Correlation between copy number variations of arm-level result and molecular subtypes
Skin Cutaneous Melanoma (Metastatic)
22 February 2013  |  analyses__2013_02_22
Maintainer Information
Citation Information
doi:10.7908/C14T6GK8
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between copy number variations of arm-level result and molecular subtypes. Broad Institute of MIT and Harvard. doi:10.7908/C14T6GK8
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 236 patients, 28 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'.

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

  • 8p gain cnv correlated to 'CN_CNMF'.

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

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

  • 20q gain cnv correlated to 'CN_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', and 'MRNASEQ_CNMF'.

  • 14q loss cnv correlated to 'CN_CNMF'.

  • 18p loss cnv correlated to 'MRNASEQ_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, 28 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 Chi-square test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test
8q gain 77 (33%) 159 5.23e-08
(3.21e-05) 		9.75e-06
(0.00592) 		0.842
(1.00) 		0.668
(1.00) 		0.000335
(0.2) 		0.0075
(1.00) 		0.558
(1.00) 		0.581
(1.00)
10p loss 102 (43%) 134 5.15e-08
(3.17e-05) 		8.69e-05
(0.0523) 		0.653
(1.00) 		0.133
(1.00) 		1.13e-06
(0.000687) 		0.000864
(0.503) 		0.576
(1.00) 		0.163
(1.00)
10q loss 111 (47%) 125 1.04e-09
(6.43e-07) 		0.000118
(0.0708) 		0.503
(1.00) 		0.314
(1.00) 		1.11e-06
(0.000676) 		0.000661
(0.39) 		0.985
(1.00) 		0.376
(1.00)
6p gain 75 (32%) 161 8.56e-08
(5.25e-05) 		3.43e-05
(0.0207) 		0.331
(1.00) 		0.571
(1.00) 		0.267
(1.00) 		0.0458
(1.00) 		0.0101
(1.00) 		0.00081
(0.473)
7q gain 102 (43%) 134 1.32e-18
(8.22e-16) 		0.305
(1.00) 		0.317
(1.00) 		0.0957
(1.00) 		0.000222
(0.133) 		0.0449
(1.00) 		0.306
(1.00) 		0.108
(1.00)
13q gain 38 (16%) 198 9.84e-06
(0.00596) 		2.95e-05
(0.0179) 		0.178
(1.00) 		0.315
(1.00) 		0.00828
(1.00) 		0.0141
(1.00) 		0.502
(1.00) 		0.308
(1.00)
20p gain 68 (29%) 168 5.01e-07
(0.000307) 		0.00306
(1.00) 		0.187
(1.00) 		0.816
(1.00) 		0.000149
(0.0892) 		0.00887
(1.00) 		0.0795
(1.00) 		0.269
(1.00)
9p loss 129 (55%) 107 1.43e-08
(8.84e-06) 		0.000136
(0.0814) 		0.0597
(1.00) 		0.0601
(1.00) 		0.00292
(1.00) 		0.00297
(1.00) 		1
(1.00) 		0.407
(1.00)
1q gain 75 (32%) 161 2.01e-06
(0.00122) 		0.0187
(1.00) 		0.308
(1.00) 		0.154
(1.00) 		0.0799
(1.00) 		0.385
(1.00) 		0.2
(1.00) 		0.0717
(1.00)
7p gain 99 (42%) 137 2.94e-15
(1.83e-12) 		0.147
(1.00) 		0.394
(1.00) 		0.145
(1.00) 		0.000485
(0.288) 		0.0592
(1.00) 		0.831
(1.00) 		0.461
(1.00)
8p gain 48 (20%) 188 2.12e-08
(1.31e-05) 		0.00879
(1.00) 		0.221
(1.00) 		0.0371
(1.00) 		0.00846
(1.00) 		0.0308
(1.00) 		0.707
(1.00) 		0.425
(1.00)
12p gain 23 (10%) 213 1.15e-06
(0.000699) 		0.16
(1.00) 		0.787
(1.00) 		0.957
(1.00) 		0.00172
(0.987) 		0.209
(1.00) 		0.306
(1.00) 		0.457
(1.00)
15q gain 32 (14%) 204 0.000156
(0.0934) 		0.442
(1.00) 		0.301
(1.00) 		0.92
(1.00) 		0.504
(1.00) 		0.492
(1.00) 		1
(1.00) 		0.123
(1.00)
20q gain 85 (36%) 151 1.72e-09
(1.07e-06) 		0.000934
(0.541) 		0.223
(1.00) 		0.928
(1.00) 		0.000771
(0.452) 		0.0585
(1.00) 		0.351
(1.00) 		0.512
(1.00)
6q loss 89 (38%) 147 3.13e-05
(0.0189) 		0.000804
(0.471) 		0.244
(1.00) 		0.159
(1.00) 		0.00416
(1.00) 		0.016
(1.00) 		0.535
(1.00) 		0.257
(1.00)
14q loss 51 (22%) 185 1.98e-08
(1.22e-05) 		0.0957
(1.00) 		0.0821
(1.00) 		0.191
(1.00) 		0.0043
(1.00) 		0.134
(1.00) 		1
(1.00) 		0.432
(1.00)
18p loss 47 (20%) 189 0.00217
(1.00) 		0.00676
(1.00) 		0.147
(1.00) 		0.228
(1.00) 		0.000288
(0.172) 		0.0291
(1.00) 		0.932
(1.00) 		0.885
(1.00)
1p gain 27 (11%) 209 0.00513
(1.00) 		0.264
(1.00) 		0.177
(1.00) 		0.387
(1.00) 		0.0811
(1.00) 		0.656
(1.00) 		0.965
(1.00) 		0.249
(1.00)
2p gain 30 (13%) 206 0.000577
(0.341) 		0.26
(1.00) 		0.00433
(1.00) 		0.587
(1.00) 		0.00974
(1.00) 		0.0496
(1.00) 		0.202
(1.00) 		0.00782
(1.00)
2q gain 27 (11%) 209 0.000777
(0.455) 		0.295
(1.00) 		0.0216
(1.00) 		0.0588
(1.00) 		0.00492
(1.00) 		0.00564
(1.00) 		0.0397
(1.00) 		0.00574
(1.00)
3p gain 23 (10%) 213 0.0509
(1.00) 		0.0685
(1.00) 		0.727
(1.00) 		0.755
(1.00) 		0.16
(1.00) 		0.0569
(1.00) 		0.53
(1.00) 		0.286
(1.00)
3q gain 29 (12%) 207 0.247
(1.00) 		0.211
(1.00) 		0.972
(1.00) 		0.438
(1.00) 		0.18
(1.00) 		0.232
(1.00) 		0.903
(1.00) 		0.482
(1.00)
4p gain 23 (10%) 213 0.0199
(1.00) 		0.0685
(1.00) 		0.845
(1.00) 		0.445
(1.00) 		0.816
(1.00) 		0.769
(1.00) 		0.778
(1.00) 		0.848
(1.00)
4q gain 20 (8%) 216 0.0126
(1.00) 		0.0292
(1.00) 		0.992
(1.00) 		0.971
(1.00) 		0.427
(1.00) 		0.538
(1.00) 		0.328
(1.00) 		0.881
(1.00)
5p gain 28 (12%) 208 0.00115
(0.661) 		0.268
(1.00) 		0.911
(1.00) 		0.813
(1.00) 		0.906
(1.00) 		0.806
(1.00) 		0.135
(1.00) 		0.748
(1.00)
5q gain 13 (6%) 223 0.0471
(1.00) 		0.448
(1.00) 		0.826
(1.00) 		0.456
(1.00) 		0.422
(1.00) 		0.49
(1.00) 		0.228
(1.00) 		0.397
(1.00)
6q gain 16 (7%) 220 0.725
(1.00) 		0.0163
(1.00) 		0.534
(1.00) 		0.887
(1.00) 		0.464
(1.00) 		0.25
(1.00) 		0.798
(1.00) 		0.676
(1.00)
9p gain 9 (4%) 227 0.834
(1.00) 		0.179
(1.00) 		0.15
(1.00) 		0.411
(1.00) 		0.395
(1.00) 		0.894
(1.00) 		0.555
(1.00) 		0.512
(1.00)
9q gain 9 (4%) 227 0.0918
(1.00) 		0.198
(1.00) 		0.356
(1.00) 		0.728
(1.00) 		0.394
(1.00) 		0.823
(1.00) 		0.555
(1.00) 		0.512
(1.00)
11p gain 16 (7%) 220 0.0675
(1.00) 		0.897
(1.00) 		0.155
(1.00) 		0.177
(1.00) 		0.0231
(1.00) 		0.0477
(1.00) 		0.371
(1.00) 		0.0963
(1.00)
11q gain 12 (5%) 224 0.0515
(1.00) 		0.484
(1.00) 		0.355
(1.00) 		0.435
(1.00) 		0.0146
(1.00) 		0.0364
(1.00) 		0.368
(1.00) 		0.154
(1.00)
12q gain 11 (5%) 225 0.00695
(1.00) 		0.629
(1.00) 		0.351
(1.00) 		0.887
(1.00) 		0.0597
(1.00) 		0.0971
(1.00) 		0.564
(1.00) 		0.124
(1.00)
14q gain 16 (7%) 220 0.0409
(1.00) 		0.047
(1.00) 		0.156
(1.00) 		0.501
(1.00) 		1
(1.00) 		0.748
(1.00) 		0.798
(1.00) 		0.676
(1.00)
16p gain 16 (7%) 220 0.000705
(0.415) 		0.0132
(1.00) 		0.318
(1.00) 		0.856
(1.00) 		0.0132
(1.00) 		0.28
(1.00) 		0.592
(1.00) 		0.623
(1.00)
16q gain 15 (6%) 221 0.00094
(0.543) 		0.0497
(1.00) 		0.215
(1.00) 		0.567
(1.00) 		0.0222
(1.00) 		0.364
(1.00) 		0.478
(1.00) 		0.342
(1.00)
17p gain 16 (7%) 220 0.0409
(1.00) 		0.246
(1.00) 		0.448
(1.00) 		0.353
(1.00) 		0.464
(1.00) 		0.388
(1.00) 		0.213
(1.00) 		0.398
(1.00)
17q gain 27 (11%) 209 0.0664
(1.00) 		0.0613
(1.00) 		0.657
(1.00) 		0.232
(1.00) 		0.409
(1.00) 		0.214
(1.00) 		0.164
(1.00) 		0.861
(1.00)
18p gain 26 (11%) 210 0.00543
(1.00) 		0.209
(1.00) 		0.106
(1.00) 		0.972
(1.00) 		0.0676
(1.00) 		0.181
(1.00) 		0.0935
(1.00) 		0.211
(1.00)
18q gain 15 (6%) 221 0.0685
(1.00) 		0.285
(1.00) 		0.417
(1.00) 		0.0844
(1.00) 		0.276
(1.00) 		0.0795
(1.00) 		0.94
(1.00) 		0.787
(1.00)
19p gain 16 (7%) 220 0.0123
(1.00) 		0.0317
(1.00) 		0.0454
(1.00) 		0.201
(1.00) 		0.00502
(1.00) 		0.0272
(1.00) 		0.165
(1.00) 		0.93
(1.00)
19q gain 20 (8%) 216 0.0154
(1.00) 		0.05
(1.00) 		0.0275
(1.00) 		0.689
(1.00) 		0.000683
(0.402) 		0.0704
(1.00) 		0.651
(1.00) 		0.735
(1.00)
21q gain 27 (11%) 209 0.0909
(1.00) 		0.000883
(0.512) 		0.354
(1.00) 		0.0977
(1.00) 		0.106
(1.00) 		0.0553
(1.00) 		0.538
(1.00) 		1
(1.00)
22q gain 57 (24%) 179 0.00307
(1.00) 		0.151
(1.00) 		0.497
(1.00) 		0.976
(1.00) 		0.324
(1.00) 		0.811
(1.00) 		0.601
(1.00) 		0.469
(1.00)
Xq gain 4 (2%) 232 0.832
(1.00) 		0.833
(1.00) 		0.403
(1.00) 		0.607
(1.00) 		0.207
(1.00) 		0.0897
(1.00) 		0.535
(1.00) 		0.577
(1.00)
1p loss 17 (7%) 219 0.01
(1.00) 		0.00397
(1.00) 		0.577
(1.00) 		0.259
(1.00) 		0.0068
(1.00) 		0.0511
(1.00) 		0.683
(1.00) 		0.557
(1.00)
1q loss 7 (3%) 229 0.00363
(1.00) 		0.343
(1.00) 		0.591
(1.00) 		0.888
(1.00) 		0.182
(1.00) 		0.602
(1.00) 		0.612
(1.00) 		1
(1.00)
2p loss 18 (8%) 218 0.146
(1.00) 		0.11
(1.00) 		0.188
(1.00) 		0.0135
(1.00) 		0.695
(1.00) 		0.0771
(1.00) 		0.000482
(0.286) 		0.00592
(1.00)
2q loss 19 (8%) 217 0.251
(1.00) 		0.0161
(1.00) 		0.0743
(1.00) 		0.0828
(1.00) 		0.341
(1.00) 		0.0784
(1.00) 		0.000852
(0.497) 		0.028
(1.00)
3p loss 22 (9%) 214 0.143
(1.00) 		0.0728
(1.00) 		0.23
(1.00) 		0.119
(1.00) 		0.00405
(1.00) 		0.031
(1.00) 		0.877
(1.00) 		0.27
(1.00)
3q loss 19 (8%) 217 0.0686
(1.00) 		0.0542
(1.00) 		0.173
(1.00) 		0.0457
(1.00) 		0.105
(1.00) 		0.252
(1.00) 		0.863
(1.00) 		0.214
(1.00)
4p loss 30 (13%) 206 0.283
(1.00) 		0.154
(1.00) 		0.158
(1.00) 		0.139
(1.00) 		0.00799
(1.00) 		0.00886
(1.00) 		0.793
(1.00) 		0.179
(1.00)
4q loss 31 (13%) 205 0.0769
(1.00) 		0.0342
(1.00) 		0.0409
(1.00) 		0.0331
(1.00) 		0.00974
(1.00) 		0.00281
(1.00) 		0.391
(1.00) 		0.0453
(1.00)
5p loss 31 (13%) 205 0.543
(1.00) 		0.306
(1.00) 		0.128
(1.00) 		0.207
(1.00) 		0.331
(1.00) 		0.142
(1.00) 		0.0624
(1.00) 		0.26
(1.00)
5q loss 46 (19%) 190 0.479
(1.00) 		0.0304
(1.00) 		0.149
(1.00) 		0.487
(1.00) 		0.273
(1.00) 		0.251
(1.00) 		0.637
(1.00) 		0.663
(1.00)
6p loss 28 (12%) 208 0.198
(1.00) 		0.226
(1.00) 		0.312
(1.00) 		0.0834
(1.00) 		0.355
(1.00) 		0.232
(1.00) 		0.332
(1.00) 		0.213
(1.00)
7p loss 7 (3%) 229 0.797
(1.00) 		0.0359
(1.00) 		0.522
(1.00) 		0.523
(1.00) 		0.795
(1.00) 		1
(1.00) 		0.419
(1.00) 		1
(1.00)
7q loss 6 (3%) 230 0.771
(1.00) 		0.127
(1.00) 		0.403
(1.00) 		0.888
(1.00) 		1
(1.00) 		0.878
(1.00) 		1
(1.00) 		0.839
(1.00)
8p loss 28 (12%) 208 0.251
(1.00) 		0.109
(1.00) 		0.128
(1.00) 		0.432
(1.00) 		0.0525
(1.00) 		0.201
(1.00) 		0.0804
(1.00) 		0.124
(1.00)
8q loss 5 (2%) 231 0.326
(1.00) 		0.05
(1.00) 		0.356
(1.00) 		0.457
(1.00) 		0.326
(1.00) 		0.0384
(1.00) 		0.844
(1.00) 		0.798
(1.00)
9q loss 97 (41%) 139 0.0239
(1.00) 		0.00115
(0.663) 		0.323
(1.00) 		0.318
(1.00) 		0.238
(1.00) 		0.895
(1.00) 		0.496
(1.00) 		0.636
(1.00)
11p loss 56 (24%) 180 0.000554
(0.328) 		0.00273
(1.00) 		0.0717
(1.00) 		0.213
(1.00) 		0.0551
(1.00) 		0.00634
(1.00) 		0.12
(1.00) 		0.665
(1.00)
11q loss 64 (27%) 172 0.000874
(0.508) 		0.0316
(1.00) 		0.744
(1.00) 		0.878
(1.00) 		0.0502
(1.00) 		0.00136
(0.782) 		0.609
(1.00) 		0.468
(1.00)
12p loss 15 (6%) 221 0.116
(1.00) 		0.00702
(1.00) 		0.219
(1.00) 		0.818
(1.00) 		0.259
(1.00) 		0.298
(1.00) 		0.88
(1.00) 		0.704
(1.00)
12q loss 23 (10%) 213 0.00288
(1.00) 		0.00381
(1.00) 		0.491
(1.00) 		0.981
(1.00) 		0.329
(1.00) 		0.241
(1.00) 		0.778
(1.00) 		0.848
(1.00)
13q loss 36 (15%) 200 0.327
(1.00) 		0.393
(1.00) 		0.513
(1.00) 		0.508
(1.00) 		0.662
(1.00) 		0.971
(1.00) 		0.0827
(1.00) 		0.224
(1.00)
15q loss 15 (6%) 221 0.00739
(1.00) 		0.0916
(1.00) 		0.812
(1.00) 		0.372
(1.00) 		0.634
(1.00) 		0.611
(1.00) 		0.267
(1.00) 		0.149
(1.00)
16p loss 21 (9%) 215 0.0413
(1.00) 		0.00889
(1.00) 		0.421
(1.00) 		0.0345
(1.00) 		0.326
(1.00) 		0.268
(1.00) 		0.0466
(1.00) 		0.175
(1.00)
16q loss 44 (19%) 192 0.0244
(1.00) 		0.0483
(1.00) 		0.13
(1.00) 		0.0297
(1.00) 		0.0884
(1.00) 		0.226
(1.00) 		0.0391
(1.00) 		0.0532
(1.00)
17p loss 48 (20%) 188 0.297
(1.00) 		0.274
(1.00) 		0.24
(1.00) 		0.773
(1.00) 		0.0673
(1.00) 		0.955
(1.00) 		0.646
(1.00) 		0.388
(1.00)
17q loss 20 (8%) 216 0.408
(1.00) 		0.728
(1.00) 		0.637
(1.00) 		0.306
(1.00) 		0.123
(1.00) 		0.357
(1.00) 		0.493
(1.00) 		1
(1.00)
18q loss 44 (19%) 192 0.00563
(1.00) 		0.0195
(1.00) 		0.0373
(1.00) 		0.107
(1.00) 		0.0518
(1.00) 		0.221
(1.00) 		0.863
(1.00) 		0.616
(1.00)
19p loss 19 (8%) 217 0.275
(1.00) 		0.471
(1.00) 		0.186
(1.00) 		0.781
(1.00) 		0.142
(1.00) 		0.367
(1.00) 		0.472
(1.00) 		0.936
(1.00)
19q loss 19 (8%) 217 0.262
(1.00) 		0.303
(1.00) 		0.474
(1.00) 		0.452
(1.00) 		0.395
(1.00) 		0.95
(1.00) 		0.707
(1.00) 		0.936
(1.00)
20p loss 12 (5%) 224 0.528
(1.00) 		0.484
(1.00) 		0.606
(1.00) 		0.838
(1.00) 		0.755
(1.00) 		0.222
(1.00) 		0.184
(1.00) 		0.552
(1.00)
20q loss 3 (1%) 233 0.632
(1.00) 		0.188
(1.00) 		0.508
(1.00) 		0.213
(1.00) 		1
(1.00) 		1
(1.00)
21q loss 28 (12%) 208 0.15
(1.00) 		0.304
(1.00) 		0.711
(1.00) 		0.435
(1.00) 		0.436
(1.00) 		0.451
(1.00) 		0.809
(1.00) 		0.305
(1.00)
22q loss 18 (8%) 218 0.262
(1.00) 		0.244
(1.00) 		0.24
(1.00) 		0.339
(1.00) 		0.644
(1.00) 		0.428
(1.00) 		0.208
(1.00) 		0.806
(1.00)
Xq loss 9 (4%) 227 0.635
(1.00) 		0.00888
(1.00) 		0.052
(1.00) 		0.389
(1.00) 		0.484
(1.00) 		0.234
(1.00) 		0.157
(1.00) 		0.285
(1.00)
'1q gain mutation analysis' versus 'CN_CNMF'

P value = 2.01e-06 (Fisher's exact test), Q value = 0.0012

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

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 80 90 66
1Q GAIN MUTATED 43 18 14
1Q GAIN WILD-TYPE 37 72 52

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 = 8.56e-08 (Fisher's exact test), Q value = 5.2e-05

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

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 80 90 66
6P GAIN MUTATED 45 16 14
6P GAIN WILD-TYPE 35 74 52

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 = 3.43e-05 (Fisher's exact test), Q value = 0.021

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

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 61 84 91
6P GAIN MUTATED 31 29 15
6P GAIN WILD-TYPE 30 55 76

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 = 2.94e-15 (Fisher's exact test), Q value = 1.8e-12

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

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 80 90 66
7P GAIN MUTATED 31 15 53
7P GAIN WILD-TYPE 49 75 13

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

'7q gain mutation analysis' versus 'CN_CNMF'

P value = 1.32e-18 (Fisher's exact test), Q value = 8.2e-16

Table S5.  Gene #14: '7q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 80 90 66
7Q GAIN MUTATED 26 18 58
7Q GAIN WILD-TYPE 54 72 8

Figure S5.  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 = 0.000222 (Fisher's exact test), Q value = 0.13

Table S6.  Gene #14: '7q gain mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 81 67 84
7Q GAIN MUTATED 50 22 29
7Q GAIN WILD-TYPE 31 45 55

Figure S6.  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 = 2.12e-08 (Fisher's exact test), Q value = 1.3e-05

Table S7.  Gene #15: '8p gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 80 90 66
8P GAIN MUTATED 19 3 26
8P GAIN WILD-TYPE 61 87 40

Figure S7.  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 = 5.23e-08 (Fisher's exact test), Q value = 3.2e-05

Table S8.  Gene #16: '8q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 80 90 66
8Q GAIN MUTATED 36 10 31
8Q GAIN WILD-TYPE 44 80 35

Figure S8.  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 = 9.75e-06 (Fisher's exact test), Q value = 0.0059

Table S9.  Gene #16: '8q gain mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 61 84 91
8Q GAIN MUTATED 35 23 19
8Q GAIN WILD-TYPE 26 61 72

Figure S9.  Get High-res Image Gene #16: '8q gain mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

'8q gain mutation analysis' versus 'MRNASEQ_CNMF'

P value = 0.000335 (Fisher's exact test), Q value = 0.2

Table S10.  Gene #16: '8q gain mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 81 67 84
8Q GAIN MUTATED 36 10 30
8Q GAIN WILD-TYPE 45 57 54

Figure S10.  Get High-res Image Gene #16: '8q gain mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

'12p gain mutation analysis' versus 'CN_CNMF'

P value = 1.15e-06 (Fisher's exact test), Q value = 7e-04

Table S11.  Gene #21: '12p gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 80 90 66
12P GAIN MUTATED 8 0 15
12P GAIN WILD-TYPE 72 90 51

Figure S11.  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 = 9.84e-06 (Fisher's exact test), Q value = 0.006

Table S12.  Gene #23: '13q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 80 90 66
13Q GAIN MUTATED 23 3 12
13Q GAIN WILD-TYPE 57 87 54

Figure S12.  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 = 2.95e-05 (Fisher's exact test), Q value = 0.018

Table S13.  Gene #23: '13q gain mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 61 84 91
13Q GAIN MUTATED 19 15 4
13Q GAIN WILD-TYPE 42 69 87

Figure S13.  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 = 0.000156 (Fisher's exact test), Q value = 0.093

Table S14.  Gene #25: '15q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 80 90 66
15Q GAIN MUTATED 8 5 19
15Q GAIN WILD-TYPE 72 85 47

Figure S14.  Get High-res Image Gene #25: '15q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'20p gain mutation analysis' versus 'CN_CNMF'

P value = 5.01e-07 (Fisher's exact test), Q value = 0.00031

Table S15.  Gene #34: '20p gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 80 90 66
20P GAIN MUTATED 29 9 30
20P GAIN WILD-TYPE 51 81 36

Figure S15.  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 = 0.000149 (Fisher's exact test), Q value = 0.089

Table S16.  Gene #34: '20p gain mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 81 67 84
20P GAIN MUTATED 37 10 21
20P GAIN WILD-TYPE 44 57 63

Figure S16.  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 = 1.72e-09 (Fisher's exact test), Q value = 1.1e-06

Table S17.  Gene #35: '20q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 80 90 66
20Q GAIN MUTATED 37 11 37
20Q GAIN WILD-TYPE 43 79 29

Figure S17.  Get High-res Image Gene #35: '20q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'6q loss mutation analysis' versus 'CN_CNMF'

P value = 3.13e-05 (Fisher's exact test), Q value = 0.019

Table S18.  Gene #50: '6q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 80 90 66
6Q LOSS MUTATED 37 18 34
6Q LOSS WILD-TYPE 43 72 32

Figure S18.  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 = 1.43e-08 (Fisher's exact test), Q value = 8.8e-06

Table S19.  Gene #55: '9p loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 80 90 66
9P LOSS MUTATED 57 27 45
9P LOSS WILD-TYPE 23 63 21

Figure S19.  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 = 0.000136 (Fisher's exact test), Q value = 0.081

Table S20.  Gene #55: '9p loss mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 61 84 91
9P LOSS MUTATED 40 55 34
9P LOSS WILD-TYPE 21 29 57

Figure S20.  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 = 5.15e-08 (Fisher's exact test), Q value = 3.2e-05

Table S21.  Gene #57: '10p loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 80 90 66
10P LOSS MUTATED 40 19 43
10P LOSS WILD-TYPE 40 71 23

Figure S21.  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 = 8.69e-05 (Fisher's exact test), Q value = 0.052

Table S22.  Gene #57: '10p loss mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 61 84 91
10P LOSS MUTATED 36 42 24
10P LOSS WILD-TYPE 25 42 67

Figure S22.  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 = 1.13e-06 (Fisher's exact test), Q value = 0.00069

Table S23.  Gene #57: '10p loss mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 81 67 84
10P LOSS MUTATED 51 14 35
10P LOSS WILD-TYPE 30 53 49

Figure S23.  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 = 1.04e-09 (Fisher's exact test), Q value = 6.4e-07

Table S24.  Gene #58: '10q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 80 90 66
10Q LOSS MUTATED 44 20 47
10Q LOSS WILD-TYPE 36 70 19

Figure S24.  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 = 0.000118 (Fisher's exact test), Q value = 0.071

Table S25.  Gene #58: '10q loss mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 61 84 91
10Q LOSS MUTATED 35 49 27
10Q LOSS WILD-TYPE 26 35 64

Figure S25.  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 = 1.11e-06 (Fisher's exact test), Q value = 0.00068

Table S26.  Gene #58: '10q loss mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 81 67 84
10Q LOSS MUTATED 55 17 38
10Q LOSS WILD-TYPE 26 50 46

Figure S26.  Get High-res Image Gene #58: '10q loss mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

'14q loss mutation analysis' versus 'CN_CNMF'

P value = 1.98e-08 (Fisher's exact test), Q value = 1.2e-05

Table S27.  Gene #64: '14q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 80 90 66
14Q LOSS MUTATED 19 4 28
14Q LOSS WILD-TYPE 61 86 38

Figure S27.  Get High-res Image Gene #64: '14q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'18p loss mutation analysis' versus 'MRNASEQ_CNMF'

P value = 0.000288 (Fisher's exact test), Q value = 0.17

Table S28.  Gene #70: '18p loss mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 81 67 84
18P LOSS MUTATED 28 6 13
18P LOSS WILD-TYPE 53 61 71

Figure S28.  Get High-res Image Gene #70: '18p loss mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

Methods & Data
Input

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

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

  • Number of patients = 236

  • 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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