Melanoma

Overview

Literature Analysis

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Tag cloud generated 29 August, 2019 using data from PubMed, MeSH and CancerIndex

Mutated Genes and Abnormal Protein Expression (229)

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, B-raf, BRAF1, RAFB1, B-RAF1 -BRAF and Melanoma
2325
CDKN2A 9p21.3 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
740
NRAS 1p13.2 NS6, CMNS, NCMS, ALPS4, N-ras, NRAS1 -NRAS and Melanoma
943
CDK4 12q14.1 CMM3, PSK-J3 -CDK4 and Melanoma
-CDK4 Germline Mutations in Melanoma Prone Families
235
KIT 4q12 PBT, SCFR, C-Kit, CD117 -KIT and Melanoma
297
CTNNB1 3p22.1 CTNNB, MRD19, armadillo -CTNNB1 and Melanoma
256
MITF 3p13 MI, WS2, CMM8, WS2A, COMMAD, bHLHe32 -MITF and Melanoma
256
AR Xq12 KD, AIS, AR8, TFM, DHTR, SBMA, HYSP1, NR3C4, SMAX1, HUMARA -AR and Melanoma
247
MC1R 16q24.3 CMM5, MSH-R, SHEP2 -MC1R Polymorphisms and Melanoma
196
FGF2 4q28.1 BFGF, FGFB, FGF-2, HBGF-2 -FGF2 and Melanoma
195
TP53 17p13.1 P53, BCC7, LFS1, TRP53 -TP53 and Melanoma
169
MCAM 11q23.3 CD146, MUC18 -MCAM and Melanoma
130
MLANA 9p24.1 MART1, MART-1 -MLANA and Melanoma
124
GNAQ 9q21.2 GAQ, SWS, CMC1, G-ALPHA-q -GNAQ and Melanoma
122
GNA11 19p13.3 FBH, FBH2, FHH2, HHC2, GNA-11, HYPOC2 -GNA11 and Melanoma
85
CDKN1A 6p21.2 P21, CIP1, SDI1, WAF1, CAP20, CDKN1, MDA-6, p21CIP1 -CDKN1A Expression in Melanoma
85
BAP1 3p21.1 UCHL2, hucep-6, HUCEP-13 Germline
-BAP1 and Melanoma
81
TERT 5p15.33 TP2, TRT, CMM9, EST2, TCS1, hTRT, DKCA2, DKCB4, hEST2, PFBMFT1 -TERT and Melanoma
71
CXCL1 4q13.3 FSP, GRO1, GROa, MGSA, NAP-3, SCYB1, MGSA-a -CXCL1 and Melanoma
65
MMP2 16q12.2 CLG4, MONA, CLG4A, MMP-2, TBE-1, MMP-II -MMP2 and Melanoma
64
HLA-B 6p21.33 AS, HLAB, B-4901 -HLA-B and Melanoma
63
SOX10 22q13.1 DOM, WS4, PCWH, WS2E, WS4C -SOX10 and Melanoma
61
CAMP 3p21.31 LL37, CAP18, CRAMP, HSD26, CAP-18, FALL39, FALL-39 -CAMP and Melanoma
55
BRCA2 13q13.1 FAD, FACD, FAD1, GLM3, BRCC2, FANCD, PNCA2, FANCD1, XRCC11, BROVCA2 -BRCA2 and Melanoma
50
HRAS 11p15.5 CTLO, HAMSV, HRAS1, RASH1, p21ras, C-H-RAS, H-RASIDX, C-BAS/HAS, C-HA-RAS1 -HRAS and Melanoma
49
PMEL 12q13-q14 P1, SI, SIL, ME20, P100, SILV, ME20M, gp100, ME20-M, PMEL17, D12S53E -PMEL and Melanoma
45
IL24 1q32.1 C49A, FISP, MDA7, MOB5, ST16, IL10B Down Regulated
-MDA1 Expression in Melanoma
43
IL4 5q31.1 BSF1, IL-4, BCGF1, BSF-1, BCGF-1 -IL4 Gene Therapy for Melanoma (Experimental)
42
TYRP1 9p23 TRP, CAS2, CATB, GP75, OCA3, TRP1, TYRP, b-PROTEIN -TYRP1 and Melanoma
41
CTLA4 2q33 CD, GSE, GRD4, ALPS5, CD152, CTLA-4, IDDM12, CELIAC3 -CTLA4 and Melanoma
41
MYB 6q23.3 efg, Cmyb, c-myb, c-myb_CDS -MYB and Melanoma
37
RAF1 3p25.2 NS5, CRAF, Raf-1, c-Raf, CMD1NN -RAF1 and Melanoma
36
MAGEA3 Xq28 HIP8, HYPD, CT1.3, MAGE3, MAGEA6 -MAGEA3 and Melanoma
36
MAGEA1 Xq28 CT1.1, MAGE1 -MAGEA1 and Melanoma
34
FOXP3 Xp11.23 JM2, AIID, IPEX, PIDX, XPID, DIETER -FOXP3 and Melanoma
33
CDKN2C 1p32.3 p18, INK4C, p18-INK4C -CDKN2C and Melanoma
33
RAC1 7p22.1 MIG5, Rac-1, TC-25, p21-Rac1 -RAC1 and Melanoma
33
PAX3 2q35 WS1, WS3, CDHS, HUP2 -PAX3 and Melanoma
31
CDK6 7q21.2 MCPH12, PLSTIRE -CDK6 and Melanoma
31
ICAM1 19p13.2 BB2, CD54, P3.58 -ICAM1 and Melanoma
31
BAD 11q13.1 BBC2, BCL2L8 -BAD and Melanoma
30
MAP2K1 15q22.31 CFC3, MEK1, MKK1, MAPKK1, PRKMK1 -MAP2K1 and Melanoma
30
TFAP2C 20q13.31 ERF1, TFAP2G, hAP-2g, AP2-GAMMA -TFAP2C and Melanoma
29
TFAP2A 6p24.3 AP-2, BOFS, AP2TF, TFAP2, AP-2alpha -TFAP2A and Melanoma
29
RREB1 6p24.3 HNT, FINB, LZ321, Zep-1, RREB-1 -RREB1 and Melanoma
25
CD63 12q13.2 MLA1, ME491, LAMP-3, OMA81H, TSPAN30 -CD63 and Melanoma
25
PARP1 1q42.12 PARP, PPOL, ADPRT, ARTD1, ADPRT1, PARP-1, ADPRT 1, pADPRT-1 -PARP1 and Melanoma
24
WNT5A 3p14.3 hWNT5A -WNT5A and Melanoma
24
CD80 3q13.33 B7, BB1, B7-1, B7.1, LAB7, CD28LG, CD28LG1 -CD80 and Melanoma
24
ATF1 12q13.12 TREB36, EWS-ATF1, FUS/ATF-1 -ATF1 and Melanoma
24
STAT1 2q32.2 CANDF7, IMD31A, IMD31B, IMD31C, ISGF-3, STAT91 -STAT1 and Melanoma
23
TIMP1 Xp11.3 EPA, EPO, HCI, CLGI, TIMP, TIMP-1 Prognostic
-TIMP1 AND Melanoma
22
ASIP 20q11.22 ASP, AGSW, AGTI, AGTIL, SHEP9 -ASIP and Melanoma
22
SPARC 5q33.1 ON, OI17, BM-40 -SPARC and Melanoma
22
MMP1 11q22.2 CLG, CLGN Prognostic
-MMP1 and Melanoma
22
PRAME 22q11.22 MAPE, OIP4, CT130, OIP-4 -PRAME and Melanoma
21
MAP2K2 19p13.3 CFC4, MEK2, MKK2, MAPKK2, PRKMK2 -MAP2K2 and Melanoma
21
PIGS 17q11.2 -PIGS and Melanoma
21
RHOC 1p13.2 H9, ARH9, ARHC, RHOH9 -RHOC and Melanoma
20
OCA2 15q12-q13.1 P, BEY, PED, BEY1, BEY2, BOCA, EYCL, HCL3, EYCL2, EYCL3, SHEP1, D15S12 -OCA2 and Melanoma
20
PRKN 6q26 PDJ, AR-JP, LPRS2, PARK2 -PARK2 and Melanoma
19
CD274 9p24.1 B7-H, B7H1, PDL1, PD-L1, PDCD1L1, PDCD1LG1 -CD274 and Melanoma
18
ABCB5 7p21.1 ABCB5beta, EST422562, ABCB5alpha -ABCB5 and Melanoma
18
CTAG1B Xq28 CTAG, ESO1, CT6.1, CTAG1, LAGE-2, LAGE2B, NY-ESO-1 -CTAG1B and Melanoma
18
HLA-C 6p21.33 MHC, HLAC, HLC-C, D6S204, PSORS1, HLA-JY3 -HLA-C and Melanoma
18
HLA-DRB1 6p21.32 SS1, DRB1, HLA-DRB, HLA-DR1B -HLA-DRB1 and Melanoma
18
MAGEA2 Xq28 CT1.2, MAGE2, MAGEA2A -Melanoma and MAGEA2
18
AKT3 1q43-q44 MPPH, PKBG, MPPH2, PRKBG, STK-2, PKB-GAMMA, RAC-gamma, RAC-PK-gamma -AKT3 and Melanoma
18
XPC 3p25.1 XP3, RAD4, XPCC, p125 -XPC and Melanoma
18
MIA 19q13.2 CD-RAP -MIA and Melanoma
17
CD68 17p13.1 GP110, LAMP4, SCARD1 -CD68 and Melanoma
17
S100A6 1q21.3 2A9, PRA, 5B10, CABP, CACY, S10A6 -S100A6 Expression in Melanoma
17
MTAP 9p21.3 BDMF, MSAP, DMSFH, LGMBF, DMSMFH, c86fus, HEL-249 -MTAP and Melanoma
17
S100B 21q22.3 NEF, S100, S100-B, S100beta Prognostic
-S100B and Melanoma
15
FAS 10q23.31 APT1, CD95, FAS1, APO-1, FASTM, ALPS1A, TNFRSF6 -FAS and Melanoma
15
IL2 4q27 IL-2, TCGF, lymphokine -IL2 and Melanoma
15
APAF1 12q23.1 CED4, APAF-1 -APAF1 and Melanoma
14
SF3B1 2q33.1 MDS, PRP10, Hsh155, PRPF10, SAP155, SF3b155 -SF3B1 and Melanoma
14
CIITA 16p13.13 C2TA, NLRA, MHC2TA, CIITAIV -CIITA and Melanoma
13
KISS1 1q32.1 HH13, KiSS-1 -KISS1 and Melanoma
12
SKI 1p36.33-p36.32 SGS, SKV -SKI and Melanoma
12
GAST 17q21.2 GAS -GAST and Melanoma
12
CXCL10 4q21.1 C7, IFI10, INP10, IP-10, crg-2, mob-1, SCYB10, gIP-10 -CXCL10 and Melanoma
12
CLPTM1L 5p15.33 CRR9 -CLPTM1L and Melanoma
11
ERBB4 2q33.3-q34 HER4, ALS19, p180erbB4 -ERBB4 and Melanoma
11
MIRLET7B 22q13.31 LET7B, let-7b, MIRNLET7B, hsa-let-7b -MicroRNA let-7b and Melanoma
11
TAP1 6p21.32 APT1, PSF1, ABC17, ABCB2, PSF-1, RING4, TAP1N, D6S114E, TAP1*0102N -TAP1 and Melanoma
11
CD86 3q13.33 B70, B7-2, B7.2, LAB72, CD28LG2 -CD86 and Melanoma
11
GSTT1 22q11.23 -GSTT1 Polymorphisms and Melanoma
11
GRM1 6q24 MGLU1, GPRC1A, MGLUR1, SCAR13, PPP1R85 -GRM1 and Melanoma
11
L1CAM Xq28 S10, HSAS, MASA, MIC5, SPG1, CAML1, CD171, HSAS1, N-CAML1, NCAM-L1, N-CAM-L1 -L1CAM and Melanoma
10
ITCH 20q11.22 AIF4, AIP4, ADMFD, NAPP1 -ITCH and Melanoma
10
POT1 7q31.33 GLM9, CMM10, HPOT1 Germline
GWS
-POT1 and Predisposition to Familial Melanoma
10
MAGEB2 Xp21.2 DAM6, CT3.2, MAGE-XP-2 -MAGEB2 and Melanoma
9
ATF2 2q32 HB16, CREB2, TREB7, CREB-2, CRE-BP1 -ATF2 and Melanoma
9
IRF4 6p25.3 MUM1, LSIRF, SHEP8, NF-EM5 -IRF4 and Melanoma
9
KDM5B 1q32.1 CT31, PLU1, PUT1, MRT65, PLU-1, JARID1B, PPP1R98, RBP2-H1, RBBP2H1A -KDM5B and Melanoma
9
BCL2A1 15q25.1 GRS, ACC1, ACC2, BFL1, ACC-1, ACC-2, HBPA1, BCL2L5 -BCL2A1 and Melanoma
9
CCR7 17q21.2 BLR2, EBI1, CCR-7, CD197, CDw197, CMKBR7, CC-CKR-7 -CCR7 and Melanoma
8
TLR3 4q35.1 CD283, IIAE2 -TLR3 and Melanoma
8
GDF15 19p13.11 PDF, MIC1, PLAB, MIC-1, NAG-1, PTGFB, GDF-15 -GDF15 and Melanoma
7
EDNRB 13q22.3 ETB, ET-B, ETB1, ETBR, ETRB, HSCR, WS4A, ABCDS, ET-BR, HSCR2 -EDNRB and Melanoma
7
CITED1 Xq13.1 MSG1 -CITED1 and Melanoma
7
TRB 7q34 TCRB, TRB@ -TRB and Melanoma
7
CD27 12p13.31 T14, S152, Tp55, TNFRSF7, S152. LPFS2 -CD27 and Melanoma
7
YES1 18p11.32 Yes, c-yes, HsT441, P61-YES -Proto-Oncogene Proteins c-yes and Melanoma
7
NEDD9 6p24.2 CAS2, CASL, HEF1, CAS-L, CASS2 -NEDD9 and Melanoma
7
TRG 7p14.1 TCRG, TRG@ -TRG and Melanoma
7
TERC 3q26.2 TR, hTR, TRC3, DKCA1, PFBMFT2, SCARNA19 -TERC and Melanoma
7
BIRC7 20q13.33 KIAP, LIVIN, MLIAP, RNF50, ML-IAP -BIRC7 and Melanoma
7
MAGEA4 Xq28 CT1.4, MAGE4, MAGE4A, MAGE4B, MAGE-41, MAGE-X2 -MAGEA4 and Melanoma
7
CEACAM1 19q13.2 BGP, BGP1, BGPI -CEACAM1 and Melanoma
7
IL18 11q23.1 IGIF, IL-18, IL-1g, IL1F4 -IL18 and Melanoma
7
SPRY4 5q31.3 HH17 -SPRY4 and Melanoma
7
YBX1 1p34.2 YB1, BP-8, CSDB, DBPB, YB-1, CBF-A, CSDA2, EFI-A, NSEP1, NSEP-1, MDR-NF1 -YBX1 and Melanoma
7
BAGE 21p11.1 BAGE1, CT2.1 -BAGE and Melanoma
6
MSN Xq12 HEL70, IMD50 -MSN and Melanoma
6
PEBP1 12q24.23 PBP, HCNP, PEBP, RKIP, HCNPpp, PEBP-1, HEL-210, HEL-S-34 -PEBP1 and Melanoma
6
ATF3 1q32.3 -ATF3 and Melanoma
6
DDB2 11p11.2 XPE, DDBB, UV-DDB2 -DDB2 and Melanoma
6
PDCD1 2q37.3 PD1, PD-1, CD279, SLEB2, hPD-1, hPD-l, hSLE1 -PDCD1 and Melanoma
6
RARB 3p24.2 HAP, RRB2, NR1B2, MCOPS12, RARbeta1 -RARB and Melanoma
6
TBX2 17q23.2 -TBX2 and Melanoma
6
PTPRD 9p24.1-p23 HPTP, PTPD, HPTPD, HPTPDELTA, RPTPDELTA -PTPRD and Melanoma
6
GAGE1 Xp11.23 CT4.1, CT4.4, GAGE4, GAGE-1, GAGE-4 -GAGE1 and Melanoma
6
AIM1 6q21 ST4, CRYBG1 -AIM1 and Melanoma
6
CXCL9 4q21.1 CMK, MIG, Humig, SCYB9, crg-10 -CXCL9 and Melanoma
5
MXI1 10q25.2 MXI, MAD2, MXD2, bHLHc11 -MXI1 and Melanoma
5
CD70 19p13.3 CD27L, CD27-L, CD27LG, TNFSF7, TNLG8A -CD70 and Melanoma
5
ULBP2 6q25 N2DL2, RAET1H, NKG2DL2, ALCAN-alpha -ULBP2 and Melanoma
5
VCAN 5q14.2-q14.3 WGN, ERVR, GHAP, PG-M, WGN1, CSPG2 -VCAN and Melanoma
5
TFEB 6p21.1 TCFEB, BHLHE35, ALPHATFEB -TFEB and Melanoma
5
HSPB1 7q11.23 CMT2F, HMN2B, HSP27, HSP28, Hsp25, SRP27, HS.76067, HEL-S-102 -HSPB1 and Melanoma
5
SMARCA2 9p24.3 BRM, SNF2, SWI2, hBRM, NCBRS, Sth1p, BAF190, SNF2L2, SNF2LA, hSNF2a -SMARCA2 and Melanoma
5
BMP7 20q13.31 OP-1 -BMP7 and Melanoma
5
SFPQ 1p34.3 PSF, POMP100, PPP1R140 -SFPQ and Melanoma
5
NGFR 17q21.33 CD271, p75NTR, TNFRSF16, p75(NTR), Gp80-LNGFR -NGFR and Melanoma
5
NFATC2 20q13.2 NFAT1, NFATP -NFATC2 and Melanoma
5
OSCAR 19q13.42 PIGR3, PIgR-3 -OSCAR and Melanoma
5
RBX1 22q13.2 ROC1, RNF75, BA554C12.1 -RBX1 and Melanoma
5
CAST 5q15 BS-17, PLACK -CAST and Melanoma
5
HSF1 8q24.3 HSTF1 -HSF1 and Melanoma
5
EFNB2 13q33.3 HTKL, EPLG5, Htk-L, LERK5 -EFNB2 expression in Melanoma
5
STAT2 12q13.3 P113, IMD44, ISGF-3, STAT113 -STAT2 and Melanoma
5
YY1AP1 1q22 GRNG, HCCA1, HCCA2, YY1AP -YY1AP1 and Melanoma
5
ETV1 7p21.2 ER81 Overexpression
-ETV1 overexpression in Melanoma
4
FABP7 6q22.31 MRG, BLBP, FABPB, B-FABP -FABP7 and Melanoma
4
IRF9 14q12 p48, IRF-9, ISGF3, ISGF3G -IRF9 and Melanoma
4
EIF3E 8q23.1 INT6, EIF3S6, EIF3-P48, eIF3-p46 -EIF3E and Melanoma
4
ADRB2 5q32 BAR, B2AR, ADRBR, ADRB2R, BETA2AR -ADRB2 and Melanoma
4
CHUK 10q24.31 IKK1, IKKA, IKBKA, TCF16, NFKBIKA, IKK-alpha -CHUK and Melanoma
4
POSTN 13q13.3 PN, OSF2, OSF-2, PDLPOSTN -POSTN and Melanoma
4
TBX3 12q24.21 UMS, XHL, TBX3-ISO -TBX3 and Melanoma
4
MAP2 2q34-q35 MAP2A, MAP2B, MAP2C -MAP2 and Melanoma
4
HLA-DRA 6p21.32 HLA-DRA1 -HLA-DRA and Melanoma
4
TIMP2 17q25.3 DDC8, CSC-21K -TIMP2 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
SLC9A1 1p36.11 APNH, NHE1, LIKNS, NHE-1, PPP1R143 -SLC9A1 and Melanoma
4
TRPM8 2q37.1 TRPP8, LTRPC6 -TRPM8 and Melanoma
4
NONO Xq13.1 P54, NMT55, NRB54, MRXS34, P54NRB, PPP1R114 -NONO and Melanoma
4
DUSP6 12q21.33 HH19, MKP3, PYST1 -DUSP6 and Melanoma
4
IGFBP7 4q12 AGM, PSF, TAF, FSTL2, IBP-7, MAC25, IGFBP-7, RAMSVPS, IGFBP-7v, IGFBPRP1 -IGFBP7 and Melanoma
4
BRMS1 11q13.2 -BRMS1 and Melanoma
4
ING4 12p13.31 my036, p29ING4 -ING4 and Melanoma
4
ITGA4 2q31.3 IA4, CD49D -ITGA4 and Melanoma
4
RAP1GAP 1p36.12 RAPGAP, RAP1GA1, RAP1GAP1, RAP1GAPII -RAP1GAP and Melanoma
4
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
MCM5 22q12.3 CDC46, MGORS8, P1-CDC46 -MCM5 and Melanoma
3
IL12B 5q33.3 CLMF, NKSF, CLMF2, IMD28, IMD29, NKSF2, IL-12B -IL12B and Melanoma
3
HOXB7 17q21.32 HOX2, HOX2C, HHO.C1, Hox-2.3 -HOXB7 and Melanoma
3
MMP3 11q22.2 SL-1, STMY, STR1, CHDS6, MMP-3, STMY1 -MMP3 and Melanoma
3
ICOS 2q33 AILIM, CD278, CVID1 -ICOS and Melanoma
3
PPP1R15A 19q13.33 GADD34 -PPP1R15A and Melanoma
3
PTPRK 6q22.33 R-PTP-kappa -PTPRK 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
ANGPTL4 19p13.2 NL2, ARP4, FIAF, HARP, PGAR, HFARP, TGQTL, UNQ171, pp1158 -ANGPTL4 and Melanoma
3
HPSE 4q21.23 HPA, HPA1, HPR1, HSE1, HPSE1 -HPSE and Melanoma
3
MMP8 11q22.2 HNC, CLG1, MMP-8, PMNL-CL -MMP8 and Melanoma
3
ARID2 12q12 p200, BAF200 -ARID2 and Melanoma
3
S100A2 1q21.3 CAN19, S100L -S100A2 Expression in Melanoma
3
FLNA Xq28 FLN, FMD, MNS, OPD, ABPX, CSBS, CVD1, FGS2, FLN1, NHBP, OPD1, OPD2, XLVD, XMVD, FLN-A, ABP-280 -FLNA and Melanoma
3
ISG15 1p36.33 G1P2, IP17, UCRP, IFI15, IMD38, hUCRP -ISG15 and Melanoma
3
TFPI2 7q21.3 PP5, REF1, TFPI-2 -TFPI2 and Melanoma
3
CXCL11 4q21.1 IP9, H174, IP-9, b-R1, I-TAC, SCYB11, SCYB9B -CXCL11 and Melanoma
3
CD163 12p13.3 M130, MM130 -CD163 and Melanoma
3
NOX4 11q14.3 KOX, KOX-1, RENOX -NOX4 and Melanoma
3
ARL11 13q14.2 ARLTS1 -ARL11 and Melanoma
3
ASAH1 8p22 AC, PHP, ASAH, PHP32, ACDase, SMAPME -ASAH1 and Melanoma
3
CTSL 9q21.33 MEP, CATL, CTSL1 -CTSL1 and Melanoma
3
LARS 5q32 LRS, LEUS, LFIS, ILFS1, LARS1, LEURS, PIG44, RNTLS, HSPC192, hr025Cl -LARS and Melanoma
3
TYRO3 15q15.1 BYK, Dtk, RSE, Rek, Sky, Tif, Etk-2 -TYRO3 and Melanoma
3
HTRA2 2p12 OMI, PARK13, PRSS25 -HTRA2 and Melanoma
3
GRASP 12q13.13 TAMALIN -GRASP and Melanoma
3
PPP2R1A 19q13.41 MRD36, PP2AA, PR65A, PP2AAALPHA, PP2A-Aalpha -PPP2R1A and Melanoma
2
MCM4 8q11.21 NKCD, CDC21, CDC54, NKGCD, hCdc21, P1-CDC21 -MCM4 and Melanoma
2
ING3 7q31.31 Eaf4, ING2, MEAF4, p47ING3 -ING3 and Melanoma
2
AQP3 9p13.3 GIL, AQP-3 -AQP3 and Melanoma
2
RIN1 11q13.2 -RIN1 and Melanoma
2
PAEP 9q34.3 GD, GdA, GdF, GdS, PEP, PAEG, PP14 -PAEP and Melanoma
2
PDCD6 5p15.33 ALG2, ALG-2, PEF1B -PDCD6 and Melanoma
2
RTEL1 20q13.33 NHL, RTEL, DKCA4, DKCB5, PFBMFT3, C20orf41 -RTEL1 and Melanoma
2
ELK4 1q32.1 SAP1 -ELK4 and Melanoma
2
MAP3K8 10p11.23 COT, EST, ESTF, TPL2, AURA2, MEKK8, Tpl-2, c-COT -MAP3K8 and Melanoma
2
TGFBI 5q31.1 CSD, CDB1, CDG2, CSD1, CSD2, CSD3, EBMD, LCD1, BIGH3, CDGG1 -TGFBI and Melanoma
2
PPP2CA 5q31.1 RP-C, PP2Ac, PP2CA, PP2Calpha -PPP2CA and Melanoma
2
TRIM24 7q33-q34 PTC6, TF1A, TIF1, RNF82, TIF1A, hTIF1, TIF1ALPHA -TRIM24 and Melanoma
2
ITGAM 16p11.2 CR3A, MO1A, CD11B, MAC-1, MAC1A, SLEB6 -ITGAM and Melanoma
2
MAFG 17q25.3 hMAF -MAFG and Melanoma
2
HAVCR2 5q33.3 TIM3, CD366, KIM-3, TIMD3, Tim-3, TIMD-3, HAVcr-2 -HAVCR2 and Melanoma
2
ARNTL 11p15.3 TIC, JAP3, MOP3, BMAL1, PASD3, BMAL1c, bHLHe5 -ARNTL and Melanoma
2
PTPRT 20q12-q13.11 RPTPrho -PTPRT and Melanoma
1
CANT1 17q25.3 DBQD, DBQD1, SCAN1, SHAPY, SCAN-1 -CANT1 and Melanoma
1
ITGAX 16p11.2 CD11C, SLEB6 -ITGAX and Melanoma
1
TNFRSF9 1p36.23 ILA, 4-1BB, CD137, CDw137 -TNFRSF9 and Melanoma
1
PTPRF 1p34.2 LAR, BNAH2 -PTPRF and Melanoma
1
PPP1R3A 7q31.1 GM, PP1G, PPP1R3 -PPP1R3A and Melanoma
1
PNN 14q21.1 DRS, DRSP, SDK3, memA -PNN and Melanoma
1
FLNC 7q32.1 ABPA, ABPL, FLN2, MFM5, MPD4, RCM5, CMH26, ABP-280, ABP280A -FLNC and Melanoma
1
ADAM7 8p21.2 EAPI, GP83, GP-83, ADAM 7, ADAM-7 -ADAM7 and Melanoma
1
KDM5A 12p13.33 RBP2, RBBP2, RBBP-2 -KDM5A and Melanoma
1
MAS1 6q25.3 MAS, MGRA -MAS1 and Melanoma
1
HOXD11 2q31.1 HOX4, HOX4F -HOXD11 and Melanoma
1
BIN1 2q14 AMPH2, AMPHL, SH3P9 -BIN1 and Melanoma
1
TNFRSF8 1p36.22 CD30, Ki-1, D1S166E -TNFRSF8 and Melanoma
1
FBXO11 2p16.3 UBR6, VIT1, FBX11, PRMT9, UG063H01 -FBXO11 and Melanoma
1
KNL1 15q15.1 D40, CT29, Spc7, CASC5, MCPH4, hKNL-1, AF15Q14, PPP1R55, hSpc105 -CASC5 and Melanoma
1
BLM 15q26.1 BS, RECQ2, RECQL2, RECQL3 -BLM and Melanoma
1
TFAP2B 6p12.3 PDA2, AP-2B, AP2-B -TFAP2B and Melanoma

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

Valentini V, Zelli V, Gaggiano E, et al.
MiRNAs as Potential Prognostic Biomarkers for Metastasis in Thin and Thick Primary Cutaneous Melanomas.
Anticancer Res. 2019; 39(8):4085-4093 [PubMed] Related Publications
BACKGROUND/AIM: The identification of novel prognostic biomarkers for melanoma metastasis is essential to improve patient outcomes. To this aim, we characterized miRNA expression profiles in relation to metastasis in melanoma and correlated miRNAs expression with clinical-pathological factors.
MATERIALS AND METHODS: MiR-145-5p, miR-150-5p, miR-182-5p, miR-203-3p, miR-205-5p and miR-211-5p expression levels were analyzed in primary cutaneous melanomas, including thin and thick melanomas, and in melanoma metastases by quantitative Real-Time PCR.
RESULTS: A significantly lower miR-205-5p expression was found in metastases compared to primary melanomas. Furthermore, a progressive down-regulation of miR-205-5p expression was observed from loco-regional to distant metastasis. Significantly lower miR-145-5p and miR-203-3p expression levels were found in cases with Breslow thickness >1 mm, high Clark level, ulceration and mitotic rate ≥1/mm

Arozarena I, Wellbrock C
Phenotype plasticity as enabler of melanoma progression and therapy resistance.
Nat Rev Cancer. 2019; 19(7):377-391 [PubMed] Related Publications
Malignant melanoma is notorious for its inter- and intratumour heterogeneity, based on transcriptionally distinct melanoma cell phenotypes. It is thought that these distinct phenotypes are plastic in nature and that their transcriptional reprogramming enables heterogeneous tumours both to undergo different stages of melanoma progression and to adjust to drug exposure during treatment. Recent advances in genomic technologies and the rapidly expanding availability of large gene expression datasets have allowed for a refined definition of the gene signatures that characterize these phenotypes and have revealed that phenotype plasticity plays a major role in the resistance to both targeted therapy and immunotherapy. In this Review we discuss the definition of melanoma phenotypes through particular transcriptional states and reveal the prognostic relevance of the related gene expression signatures. We review how the establishment of phenotypes is controlled and which roles phenotype plasticity plays in melanoma development and therapy. Because phenotype plasticity in melanoma bears a great resemblance to epithelial-mesenchymal transition, the lessons learned from melanoma will also benefit our understanding of other cancer types.

Ribas A, Lawrence D, Atkinson V, et al.
Combined BRAF and MEK inhibition with PD-1 blockade immunotherapy in BRAF-mutant melanoma.
Nat Med. 2019; 25(6):936-940 [PubMed] Related Publications
Oncogene-targeted therapy with B-Raf proto-oncogene (BRAF) and mitogen-activated protein kinase kinase (MEK) inhibitors induces a high initial response rate in patients with BRAF

Ascierto PA, Ferrucci PF, Fisher R, et al.
Dabrafenib, trametinib and pembrolizumab or placebo in BRAF-mutant melanoma.
Nat Med. 2019; 25(6):941-946 [PubMed] Related Publications
Blocking programmed death 1 (PD-1) may enhance the durability of anti-tumor responses that are induced by the combined inhibition of BRAF and MEK

Sullivan RJ, Hamid O, Gonzalez R, et al.
Atezolizumab plus cobimetinib and vemurafenib in BRAF-mutated melanoma patients.
Nat Med. 2019; 25(6):929-935 [PubMed] Related Publications
Melanoma treatment has progressed in the past decade with the development and approval of immune checkpoint inhibitors targeting programmed death 1 (PD-1) or its ligand (PD-L1) and cytotoxic T lymphocyte-associated antigen 4, as well as small molecule inhibitors of BRAF and/or MEK for the subgroup of patients with BRAF

Antunes LCM, Cartell A, de Farias CB, et al.
Tropomyosin-Related Kinase Receptor and Neurotrophin Expression in Cutaneous Melanoma Is Associated with a Poor Prognosis and Decreased Survival.
Oncology. 2019; 97(1):26-37 [PubMed] Related Publications
OBJECTIVE: Normally, activation of tropomyosin-related kinase (TRK) receptors by neurotrophins (NTs) stimulates intracellular pathways involved in cell survival and proliferation. Dysregulation of NT/TRK signaling may affect neoplasm prognosis. Data on NT and TRK expression in melanomas are limited, and it is unclear whether NT/TRK signaling pathways are involved in the origin and progression of this neoplasm.
METHODS: We examined whether NT/TRK expression differs across different cutaneous melanoma grades and subtypes, and whether it is associated with melanoma prognosis and survival. A cross-sectional study was performed in which the expression of TrkA, TrkB, nerve growth factor (NGF), and brain-derived neurotrophic factor (BDNF) was analyzed by immunohistochemistry of 154 melanoma samples. We investigated NT/TRK expression associations with prognostic factors for melanoma, relapse-free survival (RFS), and overall survival (OS).
RESULTS: Of the 154 melanoma samples, 77 (55.4%) were TrkA immunopositive, 81 (58.3%) were TrkB immunopositive, 113 (81.3%) were BDNF immunopositive, and 104 (75.4%) were NGF immunopositive. We found NT/TRK expression associated strongly with several clinical prognostic factors, including the tumor-node-metastasis stage (p < 0.001), histological subtype (p < 0.001), and Clark level (p < 0.05), as well as with a worse OS (p < 0.05 for all, except TrkB) and RFS (p < 0.05 for all).
CONCLUSIONS: Our results show strong associations of NT/TRK expression with melanoma stage progression and a poor prognosis.

Schefler AC, Koca E, Bernicker EH, Correa ZM
Relationship between clinical features, GEP class, and PRAME expression in uveal melanoma.
Graefes Arch Clin Exp Ophthalmol. 2019; 257(7):1541-1545 [PubMed] Related Publications
BACKGROUND: Metastatic risk for uveal melanoma (UM) patients can be characterized by gene expression profiling (GEP) (Castle Biosciences, Friendswood, TX). Class 1A tumors carry low metastatic risk; class 1B tumors have intermediate risk; and class 2 tumors have high risk. Preferentially expressed antigen in melanoma (PRAME) is a tumor-associated antigen which is expressed in various neoplasms including UM. Recently, PRAME expression in uveal melanoma was first recognized to confer an additional metastatic risk beyond GEP status.
METHODS: This was a retrospective, consecutive, multicenter chart review study. All patients diagnosed with UM at two major ocular oncology centers from August 2016 to February 2018 who underwent both GEP and PRAME mRNA expression testing were included. Patient age at diagnosis, gender, and tumor variables such as thickness, largest basal diameter (LBD), tumor volume, TNM stage, and GEP class and PRAME status were extracted from the medical records. Statistical analysis was performed to analyze the association of PRAME +/- status with all clinical and molecular variables.
RESULTS: One hundred forty-eight UM patients were identified. TNM was stage I in 51 (34.5%), stage IIA in 33 (22.3%), stage IIB in 34 (23%), stage IIIA in 20 (13.5%), and stage IIIB in 10 (6.8%) patients. Fifty-five patients (37%) were PRAME-positive, a significant fraction. There was no association between higher TNM stage and positive PRAME status (p = 0.129). PRAME expression was found to be independent of gender, patient age, and tumor thickness. PRAME expression was statistically associated with LBD and tumor volume. Higher GEP class was associated with higher TNM staging (p < 0.001). Worsening GEP class was associated with PRAME+ status with 28% of GEP class 1A tumors having PRAME+ status, 29% of GEP class 1B tumors having PRAME+ status, and 56% of GEP class 2 tumors having PRAME+ status.
CONCLUSIONS: In this study cohort, PRAME+ status was significantly associated with LBD and tumor volume as well as worsening GEP class. Nearly a third of GEP class 1A tumors expressed PRAME. Given the recent published data on increased metastatic risk among patients with tumors expressing PRAME, this study suggests that a significant fraction of 1A patients may harbor an increased metastatic risk. Future large, multicenter studies with long-term follow-up will clarify this finding.

He M, Chaurushiya MS, Webster JD, et al.
Intrinsic apoptosis shapes the tumor spectrum linked to inactivation of the deubiquitinase BAP1.
Science. 2019; 364(6437):283-285 [PubMed] Related Publications
Malignancies arising from mutation of tumor suppressors have unexplained tissue proclivity. For example,

Shoushtari AN
About Face: Molecular Aberrations in Head and Neck Mucosal Melanomas.
Clin Cancer Res. 2019; 25(12):3473-3475 [PubMed] Related Publications
Detailed molecular characterization of a large cohort of mucosal melanomas, most arising from head and neck primaries, suggests that chromosomal translocations and other complex rearrangements have prognostic importance. CDK4 amplification is a frequent event in these rare tumors, and CDK4/6 inhibition may represent a rational clinical trial strategy.

Lombard DB, Cierpicki T, Grembecka J
Combined MAPK Pathway and HDAC Inhibition Breaks Melanoma.
Cancer Discov. 2019; 9(4):469-471 [PubMed] Article available free on PMC after 01/04/2020 Related Publications
In this issue, Maertens and colleagues demonstrate that HDAC3 inhibition potentiates the effects of MAPK pathway inhibitors in melanoma, including difficult-to-treat

Zhao B, You Y, Wan Z, et al.
Weighted correlation network and differential expression analyses identify candidate genes associated with BRAF gene in melanoma.
BMC Med Genet. 2019; 20(1):54 [PubMed] Article available free on PMC after 01/04/2020 Related Publications
BACKGROUND: Primary cutaneous malignant melanoma is a cancer of the pigment cells of the skin, some of which are accompanied by BRAF mutation. Melanoma incidence and mortality rates have been rising around the world. As the current knowledge about pathogenesis, clinical and genetic features of cutaneous melanoma is not very clear, we aim to use bioinformatics to identify the potential key genes involved in the expression and mutation status of BRAF.
METHODS: Firstly, we used UCSC public hub datasets of melanoma (Lin et al., Cancer Res 68(3):664, 2008) to perform weighted genes co-expression network analysis (WGCNA) and differentially expressed genes analysis (DEGs), respectively. Secondly, overlapping genes between significant gene modules and DEGs were screened and validated at transcriptional levels and overall survival in TCGA and GTEx datasets. Lastly, the functional enrichment analysis was accomplished to find biological functions on the web-server database.
RESULTS: We performed weighted correlation network and differential expression analyses, using gene expression data in melanoma samples. We identified 20 genes whose expression was correlated with the mutation status of BRAF. For further validation, three of these genes (CYR61, DUSP1, and RNASE4) were found to have similar expression patterns in skin tumors from TCGA compared with normal skin samples from GTEx. We also found that weak expression of these three genes was associated with worse overall survival in the TCGA data. These three genes were involved in the nucleic acid metabolic process.
CONCLUSION: In this study, CYR61, DUSP1, and RNASE4 were identified as potential genes of interest for future molecular studies in melanoma, which would improve our understanding of its causes and underlying molecular events. These candidate genes may provide a promising avenue of future research for therapeutic targets in melanoma.

Bardi GT, Al-Rayan N, Richie JL, et al.
Detection of Inflammation-Related Melanoma Small Extracellular Vesicle (sEV) mRNA Content Using Primary Melanocyte sEVs as a Reference.
Int J Mol Sci. 2019; 20(5) [PubMed] Article available free on PMC after 01/04/2020 Related Publications
Melanoma-derived small extracellular vesicles (sEVs) participate in tumor pathogenesis. Tumor pathogenesis is highly dependent on inflammatory processes. Given the potential for melanoma sEVs to carry tumor biomarkers, we explored the hypothesis that they may contain inflammation-related mRNA content. Biophysical characterization showed that human primary melanocyte-derived sEVs trended toward being smaller and having less negative (more neutral) zeta potential than human melanoma sEVs (A-375, SKMEL-28, and C-32). Using primary melanocyte sEVs as the control population, RT-qPCR array results demonstrated similarities and differences in gene expression between melanoma sEV types. Upregulation of pro-angiogenic chemokine ligand CXCL1, CXCL2, and CXCL8 mRNAs in A-375 and SKMEL-28 melanoma sEVs was the most consistent finding. This paralleled increased production of CXCL1, CXCL2, and CXCL8 proteins by A-375 and SKMEL-28 sEV source cells. Overall, the use of primary melanocyte sEVs as a control sEV reference population facilitated the detection of inflammation-related melanoma sEV mRNA content.

Makita K, Hara H, Sano E, et al.
Interferon-β sensitizes human malignant melanoma cells to temozolomide-induced apoptosis and autophagy.
Int J Oncol. 2019; 54(5):1864-1874 [PubMed] Related Publications
Malignant melanoma is a highly aggressive skin cancer that is highly resistant to chemotherapy. Adjuvant therapy is administered to patients with melanoma that possess no microscopic metastases or have a high risk of developing microscopic metastases. Methylating agents, including dacarbazine (DTIC) and temozolomide (TMZ), pegylated interferon (IFN)‑α2b and interleukin‑2 have been approved for adjuvant immuno‑chemotherapy; however, unsatisfactory results have been reported following the administration of methylating agents. IFN‑β has been considered to be a signaling molecule with an important therapeutic potential in cancer. The aim of the present study was to elucidate whether antitumor effects could be augmented by the combination of TMZ and IFN‑β in malignant melanoma. We evaluated the efficacy of TMZ and IFN‑β by comparing O6‑methylguanine‑DNA transferase (MGMT)‑proficient and ‑deficient cells, as MGMT has been reported to be associated with the resistance to methylating agents. Cell viability was determined by counting living cells with a Coulter counter, and apoptosis was analyzed by dual staining with Annexin V Alexa Fluor® 488 and propidium iodide. The expression of proteins involved in the cell cycle, apoptosis and autophagy was evaluated by western blot analysis. The combined treatment with TMZ and IFN‑β suppressed cell proliferation and induced cell cycle arrest. We also demonstrated that a combination of TMZ and IFN‑β enhanced apoptosis and autophagy more efficiently compared with TMZ treatment alone. These findings suggest that antitumor activity may be potentiated by IFN‑β in combination with TMZ.

Wang HZ, Wang F, Chen PF, et al.
Coexpression network analysis identified that plakophilin 1 is associated with the metastasis in human melanoma.
Biomed Pharmacother. 2019; 111:1234-1242 [PubMed] Related Publications
BACKGROUND AND AIMS: Malignant melanoma is a fatal cancer with high metastatic characteristics. Approximately 80% of skin cancer deaths are caused by metastatic melanoma. It has been established that the metastatic ability of melanoma is regulated by an intricate gene interconnection network. Thus, the aim of this study was to identify and validate hub genes associated with metastatic melanoma and to further illustrate its potential mechanisms.
METHODS: The method of weighted gene coexpression network analysis (WGCNA) was applied to explore potential regulatory targets and investigate the relationship between the key module and hub genes associated with the metastasis ability of melanoma.
RESULTS: In the turquoise module, 26 hub genes were initially selected, and 6 of them were identified as "real" hub genes with high connectivity in the protein-protein interaction network. In terms of validation, PKP1 had the highest correlation with metastasis among all the "real" hub genes. Data obtained from the GEPIA database and the Gene Expression Omnibus database showed a lower expression of PKP1 in melanoma tissues compared to normal skin tissues. The results also showed that PKP1 was downregulated in metastatic melanomas (n = 367) compared with primary melanomas (n = 103) in The Cancer Genome Atlas (TCGA) database (n = 470). Furthermore, an ROC curve showed that PKP1 expression had good power in the diagnostics of both primary melanoma (p =  5.30e-06, AUC = 0.8) and metastatic melanoma (p =  1.13e-10, AUC = 0.925). We also found that PKP1 could distinguish low- and high-grade of metastatic melanomas and was associated with inflammatory melanoma. Moreover, in a tumor-bearing mouse model, melanoma tissues also showed lower mRNA expression of PKP1 than the adjacent normal skin. Finally, Gene Set Enrichment Analysis indicated that the calcium signaling was significantly enriched in metastatic melanoma with highly expressed PKP1.
CONCLUSIONS: PKP1 was identified as a new potential tumor suppressor in human melanoma, likely through regulating calcium signaling pathways.

Newman S, Fan L, Pribnow A, et al.
Clinical genome sequencing uncovers potentially targetable truncations and fusions of MAP3K8 in spitzoid and other melanomas.
Nat Med. 2019; 25(4):597-602 [PubMed] Related Publications
Spitzoid melanoma is a specific morphologic variant of melanoma that most commonly affects children and adolescents, and ranges on the spectrum of malignancy from low grade to overtly malignant. These tumors are generally driven by fusions of ALK, RET, NTRK1/3, MET, ROS1 and BRAF

Li N, Liu Y, Pang H, et al.
Methylation-Mediated Silencing of MicroRNA-211 Decreases the Sensitivity of Melanoma Cells to Cisplatin.
Med Sci Monit. 2019; 25:1590-1599 [PubMed] Article available free on PMC after 01/04/2020 Related Publications
BACKGROUND Malignant melanoma is recalcitrant to most existing chemotherapies, and aberrant expression of miR-211 plays prominent roles in progression of melanoma. However, the trigger mechanism of aberrant miR-211 expression in melanoma is still elusive. MATERIAL AND METHODS We used qRT-PCR to test miR-211 expression. Cell viability assay and mouse xenograft assay were performed to examine the role of miR-211 on the sensitivity of melanoma cells to cisplatin. The epigenetic modification of miR-211 promoter was assess by DNA methylation analysis and DAC treatment. RESULTS In this study, decreased miR-211 expression was detected. Bisulfite sequencing PCR showed that DNA hypermethylation contributed to the downregulation of miR-211 in melanoma tissues. In melanoma cells, overexpressed 211 could enhance the anticancer effect of cisplatin and restoration of miR-211 rendered susceptibility to cisplatin in cisplatin-resistant cells. And the same result was showed in vivo by mouse xenograft assay. What is more, DAC treatment could increase miR-211 expression and EZH2 expression was increased in cisplatin-resistant cells. MiR-211 could be transcriptionally repressed by EZH2 mediated promoter methylation. CONCLUSIONS Taken together, our findings revealed that epigenetic modification of miR-211 governed melanoma cell chemosensitivity and were involved in the progression of tumorigenesis.

Falzone L, Romano GL, Salemi R, et al.
Prognostic significance of deregulated microRNAs in uveal melanomas.
Mol Med Rep. 2019; 19(4):2599-2610 [PubMed] Article available free on PMC after 01/04/2020 Related Publications
Uveal melanoma (UM) represents the most frequent primary tumor of the eye. Despite the development of new drugs and screening programs, the prognosis of patients with UM remains poor and no effective prognostic biomarkers are yet able to identify high‑risk patients. Therefore, in the present study, microRNA (miRNA or miR) expression data, contained in the TCGA UM (UVM) database, were analyzed in order to identify a set of miRNAs with prognostic significance to be used as biomarkers in clinical practice. Patients were stratified into 2 groups, including tumor stage (high‑grade vs. low‑grade) and status (deceased vs. alive); differential analyses of miRNA expression among these groups were performed. A total of 20 deregulated miRNAs for each group were identified. In total 7 miRNAs were common between the groups. The majority of common miRNAs belonged to the miR‑506‑514 cluster, known to be involved in UM development. The prognostic value of the 20 selected miRNAs related to tumor stage was assessed. The deregulation of 12 miRNAs (6 upregulated and 6 downregulated) was associated with a worse prognosis of patients with UM. Subsequently, miRCancerdb and microRNA Data Integration Portal bioinformatics tools were used to identify a set of genes associated with the 20 miRNAs and to establish their interaction levels. By this approach, 53 different negatively and positively associated genes were identified. Finally, DIANA‑mirPath prediction pathway and Gene Ontology enrichment analyses were performed on the lists of genes previously generated to establish their functional involvement in biological processes and molecular pathways. All the miRNAs and genes were involved in molecular pathways usually altered in cancer, including the mitogen‑activated protein kinase (MAPK) pathway. Overall, the findings of the presents study demonstrated that the miRNAs of the miR‑506‑514 cluster, hsa‑miR‑592 and hsa‑miR‑199a‑5p were the most deregulated miRNAs in patients with high‑grade disease compared to those with low‑grade disease and were strictly related to the overall survival (OS) of the patients. However, further in vitro and translational approaches are required to validate these preliminary findings.

Litschko C, Brühmann S, Csiszár A, et al.
Functional integrity of the contractile actin cortex is safeguarded by multiple Diaphanous-related formins.
Proc Natl Acad Sci U S A. 2019; 116(9):3594-3603 [PubMed] Article available free on PMC after 01/04/2020 Related Publications
The contractile actin cortex is a thin layer of filamentous actin, myosin motors, and regulatory proteins beneath the plasma membrane crucial to cytokinesis, morphogenesis, and cell migration. However, the factors regulating actin assembly in this compartment are not well understood. Using the

Herbert K, Binet R, Lambert JP, et al.
BRN2 suppresses apoptosis, reprograms DNA damage repair, and is associated with a high somatic mutation burden in melanoma.
Genes Dev. 2019; 33(5-6):310-332 [PubMed] Article available free on PMC after 01/04/2020 Related Publications
Whether cell types exposed to a high level of environmental insults possess cell type-specific prosurvival mechanisms or enhanced DNA damage repair capacity is not well understood. BRN2 is a tissue-restricted POU domain transcription factor implicated in neural development and several cancers. In melanoma, BRN2 plays a key role in promoting invasion and regulating proliferation. Here we found, surprisingly, that rather than interacting with transcription cofactors, BRN2 is instead associated with DNA damage response proteins and directly binds PARP1 and Ku70/Ku80. Rapid PARP1-dependent BRN2 association with sites of DNA damage facilitates recruitment of Ku80 and reprograms DNA damage repair by promoting Ku-dependent nonhomologous end-joining (NHEJ) at the expense of homologous recombination. BRN2 also suppresses an apoptosis-associated gene expression program to protect against UVB-, chemotherapy- and vemurafenib-induced apoptosis. Remarkably, BRN2 expression also correlates with a high single-nucleotide variation prevalence in human melanomas. By promoting error-prone DNA damage repair via NHEJ and suppressing apoptosis of damaged cells, our results suggest that BRN2 contributes to the generation of melanomas with a high mutation burden. Our findings highlight a novel role for a key transcription factor in reprogramming DNA damage repair and suggest that BRN2 may impact the response to DNA-damaging agents in BRN2-expressing cancers.

Zoratti MJ, Devji T, Levine O, et al.
Network meta-analysis of therapies for previously untreated advanced BRAF-mutated melanoma.
Cancer Treat Rev. 2019; 74:43-48 [PubMed] Related Publications
BACKGROUND: The spectrum of treatment options for patients with metastatic BRAF-mutated melanoma is broad, spanning multiple treatment classes. However, there is a lack of head-to-head evidence comparing targeted and immunotherapies. The purpose of this study is to conduct a network meta-analysis (NMA) in previously untreated, BRAF-mutated melanoma patients and estimate the relative efficacy of systemic therapies for this patient population at the treatment level.
METHODS: The literature review included searches of MEDLINE, EMBASE, and the Cochrane Central Registry of Controlled Trials (CENTRAL) to November 2018. Randomized controlled trials of previously untreated patients with advanced melanoma were eligible if at least one intervention was either a targeted or immune therapy. Relative treatment effects were estimated by fixed effect Bayesian NMAs on progression-free survival (PFS) and overall survival (OS), based on the hazard ratio.
RESULTS: Combination dabrafenib with trametinib (HR 0.22 [95% CrI 0.17, 0.28] vs dacarbazine) and combination vemurafenib with cobimetinib (HR 0.22 [95% CrI 0.17, 0.29] vs dacarbazine) were likely to rank as the most favorable treatment options for PFS, while combination nivolumab with ipilimumab was likely to be the most efficacious in terms of OS (HR 0.33 [0.24, 0.47] vs dacarbazine).
CONCLUSIONS AND RELEVANCE: The findings highlight the efficacy of combination PD-1 with CTLA-4 inhibitors and combination BRAF with MEK inhibitors in the treatment of advanced melanoma. However, as few trials informed each treatment comparison, research is needed to further refine our understanding of this complex and rapidly evolving treatment landscape.

Giannopoulou AF, Konstantakou EG, Velentzas AD, et al.
Gene-Specific Intron Retention Serves as Molecular Signature that Distinguishes Melanoma from Non-Melanoma Cancer Cells in Greek Patients.
Int J Mol Sci. 2019; 20(4) [PubMed] Article available free on PMC after 01/04/2020 Related Publications
BACKGROUND: Skin cancer represents the most common human malignancy, and it includes BCC, SCC, and melanoma. Since melanoma is one of the most aggressive types of cancer, we have herein attempted to develop a gene-specific intron retention signature that can distinguish BCC and SCC from melanoma biopsy tumors.
METHODS: Intron retention events were examined through RT-sqPCR protocols, using total RNA preparations derived from BCC, SCC, and melanoma Greek biopsy specimens. Intron-hosted miRNA species and their target transcripts were predicted via the miRbase and miRDB bioinformatics platforms, respectively. Ιntronic ORFs were recognized through the ORF Finder application. Generation and visualization of protein interactomes were achieved by the IntAct and Cytoscape softwares, while tertiary protein structures were produced by using the I-TASSER online server.

Şükrüoğlu Erdoğan Ö, Kılıç Erciyas S, Bilir A, et al.
Methylation Changes of Primary Tumors, Monolayer, and Spheroid Tissue Culture Environments in Malignant Melanoma and Breast Carcinoma.
Biomed Res Int. 2019; 2019:1407167 [PubMed] Article available free on PMC after 01/04/2020 Related Publications
Epigenetic changes have major role in the normal development and programming of gene expression. Aberrant methylation results in carcinogenesis. The primary objective of our study is to determine whether primary tumor tissue and cultured tumor cells in 2D and 3D tissue culture systems have the same methylation signature for

Zhou R, Shi C, Tao W, et al.
Analysis of Mucosal Melanoma Whole-Genome Landscapes Reveals Clinically Relevant Genomic Aberrations.
Clin Cancer Res. 2019; 25(12):3548-3560 [PubMed] Related Publications
PURPOSE: Unlike advances in the genomics-driven precision treatment of cutaneous melanomas, the current poor understanding of the molecular basis of mucosal melanomas (MM) has hindered such progress for MM patients. Thus, we sought to characterize the genomic landscape of MM to identify genomic alterations with prognostic and/or therapeutic implications.
EXPERIMENTAL DESIGN: Whole-genome sequencing (WGS) was performed on 65 MM samples, including 63 paired tumor blood samples and 2 matched lymph node metastases, with a further droplet digital PCR-based validation study of an independent MM cohort (
RESULTS: Besides the identification of well-recognized driver mutations of
CONCLUSIONS: Our largest-to-date cohort WGS analysis of MMs defines the genomic landscape of this deadly cancer at unprecedented resolution and identifies genomic aberrations that could facilitate the delivery of precision cancer treatments.

Poźniak J, Nsengimana J, Laye JP, et al.
Genetic and Environmental Determinants of Immune Response to Cutaneous Melanoma.
Cancer Res. 2019; 79(10):2684-2696 [PubMed] Article available free on PMC after 15/11/2019 Related Publications
The immune response to melanoma improves the survival in untreated patients and predicts the response to immune checkpoint blockade. Here, we report genetic and environmental predictors of the immune response in a large primary cutaneous melanoma cohort. Bioinformatic analysis of 703 tumor transcriptomes was used to infer immune cell infiltration and to categorize tumors into immune subgroups, which were then investigated for association with biological pathways, clinicopathologic factors, and copy number alterations. Three subgroups, with "low", "intermediate", and "high" immune signals, were identified in primary tumors and replicated in metastatic tumors. Genes in the low subgroup were enriched for cell-cycle and metabolic pathways, whereas genes in the high subgroup were enriched for IFN and NF-κB signaling. We identified high MYC expression partially driven by amplification, HLA-B downregulation, and deletion of IFNγ and NF-κB pathway genes as the regulators of immune suppression. Furthermore, we showed that cigarette smoking, a globally detrimental environmental factor, modulates immunity, reducing the survival primarily in patients with a strong immune response. Together, these analyses identify a set of factors that can be easily assessed that may serve as predictors of response to immunotherapy in patients with melanoma. SIGNIFICANCE: These findings identify novel genetic and environmental modulators of the immune response against primary cutaneous melanoma and predict their impact on patient survival.

Borriello F, Galdiero MR, Varricchi G, et al.
Innate Immune Modulation by GM-CSF and IL-3 in Health and Disease.
Int J Mol Sci. 2019; 20(4) [PubMed] Article available free on PMC after 15/11/2019 Related Publications
Granulocyte-macrophage colony-stimulating factor (GM-CSF) and inteleukin-3 (IL-3) have long been known as mediators of emergency myelopoiesis, but recent evidence has highlighted their critical role in modulating innate immune effector functions in mice and humans. This new wealth of knowledge has uncovered novel aspects of the pathogenesis of a range of disorders, including infectious, neoplastic, autoimmune, allergic and cardiovascular diseases. Consequently, GM-CSF and IL-3 are now being investigated as therapeutic targets for some of these disorders, and some phase I/II clinical trials are already showing promising results. There is also pre-clinical and clinical evidence that GM-CSF can be an effective immunostimulatory agent when being combined with anti-cytotoxic T lymphocyte-associated protein 4 (anti-CTLA-4) in patients with metastatic melanoma as well as in novel cancer immunotherapy approaches. Finally, GM-CSF and to a lesser extent IL-3 play a critical role in experimental models of trained immunity by acting not only on bone marrow precursors but also directly on mature myeloid cells. Altogether, characterizing GM-CSF and IL-3 as central mediators of innate immune activation is poised to open new therapeutic avenues for several immune-mediated disorders and define their potential in the context of immunotherapies.

Vetto JT, Hsueh EC, Gastman BR, et al.
Guidance of sentinel lymph node biopsy decisions in patients with T1-T2 melanoma using gene expression profiling.
Future Oncol. 2019; 15(11):1207-1217 [PubMed] Related Publications
AIM: Can gene expression profiling be used to identify patients with T1-T2 melanoma at low risk for sentinel lymph node (SLN) positivity?
PATIENTS & METHODS: Bioinformatics modeling determined a population in which a 31-gene expression profile test predicted <5% SLN positivity. Multicenter, prospectively-tested (n = 1421) and retrospective (n = 690) cohorts were used for validation and outcomes, respectively.
RESULTS: Patients 55-64 years and ≥65 years with a class 1A (low-risk) profile had SLN positivity rates of 4.9% and 1.6%. Class 2B (high-risk) patients had SLN positivity rates of 30.8% and 11.9%. Melanoma-specific survival was 99.3% for patients ≥55 years with class 1A, T1-T2 tumors and 55.0% for class 2B, SLN-positive, T1-T2 tumors.
CONCLUSION: The 31-gene expression profile test identifies patients who could potentially avoid SLN biopsy.

Lee JS, Lee H, Lee S, et al.
Loss of SLC25A11 causes suppression of NSCLC and melanoma tumor formation.
EBioMedicine. 2019; 40:184-197 [PubMed] Article available free on PMC after 15/11/2019 Related Publications
BACKGROUND: Fast growing cancer cells require greater amounts of ATP than normal cells. Although glycolysis was suggested as a source of anabolic metabolism based on lactate production, the main source of ATP to support cancer cell metabolism remains unidentified.
METHODS: We have proposed that the oxoglutarate carrier SLC25A11 is important for ATP production in cancer by NADH transportation from the cytosol to mitochondria as a malate. We have examined not only changes of ATP and NADH but also changes of metabolites after SLC25A11 knock down in cancer cells.
FINDINGS: The mitochondrial electron transport chain was functionally active in cancer cells. The cytosolic to mitochondrial NADH ratio was higher in non-small cell lung cancer (NSCLC) and melanoma cells than in normal cells. This was consistent with higher levels of the oxoglutarate carrier SLC25A11. Blocking malate transport by knockdown of SLC25A11 significantly impaired ATP production and inhibited the growth of cancer cells, which was not observed in normal cells. In in vivo experiments, heterozygote of SLC25A11 knock out mice suppressed KRAS
INTERPRETATION: Cancer cells critically depended on the oxoglutarate carrier SLC25A11 for transporting NADH from cytosol to mitochondria as a malate form for the purpose of ATP production. Therefore blocking SLC25A11 may have an advantage in stopping cancer growth by reducing ATP production. FUND: The Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Science and ICT to SYK (NRF-2017R1A2B2003428).

Burjanivova T, Malicherova B, Grendar M, et al.
Detection of BRAFV600E Mutation in Melanoma Patients by Digital PCR of Circulating DNA.
Genet Test Mol Biomarkers. 2019; 23(4):241-245 [PubMed] Related Publications
AIMS: About 50% of melanomas have the BRAFV600E mutation. This mutation is an attractive therapeutic target. The aims of our study were to detect BRAFV600E mutations within circulating cell-free DNA in plasma ("liquid biopsy") by a droplet digital PCR (ddPCR) method, and to investigate how well the Breslow-Clark score can be predicted by ddPCR.
MATERIALS AND METHODS: We analyzed 113 patients with malignant melanoma. ddPCR was performed using the QX200 system (BIO-RAD
RESULTS: The BRAFV600E mutation was detected in 37 of 113 samples. A moderate predictive accuracy of the Breslow-Clark score can be attained with the mitotic activity and the type of melanoma as the most important predictors.
CONCLUSION: Our results show that ddPCR is a highly sensitive method and could be used for a routine laboratory detection of the BRAFV600E mutation as well as for follow-up monitoring to determine the treatment response in patients with malignant melanomas.

Roncati L
Microsatellite Instability Predicts Response to Anti-PD1 Immunotherapy in Metastatic Melanoma.
Acta Dermatovenerol Croat. 2018; 26(4):341-343 [PubMed] Related Publications
Dear Editor, Immune-checkpoint blockade is a type of passive immunotherapy aimed at enhancing preexisting anti-tumor responses of the organism, blocking self-tolerance molecular interactions between T-lymphocytes and neoplastic cells (1,2). Despite a significant increase in progression-free survival, a large proportion of patients affected by metastatic melanoma do not show durable responses even after appropriate diagnostic categorization and shared therapeutic choices (3-9). Therefore, predictive biomarkers of clinical response are urgently needed, and predictive immunohistochemistry (IHC) meets these requirements. Strong evidence suggests that DNA mismatch repair (MMR) deficiency is a frequent condition in malignant melanoma, as well as in other tumors (10). As is known, DNA MMR is a safeguard system for the detection and repair of DNA errors, which can randomly occur in the phase of DNA replication inside the cell. In humans, seven DNA MMR proteins (Mlh1, Mlh3, Msh2, Msh3, Msh6, Pms1, and Pms2) work in a coordinated and sequential manner to repair DNA mismatches. When this system is defective, the cell accumulates a series of replication errors in terms of new microsatellites; therefore, a condition of genetic hypermutability and microsatellite instability (MSI) takes place inside the cell itself (11). For this reason, my working group has started to search for MMR protein deficiency in melanoma biopsies from patients of both sexes and of all ages with metastatic spread, correlating the data with the response to pembrolizumab, the well-known anti-programmed cell death protein 1 (PD1) human monoclonal immunoglobulin G4, capable of blocking the interaction between PD1, the surface receptor of activated T-lymphocytes, and its ligand, the programmed death-ligand 1 (PD-L1), favoring melanoma cell attack by T-lymphocytes (1) rather than its depression (12). PD-L1 is highly expressed in about half of all melanomas and thus the role of PD1 in melanoma immune evasion is now well established (13). Surprisingly, the best therapeutic results to pembrolizumab, in terms of progression-free survival and overall survival, occur precisely in those patients, approximately 7% in my database, affected by deficient MMR (dMMR) melanomas. In particular, the most important benefits to pembrolizumab-based treatment have occurred in a female patient, who developed a subungual melanoma in the second finger of the left hand at the age of 41 years, together with lymph node metastases to ipsilateral axilla at the onset. The patient was promptly submitted to amputation of the first phalanx and emptying of the axillary cable. The primary tumor was a vertical growth phase melanoma with a Breslow's depth of 1.4 mm; three mitotic figures for 1 mm2 were ascertained. There was no evidence of ulceration, regression, microsatellitosis, or lymphocytic infiltration; moreover, the surgical margins tested free of disease. Further molecular analyses did not show rearrangements in B-RAF and C-KIT genes. After four years, metastases appeared in the brain and ileum; however, at present the patient is still alive and in complete pembrolizumab response with progression-free survival and overall survival of 956 days and 2546 days, respectively. The tumor was afterwards identified as a dMMR melanoma for an exclusive loss of Msh6 expression on IHC (Figure 1). This finding is in line with the fact that the U.S. Food and Drug Administration has approved the use of pembrolizumab in 2017 for unresectable or metastatic solid tumors with MMR deficiency (14). In conclusion, dMMR melanoma seems to be a particular subset of disease that can be identified with high sensibility and specificity by predictive IHC as a complete loss of one or more DNA MMR proteins and that deserves targeted therapy.

Wong K, van der Weyden L, Schott CR, et al.
Cross-species genomic landscape comparison of human mucosal melanoma with canine oral and equine melanoma.
Nat Commun. 2019; 10(1):353 [PubMed] Article available free on PMC after 15/11/2019 Related Publications
Mucosal melanoma is a rare and poorly characterized subtype of human melanoma. Here we perform a cross-species analysis by sequencing tumor-germline pairs from 46 primary human muscosal, 65 primary canine oral and 28 primary equine melanoma cases from mucosal sites. Analysis of these data reveals recurrently mutated driver genes shared between species such as NRAS, FAT4, PTPRJ, TP53 and PTEN, and pathogenic germline alleles of BRCA1, BRCA2 and TP53. We identify a UV mutation signature in a small number of samples, including human cases from the lip and nasal mucosa. A cross-species comparative analysis of recurrent copy number alterations identifies several candidate drivers including MDM2, B2M, KNSTRN and BUB1B. Comparison of somatic mutations in recurrences and metastases to those in the primary tumor suggests pervasive intra-tumor heterogeneity. Collectively, these studies suggest a convergence of some genetic changes in mucosal melanomas between species but also distinctly different paths to tumorigenesis.

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