Melanoma

Overview

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 (196)

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
BRAF 7q34 NS7, BRAF1, RAFB1, B-RAF1 -BRAF and Melanoma
1448
CDKN2A 9p21 ARF, MLM, P14, P16, P19, CMM2, INK4, MTS1, TP16, CDK4I, CDKN2, INK4A, MTS-1, P14ARF, P19ARF, P16INK4, P16INK4A, P16-INK4A -CDKN2A and Melanoma
-CDKN2A and Familial Melanoma
677
NRAS 1p13.2 NS6, CMNS, NCMS, ALPS4, N-ras, NRAS1 -NRAS and Melanoma
519
CDK4 12q14 CMM3, PSK-J3 -CDK4 and Melanoma
-CDK4 Germline Mutations in Melanoma Prone Families
208
KIT 4q12 PBT, SCFR, C-Kit, CD117 -KIT and Melanoma
228
CTNNB1 3p21 CTNNB, MRD19, armadillo -CTNNB1 and Melanoma
215
MITF 3p14.2-p14.1 MI, WS2, CMM8, WS2A, bHLHe32 -MITF and Melanoma
189
FGF2 4q26 BFGF, FGFB, FGF-2, HBGF-2 -FGF2 and Melanoma
186
AR Xq12 KD, AIS, TFM, DHTR, SBMA, HYSP1, NR3C4, SMAX1, HUMARA -AR and Melanoma
175
MC1R 16q24.3 CMM5, MSH-R, SHEP2 -MC1R Polymorphisms and Melanoma
165
TP53 17p13.1 P53, BCC7, LFS1, TRP53 -TP53 and Melanoma
127
MCAM 11q23.3 CD146, MUC18 -MCAM and Melanoma
116
MLANA 9p24.1 MART1, MART-1 -MLANA and Melanoma
115
GNAQ 9q21 GAQ, SWS, CMC1, G-ALPHA-q -GNAQ and Melanoma
85
CDKN1A 6p21.2 P21, CIP1, SDI1, WAF1, CAP20, CDKN1, MDA-6, p21CIP1 -CDKN1A Expression in Melanoma
73
CXCL1 4q21 FSP, GRO1, GROa, MGSA, NAP-3, SCYB1, MGSA-a -CXCL1 and Melanoma
60
HLA-B 6p21.3 AS, HLAB, SPDA1 -HLA-B and Melanoma
59
MMP2 16q12.2 CLG4, MONA, CLG4A, MMP-2, TBE-1, MMP-II -MMP2 and Melanoma
53
GNA11 19p13.3 FBH, FBH2, FHH2, HHC2, GNA-11, HYPOC2 -GNA11 and Melanoma
52
PMEL 12q13-q14 P1, SI, SIL, ME20, P100, SILV, ME20M, gp100, ME20-M, PMEL17, D12S53E -PMEL and Melanoma
44
HRAS 11p15.5 CTLO, HAMSV, HRAS1, RASH1, p21ras, C-H-RAS, H-RASIDX, C-BAS/HAS, C-HA-RAS1 -HRAS and Melanoma
42
BRCA2 13q12.3 FAD, FACD, FAD1, GLM3, BRCC2, FANCD, PNCA2, FANCD1, XRCC11, BROVCA2 -BRCA2 and Melanoma
42
CTLA4 2q33 CD, GSE, GRD4, ALPS5, CD152, CTLA-4, IDDM12, CELIAC3 -CTLA4 and Melanoma
41
IL4 5q31.1 BSF1, IL-4, BCGF1, BSF-1, BCGF-1 -IL4 Gene Therapy for Melanoma (Experimental)
40
IL24 1q32 C49A, FISP, MDA7, MOB5, ST16, IL10B Down Regulated
-MDA1 Expression in Melanoma
40
BAP1 3p21.1 UCHL2, hucep-6, HUCEP-13 Germline
-BAP1 and Melanoma
39
TYRP1 9p23 TRP, CAS2, CATB, GP75, OCA3, TRP1, TYRP, b-PROTEIN -TYRP1 and Melanoma
37
MAGEA3 Xq28 HIP8, HYPD, CT1.3, MAGE3, MAGEA6 -MAGEA3 and Melanoma
34
MYB 6q22-q23 efg, Cmyb, c-myb, c-myb_CDS -MYB and Melanoma
33
MAGEA1 Xq28 CT1.1, MAGE1 -MAGEA1 and Melanoma
32
PAX3 2q35 WS1, WS3, CDHS, HUP2 -PAX3 and Melanoma
31
CDK6 7q21-q22 MCPH12, PLSTIRE -CDK6 and Melanoma
29
ICAM1 19p13.3-p13.2 BB2, CD54, P3.58 -ICAM1 and Melanoma
28
TERT 5p15.33 TP2, TRT, CMM9, EST2, TCS1, hTRT, DKCA2, DKCB4, hEST2, PFBMFT1 -TERT and Melanoma
27
RAF1 3p25 NS5, CRAF, Raf-1, c-Raf, CMD1NN -RAF1 and Melanoma
27
SOX10 22q13.1 DOM, WS4, PCWH, WS2E, WS4C -SOX10 and Melanoma
25
TFAP2C 20q13.2 ERF1, TFAP2G, hAP-2g, AP2-GAMMA -TFAP2C and Melanoma
25
CD63 12q12-q13 MLA1, ME491, LAMP-3, OMA81H, TSPAN30 -CD63 and Melanoma
25
TFAP2B 6p12 AP-2B, AP2-B -TFAP2B and Melanoma
24
TFAP2A 6p24 AP-2, BOFS, AP2TF, TFAP2, AP-2alpha -TFAP2A and Melanoma
24
STAT1 2q32.2 CANDF7, IMD31A, IMD31B, IMD31C, ISGF-3, STAT91 -STAT1 and Melanoma
23
CD80 3q13.3-q21 B7, BB1, B7-1, B7.1, LAB7, CD28LG, CD28LG1 -CD80 and Melanoma
23
FOXP3 Xp11.23 JM2, AIID, IPEX, PIDX, XPID, DIETER -FOXP3 and Melanoma
22
WNT5A 3p21-p14 hWNT5A -WNT5A and Melanoma
22
ATF1 12q13 TREB36, EWS-ATF1, FUS/ATF-1 -ATF1 and Melanoma
22
MMP1 11q22.3 CLG, CLGN Prognostic
-MMP1 and Melanoma
21
SPARC 5q31.3-q32 ON -SPARC and Melanoma
21
TIMP1 Xp11.3-p11.23 EPA, EPO, HCI, CLGI, TIMP Prognostic
-TIMP1 AND Melanoma
20
PIGS 17p13.2 -PIGS and Melanoma
20
RREB1 6p25 HNT, FINB, LZ321, Zep-1, RREB-1 -RREB1 and Melanoma
20
PARP1 1q41-q42 PARP, PPOL, ADPRT, ARTD1, ADPRT1, PARP-1, ADPRT 1, pADPRT-1 -PARP1 and Melanoma
20
HLA-C 6p21.3 HLC-C, D6S204, PSORS1, HLA-JY3 -HLA-C and Melanoma
18
ASIP 20q11.2-q12 ASP, AGSW, AGTI, AGTIL, SHEP9 -ASIP and Melanoma
18
HLA-DRB1 6p21.3 SS1, DRB1, DRw10, HLA-DRB, HLA-DR1B -HLA-DRB1 and Melanoma
18
MAP2K2 19p13.3 CFC4, MEK2, MKK2, MAPKK2, PRKMK2 -MAP2K2 and Melanoma
17
MIA 19q13.2 CD-RAP -MIA and Melanoma
17
S100A6 1q21 2A9, PRA, 5B10, CABP, CACY -S100A6 Expression in Melanoma
16
OCA2 15q P, BEY, PED, BEY1, BEY2, BOCA, EYCL, HCL3, EYCL2, EYCL3, SHEP1, D15S12 -OCA2 and Melanoma
16
XPC 3p25.1 XP3, RAD4, XPCC, p125 -XPC and Melanoma
16
MTAP 9p21 BDMF, MSAP, DMSFH, LGMBF, DMSMFH, c86fus, HEL-249 -MTAP and Melanoma
16
FAS 10q24.1 APT1, CD95, FAS1, APO-1, FASTM, ALPS1A, TNFRSF6 -FAS and Melanoma
15
RHOC 1p13.1 H9, ARH9, ARHC, RHOH9 -RHOC and Melanoma
15
CTAG1B Xq28 CTAG, ESO1, CT6.1, CTAG1, LAGE-2, LAGE2B, NY-ESO-1 -CTAG1B and Melanoma
15
ABCB5 7p21.1 ABCB5beta, EST422562, ABCB5alpha -ABCB5 and Melanoma
15
CD68 17p13 GP110, LAMP4, SCARD1 -CD68 and Melanoma
14
SF3B1 2q33.1 MDS, PRP10, Hsh155, PRPF10, SAP155, SF3b155 -SF3B1 and Melanoma
14
CIITA 16p13 C2TA, NLRA, MHC2TA, CIITAIV -CIITA and Melanoma
13
APAF1 12q23 CED4, APAF-1 -APAF1 and Melanoma
13
KISS1 1q32 HH13, KiSS-1 -KISS1 and Melanoma
12
IL2 4q26-q27 IL-2, TCGF, lymphokine -IL2 and Melanoma
12
GAST 17q21 GAS -GAST and Melanoma
12
AKT3 1q44 MPPH, PKBG, MPPH2, PRKBG, STK-2, PKB-GAMMA, RAC-gamma, RAC-PK-gamma -AKT3 and Melanoma
11
ERBB4 2q33.3-q34 HER4, ALS19, p180erbB4 -ERBB4 and Melanoma
11
SKI 1p36.33 SGS, SKV -SKI and Melanoma
11
PARK2 6q25.2-q27 PDJ, PRKN, AR-JP, LPRS2 -PARK2 and Melanoma
11
GRM1 6q24 MGLU1, GPRC1A, MGLUR1, SCAR13, PPP1R85 -GRM1 and Melanoma
11
TAP1 6p21.3 APT1, PSF1, ABC17, ABCB2, PSF-1, RING4, TAP1N, D6S114E, TAP1*0102N -TAP1 and Melanoma
10
GSTT1 22q11.23 -GSTT1 Polymorphisms and Melanoma
10
CXCL10 4q21 C7, IFI10, INP10, IP-10, crg-2, mob-1, SCYB10, gIP-10 -CXCL10 and Melanoma
10
CD274 9p24 B7-H, B7H1, PDL1, PD-L1, PDCD1L1, PDCD1LG1 -CD274 and Melanoma
9
CD86 3q21 B70, B7-2, B7.2, LAB72, CD28LG2 -CD86 and Melanoma
9
MAGEA2 Xq28 CT1.2, MAGE2, MAGEA2A -Melanoma and MAGEA2
9
PTPRD 9p23-p24.3 HPTP, PTPD, HPTPD, HPTPDELTA, RPTPDELTA -PTPRD and Melanoma
9
ATF2 2q32 HB16, CREB2, TREB7, CREB-2, CRE-BP1 -ATF2 and Melanoma
9
CCR7 17q12-q21.2 BLR2, EBI1, CCR-7, CD197, CDw197, CMKBR7, CC-CKR-7 -CCR7 and Melanoma
8
MAGEB2 Xp21.3 DAM6, CT3.2, MAGE-XP-2 -MAGEB2 and Melanoma
8
PRAME 22q11.22 MAPE, OIP4, CT130, OIP-4 -PRAME and Melanoma
8
S100B 21q22.3 NEF, S100, S100-B, S100beta Prognostic
-S100B and Melanoma
8
FLNC 7q32-q35 ABPA, ABPL, FLN2, MFM5, MPD4, ABP-280, ABP280A -FLNC and Melanoma
7
GDF15 19p13.11 PDF, MIC1, PLAB, MIC-1, NAG-1, PTGFB, GDF-15 -GDF15 and Melanoma
7
TRB 7q34 TCRB, TRB@ -TRB and Melanoma
7
L1CAM Xq28 S10, HSAS, MASA, MIC5, SPG1, CAML1, CD171, HSAS1, N-CAML1, NCAM-L1, N-CAM-L1 -L1CAM and Melanoma
7
TERC 3q26 TR, hTR, TRC3, DKCA1, PFBMFT2, SCARNA19 -TERC and Melanoma
7
EDNRB 13q22 ETB, ET-B, ETBR, ETRB, HSCR, WS4A, ABCDS, ET-BR, HSCR2 -EDNRB and Melanoma
7
YES1 18p11.31-p11.21 Yes, c-yes, HsT441, P61-YES -Proto-Oncogene Proteins c-yes and Melanoma
7
IRF4 6p25-p23 MUM1, LSIRF, SHEP8, NF-EM5 -IRF4 and Melanoma
7
IL18 11q22.2-q22.3 IGIF, IL-18, IL-1g, IL1F4 -IL18 and Melanoma
7
NEDD9 6p24.2 CAS2, CASL, HEF1, CAS-L, CASS2 -NEDD9 and Melanoma
7
MAGEA4 Xq28 CT1.4, MAGE4, MAGE4A, MAGE4B, MAGE-41, MAGE-X2 -MAGEA4 and Melanoma
7
BIRC7 20q13.3 KIAP, LIVIN, MLIAP, RNF50, ML-IAP -BIRC7 and Melanoma
6
TLR3 4q35 CD283, IIAE2 -TLR3 and Melanoma
6
BAGE 21p11.1 not on ref BAGE1, CT2.1 -BAGE and Melanoma
6
BCL2A1 15q24.3 GRS, ACC1, ACC2, BFL1, ACC-1, ACC-2, HBPA1, BCL2L5 -BCL2A1 and Melanoma
6
CLPTM1L 5p15.33 CRR9 -CLPTM1L and Melanoma
6
CD27 12p13 T14, S152, Tp55, TNFRSF7, S152. LPFS2 -CD27 and Melanoma
6
DDB2 11p12-p11 DDBB, UV-DDB2 -DDB2 and Melanoma
6
PTPRT 20q12-q13 RPTPrho -PTPRT and Melanoma
6
PTPRF 1p34 LAR, BNAH2 -PTPRF and Melanoma
6
TBX2 17q23.2 -TBX2 and Melanoma
6
AIM1 6q21 ST4, CRYBG1 -AIM1 and Melanoma
6
PDCD1 2q37.3 PD1, PD-1, CD279, SLEB2, hPD-1, hPD-l, hSLE1 -PDCD1 and Melanoma
6
EFNB2 13q33 HTKL, EPLG5, Htk-L, LERK5 -EFNB2 expression in Melanoma
5
GAGE1 Xp11.23 CT4.1, GAGE-1 -GAGE1 and Melanoma
5
KDM5B 1q32.1 CT31, PLU1, PUT1, PLU-1, JARID1B, PPP1R98, RBBP2H1A -KDM5B and Melanoma
5
STAT2 12q13.3 P113, ISGF-3, STAT113 -STAT2 and Melanoma
5
MXI1 10q24-q25 MXI, MAD2, MXD2, bHLHc11 -MXI1 and Melanoma
5
SMARCA2 9p22.3 BRM, SNF2, SWI2, hBRM, NCBRS, Sth1p, BAF190, SNF2L2, SNF2LA, hSNF2a -SMARCA2 and Melanoma
5
YY1AP1 1q22 YAP, HCCA1, HCCA2, YY1AP -YY1AP1 and Melanoma
5
HSPB1 7q11.23 CMT2F, HMN2B, HSP27, HSP28, Hsp25, SRP27, HS.76067, HEL-S-102 -HSPB1 and Melanoma
5
CXCL9 4q21 CMK, MIG, Humig, SCYB9, crg-10 -CXCL9 and Melanoma
5
RARB 3p24.2 HAP, RRB2, NR1B2, MCOPS12 -RARB and Melanoma
5
BMP7 20q13 OP-1 -BMP7 and Melanoma
5
PEBP1 12q24.23 PBP, HCNP, PEBP, RKIP, HCNPpp, PEBP-1, HEL-210, HEL-S-34 -PEBP1 and Melanoma
5
TRG 7p14 TCRG, TRG@ -TRG and Melanoma
5
MIRLET7B 22q13.31 LET7B, let-7b, MIRNLET7B, hsa-let-7b -MicroRNA let-7b and Melanoma
5
IGFBP7 4q12 AGM, PSF, TAF, FSTL2, IBP-7, MAC25, IGFBP-7, RAMSVPS, IGFBP-7v, IGFBPRP1 -IGFBP7 and Melanoma
4
POT1 7q31.33 CMM10, HPOT1 Germline
GWS
-POT1 and Predisposition to Familial Melanoma
4
RAP1GAP 1p36.1-p35 RAPGAP, RAP1GA1, RAP1GAP1, RAP1GAPII -RAP1GAP and Melanoma
4
FABP7 6q22-q23 MRG, BLBP, FABPB, B-FABP, LTR2-FABP7 -FABP7 and Melanoma
4
ING4 12p13.31 my036, p29ING4 -ING4 and Melanoma
4
ULBP2 6q25 N2DL2, RAET1H, NKG2DL2, ALCAN-alpha -ULBP2 and Melanoma
4
IRF9 14q11.2 p48, IRF-9, ISGF3, ISGF3G -IRF9 and Melanoma
4
DUSP6 12q22-q23 HH19, MKP3, PYST1 -DUSP6 and Melanoma
4
BRMS1 11q13.2 -BRMS1 and Melanoma
4
ITGA4 2q31.3 IA4, CD49D -ITGA4 and Melanoma
4
CD70 19p13 CD27L, CD27LG, TNFSF7 -CD70 and Melanoma
4
VCAN 5q14.3 WGN, ERVR, GHAP, PG-M, WGN1, CSPG2 -VCAN and Melanoma
4
MAP2 2q34-q35 MAP2A, MAP2B, MAP2C -MAP2 and Melanoma
4
PERP 6q24 THW, KCP1, PIGPC1, KRTCAP1, dJ496H19.1 -PERP and Melanoma
4
MAP3K5 6q22.33 ASK1, MEKK5, MAPKKK5 -MAP3K5 and Melanoma
4
CAST 5q15 BS-17, PLACK -CAST and Melanoma
4
ATF3 1q32.3 -ATF3 and Melanoma
4
CITED1 Xq13.1 MSG1 -CITED1 and Melanoma
4
TIMP2 17q25 DDC8, CSC-21K -TIMP2 and Melanoma
4
CHUK 10q24-q25 IKK1, IKKA, IKBKA, TCF16, NFKBIKA, IKK-alpha -CHUK and Melanoma
4
HLA-DRA 6p21.3 MLRW, HLA-DRA1 -HLA-DRA and Melanoma
4
ITCH 20q11.22 AIF4, AIP4, ADMFD, NAPP1, dJ468O1.1 -ITCH and Melanoma
4
TRPM8 2q37.1 TRPP8, LTRPC6 -TRPM8 and Melanoma
4
SPRY4 5q31.3 HH17 -SPRY4 and Melanoma
3
CD163 12p13.3 M130, MM130 -CD163 and Melanoma
3
ASAH1 8p22 AC, PHP, ASAH, PHP32, ACDase, SMAPME -ASAH1 and Melanoma
3
LARS 5q32 LRS, LEUS, LFIS, ILFS1, LARS1, LEURS, PIG44, RNTLS, HSPC192, hr025Cl -LARS and Melanoma
3
MSN Xq11.1 HEL70 -MSN and Melanoma
3
ICOS 2q33 AILIM, CD278, CVID1 -ICOS and Melanoma
3
HTRA2 2p12 OMI, PARK13, PRSS25 -HTRA2 and Melanoma
3
PPP1R15A 19q13.2 GADD34 -PPP1R15A and Melanoma
3
MMP3 11q22.3 SL-1, STMY, STR1, CHDS6, MMP-3, STMY1 -MMP3 and Melanoma
3
HSPA8 11q24.1 LAP1, HSC54, HSC70, HSC71, HSP71, HSP73, LAP-1, NIP71, HEL-33, HSPA10, HEL-S-72p -HSPA8 and Melanoma
3
CD59 11p13 1F5, EJ16, EJ30, EL32, G344, MIN1, MIN2, MIN3, MIRL, HRF20, MACIF, MEM43, MIC11, MSK21, 16.3A5, HRF-20, MAC-IP, p18-20 -CD59 and Melanoma
3
HPSE 4q21.3 HPA, HPA1, HPR1, HSE1, HPSE1 -HPSE and Melanoma
3
PTPRK 6q22.2-q22.3 R-PTP-kappa -PTPRK and Melanoma
3
TNFRSF9 1p36 ILA, 4-1BB, CD137, CDw137 -TNFRSF9 and Melanoma
3
ISG15 1p36.33 G1P2, IP17, UCRP, IFI15, IMD38, hUCRP -ISG15 and Melanoma
3
CXCL11 4q21.2 IP9, H174, IP-9, b-R1, I-TAC, SCYB11, SCYB9B -CXCL11 and Melanoma
3
ADRB2 5q31-q32 BAR, B2AR, ADRBR, ADRB2R, BETA2AR -ADRB2 and Melanoma
3
CTSL 9q21.33 MEP, CATL, CTSL1 -CTSL1 and Melanoma
3
ARL11 13q14.2 ARLTS1 -ARL11 and Melanoma
3
TBX3 12q24.21 UMS, XHL, TBX3-ISO -TBX3 and Melanoma
3
MMP8 11q22.3 HNC, CLG1, MMP-8, PMNL-CL -MMP8 and Melanoma
3
TYRO3 15q15 BYK, Dtk, RSE, Rek, Sky, Tif, Etk-2 -TYRO3 and Melanoma
3
GRASP 12q13.13 TAMALIN -GRASP and Melanoma
3
ETV1 7p21.3 ER81 Overexpression
-ETV1 overexpression in Melanoma
2
MAP3K8 10p11.23 COT, EST, ESTF, TPL2, AURA2, MEKK8, Tpl-2, c-COT -MAP3K8 and Melanoma
2
IL12B 5q33.3 CLMF, NKSF, CLMF2, IMD28, IMD29, NKSF2, IL-12B -IL12B and Melanoma
2
ARNTL 11p15 TIC, JAP3, MOP3, BMAL1, PASD3, BMAL1c, bHLHe5 -ARNTL and Melanoma
2
MCM4 8q11.2 NKCD, CDC21, CDC54, NKGCD, hCdc21, P1-CDC21 -MCM4 and Melanoma
2
ING3 7q31 Eaf4, ING2, MEAF4, p47ING3 -ING3 and Melanoma
2
HAVCR2 5q33.3 TIM3, CD366, KIM-3, TIMD3, Tim-3, TIMD-3, HAVcr-2 -HAVCR2 and Melanoma
2
S100A2 1q21 CAN19, S100L -S100A2 Expression in Melanoma
2
ARID2 12q12 p200, BAF200 -ARID2 and Melanoma
2
AQP3 9p13 GIL, AQP-3 -AQP3 and Melanoma
2
RTEL1 20q13.3 NHL, RTEL, DKCA4, DKCB5, C20orf41 -RTEL1 and Melanoma
2
PDCD6 5p15.33 ALG2, ALG-2, PEF1B -PDCD6 and Melanoma
2
NOX4 11q14.2-q21 KOX, KOX-1, RENOX -NOX4 and Melanoma
2
KDM5A 12p11 RBP2, RBBP2, RBBP-2 -KDM5A and Melanoma
2
PAEP 9q34 GD, GdA, GdF, GdS, PEP, PAEG, PP14 -PAEP and Melanoma
2
PPP2R1A 19q13.41 PR65A, PP2AAALPHA, PP2A-Aalpha -PPP2R1A and Melanoma
2
FLNA Xq28 FLN, FMD, MNS, OPD, ABPX, CSBS, CVD1, FLN1, NHBP, OPD1, OPD2, XLVD, XMVD, FLN-A, ABP-280 -FLNA and Melanoma
2
BIN1 2q14 AMPH2, AMPHL, SH3P9 -BIN1 and Melanoma
1
PPP1R3A 7q31.1 GM, PP1G, PPP1R3 -PPP1R3A and Melanoma
1
PNN 14q21.1 DRS, DRSP, SDK3, memA -PNN and Melanoma
1
MAS1 6q25.3-q26 MAS, MGRA -MAS1 and Melanoma
1
HOXD11 2q31.1 HOX4, HOX4F -HOXD11 and Melanoma
1
ADAM7 8p21.2 EAPI, GP83, GP-83, ADAM 7, ADAM-7 -ADAM7 and Melanoma
1
CASC5 15q14 D40, CT29, KNL1, Spc7, hKNL-1, AF15Q14, PPP1R55, hSpc105 -CASC5 and Melanoma
1
BLM 15q26.1 BS, RECQ2, RECQL2, RECQL3 -BLM and Melanoma
1

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

Latest Research Publications

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Targeted regulation of gene expression holds huge promise for biomedical research. In a series of recent publications (Gilbert et al., 2014; Konermann et al., 2015; Zalatan et al., 2015), sophisticated, multiplex-compatible transcriptional activator systems based on the CRISPR-Cas9 technology and genome-scale libraries advance the field toward whole-transcriptome control.

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Interleukin-8 is a key mediator of FKBP51-induced melanoma growth, angiogenesis and metastasis.
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BACKGROUND: FKBP51 is overexpressed in melanoma and impacts tumour cell properties. However, its comprehensive role in melanoma pathogenesis and underlying mechanism(s) remain elusive.
METHODS: FKBP51 was stably silenced in aggressive melanoma cell lines and its effect examined in vitro and in mouse model. Histological/immunohistochemical analyses were performed to confirm metastasis, angiogenesis and neutrophil infiltration. Gene expression was analyzed by qRT-PCR, immunoblot and/or ELISA. NF-κB transcriptional activity and promoter binding were monitored by luciferase-based promoter-reporter and ChIP assays, respectively. Interleukin (IL)-8 inhibition was achieved by gene silencing or neutralising-antibody treatment.
RESULTS: FKBP51 silencing reduced melanoma growth, metastasis, angiogenesis and neutrophil infiltration and led to IL-8 downregulation through NF-κB suppression in cell lines and tumour xenografts. IL-8 inhibition drastically decreased growth, migration and invasiveness of FKPB51-overexpressing cells; whereas its treatment partially restored the suppressed phenotypes of FKBP51-silenced melanoma cells. Interleukin-8 depletion in conditioned medium (CM) of FKBP51-overexpressing melanoma cells inhibited endothelial cell proliferation and capillary-like structure formation, whereas its treatment promoted these effects in endothelial cells cultured in CM of FKBP51-silenced melanoma cells.
CONCLUSIONS: FKBP51 promotes melanoma growth, metastasis and angiogenesis, and IL-8 plays a key role in these processes. Thus, targeting of FKBP51 or its upstream or downstream regulatory pathways could lead to effective therapeutic strategies against melanoma.

Rao RC, Khan M, Badiyan SN, Harbour JW
Gene expression profiling and regression rate of irradiated uveal melanomas.
Ophthalmic Surg Lasers Imaging Retina. 2015; 46(3):333-7 [PubMed] Free Access to Full Article Related Publications
BACKGROUND AND OBJECTIVE: Uveal melanoma is the most common primary intraocular cancer; however, the molecular features that predict response to therapy are poorly understood. Our objective was to determine whether gene expression profiling (GEP) is associated with rate of tumor regression after I-125 plaque brachytherapy for uveal melanoma.
PATIENTS AND METHODS: Retrospective review of 138 patients with posterior uveal melanoma treated with I-125 plaque brachytherapy in which GEP class and 3-month post-radiation ultrasonographic tumor thickness data were available. Statistical analysis was performed using t test and Fisher's exact test.
RESULTS: GEP class assignment was class 1 in 83 (60.1%) and class 2 in 55 (39.9%) patients. Mean patient age was 60.9 years for class 1 and 68.1 years for class 2 tumors (P = .002). Mean initial tumor diameter was 13.0 mm for class 1 and 14.1 mm for class 2 tumors (P = .02). Mean initial tumor thickness was 5.2 mm for class 1 and 6.1 mm for class 2 tumors (P = .047). Three months after I-125 plaque radiotherapy, mean reduction in tumor thickness was 26.5% for class 1 and 16.7% for class 2 tumors (P = .03).
CONCLUSION: Class 1 uveal melanoma tumors exhibit more rapid early tumor regression than class 2 tumors after I-125 plaque radiotherapy.

Mann MB, Black MA, Jones DJ, et al.
Transposon mutagenesis identifies genetic drivers of Braf(V600E) melanoma.
Nat Genet. 2015; 47(5):486-95 [PubMed] Related Publications
Although nearly half of human melanomas harbor oncogenic BRAF(V600E) mutations, the genetic events that cooperate with these mutations to drive melanogenesis are still largely unknown. Here we show that Sleeping Beauty (SB) transposon-mediated mutagenesis drives melanoma progression in Braf(V600E) mutant mice and identify 1,232 recurrently mutated candidate cancer genes (CCGs) from 70 SB-driven melanomas. CCGs are enriched in Wnt, PI3K, MAPK and netrin signaling pathway components and are more highly connected to one another than predicted by chance, indicating that SB targets cooperative genetic networks in melanoma. Human orthologs of >500 CCGs are enriched for mutations in human melanoma or showed statistically significant clinical associations between RNA abundance and survival of patients with metastatic melanoma. We also functionally validate CEP350 as a new tumor-suppressor gene in human melanoma. SB mutagenesis has thus helped to catalog the cooperative molecular mechanisms driving BRAF(V600E) melanoma and discover new genes with potential clinical importance in human melanoma.

Kim SY, Kim SN, Hahn HJ, et al.
Metaanalysis of BRAF mutations and clinicopathologic characteristics in primary melanoma.
J Am Acad Dermatol. 2015; 72(6):1036-46.e2 [PubMed] Related Publications
BACKGROUND: BRAF mutations occur in some melanomas. We hypothesized that BRAF mutation rates may differ in melanomas found in Asian compared to white populations.
OBJECTIVE: We performed a metaanalysis of BRAF mutations and their associations with the clinicopathologic characteristics of primary melanoma (PM), with a subgroup analysis to compare Asian and white patients with PM.
METHODS: The PubMed, EMBASE, and Cochrane databases were searched up to November 2013. The incidence rates and odds ratios (ORs) of BRAF mutations were calculated using a fixed or random effects model.
RESULTS: BRAF mutation was associated with younger age (OR = 1.734; P < .001), trunk location (OR = 2.272; P < .001), non-chronically sun damaged skin (OR = 2.833; P < .001), superficial spreading melanoma (OR = 2.081; P < .001), and advanced melanoma stage (OR = 1.551; P = .003). The incidence of BRAF mutations in Asian patients with PM was half that of white patients with PM, but it was linked to the same clinicopathologic characteristics.
LIMITATIONS: Only a small number of studies have been conducted on Asian patients with PMs.
CONCLUSIONS: The BRAF mutation in PM was associated with age, anatomic site based on ultraviolet radiation exposure, histologic subtype, and advanced stage of melanoma. The clinicopathologic associations with BRAF mutations were similar in Asian and white patients with PM.

Johnson DB, Puzanov I
Treatment of NRAS-mutant melanoma.
Curr Treat Options Oncol. 2015; 16(4):15 [PubMed] Related Publications
NRAS mutations in codons 12, 13, and 61 arise in 15-20 % of all melanomas. These alterations have been associated with aggressive clinical behavior and a poor prognosis. Until recently, there has been a paucity of promising genetically targeted therapy approaches for NRAS-mutant melanoma (and RAS-mutant malignancies in general). MEK inhibitors, particularly binimetinib, have shown activity in this cohort. Based on pre-clinical and early clinical studies, combining MEK inhibitors with agents inhibiting the cell cycling and the PI3K-AKT pathway appears to provide additional benefit. In particular, a strategy of MEK inhibition and CDK4/6 inhibition is likely to be a viable treatment option in the future, and is the most promising genetically targeted treatment strategy for NRAS-mutant melanoma developed to date. In addition, immune-based therapies have shown increasing activity in advanced melanoma and may be particularly effective in those with NRAS mutations. Combination strategies of immune and targeted therapies may also play a role in the future although clinical trials testing these approaches are in early stages.

Lee YH, Gyu Song G
Vitamin D receptor FokI, BsmI, TaqI, ApaI, and EcoRV polymorphisms and susceptibility to melanoma: a meta-analysis.
J BUON. 2015 Jan-Feb; 20(1):235-43 [PubMed] Related Publications
PURPOSE: The purpose of this study was to examine whether vitamin D receptor (lVDR) polymorphisms are associated with susceptibility to melanoma.
METHODS: A meta-analysis was carried out to investigate the association between the VDR FokI, BsmI, TaqI, ApaI, and EcoRV polymorphisms and susceptibility to melanoma.
RESULTS: A total of 11 studies were evaluated, which included 4,413 patients and 4,072 controls (all European). The meta-analysis revealed no association between melanoma and the BsmI B allele (odds ratio/OR=0.901, 95% confidence interval/CI=0.783-1.036, p=0.144). However, an association was shown between melanoma and the Bb+bb genotype (OR=0.868, 95% CI=0.767-0.982, p=0.025). No association was noticed between melanoma and FokI polymorphism (OR for the F allele=1.016, 95% CI=0.869-1.189, p=0.839). Moreover, melanoma risk was not associated with the TaqI, ApaI, and EcoRV polymorphisms (OR for the T allele=0.986, 95% CI=0.842-1.156, p=0.864; OR for the A allele=0.949, 95% CI=0.842-1.069, p=0.388; OR for the E allele=0.993, 95% CI=0.875-1.126, p=0.911, respectively).
CONCLUSIONS: This meta-analysis demonstrated that the VDR BsmI polymorphism is associated with susceptibility to melanoma in Europeans, suggesting that carrying the VDR BsmI B allele may be a protective factor against melanoma development.

Gerami P, Cook RW, Russell MC, et al.
Gene expression profiling for molecular staging of cutaneous melanoma in patients undergoing sentinel lymph node biopsy.
J Am Acad Dermatol. 2015; 72(5):780-5.e3 [PubMed] Related Publications
BACKGROUND: A gene expression profile (GEP) test able to accurately identify risk of metastasis for patients with cutaneous melanoma has been clinically validated.
OBJECTIVE: We aimed for assessment of the prognostic accuracy of GEP and sentinel lymph node biopsy (SLNB) tests, independently and in combination, in a multicenter cohort of 217 patients.
METHODS: Reverse transcription polymerase chain reaction (RT-PCR) was performed to assess the expression of 31 genes from primary melanoma tumors, and SLNB outcome was determined from clinical data. Prognostic accuracy of each test was determined using Kaplan-Meier and Cox regression analysis of disease-free, distant metastasis-free, and overall survivals.
RESULTS: GEP outcome was a more significant and better predictor of each end point in univariate and multivariate regression analysis, compared with SLNB (P < .0001 for all). In combination with SLNB, GEP improved prognostication. For patients with a GEP high-risk outcome and a negative SLNB result, Kaplan-Meier 5-year disease-free, distant metastasis-free, and overall survivals were 35%, 49%, and 54%, respectively.
LIMITATIONS: Within the SLNB-negative cohort of patients, overall risk of metastatic events was higher (∼30%) than commonly found in the general population of patients with melanoma.
CONCLUSIONS: In this study cohort, GEP was an objective tool that accurately predicted metastatic risk in SLNB-eligible patients.

Kourtis N, Moubarak RS, Aranda-Orgilles B, et al.
FBXW7 modulates cellular stress response and metastatic potential through ​HSF1 post-translational modification.
Nat Cell Biol. 2015; 17(3):322-32 [PubMed] Article available free on PMC after 01/09/2015 Related Publications
​Heat-shock factor 1 (​HSF1) orchestrates the heat-shock response in eukaryotes. Although this pathway has evolved to help cells adapt in the presence of challenging conditions, it is co-opted in cancer to support malignancy. However, the mechanisms that regulate ​HSF1 and thus cellular stress response are poorly understood. Here we show that the ubiquitin ligase ​FBXW7α interacts with ​HSF1 through a conserved motif phosphorylated by ​GSK3β and ​ERK1. ​FBXW7α ubiquitylates ​HSF1 and loss of ​FBXW7α results in impaired degradation of nuclear ​HSF1 and defective heat-shock response attenuation. ​FBXW7α is either mutated or transcriptionally downregulated in melanoma and ​HSF1 nuclear stabilization correlates with increased metastatic potential and disease progression. ​FBXW7α deficiency and subsequent ​HSF1 accumulation activates an invasion-supportive transcriptional program and enhances the metastatic potential of human melanoma cells. These findings identify a post-translational mechanism of regulation of the ​HSF1 transcriptional program both in the presence of exogenous stress and in cancer.

Premi S, Wallisch S, Mano CM, et al.
Photochemistry. Chemiexcitation of melanin derivatives induces DNA photoproducts long after UV exposure.
Science. 2015; 347(6224):842-7 [PubMed] Article available free on PMC after 20/08/2015 Related Publications
Mutations in sunlight-induced melanoma arise from cyclobutane pyrimidine dimers (CPDs), DNA photoproducts that are typically created picoseconds after an ultraviolet (UV) photon is absorbed at thymine or cytosine. We found that in melanocytes, CPDs are generated for >3 hours after exposure to UVA, a major component of the radiation in sunlight and in tanning beds. These "dark CPDs" constitute the majority of CPDs and include the cytosine-containing CPDs that initiate UV-signature C→T mutations. Dark CPDs arise when UV-induced reactive oxygen and nitrogen species combine to excite an electron in fragments of the pigment melanin. This creates a quantum triplet state that has the energy of a UV photon but induces CPDs by energy transfer to DNA in a radiation-independent manner. Melanin may thus be carcinogenic as well as protective against cancer. These findings also validate the long-standing suggestion that chemically generated excited electronic states are relevant to mammalian biology.

Shoshan E, Mobley AK, Braeuer RR, et al.
Reduced adenosine-to-inosine miR-455-5p editing promotes melanoma growth and metastasis.
Nat Cell Biol. 2015; 17(3):311-21 [PubMed] Article available free on PMC after 01/09/2015 Related Publications
Although recent studies have shown that adenosine-to-inosine (A-to-I) RNA editing occurs in microRNAs (miRNAs), its effects on tumour growth and metastasis are not well understood. We present evidence of CREB-mediated low expression of ADAR1 in metastatic melanoma cell lines and tumour specimens. Re-expression of ADAR1 resulted in the suppression of melanoma growth and metastasis in vivo. Consequently, we identified three miRNAs undergoing A-to-I editing in the weakly metastatic melanoma but not in strongly metastatic cell lines. One of these miRNAs, miR-455-5p, has two A-to-I RNA-editing sites. The biological function of edited miR-455-5p is different from that of the unedited form, as it recognizes a different set of genes. Indeed, wild-type miR-455-5p promotes melanoma metastasis through inhibition of the tumour suppressor gene CPEB1. Moreover, wild-type miR-455 enhances melanoma growth and metastasis in vivo, whereas the edited form inhibits these features. These results demonstrate a previously unrecognized role for RNA editing in melanoma progression.

Priedigkeit N, Wolfe N, Clark NL
Evolutionary signatures amongst disease genes permit novel methods for gene prioritization and construction of informative gene-based networks.
PLoS Genet. 2015; 11(2):e1004967 [PubMed] Article available free on PMC after 01/09/2015 Related Publications
Genes involved in the same function tend to have similar evolutionary histories, in that their rates of evolution covary over time. This coevolutionary signature, termed Evolutionary Rate Covariation (ERC), is calculated using only gene sequences from a set of closely related species and has demonstrated potential as a computational tool for inferring functional relationships between genes. To further define applications of ERC, we first established that roughly 55% of genetic diseases posses an ERC signature between their contributing genes. At a false discovery rate of 5% we report 40 such diseases including cancers, developmental disorders and mitochondrial diseases. Given these coevolutionary signatures between disease genes, we then assessed ERC's ability to prioritize known disease genes out of a list of unrelated candidates. We found that in the presence of an ERC signature, the true disease gene is effectively prioritized to the top 6% of candidates on average. We then apply this strategy to a melanoma-associated region on chromosome 1 and identify MCL1 as a potential causative gene. Furthermore, to gain global insight into disease mechanisms, we used ERC to predict molecular connections between 310 nominally distinct diseases. The resulting "disease map" network associates several diseases with related pathogenic mechanisms and unveils many novel relationships between clinically distinct diseases, such as between Hirschsprung's disease and melanoma. Taken together, these results demonstrate the utility of molecular evolution as a gene discovery platform and show that evolutionary signatures can be used to build informative gene-based networks.

Davar D, Lin Y, Kirkwood JM
Unfolding the mutational landscape of human melanoma.
J Invest Dermatol. 2015; 135(3):659-62 [PubMed] Article available free on PMC after 01/09/2015 Related Publications
Over the preceding two decades, sophisticated sequencing techniques have been used to characterize the genetic drivers of adult melanoma. However, our understanding of pediatric melanomas is still rudimentary. In this report, we comment on a thorough multi-platform analysis of common pediatric melanoma subsets, including pediatric conventional melanoma (CM), congenital nevus-derived melanoma (CNM), and Spitzoid melanoma (SM), contributed by Lu et al.

Ilie M, Long-Mira E, Funck-Brentano E, et al.
Immunohistochemistry as a potential tool for routine detection of the NRAS Q61R mutation in patients with metastatic melanoma.
J Am Acad Dermatol. 2015; 72(5):786-93 [PubMed] Related Publications
BACKGROUND: It can be useful to assess the NRAS mutation status in patients with metastatic melanoma because NRAS-activating mutations confer resistance to RAF inhibitors, and NRAS-mutated patients appear to be sensitive to mitogen-activated protein kinase (MEK) inhibitors.
OBJECTIVE: We aimed to assess the diagnostic accuracy of an immunohistochemistry (IHC) approach using a novel anti-NRAS (Q61R) monoclonal antibody on formalin-fixed paraffin-embedded tissue samples from patients with metastatic melanoma.
METHODS: We conducted a retrospective multicenter cohort study on 170 patients with metastatic melanoma. The automated IHC assay was performed using the SP174 clone, and compared with results of the molecular testing.
RESULTS: Evaluation of a test cohort with knowledge of the mutation status established a specific IHC pattern for the mutation. In the independent blinded analysis of the remaining cases, the anti-NRAS (Q61R) antibody accurately identified all NRAS Q61R-mutated tumors, and demonstrated 100% sensitivity and specificity.
LIMITATIONS: Limitations include retrospective design and lack of multicenter interobserver reproducibility.
CONCLUSION: The NRAS (Q61R) IHC assay is reliable and specific for the evaluation of the Q61R mutation status in metastatic melanoma and may be an alternative to molecular biology in evaluation of metastatic melanoma in routine practice.

Sobota RS, Shriner D, Kodaman N, et al.
Addressing population-specific multiple testing burdens in genetic association studies.
Ann Hum Genet. 2015; 79(2):136-47 [PubMed] Article available free on PMC after 01/03/2016 Related Publications
The number of effectively independent tests performed in genome-wide association studies (GWAS) varies by population, making a universal P-value threshold inappropriate. We estimated the number of independent SNPs in Phase 3 HapMap samples by: (1) the LD-pruning function in PLINK, and (2) an autocorrelation-based approach. Autocorrelation was also used to estimate the number of independent SNPs in whole genome sequences from 1000 Genomes. Both approaches yielded consistent estimates of numbers of independent SNPs, which were used to calculate new population-specific thresholds for genome-wide significance. African populations had the most stringent thresholds (1.49 × 10(-7) for YRI at r(2) = 0.3), East Asian populations the least (3.75 × 10(-7) for JPT at r(2) = 0.3). We also assessed how using population-specific significance thresholds compared to using a single multiple testing threshold at the conventional 5 × 10(-8) cutoff. Applied to a previously published GWAS of melanoma in Caucasians, our approach identified two additional genes, both previously associated with the phenotype. In a Chinese breast cancer GWAS, our approach identified 48 additional genes, 19 of which were in or near genes previously associated with the phenotype. We conclude that the conventional genome-wide significance threshold generates an excess of Type 2 errors, particularly in GWAS performed on more recently founded populations.

Tell-Marti G, Puig-Butille JA, Potrony M, et al.
The MC1R melanoma risk variant p.R160W is associated with Parkinson disease.
Ann Neurol. 2015; 77(5):889-94 [PubMed] Related Publications
Epidemiological studies have reported the co-occurrence of Parkinson disease (PD) and melanoma. Common genetic variants in the MC1R (melanocortin 1 receptor) gene, which determines skin and hair color, are associated with melanoma. Here we investigated whether genetic variants in MC1R modulate the risk of PD by sequencing the entire gene in 870 PD patients and 736 controls ascertained from Spain. We found that the MC1R variant p.R160W (rs1805008) is marginally associated with PD (odds ratio = 2.10, gender- and age-adjusted p = 0.009, Bonferroni-corrected p = 0.063). Our results suggest that MC1R genetic variants modulate the risk of PD disease in the Spanish population.

Shakhova O, Cheng P, Mishra PJ, et al.
Antagonistic cross-regulation between Sox9 and Sox10 controls an anti-tumorigenic program in melanoma.
PLoS Genet. 2015; 11(1):e1004877 [PubMed] Article available free on PMC after 01/03/2016 Related Publications
Melanoma is the most fatal skin cancer, but the etiology of this devastating disease is still poorly understood. Recently, the transcription factor Sox10 has been shown to promote both melanoma initiation and progression. Reducing SOX10 expression levels in human melanoma cells and in a genetic melanoma mouse model, efficiently abolishes tumorigenesis by inducing cell cycle exit and apoptosis. Here, we show that this anti-tumorigenic effect functionally involves SOX9, a factor related to SOX10 and upregulated in melanoma cells upon loss of SOX10. Unlike SOX10, SOX9 is not required for normal melanocyte stem cell function, the formation of hyperplastic lesions, and melanoma initiation. To the contrary, SOX9 overexpression results in cell cycle arrest, apoptosis, and a gene expression profile shared by melanoma cells with reduced SOX10 expression. Moreover, SOX9 binds to the SOX10 promoter and induces downregulation of SOX10 expression, revealing a feedback loop reinforcing the SOX10 low/SOX9 high ant,m/ii-tumorigenic program. Finally, SOX9 is required in vitro and in vivo for the anti-tumorigenic effect achieved by reducing SOX10 expression. Thus, SOX10 and SOX9 are functionally antagonistic regulators of melanoma development.

Yuan H, Liu H, Liu Z, et al.
Genetic variants in Hippo pathway genes YAP1, TEAD1 and TEAD4 are associated with melanoma-specific survival.
Int J Cancer. 2015; 137(3):638-45 [PubMed] Article available free on PMC after 01/08/2016 Related Publications
Cutaneous melanoma (CM) is the most lethal form of skin cancers. The Hippo pathway controls cell migration, development and sizes of the organs in diverse species, and deregulation of this pathway may affect CM progression and prognosis. Therefore, we hypothesized that genetic variants of Hippo pathway genes might predict survival of CM patients. We used the genotyping data of 1,115 common single nucleotide polymorphisms (SNPs) in the 12 pathway core genes (i.e., MST1, MST2, SAV1, LATS1, LATS2, MOB1A, MOB1B, YAP1, TEAD1, TEAD2, TEAD3 and TEAD4) from the dataset of our previously published CM genome-wide association study and comprehensively analyzed their associations with CM-specific survival (CSS) in 858 CM patients by using the Kaplan-Meier analyses and Cox proportional hazards regression models. We found a predictive role of YAP1 rs11225163 CC, TEAD1 rs7944031 AG+GG and TEAD4 rs1990330 CA+AA in the prognosis of CM. In addition, patients with an increasing number of unfavorable genotypes (NUG) had a markedly increased risk of death. After incorporating NUG in the model with clinical variables, the new model showed a significantly improved discriminatory ability to classify CSS (AUC increased from 82.03% to 84.56%). Our findings suggest that genetic variants of Hippo pathway genes, particularly YAP1 rs11225163, TEAD1 rs7944031 and TEAD4 rs1990330, may independently or jointly modulate survival of CM patients. Additional large, prospective studies are needed to validate these findings.

Hallberg AR, Vorrink SU, Hudachek DR, et al.
Aberrant CpG methylation of the TFAP2A gene constitutes a mechanism for loss of TFAP2A expression in human metastatic melanoma.
Epigenetics. 2014; 9(12):1641-7 [PubMed] Related Publications
Metastatic melanoma is a deadly treatment-resistant form of skin cancer whose global incidence is on the rise. During melanocyte transformation and melanoma progression the expression profile of many genes changes. Among these, a gene implicated in several steps of melanocyte development, TFAP2A, is frequently silenced; however, the molecular mechanism of TFAP2A silencing in human melanoma remains unknown. In this study, we measured TFAP2A mRNA expression in primary human melanocytes compared to 11 human melanoma samples by quantitative real-time RT-PCR. In addition, we assessed CpG DNA methylation of the TFAP2A promoter in these samples using bisulfite sequencing. Compared to primary melanocytes, which showed high TFAP2A mRNA expression and no promoter methylation, human melanoma samples showed decreased TFAP2A mRNA expression and increased promoter methylation. We further show that increased CpG methylation correlates with decreased TFAP2A mRNA expression. Using The Cancer Genome Atlas, we further identified TFAP2A as a gene displaying among the most decreased expression in stage 4 melanomas vs. non-stage 4 melanomas, and whose CpG methylation was frequently associated with lack of mRNA expression. Based on our data, we conclude that TFAP2A expression in human melanomas can be silenced by aberrant CpG methylation of the TFAP2A promoter. We have identified aberrant CpG DNA methylation as an epigenetic mark associated with TFAP2A silencing in human melanoma that could have significant implications for the therapy of human melanoma using epigenetic modifying drugs.

Kampilafkos P, Melachrinou M, Kefalopoulou Z, et al.
Epigenetic modifications in cutaneous malignant melanoma: EZH2, H3K4me2, and H3K27me3 immunohistochemical expression is enhanced at the invasion front of the tumor.
Am J Dermatopathol. 2015; 37(2):138-44 [PubMed] Related Publications
Cancer stem cells and the misregulation of epigenetic modifications have been identified to possess a determinative role in carcinogenesis. The purpose of this study was to investigate the expression profile of EZH2 and H3K4me2 and H3K27me3, which constitute stem cell-like "bivalent" domains, in cutaneous malignant melanoma. A comparative analysis of their immunohistochemical expression between the invasion front (IF) and the inner tumor mass was also evaluated. Immunohistochemical methodology was performed on sections of 89 melanoma lesions from 79 patients. The 3 markers studied were identified in the cell nuclei of melanoma cells, nevus cells, and normal epidermal keratinocytes. A specific distribution pattern of H3K4me2 and H3K27me3 was found, as stronger levels were localized at the IF of the tumor (P = 0.034 and P < 0.01, respectively). In general, H3K4me2 and H3K27me3 levels were lower in metastatic with respect to primary melanoma cases (P = 0.0065 and P = 0.027, respectively). Advanced melanoma demonstrated significantly lower H3K4 immunohistochemical expression than did cases of lowest Clark level (I) (P = 0.038) or low Breslow depth (≤1 mm; P < 0.001). Furthermore, EZH2 expression in melanoma cells was higher compared with that in nevus cells (P = 0.02). A positive correlation between EZH2-H3K27me3 (P = 0.03) and H3K4me2-H3K27me3 (P < 0.01) in melanoma cells was also found. Our results suggest the possibility that combined immunohistochemical expression of EZH2, H3K4me2, and H3K27me3 might identify cancer cells with potential stem cell properties, particularly at the IF of this malignancy. This hypothesis should be further investigated, as many of the epigenetic changes are reversible via pharmacologic manipulations and new therapies, overpassing the resistance of advanced melanoma, may be developed.

Tan JM, Lin LL, Lambie D, et al.
BRAF wild-type melanoma in situ arising in a BRAF V600E mutant dysplastic nevus.
JAMA Dermatol. 2015; 151(4):417-21 [PubMed] Related Publications
IMPORTANCE: The BRAF V600E mutation accounts for the majority of BRAF mutations found in cutaneous melanoma and is also commonly found in nevi. We used dermoscopy-targeted sampling and a microbiopsy device coupled with DNA sequence analysis to highlight BRAF V600E heterogeneity within a multicomponent melanocytic proliferation. This sampling technique demonstrates the prospect of in vivo application in a clinical setting.
OBSERVATIONS: A man in his 50s with Fitzpatrick skin type II presented with an irregularly pigmented melanocytic lesion on his back that met melanoma-specific dermoscopic criteria, and diagnostic shave excision of the lesion was performed. Histopathologic analysis revealed a melanoma in situ arising in a dysplastic nevus. Dermoscopy-targeted microbiopsy specimens were taken across the lesion, and genotyping was carried out on extracted DNA samples for BRAF and NRAS mutations. The melanoma in situ showed only BRAF wild-type results, while the dysplastic nevus showed both BRAF wild-type and BRAF V600E mutations. Sequencing in all DNA samples revealed NRAS wild-type genotype.
CONCLUSIONS AND RELEVANCE: Dermoscopy-targeted sampling and genotyping of a melanoma in situ arising in a dysplastic nevus revealed a phenotype-genotype paradox that confounds the exclusive significance of BRAF and NRAS mutations in melanoma pathogenesis. Further studies are required to investigate the importance of other candidate genes linked to melanomagenesis.

Moriceau G, Hugo W, Hong A, et al.
Tunable-combinatorial mechanisms of acquired resistance limit the efficacy of BRAF/MEK cotargeting but result in melanoma drug addiction.
Cancer Cell. 2015; 27(2):240-56 [PubMed] Article available free on PMC after 09/02/2016 Related Publications
Combined BRAF- and MEK-targeted therapy improves upon BRAF inhibitor (BRAFi) therapy but is still beset by acquired resistance. We show that melanomas acquire resistance to combined BRAF and MEK inhibition by augmenting or combining mechanisms of single-agent BRAFi resistance. These double-drug resistance-associated genetic configurations significantly altered molecular interactions underlying MAPK pathway reactivation. (V600E)BRAF, expressed at supraphysiological levels because of (V600E)BRAF ultra-amplification, dimerized with and activated CRAF. In addition, MEK mutants enhanced interaction with overexpressed (V600E)BRAF via a regulatory interface at R662 of (V600E)BRAF. Importantly, melanoma cell lines selected for resistance to BRAFi+MEKi, but not those to BRAFi alone, displayed robust drug addiction, providing a potentially exploitable therapeutic opportunity.

Sargen MR, Kanetsky PA, Newton-Bishop J, et al.
Histologic features of melanoma associated with CDKN2A genotype.
J Am Acad Dermatol. 2015; 72(3):496-507.e7 [PubMed] Article available free on PMC after 01/03/2016 Related Publications
BACKGROUND: Inherited susceptibility genes have been associated with histopathologic characteristics of tumors.
OBJECTIVE: We sought to identify associations between histology of melanomas and CDKN2A genotype.
METHODS: This was a case-control study design comparing 28 histopathologic tumor features among individuals with sporadic melanomas (N = 81) and cases from melanoma families with (N = 123) and without (N = 120) CDKN2A germline mutations.
RESULTS: Compared with CDKN2A(-) cases, mutation carriers tended to have histologic features of superficial spreading melanoma subtype including higher pigmentation (Ptrend = .02) and increased pagetoid scatter (Ptrend = .07) after adjusting for age at diagnosis, sex, and American Joint Committee on Cancer thickness category. Similar associations were observed when comparing mutation carriers with a combined group of CDKN2A(-) (wild type) and sporadic melanomas. The presence of spindle cell morphology in the vertical growth phase was also an important predictor of genotype. Of the 15 cases with this phenotype, none were observed to harbor a CDKN2A mutation.
LIMITATIONS: Our study examined rare mutations and may have been underpowered to detect small, but biologically significant associations between histology and genotype.
CONCLUSION: Familial melanomas with CDKN2A mutations preferentially express a histologic phenotype of dense pigmentation, high pagetoid scatter, and a non-spindle cell morphology in the vertical growth phase.

Eriksson H, Zebary A, Vassilaki I, et al.
BRAFV600E protein expression in primary cutaneous malignant melanomas and paired metastases.
JAMA Dermatol. 2015; 151(4):410-6 [PubMed] Related Publications
IMPORTANCE: BRAFV600E mutations are present in approximately 50% of cutaneous malignant melanomas (CMMs). The use of BRAFV600E mutation-specific monoclonal antibody VE1 immunohistochemical analysis may facilitate rapid detection of BRAFV600E mutations in CMMs and demonstrate heterogeneity among tumors.
OBJECTIVES: To characterize the pattern of BRAFV600E protein expression in primary CMMs with matched metastases and to analyze the use of VE1 immunohistochemical analysis in clinical practice using formalin-fixed, paraffin-embedded tumor tissue.
DESIGN, SETTING, AND PARTICIPANTS: In this retrospective cohort study performed at Karolinska University Hospital from September 2012 to September 2013, we examined CMMs (124 primary tumors and 76 metastases) with VE1 immunohistochemical analysis, and results were compared with DNA mutation analyses.
MAIN OUTCOMES AND MEASURES: Determination of intratumoral and intertumoral heterogeneity as well as the sensitivity and specificity of VE1 immunohistochemical analysis.
RESULTS: Positive staining results with the VE1 antibody were detected in 94 of 200 tumors (47.0%). In general, VE1 staining was homogeneous. However, VE1 staining intensity varied among the primary tumors and corresponding metastases in 63 of 135 tumors (46.7%), but a change of mutational status based on DNA analysis was found in only 4 matched tumors (3.0%). Discordant findings between DNA mutation analysis and immunohistochemical analysis were observed in 12 tumors. The overall sensitivity and specificity of VE1 immunohistochemical analysis were 96.7% and 94.5%, respectively. A comparable sensitivity was obtained for primary and metastatic CMMs. The specificity was lower among primary CMMs (92.4%) compared with metastases (98.0%).
CONCLUSIONS AND RELEVANCE: We found VE1 immunohistochemical analysis to be a useful and rapid assay for BRAFV600E mutations that may contribute to the detection of intratumoral and intertumoral heterogenetic subclones. Tumors with positive results, including strong staining, should be expedited for confirmatory BRAF mutation testing. If this test result is negative, a false-negative result of the mutation analysis should be considered. Validation of VE1 immunohistochemical analysis in clinical practice is needed.

Hill R, Kalathur RK, Colaço L, et al.
TRIB2 as a biomarker for diagnosis and progression of melanoma.
Carcinogenesis. 2015; 36(4):469-77 [PubMed] Related Publications
Malignant melanoma is the most deadly form of skin cancer. There is a critical need to identify the patients that could be successfully treated by surgery alone and those that require adjuvant treatment. In this study, we demonstrate that the expression of tribbles2 (TRIB2) strongly correlates with both the presence and progression of melanocyte-derived malignancies. We examined the expression of TRIB2 in addition to 12 previously described melanoma biomarkers across three independent full genome microarray studies. TRIB2 expression was consistently and significantly increased in benign nevi and melanoma, and was highest in samples from patients with metastatic melanoma. The expression profiles for the 12 biomarkers were poorly conserved throughout these studies with only TYR, S100B and SPP1 showing consistently elevated expression in metastatic melanoma versus normal skin. Strikingly we confirmed these findings in 20 freshly obtained primary melanoma tissue samples from metastatic lesions where the expression of these biomarkers were evaluated revealing that TRIB2 expression correlated with disease stage and clinical prognosis. Our results suggest that TRIB2 is a meaningful biomarker reflecting diagnosis and progression of melanoma, as well as predicting clinical response to chemotherapy.

Slipicevic A, Herlyn M
KIT in melanoma: many shades of gray.
J Invest Dermatol. 2015; 135(2):337-8 [PubMed] Related Publications
Activating mutations in KIT have been identified in melanomas of acral and mucosal types and in those arising in chronically sun-damaged skin. Until now, KIT has been considered an oncogenic driver and a potential therapeutic target. However, data presented by Dhal et al. show that in cutaneous melanomas the KIT promoter is a target for hypermethylation, leading to its downregulation. Their observations suggest that signaling pathways downstream of KIT may have distinct and opposing roles in the pathogenesis of melanoma subtypes. This will have important implications for the use of KIT inhibitors in treating melanomas.

Griewank KG, Schadendorf D
Panel sequencing melanomas.
J Invest Dermatol. 2015; 135(2):335-6 [PubMed] Related Publications
Sequencing samples of melanoma for targetable mutations has become a standard of care for metastatic disease. In this issue, Siroy et al. demonstrate how clinical genetic analysis is moving from a single-gene Sanger-sequencing approach to targeted next-generation sequencing. They present data on a large cohort of patients with advanced melanoma, and their data support previous findings and also present novel aspects of melanoma genetics.

Bertrand D, Chng KR, Sherbaf FG, et al.
Patient-specific driver gene prediction and risk assessment through integrated network analysis of cancer omics profiles.
Nucleic Acids Res. 2015; 43(7):e44 [PubMed] Article available free on PMC after 01/03/2016 Related Publications
Extensive and multi-dimensional data sets generated from recent cancer omics profiling projects have presented new challenges and opportunities for unraveling the complexity of cancer genome landscapes. In particular, distinguishing the unique complement of genes that drive tumorigenesis in each patient from a sea of passenger mutations is necessary for translating the full benefit of cancer genome sequencing into the clinic. We address this need by presenting a data integration framework (OncoIMPACT) to nominate patient-specific driver genes based on their phenotypic impact. Extensive in silico and in vitro validation helped establish OncoIMPACT's robustness, improved precision over competing approaches and verifiable patient and cell line specific predictions (2/2 and 6/7 true positives and negatives, respectively). In particular, we computationally predicted and experimentally validated the gene TRIM24 as a putative novel amplified driver in a melanoma patient. Applying OncoIMPACT to more than 1000 tumor samples, we generated patient-specific driver gene lists in five different cancer types to identify modes of synergistic action. We also provide the first demonstration that computationally derived driver mutation signatures can be overall superior to single gene and gene expression based signatures in enabling patient stratification and prognostication. Source code and executables for OncoIMPACT are freely available from http://sourceforge.net/projects/oncoimpact.

Danilova NV, Davydova SY
Immunohistochemical characteristics of uveal melanoma assording to the age at diagnosis, histological type and extension of the tumor.
Arkh Patol. 2014 Sep-Oct; 76(5):55-60 [PubMed] Related Publications
The purpose of this study was to investigate the relationship between MMP9 expression and tumour invasion in different structures of the eye. We also examined whether there was any correlation between the growth factors (TGFb and EGF), onco-suppressor proteins (p16 and p53) and Ki-67, and the tumour histological subtypes, atypia level and age at diagnosis. Tumour specimens were obtained from 42 primary uveal melanomas immediately after enucleation at The Helmholtz Moscow Research Institute of Eye Diseases. The patients were not treated with radio- or thermotherapy. During our systematic study, we exclusively employed 10%-formalin fixed, paraffin-wax-embedded tissue sections of UM for histological diagnosis and immunohistochemistry. According to our data the hyperexpression of MMP9 and EGFR correlates with a high proportion of spindle cells in a tumour (Kruskal-Wallis test p=0,1 for each). Moreover, we have demonstrated the association between the level of EGFR, TGFb and MMP9 expression and the initial invasion stage (Spearman's test p=0,1). In addition, we have revealed the significant correlation between TGFb hyperexpression and atypia level (Spearman's test p=0,059). Our data reflect that the diagnoses at an advanced age correlate with hyperexpression of p16 (Kruskal-Wallis test p=0,068). An interesting result is that p16 level reduced in inverse proportion to that of TGFb. On the basis of our data and previous studies, we reached the conclusion that after the lapse of time the level of p16 rises significantly in order to inhibit proliferating activity of melanocytes in the normally functioning pigmented layer. However, although the probability of UM diagnoses in elderly is increasing, we have no reliable data for the relationship with high atypia levels.

Adinolfi E, Capece M, Franceschini A, et al.
Accelerated tumor progression in mice lacking the ATP receptor P2X7.
Cancer Res. 2015; 75(4):635-44 [PubMed] Related Publications
The ATP receptor P2X7 (P2X7R or P2RX7) has a key role in inflammation and immunity, but its possible roles in cancer are not firmly established. In the present study, we investigated the effect of host genetic deletion of P2X7R in the mouse on the growth of B16 melanoma or CT26 colon carcinoma cells. Tumor size and metastatic dissemination were assessed by in vivo calliper and luciferase luminescence emission measurements along with postmortem examination. In P2X7R-deficient mice, tumor growth and metastatic spreading were accelerated strongly, compared with wild-type (wt) mice. Intratumoral IL-1β and VEGF release were drastically reduced, and inflammatory cell infiltration was abrogated nearly completely. Similarly, tumor growth was also greatly accelerated in wt chimeric mice implanted with P2X7R-deficient bone marrow cells, defining hematopoietic cells as a sufficient site of P2X7R action. Finally, dendritic cells from P2X7R-deficient mice were unresponsive to stimulation with tumor cells, and chemotaxis of P2X7R-less cells was impaired. Overall, our results showed that host P2X7R expression was critical to support an antitumor immune response, and to restrict tumor growth and metastatic diffusion.

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