Ovarian Cancer

Overview

Approximately 10% of all epithelial ovarian carcinomas are associated with autosomal dominant genetic predisposition, primarily by inherited mutations in the BRCA1 or BRCA2 tumour supressor genes (Boyd 1998). Mutations of these genes are also seen in some sporadic ovarian cancers. Other genetic features tend to relate to specific types of ovarian cancer;

Invasive serous and undifferentiated ovarian carcinomas are characterized by TP53 mutations and TP53 protein accumulation. Loss of genetic material from chromosome 17 is also common.

Overexpression of BCL2 is seen in most endometrioid carcinomas (90% of cases). These tumours can also show microsatellite instability.

KRAS mutations are characteristic for mucinous carcinomas (40-50% of cases). In mucinous tumors with low malignant potential (LMP) KRAS mutations are less frequent ( about 30% of cases).

See also: Ovarian Cancer - clinical resources (31)

Literature Analysis

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Tag cloud generated 08 August, 2015 using data from PubMed, MeSH and CancerIndex

Mutated Genes and Abnormal Protein Expression (295)

How to use this data tableClicking on the Gene or Topic will take you to a separate more detailed page. Sort this list by clicking on a column heading e.g. 'Gene' or 'Topic'.

GeneLocationAliasesNotesTopicPapers
BRCA1 17q21 IRIS, PSCP, BRCAI, BRCC1, FANCS, PNCA4, RNF53, BROVCA1, PPP1R53 -BRCA1 and Ovarian Cancer
2715
BRCA2 13q12.3 FAD, FACD, FAD1, GLM3, BRCC2, FANCD, PNCA2, FANCD1, XRCC11, BROVCA2 -BRCA2 and Ovarian Cancer
2096
TP53 17p13.1 P53, BCC7, LFS1, TRP53 -TP53 and Ovarian Cancer
312
CTNNB1 3p21 CTNNB, MRD19, armadillo -CTNNB1 and Ovarian Cancer
258
ERBB2 17q12 NEU, NGL, HER2, TKR1, CD340, HER-2, MLN 19, HER-2/neu -HER2 and Ovarian Cancer
134
MSH2 2p21 FCC1, COCA1, HNPCC, LCFS2, HNPCC1 -MSH2 and Ovarian Cancer
115
PIK3CA 3q26.3 MCM, CWS5, MCAP, PI3K, CLOVE, MCMTC, p110-alpha -PIK3CA and Ovarian Cancer
96
CDKN1A 6p21.2 P21, CIP1, SDI1, WAF1, CAP20, CDKN1, MDA-6, p21CIP1 Prognostic
-CDKN1A Expression in Ovarian Cancer
84
MDM2 12q14.3-q15 HDMX, hdm2, ACTFS -MDM2 and Ovarian Cancer
81
AKT2 19q13.1-q13.2 PKBB, PRKBB, HIHGHH, PKBBETA, RAC-BETA -AKT2 and Ovarian Cancer
59
RAD51 15q15.1 RECA, BRCC5, MRMV2, HRAD51, RAD51A, HsRad51, HsT16930 -RAD51 and Ovarian Cancer
59
ERCC1 19q13.32 UV20, COFS4, RAD10 -ERCC1 and Ovarian Cancer
59
MMP2 16q12.2 CLG4, MONA, CLG4A, MMP-2, TBE-1, MMP-II -MMP2 and Ovarian Cancer
57
ARID1A 1p35.3 ELD, B120, OSA1, P270, hELD, BM029, MRD14, hOSA1, BAF250, C1orf4, BAF250a, SMARCF1 -ARID1A and Ovarian Cancer
50
TUBE1 6q21 TUBE, dJ142L7.2 -TUBE1 and Ovarian Cancer
47
BLID 11q24.1 BRCC2 -BLID and Ovarian Cancer
44
MSH6 2p16 GTBP, HSAP, p160, GTMBP, HNPCC5 -MSH6 and Ovarian Cancer
44
FOXL2 3q23 BPES, PFRK, POF3, BPES1, PINTO -FOXL2 and Ovarian Cancer
43
ACHE 7q22 YT, ACEE, ARACHE, N-ACHE -ACHE and Ovarian Cancer
43
BCL2L1 20q11.21 BCLX, BCL2L, BCLXL, BCLXS, Bcl-X, bcl-xL, bcl-xS, PPP1R52, BCL-XL/S -BCL2L1 and Ovarian Cancer
38
WT1 11p13 GUD, AWT1, WAGR, WT33, NPHS4, WIT-2, EWS-WT1 -WT1 expression in Ovarian Cancer
37
CHEK2 22q12.1 CDS1, CHK2, LFS2, RAD53, hCds1, HuCds1, PP1425 -CHEK2 and Ovarian Cancer
37
RAD51C 17q22 FANCO, R51H3, BROVCA3, RAD51L2 -RAD51C and Ovarian Cancer
36
FGF2 4q26 BFGF, FGFB, FGF-2, HBGF-2 -FGF2 and Ovarian Cancer
30
CYP19A1 15q21.1 ARO, ARO1, CPV1, CYAR, CYP19, CYPXIX, P-450AROM -CYP19A1 and Ovarian Cancer
30
PMS2 7p22.2 PMSL2, HNPCC4, PMS2CL -PMS2 and Ovarian Cancer
29
KLK3 19q13.41 APS, PSA, hK3, KLK2A1 -PSA expression in Ovarian Cancere
29
PARP1 1q41-q42 PARP, PPOL, ADPRT, ARTD1, ADPRT1, PARP-1, ADPRT 1, pADPRT-1 -PARP1 and Ovarian Cancer
28
OLAH 10p13 SAST, AURA1, THEDC1 -OLAH and Ovarian Cancer
26
MUC16 19p13.2 CA125 -MUC16 and Ovarian Cancer
26
BARD1 2q34-q35 -BARD1 and Ovarian Cancer
26
ABCC1 16p13.1 MRP, ABCC, GS-X, MRP1, ABC29 -ABCC1 (MRP1) and Ovarian Cancer
25
SERPINB5 18q21.33 PI5, maspin -SERPIN-B5 and Ovarian Cancer
23
XIAP Xq25 API3, ILP1, MIHA, XLP2, BIRC4, IAP-3, hIAP3, hIAP-3 -XIAP and Ovarian Cancer
23
DICER1 14q32.13 DCR1, MNG1, Dicer, HERNA, RMSE2, Dicer1e, K12H4.8-LIKE -DICER1 and Ovarian Cancer
22
CXCL1 4q21 FSP, GRO1, GROa, MGSA, NAP-3, SCYB1, MGSA-a -CXCL1 and Ovarian Cancer
22
BRAP 12q24 IMP, BRAP2, RNF52 -BRAP and Ovarian Cancer
22
HMGA2 12q15 BABL, LIPO, HMGIC, HMGI-C, STQTL9 -HMGA2 and Ovarian Cancer
21
ZNF217 20q13.2 ZABC1 -ZNF217 and Ovarian Cancer
20
DIRAS3 1p31 ARHI, NOEY2 -DIRAS3 and Ovarian Cancer
19
JUN 1p32-p31 AP1, AP-1, c-Jun -c-Jun and Ovarian Cancer
19
FHIT 3p14.2 FRA3B, AP3Aase -FHIT and Ovarian Cancer
17
KLK6 19q13.3 hK6, Bssp, Klk7, SP59, PRSS9, PRSS18 -KLK6 and Ovarian Cancer
15
EPHA2 1p36 ECK, CTPA, ARCC2, CTPP1, CTRCT6 -EPHA2 and Ovarian Cancer
15
NBN 8q21 ATV, NBS, P95, NBS1, AT-V1, AT-V2 -NBN and Ovarian Cancer
15
CLDN3 7q11.23 RVP1, HRVP1, C7orf1, CPE-R2, CPETR2 -CLDN3 and Ovarian Cancer
15
CLDN4 7q11.23 CPER, CPE-R, CPETR, CPETR1, WBSCR8, hCPE-R -CLDN4 and Ovarian Cancer
14
E2F3 6p22 E2F-3 -E2F3 and Ovarian Cancer
14
FH 1q42.1 MCL, FMRD, LRCC, HLRCC, MCUL1 -FH and Ovarian Cancer
14
PPP2R1A 19q13.41 PR65A, PP2AAALPHA, PP2A-Aalpha -PPP2R1A and Ovarian Cancer
14
PTER 10p12 HPHRP, RPR-1 -PTER and Ovarian Cancer
14
RAD51D 17q11 TRAD, R51H3, BROVCA4, RAD51L3 -RAD51D and Ovarian Cancer
13
L1CAM Xq28 S10, HSAS, MASA, MIC5, SPG1, CAML1, CD171, HSAS1, N-CAML1, NCAM-L1, N-CAM-L1 -L1CAM and Ovarian Cancer
13
DAB2 5p13.1 DOC2, DOC-2 -DAB2 and Ovarian Cancer
13
XRCC3 14q32.3 CMM6 -XRCC3 and Ovarian Cancer
12
OPCML 11q25 OPCM, OBCAM, IGLON1 -OPCML and Ovarian Cancer
12
XRCC2 7q36.1 -XRCC2 and Ovarian Cancer
12
EPCAM 2p21 ESA, KSA, M4S1, MK-1, DIAR5, EGP-2, EGP40, KS1/4, MIC18, TROP1, EGP314, HNPCC8, TACSTD1 -EPCAM and Ovarian Cancer
12
NOTCH3 19p13.2-p13.1 IMF2, CASIL, CADASIL -NOTCH3 and Ovarian Cancer
11
PDCD4 10q24 H731 -PDCD4 and Ovarian Cancer
11
HSD17B2 16q24.1-q24.2 HSD17, SDR9C2, EDH17B2 -HSD17B2 and Ovarian Cancer
11
KLK10 19q13 NES1, PRSSL1 -KLK10 and Ovarian Cancer
11
FSHR 2p21-p16 LGR1, ODG1, FSHRO -FSHR and Ovarian Cancer
11
COL18A1 21q22.3 KS, KNO, KNO1 -COL18A1 and Ovarian Cancer
11
SMARCA4 19p13.2 BRG1, SNF2, SWI2, MRD16, RTPS2, BAF190, SNF2L4, SNF2LB, hSNF2b, BAF190A -SMARCA4 and Ovarian Cancer
11
GATA4 8p23.1-p22 TOF, ASD2, VSD1, TACHD -GATA4 and Ovarian Cancer
11
CDH13 16q23.3 CDHH, P105 -CDH13 and Ovarian Cancer
10
FOLR1 11q13.3-q14.1 FBP, FOLR -FOLR1 and Ovarian Cancer
10
TUBB3 16q24.3 CDCBM, FEOM3, TUBB4, CDCBM1, CFEOM3, beta-4, CFEOM3A -TUBB3 and Ovarian Cancer
9
RHOC 1p13.1 H9, ARH9, ARHC, RHOH9 -RHOC and Ovarian Cancer
9
CLMP 11q24.1 ACAM, ASAM, CSBM, CSBS -CLMP and Ovarian Cancer
9
KLK5 19q13.33 SCTE, KLKL2, KLK-L2 -KLK5 and Ovarian Cancer
9
PAK1 11q13-q14 PAKalpha -PAK1 and Ovarian Cancer
9
E2F2 1p36 E2F-2 -E2F2 and Ovarian Cancer
9
TRPM2 21q22.3 KNP3, EREG1, TRPC7, LTRPC2, NUDT9H, NUDT9L1 -TRPM2 and Ovarian Cancer
9
AKT3 1q44 MPPH, PKBG, MPPH2, PRKBG, STK-2, PKB-GAMMA, RAC-gamma, RAC-PK-gamma -AKT3 and Ovarian Cancer
9
MAP2K4 17p12 JNKK, MEK4, MKK4, SEK1, SKK1, JNKK1, SERK1, MAPKK4, PRKMK4, SAPKK1, SAPKK-1 -MAP2K4 and Ovarian Cancer
9
MRE11A 11q21 ATLD, HNGS1, MRE11, MRE11B -MRE11A and Ovarian Cancer
8
POSTN 13q13.3 PN, OSF2, OSF-2, PDLPOSTN, periostin -POSTN and Ovarian Cancer
8
TSG101 11p15 TSG10, VPS23 -TSG101 and Ovarian Cancer
8
GALT 9p13 -GALT and Ovarian Cancer
8
CLU 8p21-p12 CLI, AAG4, APOJ, CLU1, CLU2, KUB1, SGP2, APO-J, SGP-2, SP-40, TRPM2, TRPM-2, NA1/NA2 -CLU and Ovarian Cancer
8
HBEGF 5q23 DTR, DTS, DTSF, HEGFL -HBEGF and Ovarian Cancer
8
ATP7B 13q14.3 WD, PWD, WC1, WND -ATP7B and Ovarian Cancer
8
IL18 11q22.2-q22.3 IGIF, IL-18, IL-1g, IL1F4 -IL18 and Ovarian Cancer
8
HNF1B 17q12 FJHN, HNF2, LFB3, TCF2, HPC11, LF-B3, MODY5, TCF-2, VHNF1, HNF-1B, HNF1beta, HNF-1-beta -HNF1B and Ovarian Cancer
8
DNMT3A 2p23 TBRS, DNMT3A2, M.HsaIIIA -DNMT3A and Ovarian Cancer
8
SPINT2 19q13.1 PB, Kop, HAI2, DIAR3, HAI-2 -SPINT2 and Ovarian Cancer
7
GNRHR 4q21.2 HH7, GRHR, LRHR, LHRHR, GNRHR1 -GNRHR and Ovarian Cancer
7
BMP4 14q22-q23 ZYME, BMP2B, OFC11, BMP2B1, MCOPS6 -BMP4 and Ovarian Cancer
7
KLK4 19q13.41 ARM1, EMSP, PSTS, AI2A1, EMSP1, KLK-L1, PRSS17, kallikrein -KLK4 and Ovarian Cancer
7
IGFBP2 2q35 IBP2, IGF-BP53 -IGFBP2 and Ovarian Cancer
7
MIRLET7B 22q13.31 LET7B, let-7b, MIRNLET7B, hsa-let-7b -MicroRNA let-7b and Ovarian Cancer
7
TACSTD2 1p32 EGP1, GP50, M1S1, EGP-1, TROP2, GA7331, GA733-1 -TACSTD2 and Ovarian Cancer
7
SCGB2A2 11q13 MGB1, UGB2 -SCGB2A2 and Ovarian Cancer
7
EP300 22q13.2 p300, KAT3B, RSTS2 -EP300 and Ovarian Cancer
7
LIN28B 6q21 CSDD2 -LIN28B and Ovarian Cancer
7
ABCC2 10q24 DJS, MRP2, cMRP, ABC30, CMOAT -ABCC2 and Ovarian Cancer
7
SNAI1 20q13.2 SNA, SNAH, SNAIL, SLUGH2, SNAIL1, dJ710H13.1 -SNAI1 and Ovarian Cancer
7
CTCFL 20q13.31 CT27, BORIS, CTCF-T, HMGB1L1, dJ579F20.2 -CTCFL and Ovarian Cancer
7
RBBP8 18q11.2 RIM, COM1, CTIP, JWDS, SAE2, SCKL2 -RBBP8 and Ovarian Cancer
7
EEF1A2 20q13.3 HS1, STN, EF1A, STNL, EEF1AL, EF-1-alpha-2 -EEF1A2 and Ovarian Cancer
7
BACH1 21q22.11 BACH-1, BTBD24 -BACH1 and Ovarian Cancer
7
TP53BP1 15q15-q21 p202, 53BP1 -TP53BP1 and Ovarian Cancer
7
HOXA10 7p15.2 PL, HOX1, HOX1H, HOX1.8 -HOXA10 and Ovarian Cancer
7
PPM1D 17q23.2 WIP1, PP2C-DELTA -PPM1D and Ovarian Cancer
6
SULF1 8q13.2 SULF-1, HSULF-1 -SULF1 and Ovarian Cancer
6
SNAI2 8q11 SLUG, WS2D, SLUGH1, SNAIL2 -SNAI2 and Ovarian Cancer
6
SKIL 3q26 SNO, SnoA, SnoI, SnoN -SKIL and Ovarian Cancer
6
MIRLET7I 12q14.1 LET7I, MIRNLET7I, hsa-let-7i -MicroRNA let-7i and Ovarian Cancer
6
SLPI 20q12 ALP, MPI, ALK1, BLPI, HUSI, WAP4, WFDC4, HUSI-I -SLPI and Ovarian Cancer
6
CCL5 17q12 SISd, eoCP, SCYA5, RANTES, TCP228, D17S136E, SIS-delta -CCL5 and Ovarian Cancer
6
ATP7A Xq21.1 MK, MNK, DSMAX, SMAX3 -ATP7A and Ovarian Cancer
6
STIP1 11q13 HOP, P60, STI1, STI1L, HEL-S-94n, IEF-SSP-3521 Prognostic
-STIP1 Expression in Ovarian Cancer
6
RAD52 12p13-p12.2 -RAD52 and Ovarian Cancer
6
SFRP5 10q24.1 SARP3 -SFRP5 and Ovarian Cancer
6
RAB25 1q22 CATX-8, RAB11C -RAB25 and Ovarian Cancer
6
CCNB2 15q22.2 HsT17299 -CCNB2 and Ovarian Cancer
6
TGFBI 5q31 CSD, CDB1, CDG2, CSD1, CSD2, CSD3, EBMD, LCD1, BIGH3, CDGG1 -TGFBI and Ovarian Cancer
6
OSCAR 19q13.42 PIGR3, PIgR-3 -OSCAR and Ovarian Cancer
6
GATA6 18q11.1-q11.2 -GATA6 and Ovarian Cancer
5
ARL11 13q14.2 ARLTS1 -ARL11 and Ovarian Cancer
5
GSTA1 6p12.1 GST2, GTH1, GSTA1-1 -GSTA1 and Ovarian Cancer
5
AIDA 1q41 C1orf80 -AIDA and Ovarian Cancer
5
GPER1 7p22.3 mER, CEPR, GPER, DRY12, FEG-1, GPR30, LERGU, LyGPR, CMKRL2, LERGU2, GPCR-Br -GPER and Ovarian Cancer
5
CD46 1q32 MCP, TLX, AHUS2, MIC10, TRA2.10 -CD46 and Ovarian Cancer
5
IGFBP1 7p12.3 AFBP, IBP1, PP12, IGF-BP25, hIGFBP-1 -IGFBP1 and Ovarian Cancer
5
ESR2 14q23.2 Erb, ESRB, ESTRB, NR3A2, ER-BETA, ESR-BETA -ESR2 and Ovarian Cancer
5
RASSF2 20p13 CENP-34, RASFADIN -RASSF2 and Ovarian Cancer
5
ETV5 3q28 ERM -ETV5 and Ovarian Cancer
5
CYP2C8 10q23.33 CPC8, CYPIIC8, MP-12/MP-20 -CYP2C8 and Ovarian Cancer
5
FOSB 19q13.32 AP-1, G0S3, GOS3, GOSB -FOSB and Ovarian Cancer
5
TOPBP1 3q22.1 TOP2BP1 -TOPBP1 and Ovarian Cancer
5
KRT7 12q13.13 K7, CK7, SCL, K2C7 -KRT7 and Ovarian Cancer
5
CCR1 3p21 CKR1, CD191, CKR-1, HM145, CMKBR1, MIP1aR, SCYAR1 -CCR1 and Ovarian Cancer
5
CDKN2D 19p13 p19, INK4D, p19-INK4D -CDKN2D and Ovarian Cancer
4
NBR1 17q21.31 IAI3B, M17S2, MIG19, 1A1-3B -NBR1 and Ovarian Cancer
4
MTDH 8q22.1 3D3, AEG1, AEG-1, LYRIC, LYRIC/3D3 -MTDH and Ovarian Cancer
4
SNCG 10q23.2-q23.3 SR, BCSG1 -SNCG and Ovarian Cancer
4
ZNF350 19q13.41 ZFQR, ZBRK1 -ZNF350 and Ovarian Cancer
4
BAGE 21p11.1 not on ref BAGE1, CT2.1 -BAGE and Ovarian Cancer
4
SMAD5 5q31 DWFC, JV5-1, MADH5 -SMAD5 and Ovarian Cancer
4
PTPN1 20q13.1-q13.2 PTP1B -PTPN1 and Ovarian Cancer
4
PYCARD 16p11.2 ASC, TMS, TMS1, CARD5, TMS-1 -PYCARD and Ovarian Cancer
4
ITGB3 17q21.32 GT, CD61, GP3A, BDPLT2, GPIIIa, BDPLT16 -ITGB3 and Ovarian Cancer
4
VCAN 5q14.3 WGN, ERVR, GHAP, PG-M, WGN1, CSPG2 -VCAN and Ovarian Cancer
4
GAST 17q21 GAS -GAST and Ovarian Cancer
4
HOXA7 7p15.2 ANTP, HOX1, HOX1A, HOX1.1 -HOXA7 and Ovarian Cancer
4
ZMYND10 3p21.3 BLU, FLU, CILD22 -ZMYND10 and Ovarian Cancer
4
DPH1 17p13.3 DPH2L, OVCA1, DPH2L1 -DPH1 and Ovarian Cancer
4
SNRPN 15q11.2 SMN, PWCR, SM-D, sm-N, RT-LI, HCERN3, SNRNP-N, SNURF-SNRPN -SNRPN and Ovarian Cancer
4
PITX2 4q25 RS, RGS, ARP1, Brx1, IDG2, IGDS, IHG2, PTX2, RIEG, IGDS2, IRID2, Otlx2, RIEG1 -PITX2 and Ovarian Cancer
4
HDAC4 2q37.3 HD4, AHO3, BDMR, HDACA, HA6116, HDAC-4, HDAC-A -HDAC4 and Ovarian Cancer
4
FGF9 13q11-q12 GAF, FGF-9, SYNS3, HBFG-9, HBGF-9 -FGF9 and Ovarian Cancer
4
HTRA1 10q26.3 L56, HtrA, ARMD7, ORF480, PRSS11, CARASIL -HTRA1 and Ovarian Cancer
4
CA12 15q22 CAXII, HsT18816 -CA12 and Ovarian Cancer
4
CDK12 17q12 CRK7, CRKR, CRKRS -CDK12 and Ovarian Cancer
4
XIST Xq13.2 SXI1, swd66, DXS1089, DXS399E, LINC00001, NCRNA00001 -XIST and Ovarian Cancer
4
PLAGL1 6q24-q25 ZAC, LOT1, ZAC1 -PLAGL1 and Ovarian Cancer
4
PEG3 19q13.4 PW1, ZNF904, ZSCAN24, ZKSCAN22 -PEG3 and Ovarian Cancer
4
SAT2 17p13.1 SSAT2 -SAT2 and Ovarian Cancer
4
PAEP 9q34 GD, GdA, GdF, GdS, PEP, PAEG, PP14 -PAEP and Ovarian Cancer
4
KL 13q12 -KL and Ovarian Cancer
4
MSLN 16p13.3 MPF, SMRP -MSLN and Ovarian Cancer
4
FMR1 Xq27.3 POF, FMRP, POF1, FRAXA -FMR1 and Ovarian Cancer
3
MIRLET7E 19q13.41 LET7E, let-7e, MIRNLET7E, hsa-let-7e -MicroRNA let-7e and Ovarian Cancer
3
KLK14 19q13.3-q13.4 KLK-L6 -KLK14 and Ovarian Cancer
3
CRABP1 15q24 RBP5, CRABP, CRABPI, CRABP-I -CRABP1 and Ovarian Cancer
3
KISS1R 19p13.3 HH8, CPPB1, GPR54, AXOR12, KISS-1R, HOT7T175 -KISS1R and Ovarian Cancer
3
ADRM1 20q13.33 ARM1, ARM-1, GP110 -ADRM1 and Ovarian Cancer
3
GAS6 13q34 AXSF, AXLLG -GAS6 and Ovarian Cancer
3
LZTS1 8p22 F37, FEZ1 -LZTS1 and Ovarian Cancer
3
CASP4 11q22.2-q22.3 TX, ICH-2, Mih1/TX, ICEREL-II, ICE(rel)II -CASP4 and Ovarian Cancer
3
CCL19 9p13 ELC, CKb11, MIP3B, MIP-3b, SCYA19 -CCL19 and Ovarian Cancer
3
POLL 10q23 BETAN, POLKAPPA -POLL and Ovarian Cancer
3
TCEAL7 Xq22.1 WEX5, MPMGp800C04260Q003 -TCEAL7 and Ovarian Cancer
3
CCNE2 8q22.1 CYCE2 -CCNE2 and Ovarian Cancer
3
ACVR1 2q23-q24 FOP, ALK2, SKR1, TSRI, ACTRI, ACVR1A, ACVRLK2 -ACVR1 and Ovarian Cancer
3
HAS2 8q24.12 -HAS2 and Ovarian Cancer
3
RUNX2 6p21 CCD, AML3, CCD1, CLCD, OSF2, CBFA1, OSF-2, PEA2aA, PEBP2aA, CBF-alpha-1 -RUNX2 and Ovarian Cancer
3
IL21 4q26-q27 Za11, IL-21, CVID11 -IL21 and Ovarian Cancer
3
HYAL1 3p21.31 MPS9, NAT6, LUCA1, HYAL-1 -HYAL1 and Ovarian Cancer
3
GAB2 11q14.1 -GAB2 and Ovarian Cancer
3
LSP1 11p15.5 WP34, pp52 -LSP1 and Ovarian Cancer
3
PPIA 7p13 CYPA, CYPH, HEL-S-69p -PPIA and Ovarian Cancer
3
IFITM1 11p15.5 9-27, CD225, IFI17, LEU13, DSPA2a -IFITM1 and Ovarian Cancer
3
SLC7A5 16q24.3 E16, CD98, LAT1, 4F2LC, MPE16, hLAT1, D16S469E -SLC7A5 and Ovarian Cancer
3
BAG3 10q25.2-q26.2 BIS, MFM6, BAG-3, CAIR-1 -BAG3 and Ovarian Cancer
3
KLK2 19q13.41 hK2, hGK-1, KLK2A2 -KLK2 and Ovarian Cancer
3
EFEMP1 2p16 DHRD, DRAD, FBNL, MLVT, MTLV, S1-5, FBLN3, FIBL-3 -EFEMP1 and Ovarian Cancer
3
MTHFD1 14q24 MTHFC, MTHFD -MTHFD1 and Ovarian Cancer
3
CUL3 2q36.2 CUL-3, PHA2E -CUL3 and Ovarian Cancer
3
AQP1 7p14 CO, CHIP28, AQP-CHIP -AQP1 and Ovarian Cancer
3
GATA5 20q13.33 GATAS, bB379O24.1 -GATA5 and Ovarian Cancer
3
ARHGDIB 12p12.3 D4, GDIA2, GDID4, LYGDI, Ly-GDI, RAP1GN1, RhoGDI2 -ARHGDIB and Ovarian Cancer
3
BTG1 12q22 -BTG1 and Ovarian Cancer
3
EBAG9 8q23 EB9, PDAF -EBAG9 and Ovarian Cancer
3
CALCA 11p15.2 CT, KC, CGRP, CALC1, CGRP1, CGRP-I -CALCA and Ovarian Cancer
3
SMAD6 15q22.31 AOVD2, MADH6, MADH7, HsT17432 -SMAD6 and Ovarian Cancer
3
PRDX6 1q25.1 PRX, p29, AOP2, 1-Cys, NSGPx, aiPLA2, HEL-S-128m -PRDX6 and Ovarian Cancer
3
E2F5 8q21.2 E2F-5 -E2F5 and Ovarian Cancer
3
SNRPF 12q23.1 SMF, Sm-F, snRNP-F -SNRPF and Ovarian Cancer
3
LHCGR 2p21 HHG, LHR, LCGR, LGR2, ULG5, LHRHR, LSH-R, LH/CGR, LH/CG-R -LHCGR and Ovarian Cancer
3
LMNA 1q22 FPL, IDC, LFP, CDDC, EMD2, FPLD, HGPS, LDP1, LMN1, LMNC, PRO1, CDCD1, CMD1A, FPLD2, LMNL1, CMT2B1, LGMD1B -LMNA and Ovarian Cancer
3
SPRY4 5q31.3 HH17 -SPRY4 and Ovarian Cancer
3
GAGE1 Xp11.23 CT4.1, GAGE-1 -GAGE1 and Ovarian Cancer
3
CHEK1 11q24.2 CHK1 -CHEK1 and Ovarian Cancer
3
PLK2 5q12.1-q13.2 SNK, hSNK, hPlk2 -PLK2 and Ovarian Cancer
3
HLA-DRA 6p21.3 MLRW, HLA-DRA1 -HLA-DRA and Ovarian Cancer
3
HAS3 16q22.1 -HAS3 and Ovarian Cancer
3
SNRPE 1q32 SME, Sm-E, B-raf, HYPT11 -SNRPE and Ovarian Cancer
3
MIRLET7D 9q22.32 LET7D, let-7d, MIRNLET7D, hsa-let-7d -MicroRNA let-d and Ovarian Cancer
3
MARS 12q13.3 MRS, METRS, MTRNS, SPG70 -MARS and Ovarian Cancer
3
LIG4 13q33-q34 LIG4S -LIG4 and Ovarian Cancer
3
TWIST2 2q37.3 FFDD3, DERMO1, SETLSS, bHLHa39 -TWIST2 and Ovarian Cancer
3
SRSF3 6p21 SFRS3, SRp20 -SRSF3 and Ovarian Cancer
3
HOXA11 7p15.2 HOX1, HOX1I -HOXA11 and Ovarian Cancer
3
MUC5B 11p15.5 MG1, MUC5, MUC9, MUC-5B -MUC5B and Ovarian Cancer
3
GNAI2 3p21.31 GIP, GNAI2B, H_LUCA15.1, H_LUCA16.1 -GNAI2 and Ovarian Cancer
3
IL1A 2q14 IL1, IL-1A, IL1F1, IL1-ALPHA -IL1A and Ovarian Cancer
3
NR5A1 9q33 ELP, SF1, FTZ1, POF7, SF-1, AD4BP, FTZF1, SPGF8, SRXY3 -NR5A1 and Ovarian Cancer
3
THBS2 6q27 TSP2 -THBS2 and Ovarian Cancer
3
HTRA2 2p12 OMI, PARK13, PRSS25 -HTRA2 and Ovarian Cancer
2
NQO2 6p25.2 QR2, DHQV, DIA6, NMOR2 -NQO2 and Ovarian Cancer
2
HSD3B2 1p13.1 HSDB, HSD3B, SDR11E2 -HSD3B2 and Ovarian Cancer
2
PTK6 20q13.3 BRK -PTK6 and Ovarian Cancer
2
FOXC2 16q24.1 LD, MFH1, MFH-1, FKHL14 -FOXC2 and Ovarian Cancer
2
TRIO 5p15.2 tgat, ARHGEF23 -TRIO and Ovarian Cancer
2
CASP2 7q34-q35 ICH1, NEDD2, CASP-2, NEDD-2, PPP1R57 -CASP2 and Ovarian Cancer
2
CARS 11p15.5 CARS1, CYSRS, MGC:11246 -CARS and Ovarian Cancer
2
FALEC 1 FAL1, ncRNA-a1, LINC00568 -FALEC and Ovarian Cancer
2
MAGEA3 Xq28 HIP8, HYPD, CT1.3, MAGE3, MAGEA6 -MAGEA3 and Ovarian Cancer
2
CSE1L 20q13 CAS, CSE1, XPO2 -CSE1L and Ovarian Cancer
2
WNT4 1p36.23-p35.1 WNT-4, SERKAL -WNT4 and Ovarian Cancer
2
IL2 4q26-q27 IL-2, TCGF, lymphokine -IL2 and Ovarian Cancer
2
CTSD 11p15.5 CPSD, CLN10, HEL-S-130P -CTSD and Ovarian Cancer
2
CCR3 3p21.3 CKR3, CD193, CMKBR3, CC-CKR-3 -CCR3 and Ovarian Cancer
2
RAC2 22q13.1 Gx, EN-7, HSPC022, p21-Rac2 -RAC2 and Ovarian Cancer
2
HLA-DQA1 6p21.3 CD, GSE, DQ-A1, CELIAC1, HLA-DQA -HLA-DQA1 and Ovarian Cancer
2
IL27 16p11 p28, IL30, IL-27, IL27A, IL-27A, IL27p28 -IL27 and Ovarian Cancer
2
HAS1 19q13.4 HAS -HAS1 and Ovarian Cancer
2
MAGEA4 Xq28 CT1.4, MAGE4, MAGE4A, MAGE4B, MAGE-41, MAGE-X2 -MAGEA4 and Ovarian Cancer
2
SALL4 20q13.2 DRRS, HSAL4, ZNF797, dJ1112F19.1 -SALL4 and Ovarian Cancer
2
BMPR1B 4q22-q24 ALK6, ALK-6, CDw293 -BMPR1B and Ovarian Cancer
2
RAG1 11p13 RAG-1, RNF74 -RAG1 and Ovarian Cancer
2
WNT7A 3p25 -WNT7A and Ovarian Cancer
2
RAD54L 1p32 HR54, hHR54, RAD54A, hRAD54 -RAD54L and Ovarian Cancer
2
LRP1 12q13.3 APR, LRP, A2MR, CD91, APOER, LRP1A, TGFBR5, IGFBP3R -LRP1 and Ovarian Cancer
2
CX3CL1 16q13 NTN, NTT, CXC3, CXC3C, SCYD1, ABCD-3, C3Xkine, fractalkine, neurotactin -CX3CL1 and Ovarian Cancer
2
GREB1 2p25.1 -GREB1 and Ovarian Cancer
2
PELP1 17p13.2 MNAR, P160 -PELP1 and Ovarian Cancer
2
PAPPA 9q33.2 PAPA, DIPLA1, PAPP-A, PAPPA1, ASBABP2, IGFBP-4ase -PAPPA and Ovarian Cancer
2
PIK3R2 19q13.2-q13.4 p85, MPPH, P85B, MPPH1, p85-BETA -PIK3R2 and Ovarian Cancer
2
PAK4 19q13.2 -PAK4 and Ovarian Cancer
2
GUSB 7q21.11 BG, MPS7 -GUSB and Ovarian Cancer
2
IRF3 19q13.3-q13.4 -IRF3 and Ovarian Cancer
2
LAMP1 13q34 LAMPA, CD107a, LGP120 -LAMP1 and Ovarian Cancer
2
MTA2 11q12-q13.1 PID, MTA1L1 -MTA2 and Ovarian Cancer
2
PEA15 1q21.1 PED, MAT1, HMAT1, MAT1H, PEA-15, HUMMAT1H -PEA15 and Ovarian Cancer
2
SLC34A2 4p15.2 NPTIIb, NAPI-3B, NAPI-IIb -SLC34A2 and Ovarian Cancer
2
MMP10 11q22.3 SL-2, STMY2 -MMP10 and Ovarian Cancer
2
GSTO2 10q25.1 GSTO 2-2, bA127L20.1 -GSTO2 and Ovarian Cancer
2
FRAT1 10q24.1 -FRAT1 and Ovarian Cancer
2
ST14 11q24-q25 HAI, MTSP1, SNC19, ARCI11, MT-SP1, PRSS14, TADG15, TMPRSS14 -ST14 and Ovarian Cancer
2
XRCC6 22q13.2 ML8, KU70, TLAA, CTC75, CTCBF, G22P1 -XRCC6 and Ovarian Cancer
2
CTSL 9q21.33 MEP, CATL, CTSL1 -CTSL and Ovarian Cancer
2
PPARGC1A 4p15.1 LEM6, PGC1, PGC1A, PGC-1v, PPARGC1, PGC-1(alpha) -PPARGC1A and Ovarian Cancer
2
TNKS 8p23.1 TIN1, ARTD5, PARPL, TINF1, TNKS1, pART5, PARP5A, PARP-5a -TNKS and Ovarian Cancer
2
HRK 12q24.22 DP5, HARAKIRI -HRK and Ovarian Cancer
2
KRT8 12q13 K8, KO, CK8, CK-8, CYK8, K2C8, CARD2 -KRT8 and Ovarian Cancer
2
MAGEA1 Xq28 CT1.1, MAGE1 -MAGEA1 and Ovarian Cancer
2
HSP90AA1 14q32.33 EL52, HSPN, LAP2, HSP86, HSPC1, HSPCA, Hsp89, Hsp90, LAP-2, HSP89A, HSP90A, HSP90N, HSPCAL1, HSPCAL4 -HSP90AA1 and Ovarian Cancer
1
REST 4q12 XBR, NRSF -REST and Ovarian Cancer
1
RNF217-AS1 6q22.33 STL -STL and Ovarian Cancer
1
TPD52L1 6q22-q23 D53, hD53 -TPD52L1 and Ovarian Cancer
1
CD276 15q23-q24 B7H3, B7-H3, B7RP-2, 4Ig-B7-H3 -CD276 and Ovarian Cancer
1
DNM2 19p13.2 DYN2, CMT2M, DYNII, LCCS5, CMTDI1, CMTDIB, DI-CMTB -DNM2 and Ovarian Cancer
1
REV1 2q11.1-q11.2 REV1L -REV1 and Ovarian Cancer
1
PATZ1 22q12.2 ZSG, MAZR, PATZ, RIAZ, ZBTB19, ZNF278, dJ400N23 -PATZ1 and Ovarian Cancer
1
SACS 13q12 SPAX6, ARSACS, DNAJC29, PPP1R138 -SACS and Ovarian Cancer
1
APOD 3q29 -APOD and Ovarian Cancer
1
TAL2 9q32 -TAL2 and Ovarian Cancer
1
ARID2 12q12 p200, BAF200 -ARID2 and Ovarian Cancer
1
CXCL13 4q21 BLC, BCA1, ANGIE, BCA-1, BLR1L, ANGIE2, SCYB13 -CXCL13 and Ovarian Cancer
1
PPP1R3A 7q31.1 GM, PP1G, PPP1R3 -PPP1R3A and Ovarian Cancer
1
MAGEB2 Xp21.3 DAM6, CT3.2, MAGE-XP-2 -MAGEB2 and Ovarian Cancer
1
GOPC 6q21 CAL, FIG, PIST, GOPC1, dJ94G16.2 -GOPC and Ovarian Cancer
1
PLA2G16 11q12.3 AdPLA, HRSL3, HRASLS3, HREV107, HREV107-1, HREV107-3, H-REV107-1 -PLA2G16 and Ovarian Cancer
1
PDGFRL 8p22-p21.3 PDGRL, PRLTS -PDGFRL and Ovarian Cancer
1
HSP90AB1 6p12 HSP84, HSPC2, HSPCB, D6S182, HSP90B -HSP90AB1 and Ovarian Cancer
1
RHBDF2 17q25.1 TEC, TOC, TOCG, RHBDL5, RHBDL6, iRhom2 -RHBDF2 and Ovarian Cancer
1
ARF1 1q42 -ARF1 and Ovarian Cancer
1
PDCD6 5p15.33 ALG2, ALG-2, PEF1B -PDCD6 and Ovarian Cancer
1
ST7 7q31.2 HELG, RAY1, SEN4, TSG7, ETS7q, FAM4A, FAM4A1 -ST7 and Ovarian Cancer
1
ST8 6q25-q27 OVC, OVCS LOH
-LOH in 6q27 in Serous Ovarian Carcinoma

Note: list is not exhaustive. Number of papers are based on searches of PubMed (click on topic title for arbitrary criteria used).

Latest Publications

Patch AM, Christie EL, Etemadmoghadam D, et al.
Whole-genome characterization of chemoresistant ovarian cancer.
Nature. 2015; 521(7553):489-94 [PubMed] Related Publications
Patients with high-grade serous ovarian cancer (HGSC) have experienced little improvement in overall survival, and standard treatment has not advanced beyond platinum-based combination chemotherapy, during the past 30 years. To understand the drivers of clinical phenotypes better, here we use whole-genome sequencing of tumour and germline DNA samples from 92 patients with primary refractory, resistant, sensitive and matched acquired resistant disease. We show that gene breakage commonly inactivates the tumour suppressors RB1, NF1, RAD51B and PTEN in HGSC, and contributes to acquired chemotherapy resistance. CCNE1 amplification was common in primary resistant and refractory disease. We observed several molecular events associated with acquired resistance, including multiple independent reversions of germline BRCA1 or BRCA2 mutations in individual patients, loss of BRCA1 promoter methylation, an alteration in molecular subtype, and recurrent promoter fusion associated with overexpression of the drug efflux pump MDR1.

Kondakova IV, Iunusova NV, Spirina LV, et al.
[Association of intracellular proteinase activities with the content of locomotor proteins in tissues of primary tumors and metastasis in ovarian cancer].
Bioorg Khim. 2014 Nov-Dec; 40(6):735-42 [PubMed] Related Publications
The ability to active movement in extracellular matrix wherein significant role plays remodeling of the cytoskeleton by actin-binding proteins may influence on the metastatic potential of tumor cells. We studied the expression of actin-binding proteins and β-catenin in connection with proteasome and calpain functioning in the tissues of primary tumors and metastases of ovarian cancer. The chymotrypsin-like proteasome activity and calpain activity were shown to be significantly higher in ovarian cancer than in normal tissues. Furthermore, the activity of the proteasome and calpain were significantly higher in the peritoneal metastases in comparison with primary tumors. Correlation analysis showed in the primary tumor tissue the presence of a positive relationship between the activity of calpain and chymotrypsin-like proteasome activity (r = 0.82; p = 0.0005), whereas in metastases this connection was not revealed. Contents of p45 Ser β-catenin and the actin-severing protein gelzolin were decreased in metastases relative to primary tumors. Level of cofilin, functionally similar to gelzolin protein, was significantly higher in metastases compared to primary ovarian tumor tissue. In ovarian cancer significant reduction in the number of the monomer binder protein thymosin-β4 was observed in primary tumors and metastases as compared to normal tissues, but significant differences between the primary tumor and metastases were not observed. In the tissues of primary tumors negative correlations were observed between the chymotrypsin-like activity of the proteasome and the amount of p45 Ser β-catenin and protein Arp3, a member of the Arp2/3 complex. In metastasis negative correlation were revealed between the activity of calpain and content Arp3, cofilin, thymosin. The data obtained suggest the existence of different mechanisms of proteolytic regulation of locomotor proteins in primary tumors and metastases in ovarian cancer.

Wang Y, Ye Y, Lin J, et al.
Genetic variants in matrix metalloproteinase genes as disposition factors for ovarian cancer risk, survival, and clinical outcome.
Mol Carcinog. 2015; 54(6):430-9 [PubMed] Related Publications
Ovarian cancer is one of the leading female cancers in the United States. Challenges remain in early diagnosis of this deadly disease. Matrix metalloproteinases (MMPs) family genes are paradoxically involved in cancer promotion and suppression. We hypothesize that genetic variants in MMP genes are associated with ovarian cancer development, so they could be potential markers for ovarian cancer diagnosis and prognosis. In this study of 417 ovarian cancer cases and 417 healthy controls, we genotyped a comprehensive panel of 266 single nucleotide polymorphisms (SNPs) in 23 MMP genes and analysed their associations with ovarian cancer risk, overall survival and treatment response in ovarian cancer cases who received platinum-based chemotherapy with surgery. In the analysis on 339 Caucasian cases and 349 Caucasian controls, 4 SNPs were significantly associated with cancer risk. The most significant association was observed for rs2292730 (OR = 2.03, 95% CI = 1.39-2.96, P = 0.0002). Classification and regression tree analysis identified four terminal nodes with differential risk of ovarian cancer. Thirty-four SNPs were significantly associated with overall survival and four of which showed significant association with response to chemotherapy. Unfavourable genotype analysis of top SNPs on overall risk of death showed significant gene-dosage effect, survival tree analysis differentiated patients into distinct risk groups based on their genetic profiles with median survival times (MSTs) ranging from 17.7 to 151.7 months. In conclusion, our results suggest that genetic variants in MMP pathway genes may modulate the risk and clinical outcomes of ovarian cancer, both individually and jointly.

Zhu LY, Zhang WM, Yang XM, et al.
Silencing of MICAL-L2 suppresses malignancy of ovarian cancer by inducing mesenchymal-epithelial transition.
Cancer Lett. 2015; 363(1):71-82 [PubMed] Related Publications
Ovarian cancer remains the disease with the highest associated mortality rate of gynecologic malignancy due to cancer metastasis. Rearrangement of actin cytoskeleton by cytoskeleton protein plays a critical role in tumor cell metastasis. MICAL-L2, a member of MICAL family, can interact with actin-binding proteins, regulate actin cross-linking and coordinate the assembly of adherens junctions and tight junctions. However, the roles of MICAL-L2 in tumors and diseases have not been explored. In this study, we found that MICAL-L2 protein is significantly up-regulated in ovarian cancer tissues along with FIGO stage and associated with histologic subgroups of ovarian cancer. Silencing of MICAL-L2 suppressed ovarian cancer cell proliferation, migration and invasion ability. Moreover, silencing of MICAL-L2 prevented nuclear translocation of β-catenin, inhibited canonical wnt/β-catenin signaling and induced the mesenchymal-epithelial transition (MET). Taken together, our data indicated that MICAL-L2 may be an important regulator of epithelial-mesenchymal transition (EMT) in ovarian cancer cells and a new therapeutic target for interventions against ovarian cancer invasion and metastasis.

Kreuzinger C, Gamperl M, Wolf A, et al.
Molecular characterization of 7 new established cell lines from high grade serous ovarian cancer.
Cancer Lett. 2015; 362(2):218-28 [PubMed] Related Publications
Cancer cell lines are good in vitro models to study molecular mechanisms underlying chemoresistance and cancer recurrence. Recent works have demonstrated that most of the available ovarian cancer cell lines are most unlikely high grade serous (HGSOC), the major type of epithelial ovarian cancer. We aimed at establishing well characterized HGSOC cell lines, which can be used as optimal models for ovarian cancer research. We successfully established seven cell lines from HGSOC and provided the major genomic alterations and the transcriptomic landscapes of them. They exhibited different gene expression patterns in the key pathways involved in cancer resistance. Each cell line harbored a unique TP53 mutation as their corresponding tumors and expressed cytokeratins 8/18/19 and EpCAM. Two matched lines were established from the same patient, one at diagnosis and being sensitive to carboplatin and the other during chemotherapy and being resistant. Two cell lines presented respective BRCA1 and BRCA2 mutations. To conclude, we have established seven cell lines and well characterized them at genomic and transcriptomic levels. They are optimal models to investigate the molecular mechanisms underlying the progression, chemo resistance and recurrence of HGSOC.

Quitadamo A, Tian L, Hall B, Shi X
An integrated network of microRNA and gene expression in ovarian cancer.
BMC Bioinformatics. 2015; 16 Suppl 5:S5 [PubMed] Free Access to Full Article Related Publications
BACKGROUND: Ovarian cancer is a deadly female reproductive cancer. Understanding the biological mechanisms underlying ovarian cancer could help lead to quicker and more accurate diagnosis and more effective treatments. Both changes in microRNA(miRNA) expression and miRNA/mRNA dysregulation have been associated with ovarian cancer. With the availability of whole-genome miRNA and mRNA sequencing we now have new potentials to study these associations. In this study, we performed a comprehensive analysis of miRNA and mRNA expression in ovarian cancer using an integrative network approach combined with association analysis.
RESULTS: We developed an integrative approach to construct a network that illustrates the complex interplay among miRNA and gene expression from a systems perspective. Our method is composed of expanding networks from eQTL associations, building network associations in eQTL analysis, and then combine the networks into an integrated network. This integrated network takes account of miRNA expression quantitative trait loci (eQTL) associations, miRNAs and their targets, protein-protein interactions, co-expressions among miRNAs and genes respectively. Applied to the ovarian cancer data set from The Cancer Genome Atlas (TCGA), we created an integrated network with 167 nodes containing 108 miRNA-target interactions and 145 from protein-protein interactions, starting from 44 initial eQTLs. This integrated network encompassed 26 genes and 14 miRNAs associated with cancer. In particular, 11 genes and 12 miRNAs in the integrated network are associated with ovarian cancer.
CONCLUSION: We demonstrated an integrated network approach that integrates multiple data sources at a systems level. We applied this approach to the TCGA ovarian cancer dataset, and constructed a network that provided a more inclusive view of miRNA and gene expression in ovarian cancer. This network included four separate types of interactions among miRNAs and genes. Simply analyzing each interaction component in isolation, such as the eQTL associations, the miRNA-target interactions or the protein-protein interactions, would create a much more limited network than the integrated one.

Nasedkina TV, Gromyko OE, Emel'ianova MA, et al.
[Genotyping of BRCA1, BRCA2 and CHEK2 germline mutations in Russian breast cancer patients using diagnostic biochips].
Mol Biol (Mosk). 2014 Mar-Apr; 48(2):243-50 [PubMed] Related Publications
Germline mutations of BRCA1/2 genes cause the predisposition of their carriers to breast or/and ovary cancers (BC or/and OC) during the lifetime. Identification of these mutations is a basis of molecular diagnosis for BC susceptibility. Rapid genotyping technique using microarrays for identification of BRCA1 185delAG, 300T>G, 4153delA, 5382insC mutations and 4158 A>G sequence variant; BRCA2 695insT and 6174delT mutations; 1100delC mutation in CHEK2 gene was applied for 412 randomly collected breast cancer samples from the central region of European area of Russia. In 25 (6.0%) patients (6.0%) BC was associated with other tumours: OC, cervical cancer, colorectal cancer etc. BRCA1/2 and CHEK2 mutations were found in 33 (8.0%) BC patients. The most frequent mutation was BRCA1 5382insC, occurred in 16 (3.9%) BC patients, and CHEK2 1100delC, revealed in 7 (1.7%) BC patients. An application of diagnostic BC-microarray for genetic testing of BRCA1/2 and CHEK2 founder mutations has been discussed.

Rebbeck TR, Mitra N, Wan F, et al.
Association of type and location of BRCA1 and BRCA2 mutations with risk of breast and ovarian cancer.
JAMA. 2015; 313(13):1347-61 [PubMed] Related Publications
IMPORTANCE: Limited information about the relationship between specific mutations in BRCA1 or BRCA2 (BRCA1/2) and cancer risk exists.
OBJECTIVE: To identify mutation-specific cancer risks for carriers of BRCA1/2.
DESIGN, SETTING, AND PARTICIPANTS: Observational study of women who were ascertained between 1937 and 2011 (median, 1999) and found to carry disease-associated BRCA1 or BRCA2 mutations. The international sample comprised 19,581 carriers of BRCA1 mutations and 11,900 carriers of BRCA2 mutations from 55 centers in 33 countries on 6 continents. We estimated hazard ratios for breast and ovarian cancer based on mutation type, function, and nucleotide position. We also estimated RHR, the ratio of breast vs ovarian cancer hazard ratios. A value of RHR greater than 1 indicated elevated breast cancer risk; a value of RHR less than 1 indicated elevated ovarian cancer risk.
EXPOSURES: Mutations of BRCA1 or BRCA2.
MAIN OUTCOMES AND MEASURES: Breast and ovarian cancer risks.
RESULTS: Among BRCA1 mutation carriers, 9052 women (46%) were diagnosed with breast cancer, 2317 (12%) with ovarian cancer, 1041 (5%) with breast and ovarian cancer, and 7171 (37%) without cancer. Among BRCA2 mutation carriers, 6180 women (52%) were diagnosed with breast cancer, 682 (6%) with ovarian cancer, 272 (2%) with breast and ovarian cancer, and 4766 (40%) without cancer. In BRCA1, we identified 3 breast cancer cluster regions (BCCRs) located at c.179 to c.505 (BCCR1; RHR = 1.46; 95% CI, 1.22-1.74; P = 2 × 10(-6)), c.4328 to c.4945 (BCCR2; RHR = 1.34; 95% CI, 1.01-1.78; P = .04), and c. 5261 to c.5563 (BCCR2', RHR = 1.38; 95% CI, 1.22-1.55; P = 6 × 10(-9)). We also identified an ovarian cancer cluster region (OCCR) from c.1380 to c.4062 (approximately exon 11) with RHR = 0.62 (95% CI, 0.56-0.70; P = 9 × 10(-17)). In BRCA2, we observed multiple BCCRs spanning c.1 to c.596 (BCCR1; RHR = 1.71; 95% CI, 1.06-2.78; P = .03), c.772 to c.1806 (BCCR1'; RHR = 1.63; 95% CI, 1.10-2.40; P = .01), and c.7394 to c.8904 (BCCR2; RHR = 2.31; 95% CI, 1.69-3.16; P = .00002). We also identified 3 OCCRs: the first (OCCR1) spanned c.3249 to c.5681 that was adjacent to c.5946delT (6174delT; RHR = 0.51; 95% CI, 0.44-0.60; P = 6 × 10(-17)). The second OCCR spanned c.6645 to c.7471 (OCCR2; RHR = 0.57; 95% CI, 0.41-0.80; P = .001). Mutations conferring nonsense-mediated decay were associated with differential breast or ovarian cancer risks and an earlier age of breast cancer diagnosis for both BRCA1 and BRCA2 mutation carriers.
CONCLUSIONS AND RELEVANCE: Breast and ovarian cancer risks varied by type and location of BRCA1/2 mutations. With appropriate validation, these data may have implications for risk assessment and cancer prevention decision making for carriers of BRCA1 and BRCA2 mutations.

de Kock L, Druker H, Weber E, et al.
Ovarian embryonal rhabdomyosarcoma is a rare manifestation of the DICER1 syndrome.
Hum Pathol. 2015; 46(6):917-22 [PubMed] Related Publications
Embryonal rhabdomyosarcoma (ERMS), a soft tissue sarcoma, is one of the most common pediatric cancers. Certain ERMSs are associated with the DICER1 syndrome, a tumor predisposition syndrome caused by germ-line DICER1 mutations. Characteristic somatic mutations have also been identified in DICER1-associated tumor types. These "hotspot" mutations affect the catalytic activity of the DICER1 ribonuclease IIIb domain. Primary ovarian ERMS (oERMS) is extremely rare. We present a case of a 6-year-old girl with an oERMS harboring 2 DICER1 mutations. The girl also exhibited other DICER1 phenotypes: cystic nephroma (CN) and multinodular goiter. Somatic investigations of the CN identified a hotspot DICER1 mutation different from that in the oERMS. Significantly, the CN presented at 12 years of age, which is much older than the previously reported age range of susceptibility. This report documents the occurrence of DICER1 mutations in a case of oERMS, expanding the spectrum of DICER1-associated tumors.

Srivastava AK, Han C, Zhao R, et al.
Enhanced expression of DNA polymerase eta contributes to cisplatin resistance of ovarian cancer stem cells.
Proc Natl Acad Sci U S A. 2015; 112(14):4411-6 [PubMed] Article available free on PMC after 07/10/2015 Related Publications
Cancer stem cells (CSCs) with enhanced tumorigenicity and chemoresistance are believed to be responsible for treatment failure and tumor relapse in ovarian cancer patients. However, it is still unclear how CSCs survive DNA-damaging agent treatment. Here, we report an elevated expression of DNA polymerase η (Pol η) in ovarian CSCs isolated from both ovarian cancer cell lines and primary tumors, indicating that CSCs may have intrinsically enhanced translesion DNA synthesis (TLS). Down-regulation of Pol η blocked cisplatin-induced CSC enrichment both in vitro and in vivo through the enhancement of cisplatin-induced apoptosis in CSCs, indicating that Pol η-mediated TLS contributes to the survival of CSCs upon cisplatin treatment. Furthermore, our data demonstrated a depletion of miR-93 in ovarian CSCs. Enforced expression of miR-93 in ovarian CSCs reduced Pol η expression and increased their sensitivity to cisplatin. Taken together, our data suggest that ovarian CSCs have intrinsically enhanced Pol η-mediated TLS, allowing CSCs to survive cisplatin treatment, leading to tumor relapse. Targeting Pol η, probably through enhancement of miR-93 expression, might be exploited as a strategy to increase the efficacy of cisplatin treatment.

Chen S, Chen X, Xiu YL, et al.
MicroRNA-490-3P targets CDK1 and inhibits ovarian epithelial carcinoma tumorigenesis and progression.
Cancer Lett. 2015; 362(1):122-30 [PubMed] Related Publications
The expression of microRNA-490-3P has been reported to regulate hepatocellular carcinoma cell proliferation, migration and invasion, and its overexpression significantly inhibits A549 lung cancer cell proliferation. Here, we demonstrated for the first time that miR-490 mRNA expression was significantly lower in ovarian carcinoma and borderline tumors compared to benign tumors, and lower in metastatic ovarian carcinoma (omentum) than primary ovarian carcinoma, and was negatively associated with differentiation and International Federation of Gynecology and Obstetrics (FIGO) staging. MiR-490-3P overexpression promoted G1/S or G2/M arrest and apoptosis; reduced cell proliferation, migration and invasion; reduced CDK1, Bcl-xL, MMP2/9, CCND1, SMARCD1 mRNA or protein expression; and induced P53 expression. Dual-luciferase reporter assay indicated miR-490-3P directly targeted CDK1. In vivo studies showed that miR-490-3P transfection suppressed tumor development and CDK1, Bcl-xL, MMP2/9 expression while inducing P53 expression. These findings indicate that miR-490-3P may target CDK1 and inhibit ovarian epithelial carcinoma tumorigenesis and progression.

Qiu JJ, Wang Y, Ding JX, et al.
The long non-coding RNA HOTAIR promotes the proliferation of serous ovarian cancer cells through the regulation of cell cycle arrest and apoptosis.
Exp Cell Res. 2015; 333(2):238-48 [PubMed] Related Publications
HOX transcript antisense RNA (HOTAIR) is a well-known long non-coding RNA (lncRNA) whose dysregulation correlates with poor prognosis and malignant progression in many forms of cancer. Here, we investigate the expression pattern, clinical significance, and biological function of HOTAIR in serous ovarian cancer (SOC). Clinically, we found that HOTAIR levels were overexpressed in SOC tissues compared with normal controls and that HOTAIR overexpression was correlated with an advanced FIGO stage and a high histological grade. Multivariate analysis revealed that HOTAIR is an independent prognostic factor for predicting overall survival in SOC patients. We demonstrated that HOTAIR silencing inhibited A2780 and OVCA429 SOC cell proliferation in vitro and that the anti-proliferative effects of HOTAIR silencing also occurred in vivo. Further investigation into the mechanisms responsible for the growth inhibitory effects by HOTAIR silencing revealed that its knockdown resulted in the induction of cell cycle arrest and apoptosis through certain cell cycle-related and apoptosis-related proteins. Together, these results highlight a critical role of HOTAIR in SOC cell proliferation and contribute to a better understanding of the importance of dysregulated lncRNAs in SOC progression.

Perri T, Lifshitz D, Sadetzki S, et al.
Fertility treatments and invasive epithelial ovarian cancer risk in Jewish Israeli BRCA1 or BRCA2 mutation carriers.
Fertil Steril. 2015; 103(5):1305-12 [PubMed] Related Publications
OBJECTIVE: To determine whether BRCA mutation carriers who undergo fertility treatments are at increased risk of developing invasive epithelial ovarian cancer (IEOC).
DESIGN: Historical cohort study.
SETTING: Tertiary university-affiliated medical center and the National Cancer Registry.
PATIENT(S): A total of 1,073 Jewish Israeli BRCA mutation carriers diagnosed in a single institution between 1995 and 2013, including 164 carriers (15.2%) who had fertility treatments that included clomiphene citrate (n = 82), gonadotropin (n = 69), in vitro fertilization (IVF) (n = 66), or a combination (n = 50), and 909 carriers not treated for infertility.
INTERVENTION(S): None.
MAIN OUTCOME MEASURE(S): Odds ratios (OR) and 95% confidence intervals (CI) for IEOC association with fertility treatments and other hormone and reproductive variables.
RESULT(S): In 175 (16.3%) mutation carriers, IEOC was diagnosed; 139 women carried BRCA1, 33 carried BRCA2, and 3 had unknown mutations. Fertility treatments were not associated with IEOC risk (age-adjusted OR 0.63; 95% CI, 0.38-1.05) regardless of treatment type (with clomiphene citrate, OR 0.87; 95% CI, 0.46-1.63; with gonadotropin, OR 0.59; 95% CI, 0.26-1.31; with IVF, OR 1.08, 95% CI, 0.57-2.06). Multivariate analysis indicated an increased risk of IEOC with hormone-replacement therapy (OR 2.22; 95% CI, 1.33-3.69) and a reduced risk with oral contraceptives (OR 0.19; 95% CI, 0.13-0.28) in both BRCA1 and BRCA2 mutation carriers. Parity was a risk factor for IEOC by univariate but not multivariate analysis.
CONCLUSION(S): According to our results, treatments for infertile BRCA mutation carriers should not be contraindicated or viewed as risk modifiers for IEOC. Parity as a risk factor in BRCA mutation carriers warrants further investigation.

Yoneyama K, Ishibashi O, Kawase R, et al.
miR-200a, miR-200b and miR-429 are onco-miRs that target the PTEN gene in endometrioid endometrial carcinoma.
Anticancer Res. 2015; 35(3):1401-10 [PubMed] Related Publications
Endometrioid endometrial carcinoma (EEC) is a common malignancy of the female genital tract. However, no adequate biomarker is currently available for predicting the prognosis of this cancer. Recent studies have revealed dysregulated expression of several microRNAs (miRNAs) in various cancer tissues, and therefore, these cancer-associated miRNAs (also called onco-miRs) could be promising prognostic biomarkers of cancer progression or metastasis. In this study, in order to identify onco-miRs and their possible targets involved in EEC, we performed microarray-based integrative analyses of miRNA and mRNA expression in specimens excised from EEC lesions and adjacent normal endometrial tissues. Using integrated statistical analyses, we identified miR-200a, miR-200b and miR-429 as highly up-regulated onco-miRs in EECs. Conversely, we detected expression of a tumor-suppressor gene, phosphatase and tensin homolog (PTEN), which was predicted in silico using a miRNA-targeting mRNA prediction algorithm, as a target of the three miRNAs and which was down-regulated in EECs. Furthermore, these miRNAs were validated to target PTEN experimentally using luciferase assays and real-time polymerase chain reaction. These results suggest that the occurrence of EEC is, at least in part, mediated by miRNA-induced suppression of PTEN expression.

Guillemette S, Serra RW, Peng M, et al.
Resistance to therapy in BRCA2 mutant cells due to loss of the nucleosome remodeling factor CHD4.
Genes Dev. 2015; 29(5):489-94 [PubMed] Article available free on PMC after 01/09/2015 Related Publications
Hereditary cancers derive from gene defects that often compromise DNA repair. Thus, BRCA-associated cancers are sensitive to DNA-damaging agents such as cisplatin. The efficacy of cisplatin is limited, however, by the development of resistance. One cisplatin resistance mechanism is restoration of homologous recombination (HR), which can result from BRCA reversion mutations. However, in BRCA2 mutant cancers, cisplatin resistance can occur independently of restored HR by a mechanism that remains unknown. Here we performed a genome-wide shRNA screen and found that loss of the nucleosome remodeling factor CHD4 confers cisplatin resistance. Restoration of cisplatin resistance is independent of HR but correlates with restored cell cycle progression, reduced chromosomal aberrations, and enhanced DNA damage tolerance. Suggesting clinical relevance, cisplatin-resistant clones lacking genetic reversion of BRCA2 show de novo loss of CHD4 expression in vitro. Moreover, BRCA2 mutant ovarian cancers with reduced CHD4 expression significantly correlate with shorter progression-free survival and shorter overall survival. Collectively, our findings indicate that CHD4 modulates therapeutic response in BRCA2 mutant cancer cells.

Kannan K, Coarfa C, Chao PW, et al.
Recurrent BCAM-AKT2 fusion gene leads to a constitutively activated AKT2 fusion kinase in high-grade serous ovarian carcinoma.
Proc Natl Acad Sci U S A. 2015; 112(11):E1272-7 [PubMed] Article available free on PMC after 17/09/2015 Related Publications
High-grade serous ovarian cancer (HGSC) is among the most lethal forms of cancer in women. Excessive genomic rearrangements, which are expected to create fusion oncogenes, are the hallmark of this cancer. Here we report a cancer-specific gene fusion between BCAM, a membrane adhesion molecule, and AKT2, a key kinase in the PI3K signaling pathway. This fusion is present in 7% of the 60 patient cancers tested, a significant frequency considering the highly heterogeneous nature of this malignancy. Further, we provide direct evidence that BCAM-AKT2 is translated into an in-frame fusion protein in the patient's tumor. The resulting AKT2 fusion kinase is membrane-associated, constitutively phosphorylated, and activated as a functional kinase in cells. Unlike endogenous AKT2, whose activity is tightly regulated by external stimuli, BCAM-AKT2 escapes the regulation from external stimuli. Moreover, a BCAM-AKT2 fusion gene generated via chromosomal translocation using the CRISPR/Cas9 system leads to focus formation in both OVCAR8 and HEK-293T cell lines, suggesting that BCAM-AKT2 is oncogenic. Together, the results indicate that BCAM-AKT2 expression is a new mechanism of AKT2 kinase activation in HGSC. BCAM-AKT2 is the only fusion gene in HGSC that is proven to translate an aberrant yet functional kinase fusion protein with oncogenic properties. This recurrent genomic alteration is a potential therapeutic target and marker of a clinically relevant subtype for tailored therapy of HGSC.

Iunusova NV, Spirina LV, Kondakova IV, et al.
[Expression and activity of proteases in metastasis of ovarian cancer].
Izv Akad Nauk Ser Biol. 2014 Sep-Oct; (5):448-55 [PubMed] Related Publications
The total chymotrypsin-like activity of proteasornes, the activity of 20S- and 26S-proteasome pools and calpains, and the expression of metalloproteinase PAPP-A in primary tumors and metastasized tissues were studied in 13 patients with epithelial ovarian cancer. It was shown that initiation of the process of tumor dissemination occurs against the background of active proteolytic processes; A decrease in activity of 26S-proteasomes and total calpain activity and increased expression ofmetalloproteinase PAPP-A in the primary tu mors were found in patients with ascites as compared with patients without ascites. The disease progression after treatment and achieved stabilization were found in patients with decreased activity of intracellular proteases and a high content of PAPP-A in the primary tumors.

Wang H, Bao W, Jiang F, et al.
Mutant p53 (p53-R248Q) functions as an oncogene in promoting endometrial cancer by up-regulating REGγ.
Cancer Lett. 2015; 360(2):269-79 [PubMed] Related Publications
P53 mutation plays a pivotal role in tumorigenesis of endometrial cancer (EC), here we report that the gain-of-function mutant p53-R248Q targets the proteasome activator REGγ to promote EC progression. Increased p53 expression significantly correlated with high pathological grade and lymph node metastasis in EC specimens. Manipulation of p53-R248Q in EC cells caused coincident changes in REGγ expression, and chromatin immunoprecipitation coupled with PCR further indicated that p53-R248Q bound to the REGγ gene promoter at a p53 responsive element. Silencing of REGγ in EC cells attenuated the cell proliferation, migration and invasion abilities, whereas overexpression of p53-R248Q rescued these activities. Overexpression of REGγ also induced an epithelial-mesenchymal transition phenotype. Moreover, a mouse xenograft tumor model showed that REGγ promoted tumor growth, further demonstrating a p53-R248Q-REGγ oncogenic pathway. Finally, examination of EC and normal endometrium specimens confirmed the oncogenic role of REGγ, in that REGγ was more highly overexpressed in p53-positive specimens than in p53-negative specimens. Our data suggest that REGγ is a promising therapeutic target for EC with the p53-R248Q mutation.

Anglesio MS, Bashashati A, Wang YK, et al.
Multifocal endometriotic lesions associated with cancer are clonal and carry a high mutation burden.
J Pathol. 2015; 236(2):201-9 [PubMed] Related Publications
Endometriosis is a significant risk factor for clear cell and endometrioid ovarian cancers and is often found contiguous with these cancers. Using whole-genome shotgun sequencing of seven clear cell ovarian carcinomas (CCC) and targeted sequencing in synchronous endometriosis, we have investigated how this carcinoma may evolve from endometriosis. In every case we observed multiple tumour-associated somatic mutations in at least one concurrent endometriotic lesion. ARID1A and PIK3CA mutations appeared consistently in concurrent endometriosis when present in the primary CCC. In several cases, one or more endometriotic lesions carried the near-complete complement of somatic mutations present in the index CCC tumour. Ancestral mutations were detected in both tumour-adjacent and -distant endometriotic lesions, regardless of any cytological atypia. These findings provide objective evidence that multifocal benign endometriotic lesions are clonally related and that CCCs arising in these patients progress from endometriotic lesions that may already carry sufficient cancer-associated mutations to be considered neoplasms themselves, albeit with low malignant potential. We speculate that genomically distinct classes of endometriosis exist and that ovarian endometriosis with high mutational burden represents one class at high risk for malignant transformation.

Bitler BG, Aird KM, Garipov A, et al.
Synthetic lethality by targeting EZH2 methyltransferase activity in ARID1A-mutated cancers.
Nat Med. 2015; 21(3):231-8 [PubMed] Article available free on PMC after 01/09/2015 Related Publications
The gene encoding ARID1A, a chromatin remodeler, shows one of the highest mutation rates across many cancer types. Notably, ARID1A is mutated in over 50% of ovarian clear cell carcinomas, which currently have no effective therapy. To date, clinically applicable targeted cancer therapy based on ARID1A mutational status has not been described. Here we show that inhibition of the EZH2 methyltransferase acts in a synthetic lethal manner in ARID1A-mutated ovarian cancer cells and that ARID1A mutational status correlated with response to the EZH2 inhibitor. We identified PIK3IP1 as a direct target of ARID1A and EZH2 that is upregulated by EZH2 inhibition and contributed to the observed synthetic lethality by inhibiting PI3K-AKT signaling. Importantly, EZH2 inhibition caused regression of ARID1A-mutated ovarian tumors in vivo. To our knowledge, this is the first data set to demonstrate a synthetic lethality between ARID1A mutation and EZH2 inhibition. Our data indicate that pharmacological inhibition of EZH2 represents a novel treatment strategy for cancers involving ARID1A mutations.

Coenegrachts L, Garcia-Dios DA, Depreeuw J, et al.
Mutation profile and clinical outcome of mixed endometrioid-serous endometrial carcinomas are different from that of pure endometrioid or serous carcinomas.
Virchows Arch. 2015; 466(4):415-22 [PubMed] Related Publications
Clinical outcome of 23 patients with mixed endometrioid and serous endometrial carcinomas (mixed EEC-SC) was compared to that of pure endometrioid (EEC) and pure serous (SC) carcinomas. Hotspot mutation frequencies in KRAS, PIK3CA, PTEN, and TP53 and microsatellite instability (MSI) status were determined in mixed EEC-SC, as well as in their EEC and SC microdissected components separately, and alterations were compared to frequencies in pure EEC and SC. Relapse-free (RFS) and overall survival (OS) differed significantly between mixed EEC-SC and pure EEC and SC, revealing that outcome of mixed EEC-SCs was intermediate to that of pure EEC and pure SC. PTEN mutations were absent in pure SC, but occurred in 20 % of pure EEC, and 13 % of mixed EEC-SC. In contrast, TP53 mutations were more frequent in pure SC (17 %) and mixed EEC-SC (22 %) than in pure EEC (2 %). Mutations in mixed EEC-SC were shared by the two microdissected components in 30 %, whereas in 35 %, some mutations were component-specific. Mutation analysis confirms similarities between the EEC and SC components of mixed EEC-SC with pure EEC and pure SC, respectively. However, PTEN and KRAS mutations were more frequent in the SC component of mixed EEC-SC than in pure SC, while TP53 mutations were more frequent in the EEC component of mixed EEC-SC than in pure EEC. Presence of different clonal mutation pattern between EEC and SC components of mixed EEC-SC raises the possibility of divergent tumor heterogeneity or biclonal origin in some cases.

Cybulski M, Jeleniewicz W, Nowakowski A, et al.
Cyclin I mRNA expression correlates with kinase insert domain receptor expression in human epithelial ovarian cancer.
Anticancer Res. 2015; 35(2):1115-9 [PubMed] Related Publications
BACKGROUND/AIM: Ovarian cancer is the second most common gynecological malignancy after cancer of the uterine corpus, and the fifth leading cause of cancer-related death among women. It has been discovered that cyclin I (CCNI) protein expression correlates with the proliferation of cancer cells and expression of angiogenesis-related proteins, such as vascular endothelial growth factor (VEGF) and VEGF receptor 2/kinase insert domain receptor (VEGFR2/KDR). We examined whether any association exists between mRNA expression of CCNI and KDR genes in epithelial ovarian cancer (EOC) tissues, clinicopathological parameters and patients' response to chemotherapy.
MATERIALS AND METHODS: Expression of CCNI and KDR genes was analyzed by quantitative real-time reverse transcription PCR in 40 human primary EOC tissues and four human ovarian cancer cell lines (TOV-112D, OV-90, OVCAR-3 and Caov-3).
RESULTS: CCNI and KDR mRNA expression was detected in all EOC tissues and ovarian cancer cell lines. The mRNA levels of both genes were significantly higher in EOC than in ovarian cancer cell lines (p<0.001). Neither CCNI nor KDR mRNA expression in EOC tissues was significantly associated with variables such as age, menopausal status, International Federation of Gynecology and Obstetrics (FIGO) stage, residual disease, patients' response to chemotherapy, tumor histology, grade or sensitivity to chemotherapy. However, we demonstrated a significant positive correlation between the mRNA expression of KDR and CCNI in EOC tissues (R=0.530, p<0.001).
CONCLUSION: Neither CCNI nor KDR mRNA expression predicts response of patients with EOC to platinum-based first-line chemotherapy. Cyclin I may be involved in angiogenesis in EOC, which needs further investigation.

Wang L, Mao Y, Du G, et al.
Overexpression of JARID1B is associated with poor prognosis and chemotherapy resistance in epithelial ovarian cancer.
Tumour Biol. 2015; 36(4):2465-72 [PubMed] Article available free on PMC after 01/09/2015 Related Publications
JARID1B, a histone demethylase, has been reported to be highly expressed in various human cancers. In the present study, we investigated the association of JARID1B level with epithelial ovarian cancer (EOC) and prognosis of patients with EOC. We analyzed JARID1B expression in 20 normal ovaries, 20 benign ovarian tumor (BOT) samples, and 45 epithelial ovarian carcinoma specimens by quantitative PCR (qRT-PCR) and western blotting analyses. JARID1B was further examined in 120 EOC specimens from patients with different histological stages via immunohistochemistry. Possible correlations between JARID1B levels and prognosis as well as chemotherapy resistance of EOC patients were determined by univariate and multivariate analyses. JARID1B level was significantly increased in EOC, as compared to normal ovaries and BOT. Among 120 EOC cases examined, the 5-year progression-free survival (PFS) rates were 17 and 85% in patients with high and low JARID1B expression, respectively (hazard ratio = 17.85, 95% confidence interval (CI) 6.31-50.51, P < 0.001). Similarly, the 5-year overall survival (OS) rates for patients with high and low JARID1B expression were 28 and 92% respectively (hazard ratio = 21.8, 95% CI 5.92-71.81, P < 0.001). Positive correlation between JARID1B level and chemotherapy resistance was observed in patients with EOC (odds ratio (OR) 36.81, 95% CI 4.84-280.11, P < 0.001). JARID1B could serve as an important biomarker for prognosis and chemotherapy resistance of EOC patients.

Stefanou DT, Bamias A, Episkopou H, et al.
Aberrant DNA damage response pathways may predict the outcome of platinum chemotherapy in ovarian cancer.
PLoS One. 2015; 10(2):e0117654 [PubMed] Article available free on PMC after 01/09/2015 Related Publications
Ovarian carcinoma (OC) is the most lethal gynecological malignancy. Despite the advances in the treatment of OC with combinatorial regimens, including surgery and platinum-based chemotherapy, patients generally exhibit poor prognosis due to high chemotherapy resistance. Herein, we tested the hypothesis that DNA damage response (DDR) pathways are involved in resistance of OC patients to platinum chemotherapy. Selected DDR signals were evaluated in two human ovarian carcinoma cell lines, one sensitive (A2780) and one resistant (A2780/C30) to platinum treatment as well as in peripheral blood mononuclear cells (PBMCs) from OC patients, sensitive (n = 7) or resistant (n = 4) to subsequent chemotherapy. PBMCs from healthy volunteers (n = 9) were studied in parallel. DNA damage was evaluated by immunofluorescence γH2AX staining and comet assay. Higher levels of intrinsic DNA damage were found in A2780 than in A2780/C30 cells. Moreover, the intrinsic DNA damage levels were significantly higher in OC patients relative to healthy volunteers, as well as in platinum-sensitive patients relative to platinum-resistant ones (all P<0.05). Following carboplatin treatment, A2780 cells showed lower DNA repair efficiency than A2780/C30 cells. Also, following carboplatin treatment of PBMCs ex vivo, the DNA repair efficiency was significantly higher in healthy volunteers than in platinum-resistant patients and lowest in platinum-sensitive ones (t1/2 for loss of γH2AX foci: 2.7±0.5h, 8.8±1.9h and 15.4±3.2h, respectively; using comet assay, t1/2 of platinum-induced damage repair: 4.8±1.4h, 12.9±1.9h and 21.4±2.6h, respectively; all P<0.03). Additionally, the carboplatin-induced apoptosis rate was higher in A2780 than in A2780/C30 cells. In PBMCs, apoptosis rates were inversely correlated with DNA repair efficiencies of these cells, being significantly higher in platinum-sensitive than in platinum-resistant patients and lowest in healthy volunteers (all P<0.05). We conclude that perturbations of DNA repair pathways as measured in PBMCs from OC patients correlate with the drug sensitivity of these cells and reflect the individualized response to platinum-based chemotherapy.

Shah RH, Scott SN, Brannon AR, et al.
Comprehensive mutation profiling by next-generation sequencing of effusion fluids from patients with high-grade serous ovarian carcinoma.
Cancer Cytopathol. 2015; 123(5):289-97 [PubMed] Related Publications
BACKGROUND: Mutation analysis for personalized treatment has become increasingly important in the management of different types of cancer. The advent of new DNA extraction protocols and sequencing platforms with reduced DNA input requirements might allow the use of cytology specimens for high-throughput mutation analysis. In this study, the authors evaluated the use of effusion fluid for next-generation sequencing-based, multigene mutation profiling.
METHODS: Four specimens from each of 5 patients who had at least stage III, high-grade serous ovarian carcinoma were selected: effusion fluid; frozen tumor; formalin-fixed, paraffin embedded tumor; and matched normal blood. Frozen tumors from each patient were previously characterized by The Cancer Genomic Atlas (TCGA). DNA was extracted from all specimens and was sequenced using a custom hybridization capture-based assay. Genomic alterations were compared among all specimens from each patient as well as with mutations reported in TCGA for the same tumors.
RESULTS: In total, 17 distinct somatic mutations were identified in the cohort. Ten of 17 mutations were reported in TCGA and were called in all 3 malignant specimens procured from the patients. Of the remaining 7 mutations, 2 were called in all 3 specimens, and the other 5 were sample-specific. Two mutations were detected only in the cytology specimens. Copy number profiles were concordant among the tumors analyzed.
CONCLUSIONS: Cytology specimens represent suitable material for high-throughput sequencing, because all mutations described by TCGA were independently identified in the effusion fluid. Differences in mutations detected in samples procured from the same patient may reflect tumor heterogeneity.

Zhang K, Song H, Yang P, et al.
Silencing dishevelled-1 sensitizes paclitaxel-resistant human ovarian cancer cells via AKT/GSK-3β/β-catenin signalling.
Cell Prolif. 2015; 48(2):249-58 [PubMed] Related Publications
OBJECTIVES: Expression of dishevelled-1 (DVL1) has recently been linked to cancer progression, however, its role in resistance to cancer therapy is unclear. In this study, we aimed to explore the function of DVL1 in paclitaxel-resistant human ovarian cancer cells.
MATERIALS AND METHODS: The MTT assay was used to assess effects of DVL1 silencing on sensitivity of cells that were otherwise resistant to paclitaxel (Taxol). Western blotting and immunofluorescence staining were used to examine effects of DVL1 on AKT/GSK-3β/β-catenin signalling.
RESULTS: Dishevelled-1 was found to be over-expressed in a paclitaxel-resistant cell line derived from human ovarian cancer cell line A2780 (A2780/Taxol line) as well as parental A2780 cells. Down-regulation of DVL1 (using the inhibitor 3289-8625 or siRNA (siDVL1) against DVL1) sensitized A2780/Taxol cells to paclitaxel. Over-expression of DVL1 in A2780 cells increased protein levels of P-gp, BCRP and Bcl-2, which are known targets of β-catenin. Silencing DVL1 in A2780/Taxol cells also reduced levels of these proteins, and led to accumulation of β-catenin. In addition, DVL1 aberrantly activated AKT/GSK-3β/β-catenin signalling. Inactivation of AKT signalling attenuated DVL1-mediated inhibition of GSK-3β and accumulation of β-catenin, in both A2780 and A2780/Taxol cells.
CONCLUSIONS: Taken together, these results suggest that silencing DVL1 sensitized A2780/Taxol cells to paclitaxel, by down-regulating AKT/GSK-3β/β-catenin signalling, providing a novel strategy for chemosensitization of ovarian cancer to paclitaxel-induced cytotoxicity.

Ceccaldi R, Liu JC, Amunugama R, et al.
Homologous-recombination-deficient tumours are dependent on Polθ-mediated repair.
Nature. 2015; 518(7538):258-62 [PubMed] Article available free on PMC after 12/08/2015 Related Publications
Large-scale genomic studies have shown that half of epithelial ovarian cancers (EOCs) have alterations in genes regulating homologous recombination (HR) repair. Loss of HR accounts for the genomic instability of EOCs and for their cellular hyper-dependence on alternative poly-ADP ribose polymerase (PARP)-mediated DNA repair mechanisms. Previous studies have implicated the DNA polymerase θ (Polθ also known as POLQ, encoded by POLQ) in a pathway required for the repair of DNA double-strand breaks, referred to as the error-prone microhomology-mediated end-joining (MMEJ) pathway. Whether Polθ interacts with canonical DNA repair pathways to prevent genomic instability remains unknown. Here we report an inverse correlation between HR activity and Polθ expression in EOCs. Knockdown of Polθ in HR-proficient cells upregulates HR activity and RAD51 nucleofilament assembly, while knockdown of Polθ in HR-deficient EOCs enhances cell death. Consistent with these results, genetic inactivation of an HR gene (Fancd2) and Polq in mice results in embryonic lethality. Moreover, Polθ contains RAD51 binding motifs and it blocks RAD51-mediated recombination. Our results reveal a synthetic lethal relationship between the HR pathway and Polθ-mediated repair in EOCs, and identify Polθ as a novel druggable target for cancer therapy.

Ceccaldi R, O'Connor KW, Mouw KW, et al.
A unique subset of epithelial ovarian cancers with platinum sensitivity and PARP inhibitor resistance.
Cancer Res. 2015; 75(4):628-34 [PubMed] Article available free on PMC after 12/08/2015 Related Publications
Platinum and PARP inhibitor (PARPi) sensitivity commonly coexist in epithelial ovarian cancer (EOC) due to the high prevalence of alterations in the homologous recombination (HR) DNA repair pathway that confer sensitivity to both drugs. In this report, we describe a unique subset of EOC with alterations in another DNA repair pathway, the nucleotide excision repair (NER) pathway, which may exhibit a discordance in sensitivities to these drugs. Specifically, 8% of high-grade serous EOC from The Cancer Genome Atlas dataset exhibited NER alterations, including nonsynonymous or splice site mutations and homozygous deletions of NER genes. Tumors with NER alterations were associated with improved overall survival (OS) and progression-free survival (PFS), compared with patients without NER alterations or BRCA1/2 mutations. Furthermore, patients with tumors with NER alterations had similar OS and PFS as BRCA1/2-mutated patients, suggesting that NER pathway inactivation in EOC conferred enhanced platinum sensitivity, similar to BRCA1/2-mutated tumors. Moreover, two NER mutations (ERCC6-Q524* and ERCC4-A583T), identified in the two most platinum-sensitive tumors, were functionally associated with platinum sensitivity in vitro. Importantly, neither NER alteration affected HR or conferred sensitivity to PARPi or other double-strand break-inducing agents. Overall, our findings reveal a new mechanism of platinum sensitivity in EOC that, unlike defective HR, may lead to a discordance in sensitivity to platinum and PARPi, with potential implications for previously reported and ongoing PARPi trials in this disease.

Niskakoski A, Kaur S, Staff S, et al.
Epigenetic analysis of sporadic and Lynch-associated ovarian cancers reveals histology-specific patterns of DNA methylation.
Epigenetics. 2014; 9(12):1577-87 [PubMed] Related Publications
Diagnosis and treatment of epithelial ovarian cancer is challenging due to the poor understanding of the pathogenesis of the disease. Our aim was to investigate epigenetic mechanisms in ovarian tumorigenesis and, especially, whether tumors with different histological subtypes or hereditary background (Lynch syndrome) exhibit differential susceptibility to epigenetic inactivation of growth regulatory genes. Gene candidates for epigenetic regulation were identified from the literature and by expression profiling of ovarian and endometrial cancer cell lines treated with demethylating agents. Thirteen genes were chosen for methylation-specific multiplex ligation-dependent probe amplification assays on 104 (85 sporadic and 19 Lynch syndrome-associated) ovarian carcinomas. Increased methylation (i.e., hypermethylation) of variable degree was characteristic of ovarian carcinomas relative to the corresponding normal tissues, and hypermethylation was consistently more prominent in non-serous than serous tumors for individual genes and gene sets investigated. Lynch syndrome-associated clear cell carcinomas showed the highest frequencies of hypermethylation. Among endometrioid ovarian carcinomas, lower levels of promoter methylation of RSK4, SPARC, and HOXA9 were significantly associated with higher tumor grade; thus, the methylation patterns showed a shift to the direction of high-grade serous tumors. In conclusion, we provide evidence of a frequent epigenetic inactivation of RSK4, SPARC, PROM1, HOXA10, HOXA9, WT1-AS, SFRP2, SFRP5, OPCML, and MIR34B in the development of non-serous ovarian carcinomas of Lynch and sporadic origin, as compared to serous tumors. Our findings shed light on the role of epigenetic mechanisms in ovarian tumorigenesis and identify potential targets for translational applications.

Minion LE, Dolinsky JS, Chase DM, et al.
Hereditary predisposition to ovarian cancer, looking beyond BRCA1/BRCA2.
Gynecol Oncol. 2015; 137(1):86-92 [PubMed] Related Publications
OBJECTIVE: Genetic predisposition to ovarian cancer is well documented. With the advent of next generation sequencing, hereditary panel testing provides an efficient method for evaluating multiple genes simultaneously. Therefore, we sought to investigate the contribution of 19 genes identified in the literature as increasing the risk of hereditary breast and ovarian cancer (HBOC) in a BRCA1 and BRCA2 negative population of patients with a personal history of breast and/or ovarian cancer by means of a hereditary cancer panel.
METHODS: Subjects were referred for multi-gene panel testing between February 2012 and March 2014. Clinical data was ascertained from requisition forms. The incidence of pathogenic mutations (including likely pathogenic), and variant of unknown significance were then calculated for each gene and/or patient cohort.
RESULTS: In this cohort of 911 subjects, panel testing identified 67 mutations. With 7.4% of subjects harboring a mutation on this multi-gene panel, the diagnostic yield was increased, compared to testing for BRCA1 and BRCA2 mutations alone. In the ovarian cancer probands, the most frequently mutated genes were BRIP1 (n=8; 1.72%) and MSH6 (n=6; 1.29%). In the breast cancer probands, mutations were most commonly observed in CHEK2 (n=9; 2.54%), ATM (n=3; 0.85%), and TP53 (n=3; 0.85%).
CONCLUSIONS: Although further studies are needed to clarify the exact management of patients with a mutation in each gene, this study highlights information that can be captured with panel testing and provides support for incorporation of panel testing into clinical practice.

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