Skin Cutaneous Melanoma: Correlation between copy number variations of arm-level result and molecular subtypes
(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 significant arm-level copy number variations (cnvs) and subtypes.

Summary

Testing the association between copy number variation 72 arm-level results and 8 molecular subtypes across 112 patients, 13 significant findings detected with Q value < 0.25.

  • 7p gain cnv correlated to 'CN_CNMF'.

  • 7q gain cnv correlated to 'CN_CNMF'.

  • 8p gain cnv correlated to 'CN_CNMF'.

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

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

  • 20q gain cnv correlated to 'CN_CNMF'.

  • 6q loss cnv correlated to 'CN_CNMF'.

  • 9p loss cnv correlated to 'CN_CNMF'.

  • 10q loss cnv correlated to 'MRNASEQ_CNMF'.

  • 11q loss cnv correlated to 'CN_CNMF'.

  • 17p loss cnv correlated to 'METHLYATION_CNMF'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between significant copy number variation of 72 arm-level results and 8 molecular subtypes. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 13 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 31 (28%) 81 1.49e-05
(0.00849) 		1.66e-05
(0.00944) 		0.499
(1.00) 		0.65
(1.00) 		0.000599
(0.334) 		0.0247
(1.00) 		0.856
(1.00) 		0.226
(1.00)
20p gain 31 (28%) 81 1.1e-05
(0.00627) 		0.00034
(0.189) 		0.579
(1.00) 		0.953
(1.00) 		0.00392
(1.00) 		0.0253
(1.00) 		0.218
(1.00) 		0.802
(1.00)
7p gain 44 (39%) 68 0.000228
(0.128) 		0.0217
(1.00) 		0.968
(1.00) 		0.465
(1.00) 		0.0259
(1.00) 		0.35
(1.00) 		0.212
(1.00) 		0.13
(1.00)
7q gain 44 (39%) 68 0.000286
(0.16) 		0.00103
(0.567) 		0.873
(1.00) 		0.303
(1.00) 		0.0318
(1.00) 		0.0915
(1.00) 		0.0747
(1.00) 		0.0846
(1.00)
8p gain 18 (16%) 94 1.97e-05
(0.0111) 		0.00284
(1.00) 		0.416
(1.00) 		0.297
(1.00) 		0.00611
(1.00) 		0.108
(1.00) 		0.635
(1.00) 		0.236
(1.00)
20q gain 39 (35%) 73 2.24e-05
(0.0126) 		0.000691
(0.384) 		0.813
(1.00) 		0.614
(1.00) 		0.00529
(1.00) 		0.0612
(1.00) 		0.127
(1.00) 		0.604
(1.00)
6q loss 40 (36%) 72 4.79e-05
(0.027) 		0.0166
(1.00) 		0.217
(1.00) 		0.652
(1.00) 		0.0359
(1.00) 		0.073
(1.00) 		0.0593
(1.00) 		0.13
(1.00)
9p loss 57 (51%) 55 1.55e-05
(0.00878) 		0.000905
(0.5) 		0.614
(1.00) 		0.855
(1.00) 		0.00237
(1.00) 		0.167
(1.00) 		0.0306
(1.00) 		0.242
(1.00)
10q loss 49 (44%) 63 0.0168
(1.00) 		0.0103
(1.00) 		0.242
(1.00) 		0.115
(1.00) 		0.000272
(0.153) 		0.00199
(1.00) 		0.0219
(1.00) 		0.163
(1.00)
11q loss 30 (27%) 82 0.000137
(0.077) 		0.00276
(1.00) 		0.327
(1.00) 		0.576
(1.00) 		0.0111
(1.00) 		0.00709
(1.00) 		0.131
(1.00) 		0.134
(1.00)
17p loss 28 (25%) 84 0.325
(1.00) 		0.00013
(0.0729) 		0.381
(1.00) 		0.746
(1.00) 		0.0622
(1.00) 		0.0279
(1.00) 		0.127
(1.00) 		0.209
(1.00)
1p gain 12 (11%) 100 0.262
(1.00) 		0.00106
(0.583) 		0.307
(1.00) 		0.726
(1.00) 		0.143
(1.00) 		0.114
(1.00) 		0.182
(1.00) 		0.0935
(1.00)
1q gain 33 (29%) 79 0.0179
(1.00) 		0.189
(1.00) 		0.547
(1.00) 		0.78
(1.00) 		0.0107
(1.00) 		0.0385
(1.00) 		0.223
(1.00) 		0.248
(1.00)
2p gain 10 (9%) 102 0.424
(1.00) 		0.91
(1.00) 		0.156
(1.00) 		0.571
(1.00) 		0.408
(1.00) 		0.657
(1.00) 		0.82
(1.00) 		0.554
(1.00)
2q gain 9 (8%) 103 0.719
(1.00) 		0.929
(1.00) 		0.156
(1.00) 		0.571
(1.00) 		0.621
(1.00) 		0.383
(1.00) 		0.899
(1.00) 		0.41
(1.00)
3p gain 7 (6%) 105 0.00907
(1.00) 		0.0243
(1.00) 		0.589
(1.00) 		0.482
(1.00) 		0.256
(1.00) 		0.333
(1.00) 		1
(1.00) 		1
(1.00)
3q gain 8 (7%) 104 0.0198
(1.00) 		0.158
(1.00) 		0.362
(1.00) 		0.188
(1.00) 		0.0665
(1.00) 		0.0177
(1.00) 		1
(1.00) 		0.864
(1.00)
4p gain 10 (9%) 102 0.215
(1.00) 		0.00919
(1.00) 		0.705
(1.00) 		0.445
(1.00) 		0.636
(1.00) 		0.641
(1.00) 		0.0246
(1.00) 		0.5
(1.00)
4q gain 10 (9%) 102 0.00335
(1.00) 		0.029
(1.00) 		0.552
(1.00) 		0.483
(1.00) 		0.408
(1.00) 		0.745
(1.00) 		0.0246
(1.00) 		0.5
(1.00)
5p gain 12 (11%) 100 0.0314
(1.00) 		0.564
(1.00) 		0.703
(1.00) 		0.894
(1.00) 		0.246
(1.00) 		0.19
(1.00) 		0.197
(1.00) 		0.574
(1.00)
5q gain 5 (4%) 107 0.127
(1.00) 		0.0994
(1.00) 		0.127
(1.00) 		0.561
(1.00) 		0.365
(1.00) 		0.429
(1.00) 		0.628
(1.00) 		1
(1.00)
6p gain 35 (31%) 77 0.00182
(0.992) 		0.0254
(1.00) 		0.399
(1.00) 		0.814
(1.00) 		0.499
(1.00) 		0.404
(1.00) 		0.562
(1.00) 		0.0477
(1.00)
6q gain 8 (7%) 104 0.594
(1.00) 		0.734
(1.00) 		0.75
(1.00) 		0.937
(1.00) 		0.215
(1.00) 		0.743
(1.00) 		0.318
(1.00) 		0.61
(1.00)
9q gain 4 (4%) 108 0.695
(1.00) 		0.909
(1.00) 		0.203
(1.00) 		0.372
(1.00) 		0.0605
(1.00) 		0.0122
(1.00) 		0.0574
(1.00) 		0.387
(1.00)
11p gain 8 (7%) 104 0.522
(1.00) 		0.0854
(1.00) 		0.459
(1.00) 		0.608
(1.00) 		0.0904
(1.00) 		0.0689
(1.00) 		0.283
(1.00) 		0.000829
(0.46)
11q gain 5 (4%) 107 0.264
(1.00) 		0.174
(1.00) 		0.122
(1.00) 		0.631
(1.00) 		0.0862
(1.00) 		0.404
(1.00) 		0.84
(1.00) 		0.0171
(1.00)
12p gain 10 (9%) 102 0.0097
(1.00) 		0.0251
(1.00) 		0.648
(1.00) 		1
(1.00) 		0.0397
(1.00) 		0.171
(1.00) 		0.0764
(1.00) 		0.226
(1.00)
12q gain 4 (4%) 108 0.152
(1.00) 		0.0341
(1.00) 		0.674
(1.00) 		0.0881
(1.00) 		0.0282
(1.00) 		0.0299
(1.00)
13q gain 15 (13%) 97 0.00505
(1.00) 		0.0748
(1.00) 		0.256
(1.00) 		0.636
(1.00) 		0.112
(1.00) 		0.91
(1.00) 		0.936
(1.00) 		0.621
(1.00)
14q gain 9 (8%) 103 0.162
(1.00) 		0.353
(1.00) 		0.25
(1.00) 		0.35
(1.00) 		0.183
(1.00) 		0.05
(1.00) 		0.907
(1.00) 		0.229
(1.00)
15q gain 16 (14%) 96 0.00354
(1.00) 		0.602
(1.00) 		0.116
(1.00) 		0.276
(1.00) 		0.941
(1.00) 		0.347
(1.00) 		0.454
(1.00) 		0.204
(1.00)
16p gain 6 (5%) 106 0.0286
(1.00) 		0.00944
(1.00) 		0.635
(1.00) 		1
(1.00) 		0.192
(1.00) 		0.0208
(1.00) 		0.206
(1.00) 		0.503
(1.00)
16q gain 6 (5%) 106 0.0703
(1.00) 		0.062
(1.00) 		0.857
(1.00) 		0.285
(1.00) 		0.383
(1.00) 		0.236
(1.00) 		0.0153
(1.00) 		0.0613
(1.00)
17p gain 8 (7%) 104 0.49
(1.00) 		0.369
(1.00) 		0.389
(1.00) 		0.0303
(1.00) 		0.407
(1.00) 		0.793
(1.00) 		0.455
(1.00) 		0.864
(1.00)
17q gain 15 (13%) 97 0.0414
(1.00) 		0.125
(1.00) 		0.804
(1.00) 		0.47
(1.00) 		0.434
(1.00) 		0.793
(1.00) 		0.0414
(1.00) 		0.682
(1.00)
18p gain 14 (12%) 98 0.0792
(1.00) 		0.0203
(1.00) 		0.977
(1.00) 		0.723
(1.00) 		0.204
(1.00) 		0.407
(1.00) 		0.0125
(1.00) 		0.232
(1.00)
18q gain 6 (5%) 106 0.179
(1.00) 		0.0721
(1.00) 		0.512
(1.00) 		0.937
(1.00) 		0.237
(1.00) 		0.147
(1.00) 		0.747
(1.00) 		1
(1.00)
19p gain 8 (7%) 104 0.273
(1.00) 		0.0385
(1.00) 		0.5
(1.00) 		0.429
(1.00) 		0.0128
(1.00) 		0.00525
(1.00) 		0.508
(1.00) 		0.153
(1.00)
19q gain 9 (8%) 103 0.0174
(1.00) 		0.0037
(1.00) 		0.886
(1.00) 		0.843
(1.00) 		0.0043
(1.00) 		0.00134
(0.732) 		0.489
(1.00) 		0.0679
(1.00)
21q gain 11 (10%) 101 0.596
(1.00) 		0.0441
(1.00) 		0.311
(1.00) 		0.571
(1.00) 		0.477
(1.00) 		0.452
(1.00) 		0.66
(1.00) 		1
(1.00)
22q gain 24 (21%) 88 0.191
(1.00) 		0.364
(1.00) 		0.348
(1.00) 		0.401
(1.00) 		0.734
(1.00) 		0.0914
(1.00) 		0.755
(1.00) 		0.817
(1.00)
1p loss 8 (7%) 104 0.054
(1.00) 		0.147
(1.00) 		0.648
(1.00) 		0.514
(1.00) 		0.272
(1.00) 		0.217
(1.00) 		1
(1.00) 		0.285
(1.00)
2p loss 12 (11%) 100 0.168
(1.00) 		0.148
(1.00) 		0.506
(1.00) 		0.184
(1.00) 		0.246
(1.00) 		0.759
(1.00) 		0.0249
(1.00) 		0.0414
(1.00)
2q loss 11 (10%) 101 0.0203
(1.00) 		0.192
(1.00) 		0.673
(1.00) 		0.68
(1.00) 		0.4
(1.00) 		0.719
(1.00) 		0.0631
(1.00) 		0.0595
(1.00)
3p loss 5 (4%) 107 0.00183
(0.998) 		0.16
(1.00) 		0.436
(1.00) 		0.729
(1.00) 		0.209
(1.00) 		0.061
(1.00) 		0.352
(1.00) 		0.765
(1.00)
3q loss 8 (7%) 104 0.0107
(1.00) 		0.432
(1.00) 		0.142
(1.00) 		0.279
(1.00) 		0.188
(1.00) 		0.232
(1.00) 		0.406
(1.00) 		0.61
(1.00)
4p loss 9 (8%) 103 0.494
(1.00) 		0.0405
(1.00) 		0.587
(1.00) 		0.445
(1.00) 		0.101
(1.00) 		0.169
(1.00) 		0.131
(1.00) 		0.127
(1.00)
4q loss 10 (9%) 102 0.54
(1.00) 		0.503
(1.00) 		0.612
(1.00) 		0.627
(1.00) 		0.636
(1.00) 		0.377
(1.00) 		0.142
(1.00) 		0.435
(1.00)
5p loss 14 (12%) 98 0.36
(1.00) 		0.00112
(0.613) 		0.291
(1.00) 		0.636
(1.00) 		0.151
(1.00) 		0.0147
(1.00) 		0.123
(1.00) 		0.359
(1.00)
5q loss 24 (21%) 88 0.217
(1.00) 		0.154
(1.00) 		0.455
(1.00) 		0.664
(1.00) 		0.411
(1.00) 		0.0662
(1.00) 		0.346
(1.00) 		0.768
(1.00)
6p loss 9 (8%) 103 0.396
(1.00) 		0.766
(1.00) 		0.1
(1.00) 		0.926
(1.00) 		1
(1.00) 		0.964
(1.00) 		0.235
(1.00) 		0.0547
(1.00)
8p loss 14 (12%) 98 0.583
(1.00) 		0.0727
(1.00) 		0.653
(1.00) 		0.517
(1.00) 		0.386
(1.00) 		0.285
(1.00) 		0.0137
(1.00) 		0.0163
(1.00)
8q loss 3 (3%) 109 1
(1.00) 		0.0656
(1.00) 		0.122
(1.00) 		1
(1.00) 		0.62
(1.00) 		0.705
(1.00) 		0.163
(1.00) 		0.687
(1.00)
9q loss 42 (38%) 70 0.0216
(1.00) 		0.0186
(1.00) 		0.656
(1.00) 		0.397
(1.00) 		0.0295
(1.00) 		0.353
(1.00) 		0.0143
(1.00) 		0.243
(1.00)
10p loss 45 (40%) 67 0.0139
(1.00) 		0.0254
(1.00) 		0.0945
(1.00) 		0.0517
(1.00) 		0.000965
(0.532) 		0.00548
(1.00) 		0.00179
(0.98) 		0.113
(1.00)
11p loss 26 (23%) 86 0.000879
(0.487) 		0.0091
(1.00) 		0.401
(1.00) 		0.386
(1.00) 		0.028
(1.00) 		0.199
(1.00) 		0.137
(1.00) 		0.33
(1.00)
12p loss 5 (4%) 107 0.48
(1.00) 		0.76
(1.00) 		0.203
(1.00) 		0.518
(1.00) 		0.739
(1.00) 		0.813
(1.00) 		0.438
(1.00) 		0.606
(1.00)
12q loss 8 (7%) 104 0.172
(1.00) 		0.104
(1.00) 		0.959
(1.00) 		0.81
(1.00) 		0.815
(1.00) 		0.696
(1.00) 		0.172
(1.00) 		0.339
(1.00)
13q loss 17 (15%) 95 0.0037
(1.00) 		0.605
(1.00) 		0.54
(1.00) 		0.771
(1.00) 		0.796
(1.00) 		0.878
(1.00) 		0.0491
(1.00) 		0.267
(1.00)
14q loss 25 (22%) 87 0.00487
(1.00) 		0.0528
(1.00) 		0.143
(1.00) 		0.569
(1.00) 		0.221
(1.00) 		0.0879
(1.00) 		0.397
(1.00) 		0.587
(1.00)
15q loss 7 (6%) 105 0.0843
(1.00) 		0.0708
(1.00) 		0.999
(1.00) 		1
(1.00) 		0.256
(1.00) 		0.947
(1.00) 		0.692
(1.00) 		0.703
(1.00)
16p loss 8 (7%) 104 0.447
(1.00) 		0.432
(1.00) 		0.101
(1.00) 		0.561
(1.00) 		0.272
(1.00) 		0.819
(1.00) 		0.00541
(1.00) 		0.61
(1.00)
16q loss 16 (14%) 96 0.62
(1.00) 		0.246
(1.00) 		0.108
(1.00) 		0.196
(1.00) 		0.0736
(1.00) 		0.261
(1.00) 		0.0134
(1.00) 		0.145
(1.00)
17q loss 13 (12%) 99 0.399
(1.00) 		0.473
(1.00) 		0.754
(1.00) 		0.726
(1.00) 		0.199
(1.00) 		0.496
(1.00) 		1
(1.00) 		0.509
(1.00)
18p loss 18 (16%) 94 0.289
(1.00) 		0.00371
(1.00) 		0.501
(1.00) 		0.506
(1.00) 		0.00437
(1.00) 		0.018
(1.00) 		0.0372
(1.00) 		0.659
(1.00)
18q loss 18 (16%) 94 0.37
(1.00) 		0.00255
(1.00) 		0.855
(1.00) 		0.0884
(1.00) 		0.0405
(1.00) 		0.0341
(1.00) 		0.0892
(1.00) 		0.923
(1.00)
19p loss 12 (11%) 100 0.708
(1.00) 		0.756
(1.00) 		0.481
(1.00) 		0.736
(1.00) 		0.289
(1.00) 		0.0161
(1.00) 		0.0194
(1.00) 		0.337
(1.00)
19q loss 12 (11%) 100 0.0849
(1.00) 		0.609
(1.00) 		0.471
(1.00) 		0.795
(1.00) 		0.143
(1.00) 		0.0499
(1.00) 		0.182
(1.00) 		0.448
(1.00)
20p loss 3 (3%) 109 0.457
(1.00) 		0.599
(1.00) 		0.248
(1.00) 		0.297
(1.00) 		0.163
(1.00) 		0.687
(1.00)
21q loss 15 (13%) 97 0.548
(1.00) 		0.149
(1.00) 		0.757
(1.00) 		0.579
(1.00) 		0.569
(1.00) 		0.91
(1.00) 		0.00288
(1.00) 		0.621
(1.00)
22q loss 7 (6%) 105 0.178
(1.00) 		0.697
(1.00) 		0.494
(1.00) 		1
(1.00) 		0.256
(1.00) 		0.77
(1.00) 		0.149
(1.00) 		0.848
(1.00)
Xq loss 3 (3%) 109 0.236
(1.00) 		0.389
(1.00) 		0.786
(1.00) 		0.486
(1.00) 		0.606
(1.00) 		0.687
(1.00)
'7p gain mutation analysis' versus 'CN_CNMF'

P value = 0.000228 (Fisher's exact test), Q value = 0.13

Table S1.  Gene #13: '7p gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 30 18 38 26
7P GAIN MUTATED 14 14 7 9
7P GAIN WILD-TYPE 16 4 31 17

Figure S1.  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 = 0.000286 (Fisher's exact test), Q value = 0.16

Table S2.  Gene #14: '7q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 30 18 38 26
7Q GAIN MUTATED 10 15 9 10
7Q GAIN WILD-TYPE 20 3 29 16

Figure S2.  Get High-res Image Gene #14: '7q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'8p gain mutation analysis' versus 'CN_CNMF'

P value = 1.97e-05 (Fisher's exact test), Q value = 0.011

Table S3.  Gene #15: '8p gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 30 18 38 26
8P GAIN MUTATED 3 10 1 4
8P GAIN WILD-TYPE 27 8 37 22

Figure S3.  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 = 1.49e-05 (Fisher's exact test), Q value = 0.0085

Table S4.  Gene #16: '8q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 30 18 38 26
8Q GAIN MUTATED 10 12 2 7
8Q GAIN WILD-TYPE 20 6 36 19

Figure S4.  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 = 1.66e-05 (Fisher's exact test), Q value = 0.0094

Table S5.  Gene #16: '8q gain mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 18 40 26 28
8Q GAIN MUTATED 11 3 12 5
8Q GAIN WILD-TYPE 7 37 14 23

Figure S5.  Get High-res Image Gene #16: '8q gain mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

'20p gain mutation analysis' versus 'CN_CNMF'

P value = 1.1e-05 (Fisher's exact test), Q value = 0.0063

Table S6.  Gene #33: '20p gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 30 18 38 26
20P GAIN MUTATED 6 10 2 13
20P GAIN WILD-TYPE 24 8 36 13

Figure S6.  Get High-res Image Gene #33: '20p gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'20p gain mutation analysis' versus 'METHLYATION_CNMF'

P value = 0.00034 (Fisher's exact test), Q value = 0.19

Table S7.  Gene #33: '20p gain mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 18 40 26 28
20P GAIN MUTATED 10 6 12 3
20P GAIN WILD-TYPE 8 34 14 25

Figure S7.  Get High-res Image Gene #33: '20p gain mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

'20q gain mutation analysis' versus 'CN_CNMF'

P value = 2.24e-05 (Fisher's exact test), Q value = 0.013

Table S8.  Gene #34: '20q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 30 18 38 26
20Q GAIN MUTATED 11 11 3 14
20Q GAIN WILD-TYPE 19 7 35 12

Figure S8.  Get High-res Image Gene #34: '20q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'6q loss mutation analysis' versus 'CN_CNMF'

P value = 4.79e-05 (Fisher's exact test), Q value = 0.027

Table S9.  Gene #47: '6q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 30 18 38 26
6Q LOSS MUTATED 15 5 4 16
6Q LOSS WILD-TYPE 15 13 34 10

Figure S9.  Get High-res Image Gene #47: '6q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'9p loss mutation analysis' versus 'CN_CNMF'

P value = 1.55e-05 (Fisher's exact test), Q value = 0.0088

Table S10.  Gene #50: '9p loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 30 18 38 26
9P LOSS MUTATED 24 13 10 10
9P LOSS WILD-TYPE 6 5 28 16

Figure S10.  Get High-res Image Gene #50: '9p loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'10q loss mutation analysis' versus 'MRNASEQ_CNMF'

P value = 0.000272 (Fisher's exact test), Q value = 0.15

Table S11.  Gene #53: '10q loss mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 42 27 43
10Q LOSS MUTATED 23 3 23
10Q LOSS WILD-TYPE 19 24 20

Figure S11.  Get High-res Image Gene #53: '10q loss mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

'11q loss mutation analysis' versus 'CN_CNMF'

P value = 0.000137 (Fisher's exact test), Q value = 0.077

Table S12.  Gene #55: '11q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 30 18 38 26
11Q LOSS MUTATED 15 8 3 4
11Q LOSS WILD-TYPE 15 10 35 22

Figure S12.  Get High-res Image Gene #55: '11q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'17p loss mutation analysis' versus 'METHLYATION_CNMF'

P value = 0.00013 (Fisher's exact test), Q value = 0.073

Table S13.  Gene #63: '17p loss mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 18 40 26 28
17P LOSS MUTATED 4 4 4 16
17P LOSS WILD-TYPE 14 36 22 12

Figure S13.  Get High-res Image Gene #63: '17p loss mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

Methods & Data
Input

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

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

  • Number of patients = 112

  • Number of significantly arm-level cnvs = 72

  • 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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