zhao,K. Komura, E. O'Connor, P.W. KantoffAcquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): Y.Z. Mazzu, Y. Yoshikawa, S.A. Coggins, M. Atiq,K. Komura, C. Bell, E. O'Connor, B. KimAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): Y.Z. Mazzu, J. Armenia, G. Chakraborty,S. Nandakumar, T.A. Gerke, M.M. Pomerantz, X. Qiu, K. Komura, G.-S.M.Lee, S.W. Fine, H.W. Long, M.L. Freedman, P.W. KantoffWriting, review, and/or revision of the manuscript: Y.Z. Mazzu, J. Armenia,S. Nandakumar, T.A. Gerke, M.M. Pomerantz, M. Atiq, N. Khan, S.W. Fine,M.L. Freedman, P.W. Kantoff

Administrative, technical, or material support (i.e., reporting or organizingdata, constructing databases): Y.Z. Mazzu, T.A. Gerke, X. Qiu, H. Zhao,S.W. FineStudy supervision: Y.Z. Mazzu, G.-S.M. Lee, P.W. Kantoff

AcknowledgmentsThis research was funded in part through the NIH/NCI Cancer Center

Support Grant to Memorial Sloan Kettering Cancer Center (P30 CA008748),and dNTP assays were funded in part through NIH/NIGMS R01 GM104198 toB. Kim and NIH/NIAID R01 AI136581 to B. Kim.

The costs of publication of this article were defrayed in part by thepayment of page charges. This article must therefore be hereby markedadvertisement in accordance with 18 U.S.C. Section 1734 solely to indicatethis fact.

ReceivedDecember 14, 2018; revisedMarch 14, 2019; accepted April 8, 2019;published first April 17, 2019.

References1. Baca SC, Prandi D, Lawrence MS, Mosquera JM, Romanel A, Drier Y,

et al. Punctuated evolution of prostate cancer genomes. Cell 2013;153:666–77.

2. Berger MF, Lawrence MS, Demichelis F, Drier Y, Cibulskis K, SivachenkoAY, et al. The genomic complexity of primary human prostate cancer.Nature 2011;470:214–20.

3. Cancer Genome Atlas Research Network. The molecular taxonomy ofprimary prostate cancer. Cell 2015;163:1011–25.

4. Robinson D, Van Allen EM, Wu YM, Schultz N, Lonigro RJ, Mosquera JM,et al. Integrative clinical genomics of advanced prostate cancer. Cell 2015;161:1215–28.

5. Beer TM, Kwon ED, Drake CG, Fizazi K, Logothetis C, Gravis G, et al.Randomized, double-blind, phase III trial of ipilimumab versus placebo inasymptomatic or minimally symptomatic patients with metastatic chemo-therapy-naive castration-resistant prostate cancer. J Clin Oncol 2017;35:40–7.

6. Farmer H, McCabe N, Lord CJ, Tutt AN, Johnson DA, Richardson TB, et al.Targeting the DNA repair defect in BRCA mutant cells as a therapeuticstrategy. Nature 2005;434:917–21.

7. Fiorentino M, Judson G, Penney K, Flavin R, Stark J, Fiore C, et al.Immunohistochemical expression of BRCA1 and lethal prostate cancer.Cancer Res 2010;70:3136–9.

8. Kumar D, Abdulovic AL, Viberg J, Nilsson AK, Kunkel TA, Chabes A.Mechanisms of mutagenesis in vivo due to imbalanced dNTP pools.Nucleic Acids Res 2011;39:1360–71.

9. Aye Y, Li M, Long MJ, Weiss RS. Ribonucleotide reductase and cancer:biological mechanisms and targeted therapies. Oncogene 2015;34:2011–21.

10. Chabes A, Thelander L. Controlled protein degradation regulates ribonu-cleotide reductase activity in proliferating mammalian cells during thenormal cell cycle and in response to DNA damage and replication blocks.J Biol Chem 2000;275:17747–53.

11. Grade M, Hummon AB, Camps J, Emons G, Spitzner M, Gaedcke J, et al.A genomic strategy for the functional validation of colorectal cancergenes identifies potential therapeutic targets. Int J Cancer 2011;128:1069–79.

12. Kretschmer C, Sterner-Kock A, Siedentopf F, Schoenegg W, Schlag PM,Kemmner W. Identification of early molecular markers for breast cancer.Mol Cancer 2011;10:15.

13. Xu X, Page JL, Surtees JA, Liu H, Lagedrost S, Lu Y, et al. Broad over-expression of ribonucleotide reductase genes in mice specifically induceslung neoplasms. Cancer Res 2008;68:2652–60.

14. Shah KN, Wilson EA, Malla R, Elford HL, Faridi JS. Targeting ribonu-cleotide reductase M2 and NF-kappaB activation with didox to circum-vent tamoxifen resistance in breast cancer. Mol Cancer Ther 2015;14:2411–21.

15. Zhang K, Hu S, Wu J, Chen L, Lu J, Wang X, et al. Overexpression of RRM2decreases thrombspondin-1 and increases VEGF production in human

cancer cells in vitro and in vivo: implication of RRM2 in angiogenesis.Mol Cancer 2009;8:11.

16. Giovannucci E, Liu Y, Platz EA, Stampfer MJ, Willett WC. Risk factors forprostate cancer incidence and progression in the Health ProfessionalsFollow-Up Study. Int J Cancer 2007;121:1571–8.

17. Steering Committee of the Physicians' Health Study Research Group. Finalreport on the aspirin component of the ongoing Physicians' Health Study.N Engl J Med 1989;321:129–35.

18. Penney KL, Sinnott JA, Tyekucheva S, Gerke T, Shui IM, Kraft P, et al.Association of prostate cancer risk variants with gene expression in normaland tumor tissue. Cancer Epidemiol Biomarkers Prev 2015;24:255–60.

19. Zhang Y, Xie RL, Croce CM, Stein JL, Lian JB, van Wijnen AJ, et al. Aprogram of microRNAs controls osteogenic lineage progression bytargeting transcription factor Runx2. Proc Natl Acad Sci U S A 2011;108:9863–8.

20. Pomerantz MM, Li F, Takeda DY, Lenci R, Chonkar A, Chabot M, et al. Theandrogen receptor cistrome is extensively reprogrammed in human pros-tate tumorigenesis. Nat Genet 2015;47:1346–51.

21. Hollenbaugh JA, Tao S, Lenzi GM, Ryu S, Kim DH, Diaz-Griffero F, et al.dNTP pool modulation dynamics by SAMHD1 protein in monocyte-derived macrophages. Retrovirology 2014;11:63.

22. Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, et al.Integrative analysis of complex cancer genomics and clinical profiles usingthe cBioPortal. Sci Signal 2013;6:pl1.

23. Rhodes DR, Yu J, Shanker K, Deshpande N, Varambally R, Ghosh D, et al.ONCOMINE: a cancer microarray database and integrated data-miningplatform. Neoplasia 2004;6:1–6.

24. SubramanianA, TamayoP,Mootha VK,Mukherjee S, Ebert BL,GilletteMA,et al. Gene set enrichment analysis: a knowledge-based approach forinterpreting genome-wide expression profiles. Proc Natl Acad Sci U S A2005;102:15545–50.

25. Chen J, Bardes EE, Aronow BJ, Jegga AG. ToppGene Suite for gene listenrichment analysis and candidate gene prioritization. Nucleic Acids Res2009;37:W305–11.

26. Huang Y, Liu X, Wang YH, Yeh SD, Chen CL, Nelson RA, et al. Theprognostic value of ribonucleotide reductase small subunit M2 in predict-ing recurrence for prostate cancers. Urol Oncol 2014;32:51.e9–19.

27. Patel IS, Madan P, Getsios S, Bertrand MA, MacCalman CD. Cadherinswitching in ovarian cancer progression. Int J Cancer 2003;106:172–7.

28. Ribeiro AS, Sousa B, Carreto L, Mendes N, Nobre AR, Ricardo S, et al.P-cadherin functional role is dependent on E-cadherin cellular context:a proof of concept using the breast cancer model. J Pathol 2013;229:705–18.

29. D'Angiolella V,Donato V, Forrester FM, Jeong YT, PellacaniC, Kudo Y, et al.Cyclin F-mediated degradation of ribonucleotide reductase M2 controlsgenome integrity and DNA repair. Cell 2012;149:1023–34.

30. Bedard PL, Hansen AR, Ratain MJ, Siu LL. Tumour heterogeneity in theclinic. Nature 2013;501:355–64.

Mazzu et al.

Clin Cancer Res; 2019 Clinical Cancer ResearchOF12

Cancer Research. on December 18, 2020. © 2019 American Association forclincancerres.aacrjournals.org Downloaded from

Published OnlineFirst April 17, 2019; DOI: 10.1158/1078-0432.CCR-18-4046

31. Hieronymus H, Schultz N, Gopalan A, Carver BS, Chang MT, Xiao Y, et al.Copy number alteration burden predicts prostate cancer relapse. Proc NatlAcad Sci U S A 2014;111:11139–44.

32. Taylor BS, Schultz N, Hieronymus H, Gopalan A, Xiao Y, Carver BS, et al.Integrative genomic profiling of human prostate cancer. Cancer Cell 2010;18:11–22.

33. Kumar A, Coleman I, Morrissey C, Zhang X, True LD, Gulati R, et al.Substantial interindividual and limited intraindividual genomic diversityamong tumors from men with metastatic prostate cancer. Nat Med 2016;22:369–78.

34. GrassoCS,WuYM,RobinsonDR, CaoX,Dhanasekaran SM,KhanAP, et al.The mutational landscape of lethal castration-resistant prostate cancer.Nature 2012;487:239–43.

35. Glinsky GV, Glinskii AB, Stephenson AJ, Hoffman RM, Gerald WL. Geneexpression profiling predicts clinical outcome of prostate cancer. J ClinInvest 2004;113:913–23.

36. Groger CJ, Grubinger M, Waldhor T, Vierlinger K, Mikulits W. Meta-analysis of gene expression signatures defining the epithelial to mes-enchymal transition during cancer progression. PLoS One 2012;7:e51136.

37. Masiero M, Simoes FC, Han HD, Snell C, Peterkin T, Bridges E, et al. A corehuman primary tumor angiogenesis signature identifies the endothelialorphan receptor ELTD1 as a key regulator of angiogenesis. Cancer Cell2013;24:229–41.

38. Chen MC, Zhou B, Zhang K, Yuan YC, Un F, Hu S, et al. The novelribonucleotide reductase inhibitor COH29 inhibits DNA repair in vitro.Mol Pharmacol 2015;87:996–1005.

39. Zhou B, Su L, Hu S, Hu W, Yip ML, Wu J, et al. A small-molecule blockingribonucleotide reductase holoenzyme formation inhibits cancer cellgrowth and overcomes drug resistance. Cancer Res 2013;73:6484–93.

40. Curtis C, Shah SP, Chin SF, Turashvili G, RuedaOM,DunningMJ, et al. Thegenomic and transcriptomic architecture of 2,000 breast tumours revealsnovel subgroups. Nature 2012;486:346–52.

41. Gaedcke J, Grade M, Jung K, Camps J, Jo P, Emons G, et al. Mutated KRASresults in overexpression of DUSP4, a MAP-kinase phosphatase, and

SMYD3, a histonemethyltransferase, in rectal carcinomas. Genes Chromo-somes Cancer 2010;49:1024–34.

42. Rajan P, Stockley J, Sudbery IM, Fleming JT, Hedley A, Kalna G, et al.Identification of a candidate prognostic gene signature by transcrip-tome analysis of matched pre- and post-treatment prostatic biopsiesfrom patients with advanced prostate cancer. BMC Cancer 2014;14:977.

43. Fang Z, Gong C, Liu H, Zhang X, Mei L, Song M, et al. E2F1 promote theaggressiveness of human colorectal cancer by activating the ribonucleotidereductase small subunit M2. Biochem Biophys Res Commun 2015;464:407–15.

44. Gong C, Liu H, Song R, Zhong T, Lou M, Wang T, et al. ATR-CHK1-E2F3signaling transactivates human ribonucleotide reductase small subunitM2for DNA repair induced by the chemical carcinogen MNNG. BiochimBiophys Acta 2016;1859:612–26.

45. Valentijn LJ, Koster J, Haneveld F, Aissa RA, van Sluis P, Broekmans ME,et al. Functional MYCN signature predicts outcome of neuroblastomairrespective of MYCN amplification. Proc Natl Acad Sci U S A 2012;109:19190–5.

46. Aytes A,Mitrofanova A, LefebvreC, AlvarezMJ, Castillo-MartinM,Zheng T,et al. Cross-species regulatory network analysis identifies a synergisticinteraction between FOXM1 and CENPF that drives prostate cancer malig-nancy. Cancer Cell 2014;25:638–51.

47. Celia-Terrassa T, Meca-Cortes O, Mateo F, Martinez de Paz A, Rubio N,Arnal-Estape A, et al. Epithelial-mesenchymal transition can suppressmajor attributes of human epithelial tumor-initiating cells. J Clin Invest2012;122:1849–68.

48. Elisha Y, Kalchenko V, Kuznetsov Y, Geiger B.Dual role of E-cadherin in theregulation of invasive collective migration of mammary carcinoma cells.Sci Rep 2018;8:4986.

49. Vieira AF, Paredes J. P-cadherin and the journey to cancer metastasis.Mol Cancer 2015;14:178.

50. Ketola K, Munuganti RSN, Davies A, Nip KM, Bishop JL, Zoubeidi A.Targeting prostate cancer subtype 1 by Forkhead box M1 pathway inhibi-tion. Clin Cancer Res 2017;23:6923–33.

www.aacrjournals.org Clin Cancer Res; 2019 OF13

Targeting RRM2 Suppresses Poor-Prognosis Prostate Cancer

Cancer Research. on December 18, 2020. © 2019 American Association forclincancerres.aacrjournals.org Downloaded from

Published OnlineFirst April 17, 2019; DOI: 10.1158/1078-0432.CCR-18-4046

Published OnlineFirst April 17, 2019.Clin Cancer Res   Ying Z. Mazzu, Joshua Armenia, Goutam Chakraborty, et al.   Ribonucleotide Reductase Small Subunit M2 (RRM2)Cancer: Overexpression of the DNA Repair Gene, A Novel Mechanism Driving Poor-Prognosis Prostate

  Updated version

  10.1158/1078-0432.CCR-18-4046doi:

Access the most recent version of this article at:

  Material

Supplementary

 

http://clincancerres.aacrjournals.org/content/suppl/2019/04/13/1078-0432.CCR-18-4046.DC1Access the most recent supplemental material at:

   

   

   

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

  Subscriptions

Reprints and

  .pubs@aacr.orgDepartment at

To order reprints of this article or to subscribe to the journal, contact the AACR Publications

  Permissions

  Rightslink site. (CCC)Click on "Request Permissions" which will take you to the Copyright Clearance Center's

.http://clincancerres.aacrjournals.org/content/early/2019/05/14/1078-0432.CCR-18-4046To request permission to re-use all or part of this article, use this link

Cancer Research. on December 18, 2020. © 2019 American Association forclincancerres.aacrjournals.org Downloaded from

Published OnlineFirst April 17, 2019; DOI: 10.1158/1078-0432.CCR-18-4046