Correlation between copy number variations of arm-level result and molecular subtypes
Bladder Urothelial Carcinoma (Primary solid tumor)
15 January 2014  |  analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C15X279M
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between copy number variations of arm-level result and molecular subtypes. Broad Institute of MIT and Harvard. doi:10.7908/C15X279M
Overview
Introduction

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

Summary

Testing the association between copy number variation 80 arm-level events and 10 molecular subtypes across 221 patients, 24 significant findings detected with P value < 0.05 and Q value < 0.25.

  • 2p gain cnv correlated to 'CN_CNMF'.

  • 7p gain cnv correlated to 'CN_CNMF'.

  • 9p gain cnv correlated to 'CN_CNMF'.

  • 10p gain cnv correlated to 'CN_CNMF'.

  • 12p gain cnv correlated to 'CN_CNMF'.

  • 17p gain cnv correlated to 'MIRSEQ_CHIERARCHICAL'.

  • 18p gain cnv correlated to 'CN_CNMF'.

  • 19q gain cnv correlated to 'CN_CNMF'.

  • 21q gain cnv correlated to 'CN_CNMF',  'MIRSEQ_CNMF', and 'MIRSEQ_CHIERARCHICAL'.

  • 2q loss cnv correlated to 'CN_CNMF'.

  • 3p loss cnv correlated to 'MRNASEQ_CNMF',  'MRNASEQ_CHIERARCHICAL', and 'MIRSEQ_MATURE_CNMF'.

  • 6q loss cnv correlated to 'CN_CNMF'.

  • 8p loss cnv correlated to 'CN_CNMF'.

  • 9q loss cnv correlated to 'MRNASEQ_CHIERARCHICAL'.

  • 11p loss cnv correlated to 'CN_CNMF' and 'METHLYATION_CNMF'.

  • 11q loss cnv correlated to 'CN_CNMF'.

  • 16p loss cnv correlated to 'CN_CNMF'.

  • 16q 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 80 arm-level events and 10 molecular subtypes. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 24 significant findings detected.

Clinical
Features 		CN
CNMF 		METHLYATION
CNMF 		RPPA
CNMF 		RPPA
CHIERARCHICAL 		MRNASEQ
CNMF 		MRNASEQ
CHIERARCHICAL 		MIRSEQ
CNMF 		MIRSEQ
CHIERARCHICAL 		MIRSEQ
MATURE
CNMF 		MIRSEQ
MATURE
CHIERARCHICAL
nCNV (%) nWild-Type Chi-square 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 Fisher's exact test Fisher's exact test
21q gain 67 (30%) 154 0.000204
(0.159) 		0.0245
(1.00) 		0.614
(1.00) 		0.568
(1.00) 		0.239
(1.00) 		0.209
(1.00) 		0.000176
(0.137) 		3.34e-05
(0.0263) 		0.0936
(1.00) 		0.00322
(1.00)
3p loss 29 (13%) 192 0.0967
(1.00) 		0.0938
(1.00) 		0.0181
(1.00) 		0.0612
(1.00) 		1.15e-07
(9.17e-05) 		4.12e-05
(0.0325) 		0.283
(1.00) 		0.0408
(1.00) 		0.000109
(0.0853) 		0.000414
(0.318)
11p loss 89 (40%) 132 1.08e-05
(0.00857) 		0.000101
(0.0789) 		0.256
(1.00) 		0.803
(1.00) 		0.126
(1.00) 		0.00244
(1.00) 		0.0422
(1.00) 		0.0488
(1.00) 		0.00852
(1.00) 		0.42
(1.00)
2p gain 66 (30%) 155 6.97e-06
(0.00554) 		0.000868
(0.658) 		0.394
(1.00) 		0.783
(1.00) 		0.146
(1.00) 		0.29
(1.00) 		0.122
(1.00) 		0.0459
(1.00) 		0.175
(1.00) 		0.00973
(1.00)
7p gain 94 (43%) 127 0.000108
(0.0842) 		0.000934
(0.707) 		0.0232
(1.00) 		0.858
(1.00) 		0.327
(1.00) 		0.89
(1.00) 		0.249
(1.00) 		0.249
(1.00) 		0.192
(1.00) 		0.21
(1.00)
9p gain 35 (16%) 186 4.39e-05
(0.0346) 		0.756
(1.00) 		0.725
(1.00) 		0.595
(1.00) 		0.182
(1.00) 		0.282
(1.00) 		0.789
(1.00) 		0.94
(1.00) 		0.267
(1.00) 		0.126
(1.00)
10p gain 64 (29%) 157 2.63e-06
(0.0021) 		0.332
(1.00) 		0.854
(1.00) 		0.493
(1.00) 		0.264
(1.00) 		0.0787
(1.00) 		0.675
(1.00) 		0.499
(1.00) 		0.498
(1.00) 		0.905
(1.00)
12p gain 67 (30%) 154 9.03e-05
(0.0709) 		0.115
(1.00) 		0.732
(1.00) 		0.526
(1.00) 		0.194
(1.00) 		0.52
(1.00) 		0.521
(1.00) 		0.0474
(1.00) 		0.623
(1.00) 		0.0835
(1.00)
17p gain 32 (14%) 189 0.00843
(1.00) 		0.11
(1.00) 		0.475
(1.00) 		0.467
(1.00) 		0.633
(1.00) 		0.615
(1.00) 		0.00125
(0.946) 		0.000313
(0.243) 		0.0442
(1.00) 		0.00857
(1.00)
18p gain 67 (30%) 154 1.04e-07
(8.29e-05) 		0.000325
(0.252) 		0.935
(1.00) 		0.824
(1.00) 		0.000381
(0.294) 		0.181
(1.00) 		0.537
(1.00) 		0.231
(1.00) 		0.24
(1.00) 		0.0769
(1.00)
19q gain 75 (34%) 146 3.13e-05
(0.0247) 		0.00364
(1.00) 		0.357
(1.00) 		0.773
(1.00) 		0.67
(1.00) 		0.0646
(1.00) 		0.26
(1.00) 		0.125
(1.00) 		0.497
(1.00) 		0.232
(1.00)
2q loss 50 (23%) 171 0.000228
(0.178) 		0.178
(1.00) 		0.602
(1.00) 		0.308
(1.00) 		0.21
(1.00) 		0.0485
(1.00) 		0.853
(1.00) 		0.884
(1.00) 		0.221
(1.00) 		0.29
(1.00)
6q loss 79 (36%) 142 4.84e-06
(0.00385) 		0.000744
(0.567) 		0.837
(1.00) 		0.559
(1.00) 		0.578
(1.00) 		0.178
(1.00) 		0.808
(1.00) 		0.0237
(1.00) 		0.0609
(1.00) 		0.0569
(1.00)
8p loss 99 (45%) 122 6.07e-05
(0.0477) 		0.103
(1.00) 		0.109
(1.00) 		0.0548
(1.00) 		0.0264
(1.00) 		0.0485
(1.00) 		0.0484
(1.00) 		0.0493
(1.00) 		0.02
(1.00) 		0.0686
(1.00)
9q loss 87 (39%) 134 0.00547
(1.00) 		0.00755
(1.00) 		0.0319
(1.00) 		0.00701
(1.00) 		0.00847
(1.00) 		1.78e-05
(0.0141) 		0.666
(1.00) 		0.331
(1.00) 		0.00924
(1.00) 		0.00883
(1.00)
11q loss 69 (31%) 152 2.59e-06
(0.00207) 		0.00066
(0.503) 		0.0631
(1.00) 		0.136
(1.00) 		0.0633
(1.00) 		0.0244
(1.00) 		0.645
(1.00) 		0.663
(1.00) 		0.387
(1.00) 		0.667
(1.00)
16p loss 57 (26%) 164 1.86e-05
(0.0147) 		0.0174
(1.00) 		0.564
(1.00) 		0.471
(1.00) 		0.195
(1.00) 		0.399
(1.00) 		1
(1.00) 		0.307
(1.00) 		0.419
(1.00) 		0.165
(1.00)
16q loss 52 (24%) 169 0.000184
(0.143) 		0.0228
(1.00) 		0.176
(1.00) 		0.775
(1.00) 		0.417
(1.00) 		0.406
(1.00) 		0.749
(1.00) 		0.157
(1.00) 		0.984
(1.00) 		0.263
(1.00)
22q loss 84 (38%) 137 3.28e-07
(0.000262) 		0.00503
(1.00) 		0.548
(1.00) 		0.0155
(1.00) 		0.00291
(1.00) 		0.0171
(1.00) 		0.646
(1.00) 		0.034
(1.00) 		0.00938
(1.00) 		0.0195
(1.00)
1p gain 51 (23%) 170 0.0279
(1.00) 		0.0326
(1.00) 		0.387
(1.00) 		0.449
(1.00) 		0.506
(1.00) 		0.868
(1.00) 		0.599
(1.00) 		0.235
(1.00) 		0.881
(1.00) 		0.885
(1.00)
1q gain 70 (32%) 151 0.226
(1.00) 		0.238
(1.00) 		0.498
(1.00) 		0.759
(1.00) 		0.296
(1.00) 		0.969
(1.00) 		0.709
(1.00) 		0.572
(1.00) 		0.908
(1.00) 		0.835
(1.00)
2q gain 31 (14%) 190 0.125
(1.00) 		0.0725
(1.00) 		0.529
(1.00) 		0.702
(1.00) 		0.122
(1.00) 		0.873
(1.00) 		0.308
(1.00) 		0.338
(1.00) 		0.329
(1.00) 		0.206
(1.00)
3p gain 75 (34%) 146 0.000809
(0.616) 		0.18
(1.00) 		0.0639
(1.00) 		0.309
(1.00) 		0.00507
(1.00) 		0.0667
(1.00) 		0.000818
(0.622) 		0.000452
(0.347) 		0.000825
(0.626) 		0.00245
(1.00)
3q gain 98 (44%) 123 0.00188
(1.00) 		0.851
(1.00) 		0.431
(1.00) 		0.926
(1.00) 		0.229
(1.00) 		0.217
(1.00) 		0.00331
(1.00) 		0.00623
(1.00) 		0.00035
(0.271) 		0.00233
(1.00)
4p gain 23 (10%) 198 0.00393
(1.00) 		0.0761
(1.00) 		0.478
(1.00) 		0.655
(1.00) 		0.316
(1.00) 		0.17
(1.00) 		0.0476
(1.00) 		1
(1.00) 		0.0781
(1.00) 		0.122
(1.00)
4q gain 14 (6%) 207 0.00141
(1.00) 		0.186
(1.00) 		0.569
(1.00) 		0.161
(1.00) 		0.759
(1.00) 		0.549
(1.00) 		0.0175
(1.00) 		0.436
(1.00) 		0.0674
(1.00) 		0.825
(1.00)
5p gain 91 (41%) 130 0.00178
(1.00) 		0.179
(1.00) 		0.0422
(1.00) 		0.237
(1.00) 		0.08
(1.00) 		0.0125
(1.00) 		0.0548
(1.00) 		0.1
(1.00) 		0.136
(1.00) 		0.109
(1.00)
5q gain 31 (14%) 190 0.007
(1.00) 		0.367
(1.00) 		0.0172
(1.00) 		0.659
(1.00) 		0.96
(1.00) 		0.388
(1.00) 		0.113
(1.00) 		0.056
(1.00) 		0.334
(1.00) 		0.551
(1.00)
6p gain 33 (15%) 188 0.384
(1.00) 		0.688
(1.00) 		0.786
(1.00) 		0.177
(1.00) 		0.37
(1.00) 		0.258
(1.00) 		0.384
(1.00) 		0.331
(1.00) 		0.646
(1.00) 		0.378
(1.00)
6q gain 20 (9%) 201 0.544
(1.00) 		0.636
(1.00) 		0.934
(1.00) 		0.101
(1.00) 		0.0771
(1.00) 		0.273
(1.00) 		0.175
(1.00) 		0.13
(1.00) 		0.333
(1.00) 		0.149
(1.00)
7q gain 82 (37%) 139 0.000494
(0.379) 		0.00702
(1.00) 		0.183
(1.00) 		0.917
(1.00) 		0.814
(1.00) 		0.372
(1.00) 		0.452
(1.00) 		0.187
(1.00) 		0.35
(1.00) 		0.343
(1.00)
8p gain 44 (20%) 177 0.0112
(1.00) 		0.119
(1.00) 		0.099
(1.00) 		0.025
(1.00) 		0.0871
(1.00) 		0.451
(1.00) 		0.576
(1.00) 		0.358
(1.00) 		0.0997
(1.00) 		0.00633
(1.00)
8q gain 101 (46%) 120 0.218
(1.00) 		0.0679
(1.00) 		0.0453
(1.00) 		0.162
(1.00) 		0.619
(1.00) 		0.609
(1.00) 		0.954
(1.00) 		0.864
(1.00) 		0.586
(1.00) 		0.444
(1.00)
9q gain 29 (13%) 192 0.29
(1.00) 		0.0652
(1.00) 		0.776
(1.00) 		0.0315
(1.00) 		0.0216
(1.00) 		0.0126
(1.00) 		0.743
(1.00) 		0.863
(1.00) 		0.016
(1.00) 		0.0693
(1.00)
10q gain 18 (8%) 203 0.324
(1.00) 		0.502
(1.00) 		0.335
(1.00) 		0.364
(1.00) 		0.617
(1.00) 		0.0475
(1.00) 		0.384
(1.00) 		0.371
(1.00) 		0.487
(1.00) 		0.226
(1.00)
11p gain 21 (10%) 200 0.0464
(1.00) 		0.18
(1.00) 		0.105
(1.00) 		0.0836
(1.00) 		0.0125
(1.00) 		0.0421
(1.00) 		0.164
(1.00) 		0.376
(1.00) 		0.0527
(1.00) 		0.153
(1.00)
11q gain 29 (13%) 192 0.000633
(0.484) 		0.297
(1.00) 		0.158
(1.00) 		0.0176
(1.00) 		0.0444
(1.00) 		0.0271
(1.00) 		0.481
(1.00) 		0.882
(1.00) 		0.0696
(1.00) 		0.503
(1.00)
12q gain 49 (22%) 172 0.00233
(1.00) 		0.121
(1.00) 		0.0991
(1.00) 		0.0241
(1.00) 		0.182
(1.00) 		0.273
(1.00) 		0.231
(1.00) 		0.158
(1.00) 		0.368
(1.00) 		0.201
(1.00)
13q gain 57 (26%) 164 0.154
(1.00) 		0.78
(1.00) 		0.94
(1.00) 		0.93
(1.00) 		0.191
(1.00) 		0.69
(1.00) 		0.742
(1.00) 		0.514
(1.00) 		0.728
(1.00) 		0.139
(1.00)
14q gain 37 (17%) 184 0.0097
(1.00) 		0.126
(1.00) 		0.364
(1.00) 		0.525
(1.00) 		0.0148
(1.00) 		0.1
(1.00) 		0.908
(1.00) 		0.775
(1.00) 		0.185
(1.00) 		0.406
(1.00)
15q gain 22 (10%) 199 0.168
(1.00) 		0.0901
(1.00) 		0.576
(1.00) 		0.396
(1.00) 		0.297
(1.00) 		0.156
(1.00) 		0.172
(1.00) 		0.802
(1.00) 		0.829
(1.00) 		0.908
(1.00)
16p gain 39 (18%) 182 0.0761
(1.00) 		0.963
(1.00) 		0.238
(1.00) 		0.304
(1.00) 		0.63
(1.00) 		0.0708
(1.00) 		0.841
(1.00) 		0.575
(1.00) 		0.773
(1.00) 		0.861
(1.00)
16q gain 48 (22%) 173 0.1
(1.00) 		0.815
(1.00) 		0.0986
(1.00) 		0.438
(1.00) 		0.642
(1.00) 		0.163
(1.00) 		0.61
(1.00) 		0.489
(1.00) 		0.687
(1.00) 		0.924
(1.00)
17q gain 77 (35%) 144 0.0704
(1.00) 		0.00753
(1.00) 		0.364
(1.00) 		0.626
(1.00) 		0.254
(1.00) 		0.181
(1.00) 		0.0204
(1.00) 		0.0109
(1.00) 		0.0272
(1.00) 		0.0212
(1.00)
18q gain 32 (14%) 189 0.0206
(1.00) 		0.0731
(1.00) 		0.199
(1.00) 		0.171
(1.00) 		0.00698
(1.00) 		0.58
(1.00) 		0.0672
(1.00) 		0.265
(1.00) 		0.341
(1.00) 		0.623
(1.00)
19p gain 44 (20%) 177 0.0678
(1.00) 		0.0526
(1.00) 		0.44
(1.00) 		0.775
(1.00) 		0.0774
(1.00) 		0.00779
(1.00) 		0.295
(1.00) 		0.267
(1.00) 		0.238
(1.00) 		0.11
(1.00)
20p gain 114 (52%) 107 0.0105
(1.00) 		0.0298
(1.00) 		0.863
(1.00) 		0.0188
(1.00) 		0.596
(1.00) 		0.0299
(1.00) 		0.141
(1.00) 		0.0349
(1.00) 		0.0265
(1.00) 		0.0802
(1.00)
20q gain 127 (57%) 94 0.00193
(1.00) 		0.0197
(1.00) 		0.737
(1.00) 		0.12
(1.00) 		0.837
(1.00) 		0.253
(1.00) 		0.253
(1.00) 		0.0531
(1.00) 		0.042
(1.00) 		0.169
(1.00)
22q gain 32 (14%) 189 0.000496
(0.379) 		0.0151
(1.00) 		0.475
(1.00) 		0.62
(1.00) 		0.148
(1.00) 		0.123
(1.00) 		0.0434
(1.00) 		0.65
(1.00) 		0.252
(1.00) 		0.412
(1.00)
xq gain 23 (10%) 198 0.709
(1.00) 		0.128
(1.00) 		0.401
(1.00) 		0.848
(1.00) 		0.0584
(1.00) 		0.836
(1.00) 		0.741
(1.00) 		0.342
(1.00) 		0.637
(1.00) 		0.796
(1.00)
1p loss 18 (8%) 203 0.0178
(1.00) 		0.103
(1.00) 		0.333
(1.00) 		0.143
(1.00) 		0.415
(1.00) 		0.0747
(1.00) 		0.706
(1.00) 		0.173
(1.00) 		0.751
(1.00) 		0.617
(1.00)
1q loss 20 (9%) 201 0.0823
(1.00) 		0.188
(1.00) 		0.342
(1.00) 		0.96
(1.00) 		0.444
(1.00) 		0.041
(1.00) 		1
(1.00) 		0.0812
(1.00) 		0.382
(1.00) 		0.271
(1.00)
2p loss 23 (10%) 198 0.165
(1.00) 		0.419
(1.00) 		0.841
(1.00) 		0.759
(1.00) 		0.475
(1.00) 		0.14
(1.00) 		0.366
(1.00) 		0.809
(1.00) 		0.354
(1.00) 		0.841
(1.00)
3q loss 11 (5%) 210 0.632
(1.00) 		0.275
(1.00) 		0.00159
(1.00) 		0.0123
(1.00) 		0.0142
(1.00) 		0.0777
(1.00) 		0.589
(1.00) 		0.463
(1.00) 		0.0275
(1.00) 		0.0227
(1.00)
4p loss 77 (35%) 144 0.00112
(0.849) 		0.568
(1.00) 		0.627
(1.00) 		0.443
(1.00) 		0.0629
(1.00) 		0.00869
(1.00) 		0.0688
(1.00) 		0.426
(1.00) 		0.0409
(1.00) 		0.785
(1.00)
4q loss 80 (36%) 141 0.000402
(0.309) 		0.249
(1.00) 		0.239
(1.00) 		0.158
(1.00) 		0.0712
(1.00) 		0.0356
(1.00) 		0.0394
(1.00) 		0.333
(1.00) 		0.00734
(1.00) 		0.351
(1.00)
5p loss 34 (15%) 187 0.748
(1.00) 		0.836
(1.00) 		0.391
(1.00) 		0.602
(1.00) 		0.324
(1.00) 		0.069
(1.00) 		0.628
(1.00) 		0.756
(1.00) 		0.413
(1.00) 		0.905
(1.00)
5q loss 84 (38%) 137 0.0944
(1.00) 		0.149
(1.00) 		0.8
(1.00) 		0.266
(1.00) 		0.0156
(1.00) 		0.000361
(0.279) 		0.749
(1.00) 		0.0251
(1.00) 		0.217
(1.00) 		0.159
(1.00)
6p loss 54 (24%) 167 0.0388
(1.00) 		0.00329
(1.00) 		0.815
(1.00) 		0.741
(1.00) 		0.945
(1.00) 		0.181
(1.00) 		0.395
(1.00) 		0.123
(1.00) 		0.381
(1.00) 		0.153
(1.00)
7p loss 8 (4%) 213 0.667
(1.00) 		1
(1.00) 		0.385
(1.00) 		0.924
(1.00) 		0.461
(1.00) 		0.575
(1.00) 		0.373
(1.00) 		0.117
(1.00) 		0.287
(1.00) 		0.61
(1.00)
7q loss 13 (6%) 208 0.507
(1.00) 		1
(1.00) 		0.312
(1.00) 		0.833
(1.00) 		0.261
(1.00) 		0.607
(1.00) 		0.365
(1.00) 		0.00218
(1.00) 		0.605
(1.00) 		0.0744
(1.00)
8q loss 21 (10%) 200 0.0642
(1.00) 		0.624
(1.00) 		0.333
(1.00) 		0.0167
(1.00) 		0.127
(1.00) 		0.0556
(1.00) 		0.0517
(1.00) 		0.0109
(1.00) 		0.0136
(1.00) 		0.0412
(1.00)
9p loss 93 (42%) 128 0.000344
(0.266) 		0.0776
(1.00) 		0.3
(1.00) 		0.0697
(1.00) 		0.0581
(1.00) 		0.0201
(1.00) 		0.613
(1.00) 		0.256
(1.00) 		0.231
(1.00) 		0.2
(1.00)
10p loss 41 (19%) 180 0.00931
(1.00) 		0.0881
(1.00) 		0.699
(1.00) 		0.585
(1.00) 		0.182
(1.00) 		0.141
(1.00) 		0.692
(1.00) 		0.267
(1.00) 		0.0609
(1.00) 		0.0628
(1.00)
10q loss 75 (34%) 146 0.00118
(0.892) 		0.00233
(1.00) 		0.468
(1.00) 		0.904
(1.00) 		0.141
(1.00) 		0.145
(1.00) 		0.124
(1.00) 		0.265
(1.00) 		0.193
(1.00) 		0.205
(1.00)
12p loss 21 (10%) 200 0.488
(1.00) 		0.451
(1.00) 		0.87
(1.00) 		0.41
(1.00) 		0.724
(1.00) 		0.784
(1.00) 		0.336
(1.00) 		0.232
(1.00) 		0.918
(1.00) 		0.845
(1.00)
12q loss 28 (13%) 193 0.103
(1.00) 		0.815
(1.00) 		0.333
(1.00) 		0.542
(1.00) 		0.608
(1.00) 		0.172
(1.00) 		0.78
(1.00) 		0.521
(1.00) 		0.617
(1.00) 		0.232
(1.00)
13q loss 47 (21%) 174 0.0168
(1.00) 		0.151
(1.00) 		0.00573
(1.00) 		0.0292
(1.00) 		0.00398
(1.00) 		0.303
(1.00) 		0.647
(1.00) 		0.486
(1.00) 		0.0873
(1.00) 		0.00204
(1.00)
14q loss 62 (28%) 159 0.00457
(1.00) 		0.159
(1.00) 		0.118
(1.00) 		0.048
(1.00) 		0.15
(1.00) 		0.0404
(1.00) 		0.0109
(1.00) 		0.0386
(1.00) 		0.0407
(1.00) 		0.206
(1.00)
15q loss 74 (33%) 147 0.000383
(0.295) 		0.00133
(0.998) 		0.0999
(1.00) 		0.289
(1.00) 		0.00908
(1.00) 		0.0435
(1.00) 		0.17
(1.00) 		0.131
(1.00) 		0.146
(1.00) 		0.259
(1.00)
17p loss 97 (44%) 124 0.00459
(1.00) 		0.542
(1.00) 		0.805
(1.00) 		0.554
(1.00) 		0.106
(1.00) 		0.569
(1.00) 		0.437
(1.00) 		0.693
(1.00) 		0.418
(1.00) 		0.299
(1.00)
17q loss 26 (12%) 195 0.42
(1.00) 		0.391
(1.00) 		0.896
(1.00) 		0.783
(1.00) 		0.11
(1.00) 		0.77
(1.00) 		0.863
(1.00) 		0.882
(1.00) 		0.568
(1.00) 		0.582
(1.00)
18p loss 50 (23%) 171 0.199
(1.00) 		0.781
(1.00) 		0.478
(1.00) 		0.257
(1.00) 		0.483
(1.00) 		0.133
(1.00) 		0.43
(1.00) 		0.053
(1.00) 		0.319
(1.00) 		0.647
(1.00)
18q loss 78 (35%) 143 0.023
(1.00) 		0.455
(1.00) 		0.561
(1.00) 		0.955
(1.00) 		0.462
(1.00) 		0.957
(1.00) 		0.0232
(1.00) 		0.0013
(0.976) 		0.252
(1.00) 		0.435
(1.00)
19p loss 57 (26%) 164 0.00502
(1.00) 		0.481
(1.00) 		0.767
(1.00) 		0.37
(1.00) 		0.00251
(1.00) 		0.0375
(1.00) 		0.674
(1.00) 		0.125
(1.00) 		0.488
(1.00) 		0.593
(1.00)
19q loss 33 (15%) 188 0.0877
(1.00) 		1
(1.00) 		0.208
(1.00) 		0.101
(1.00) 		0.284
(1.00) 		0.478
(1.00) 		0.209
(1.00) 		0.894
(1.00) 		0.207
(1.00) 		0.166
(1.00)
20p loss 14 (6%) 207 0.0969
(1.00) 		0.547
(1.00) 		0.282
(1.00) 		0.0238
(1.00) 		0.734
(1.00) 		0.252
(1.00) 		0.392
(1.00) 		0.524
(1.00) 		0.325
(1.00) 		0.944
(1.00)
20q loss 6 (3%) 215 0.0444
(1.00) 		0.513
(1.00) 		0.331
(1.00) 		0.287
(1.00) 		0.618
(1.00) 		0.868
(1.00) 		1
(1.00) 		0.593
(1.00) 		1
(1.00) 		0.804
(1.00)
21q loss 35 (16%) 186 0.694
(1.00) 		0.324
(1.00) 		0.114
(1.00) 		0.189
(1.00) 		0.0468
(1.00) 		0.176
(1.00) 		0.583
(1.00) 		0.753
(1.00) 		0.0772
(1.00) 		0.326
(1.00)
xq loss 48 (22%) 173 0.000495
(0.379) 		0.115
(1.00) 		0.981
(1.00) 		1
(1.00) 		0.466
(1.00) 		0.59
(1.00) 		0.082
(1.00) 		0.789
(1.00) 		0.494
(1.00) 		0.482
(1.00)
'2p gain' versus 'CN_CNMF'

P value = 6.97e-06 (Chi-square test), Q value = 0.0055

Table S1.  Gene #3: '2p gain' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6 CLUS_7 CLUS_8
ALL 18 79 23 14 38 25 14 10
2P GAIN MUTATED 6 7 5 9 17 10 8 4
2P GAIN WILD-TYPE 12 72 18 5 21 15 6 6

Figure S1.  Get High-res Image Gene #3: '2p gain' versus Molecular Subtype #1: 'CN_CNMF'

'7p gain' versus 'CN_CNMF'

P value = 0.000108 (Chi-square test), Q value = 0.084

Table S2.  Gene #13: '7p gain' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6 CLUS_7 CLUS_8
ALL 18 79 23 14 38 25 14 10
7P GAIN MUTATED 10 16 8 8 23 15 8 6
7P GAIN WILD-TYPE 8 63 15 6 15 10 6 4

Figure S2.  Get High-res Image Gene #13: '7p gain' versus Molecular Subtype #1: 'CN_CNMF'

'9p gain' versus 'CN_CNMF'

P value = 4.39e-05 (Chi-square test), Q value = 0.035

Table S3.  Gene #17: '9p gain' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6 CLUS_7 CLUS_8
ALL 18 79 23 14 38 25 14 10
9P GAIN MUTATED 3 3 9 5 3 9 2 1
9P GAIN WILD-TYPE 15 76 14 9 35 16 12 9

Figure S3.  Get High-res Image Gene #17: '9p gain' versus Molecular Subtype #1: 'CN_CNMF'

'10p gain' versus 'CN_CNMF'

P value = 2.63e-06 (Chi-square test), Q value = 0.0021

Table S4.  Gene #19: '10p gain' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6 CLUS_7 CLUS_8
ALL 18 79 23 14 38 25 14 10
10P GAIN MUTATED 10 5 7 6 14 10 9 3
10P GAIN WILD-TYPE 8 74 16 8 24 15 5 7

Figure S4.  Get High-res Image Gene #19: '10p gain' versus Molecular Subtype #1: 'CN_CNMF'

'12p gain' versus 'CN_CNMF'

P value = 9.03e-05 (Chi-square test), Q value = 0.071

Table S5.  Gene #23: '12p gain' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6 CLUS_7 CLUS_8
ALL 18 79 23 14 38 25 14 10
12P GAIN MUTATED 11 11 5 8 16 9 2 5
12P GAIN WILD-TYPE 7 68 18 6 22 16 12 5

Figure S5.  Get High-res Image Gene #23: '12p gain' versus Molecular Subtype #1: 'CN_CNMF'

'17p gain' versus 'MIRSEQ_CHIERARCHICAL'

P value = 0.000313 (Fisher's exact test), Q value = 0.24

Table S6.  Gene #30: '17p gain' versus Molecular Subtype #8: 'MIRSEQ_CHIERARCHICAL'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 145 16 48
17P GAIN MUTATED 13 1 16
17P GAIN WILD-TYPE 132 15 32

Figure S6.  Get High-res Image Gene #30: '17p gain' versus Molecular Subtype #8: 'MIRSEQ_CHIERARCHICAL'

'18p gain' versus 'CN_CNMF'

P value = 1.04e-07 (Chi-square test), Q value = 8.3e-05

Table S7.  Gene #32: '18p gain' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6 CLUS_7 CLUS_8
ALL 18 79 23 14 38 25 14 10
18P GAIN MUTATED 5 4 14 6 17 12 7 2
18P GAIN WILD-TYPE 13 75 9 8 21 13 7 8

Figure S7.  Get High-res Image Gene #32: '18p gain' versus Molecular Subtype #1: 'CN_CNMF'

'19q gain' versus 'CN_CNMF'

P value = 3.13e-05 (Chi-square test), Q value = 0.025

Table S8.  Gene #35: '19q gain' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6 CLUS_7 CLUS_8
ALL 18 79 23 14 38 25 14 10
19Q GAIN MUTATED 5 13 4 8 22 12 8 3
19Q GAIN WILD-TYPE 13 66 19 6 16 13 6 7

Figure S8.  Get High-res Image Gene #35: '19q gain' versus Molecular Subtype #1: 'CN_CNMF'

'21q gain' versus 'CN_CNMF'

P value = 0.000204 (Chi-square test), Q value = 0.16

Table S9.  Gene #38: '21q gain' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6 CLUS_7 CLUS_8
ALL 18 79 23 14 38 25 14 10
21Q GAIN MUTATED 7 8 8 8 13 13 6 4
21Q GAIN WILD-TYPE 11 71 15 6 25 12 8 6

Figure S9.  Get High-res Image Gene #38: '21q gain' versus Molecular Subtype #1: 'CN_CNMF'

'21q gain' versus 'MIRSEQ_CNMF'

P value = 0.000176 (Fisher's exact test), Q value = 0.14

Table S10.  Gene #38: '21q gain' versus Molecular Subtype #7: 'MIRSEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 47 97 65
21Q GAIN MUTATED 26 21 16
21Q GAIN WILD-TYPE 21 76 49

Figure S10.  Get High-res Image Gene #38: '21q gain' versus Molecular Subtype #7: 'MIRSEQ_CNMF'

'21q gain' versus 'MIRSEQ_CHIERARCHICAL'

P value = 3.34e-05 (Fisher's exact test), Q value = 0.026

Table S11.  Gene #38: '21q gain' versus Molecular Subtype #8: 'MIRSEQ_CHIERARCHICAL'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 145 16 48
21Q GAIN MUTATED 30 7 26
21Q GAIN WILD-TYPE 115 9 22

Figure S11.  Get High-res Image Gene #38: '21q gain' versus Molecular Subtype #8: 'MIRSEQ_CHIERARCHICAL'

'2q loss' versus 'CN_CNMF'

P value = 0.000228 (Chi-square test), Q value = 0.18

Table S12.  Gene #44: '2q loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6 CLUS_7 CLUS_8
ALL 18 79 23 14 38 25 14 10
2Q LOSS MUTATED 4 5 7 2 16 6 7 3
2Q LOSS WILD-TYPE 14 74 16 12 22 19 7 7

Figure S12.  Get High-res Image Gene #44: '2q loss' versus Molecular Subtype #1: 'CN_CNMF'

'3p loss' versus 'MRNASEQ_CNMF'

P value = 1.15e-07 (Fisher's exact test), Q value = 9.2e-05

Table S13.  Gene #45: '3p loss' versus Molecular Subtype #5: 'MRNASEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 33 63 45 79
3P LOSS MUTATED 3 22 0 4
3P LOSS WILD-TYPE 30 41 45 75

Figure S13.  Get High-res Image Gene #45: '3p loss' versus Molecular Subtype #5: 'MRNASEQ_CNMF'

'3p loss' versus 'MRNASEQ_CHIERARCHICAL'

P value = 4.12e-05 (Fisher's exact test), Q value = 0.032

Table S14.  Gene #45: '3p loss' versus Molecular Subtype #6: 'MRNASEQ_CHIERARCHICAL'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 93 39 88
3P LOSS MUTATED 7 0 22
3P LOSS WILD-TYPE 86 39 66

Figure S14.  Get High-res Image Gene #45: '3p loss' versus Molecular Subtype #6: 'MRNASEQ_CHIERARCHICAL'

'3p loss' versus 'MIRSEQ_MATURE_CNMF'

P value = 0.000109 (Fisher's exact test), Q value = 0.085

Table S15.  Gene #45: '3p loss' versus Molecular Subtype #9: 'MIRSEQ_MATURE_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 33 60 67 49
3P LOSS MUTATED 2 19 4 3
3P LOSS WILD-TYPE 31 41 63 46

Figure S15.  Get High-res Image Gene #45: '3p loss' versus Molecular Subtype #9: 'MIRSEQ_MATURE_CNMF'

'6q loss' versus 'CN_CNMF'

P value = 4.84e-06 (Chi-square test), Q value = 0.0038

Table S16.  Gene #52: '6q loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6 CLUS_7 CLUS_8
ALL 18 79 23 14 38 25 14 10
6Q LOSS MUTATED 10 10 10 7 23 7 6 6
6Q LOSS WILD-TYPE 8 69 13 7 15 18 8 4

Figure S16.  Get High-res Image Gene #52: '6q loss' versus Molecular Subtype #1: 'CN_CNMF'

'8p loss' versus 'CN_CNMF'

P value = 6.07e-05 (Chi-square test), Q value = 0.048

Table S17.  Gene #55: '8p loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6 CLUS_7 CLUS_8
ALL 18 79 23 14 38 25 14 10
8P LOSS MUTATED 9 20 14 8 20 18 9 1
8P LOSS WILD-TYPE 9 59 9 6 18 7 5 9

Figure S17.  Get High-res Image Gene #55: '8p loss' versus Molecular Subtype #1: 'CN_CNMF'

'9q loss' versus 'MRNASEQ_CHIERARCHICAL'

P value = 1.78e-05 (Fisher's exact test), Q value = 0.014

Table S18.  Gene #58: '9q loss' versus Molecular Subtype #6: 'MRNASEQ_CHIERARCHICAL'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 93 39 88
9Q LOSS MUTATED 42 25 20
9Q LOSS WILD-TYPE 51 14 68

Figure S18.  Get High-res Image Gene #58: '9q loss' versus Molecular Subtype #6: 'MRNASEQ_CHIERARCHICAL'

'11p loss' versus 'CN_CNMF'

P value = 1.08e-05 (Chi-square test), Q value = 0.0086

Table S19.  Gene #61: '11p loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6 CLUS_7 CLUS_8
ALL 18 79 23 14 38 25 14 10
11P LOSS MUTATED 11 16 5 7 26 11 7 6
11P LOSS WILD-TYPE 7 63 18 7 12 14 7 4

Figure S19.  Get High-res Image Gene #61: '11p loss' versus Molecular Subtype #1: 'CN_CNMF'

'11p loss' versus 'METHLYATION_CNMF'

P value = 0.000101 (Fisher's exact test), Q value = 0.079

Table S20.  Gene #61: '11p loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 81 88 52
11P LOSS MUTATED 45 21 23
11P LOSS WILD-TYPE 36 67 29

Figure S20.  Get High-res Image Gene #61: '11p loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'

'11q loss' versus 'CN_CNMF'

P value = 2.59e-06 (Chi-square test), Q value = 0.0021

Table S21.  Gene #62: '11q loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6 CLUS_7 CLUS_8
ALL 18 79 23 14 38 25 14 10
11Q LOSS MUTATED 8 8 4 5 23 11 6 4
11Q LOSS WILD-TYPE 10 71 19 9 15 14 8 6

Figure S21.  Get High-res Image Gene #62: '11q loss' versus Molecular Subtype #1: 'CN_CNMF'

'16p loss' versus 'CN_CNMF'

P value = 1.86e-05 (Chi-square test), Q value = 0.015

Table S22.  Gene #68: '16p loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6 CLUS_7 CLUS_8
ALL 18 79 23 14 38 25 14 10
16P LOSS MUTATED 7 4 12 3 15 9 5 2
16P LOSS WILD-TYPE 11 75 11 11 23 16 9 8

Figure S22.  Get High-res Image Gene #68: '16p loss' versus Molecular Subtype #1: 'CN_CNMF'

'16q loss' versus 'CN_CNMF'

P value = 0.000184 (Chi-square test), Q value = 0.14

Table S23.  Gene #69: '16q loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6 CLUS_7 CLUS_8
ALL 18 79 23 14 38 25 14 10
16Q LOSS MUTATED 8 4 8 2 15 8 4 3
16Q LOSS WILD-TYPE 10 75 15 12 23 17 10 7

Figure S23.  Get High-res Image Gene #69: '16q loss' versus Molecular Subtype #1: 'CN_CNMF'

'22q loss' versus 'CN_CNMF'

P value = 3.28e-07 (Chi-square test), Q value = 0.00026

Table S24.  Gene #79: '22q loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6 CLUS_7 CLUS_8
ALL 18 79 23 14 38 25 14 10
22Q LOSS MUTATED 5 12 17 6 22 14 3 5
22Q LOSS WILD-TYPE 13 67 6 8 16 11 11 5

Figure S24.  Get High-res Image Gene #79: '22q loss' versus Molecular Subtype #1: 'CN_CNMF'

Methods & Data
Input

  • Copy number data file = transformed.cor.cli.txt

  • Molecular subtypes file = BLCA-TP.transferedmergedcluster.txt

  • Number of patients = 221

  • Number of significantly arm-level cnvs = 80

  • Number of molecular subtypes = 10

  • 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

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

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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