Kidney Renal Papillary Cell Carcinoma: Copy number analysis (GISTIC2)
Maintained by Dan DiCara (Broad Institute)
Overview
Introduction

GISTIC identifies genomic regions that are significantly gained or lost across a set of tumors. The pipeline first filters out normal samples from the segmented copy-number data by inspecting the TCGA barcodes and then executes GISTIC version 2.0.16 (cga svn revision 38839).

Summary

There were 75 tumor samples used in this analysis: 15 significant arm-level results, 0 significant focal amplifications, and 6 significant focal deletions were found.

Results
Focal results

Figure 1. Genomic positions of amplified regions: the X-axis represents the normalized amplification signals (top) and significance by Q value (bottom). The green line represents the significance cutoff at Q value=0.25.

Figure 2. Genomic positions of deleted regions: the X-axis represents the normalized deletion signals (top) and significance by Q value (bottom). The green line represents the significance cutoff at Q value=0.25.

Table 1. Get Full Table Deletions Table - 6 significant deletions found. Click the link in the last column to view a comprehensive list of candidate genes. If no genes were identified within the peak, the nearest gene appears in brackets.

Cytoband | Q value | Residual Q value | Wide Peak Boundaries | # Genes in Wide Peak
--- | --- | --- | --- | ---
9p21.3 | 4.8772e-08 | 4.8772e-08 | chr9:21865498-22448737 | 4
2q37.2 | 0.02879 | 0.02879 | chr2:229039385-243199373 | 161
1p36.31 | 0.02879 | 0.02879 | chr1:1-8925111 | 146
5q12.1 | 0.029846 | 0.029846 | chr5:58260298-60047770 | 4
4q32.1 | 0.13468 | 0.13468 | chr4:101108133-178352218 | 297
4q32.1 | 0.13468 | 1 | chr4:1-191154276 | 903

Genes in Wide Peak

This is the comprehensive list of deleted genes in the wide peak for 9p21.3.

Table S1. Genes in bold are cancer genes as defined by The Sanger Institute's Cancer Gene Census [7].

Genes
CDKN2A
CDKN2B
C9orf53
CDKN2B-AS1

Genes in Wide Peak

This is the comprehensive list of deleted genes in the wide peak for 2q37.2.

Table S2. Genes in bold are cancer genes as defined by The Sanger Institute's Cancer Gene Census [7].

Genes
hsa-mir-3133
hsa-mir-149
hsa-mir-4269
hsa-mir-562
hsa-mir-1471
hsa-mir-1244-1
AGXT
ALPI
ALPP
ALPPL2
KIF1A
BOK
CHRND
CHRNG
COL6A3
DTYMK
GBX2
GPC1
GPR35
HDLBP
HTR2B
SP110
INPP5D
KCNJ13
NCL
NDUFA10
SEPT2
NEU2
NPPC
PDCD1
PDE6D
PPP1R7
PSMD1
PTMA
SNORD20
SAG
SP100
SPP2
DGKD
PER2
LRRFIP1
GPR55
TRIP12
ECEL1
EIF4E2
HDAC4
FARP2
ARL4C
RAMP1
NMUR1
STK25
COPS8
CAPN10
SP140
PASK
ATG4B
SH3BP4
NGEF
SNORD82
SNED1
GIGYF2
TRAF3IP1
ANO7
PRLH
THAP4
ANKMY1
SCLY
ASB1
CAB39
UGT1A10
UGT1A8
UGT1A7
UGT1A6
UGT1A5
UGT1A9
UGT1A4
UGT1A1
UGT1A3
PID1
ATG16L1
USP40
HJURP
HES6
CXCR7
RNPEPL1
GAL3ST2
RAB17
COPS7B
TRPM8
MLPH
IQCA1
C2orf54
ARMC9
EFHD1
ILKAP
ITM2C
ING5
MGC16025
DNER
B3GNT7
SP140L
AGAP1
TWIST2
DIS3L2
NEU4
SPATA3
FBXO36
MTERFD2
UBE2F
OTOS
MYEOV2
OR6B3
LOC150935
LOC151171
LOC151174
SLC16A14
LOC151475
LINC00471
LOC151484
MSL3P1
C2orf57
TIGD1
LOC200772
C2orf72
CXXC11
DUSP28
ESPNL
ECEL1P2
LOC348761
RBM44
AQP12A
KLHL30
C2orf82
OR6B2
ASB18
FLJ43879
MIR149
DNAJB3
LOC643387
PRR21
PRSS56
AQP12B
SNORA75
SCARNA6
SCARNA5
D2HGDH
LOC728323
PP14571
LOC100286922
MIR1471
MIR1244-1
BOK-AS1
MIR1244-3
MIR1244-2
MIR4269
UBE2F-SCLY
MIR4777
MIR2467
MIR4440
MIR4786
MIR4441

Genes in Wide Peak

This is the comprehensive list of deleted genes in the wide peak for 1p36.31.

Table S3. Genes in bold are cancer genes as defined by The Sanger Institute's Cancer Gene Census [7].

Genes
RPL22
TNFRSF14
PRDM16
hsa-mir-4252
hsa-mir-551a
hsa-mir-4251
hsa-mir-429
hsa-mir-1302-2
RERE
CDK11B
DFFB
DVL1
MEGF6
GABRD
GNB1
ZBTB48
TNFRSF9
PEX10
PRKCZ
SCNN1D
SKI
TP73
TNFRSF4
MMP23B
MMP23A
KCNAB2
TNFRSF25
TNFRSF18
PER3
VAMP3
ISG15
PLCH2
CEP104
KLHL21
SLC35E2
UTS2
RER1
PARK7
ACOT7
CAMTA1
ICMT
CHD5
NOC2L
OR4F3
ARHGEF16
SSU72
WRAP73
SLC45A1
SDF4
ERRFI1
MXRA8
HES2
CPSF3L
C1orf159
AURKAIP1
MRPL20
ATAD3A
PANK4
DNAJC11
AJAP1
TP73-AS1
PLEKHG5
LRRC47
HES4
VWA1
NADK
MMEL1
OR4F5
NOL9
LINC00115
MORN1
GLTPD1
TAS1R1
OR4F16
CCNL2
ESPN
TAS1R3
ATAD3B
PLEKHN1
C1orf170
KIAA1751
THAP3
LOC115110
ACAP3
UBE2J2
PUSL1
B3GALT6
TPRG1L
FAM213B
ACTRT2
MIB2
SAMD11
LOC148413
PHF13
CCDC27
CALML6
C1orf86
ATAD3C
LOC254099
TTLL10
NPHP4
FAM41C
LOC284661
C1orf174
KLHL17
TMEM240
TMEM52
AGRN
GPR153
FAM132A
HES5
LOC388588
RNF207
HES3
RNF223
MIR200A
MIR200B
FLJ42875
ANKRD65
MIR429
FAM138F
LOC643837
TMEM88B
C1orf233
FAM138A
WASH7P
MIR551A
CDK11A
SLC35E2B
LOC728716
LOC729737
OR4F29
LOC100129534
LOC100130417
LOC100132062
LOC100132287
LOC100133331
LOC100133445
LOC100133612
DDX11L1
TTC34
LOC100288069
MIR4251
MIR4252
MIR4689
MIR4417

Genes in Wide Peak

This is the comprehensive list of deleted genes in the wide peak for 5q12.1.

Table S4. Genes in bold are cancer genes as defined by The Sanger Institute's Cancer Gene Census [7].

Genes
hsa-mir-582
PDE4D
PART1
DEPDC1B

Genes in Wide Peak

This is the comprehensive list of deleted genes in the wide peak for 4q32.1.

Table S5. Genes in bold are cancer genes as defined by The Sanger Institute's Cancer Gene Census [7].

Genes
IL2
TET2
FBXW7
hsa-mir-4276
hsa-mir-548t
hsa-mir-1979
hsa-mir-3140
hsa-mir-548g
hsa-mir-3139
hsa-mir-2054
hsa-mir-1973
hsa-mir-577
hsa-mir-1243
hsa-mir-302b
hsa-mir-297
hsa-mir-576
hsa-mir-1255a
ANK2
ANXA2P1
ANXA5
CAMK2D
CASP6
CCNA2
LRBA
CLGN
CENPE
CLCN3
CPE
CTSO
EDNRA
EGF
ELF2
ENPEP
ETFDH
FABP2
FGA
FGB
FGF2
FGG
GAB1
GK3P
GLRB
GPM6A
GRIA2
GUCY1A3
GUCY1B3
GYPA
GYPB
GYPE
HADH
HMGB2
HPGD
CFI
IL15
MAD2L1
SMAD1
MANBA
MGST2
NR3C2
NDUFC1
NEK1
NFKB1
NPY1R
NPY2R
NPY5R
PET112
PITX2
PLRG1
EXOSC9
POU4F2
PPID
PPP3CA
ABCE1
RPL34
RPS3A
MSMO1
SFRP2
TACR3
TDO2
TLL1
TLR2
TRPC3
UBE2D3
UCP1
UGT8
VEGFC
GLRA3
SMARCA5
PRSS12
PDE5A
SAP30
INPP4B
SNORD73A
PAPSS1
LRAT
AIMP1
NDST3
HAND2
RAPGEF2
MFAP3L
SEC24D
SPRY1
ANAPC10
PGRMC2
SEC24B
MAB21L2
RRH
PLK4
ADAM29
PRDM5
LSM6
NUDT6
ANXA10
KLHL2
SCRG1
HSPA4L
PALLD
TBC1D9
KIAA0922
TRIM2
ANP32C
SLC7A11
CCRN4L
FBXO8
PPA2
DKK2
INTU
ARFIP1
ZNF330
SPOCK3
AADAT
LEF1
FAM198B
LARP7
EMCN
MYOZ2
ACCN5
GALNT7
GAR1
PCDH18
USP53
DKFZP434I0714
OTUD4
DCHS2
ARHGEF38
C4orf27
CCDC109B
MARCH1
BANK1
LARP1B
BBS7
NEIL3
TMEM144
C4orf43
C4orf21
AP1AR
MAML3
DDX60
TMEM184C
PDGFC
FSTL5
BDH2
INTS12
ANKRD50
RNF150
PCDH10
FNIP2
SH3RF1
METTL14
OSTC
IL21
RXFP1
SPCS3
SCOC
NEUROG2
SLC39A8
HHIP
NDST4
AGXT2L1
ELOVL6
NDNF
FAT4
ARSJ
ARHGAP10
NBLA00301
GSTCD
MAP9
TNIP3
PHF17
NAA15
C4orf29
ALPK1
CXXC4
CEP44
SETD7
PLA2G12A
SLC25A31
RAB33B
TTC29
MND1
SLC10A7
TKTL2
QRFPR
KIAA1109
COL25A1
USP38
C4orf49
CBR4
FHDC1
FLJ20021
PRMT10
DDX60L
NAF1
TIFA
TBCK
CYP2U1
WDR17
SCLT1
C4orf33
TMEM155
PABPC4L
ADAD1
C4orf32
SPATA4
TRAM1L1
SLC9B2
ASB5
SLC9B1
ZNF827
SH3D19
C4orf39
C4orf45
SPATA5
BBS12
DCLK2
TRIM60
FREM3
MMAA
RBM46
SGMS2
SYNPO2
C4orf46
TIGD4
TMEM154
TMEM192
ELMOD2
NPNT
MFSD8
LOC285419
LOC285456
RNF175
LOC340017
PRSS48
LRIT3
TRIM61
FLJ14186
C4orf3
MIR302A
GUSBP5
HSP90AA6P
GALNTL6
MIR302B
MIR302C
MIR302D
MIR367
CISD2
LOC641364
LOC641365
LOC641518
CEP170P1
LOC645513
LOC646576
C4orf51
SNORA24
MIR577
MIR578
CETN4P
FAM160A1
SNHG8
LOC100129858
PP12613
MIR1243
MIR2054
MIR1973
MIR3140
MIR4276
MIR3688-1
LOC100505545
LOC100505989
LOC100506013
LOC100506085
LOC100506122
LOC100507096
1/2-SBSRNA4
MIR4453
MIR4799
MIR3688-2

Genes in Wide Peak

This is the comprehensive list of deleted genes in the wide peak for 4q32.1.

Table S6. Genes in bold are cancer genes as defined by The Sanger Institute's Cancer Gene Census [7].

Genes
FGFR3
IL2
KDR
KIT
PDGFRA
RAP1GDS1
WHSC1
PHOX2B
DUX4
CHIC2
TET2
FBXW7
FIP1L1
hsa-mir-1305
hsa-mir-4276
hsa-mir-548t
hsa-mir-1979
hsa-mir-3140
hsa-mir-548g
hsa-mir-3139
hsa-mir-2054
hsa-mir-1973
hsa-mir-577
hsa-mir-1243
hsa-mir-302b
hsa-mir-297
hsa-mir-576
hsa-mir-1255a
hsa-mir-575
hsa-mir-1269
hsa-mir-574
hsa-mir-1255b-1
hsa-mir-4275
hsa-mir-573
hsa-mir-218-1
hsa-mir-572
hsa-mir-3138
hsa-mir-548i-2
hsa-mir-95
hsa-mir-4274
hsa-mir-943
hsa-mir-571
ADD1
ADH1A
ADH1B
ADH1C
ADH4
ADH5
ADH6
ADH7
ADRA2C
AFM
AFP
AGA
ALB
AMBN
ANK2
SLC25A4
ANXA2P1
ANXA3
ANXA5
APBB2
AREG
RHOH
ART3
ATOH1
ATP5I
NKX3-2
BMP3
BMPR1B
BST1
BTC
CAMK2D
CASP3
CASP6
CCKAR
CCNA2
CCNG2
SCARB2
CD38
LRBA
CDS1
CLGN
CENPC1
CENPE
CLCN3
CNGA1
CPE
CRMP1
CSN1S1
CSN2
CSN3
CTBP1
CTSO
DGKQ
DCK
DCTD
DHX15
DMP1
DRD5
DSPP
EDNRA
EGF
EIF4E
ELF2
ENPEP
EPHA5
EREG
ETFDH
EVC
F11
FABP2
ACSL1
FAT1
FGA
FGB
FGF2
FGF5
FGG
FRG1
GAB1
GABRA2
GABRA4
GABRB1
GABRG1
GAK
GC
GK2
GK3P
GLRB
GNRHR
GPM6A
GRK4
GRIA2
GRID2
CXCL1
CXCL2
CXCL3
GRSF1
GUCY1A3
GUCY1B3
GYPA
GYPB
GYPE
H2AFZ
HADH
HTT
HGFAC
UBE2K
HMGB2
HMX1
HNRNPD
HPGD
HSP90AA4P
HSP90AB3P
HTN1
HTN3
IBSP
IDUA
CFI
IGFBP7
IGJ
RBPJ
IL8
IL15
ING2
CXCL10
IRF2
KLKB1
LETM1
LRPAP1
MAD2L1
SMAD1
MANBA
MGST2
CXCL9
AFF1
NR3C2
MSX1
MTNR1A
MTTP
MUC7
MYL5
NDUFC1
NEK1
NFKB1
NKX6-1
NPY1R
NPY2R
NPY5R
PCDH7
PDE6B
PDHA2
PET112
PF4
PF4V1
PITX2
PKD2
PLRG1
EXOSC9
POLR2B
POU4F2
PPEF2
PPAT
PPBP
PPID
PPP2R2C
PPP3CA
PRKG2
MAPK10
PTPN13
QDPR
REST
RFC1
RGS12
RNF4
ABCE1
RPL9
RPL34
RPS3A
S100P
MSMO1
CXCL6
CXCL11
CXCL5
SFRP2
SGCB
SH3BP2
SNCA
SOD3
SPINK2
SPP1
SRP72
STATH
SULT1E1
TACR3
TDO2
TEC
TLL1
TLR1
TLR2
TLR3
TRPC3
TXK
UBE2D3
UCHL1
UCP1
UGDH
UGT2B4
UGT2B7
UGT2B10
UGT2B15
UGT2B17
UGT8
VEGFC
WFS1
WHSC2
ZNF141
SLBP
GLRA3
ACOX3
SPARCL1
SMARCA5
SORBS2
PRSS12
CPZ
NOP14
FAM193A
USO1
UNC5C
LAMTOR3
PDE5A
SLC4A4
SAP30
INPP4B
PROM1
HERC3
SNORD73A
STBD1
CDKL2
PAPSS1
LDB2
LRAT
AIMP1
NDST3
SLIT2
RAB28
TMPRSS11D
ABCG2
HAND2
ADAMTS3
CLOCK
CEP135
RAPGEF2
KIAA0232
MFAP3L
SEC24D
G3BP2
WDR1
HS3ST1
FGFBP1
HNRPDL
TSPAN5
ENAM
C4orf6
FAM13A
MFSD10
SPRY1
MAEA
TLR6
PCGF3
ANAPC10
ATP8A1
SPON2
PGRMC2
SEC24B
TACC3
SLC30A9
CXCL13
SLC34A2
MAB21L2
PAICS
MXD4
PDLIM5
RRH
CORIN
UGT2B11
PLK4
PTTG2
CPLX1
HPSE
SLC26A1
NMU
SMR3B
NPFFR2
PPARGC1A
PPBPL2
UGT2A1
CCNI
LIAS
ADAM29
PRDM5
LSM6
NUDT6
ANXA10
KLHL2
SCRG1
HSPA4L
SEC31A
RUFY3
MMRN1
LIMCH1
WDFY3
PALLD
DCUN1D4
TBC1D9
METAP1
TBC1D1
SEL1L3
KIAA0922
PDS5A
LPHN3
TRIM2
MAN2B2
ANP32C
SLC7A11
CCRN4L
PARM1
FAM149A
RCHY1
ANKRD17
STAP1
FBXL5
FBXO8
DUX2
SMR3A
D4S234E
PPA2
DAPP1
DKK2
FAM184B
INTU
NAAA
GPR78
COQ2
ARFIP1
SULT1B1
PDLIM3
HPGDS
ZNF330
TMPRSS11E
ZCCHC4
ANAPC4
SPOCK3
MRPS18C
LAP3
KLHL5
SEPSECS
COPS4
AADAT
HSD17B11
LEF1
HERC5
KLF3
FAM198B
PLAC8
LARP7
EMCN
MYOZ2
ACCN5
GALNT7
NUDT9
NUP54
STX18
FGFRL1
CLDN22
CYTL1
GAR1
SH3TC1
UGT2B28
RBM47
PCDH18
USP53
DKFZP434I0714
OTUD4
DCHS2
ARHGEF38
PIGG
DCAF16
OCIAD1
COMMD8
ODAM
C4orf27
HERC6
CCDC109B
MARCH1
BANK1
LARP1B
SDAD1
TMEM33
LGI2
BBS7
UBA6
NEIL3
PGM2
C4orf19
TBC1D19
PI4K2B
TMEM144
C4orf43
UFSP2
CNO
C4orf21
STK32B
AP1AR
MAML3
CHRNA9
BMP2K
DDX60
CDKN2AIP
LYAR
ODZ3
N4BP2
TMEM184C
SEPT11
EXOC1
LRP2BP
TMEM165
PDGFC
SLC2A9
FSTL5
BDH2
SMARCAD1
MEPE
STOX2
PRDM8
UTP3
INTS12
ANKRD50
ATP10D
DANCR
KIAA1211
RNF150
KIAA1239
TBC1D14
SORCS2
CC2D2A
KLHL8
PCDH10
KIAA1430
FNIP2
SLAIN2
SHROOM3
STIM2
SH3RF1
KIAA1530
METTL14
WDR19
ZFYVE28
GBA3
ENOPH1
PROL1
OSTC
IL21
RXFP1
AFAP1
GUF1
SPCS3
SCOC
TRAPPC11
NEUROG2
SLC39A8
NCAPG
HHIP
NDST4
AGXT2L1
USP46
MRPL1
RASL11B
ELOVL6
TNIP2
HAUS3
NDNF
FAT4
ARSJ
SRD5A3
ARHGAP10
FLJ13197
MLF1IP
THAP9
NSUN7
UGT2A3
NBLA00301
GSTCD
MAP9
TNIP3
PHF17
ABCA11P
SCD5
DNAJB14
TMEM156
WWC2
FRAS1
NAA15
CWH43
C4orf29
ALPK1
GRPEL1
MED28
CXXC4
KCNIP4
CEP44
SETD7
PLA2G12A
TLR10
SLC25A31
RAB33B
ARHGAP24
FGFBP2
SNX25
TTC29
MND1
SLC10A7
TKTL2
C4orf17
QRFPR
FAM175A
KIAA1109
MFSD7
NOA1
TMEM175
ABLIM2
HOPX
COL25A1
USP38
LNX1
C4orf49
AFAP1-AS1
NAA11
AGPAT9
CBR4
PIGY
TMEM128
CABS1
ZNF518B
FHDC1
FLJ20021
MGC45800
PRMT10
CCDC149
DDX60L
YTHDC1
C4orf42
TMEM129
NAF1
MOB1B
TIFA
FAM114A1
RG9MTD2
MRFAP1
LOC93622
TADA2B
TBCK
HTRA3
HELQ
CYP2U1
C1QTNF7
MRFAP1L1
DDIT4L
CLNK
WDR17
ARAP2
GDEP
ANTXR2
OCIAD2
SCLT1
C4orf33
TMEM155
PABPC4L
ADAD1
ZFP42
LIN54
SPATA18
C4orf32
TMPRSS11B
GNPDA2
SPATA4
CPEB2
EVC2
ARL9
AASDH
PDCL2
C4orf36
PACRGL
TRAM1L1
OTOP1
ENPP6
SLC9B2
ASB5
SLC9B1
ZNF827
SH3D19
NFXL1
NIPAL1
PAQR3
SHISA3
LOC152578
SCFD2
C4orf38
ZNF595
LOC152742
C4orf39
JAKMIP1
THAP6
C4orf26
KLB
FAM53A
PPM1K
C4orf45
METTL19
RASGEF1B
SPATA5
BBS12
DCLK2
GPR125
TRIM60
FREM3
MMAA
ZBTB49
TIGD2
RASSF6
RBM46
SGMS2
COX7B2
GSX2
ZNF721
SYNPO2
C4orf46
SLC10A4
TIGD4
TMEM154
C4orf34
TMEM192
RWDD4
CCDC111
TAPT1
FLJ39653
TRIML2
CNOT6L
TECRL
LCORL
C4orf22
LOC255130
EPGN
ZNF718
ELMOD2
NPNT
CCDC110
MFSD8
LOC256880
CCDC96
BOD1L
FDCSP
NAP1L5
LOC285419
DCAF4L1
CYP4V2
LOC285441
LOC285456
CRIPAK
LOC285484
DOK7
FLJ35424
RNF212
LOC285501
FAM13A-AS1
GPRIN3
COX18
YIPF7
FRYL
RNF175
LOC285540
LOC285547
LOC285548
FAM200B
C4orf37
CSN1S2AP
C4orf10
ZAR1
CCDC158
TMPRSS11A
LOC339975
TRIML1
LRRC66
NAT8L
LOC340017
LOC344967
PRSS48
SOWAHB
LRIT3
C4orf44
SLC10A6
HSD17B13
FAM86EP
ANKRD37
POLN
PCNAP1
KCNIP4-IT1
KCTD8
C4orf52
BEND4
GRXCR1
TMPRSS11F
LOC389247
USP17L6P
USP17
HSP90AB2P
TRIM61
HELT
C4orf48
DTHD1
LOC401127
LOC401134
SYT14L
TMPRSS11BNL
C4orf40
AMTN
FAM190A
FLJ14186
C4orf3
LOC401164
LOC402160
RPL21P44
FAM92A3
WDFY3-AS2
MIR218-1
MIR302A
C4orf11
LOC441009
MTHFD2L
LOC441025
TMEM150C
GUSBP5
HSP90AA6P
C4orf47
DUX4L4
GALNTL6
MIR302B
MIR302C
MIR302D
MIR367
FRG2
CISD2
LOC550112
LOC550113
UGT2A2
LOC641364
LOC641365
LOC641518
ZNF876P
SLED1
LOC644145
LOC644248
DEFB131
TMPRSS11GP
CLRN2
CEP170P1
LOC645513
LOC646576
C4orf51
LOC650293
FLJ38576
DUX4L6
DUX4L5
DUX4L3
ZNF732
SCARNA22
SNORA24
SNORA26
MIR572
MIR573
MIR574
MIR575
MIR577
MIR578
LINC00290
LOC728175
LOC728369
LOC728373
LOC728379
USP17L5
LOC728393
LOC728400
LOC728405
DUX4L2
CETN4P
FAM160A1
FLJ36777
LOC731424
RELL1
PSAPL1
SNHG8
MIR943
FAM47E
LOC100129858
LOC100129917
LOC100129931
FTLP10
LOC100130872
CLDN24
LOC100133461
LOC100144602
PP12613
LOC100288255
ERVMER34-1
MIR1243
MIR2054
MIR1305
MIR548I2
MIR1973
CSN1S2BP
TMED11P
MIR4274
MIR3140
MIR4275
MIR3138
MIR4276
LOC100499177
MIR3945
MIR3684
MIR3688-1
LOC100505545
LOC100505702
LOC100505875
SLIT2-IT1
LOC100505912
LOC100505989
LOC100506013
LOC100506035
LOC100506085
LOC100506122
LOC100506229
LOC100506462
LOC100506564
LOC100506746
LOC100507053
LOC100507096
LOC100507266
1/2-SBSRNA4
MIR4453
MIR378D1
MIR4799
MIR548AJ2
MIR4802
MIR4450
MIR3688-2
MIR4451
MIR4800
MIR4801
MIR4449
MIR4798
FAM47E-STBD1
HTT-AS1

Arm-level results

Table 2. Get Full Table Arm-level significance table - 15 significant results found.

Arm | # Genes | Amp Frequency | Amp Z score | Amp Q value | Del Frequency | Del Z score | Del Q value
--- | --- | --- | --- | --- | --- | --- | ---
1p | 2121 | 0.01 | -1.62 | 0.997 | 0.09 | 1.16 | 0.994
1q | 1955 | 0.08 | 0.531 | 0.683 | 0.03 | -1.26 | 0.994
2p | 924 | 0.14 | 1.2 | 0.318 | 0.03 | -1.78 | 0.994
2q | 1556 | 0.15 | 2.27 | 0.0346 | 0.02 | -1.86 | 0.994
3p | 1062 | 0.29 | 5.62 | 3.62e-08 | 0.09 | -0.0202 | 0.994
3q | 1139 | 0.30 | 6.34 | 5.64e-10 | 0.04 | -1.32 | 0.994
4p | 489 | 0.06 | -1.37 | 0.997 | 0.07 | -1.01 | 0.994
4q | 1049 | 0.04 | -1.42 | 0.997 | 0.07 | -0.664 | 0.994
5p | 270 | 0.11 | -0.0315 | 0.997 | 0.06 | -1.4 | 0.994
5q | 1427 | 0.11 | 0.961 | 0.41 | 0.06 | -0.652 | 0.994
6p | 1173 | 0.04 | -1.26 | 0.997 | 0.11 | 0.673 | 0.994
6q | 839 | 0.05 | -1.43 | 0.997 | 0.12 | 0.773 | 0.994
7p | 641 | 0.51 | 11.5 | 0 | 0.00 | -2.07 | 0.994
7q | 1277 | 0.51 | 13 | 0 | 0.00 | -1.86 | 0.994
8p | 580 | 0.06 | -1.37 | 0.997 | 0.04 | -1.73 | 0.994
8q | 859 | 0.10 | -0.0428 | 0.997 | 0.03 | -1.89 | 0.994
9p | 422 | 0.00 | -2.81 | 0.997 | 0.15 | 1.03 | 0.994
9q | 1113 | 0.00 | -2.52 | 0.997 | 0.15 | 1.7 | 0.574
10p | 409 | 0.04 | -1.85 | 0.997 | 0.04 | -1.85 | 0.994
10q | 1268 | 0.03 | -1.74 | 0.997 | 0.05 | -0.949 | 0.994
11p | 862 | 0.01 | -2.32 | 0.997 | 0.08 | -0.471 | 0.994
11q | 1515 | 0.01 | -1.97 | 0.997 | 0.09 | 0.497 | 0.994
12p | 575 | 0.29 | 5.3 | 2.01e-07 | 0.00 | -2.5 | 0.994
12q | 1447 | 0.29 | 6.75 | 4.24e-11 | 0.00 | -2.16 | 0.994
13q | 654 | 0.14 | 1.09 | 0.356 | 0.08 | -0.694 | 0.994
14q | 1341 | 0.00 | -2.38 | 0.997 | 0.17 | 2.79 | 0.0507
15q | 1355 | 0.00 | -2.51 | 0.997 | 0.08 | -0.109 | 0.994
16p | 872 | 0.47 | 10.6 | 0 | 0.05 | -1.04 | 0.994
16q | 702 | 0.41 | 8.74 | 0 | 0.04 | -1.27 | 0.994
17p | 683 | 0.49 | 10.6 | 0 | 0.12 | 0.415 | 0.994
17q | 1592 | 0.61 | 17.2 | 0 | 0.03 | -0.895 | 0.994
18p | 143 | 0.09 | -0.669 | 0.997 | 0.13 | 0.336 | 0.994
18q | 446 | 0.06 | -1.23 | 0.997 | 0.14 | 0.863 | 0.994
19p | 995 | 0.00 | -2.76 | 0.997 | 0.01 | -2.39 | 0.994
19q | 1709 | 0.00 | -2.41 | 0.997 | 0.03 | -1.56 | 0.994
20p | 355 | 0.27 | 4.34 | 2.32e-05 | 0.02 | -2.2 | 0.994
20q | 753 | 0.30 | 5.64 | 3.62e-08 | 0.02 | -1.97 | 0.994
21q | 509 | 0.02 | -2.39 | 0.997 | 0.14 | 0.778 | 0.994
22q | 921 | 0.00 | -2.52 | 0.997 | 0.20 | 3.07 | 0.0413

Methods & Data
Input

List of inputs used for this run of GISTIC2. All files listed should be included in the archived results.

- Segmentation File = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_MergeDataFilesPipeline/KIRP/1269621/2.GDAC_MergeDataFiles.Finished/KIRP.snp__genome_wide_snp_6__broad_mit_edu__Level_3__segmented_scna_minus_germline_cnv_hg19__seg.seg.txt

- Markers File = /xchip/tcga/CancerGenomeAnalysisData/trunk/copynumber/SNP6_annotations/genome.info.6.0_hg19.na31_minus_frequent_nan_probes_sorted_2.1.txt

- Reference Genome = /xchip/gistic/variables/hg19/hg19_with_miR_20120227.mat

- CNV Files = /xchip/gistic/CNV/blood_normals/CNV.hg19_111204/CNV.hg19.bypos.111213.txt

- Amplification Threshold = 0.30

- Deletion Threshold = 0.30

- Cap Values = 2.0

- Broad Length Cutoff = 0.5

- Remove X-Chromosome = 1

- Confidence Level = 0.99

- Join Segment Size = 10

- Arm Level Peel Off = 1

- Maximum Sample Segments = 10000

Table 3. Get Full Table First 10 out of 75 Input Tumor Samples.

Tumor Sample Names
TCGA-AL-3466-01A-01D-1190-01
TCGA-AL-3467-01A-01D-1190-01
TCGA-AL-3468-01A-02D-1348-01
TCGA-AL-3471-01A-02D-1348-01
TCGA-AL-3472-01A-01D-1190-01
TCGA-AL-3473-01A-01D-1190-01
TCGA-B1-5398-01A-02D-1588-01
TCGA-B3-3925-01A-02D-1348-01
TCGA-B3-3926-01A-02D-1348-01
TCGA-B3-4103-01A-02D-1348-01

Figure 3. Segmented copy number profiles in the input data

GISTIC

GISTIC identifies genomic regions that are significantly gained or lost across a set of tumors. It takes segmented copy number ratios as input, separates arm-level events from focal events, and then performs two tests: (i) identifies significantly amplified/deleted chromosome arms; and (ii) identifies regions that are significantly focally amplified or deleted. For the focal analysis, the significance levels (Q values) are calculated by comparing the observed gains/losses at each locus to those obtained by randomly permuting the events along the genome to reflect the null hypothesis that they are all 'passengers' and could have occurred anywhere. The locus-specific significance levels are then corrected for multiple hypothesis testing. The arm-level significance is calculated by comparing the frequency of gains/losses of each arm to the expected rate given its size. The method outputs genomic views of significantly amplified and deleted regions, as well as a table of genes with gain or loss scores. A more in depth discussion of the GISTIC algorithm and its utility is given in [1], [3], and [5].

CNV Description

Regions of the genome that are prone to germ line variations in copy number are excluded from the GISTIC analysis using a list of germ line copy number variations (CNVs). A CNV is a DNA sequence that may be found at different copy numbers in the germ line of two different individuals. Such germ line variations can confound a GISTIC analysis, which finds significant somatic copy number variations in cancer. A more in depth discussion is provided in [6]. GISTIC currently uses two CNV exclusion lists. One is based on the literature describing copy number variation, and a second one comes from an analysis of significant variations among the blood normals in the TCGA data set.

Download Results

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

References
[1] Beroukhim et al, Assessing the significance of chromosomal aberrations in cancer: Methodology and application to glioma, Proc Natl Acad Sci U S A. Vol. 104:50 (2007)
[2] GISTIC version 1
[3] Mermel et al, GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers, Genome Biology Vol. 12:4 (2011)
[4] GISTIC version 2
[5] Beroukhim et al., The landscape of somatic copy-number alteration across human cancers, Nature Vol. 463:7283 (2010)
[6] McCarroll, S. A. et al., Integrated detection and population-genetic analysis of SNPs and copy number variation, Nat Genet Vol. 40(10):1166-1174 (2008)
[7] The Sanger Institute: Cancer Gene Census
Meta

- Maintainer = Dan DiCara

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