Skin Cutaneous Melanoma: Correlation between copy number variations of arm-level result and molecular subtypes
(Regional_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 73 arm-level results and 8 molecular subtypes across 147 patients, 23 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',  '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'.

  • 20q gain cnv correlated to 'CN_CNMF'.

  • 22q gain cnv correlated to 'CN_CNMF'.

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

  • 9q loss cnv correlated to 'METHLYATION_CNMF'.

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

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

  • 11p loss cnv correlated to 'CN_CNMF'.

  • 11q loss cnv correlated to 'CN_CNMF'.

  • 14q 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 73 arm-level results and 8 molecular subtypes. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 23 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 Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test
8q gain 46 (31%) 101 2.77e-06
(0.00158) 		4.85e-05
(0.0271) 		0.238
(1.00) 		0.133
(1.00) 		3.43e-05
(0.0192) 		0.00886
(1.00) 		0.166
(1.00) 		0.224
(1.00)
13q gain 21 (14%) 126 7.23e-05
(0.0402) 		0.0003
(0.166) 		0.196
(1.00) 		0.312
(1.00) 		0.0947
(1.00) 		0.215
(1.00) 		0.866
(1.00) 		0.94
(1.00)
9p loss 82 (56%) 65 5.64e-06
(0.0032) 		0.00016
(0.0887) 		0.452
(1.00) 		0.288
(1.00) 		0.00624
(1.00) 		0.0206
(1.00) 		0.454
(1.00) 		0.206
(1.00)
10p loss 64 (44%) 83 4.3e-06
(0.00244) 		0.000667
(0.365) 		0.476
(1.00) 		0.259
(1.00) 		0.000103
(0.0569) 		0.00125
(0.674) 		0.526
(1.00) 		0.162
(1.00)
10q loss 71 (48%) 76 1.26e-06
(0.000721) 		0.0044
(1.00) 		0.607
(1.00) 		0.757
(1.00) 		1.47e-05
(0.00825) 		0.00228
(1.00) 		0.531
(1.00) 		0.543
(1.00)
7p gain 63 (43%) 84 8.31e-05
(0.0461) 		0.513
(1.00) 		0.159
(1.00) 		0.153
(1.00) 		0.0615
(1.00) 		0.428
(1.00) 		0.526
(1.00) 		0.822
(1.00)
7q gain 62 (42%) 85 4.16e-06
(0.00237) 		0.891
(1.00) 		0.0928
(1.00) 		0.0661
(1.00) 		0.0584
(1.00) 		0.29
(1.00) 		0.0758
(1.00) 		0.242
(1.00)
8p gain 29 (20%) 118 1e-05
(0.00569) 		0.00492
(1.00) 		0.248
(1.00) 		0.051
(1.00) 		0.00056
(0.307) 		0.00193
(1.00) 		0.0861
(1.00) 		0.103
(1.00)
12p gain 16 (11%) 131 1.31e-05
(0.00739) 		0.298
(1.00) 		0.768
(1.00) 		0.677
(1.00) 		0.00136
(0.734) 		0.054
(1.00) 		0.101
(1.00) 		0.117
(1.00)
15q gain 21 (14%) 126 6.51e-05
(0.0363) 		0.762
(1.00) 		0.0483
(1.00) 		0.488
(1.00) 		0.913
(1.00) 		0.684
(1.00) 		0.448
(1.00) 		0.136
(1.00)
20p gain 43 (29%) 104 0.000381
(0.209) 		0.00377
(1.00) 		0.522
(1.00) 		0.728
(1.00) 		0.00622
(1.00) 		0.202
(1.00) 		0.0465
(1.00) 		0.63
(1.00)
20q gain 53 (36%) 94 3.79e-05
(0.0212) 		0.00536
(1.00) 		0.43
(1.00) 		0.622
(1.00) 		0.0238
(1.00) 		0.271
(1.00) 		0.165
(1.00) 		0.417
(1.00)
22q gain 37 (25%) 110 4.54e-05
(0.0254) 		0.198
(1.00) 		0.35
(1.00) 		0.544
(1.00) 		0.48
(1.00) 		0.384
(1.00) 		0.39
(1.00) 		0.628
(1.00)
9q loss 61 (41%) 86 0.0092
(1.00) 		0.000166
(0.0917) 		0.622
(1.00) 		0.474
(1.00) 		0.344
(1.00) 		0.333
(1.00) 		0.054
(1.00) 		0.238
(1.00)
11p loss 35 (24%) 112 2.22e-06
(0.00127) 		0.000844
(0.458) 		0.664
(1.00) 		0.91
(1.00) 		0.0162
(1.00) 		0.799
(1.00) 		0.477
(1.00) 		0.735
(1.00)
11q loss 39 (27%) 108 1.13e-05
(0.00636) 		0.0053
(1.00) 		0.683
(1.00) 		1
(1.00) 		0.013
(1.00) 		0.835
(1.00) 		0.449
(1.00) 		0.665
(1.00)
14q loss 35 (24%) 112 3.23e-05
(0.0181) 		0.092
(1.00) 		0.735
(1.00) 		0.213
(1.00) 		0.0173
(1.00) 		0.0825
(1.00) 		0.791
(1.00) 		0.0817
(1.00)
1p gain 20 (14%) 127 0.799
(1.00) 		0.54
(1.00) 		0.595
(1.00) 		0.255
(1.00) 		0.109
(1.00) 		0.3
(1.00) 		1
(1.00) 		1
(1.00)
1q gain 48 (33%) 99 0.00049
(0.269) 		0.0259
(1.00) 		0.439
(1.00) 		0.266
(1.00) 		0.138
(1.00) 		0.948
(1.00) 		0.717
(1.00) 		0.481
(1.00)
2p gain 16 (11%) 131 0.167
(1.00) 		0.531
(1.00) 		0.298
(1.00) 		0.234
(1.00) 		0.71
(1.00) 		0.944
(1.00) 		0.882
(1.00) 		0.46
(1.00)
2q gain 15 (10%) 132 0.127
(1.00) 		0.697
(1.00) 		0.298
(1.00) 		0.234
(1.00) 		0.887
(1.00) 		0.73
(1.00) 		0.815
(1.00) 		0.339
(1.00)
3p gain 12 (8%) 135 0.00165
(0.884) 		0.191
(1.00) 		0.25
(1.00) 		0.752
(1.00) 		0.0809
(1.00) 		0.48
(1.00) 		0.925
(1.00) 		1
(1.00)
3q gain 15 (10%) 132 0.00477
(1.00) 		0.331
(1.00) 		0.047
(1.00) 		0.529
(1.00) 		0.000944
(0.511) 		0.256
(1.00) 		0.596
(1.00) 		0.46
(1.00)
4p gain 14 (10%) 133 0.0221
(1.00) 		0.103
(1.00) 		0.262
(1.00) 		0.593
(1.00) 		1
(1.00) 		0.232
(1.00) 		0.312
(1.00) 		0.766
(1.00)
4q gain 12 (8%) 135 0.00446
(1.00) 		0.0432
(1.00) 		0.182
(1.00) 		0.861
(1.00) 		0.553
(1.00) 		0.221
(1.00) 		0.177
(1.00) 		0.812
(1.00)
5p gain 13 (9%) 134 0.000678
(0.37) 		0.543
(1.00) 		0.768
(1.00) 		0.805
(1.00) 		0.874
(1.00) 		0.811
(1.00) 		0.26
(1.00) 		0.91
(1.00)
5q gain 5 (3%) 142 0.0588
(1.00) 		0.627
(1.00) 		0.734
(1.00) 		0.704
(1.00) 		0.739
(1.00) 		0.614
(1.00) 		0.627
(1.00) 		1
(1.00)
6p gain 45 (31%) 102 0.000822
(0.447) 		0.191
(1.00) 		0.395
(1.00) 		0.927
(1.00) 		0.745
(1.00) 		0.921
(1.00) 		0.285
(1.00) 		0.0684
(1.00)
6q gain 9 (6%) 138 0.93
(1.00) 		0.0521
(1.00) 		0.605
(1.00) 		1
(1.00) 		0.628
(1.00) 		0.244
(1.00) 		0.739
(1.00) 		0.872
(1.00)
9p gain 3 (2%) 144 0.0228
(1.00) 		0.188
(1.00) 		1
(1.00) 		0.438
(1.00)
9q gain 3 (2%) 144 0.42
(1.00) 		1
(1.00) 		0.363
(1.00) 		0.457
(1.00)
11p gain 9 (6%) 138 0.0598
(1.00) 		0.124
(1.00) 		0.0626
(1.00) 		0.555
(1.00) 		0.0565
(1.00) 		0.687
(1.00) 		0.152
(1.00) 		0.00514
(1.00)
11q gain 6 (4%) 141 0.0101
(1.00) 		0.01
(1.00) 		0.22
(1.00) 		0.632
(1.00) 		0.0586
(1.00) 		0.763
(1.00) 		0.569
(1.00) 		0.0547
(1.00)
12q gain 7 (5%) 140 0.00115
(0.62) 		0.704
(1.00) 		0.634
(1.00) 		0.129
(1.00) 		0.7
(1.00) 		0.243
(1.00) 		0.211
(1.00) 		0.0626
(1.00)
14q gain 12 (8%) 135 0.00676
(1.00) 		0.125
(1.00) 		0.209
(1.00) 		0.292
(1.00) 		0.696
(1.00) 		0.737
(1.00) 		0.925
(1.00) 		1
(1.00)
16p gain 12 (8%) 135 0.071
(1.00) 		0.125
(1.00) 		0.25
(1.00) 		0.521
(1.00) 		0.0352
(1.00) 		0.0462
(1.00) 		0.583
(1.00) 		0.244
(1.00)
16q gain 10 (7%) 137 0.019
(1.00) 		0.707
(1.00) 		0.185
(1.00) 		0.473
(1.00) 		0.162
(1.00) 		0.252
(1.00) 		0.0764
(1.00) 		0.0294
(1.00)
17p gain 13 (9%) 134 0.0149
(1.00) 		0.107
(1.00) 		0.399
(1.00) 		0.164
(1.00) 		0.306
(1.00) 		0.271
(1.00) 		0.26
(1.00) 		0.195
(1.00)
17q gain 21 (14%) 126 0.0251
(1.00) 		0.458
(1.00) 		0.319
(1.00) 		0.228
(1.00) 		0.459
(1.00) 		0.91
(1.00) 		0.196
(1.00) 		1
(1.00)
18p gain 19 (13%) 128 0.113
(1.00) 		0.213
(1.00) 		0.87
(1.00) 		0.325
(1.00) 		0.109
(1.00) 		0.134
(1.00) 		0.0511
(1.00) 		0.433
(1.00)
18q gain 10 (7%) 137 0.012
(1.00) 		0.424
(1.00) 		0.89
(1.00) 		0.823
(1.00) 		0.26
(1.00) 		0.126
(1.00) 		0.912
(1.00) 		0.622
(1.00)
19p gain 7 (5%) 140 0.423
(1.00) 		0.0357
(1.00) 		0.605
(1.00) 		0.293
(1.00) 		0.0488
(1.00) 		0.00379
(1.00) 		0.881
(1.00) 		0.856
(1.00)
19q gain 11 (7%) 136 0.0631
(1.00) 		0.0326
(1.00) 		0.45
(1.00) 		0.864
(1.00) 		0.00232
(1.00) 		0.0214
(1.00) 		0.328
(1.00) 		0.351
(1.00)
21q gain 17 (12%) 130 0.0944
(1.00) 		0.00155
(0.833) 		0.263
(1.00) 		0.485
(1.00) 		0.282
(1.00) 		0.00649
(1.00) 		0.944
(1.00) 		1
(1.00)
1p loss 12 (8%) 135 0.00771
(1.00) 		0.00552
(1.00) 		0.809
(1.00) 		0.55
(1.00) 		0.0701
(1.00) 		0.0187
(1.00) 		0.925
(1.00) 		0.904
(1.00)
1q loss 4 (3%) 143 0.00137
(0.737) 		0.188
(1.00) 		0.564
(1.00) 		0.819
(1.00) 		1
(1.00) 		0.286
(1.00)
2p loss 14 (10%) 133 0.193
(1.00) 		0.333
(1.00) 		0.631
(1.00) 		0.0344
(1.00) 		0.429
(1.00) 		0.476
(1.00) 		0.0296
(1.00) 		0.0509
(1.00)
2q loss 13 (9%) 134 0.141
(1.00) 		0.159
(1.00) 		0.817
(1.00) 		0.263
(1.00) 		0.374
(1.00) 		0.564
(1.00) 		0.00547
(1.00) 		0.0594
(1.00)
3p loss 10 (7%) 137 0.0123
(1.00) 		0.0936
(1.00) 		0.373
(1.00) 		0.663
(1.00) 		0.00232
(1.00) 		0.00652
(1.00) 		0.247
(1.00) 		1
(1.00)
3q loss 12 (8%) 135 0.0362
(1.00) 		0.0605
(1.00) 		0.0734
(1.00) 		0.142
(1.00) 		0.065
(1.00) 		0.314
(1.00) 		1
(1.00) 		0.187
(1.00)
4p loss 15 (10%) 132 0.00353
(1.00) 		0.431
(1.00) 		0.768
(1.00) 		0.697
(1.00) 		0.0226
(1.00) 		0.113
(1.00) 		0.312
(1.00) 		0.847
(1.00)
4q loss 16 (11%) 131 0.00522
(1.00) 		0.841
(1.00) 		0.712
(1.00) 		0.458
(1.00) 		0.135
(1.00) 		0.205
(1.00) 		0.616
(1.00) 		0.924
(1.00)
5p loss 18 (12%) 129 0.0641
(1.00) 		0.0459
(1.00) 		0.486
(1.00) 		0.319
(1.00) 		0.123
(1.00) 		0.00213
(1.00) 		0.138
(1.00) 		0.325
(1.00)
5q loss 31 (21%) 116 0.433
(1.00) 		0.0186
(1.00) 		0.264
(1.00) 		0.707
(1.00) 		0.14
(1.00) 		0.0839
(1.00) 		0.314
(1.00) 		0.683
(1.00)
6p loss 11 (7%) 136 0.0704
(1.00) 		0.0808
(1.00) 		0.0763
(1.00) 		0.793
(1.00) 		0.418
(1.00) 		0.48
(1.00) 		0.845
(1.00) 		0.276
(1.00)
6q loss 53 (36%) 94 0.000762
(0.415) 		0.00285
(1.00) 		0.514
(1.00) 		0.879
(1.00) 		0.0553
(1.00) 		0.285
(1.00) 		0.359
(1.00) 		0.436
(1.00)
8p loss 16 (11%) 131 0.747
(1.00) 		0.471
(1.00) 		0.353
(1.00) 		0.783
(1.00) 		0.313
(1.00) 		0.944
(1.00) 		0.0145
(1.00) 		0.00478
(1.00)
12p loss 8 (5%) 139 0.0648
(1.00) 		0.193
(1.00) 		0.361
(1.00) 		0.359
(1.00) 		0.308
(1.00) 		0.809
(1.00) 		0.713
(1.00) 		1
(1.00)
12q loss 15 (10%) 132 0.00241
(1.00) 		0.0128
(1.00) 		0.809
(1.00) 		0.956
(1.00) 		0.377
(1.00) 		0.884
(1.00) 		1
(1.00) 		0.71
(1.00)
13q loss 23 (16%) 124 0.113
(1.00) 		0.463
(1.00) 		1
(1.00) 		0.947
(1.00) 		0.23
(1.00) 		0.916
(1.00) 		0.16
(1.00) 		0.49
(1.00)
15q loss 12 (8%) 135 0.0247
(1.00) 		0.0432
(1.00) 		0.809
(1.00) 		0.917
(1.00) 		0.122
(1.00) 		0.504
(1.00) 		0.925
(1.00) 		0.904
(1.00)
16p loss 9 (6%) 138 0.554
(1.00) 		0.188
(1.00) 		0.32
(1.00) 		0.499
(1.00) 		0.123
(1.00) 		0.758
(1.00) 		0.0109
(1.00) 		0.0959
(1.00)
16q loss 20 (14%) 127 0.0725
(1.00) 		0.623
(1.00) 		0.266
(1.00) 		0.23
(1.00) 		0.00617
(1.00) 		0.613
(1.00) 		0.0154
(1.00) 		0.00411
(1.00)
17p loss 33 (22%) 114 0.133
(1.00) 		0.128
(1.00) 		0.783
(1.00) 		0.826
(1.00) 		0.0926
(1.00) 		0.243
(1.00) 		0.171
(1.00) 		0.0744
(1.00)
17q loss 12 (8%) 135 0.489
(1.00) 		1
(1.00) 		0.907
(1.00) 		0.864
(1.00) 		0.553
(1.00) 		0.266
(1.00) 		0.512
(1.00) 		0.464
(1.00)
18p loss 26 (18%) 121 0.0255
(1.00) 		0.0148
(1.00) 		0.879
(1.00) 		0.567
(1.00) 		0.00225
(1.00) 		0.0894
(1.00) 		0.256
(1.00) 		0.947
(1.00)
18q loss 24 (16%) 123 0.00701
(1.00) 		0.0356
(1.00) 		0.76
(1.00) 		0.0337
(1.00) 		0.112
(1.00) 		0.315
(1.00) 		0.269
(1.00) 		0.844
(1.00)
19p loss 14 (10%) 133 0.0209
(1.00) 		0.0883
(1.00) 		0.559
(1.00) 		0.647
(1.00) 		0.22
(1.00) 		0.628
(1.00) 		0.0723
(1.00) 		1
(1.00)
19q loss 15 (10%) 132 0.0129
(1.00) 		0.0228
(1.00) 		0.923
(1.00) 		0.639
(1.00) 		0.329
(1.00) 		0.779
(1.00) 		0.156
(1.00) 		1
(1.00)
20p loss 6 (4%) 141 0.651
(1.00) 		1
(1.00) 		0.661
(1.00) 		0.664
(1.00) 		0.314
(1.00) 		0.353
(1.00)
21q loss 18 (12%) 129 0.523
(1.00) 		0.124
(1.00) 		0.33
(1.00) 		0.677
(1.00) 		0.902
(1.00) 		0.375
(1.00) 		0.0561
(1.00) 		0.699
(1.00)
22q loss 12 (8%) 135 0.677
(1.00) 		0.554
(1.00) 		0.911
(1.00) 		0.905
(1.00) 		0.353
(1.00) 		0.118
(1.00) 		0.359
(1.00) 		0.541
(1.00)
Xq loss 4 (3%) 143 0.562
(1.00) 		0.137
(1.00) 		0.788
(1.00) 		0.273
(1.00) 		0.564
(1.00) 		0.819
(1.00) 		0.838
(1.00) 		0.75
(1.00)
'7p gain mutation analysis' versus 'CN_CNMF'

P value = 8.31e-05 (Fisher's exact test), Q value = 0.046

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

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 24 26 41 56
7P GAIN MUTATED 10 18 23 12
7P GAIN WILD-TYPE 14 8 18 44

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 = 4.16e-06 (Fisher's exact test), Q value = 0.0024

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

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 24 26 41 56
7Q GAIN MUTATED 6 20 23 13
7Q GAIN WILD-TYPE 18 6 18 43

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 = 1e-05 (Fisher's exact test), Q value = 0.0057

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

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 24 26 41 56
8P GAIN MUTATED 5 10 13 1
8P GAIN WILD-TYPE 19 16 28 55

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 = 2.77e-06 (Fisher's exact test), Q value = 0.0016

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

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 24 26 41 56
8Q GAIN MUTATED 12 12 18 4
8Q GAIN WILD-TYPE 12 14 23 52

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 = 4.85e-05 (Fisher's exact test), Q value = 0.027

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

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 40 57 50
8Q GAIN MUTATED 24 11 11
8Q GAIN WILD-TYPE 16 46 39

Figure S5.  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 = 3.43e-05 (Fisher's exact test), Q value = 0.019

Table S6.  Gene #16: '8q gain mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 56 38 51
8Q GAIN MUTATED 28 3 14
8Q GAIN WILD-TYPE 28 35 37

Figure S6.  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 = 1.31e-05 (Fisher's exact test), Q value = 0.0074

Table S7.  Gene #21: '12p gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 24 26 41 56
12P GAIN MUTATED 1 9 6 0
12P GAIN WILD-TYPE 23 17 35 56

Figure S7.  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 = 7.23e-05 (Fisher's exact test), Q value = 0.04

Table S8.  Gene #23: '13q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 24 26 41 56
13Q GAIN MUTATED 6 5 10 0
13Q GAIN WILD-TYPE 18 21 31 56

Figure S8.  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 = 3e-04 (Fisher's exact test), Q value = 0.17

Table S9.  Gene #23: '13q gain mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 40 57 50
13Q GAIN MUTATED 13 2 6
13Q GAIN WILD-TYPE 27 55 44

Figure S9.  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 = 6.51e-05 (Fisher's exact test), Q value = 0.036

Table S10.  Gene #25: '15q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 24 26 41 56
15Q GAIN MUTATED 1 12 4 4
15Q GAIN WILD-TYPE 23 14 37 52

Figure S10.  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 = 0.000381 (Fisher's exact test), Q value = 0.21

Table S11.  Gene #34: '20p gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 24 26 41 56
20P GAIN MUTATED 8 15 13 7
20P GAIN WILD-TYPE 16 11 28 49

Figure S11.  Get High-res Image Gene #34: '20p gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'20q gain mutation analysis' versus 'CN_CNMF'

P value = 3.79e-05 (Fisher's exact test), Q value = 0.021

Table S12.  Gene #35: '20q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 24 26 41 56
20Q GAIN MUTATED 10 18 16 9
20Q GAIN WILD-TYPE 14 8 25 47

Figure S12.  Get High-res Image Gene #35: '20q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'22q gain mutation analysis' versus 'CN_CNMF'

P value = 4.54e-05 (Fisher's exact test), Q value = 0.025

Table S13.  Gene #37: '22q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 24 26 41 56
22Q GAIN MUTATED 9 15 5 8
22Q GAIN WILD-TYPE 15 11 36 48

Figure S13.  Get High-res Image Gene #37: '22q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'9p loss mutation analysis' versus 'CN_CNMF'

P value = 5.64e-06 (Fisher's exact test), Q value = 0.0032

Table S14.  Gene #51: '9p loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 24 26 41 56
9P LOSS MUTATED 16 16 33 17
9P LOSS WILD-TYPE 8 10 8 39

Figure S14.  Get High-res Image Gene #51: '9p loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'9p loss mutation analysis' versus 'METHLYATION_CNMF'

P value = 0.00016 (Fisher's exact test), Q value = 0.089

Table S15.  Gene #51: '9p loss mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 40 57 50
9P LOSS MUTATED 25 20 37
9P LOSS WILD-TYPE 15 37 13

Figure S15.  Get High-res Image Gene #51: '9p loss mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

'9q loss mutation analysis' versus 'METHLYATION_CNMF'

P value = 0.000166 (Fisher's exact test), Q value = 0.092

Table S16.  Gene #52: '9q loss mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 40 57 50
9Q LOSS MUTATED 15 14 32
9Q LOSS WILD-TYPE 25 43 18

Figure S16.  Get High-res Image Gene #52: '9q loss mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

'10p loss mutation analysis' versus 'CN_CNMF'

P value = 4.3e-06 (Fisher's exact test), Q value = 0.0024

Table S17.  Gene #53: '10p loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 24 26 41 56
10P LOSS MUTATED 16 17 21 10
10P LOSS WILD-TYPE 8 9 20 46

Figure S17.  Get High-res Image Gene #53: '10p loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'10p loss mutation analysis' versus 'MRNASEQ_CNMF'

P value = 0.000103 (Fisher's exact test), Q value = 0.057

Table S18.  Gene #53: '10p loss mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 56 38 51
10P LOSS MUTATED 35 7 21
10P LOSS WILD-TYPE 21 31 30

Figure S18.  Get High-res Image Gene #53: '10p loss mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

'10q loss mutation analysis' versus 'CN_CNMF'

P value = 1.26e-06 (Fisher's exact test), Q value = 0.00072

Table S19.  Gene #54: '10q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 24 26 41 56
10Q LOSS MUTATED 16 20 23 12
10Q LOSS WILD-TYPE 8 6 18 44

Figure S19.  Get High-res Image Gene #54: '10q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'10q loss mutation analysis' versus 'MRNASEQ_CNMF'

P value = 1.47e-05 (Fisher's exact test), Q value = 0.0083

Table S20.  Gene #54: '10q loss mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 56 38 51
10Q LOSS MUTATED 39 8 24
10Q LOSS WILD-TYPE 17 30 27

Figure S20.  Get High-res Image Gene #54: '10q loss mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

'11p loss mutation analysis' versus 'CN_CNMF'

P value = 2.22e-06 (Fisher's exact test), Q value = 0.0013

Table S21.  Gene #55: '11p loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 24 26 41 56
11P LOSS MUTATED 13 12 5 5
11P LOSS WILD-TYPE 11 14 36 51

Figure S21.  Get High-res Image Gene #55: '11p loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'11q loss mutation analysis' versus 'CN_CNMF'

P value = 1.13e-05 (Fisher's exact test), Q value = 0.0064

Table S22.  Gene #56: '11q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 24 26 41 56
11Q LOSS MUTATED 14 11 9 5
11Q LOSS WILD-TYPE 10 15 32 51

Figure S22.  Get High-res Image Gene #56: '11q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'14q loss mutation analysis' versus 'CN_CNMF'

P value = 3.23e-05 (Fisher's exact test), Q value = 0.018

Table S23.  Gene #60: '14q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 24 26 41 56
14Q LOSS MUTATED 5 11 16 3
14Q LOSS WILD-TYPE 19 15 25 53

Figure S23.  Get High-res Image Gene #60: '14q 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-Regional_Metastatic.transferedmergedcluster.txt

  • Number of patients = 147

  • Number of significantly arm-level cnvs = 73

  • 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

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] 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)
Copyright © 2013 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle