Skin Cutaneous Melanoma: Correlation between copy number variations of arm-level result and molecular subtypes
(All_Metastatic cohort)
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Overview
Introduction

This pipeline computes the correlation between significant arm-level copy number variations (cnvs) and subtypes.

Summary

Testing the association between copy number variation 78 arm-level results and 8 molecular subtypes across 244 patients, 33 significant findings detected with Q value < 0.25.

  • 1q gain cnv correlated to 'CN_CNMF'.

  • 6p gain cnv correlated to 'CN_CNMF' and 'METHLYATION_CNMF'.

  • 7p gain cnv correlated to 'CN_CNMF' and 'MRNASEQ_CNMF'.

  • 7q gain cnv correlated to 'CN_CNMF' and 'MRNASEQ_CNMF'.

  • 8p gain cnv correlated to 'CN_CNMF'.

  • 8q gain cnv correlated to 'CN_CNMF' and 'METHLYATION_CNMF'.

  • 11q gain cnv correlated to 'CN_CNMF'.

  • 12p gain cnv correlated to 'CN_CNMF'.

  • 13q gain cnv correlated to 'CN_CNMF' and 'METHLYATION_CNMF'.

  • 15q gain cnv correlated to 'CN_CNMF'.

  • 19q gain cnv correlated to 'MRNASEQ_CNMF'.

  • 20p gain cnv correlated to 'CN_CNMF' and 'MRNASEQ_CNMF'.

  • 20q gain cnv correlated to 'CN_CNMF'.

  • 21q gain cnv correlated to 'METHLYATION_CNMF'.

  • 6q loss cnv correlated to 'CN_CNMF'.

  • 9p loss cnv correlated to 'CN_CNMF' and 'METHLYATION_CNMF'.

  • 10p loss cnv correlated to 'CN_CNMF',  'METHLYATION_CNMF', and 'MRNASEQ_CNMF'.

  • 10q loss cnv correlated to 'CN_CNMF',  'METHLYATION_CNMF',  'MRNASEQ_CNMF', and 'MRNASEQ_CHIERARCHICAL'.

  • 11q loss cnv correlated to 'CN_CNMF'.

  • 14q loss cnv correlated to 'CN_CNMF'.

  • 15q loss cnv correlated to 'CN_CNMF'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between significant copy number variation of 78 arm-level results and 8 molecular subtypes. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 33 significant findings detected.

Molecular
subtypes 		CN
CNMF 		METHLYATION
CNMF 		RPPA
CNMF 		RPPA
CHIERARCHICAL 		MRNASEQ
CNMF 		MRNASEQ
CHIERARCHICAL 		MIRSEQ
CNMF 		MIRSEQ
CHIERARCHICAL
nCNV (%) nWild-Type Fisher's exact test Fisher's exact test Fisher's exact test Chi-square test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test
10q loss 112 (46%) 132 3.02e-10
(1.87e-07) 		9.78e-05
(0.0587) 		0.539
(1.00) 		0.192
(1.00) 		2.52e-07
(0.000154) 		0.000187
(0.112) 		0.516
(1.00) 		0.659
(1.00)
10p loss 103 (42%) 141 2.19e-07
(0.000134) 		5.53e-05
(0.0333) 		0.44
(1.00) 		0.158
(1.00) 		3.92e-07
(0.000239) 		0.000441
(0.259) 		0.202
(1.00) 		0.323
(1.00)
6p gain 76 (31%) 168 2.21e-10
(1.37e-07) 		1.87e-05
(0.0113) 		0.0894
(1.00) 		0.589
(1.00) 		0.17
(1.00) 		0.133
(1.00) 		0.012
(1.00) 		0.000527
(0.309)
7p gain 101 (41%) 143 3.05e-10
(1.89e-07) 		0.109
(1.00) 		0.195
(1.00) 		0.298
(1.00) 		0.000225
(0.134) 		0.0343
(1.00) 		0.826
(1.00) 		0.431
(1.00)
7q gain 104 (43%) 140 6.52e-12
(4.05e-09) 		0.236
(1.00) 		0.13
(1.00) 		0.167
(1.00) 		0.00014
(0.0839) 		0.0308
(1.00) 		0.319
(1.00) 		0.108
(1.00)
8q gain 78 (32%) 166 1.6e-09
(9.87e-07) 		2.01e-05
(0.0121) 		0.603
(1.00) 		0.924
(1.00) 		0.000609
(0.356) 		0.00968
(1.00) 		0.424
(1.00) 		0.62
(1.00)
13q gain 39 (16%) 205 7e-06
(0.00424) 		2.3e-05
(0.0138) 		0.399
(1.00) 		0.174
(1.00) 		0.0193
(1.00) 		0.0545
(1.00) 		0.394
(1.00) 		0.477
(1.00)
20p gain 70 (29%) 174 6.08e-06
(0.00369) 		0.00195
(1.00) 		0.345
(1.00) 		0.904
(1.00) 		0.000225
(0.134) 		0.00204
(1.00) 		0.05
(1.00) 		0.167
(1.00)
9p loss 134 (55%) 110 3.31e-08
(2.03e-05) 		1.93e-05
(0.0117) 		0.188
(1.00) 		0.131
(1.00) 		0.000721
(0.419) 		0.000627
(0.366) 		0.74
(1.00) 		0.532
(1.00)
1q gain 77 (32%) 167 7.15e-09
(4.4e-06) 		0.00845
(1.00) 		0.366
(1.00) 		0.172
(1.00) 		0.0745
(1.00) 		0.625
(1.00) 		0.205
(1.00) 		0.0163
(1.00)
8p gain 49 (20%) 195 1.16e-09
(7.16e-07) 		0.0118
(1.00) 		0.0562
(1.00) 		0.364
(1.00) 		0.0153
(1.00) 		0.0398
(1.00) 		0.733
(1.00) 		0.438
(1.00)
11q gain 15 (6%) 229 0.000314
(0.186) 		0.413
(1.00) 		0.307
(1.00) 		0.72
(1.00) 		0.0213
(1.00) 		0.298
(1.00) 		0.776
(1.00) 		0.0625
(1.00)
12p gain 23 (9%) 221 4.41e-07
(0.000268) 		0.147
(1.00) 		0.883
(1.00) 		0.928
(1.00) 		0.00711
(1.00) 		0.0841
(1.00) 		0.317
(1.00) 		0.441
(1.00)
15q gain 35 (14%) 209 2e-06
(0.00122) 		0.42
(1.00) 		0.567
(1.00) 		0.912
(1.00) 		0.546
(1.00) 		0.243
(1.00) 		0.833
(1.00) 		0.0954
(1.00)
19q gain 20 (8%) 224 0.00761
(1.00) 		0.0396
(1.00) 		0.415
(1.00) 		0.678
(1.00) 		0.000299
(0.177) 		0.0684
(1.00) 		0.727
(1.00) 		0.834
(1.00)
20q gain 89 (36%) 155 1.66e-08
(1.02e-05) 		0.00199
(1.00) 		0.448
(1.00) 		0.802
(1.00) 		0.0026
(1.00) 		0.0664
(1.00) 		0.382
(1.00) 		0.42
(1.00)
21q gain 29 (12%) 215 0.0384
(1.00) 		0.000261
(0.155) 		0.182
(1.00) 		0.402
(1.00) 		0.0461
(1.00) 		0.00908
(1.00) 		0.47
(1.00) 		0.956
(1.00)
6q loss 90 (37%) 154 0.000111
(0.0664) 		0.000705
(0.41) 		0.354
(1.00) 		0.454
(1.00) 		0.00158
(0.91) 		0.0675
(1.00) 		0.622
(1.00) 		0.278
(1.00)
11q loss 64 (26%) 180 0.000382
(0.225) 		0.0246
(1.00) 		0.916
(1.00) 		0.764
(1.00) 		0.0965
(1.00) 		0.00748
(1.00) 		0.68
(1.00) 		0.645
(1.00)
14q loss 54 (22%) 190 6.92e-08
(4.24e-05) 		0.0508
(1.00) 		0.411
(1.00) 		0.276
(1.00) 		0.00397
(1.00) 		0.328
(1.00) 		0.522
(1.00) 		0.436
(1.00)
15q loss 15 (6%) 229 0.00027
(0.16) 		0.0849
(1.00) 		0.651
(1.00) 		0.138
(1.00) 		0.632
(1.00) 		0.884
(1.00) 		0.208
(1.00) 		0.0568
(1.00)
1p gain 28 (11%) 216 0.00929
(1.00) 		0.265
(1.00) 		0.148
(1.00) 		0.517
(1.00) 		0.0178
(1.00) 		0.139
(1.00) 		0.711
(1.00) 		0.223
(1.00)
2p gain 30 (12%) 214 0.00321
(1.00) 		0.307
(1.00) 		0.0696
(1.00) 		0.391
(1.00) 		0.0113
(1.00) 		0.0173
(1.00) 		0.0831
(1.00) 		0.0226
(1.00)
2q gain 28 (11%) 216 0.00246
(1.00) 		0.475
(1.00) 		0.0198
(1.00) 		0.384
(1.00) 		0.0035
(1.00) 		0.00468
(1.00) 		0.0083
(1.00) 		0.00169
(0.973)
3p gain 23 (9%) 221 0.058
(1.00) 		0.0677
(1.00) 		1
(1.00) 		0.771
(1.00) 		0.174
(1.00) 		0.271
(1.00) 		0.577
(1.00) 		0.758
(1.00)
3q gain 30 (12%) 214 0.158
(1.00) 		0.164
(1.00) 		0.565
(1.00) 		0.594
(1.00) 		0.366
(1.00) 		0.497
(1.00) 		0.848
(1.00) 		0.67
(1.00)
4p gain 24 (10%) 220 0.00178
(1.00) 		0.0523
(1.00) 		0.979
(1.00) 		0.469
(1.00) 		0.733
(1.00) 		0.526
(1.00) 		0.697
(1.00) 		0.586
(1.00)
4q gain 20 (8%) 224 0.00101
(0.588) 		0.0357
(1.00) 		0.923
(1.00) 		0.945
(1.00) 		0.514
(1.00) 		0.535
(1.00) 		0.458
(1.00) 		0.614
(1.00)
5p gain 28 (11%) 216 0.00966
(1.00) 		0.265
(1.00) 		0.758
(1.00) 		0.819
(1.00) 		0.849
(1.00) 		0.201
(1.00) 		0.249
(1.00) 		0.754
(1.00)
5q gain 14 (6%) 230 0.0213
(1.00) 		0.389
(1.00) 		0.629
(1.00) 		0.404
(1.00) 		0.45
(1.00) 		0.167
(1.00) 		0.359
(1.00) 		0.193
(1.00)
6q gain 18 (7%) 226 0.25
(1.00) 		0.0564
(1.00) 		1
(1.00) 		0.498
(1.00) 		0.746
(1.00) 		0.245
(1.00) 		0.454
(1.00) 		0.698
(1.00)
9p gain 9 (4%) 235 0.761
(1.00) 		0.144
(1.00) 		0.12
(1.00) 		0.435
(1.00) 		0.395
(1.00) 		0.805
(1.00) 		0.766
(1.00) 		0.459
(1.00)
9q gain 10 (4%) 234 0.0983
(1.00) 		0.468
(1.00) 		0.236
(1.00) 		0.6
(1.00) 		0.784
(1.00) 		0.836
(1.00) 		0.766
(1.00) 		0.341
(1.00)
11p gain 17 (7%) 227 0.00986
(1.00) 		0.901
(1.00) 		0.147
(1.00) 		0.474
(1.00) 		0.0172
(1.00) 		0.483
(1.00) 		0.541
(1.00) 		0.0761
(1.00)
12q gain 11 (5%) 233 0.000691
(0.403) 		0.629
(1.00) 		0.847
(1.00) 		0.807
(1.00) 		0.046
(1.00) 		0.0261
(1.00) 		0.421
(1.00) 		0.231
(1.00)
14q gain 16 (7%) 228 0.0348
(1.00) 		0.0614
(1.00) 		0.0431
(1.00) 		0.257
(1.00) 		1
(1.00) 		0.791
(1.00) 		0.484
(1.00) 		0.801
(1.00)
16p gain 16 (7%) 228 0.000554
(0.324) 		0.00913
(1.00) 		0.452
(1.00) 		0.724
(1.00) 		0.0378
(1.00) 		0.125
(1.00) 		0.674
(1.00) 		0.512
(1.00)
16q gain 15 (6%) 229 0.0154
(1.00) 		0.0462
(1.00) 		0.192
(1.00) 		0.527
(1.00) 		0.0373
(1.00) 		0.231
(1.00) 		0.743
(1.00) 		0.228
(1.00)
17p gain 16 (7%) 228 0.0636
(1.00) 		0.207
(1.00) 		0.719
(1.00) 		0.79
(1.00) 		0.317
(1.00) 		0.465
(1.00) 		0.432
(1.00) 		0.475
(1.00)
17q gain 28 (11%) 216 0.0214
(1.00) 		0.0441
(1.00) 		0.702
(1.00) 		0.293
(1.00) 		0.408
(1.00) 		0.193
(1.00) 		0.153
(1.00) 		0.542
(1.00)
18p gain 27 (11%) 217 0.0511
(1.00) 		0.161
(1.00) 		0.91
(1.00) 		0.801
(1.00) 		0.0754
(1.00) 		0.0714
(1.00) 		0.356
(1.00) 		0.293
(1.00)
18q gain 16 (7%) 228 0.0348
(1.00) 		0.219
(1.00) 		0.536
(1.00) 		0.308
(1.00) 		0.236
(1.00) 		0.0449
(1.00) 		1
(1.00) 		0.927
(1.00)
19p gain 16 (7%) 228 0.00812
(1.00) 		0.028
(1.00) 		0.623
(1.00) 		0.204
(1.00) 		0.00382
(1.00) 		0.00207
(1.00) 		0.285
(1.00) 		0.801
(1.00)
22q gain 59 (24%) 185 0.00209
(1.00) 		0.197
(1.00) 		0.803
(1.00) 		0.933
(1.00) 		0.527
(1.00) 		0.867
(1.00) 		0.717
(1.00) 		0.581
(1.00)
Xq gain 4 (2%) 240 0.829
(1.00) 		0.835
(1.00) 		1
(1.00) 		0.507
(1.00) 		0.038
(1.00) 		0.118
(1.00) 		0.566
(1.00) 		0.422
(1.00)
1p loss 15 (6%) 229 0.0605
(1.00) 		0.0256
(1.00) 		0.383
(1.00) 		0.416
(1.00) 		0.116
(1.00) 		0.359
(1.00) 		0.459
(1.00) 		0.927
(1.00)
1q loss 7 (3%) 237 0.0637
(1.00) 		0.345
(1.00) 		0.545
(1.00) 		0.563
(1.00) 		0.179
(1.00) 		0.525
(1.00) 		0.7
(1.00) 		0.734
(1.00)
2p loss 18 (7%) 226 0.00883
(1.00) 		0.106
(1.00) 		0.306
(1.00) 		0.0384
(1.00) 		0.533
(1.00) 		0.276
(1.00) 		0.00126
(0.73) 		0.0017
(0.976)
2q loss 19 (8%) 225 0.0225
(1.00) 		0.0171
(1.00) 		0.208
(1.00) 		0.15
(1.00) 		0.376
(1.00) 		0.237
(1.00) 		0.0023
(1.00) 		0.00987
(1.00)
3p loss 22 (9%) 222 0.0585
(1.00) 		0.0761
(1.00) 		0.382
(1.00) 		0.364
(1.00) 		0.0021
(1.00) 		0.027
(1.00) 		0.778
(1.00) 		0.479
(1.00)
3q loss 19 (8%) 225 0.0735
(1.00) 		0.0606
(1.00) 		0.421
(1.00) 		0.209
(1.00) 		0.0675
(1.00) 		0.343
(1.00) 		0.954
(1.00) 		0.315
(1.00)
4p loss 30 (12%) 214 0.322
(1.00) 		0.193
(1.00) 		0.342
(1.00) 		0.132
(1.00) 		0.00477
(1.00) 		0.221
(1.00) 		0.403
(1.00) 		0.482
(1.00)
4q loss 31 (13%) 213 0.142
(1.00) 		0.0664
(1.00) 		0.308
(1.00) 		0.0206
(1.00) 		0.00645
(1.00) 		0.0285
(1.00) 		0.291
(1.00) 		0.143
(1.00)
5p loss 32 (13%) 212 0.701
(1.00) 		0.262
(1.00) 		0.272
(1.00) 		0.333
(1.00) 		0.366
(1.00) 		0.283
(1.00) 		0.0912
(1.00) 		0.166
(1.00)
5q loss 46 (19%) 198 0.515
(1.00) 		0.0436
(1.00) 		0.0689
(1.00) 		0.553
(1.00) 		0.47
(1.00) 		0.536
(1.00) 		0.578
(1.00) 		0.86
(1.00)
6p loss 28 (11%) 216 0.00857
(1.00) 		0.215
(1.00) 		0.467
(1.00) 		0.347
(1.00) 		0.332
(1.00) 		0.251
(1.00) 		0.209
(1.00) 		0.11
(1.00)
7p loss 7 (3%) 237 0.799
(1.00) 		0.0348
(1.00) 		0.664
(1.00) 		0.471
(1.00) 		0.716
(1.00) 		0.786
(1.00) 		0.618
(1.00) 		1
(1.00)
7q loss 6 (2%) 238 0.676
(1.00) 		0.0911
(1.00) 		0.779
(1.00) 		0.703
(1.00) 		1
(1.00) 		0.875
(1.00) 		1
(1.00) 		1
(1.00)
8p loss 28 (11%) 216 0.0679
(1.00) 		0.111
(1.00) 		0.057
(1.00) 		0.486
(1.00) 		0.0594
(1.00) 		0.349
(1.00) 		0.202
(1.00) 		0.133
(1.00)
8q loss 5 (2%) 239 1
(1.00) 		0.0705
(1.00) 		0.139
(1.00) 		0.515
(1.00) 		0.327
(1.00) 		0.285
(1.00) 		0.853
(1.00) 		1
(1.00)
9q loss 100 (41%) 144 0.00566
(1.00) 		0.000441
(0.259) 		0.448
(1.00) 		0.364
(1.00) 		0.346
(1.00) 		0.794
(1.00) 		0.766
(1.00) 		0.464
(1.00)
11p loss 57 (23%) 187 0.00219
(1.00) 		0.0018
(1.00) 		0.484
(1.00) 		0.27
(1.00) 		0.0799
(1.00) 		0.00919
(1.00) 		0.223
(1.00) 		0.417
(1.00)
12p loss 15 (6%) 229 0.167
(1.00) 		0.00881
(1.00) 		0.5
(1.00) 		0.814
(1.00) 		0.277
(1.00) 		0.298
(1.00) 		0.776
(1.00) 		0.599
(1.00)
12q loss 24 (10%) 220 0.00538
(1.00) 		0.00239
(1.00) 		0.839
(1.00) 		0.875
(1.00) 		0.546
(1.00) 		0.356
(1.00) 		0.886
(1.00) 		0.947
(1.00)
13q loss 36 (15%) 208 0.322
(1.00) 		0.454
(1.00) 		0.172
(1.00) 		0.656
(1.00) 		0.389
(1.00) 		0.609
(1.00) 		0.121
(1.00) 		0.236
(1.00)
16p loss 22 (9%) 222 0.0216
(1.00) 		0.00567
(1.00) 		0.956
(1.00) 		0.204
(1.00) 		0.313
(1.00) 		0.39
(1.00) 		0.109
(1.00) 		0.321
(1.00)
16q loss 46 (19%) 198 0.00583
(1.00) 		0.0292
(1.00) 		0.571
(1.00) 		0.373
(1.00) 		0.0213
(1.00) 		0.0791
(1.00) 		0.134
(1.00) 		0.146
(1.00)
17p loss 50 (20%) 194 0.619
(1.00) 		0.175
(1.00) 		0.493
(1.00) 		0.688
(1.00) 		0.0519
(1.00) 		0.978
(1.00) 		0.749
(1.00) 		0.398
(1.00)
17q loss 22 (9%) 222 0.0805
(1.00) 		0.514
(1.00) 		0.387
(1.00) 		0.47
(1.00) 		0.0553
(1.00) 		0.562
(1.00) 		0.663
(1.00) 		1
(1.00)
18p loss 48 (20%) 196 0.00385
(1.00) 		0.00436
(1.00) 		0.372
(1.00) 		0.415
(1.00) 		0.000886
(0.514) 		0.0473
(1.00) 		0.978
(1.00) 		0.815
(1.00)
18q loss 44 (18%) 200 0.0128
(1.00) 		0.0182
(1.00) 		0.0502
(1.00) 		0.188
(1.00) 		0.0934
(1.00) 		0.55
(1.00) 		0.932
(1.00) 		0.666
(1.00)
19p loss 19 (8%) 225 0.21
(1.00) 		0.54
(1.00) 		0.00578
(1.00) 		0.536
(1.00) 		0.149
(1.00) 		0.137
(1.00) 		0.485
(1.00) 		0.641
(1.00)
19q loss 19 (8%) 225 0.22
(1.00) 		0.366
(1.00) 		0.654
(1.00) 		0.347
(1.00) 		0.376
(1.00) 		0.544
(1.00) 		0.685
(1.00) 		0.641
(1.00)
20p loss 12 (5%) 232 0.528
(1.00) 		0.422
(1.00) 		0.153
(1.00) 		0.744
(1.00) 		0.615
(1.00) 		0.288
(1.00) 		0.0858
(1.00) 		0.459
(1.00)
20q loss 3 (1%) 241 0.503
(1.00) 		0.187
(1.00) 		0.636
(1.00) 		0.303
(1.00) 		0.793
(1.00) 		1
(1.00)
21q loss 28 (11%) 216 0.261
(1.00) 		0.265
(1.00) 		0.823
(1.00) 		0.634
(1.00) 		0.436
(1.00) 		0.751
(1.00) 		0.903
(1.00) 		0.552
(1.00)
22q loss 19 (8%) 225 0.498
(1.00) 		0.205
(1.00) 		0.591
(1.00) 		0.428
(1.00) 		0.598
(1.00) 		0.517
(1.00) 		0.103
(1.00) 		0.583
(1.00)
Xq loss 10 (4%) 234 0.277
(1.00) 		0.00329
(1.00) 		0.283
(1.00) 		0.188
(1.00) 		0.335
(1.00) 		0.0616
(1.00) 		0.243
(1.00) 		0.501
(1.00)
'1q gain mutation analysis' versus 'CN_CNMF'

P value = 7.15e-09 (Fisher's exact test), Q value = 4.4e-06

Table S1.  Gene #2: '1q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 80 69 95
1Q GAIN MUTATED 46 16 15
1Q GAIN WILD-TYPE 34 53 80

Figure S1.  Get High-res Image Gene #2: '1q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'6p gain mutation analysis' versus 'CN_CNMF'

P value = 2.21e-10 (Fisher's exact test), Q value = 1.4e-07

Table S2.  Gene #11: '6p gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 80 69 95
6P GAIN MUTATED 48 12 16
6P GAIN WILD-TYPE 32 57 79

Figure S2.  Get High-res Image Gene #11: '6p gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'6p gain mutation analysis' versus 'METHLYATION_CNMF'

P value = 1.87e-05 (Fisher's exact test), Q value = 0.011

Table S3.  Gene #11: '6p gain mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 61 89 94
6P GAIN MUTATED 31 30 15
6P GAIN WILD-TYPE 30 59 79

Figure S3.  Get High-res Image Gene #11: '6p gain mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

'7p gain mutation analysis' versus 'CN_CNMF'

P value = 3.05e-10 (Fisher's exact test), Q value = 1.9e-07

Table S4.  Gene #13: '7p gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 80 69 95
7P GAIN MUTATED 35 48 18
7P GAIN WILD-TYPE 45 21 77

Figure S4.  Get High-res Image Gene #13: '7p gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'7p gain mutation analysis' versus 'MRNASEQ_CNMF'

P value = 0.000225 (Fisher's exact test), Q value = 0.13

Table S5.  Gene #13: '7p gain mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 79 70 92
7P GAIN MUTATED 47 19 35
7P GAIN WILD-TYPE 32 51 57

Figure S5.  Get High-res Image Gene #13: '7p gain mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

'7q gain mutation analysis' versus 'CN_CNMF'

P value = 6.52e-12 (Fisher's exact test), Q value = 4.1e-09

Table S6.  Gene #14: '7q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 80 69 95
7Q GAIN MUTATED 30 53 21
7Q GAIN WILD-TYPE 50 16 74

Figure S6.  Get High-res Image Gene #14: '7q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'7q gain mutation analysis' versus 'MRNASEQ_CNMF'

P value = 0.00014 (Fisher's exact test), Q value = 0.084

Table S7.  Gene #14: '7q gain mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 79 70 92
7Q GAIN MUTATED 49 21 34
7Q GAIN WILD-TYPE 30 49 58

Figure S7.  Get High-res Image Gene #14: '7q gain mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

'8p gain mutation analysis' versus 'CN_CNMF'

P value = 1.16e-09 (Fisher's exact test), Q value = 7.2e-07

Table S8.  Gene #15: '8p gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 80 69 95
8P GAIN MUTATED 17 29 3
8P GAIN WILD-TYPE 63 40 92

Figure S8.  Get High-res Image Gene #15: '8p gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'8q gain mutation analysis' versus 'CN_CNMF'

P value = 1.6e-09 (Fisher's exact test), Q value = 9.9e-07

Table S9.  Gene #16: '8q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 80 69 95
8Q GAIN MUTATED 35 34 9
8Q GAIN WILD-TYPE 45 35 86

Figure S9.  Get High-res Image Gene #16: '8q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'8q gain mutation analysis' versus 'METHLYATION_CNMF'

P value = 2.01e-05 (Fisher's exact test), Q value = 0.012

Table S10.  Gene #16: '8q gain mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 61 89 94
8Q GAIN MUTATED 34 25 19
8Q GAIN WILD-TYPE 27 64 75

Figure S10.  Get High-res Image Gene #16: '8q gain mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

'11q gain mutation analysis' versus 'CN_CNMF'

P value = 0.000314 (Fisher's exact test), Q value = 0.19

Table S11.  Gene #20: '11q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 80 69 95
11Q GAIN MUTATED 3 11 1
11Q GAIN WILD-TYPE 77 58 94

Figure S11.  Get High-res Image Gene #20: '11q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'12p gain mutation analysis' versus 'CN_CNMF'

P value = 4.41e-07 (Fisher's exact test), Q value = 0.00027

Table S12.  Gene #21: '12p gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 80 69 95
12P GAIN MUTATED 7 16 0
12P GAIN WILD-TYPE 73 53 95

Figure S12.  Get High-res Image Gene #21: '12p gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'13q gain mutation analysis' versus 'CN_CNMF'

P value = 7e-06 (Fisher's exact test), Q value = 0.0042

Table S13.  Gene #23: '13q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 80 69 95
13Q GAIN MUTATED 22 14 3
13Q GAIN WILD-TYPE 58 55 92

Figure S13.  Get High-res Image Gene #23: '13q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'13q gain mutation analysis' versus 'METHLYATION_CNMF'

P value = 2.3e-05 (Fisher's exact test), Q value = 0.014

Table S14.  Gene #23: '13q gain mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 61 89 94
13Q GAIN MUTATED 19 16 4
13Q GAIN WILD-TYPE 42 73 90

Figure S14.  Get High-res Image Gene #23: '13q gain mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

'15q gain mutation analysis' versus 'CN_CNMF'

P value = 2e-06 (Fisher's exact test), Q value = 0.0012

Table S15.  Gene #25: '15q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 80 69 95
15Q GAIN MUTATED 7 23 5
15Q GAIN WILD-TYPE 73 46 90

Figure S15.  Get High-res Image Gene #25: '15q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'19q gain mutation analysis' versus 'MRNASEQ_CNMF'

P value = 0.000299 (Fisher's exact test), Q value = 0.18

Table S16.  Gene #33: '19q gain mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 79 70 92
19Q GAIN MUTATED 13 0 6
19Q GAIN WILD-TYPE 66 70 86

Figure S16.  Get High-res Image Gene #33: '19q gain mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

'20p gain mutation analysis' versus 'CN_CNMF'

P value = 6.08e-06 (Fisher's exact test), Q value = 0.0037

Table S17.  Gene #34: '20p gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 80 69 95
20P GAIN MUTATED 26 32 12
20P GAIN WILD-TYPE 54 37 83

Figure S17.  Get High-res Image Gene #34: '20p gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'20p gain mutation analysis' versus 'MRNASEQ_CNMF'

P value = 0.000225 (Fisher's exact test), Q value = 0.13

Table S18.  Gene #34: '20p gain mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 79 70 92
20P GAIN MUTATED 36 11 23
20P GAIN WILD-TYPE 43 59 69

Figure S18.  Get High-res Image Gene #34: '20p gain mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

'20q gain mutation analysis' versus 'CN_CNMF'

P value = 1.66e-08 (Fisher's exact test), Q value = 1e-05

Table S19.  Gene #35: '20q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 80 69 95
20Q GAIN MUTATED 36 39 14
20Q GAIN WILD-TYPE 44 30 81

Figure S19.  Get High-res Image Gene #35: '20q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'21q gain mutation analysis' versus 'METHLYATION_CNMF'

P value = 0.000261 (Fisher's exact test), Q value = 0.15

Table S20.  Gene #36: '21q gain mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 61 89 94
21Q GAIN MUTATED 15 11 3
21Q GAIN WILD-TYPE 46 78 91

Figure S20.  Get High-res Image Gene #36: '21q gain mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

'6q loss mutation analysis' versus 'CN_CNMF'

P value = 0.000111 (Fisher's exact test), Q value = 0.066

Table S21.  Gene #50: '6q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 80 69 95
6Q LOSS MUTATED 35 35 20
6Q LOSS WILD-TYPE 45 34 75

Figure S21.  Get High-res Image Gene #50: '6q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'9p loss mutation analysis' versus 'CN_CNMF'

P value = 3.31e-08 (Fisher's exact test), Q value = 2e-05

Table S22.  Gene #55: '9p loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 80 69 95
9P LOSS MUTATED 56 48 30
9P LOSS WILD-TYPE 24 21 65

Figure S22.  Get High-res Image Gene #55: '9p loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'9p loss mutation analysis' versus 'METHLYATION_CNMF'

P value = 1.93e-05 (Fisher's exact test), Q value = 0.012

Table S23.  Gene #55: '9p loss mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 61 89 94
9P LOSS MUTATED 40 60 34
9P LOSS WILD-TYPE 21 29 60

Figure S23.  Get High-res Image Gene #55: '9p loss mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

'10p loss mutation analysis' versus 'CN_CNMF'

P value = 2.19e-07 (Fisher's exact test), Q value = 0.00013

Table S24.  Gene #57: '10p loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 80 69 95
10P LOSS MUTATED 38 44 21
10P LOSS WILD-TYPE 42 25 74

Figure S24.  Get High-res Image Gene #57: '10p loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'10p loss mutation analysis' versus 'METHLYATION_CNMF'

P value = 5.53e-05 (Fisher's exact test), Q value = 0.033

Table S25.  Gene #57: '10p loss mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 61 89 94
10P LOSS MUTATED 36 43 24
10P LOSS WILD-TYPE 25 46 70

Figure S25.  Get High-res Image Gene #57: '10p loss mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

'10p loss mutation analysis' versus 'MRNASEQ_CNMF'

P value = 3.92e-07 (Fisher's exact test), Q value = 0.00024

Table S26.  Gene #57: '10p loss mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 79 70 92
10P LOSS MUTATED 51 15 36
10P LOSS WILD-TYPE 28 55 56

Figure S26.  Get High-res Image Gene #57: '10p loss mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

'10q loss mutation analysis' versus 'CN_CNMF'

P value = 3.02e-10 (Fisher's exact test), Q value = 1.9e-07

Table S27.  Gene #58: '10q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 80 69 95
10Q LOSS MUTATED 41 50 21
10Q LOSS WILD-TYPE 39 19 74

Figure S27.  Get High-res Image Gene #58: '10q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'10q loss mutation analysis' versus 'METHLYATION_CNMF'

P value = 9.78e-05 (Fisher's exact test), Q value = 0.059

Table S28.  Gene #58: '10q loss mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 61 89 94
10Q LOSS MUTATED 35 50 27
10Q LOSS WILD-TYPE 26 39 67

Figure S28.  Get High-res Image Gene #58: '10q loss mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

'10q loss mutation analysis' versus 'MRNASEQ_CNMF'

P value = 2.52e-07 (Fisher's exact test), Q value = 0.00015

Table S29.  Gene #58: '10q loss mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 79 70 92
10Q LOSS MUTATED 55 18 39
10Q LOSS WILD-TYPE 24 52 53

Figure S29.  Get High-res Image Gene #58: '10q loss mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

'10q loss mutation analysis' versus 'MRNASEQ_CHIERARCHICAL'

P value = 0.000187 (Fisher's exact test), Q value = 0.11

Table S30.  Gene #58: '10q loss mutation analysis' versus Clinical Feature #6: 'MRNASEQ_CHIERARCHICAL'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 62 57 122
10Q LOSS MUTATED 26 40 46
10Q LOSS WILD-TYPE 36 17 76

Figure S30.  Get High-res Image Gene #58: '10q loss mutation analysis' versus Clinical Feature #6: 'MRNASEQ_CHIERARCHICAL'

'11q loss mutation analysis' versus 'CN_CNMF'

P value = 0.000382 (Fisher's exact test), Q value = 0.23

Table S31.  Gene #60: '11q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 80 69 95
11Q LOSS MUTATED 33 17 14
11Q LOSS WILD-TYPE 47 52 81

Figure S31.  Get High-res Image Gene #60: '11q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'14q loss mutation analysis' versus 'CN_CNMF'

P value = 6.92e-08 (Fisher's exact test), Q value = 4.2e-05

Table S32.  Gene #64: '14q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 80 69 95
14Q LOSS MUTATED 18 30 6
14Q LOSS WILD-TYPE 62 39 89

Figure S32.  Get High-res Image Gene #64: '14q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'15q loss mutation analysis' versus 'CN_CNMF'

P value = 0.00027 (Fisher's exact test), Q value = 0.16

Table S33.  Gene #65: '15q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 80 69 95
15Q LOSS MUTATED 12 2 1
15Q LOSS WILD-TYPE 68 67 94

Figure S33.  Get High-res Image Gene #65: '15q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

Methods & Data
Input

  • Mutation data file = broad_values_by_arm.mutsig.cluster.txt

  • Molecular subtypes file = SKCM-All_Metastatic.transferedmergedcluster.txt

  • Number of patients = 244

  • Number of significantly arm-level cnvs = 78

  • Number of molecular subtypes = 8

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

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] 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)
[2] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[3] 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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