Skin Cutaneous Melanoma: Correlation between gene mutation status and selected clinical features
(Regional_LN cohort)
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Overview
Introduction

This pipeline computes the correlation between significantly recurrent gene mutations and selected clinical features.

Summary

Testing the association between mutation status of 74 genes and 7 clinical features across 105 patients, 7 significant findings detected with Q value < 0.25.

  • CCNE2 mutation correlated to 'Time to Death'.

  • BAGE2 mutation correlated to 'LYMPH.NODE.METASTASIS'.

  • PPP6C mutation correlated to 'LYMPH.NODE.METASTASIS'.

  • OXA1L mutation correlated to 'LYMPH.NODE.METASTASIS'.

  • LIN7A mutation correlated to 'Time to Death'.

  • VGLL1 mutation correlated to 'LYMPH.NODE.METASTASIS'.

  • ACTL7B mutation correlated to 'LYMPH.NODE.METASTASIS'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between mutation status of 74 genes and 7 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 7 significant findings detected.

Clinical
Features 		Time
to
Death 		AGE GENDER DISTANT
METASTASIS 		LYMPH
NODE
METASTASIS 		TUMOR
STAGECODE 		NEOPLASM
DISEASESTAGE
nMutated (%) nWild-Type logrank test t-test Fisher's exact test Fisher's exact test Chi-square test t-test Chi-square test
CCNE2 10 (10%) 95 1.6e-05
(0.00697) 		0.804
(1.00) 		0.477
(1.00) 		1
(1.00) 		0.939
(1.00) 		0.344
(1.00)
BAGE2 7 (7%) 98 0.478
(1.00) 		0.203
(1.00) 		0.419
(1.00) 		0.126
(1.00) 		0.000138
(0.0598) 		0.0694
(1.00)
PPP6C 10 (10%) 95 0.934
(1.00) 		0.843
(1.00) 		0.72
(1.00) 		0.209
(1.00) 		0.000234
(0.101) 		0.58
(1.00)
OXA1L 4 (4%) 101 0.136
(1.00) 		1
(1.00) 		1
(1.00) 		0.000303
(0.13) 		0.0748
(1.00)
LIN7A 8 (8%) 97 0.000224
(0.0967) 		0.662
(1.00) 		1
(1.00) 		1
(1.00) 		0.052
(1.00) 		0.712
(1.00)
VGLL1 3 (3%) 102 0.111
(1.00) 		0.553
(1.00) 		1
(1.00) 		0.00012
(0.0523) 		0.451
(1.00)
ACTL7B 7 (7%) 98 0.848
(1.00) 		0.419
(1.00) 		1
(1.00) 		1.49e-05
(0.0065) 		0.0874
(1.00)
BRAF 59 (56%) 46 0.435
(1.00) 		0.298
(1.00) 		1
(1.00) 		1
(1.00) 		0.564
(1.00) 		0.817
(1.00)
CDKN2A 16 (15%) 89 0.871
(1.00) 		0.919
(1.00) 		0.773
(1.00) 		1
(1.00) 		0.871
(1.00) 		0.686
(1.00)
NRAS 29 (28%) 76 0.635
(1.00) 		0.266
(1.00) 		0.816
(1.00) 		0.744
(1.00) 		0.72
(1.00) 		0.949
(1.00)
STK19 7 (7%) 98 0.424
(1.00) 		0.598
(1.00) 		0.419
(1.00) 		1
(1.00) 		0.997
(1.00) 		0.909
(1.00)
TTN 83 (79%) 22 0.208
(1.00) 		0.888
(1.00) 		0.44
(1.00) 		0.177
(1.00) 		0.0997
(1.00) 		0.301
(1.00)
TP53 18 (17%) 87 0.59
(1.00) 		0.00159
(0.682) 		0.397
(1.00) 		1
(1.00) 		0.386
(1.00) 		0.383
(1.00)
PTEN 11 (10%) 94 0.163
(1.00) 		0.163
(1.00) 		0.295
(1.00) 		0.229
(1.00) 		0.937
(1.00) 		0.155
(1.00)
TPTE 29 (28%) 76 0.612
(1.00) 		0.223
(1.00) 		0.816
(1.00) 		1
(1.00) 		0.856
(1.00) 		0.487
(1.00)
PRB4 15 (14%) 90 0.305
(1.00) 		0.0512
(1.00) 		0.221
(1.00) 		1
(1.00) 		0.319
(1.00) 		0.839
(1.00)
CDH9 22 (21%) 83 0.738
(1.00) 		0.102
(1.00) 		1
(1.00) 		1
(1.00) 		0.517
(1.00) 		0.309
(1.00)
SERPINB4 17 (16%) 88 0.412
(1.00) 		0.417
(1.00) 		0.0904
(1.00) 		1
(1.00) 		0.226
(1.00) 		0.768
(1.00)
PRB2 19 (18%) 86 0.124
(1.00) 		0.00736
(1.00) 		0.175
(1.00) 		1
(1.00) 		0.595
(1.00) 		0.466
(1.00)
TCEB3C 14 (13%) 91 0.47
(1.00) 		0.481
(1.00) 		0.223
(1.00) 		1
(1.00) 		0.956
(1.00) 		0.845
(1.00)
ADH1C 17 (16%) 88 0.438
(1.00) 		0.0388
(1.00) 		0.773
(1.00) 		1
(1.00) 		0.0633
(1.00) 		0.231
(1.00)
C15ORF23 7 (7%) 98 0.469
(1.00) 		0.78
(1.00) 		1
(1.00) 		1
(1.00) 		0.798
(1.00) 		0.903
(1.00)
SDR16C5 16 (15%) 89 0.221
(1.00) 		0.114
(1.00) 		0.773
(1.00) 		0.503
(1.00) 		0.597
(1.00) 		0.342
(1.00)
PDE1A 19 (18%) 86 0.714
(1.00) 		0.141
(1.00) 		1
(1.00) 		1
(1.00) 		0.109
(1.00) 		0.522
(1.00)
TLL1 24 (23%) 81 0.747
(1.00) 		0.144
(1.00) 		0.799
(1.00) 		1
(1.00) 		0.224
(1.00) 		0.373
(1.00)
RAC1 7 (7%) 98 0.83
(1.00) 		0.522
(1.00) 		0.671
(1.00) 		1
(1.00) 		0.922
(1.00) 		0.679
(1.00)
RERG 8 (8%) 97 0.734
(1.00) 		0.0304
(1.00) 		1
(1.00) 		1
(1.00) 		0.0311
(1.00) 		0.304
(1.00)
OR52L1 12 (11%) 93 0.704
(1.00) 		0.845
(1.00) 		1
(1.00) 		1
(1.00) 		0.0469
(1.00) 		0.799
(1.00)
HBD 8 (8%) 97 0.546
(1.00) 		0.858
(1.00) 		0.691
(1.00) 		1
(1.00) 		0.992
(1.00) 		0.562
(1.00)
VEGFC 12 (11%) 93 0.509
(1.00) 		0.823
(1.00) 		1
(1.00) 		1
(1.00) 		0.24
(1.00) 		0.337
(1.00)
OR1N2 13 (12%) 92 0.897
(1.00) 		0.726
(1.00) 		0.336
(1.00) 		1
(1.00) 		0.223
(1.00) 		0.685
(1.00)
HNF4G 12 (11%) 93 0.259
(1.00) 		0.105
(1.00) 		1
(1.00) 		0.0231
(1.00) 		0.559
(1.00)
TRAT1 6 (6%) 99 0.00681
(1.00) 		0.331
(1.00) 		1
(1.00) 		1
(1.00) 		0.0349
(1.00) 		0.721
(1.00)
AREG 5 (5%) 100 0.445
(1.00) 		0.631
(1.00) 		1
(1.00) 		0.00334
(1.00) 		0.528
(1.00)
ST18 24 (23%) 81 0.662
(1.00) 		0.0401
(1.00) 		0.621
(1.00) 		1
(1.00) 		0.585
(1.00) 		0.545
(1.00)
TAF1A 8 (8%) 97 0.135
(1.00) 		0.843
(1.00) 		0.691
(1.00) 		0.301
(1.00) 		0.678
(1.00) 		0.735
(1.00)
GRXCR2 9 (9%) 96 0.568
(1.00) 		0.455
(1.00) 		0.722
(1.00) 		1
(1.00) 		0.0863
(1.00) 		0.474
(1.00)
OR9K2 11 (10%) 94 0.687
(1.00) 		0.058
(1.00) 		0.501
(1.00) 		1
(1.00) 		0.186
(1.00) 		0.457
(1.00)
A2BP1 14 (13%) 91 0.66
(1.00) 		0.297
(1.00) 		0.754
(1.00) 		1
(1.00) 		0.315
(1.00) 		0.215
(1.00)
HIST1H2AA 5 (5%) 100 0.787
(1.00) 		0.631
(1.00) 		1
(1.00) 		0.979
(1.00) 		0.99
(1.00)
LILRA1 21 (20%) 84 0.407
(1.00) 		0.618
(1.00) 		0.294
(1.00) 		0.597
(1.00) 		0.313
(1.00) 		0.974
(1.00)
GALNTL5 10 (10%) 95 0.384
(1.00) 		0.428
(1.00) 		0.275
(1.00) 		1
(1.00) 		0.028
(1.00) 		0.503
(1.00)
HSD11B1 6 (6%) 99 0.499
(1.00) 		0.177
(1.00) 		0.357
(1.00) 		1
(1.00) 		0.0131
(1.00) 		0.514
(1.00)
ANKRD20A4 4 (4%) 101 0.218
(1.00) 		0.455
(1.00) 		0.317
(1.00) 		1
(1.00) 		0.00258
(1.00) 		0.297
(1.00)
GPR141 8 (8%) 97 0.0436
(1.00) 		0.375
(1.00) 		1
(1.00) 		1
(1.00) 		0.0799
(1.00) 		0.452
(1.00)
NAP1L2 10 (10%) 95 0.829
(1.00) 		0.0919
(1.00) 		0.72
(1.00) 		1
(1.00) 		0.0586
(1.00) 		0.855
(1.00)
C18ORF26 13 (12%) 92 0.06
(1.00) 		0.704
(1.00) 		0.336
(1.00) 		1
(1.00) 		0.18
(1.00) 		0.312
(1.00)
DEFB110 5 (5%) 100 0.28
(1.00) 		0.963
(1.00) 		0.318
(1.00) 		1
(1.00) 		0.00258
(1.00) 		0.378
(1.00)
IDH1 6 (6%) 99 0.963
(1.00) 		0.0206
(1.00) 		0.357
(1.00) 		1
(1.00) 		0.984
(1.00) 		0.378
(1.00)
SNAP91 15 (14%) 90 0.453
(1.00) 		0.238
(1.00) 		0.221
(1.00) 		0.503
(1.00) 		0.515
(1.00) 		0.202
(1.00)
CA1 6 (6%) 99 0.152
(1.00) 		0.668
(1.00) 		1
(1.00) 		0.712
(1.00) 		0.891
(1.00)
SERPINB11 12 (11%) 93 0.874
(1.00) 		0.675
(1.00) 		0.0166
(1.00) 		1
(1.00) 		0.177
(1.00) 		0.488
(1.00)
UGT2B15 13 (12%) 92 0.887
(1.00) 		0.359
(1.00) 		0.102
(1.00) 		0.394
(1.00) 		0.211
(1.00) 		0.484
(1.00)
ACSM2B 21 (20%) 84 0.504
(1.00) 		0.95
(1.00) 		0.00611
(1.00) 		1
(1.00) 		0.614
(1.00) 		0.946
(1.00)
C16ORF78 8 (8%) 97 0.671
(1.00) 		0.383
(1.00) 		1
(1.00) 		1
(1.00) 		0.0585
(1.00) 		0.507
(1.00)
TFEC 10 (10%) 95 0.128
(1.00) 		0.158
(1.00) 		0.72
(1.00) 		1
(1.00) 		0.00184
(0.789) 		0.533
(1.00)
TRHDE 24 (23%) 81 0.276
(1.00) 		0.0878
(1.00) 		0.134
(1.00) 		1
(1.00) 		0.442
(1.00) 		0.48
(1.00)
OR5AC2 13 (12%) 92 0.322
(1.00) 		0.783
(1.00) 		0.751
(1.00) 		1
(1.00) 		0.838
(1.00) 		0.394
(1.00)
CCDC102B 11 (10%) 94 0.465
(1.00) 		0.928
(1.00) 		1
(1.00) 		1
(1.00) 		0.187
(1.00) 		0.6
(1.00)
GRXCR1 12 (11%) 93 0.501
(1.00) 		0.019
(1.00) 		1
(1.00) 		1
(1.00) 		0.937
(1.00) 		0.42
(1.00)
OR4M2 13 (12%) 92 0.858
(1.00) 		0.00192
(0.82) 		1
(1.00) 		1
(1.00) 		0.475
(1.00) 		0.537
(1.00)
PLAC8L1 3 (3%) 102 0.226
(1.00) 		0.553
(1.00) 		1
(1.00) 		0.991
(1.00) 		0.783
(1.00)
POM121 10 (10%) 95 0.0543
(1.00) 		0.921
(1.00) 		0.72
(1.00) 		1
(1.00) 		0.0981
(1.00) 		0.0476
(1.00)
RBM11 9 (9%) 96 0.238
(1.00) 		0.0272
(1.00) 		1
(1.00) 		1
(1.00) 		0.0733
(1.00) 		0.644
(1.00)
RUNX1T1 16 (15%) 89 0.814
(1.00) 		0.127
(1.00) 		0.141
(1.00) 		1
(1.00) 		0.4
(1.00) 		0.984
(1.00)
RGS7 17 (16%) 88 0.339
(1.00) 		0.0121
(1.00) 		1
(1.00) 		0.27
(1.00) 		0.0912
(1.00) 		0.845
(1.00)
CLVS2 11 (10%) 94 0.496
(1.00) 		0.442
(1.00) 		0.501
(1.00) 		1
(1.00) 		0.147
(1.00) 		0.845
(1.00)
DEFB118 6 (6%) 99 0.66
(1.00) 		0.715
(1.00) 		1
(1.00) 		1
(1.00) 		0.0446
(1.00) 		0.589
(1.00)
OR52A5 12 (11%) 93 0.221
(1.00) 		0.272
(1.00) 		1
(1.00) 		1
(1.00) 		0.18
(1.00) 		0.623
(1.00)
SPTLC3 13 (12%) 92 0.689
(1.00) 		0.875
(1.00) 		1
(1.00) 		1
(1.00) 		0.319
(1.00) 		0.281
(1.00)
PHGDH 7 (7%) 98 0.59
(1.00) 		0.156
(1.00) 		0.102
(1.00) 		1
(1.00) 		0.0446
(1.00) 		0.0424
(1.00)
RGPD3 18 (17%) 87 0.494
(1.00) 		0.00612
(1.00) 		0.0204
(1.00) 		0.575
(1.00) 		0.152
(1.00) 		0.774
(1.00)
WDR12 7 (7%) 98 0.0135
(1.00) 		0.552
(1.00) 		0.102
(1.00) 		1
(1.00) 		0.97
(1.00) 		0.968
(1.00)
MPP7 13 (12%) 92 0.816
(1.00) 		0.138
(1.00) 		0.336
(1.00) 		0.25
(1.00) 		0.323
(1.00) 		0.67
(1.00)
'CCNE2 MUTATION STATUS' versus 'Time to Death'

P value = 1.6e-05 (logrank test), Q value = 0.007

Table S1.  Gene #16: 'CCNE2 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 55 32 1.0 - 98.8 (13.3)
CCNE2 MUTATED 7 7 1.1 - 13.3 (10.3)
CCNE2 WILD-TYPE 48 25 1.0 - 98.8 (15.0)

Figure S1.  Get High-res Image Gene #16: 'CCNE2 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'BAGE2 MUTATION STATUS' versus 'LYMPH.NODE.METASTASIS'

P value = 0.000138 (Chi-square test), Q value = 0.06

Table S2.  Gene #25: 'BAGE2 MUTATION STATUS' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 N1A N1B N2 N2A N2B N2C N3 NX
ALL 58 1 3 9 1 3 7 1 12 1
BAGE2 MUTATED 2 0 1 0 1 0 0 1 2 0
BAGE2 WILD-TYPE 56 1 2 9 0 3 7 0 10 1

Figure S2.  Get High-res Image Gene #25: 'BAGE2 MUTATION STATUS' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

'PPP6C MUTATION STATUS' versus 'LYMPH.NODE.METASTASIS'

P value = 0.000234 (Chi-square test), Q value = 0.1

Table S3.  Gene #27: 'PPP6C MUTATION STATUS' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 N1A N1B N2 N2A N2B N2C N3 NX
ALL 58 1 3 9 1 3 7 1 12 1
PPP6C MUTATED 2 0 2 1 1 0 1 1 2 0
PPP6C WILD-TYPE 56 1 1 8 0 3 6 0 10 1

Figure S3.  Get High-res Image Gene #27: 'PPP6C MUTATION STATUS' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

'OXA1L MUTATION STATUS' versus 'LYMPH.NODE.METASTASIS'

P value = 0.000303 (Chi-square test), Q value = 0.13

Table S4.  Gene #35: 'OXA1L MUTATION STATUS' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 N1A N1B N2 N2A N2B N2C N3 NX
ALL 58 1 3 9 1 3 7 1 12 1
OXA1L MUTATED 2 0 1 0 1 0 0 0 0 0
OXA1L WILD-TYPE 56 1 2 9 0 3 7 1 12 1

Figure S4.  Get High-res Image Gene #35: 'OXA1L MUTATION STATUS' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

'LIN7A MUTATION STATUS' versus 'Time to Death'

P value = 0.000224 (logrank test), Q value = 0.097

Table S5.  Gene #39: 'LIN7A MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 55 32 1.0 - 98.8 (13.3)
LIN7A MUTATED 4 3 1.0 - 12.2 (1.1)
LIN7A WILD-TYPE 51 29 2.9 - 98.8 (14.7)

Figure S5.  Get High-res Image Gene #39: 'LIN7A MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'VGLL1 MUTATION STATUS' versus 'LYMPH.NODE.METASTASIS'

P value = 0.00012 (Chi-square test), Q value = 0.052

Table S6.  Gene #40: 'VGLL1 MUTATION STATUS' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 N1A N1B N2 N2A N2B N2C N3 NX
ALL 58 1 3 9 1 3 7 1 12 1
VGLL1 MUTATED 1 0 0 0 1 0 0 0 1 0
VGLL1 WILD-TYPE 57 1 3 9 0 3 7 1 11 1

Figure S6.  Get High-res Image Gene #40: 'VGLL1 MUTATION STATUS' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

'ACTL7B MUTATION STATUS' versus 'LYMPH.NODE.METASTASIS'

P value = 1.49e-05 (Chi-square test), Q value = 0.0065

Table S7.  Gene #70: 'ACTL7B MUTATION STATUS' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 N1A N1B N2 N2A N2B N2C N3 NX
ALL 58 1 3 9 1 3 7 1 12 1
ACTL7B MUTATED 3 0 0 0 1 0 0 0 0 1
ACTL7B WILD-TYPE 55 1 3 9 0 3 7 1 12 0

Figure S7.  Get High-res Image Gene #70: 'ACTL7B MUTATION STATUS' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

Methods & Data
Input

  • Mutation data file = SKCM-Regional_LN.mutsig.cluster.txt

  • Clinical data file = SKCM-Regional_LN.clin.merged.picked.txt

  • Number of patients = 105

  • Number of significantly mutated genes = 74

  • Number of selected clinical features = 7

  • Exclude genes that fewer than K tumors have mutations, K = 3

Survival analysis

For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R

Student's t-test analysis

For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between tumors with and without gene mutations using 't.test' function in R

Fisher's exact test

For binary or multi-class clinical features (nominal or ordinal), two-tailed Fisher's exact tests (Fisher 1922) were used to estimate the P values using the 'fisher.test' function in R

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.test' function in R

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

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.

References
[1] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[2] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[3] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)
[4] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[5] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)
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