Bladder Urothelial Carcinoma: Correlation between copy number variations of arm-level result and molecular subtypes
(primary solid tumor 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 76 arm-level results and 8 molecular subtypes across 150 patients, 15 significant findings detected with Q value < 0.25.

  • 2p gain cnv correlated to 'CN_CNMF'.

  • 3p gain cnv correlated to 'CN_CNMF'.

  • 17q gain cnv correlated to 'CN_CNMF'.

  • 18p gain cnv correlated to 'CN_CNMF'.

  • 6q loss cnv correlated to 'CN_CNMF'.

  • 8p loss cnv correlated to 'CN_CNMF' and 'METHLYATION_CNMF'.

  • 9p loss cnv correlated to 'CN_CNMF'.

  • 9q loss cnv correlated to 'CN_CNMF' and 'MRNASEQ_CHIERARCHICAL'.

  • 11p loss cnv correlated to 'CN_CNMF'.

  • 11q loss cnv correlated to 'CN_CNMF'.

  • 14q loss cnv correlated to 'CN_CNMF'.

  • 18q loss cnv correlated to 'CN_CNMF'.

  • 22q 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 76 arm-level results and 8 molecular subtypes. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 15 significant findings detected.

Molecular
subtypes 		CN
CNMF 		METHLYATION
CNMF 		RPPA
CNMF 		RPPA
CHIERARCHICAL 		MRNASEQ
CNMF 		MRNASEQ
CHIERARCHICAL 		MIRSEQ
CNMF 		MIRSEQ
CHIERARCHICAL
nCNV (%) nWild-Type Chi-square 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
8p loss 49 (33%) 101 6.4e-07
(0.000362) 		3.08e-05
(0.0172) 		0.143
(1.00) 		0.287
(1.00) 		0.643
(1.00) 		0.441
(1.00) 		0.0072
(1.00) 		0.00277
(1.00)
9q loss 41 (27%) 109 7.82e-05
(0.0437) 		0.00545
(1.00) 		0.00121
(0.654) 		0.00833
(1.00) 		0.00274
(1.00) 		0.000115
(0.0639) 		0.841
(1.00) 		0.718
(1.00)
2p gain 28 (19%) 122 0.000111
(0.0617) 		0.00102
(0.557) 		0.214
(1.00) 		0.637
(1.00) 		0.282
(1.00) 		0.738
(1.00) 		0.0571
(1.00) 		0.0721
(1.00)
3p gain 30 (20%) 120 3.51e-06
(0.00198) 		0.0447
(1.00) 		0.0287
(1.00) 		0.0146
(1.00) 		0.0909
(1.00) 		0.005
(1.00) 		0.0254
(1.00) 		0.0793
(1.00)
17q gain 25 (17%) 125 0.000383
(0.212) 		0.0103
(1.00) 		0.497
(1.00) 		0.168
(1.00) 		0.0233
(1.00) 		0.108
(1.00) 		0.124
(1.00) 		0.107
(1.00)
18p gain 26 (17%) 124 4.9e-06
(0.00276) 		0.221
(1.00) 		0.933
(1.00) 		0.254
(1.00) 		0.0652
(1.00) 		0.579
(1.00) 		0.383
(1.00) 		0.0848
(1.00)
6q loss 29 (19%) 121 6.05e-06
(0.0034) 		0.00883
(1.00) 		0.247
(1.00) 		0.802
(1.00) 		0.26
(1.00) 		0.0477
(1.00) 		0.774
(1.00) 		1
(1.00)
9p loss 43 (29%) 107 0.0002
(0.111) 		0.00652
(1.00) 		0.0374
(1.00) 		0.363
(1.00) 		0.0341
(1.00) 		0.000813
(0.444) 		0.894
(1.00) 		0.612
(1.00)
11p loss 48 (32%) 102 1.36e-06
(0.000769) 		0.0019
(1.00) 		0.232
(1.00) 		0.662
(1.00) 		0.0739
(1.00) 		0.00248
(1.00) 		0.178
(1.00) 		0.728
(1.00)
11q loss 35 (23%) 115 0.000188
(0.104) 		0.0298
(1.00) 		0.3
(1.00) 		0.556
(1.00) 		0.211
(1.00) 		0.424
(1.00) 		0.276
(1.00) 		0.678
(1.00)
14q loss 25 (17%) 125 0.000374
(0.207) 		0.0272
(1.00) 		0.818
(1.00) 		0.317
(1.00) 		0.746
(1.00) 		0.47
(1.00) 		0.039
(1.00) 		0.181
(1.00)
18q loss 39 (26%) 111 1.54e-05
(0.00863) 		0.709
(1.00) 		0.813
(1.00) 		0.189
(1.00) 		0.182
(1.00) 		0.559
(1.00) 		0.42
(1.00) 		0.156
(1.00)
22q loss 29 (19%) 121 6.3e-05
(0.0352) 		0.669
(1.00) 		0.0706
(1.00) 		0.126
(1.00) 		0.0686
(1.00) 		0.12
(1.00) 		0.77
(1.00) 		0.0138
(1.00)
1p gain 16 (11%) 134 0.00303
(1.00) 		0.172
(1.00) 		0.466
(1.00) 		0.744
(1.00) 		0.518
(1.00) 		0.386
(1.00) 		0.202
(1.00) 		0.26
(1.00)
1q gain 32 (21%) 118 0.127
(1.00) 		0.478
(1.00) 		0.941
(1.00) 		0.637
(1.00) 		0.822
(1.00) 		0.655
(1.00) 		0.463
(1.00) 		0.708
(1.00)
2q gain 11 (7%) 139 0.059
(1.00) 		0.241
(1.00) 		0.69
(1.00) 		0.605
(1.00) 		0.342
(1.00) 		0.359
(1.00) 		0.182
(1.00) 		0.187
(1.00)
3q gain 42 (28%) 108 0.000559
(0.308) 		0.264
(1.00) 		1
(1.00) 		0.142
(1.00) 		0.586
(1.00) 		0.0857
(1.00) 		0.219
(1.00) 		0.134
(1.00)
4p gain 8 (5%) 142 0.443
(1.00) 		0.625
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		0.613
(1.00) 		0.52
(1.00) 		0.386
(1.00)
4q gain 3 (2%) 147 0.296
(1.00) 		0.405
(1.00) 		0.782
(1.00) 		0.727
(1.00) 		0.326
(1.00) 		0.163
(1.00)
5p gain 40 (27%) 110 0.0862
(1.00) 		0.402
(1.00) 		0.0287
(1.00) 		0.436
(1.00) 		0.354
(1.00) 		0.601
(1.00) 		0.22
(1.00) 		0.33
(1.00)
5q gain 18 (12%) 132 0.0903
(1.00) 		0.0769
(1.00) 		0.228
(1.00) 		0.261
(1.00) 		0.703
(1.00) 		1
(1.00) 		0.122
(1.00) 		0.33
(1.00)
6p gain 8 (5%) 142 0.545
(1.00) 		0.182
(1.00) 		1
(1.00) 		0.227
(1.00) 		0.462
(1.00) 		0.169
(1.00)
6q gain 5 (3%) 145 0.402
(1.00) 		0.382
(1.00) 		0.115
(1.00) 		0.131
(1.00)
7p gain 46 (31%) 104 0.00188
(1.00) 		0.00172
(0.931) 		0.835
(1.00) 		0.211
(1.00) 		0.968
(1.00) 		0.963
(1.00) 		0.0175
(1.00) 		0.257
(1.00)
7q gain 43 (29%) 107 0.00249
(1.00) 		0.0504
(1.00) 		1
(1.00) 		0.258
(1.00) 		0.968
(1.00) 		0.891
(1.00) 		0.407
(1.00) 		0.311
(1.00)
8p gain 15 (10%) 135 0.104
(1.00) 		0.0034
(1.00) 		0.192
(1.00) 		0.252
(1.00) 		0.0776
(1.00) 		1
(1.00) 		0.537
(1.00) 		1
(1.00)
8q gain 39 (26%) 111 0.0523
(1.00) 		0.0887
(1.00) 		0.623
(1.00) 		0.459
(1.00) 		0.278
(1.00) 		0.889
(1.00) 		0.533
(1.00) 		0.888
(1.00)
9p gain 15 (10%) 135 0.00336
(1.00) 		0.437
(1.00) 		0.189
(1.00) 		1
(1.00) 		0.518
(1.00) 		0.0987
(1.00) 		0.0434
(1.00) 		0.894
(1.00)
9q gain 11 (7%) 139 0.494
(1.00) 		0.323
(1.00) 		0.0979
(1.00) 		0.667
(1.00) 		0.0556
(1.00) 		0.038
(1.00) 		0.717
(1.00) 		0.71
(1.00)
10p gain 28 (19%) 122 0.00114
(0.617) 		0.00192
(1.00) 		0.382
(1.00) 		0.017
(1.00) 		0.523
(1.00) 		0.651
(1.00) 		0.388
(1.00) 		0.162
(1.00)
10q gain 7 (5%) 143 0.341
(1.00) 		0.188
(1.00) 		0.512
(1.00) 		0.294
(1.00) 		0.553
(1.00) 		0.164
(1.00) 		0.61
(1.00) 		0.769
(1.00)
11p gain 8 (5%) 142 0.0507
(1.00) 		0.956
(1.00) 		0.724
(1.00) 		1
(1.00) 		0.323
(1.00) 		0.49
(1.00)
11q gain 8 (5%) 142 0.247
(1.00) 		0.518
(1.00) 		0.483
(1.00) 		0.36
(1.00) 		0.52
(1.00) 		0.386
(1.00)
12p gain 24 (16%) 126 0.00378
(1.00) 		0.624
(1.00) 		0.13
(1.00) 		0.191
(1.00) 		0.914
(1.00) 		0.493
(1.00) 		0.252
(1.00) 		0.775
(1.00)
12q gain 17 (11%) 133 0.0139
(1.00) 		0.308
(1.00) 		0.419
(1.00) 		0.551
(1.00) 		0.409
(1.00) 		0.759
(1.00) 		0.0226
(1.00) 		0.233
(1.00)
13q gain 25 (17%) 125 0.218
(1.00) 		0.885
(1.00) 		0.0318
(1.00) 		0.0844
(1.00) 		0.0442
(1.00) 		0.0452
(1.00) 		0.274
(1.00) 		0.667
(1.00)
14q gain 11 (7%) 139 0.0903
(1.00) 		0.898
(1.00) 		0.206
(1.00) 		0.576
(1.00) 		0.278
(1.00) 		0.176
(1.00) 		0.702
(1.00) 		0.692
(1.00)
15q gain 5 (3%) 145 0.0382
(1.00) 		0.0314
(1.00) 		0.296
(1.00) 		0.328
(1.00) 		0.683
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00)
16p gain 10 (7%) 140 0.0499
(1.00) 		0.609
(1.00) 		0.638
(1.00) 		1
(1.00) 		0.751
(1.00) 		0.717
(1.00) 		0.618
(1.00) 		1
(1.00)
16q gain 13 (9%) 137 0.0043
(1.00) 		0.266
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		0.256
(1.00) 		0.739
(1.00) 		0.733
(1.00)
17p gain 9 (6%) 141 0.193
(1.00) 		0.551
(1.00) 		0.724
(1.00) 		0.545
(1.00) 		0.52
(1.00) 		0.386
(1.00)
18q gain 9 (6%) 141 0.0331
(1.00) 		0.599
(1.00) 		0.465
(1.00) 		0.0836
(1.00) 		0.431
(1.00) 		0.289
(1.00) 		0.808
(1.00) 		0.49
(1.00)
19p gain 15 (10%) 135 0.0331
(1.00) 		0.0585
(1.00) 		0.109
(1.00) 		0.489
(1.00) 		0.32
(1.00) 		0.61
(1.00)
19q gain 29 (19%) 121 0.0007
(0.384) 		0.117
(1.00) 		0.633
(1.00) 		1
(1.00) 		0.0467
(1.00) 		0.105
(1.00) 		0.319
(1.00) 		0.75
(1.00)
20p gain 54 (36%) 96 0.0335
(1.00) 		0.0771
(1.00) 		0.415
(1.00) 		0.805
(1.00) 		0.475
(1.00) 		0.151
(1.00) 		0.403
(1.00) 		0.477
(1.00)
20q gain 59 (39%) 91 0.0206
(1.00) 		0.0511
(1.00) 		0.439
(1.00) 		0.494
(1.00) 		0.0775
(1.00) 		0.088
(1.00) 		0.0265
(1.00) 		0.214
(1.00)
21q gain 27 (18%) 123 0.00187
(1.00) 		0.0011
(0.601) 		0.598
(1.00) 		0.539
(1.00) 		0.191
(1.00) 		0.102
(1.00) 		0.275
(1.00) 		0.152
(1.00)
22q gain 12 (8%) 138 0.000744
(0.407) 		0.165
(1.00) 		0.536
(1.00) 		0.572
(1.00) 		0.451
(1.00) 		0.903
(1.00) 		0.0213
(1.00) 		1
(1.00)
Xq gain 6 (4%) 144 0.113
(1.00) 		0.801
(1.00) 		0.763
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00)
1p loss 4 (3%) 146 0.456
(1.00) 		0.0509
(1.00) 		0.358
(1.00) 		0.229
(1.00)
2p loss 8 (5%) 142 0.828
(1.00) 		0.0229
(1.00) 		0.368
(1.00) 		0.164
(1.00) 		1
(1.00) 		1
(1.00)
2q loss 17 (11%) 133 0.0112
(1.00) 		0.472
(1.00) 		1
(1.00) 		0.294
(1.00) 		0.463
(1.00) 		0.0323
(1.00) 		0.756
(1.00) 		0.572
(1.00)
3p loss 9 (6%) 141 0.0633
(1.00) 		0.773
(1.00) 		0.345
(1.00) 		0.467
(1.00) 		0.0858
(1.00) 		0.0719
(1.00) 		0.175
(1.00) 		0.157
(1.00)
4p loss 29 (19%) 121 0.000739
(0.405) 		0.292
(1.00) 		0.853
(1.00) 		0.767
(1.00) 		0.425
(1.00) 		0.342
(1.00) 		0.535
(1.00) 		0.75
(1.00)
4q loss 26 (17%) 124 0.0777
(1.00) 		0.78
(1.00) 		0.68
(1.00) 		0.709
(1.00) 		0.593
(1.00) 		0.601
(1.00) 		0.726
(1.00) 		0.21
(1.00)
5p loss 14 (9%) 136 0.000607
(0.334) 		0.609
(1.00) 		0.517
(1.00) 		0.867
(1.00) 		0.532
(1.00) 		0.721
(1.00) 		0.442
(1.00) 		0.589
(1.00)
5q loss 36 (24%) 114 0.00112
(0.61) 		0.0923
(1.00) 		0.767
(1.00) 		0.906
(1.00) 		0.895
(1.00) 		0.772
(1.00) 		0.426
(1.00) 		0.308
(1.00)
6p loss 18 (12%) 132 0.0517
(1.00) 		0.238
(1.00) 		0.233
(1.00) 		0.456
(1.00) 		0.384
(1.00) 		0.812
(1.00) 		0.653
(1.00) 		0.904
(1.00)
7q loss 3 (2%) 147 0.375
(1.00) 		0.292
(1.00) 		0.233
(1.00) 		0.263
(1.00)
8q loss 5 (3%) 145 0.285
(1.00) 		0.192
(1.00) 		0.782
(1.00) 		1
(1.00) 		0.151
(1.00) 		0.0191
(1.00)
10p loss 19 (13%) 131 0.763
(1.00) 		0.878
(1.00) 		0.189
(1.00) 		0.528
(1.00) 		0.252
(1.00) 		0.275
(1.00) 		0.291
(1.00) 		0.0691
(1.00)
10q loss 30 (20%) 120 0.118
(1.00) 		0.0538
(1.00) 		0.921
(1.00) 		0.843
(1.00) 		0.209
(1.00) 		0.0406
(1.00) 		0.0475
(1.00) 		0.0104
(1.00)
12p loss 6 (4%) 144 0.159
(1.00) 		0.161
(1.00) 		0.325
(1.00) 		0.284
(1.00) 		0.666
(1.00) 		1
(1.00)
12q loss 9 (6%) 141 0.159
(1.00) 		1
(1.00) 		0.15
(1.00) 		0.36
(1.00) 		0.405
(1.00) 		0.526
(1.00)
13q loss 20 (13%) 130 0.0157
(1.00) 		0.311
(1.00) 		0.104
(1.00) 		0.141
(1.00) 		0.111
(1.00) 		0.418
(1.00) 		0.711
(1.00) 		0.369
(1.00)
15q loss 19 (13%) 131 0.168
(1.00) 		0.095
(1.00) 		0.121
(1.00) 		0.411
(1.00) 		0.0776
(1.00) 		0.0576
(1.00) 		0.192
(1.00) 		0.0156
(1.00)
16p loss 17 (11%) 133 0.0179
(1.00) 		0.276
(1.00) 		0.603
(1.00) 		1
(1.00) 		0.343
(1.00) 		0.864
(1.00) 		0.158
(1.00) 		0.00217
(1.00)
16q loss 17 (11%) 133 0.00477
(1.00) 		0.399
(1.00) 		0.75
(1.00) 		1
(1.00) 		0.735
(1.00) 		0.806
(1.00) 		0.712
(1.00) 		0.0132
(1.00)
17p loss 42 (28%) 108 0.00208
(1.00) 		0.108
(1.00) 		0.541
(1.00) 		0.805
(1.00) 		0.00626
(1.00) 		0.161
(1.00) 		0.0516
(1.00) 		0.035
(1.00)
17q loss 6 (4%) 144 0.72
(1.00) 		0.0394
(1.00) 		0.857
(1.00) 		0.845
(1.00) 		0.235
(1.00) 		0.527
(1.00)
18p loss 22 (15%) 128 0.005
(1.00) 		0.0335
(1.00) 		0.507
(1.00) 		0.584
(1.00) 		1
(1.00) 		0.172
(1.00) 		0.46
(1.00) 		0.00935
(1.00)
19p loss 9 (6%) 141 0.3
(1.00) 		0.933
(1.00) 		0.252
(1.00) 		0.14
(1.00) 		0.415
(1.00) 		0.356
(1.00) 		0.445
(1.00) 		0.0737
(1.00)
19q loss 4 (3%) 146 0.545
(1.00) 		0.702
(1.00) 		0.48
(1.00) 		0.727
(1.00) 		0.148
(1.00) 		0.144
(1.00)
20p loss 7 (5%) 143 0.313
(1.00) 		0.251
(1.00) 		1
(1.00) 		1
(1.00) 		0.69
(1.00) 		0.0577
(1.00)
21q loss 17 (11%) 133 0.556
(1.00) 		0.533
(1.00) 		0.466
(1.00) 		0.216
(1.00) 		0.674
(1.00) 		0.584
(1.00) 		0.848
(1.00) 		0.267
(1.00)
Xq loss 4 (3%) 146 0.252
(1.00) 		0.285
(1.00) 		0.48
(1.00) 		0.562
(1.00) 		0.689
(1.00) 		0.659
(1.00)
'2p gain mutation analysis' versus 'CN_CNMF'

P value = 0.000111 (Chi-square test), Q value = 0.062

Table S1.  Gene #3: '2p gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6
ALL 14 53 24 29 13 17
2P GAIN MUTATED 4 1 6 4 7 6
2P GAIN WILD-TYPE 10 52 18 25 6 11

Figure S1.  Get High-res Image Gene #3: '2p gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'3p gain mutation analysis' versus 'CN_CNMF'

P value = 3.51e-06 (Chi-square test), Q value = 0.002

Table S2.  Gene #5: '3p gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6
ALL 14 53 24 29 13 17
3P GAIN MUTATED 2 3 7 7 0 11
3P GAIN WILD-TYPE 12 50 17 22 13 6

Figure S2.  Get High-res Image Gene #5: '3p gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'17q gain mutation analysis' versus 'CN_CNMF'

P value = 0.000383 (Chi-square test), Q value = 0.21

Table S3.  Gene #31: '17q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6
ALL 14 53 24 29 13 17
17Q GAIN MUTATED 6 0 7 7 1 4
17Q GAIN WILD-TYPE 8 53 17 22 12 13

Figure S3.  Get High-res Image Gene #31: '17q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'18p gain mutation analysis' versus 'CN_CNMF'

P value = 4.9e-06 (Chi-square test), Q value = 0.0028

Table S4.  Gene #32: '18p gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6
ALL 14 53 24 29 13 17
18P GAIN MUTATED 1 1 11 3 6 4
18P GAIN WILD-TYPE 13 52 13 26 7 13

Figure S4.  Get High-res Image Gene #32: '18p gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'6q loss mutation analysis' versus 'CN_CNMF'

P value = 6.05e-06 (Chi-square test), Q value = 0.0034

Table S5.  Gene #50: '6q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6
ALL 14 53 24 29 13 17
6Q LOSS MUTATED 4 0 4 8 3 10
6Q LOSS WILD-TYPE 10 53 20 21 10 7

Figure S5.  Get High-res Image Gene #50: '6q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'8p loss mutation analysis' versus 'CN_CNMF'

P value = 6.4e-07 (Chi-square test), Q value = 0.00036

Table S6.  Gene #52: '8p loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6
ALL 14 53 24 29 13 17
8P LOSS MUTATED 10 11 12 0 8 8
8P LOSS WILD-TYPE 4 42 12 29 5 9

Figure S6.  Get High-res Image Gene #52: '8p loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'8p loss mutation analysis' versus 'METHLYATION_CNMF'

P value = 3.08e-05 (Fisher's exact test), Q value = 0.017

Table S7.  Gene #52: '8p loss mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 36 50 39 25
8P LOSS MUTATED 23 15 5 6
8P LOSS WILD-TYPE 13 35 34 19

Figure S7.  Get High-res Image Gene #52: '8p loss mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

'9p loss mutation analysis' versus 'CN_CNMF'

P value = 2e-04 (Chi-square test), Q value = 0.11

Table S8.  Gene #54: '9p loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6
ALL 14 53 24 29 13 17
9P LOSS MUTATED 2 9 9 17 0 6
9P LOSS WILD-TYPE 12 44 15 12 13 11

Figure S8.  Get High-res Image Gene #54: '9p loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'9q loss mutation analysis' versus 'CN_CNMF'

P value = 7.82e-05 (Chi-square test), Q value = 0.044

Table S9.  Gene #55: '9q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6
ALL 14 53 24 29 13 17
9Q LOSS MUTATED 1 8 10 17 1 4
9Q LOSS WILD-TYPE 13 45 14 12 12 13

Figure S9.  Get High-res Image Gene #55: '9q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'9q loss mutation analysis' versus 'MRNASEQ_CHIERARCHICAL'

P value = 0.000115 (Fisher's exact test), Q value = 0.064

Table S10.  Gene #55: '9q loss mutation analysis' versus Clinical Feature #6: 'MRNASEQ_CHIERARCHICAL'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 39 18 64
9Q LOSS MUTATED 4 2 29
9Q LOSS WILD-TYPE 35 16 35

Figure S10.  Get High-res Image Gene #55: '9q loss mutation analysis' versus Clinical Feature #6: 'MRNASEQ_CHIERARCHICAL'

'11p loss mutation analysis' versus 'CN_CNMF'

P value = 1.36e-06 (Chi-square test), Q value = 0.00077

Table S11.  Gene #58: '11p loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6
ALL 14 53 24 29 13 17
11P LOSS MUTATED 4 4 11 15 2 12
11P LOSS WILD-TYPE 10 49 13 14 11 5

Figure S11.  Get High-res Image Gene #58: '11p loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'11q loss mutation analysis' versus 'CN_CNMF'

P value = 0.000188 (Chi-square test), Q value = 0.1

Table S12.  Gene #59: '11q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6
ALL 14 53 24 29 13 17
11Q LOSS MUTATED 2 2 10 13 3 5
11Q LOSS WILD-TYPE 12 51 14 16 10 12

Figure S12.  Get High-res Image Gene #59: '11q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'14q loss mutation analysis' versus 'CN_CNMF'

P value = 0.000374 (Chi-square test), Q value = 0.21

Table S13.  Gene #63: '14q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6
ALL 14 53 24 29 13 17
14Q LOSS MUTATED 4 1 8 3 2 7
14Q LOSS WILD-TYPE 10 52 16 26 11 10

Figure S13.  Get High-res Image Gene #63: '14q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'18q loss mutation analysis' versus 'CN_CNMF'

P value = 1.54e-05 (Chi-square test), Q value = 0.0086

Table S14.  Gene #70: '18q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6
ALL 14 53 24 29 13 17
18Q LOSS MUTATED 5 4 15 6 2 7
18Q LOSS WILD-TYPE 9 49 9 23 11 10

Figure S14.  Get High-res Image Gene #70: '18q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'22q loss mutation analysis' versus 'CN_CNMF'

P value = 6.3e-05 (Chi-square test), Q value = 0.035

Table S15.  Gene #75: '22q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6
ALL 14 53 24 29 13 17
22Q LOSS MUTATED 2 6 7 3 9 2
22Q LOSS WILD-TYPE 12 47 17 26 4 15

Figure S15.  Get High-res Image Gene #75: '22q 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 = BLCA-TP.transferedmergedcluster.txt

  • Number of patients = 150

  • Number of significantly arm-level cnvs = 76

  • Number of molecular subtypes = 8

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

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

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