Correlation between copy number variations of arm-level result and molecular subtypes
Liver Hepatocellular Carcinoma (Primary solid tumor)
15 January 2014  |  analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C17P8WV4
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/C17P8WV4
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 8 molecular subtypes across 165 patients, 21 significant findings detected with P value < 0.05 and Q value < 0.25.

  • 1q gain cnv correlated to 'CN_CNMF'.

  • 6p gain cnv correlated to 'CN_CNMF'.

  • 8p gain cnv correlated to 'MRNASEQ_CNMF'.

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

  • 19p gain cnv correlated to 'METHLYATION_CNMF'.

  • 19q gain cnv correlated to 'METHLYATION_CNMF'.

  • 1p loss cnv correlated to 'METHLYATION_CNMF'.

  • 3p loss cnv correlated to 'MRNASEQ_CNMF'.

  • 4p loss cnv correlated to 'MRNASEQ_CNMF'.

  • 4q loss cnv correlated to 'CN_CNMF'.

  • 7q loss cnv correlated to 'MRNASEQ_CNMF'.

  • 8q loss cnv correlated to 'CN_CNMF'.

  • 10q loss cnv correlated to 'MRNASEQ_CNMF'.

  • 13q loss cnv correlated to 'MRNASEQ_CNMF'.

  • 14q loss cnv correlated to 'MRNASEQ_CNMF'.

  • 16p loss cnv correlated to 'MRNASEQ_CNMF' and 'MRNASEQ_CHIERARCHICAL'.

  • 16q loss cnv correlated to 'MRNASEQ_CNMF' and 'MRNASEQ_CHIERARCHICAL'.

  • 17p 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 8 molecular subtypes. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 21 significant findings detected.

Clinical
Features 		CN
CNMF 		METHLYATION
CNMF 		MRNASEQ
CNMF 		MRNASEQ
CHIERARCHICAL 		MIRSEQ
CNMF 		MIRSEQ
CHIERARCHICAL 		MIRSEQ
MATURE
CNMF 		MIRSEQ
MATURE
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 83 (50%) 82 3.27e-14
(2.09e-11) 		0.74
(1.00) 		0.000154
(0.0963) 		0.00326
(1.00) 		0.116
(1.00) 		0.792
(1.00) 		0.545
(1.00) 		0.626
(1.00)
16p loss 49 (30%) 116 0.0276
(1.00) 		0.111
(1.00) 		8.06e-09
(5.15e-06) 		0.000323
(0.201) 		0.21
(1.00) 		0.00552
(1.00) 		0.00189
(1.00) 		0.00275
(1.00)
16q loss 61 (37%) 104 0.00344
(1.00) 		0.0197
(1.00) 		1.77e-07
(0.000113) 		0.000208
(0.13) 		0.0342
(1.00) 		0.0732
(1.00) 		0.331
(1.00) 		0.117
(1.00)
1q gain 93 (56%) 72 6.74e-07
(0.000428) 		0.0102
(1.00) 		0.00801
(1.00) 		0.0232
(1.00) 		0.2
(1.00) 		0.864
(1.00) 		0.607
(1.00) 		0.57
(1.00)
6p gain 43 (26%) 122 0.000343
(0.213) 		0.0267
(1.00) 		0.133
(1.00) 		0.143
(1.00) 		0.942
(1.00) 		0.784
(1.00) 		0.877
(1.00) 		0.666
(1.00)
8p gain 33 (20%) 132 0.0393
(1.00) 		0.517
(1.00) 		0.000192
(0.12) 		0.0137
(1.00) 		0.843
(1.00) 		0.113
(1.00) 		0.0992
(1.00) 		0.098
(1.00)
19p gain 33 (20%) 132 0.0601
(1.00) 		6.25e-05
(0.0395) 		0.0274
(1.00) 		0.0235
(1.00) 		0.126
(1.00) 		0.123
(1.00) 		0.0138
(1.00) 		0.108
(1.00)
19q gain 37 (22%) 128 0.0374
(1.00) 		0.000152
(0.0952) 		0.00907
(1.00) 		0.0229
(1.00) 		0.145
(1.00) 		0.067
(1.00) 		0.00637
(1.00) 		0.0471
(1.00)
1p loss 38 (23%) 127 0.00571
(1.00) 		6.49e-05
(0.041) 		0.00234
(1.00) 		0.00936
(1.00) 		0.0299
(1.00) 		0.094
(1.00) 		0.639
(1.00) 		0.157
(1.00)
3p loss 19 (12%) 146 0.149
(1.00) 		0.0263
(1.00) 		0.000133
(0.0834) 		0.000772
(0.475) 		0.568
(1.00) 		0.127
(1.00) 		0.0305
(1.00) 		0.0495
(1.00)
4p loss 38 (23%) 127 0.00119
(0.73) 		0.158
(1.00) 		0.000126
(0.0797) 		0.269
(1.00) 		0.943
(1.00) 		0.571
(1.00) 		0.298
(1.00) 		0.414
(1.00)
4q loss 54 (33%) 111 9.88e-09
(6.3e-06) 		0.0225
(1.00) 		0.000802
(0.493) 		0.541
(1.00) 		0.48
(1.00) 		0.85
(1.00) 		0.385
(1.00) 		0.907
(1.00)
7q loss 14 (8%) 151 0.74
(1.00) 		0.68
(1.00) 		0.000175
(0.11) 		0.7
(1.00) 		0.934
(1.00) 		0.0929
(1.00) 		0.856
(1.00) 		0.29
(1.00)
8q loss 16 (10%) 149 0.000229
(0.142) 		0.591
(1.00) 		0.000704
(0.434) 		0.256
(1.00) 		1
(1.00) 		0.116
(1.00) 		0.176
(1.00) 		0.205
(1.00)
10q loss 38 (23%) 127 0.0487
(1.00) 		1
(1.00) 		2.12e-05
(0.0134) 		0.0813
(1.00) 		0.293
(1.00) 		0.178
(1.00) 		0.457
(1.00) 		0.123
(1.00)
13q loss 57 (35%) 108 0.00438
(1.00) 		0.129
(1.00) 		2.56e-05
(0.0162) 		0.0165
(1.00) 		0.595
(1.00) 		0.299
(1.00) 		0.0576
(1.00) 		0.32
(1.00)
14q loss 47 (28%) 118 0.000429
(0.265) 		0.248
(1.00) 		0.000309
(0.192) 		0.00191
(1.00) 		0.303
(1.00) 		0.859
(1.00) 		0.566
(1.00) 		0.671
(1.00)
17p loss 86 (52%) 79 2.53e-06
(0.00161) 		0.326
(1.00) 		0.188
(1.00) 		0.203
(1.00) 		0.00798
(1.00) 		0.0046
(1.00) 		0.595
(1.00) 		0.0114
(1.00)
1p gain 25 (15%) 140 0.303
(1.00) 		0.246
(1.00) 		0.0925
(1.00) 		0.272
(1.00) 		0.567
(1.00) 		0.109
(1.00) 		0.641
(1.00) 		0.0875
(1.00)
2p gain 23 (14%) 142 0.0504
(1.00) 		0.481
(1.00) 		0.028
(1.00) 		0.284
(1.00) 		0.312
(1.00) 		0.492
(1.00) 		0.105
(1.00) 		0.773
(1.00)
2q gain 19 (12%) 146 0.0127
(1.00) 		0.648
(1.00) 		0.287
(1.00) 		0.495
(1.00) 		0.139
(1.00) 		0.469
(1.00) 		0.429
(1.00) 		0.784
(1.00)
3p gain 16 (10%) 149 0.121
(1.00) 		0.211
(1.00) 		0.0139
(1.00) 		0.103
(1.00) 		0.441
(1.00) 		0.396
(1.00) 		0.131
(1.00) 		0.324
(1.00)
3q gain 17 (10%) 148 0.0686
(1.00) 		0.222
(1.00) 		0.0201
(1.00) 		0.0876
(1.00) 		0.289
(1.00) 		0.595
(1.00) 		0.142
(1.00) 		0.554
(1.00)
4p gain 11 (7%) 154 0.117
(1.00) 		0.67
(1.00) 		0.056
(1.00) 		0.594
(1.00) 		0.182
(1.00) 		0.546
(1.00) 		0.731
(1.00) 		0.786
(1.00)
4q gain 4 (2%) 161 1
(1.00) 		0.814
(1.00) 		0.21
(1.00) 		1
(1.00) 		1
(1.00) 		0.593
(1.00) 		1
(1.00) 		0.574
(1.00)
5p gain 63 (38%) 102 0.00263
(1.00) 		0.0126
(1.00) 		0.327
(1.00) 		0.45
(1.00) 		0.842
(1.00) 		0.252
(1.00) 		0.913
(1.00) 		0.673
(1.00)
5q gain 47 (28%) 118 0.00144
(0.877) 		0.142
(1.00) 		0.0747
(1.00) 		0.437
(1.00) 		0.931
(1.00) 		0.802
(1.00) 		0.82
(1.00) 		0.778
(1.00)
6q gain 30 (18%) 135 0.0119
(1.00) 		0.179
(1.00) 		0.0832
(1.00) 		0.113
(1.00) 		0.868
(1.00) 		0.677
(1.00) 		0.374
(1.00) 		0.69
(1.00)
7p gain 50 (30%) 115 0.0392
(1.00) 		0.0938
(1.00) 		0.92
(1.00) 		0.674
(1.00) 		0.201
(1.00) 		0.108
(1.00) 		0.396
(1.00) 		0.474
(1.00)
7q gain 51 (31%) 114 0.225
(1.00) 		0.0121
(1.00) 		0.569
(1.00) 		0.658
(1.00) 		0.147
(1.00) 		0.162
(1.00) 		0.367
(1.00) 		0.652
(1.00)
9p gain 8 (5%) 157 0.248
(1.00) 		0.0882
(1.00) 		0.27
(1.00) 		0.285
(1.00) 		0.904
(1.00) 		0.58
(1.00) 		0.437
(1.00) 		1
(1.00)
9q gain 8 (5%) 157 0.534
(1.00) 		0.528
(1.00) 		0.328
(1.00) 		0.285
(1.00) 		0.435
(1.00) 		0.58
(1.00) 		0.437
(1.00) 		1
(1.00)
10p gain 23 (14%) 142 0.396
(1.00) 		0.0822
(1.00) 		0.104
(1.00) 		0.0199
(1.00) 		0.276
(1.00) 		1
(1.00) 		0.237
(1.00) 		0.848
(1.00)
10q gain 12 (7%) 153 0.0218
(1.00) 		0.0814
(1.00) 		0.345
(1.00) 		0.69
(1.00) 		0.0602
(1.00) 		0.676
(1.00) 		0.637
(1.00) 		0.807
(1.00)
11p gain 10 (6%) 155 0.268
(1.00) 		0.313
(1.00) 		0.389
(1.00) 		0.88
(1.00) 		0.1
(1.00) 		0.582
(1.00) 		0.501
(1.00) 		0.261
(1.00)
11q gain 11 (7%) 154 0.455
(1.00) 		0.244
(1.00) 		0.438
(1.00) 		0.555
(1.00) 		0.256
(1.00) 		0.742
(1.00) 		0.918
(1.00) 		0.638
(1.00)
12p gain 14 (8%) 151 0.294
(1.00) 		0.284
(1.00) 		0.404
(1.00) 		0.171
(1.00) 		0.854
(1.00) 		0.127
(1.00) 		1
(1.00) 		0.162
(1.00)
12q gain 19 (12%) 146 0.814
(1.00) 		0.694
(1.00) 		0.212
(1.00) 		0.141
(1.00) 		1
(1.00) 		0.277
(1.00) 		0.759
(1.00) 		0.381
(1.00)
13q gain 10 (6%) 155 0.105
(1.00) 		0.313
(1.00) 		0.0499
(1.00) 		0.212
(1.00) 		0.111
(1.00) 		0.16
(1.00) 		0.136
(1.00) 		0.113
(1.00)
14q gain 10 (6%) 155 0.224
(1.00) 		0.338
(1.00) 		0.837
(1.00) 		0.643
(1.00) 		0.357
(1.00) 		0.142
(1.00) 		0.552
(1.00) 		0.35
(1.00)
15q gain 14 (8%) 151 1
(1.00) 		0.874
(1.00) 		0.554
(1.00) 		0.209
(1.00) 		0.0895
(1.00) 		0.153
(1.00) 		0.393
(1.00) 		0.158
(1.00)
16p gain 11 (7%) 154 0.0312
(1.00) 		1
(1.00) 		0.72
(1.00) 		0.907
(1.00) 		0.244
(1.00) 		0.519
(1.00) 		0.826
(1.00) 		0.507
(1.00)
16q gain 5 (3%) 160 0.379
(1.00) 		1
(1.00) 		0.235
(1.00) 		0.562
(1.00) 		0.239
(1.00) 		0.459
(1.00) 		0.296
(1.00) 		0.443
(1.00)
17p gain 14 (8%) 151 0.451
(1.00) 		0.0648
(1.00) 		0.641
(1.00) 		0.396
(1.00) 		0.934
(1.00) 		0.666
(1.00) 		0.856
(1.00) 		0.889
(1.00)
17q gain 42 (25%) 123 0.028
(1.00) 		0.22
(1.00) 		0.0455
(1.00) 		0.0347
(1.00) 		0.763
(1.00) 		0.52
(1.00) 		1
(1.00) 		0.699
(1.00)
18p gain 13 (8%) 152 0.0762
(1.00) 		0.26
(1.00) 		0.0245
(1.00) 		0.0203
(1.00) 		0.325
(1.00) 		0.116
(1.00) 		0.393
(1.00) 		0.0935
(1.00)
18q gain 12 (7%) 153 0.145
(1.00) 		0.275
(1.00) 		0.0284
(1.00) 		0.043
(1.00) 		0.535
(1.00) 		0.127
(1.00) 		0.53
(1.00) 		0.108
(1.00)
20p gain 47 (28%) 118 0.00125
(0.763) 		0.0036
(1.00) 		0.00257
(1.00) 		0.00872
(1.00) 		0.717
(1.00) 		0.166
(1.00) 		0.0241
(1.00) 		0.135
(1.00)
20q gain 49 (30%) 116 0.0027
(1.00) 		0.0193
(1.00) 		0.0113
(1.00) 		0.0131
(1.00) 		0.611
(1.00) 		0.328
(1.00) 		0.0746
(1.00) 		0.287
(1.00)
21q gain 15 (9%) 150 0.103
(1.00) 		0.453
(1.00) 		0.0369
(1.00) 		0.103
(1.00) 		0.88
(1.00) 		0.29
(1.00) 		0.93
(1.00) 		0.259
(1.00)
22q gain 19 (12%) 146 0.0181
(1.00) 		0.414
(1.00) 		0.095
(1.00) 		0.028
(1.00) 		0.253
(1.00) 		0.707
(1.00) 		0.326
(1.00) 		0.637
(1.00)
xq gain 30 (18%) 135 0.000716
(0.441) 		0.0152
(1.00) 		0.35
(1.00) 		0.0499
(1.00) 		0.361
(1.00) 		0.133
(1.00) 		0.016
(1.00) 		0.128
(1.00)
1q loss 11 (7%) 154 1
(1.00) 		0.257
(1.00) 		0.587
(1.00) 		0.259
(1.00) 		0.235
(1.00) 		0.427
(1.00) 		0.152
(1.00) 		0.295
(1.00)
2p loss 15 (9%) 150 0.0481
(1.00) 		0.542
(1.00) 		0.143
(1.00) 		0.784
(1.00) 		0.0738
(1.00) 		0.179
(1.00) 		0.428
(1.00) 		0.178
(1.00)
2q loss 16 (10%) 149 0.0469
(1.00) 		0.443
(1.00) 		0.356
(1.00) 		0.784
(1.00) 		0.00944
(1.00) 		0.102
(1.00) 		0.171
(1.00) 		0.0901
(1.00)
3q loss 11 (7%) 154 0.455
(1.00) 		0.29
(1.00) 		0.598
(1.00) 		0.316
(1.00) 		0.0138
(1.00) 		0.501
(1.00) 		0.385
(1.00) 		0.486
(1.00)
5p loss 10 (6%) 155 0.203
(1.00) 		1
(1.00) 		0.445
(1.00) 		0.267
(1.00) 		0.782
(1.00) 		0.142
(1.00) 		0.0788
(1.00) 		0.892
(1.00)
5q loss 15 (9%) 150 0.304
(1.00) 		0.0962
(1.00) 		0.731
(1.00) 		0.406
(1.00) 		0.941
(1.00) 		0.64
(1.00) 		0.822
(1.00) 		1
(1.00)
6p loss 19 (12%) 146 0.302
(1.00) 		0.939
(1.00) 		0.0328
(1.00) 		0.732
(1.00) 		0.0539
(1.00) 		0.384
(1.00) 		0.325
(1.00) 		0.436
(1.00)
6q loss 45 (27%) 120 0.242
(1.00) 		0.78
(1.00) 		0.0347
(1.00) 		0.624
(1.00) 		0.48
(1.00) 		0.269
(1.00) 		0.238
(1.00) 		0.375
(1.00)
7p loss 10 (6%) 155 0.847
(1.00) 		0.212
(1.00) 		0.00255
(1.00) 		0.716
(1.00) 		0.498
(1.00) 		0.651
(1.00) 		1
(1.00) 		0.642
(1.00)
8p loss 78 (47%) 87 0.00369
(1.00) 		0.00248
(1.00) 		0.263
(1.00) 		0.845
(1.00) 		0.051
(1.00) 		0.129
(1.00) 		0.136
(1.00) 		0.0693
(1.00)
9p loss 51 (31%) 114 0.0255
(1.00) 		0.7
(1.00) 		0.44
(1.00) 		0.261
(1.00) 		0.224
(1.00) 		0.357
(1.00) 		0.569
(1.00) 		0.504
(1.00)
9q loss 51 (31%) 114 0.125
(1.00) 		0.771
(1.00) 		0.0326
(1.00) 		0.0321
(1.00) 		0.343
(1.00) 		0.341
(1.00) 		0.271
(1.00) 		0.248
(1.00)
10p loss 22 (13%) 143 0.226
(1.00) 		0.862
(1.00) 		0.0017
(1.00) 		0.158
(1.00) 		0.114
(1.00) 		0.377
(1.00) 		0.811
(1.00) 		0.349
(1.00)
11p loss 30 (18%) 135 0.562
(1.00) 		0.784
(1.00) 		0.226
(1.00) 		0.217
(1.00) 		0.624
(1.00) 		0.395
(1.00) 		0.273
(1.00) 		0.67
(1.00)
11q loss 33 (20%) 132 0.794
(1.00) 		0.783
(1.00) 		0.23
(1.00) 		0.314
(1.00) 		1
(1.00) 		0.922
(1.00) 		0.832
(1.00) 		1
(1.00)
12p loss 36 (22%) 129 0.327
(1.00) 		0.192
(1.00) 		0.0277
(1.00) 		0.51
(1.00) 		0.274
(1.00) 		0.767
(1.00) 		0.756
(1.00) 		0.65
(1.00)
12q loss 21 (13%) 144 0.103
(1.00) 		0.513
(1.00) 		0.0409
(1.00) 		0.609
(1.00) 		0.913
(1.00) 		0.539
(1.00) 		0.883
(1.00) 		0.471
(1.00)
15q loss 32 (19%) 133 0.00324
(1.00) 		0.331
(1.00) 		0.0797
(1.00) 		0.254
(1.00) 		0.929
(1.00) 		0.654
(1.00) 		0.248
(1.00) 		0.636
(1.00)
17q loss 22 (13%) 143 0.247
(1.00) 		0.885
(1.00) 		0.0167
(1.00) 		0.15
(1.00) 		0.649
(1.00) 		0.795
(1.00) 		0.0117
(1.00) 		1
(1.00)
18p loss 30 (18%) 135 0.0131
(1.00) 		0.0188
(1.00) 		0.331
(1.00) 		0.803
(1.00) 		0.443
(1.00) 		0.545
(1.00) 		0.741
(1.00) 		0.708
(1.00)
18q loss 33 (20%) 132 0.00172
(1.00) 		0.0219
(1.00) 		0.192
(1.00) 		0.283
(1.00) 		0.572
(1.00) 		0.566
(1.00) 		0.501
(1.00) 		0.463
(1.00)
19p loss 21 (13%) 144 0.023
(1.00) 		0.513
(1.00) 		0.0556
(1.00) 		0.904
(1.00) 		0.333
(1.00) 		0.284
(1.00) 		0.579
(1.00) 		0.24
(1.00)
19q loss 15 (9%) 150 0.0481
(1.00) 		0.493
(1.00) 		0.109
(1.00) 		0.595
(1.00) 		0.385
(1.00) 		0.614
(1.00) 		0.386
(1.00) 		0.621
(1.00)
20p loss 10 (6%) 155 0.0953
(1.00) 		0.919
(1.00) 		0.000502
(0.31) 		0.594
(1.00) 		0.348
(1.00) 		0.481
(1.00) 		0.235
(1.00) 		0.405
(1.00)
20q loss 6 (4%) 159 0.131
(1.00) 		0.875
(1.00) 		0.126
(1.00) 		0.924
(1.00) 		0.324
(1.00) 		0.522
(1.00) 		0.574
(1.00) 		0.515
(1.00)
21q loss 45 (27%) 120 0.0651
(1.00) 		0.5
(1.00) 		0.0286
(1.00) 		0.365
(1.00) 		0.012
(1.00) 		0.353
(1.00) 		0.266
(1.00) 		0.259
(1.00)
22q loss 40 (24%) 125 0.408
(1.00) 		0.195
(1.00) 		0.0731
(1.00) 		0.436
(1.00) 		0.517
(1.00) 		0.853
(1.00) 		0.917
(1.00) 		0.883
(1.00)
xq loss 24 (15%) 141 0.012
(1.00) 		0.0548
(1.00) 		0.274
(1.00) 		0.411
(1.00) 		0.322
(1.00) 		0.765
(1.00) 		0.576
(1.00) 		0.748
(1.00)
'1q gain' versus 'CN_CNMF'

P value = 6.74e-07 (Fisher's exact test), Q value = 0.00043

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

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 46 63 56
1Q GAIN MUTATED 37 20 36
1Q GAIN WILD-TYPE 9 43 20

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

'6p gain' versus 'CN_CNMF'

P value = 0.000343 (Fisher's exact test), Q value = 0.21

Table S2.  Gene #11: '6p gain' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 46 63 56
6P GAIN MUTATED 16 6 21
6P GAIN WILD-TYPE 30 57 35

Figure S2.  Get High-res Image Gene #11: '6p gain' versus Molecular Subtype #1: 'CN_CNMF'

'8p gain' versus 'MRNASEQ_CNMF'

P value = 0.000192 (Chi-square test), Q value = 0.12

Table S3.  Gene #15: '8p gain' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6 CLUS_7 CLUS_8 CLUS_9
ALL 17 14 14 29 21 6 17 16 9
8P GAIN MUTATED 8 0 2 1 2 1 9 3 3
8P GAIN WILD-TYPE 9 14 12 28 19 5 8 13 6

Figure S3.  Get High-res Image Gene #15: '8p gain' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

'8q gain' versus 'CN_CNMF'

P value = 3.27e-14 (Fisher's exact test), Q value = 2.1e-11

Table S4.  Gene #16: '8q gain' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 46 63 56
8Q GAIN MUTATED 44 17 22
8Q GAIN WILD-TYPE 2 46 34

Figure S4.  Get High-res Image Gene #16: '8q gain' versus Molecular Subtype #1: 'CN_CNMF'

'8q gain' versus 'MRNASEQ_CNMF'

P value = 0.000154 (Chi-square test), Q value = 0.096

Table S5.  Gene #16: '8q gain' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6 CLUS_7 CLUS_8 CLUS_9
ALL 17 14 14 29 21 6 17 16 9
8Q GAIN MUTATED 12 3 5 7 13 1 12 11 8
8Q GAIN WILD-TYPE 5 11 9 22 8 5 5 5 1

Figure S5.  Get High-res Image Gene #16: '8q gain' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

'19p gain' versus 'METHLYATION_CNMF'

P value = 6.25e-05 (Fisher's exact test), Q value = 0.039

Table S6.  Gene #34: '19p gain' versus Molecular Subtype #2: 'METHLYATION_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 49 42 73
19P GAIN MUTATED 8 18 6
19P GAIN WILD-TYPE 41 24 67

Figure S6.  Get High-res Image Gene #34: '19p gain' versus Molecular Subtype #2: 'METHLYATION_CNMF'

'19q gain' versus 'METHLYATION_CNMF'

P value = 0.000152 (Fisher's exact test), Q value = 0.095

Table S7.  Gene #35: '19q gain' versus Molecular Subtype #2: 'METHLYATION_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 49 42 73
19Q GAIN MUTATED 9 19 8
19Q GAIN WILD-TYPE 40 23 65

Figure S7.  Get High-res Image Gene #35: '19q gain' versus Molecular Subtype #2: 'METHLYATION_CNMF'

'1p loss' versus 'METHLYATION_CNMF'

P value = 6.49e-05 (Fisher's exact test), Q value = 0.041

Table S8.  Gene #41: '1p loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 49 42 73
1P LOSS MUTATED 5 20 12
1P LOSS WILD-TYPE 44 22 61

Figure S8.  Get High-res Image Gene #41: '1p loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'

'3p loss' versus 'MRNASEQ_CNMF'

P value = 0.000133 (Chi-square test), Q value = 0.083

Table S9.  Gene #45: '3p loss' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6 CLUS_7 CLUS_8 CLUS_9
ALL 17 14 14 29 21 6 17 16 9
3P LOSS MUTATED 4 7 0 1 0 0 2 1 1
3P LOSS WILD-TYPE 13 7 14 28 21 6 15 15 8

Figure S9.  Get High-res Image Gene #45: '3p loss' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

'4p loss' versus 'MRNASEQ_CNMF'

P value = 0.000126 (Chi-square test), Q value = 0.08

Table S10.  Gene #47: '4p loss' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6 CLUS_7 CLUS_8 CLUS_9
ALL 17 14 14 29 21 6 17 16 9
4P LOSS MUTATED 6 1 0 5 2 5 8 4 5
4P LOSS WILD-TYPE 11 13 14 24 19 1 9 12 4

Figure S10.  Get High-res Image Gene #47: '4p loss' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

'4q loss' versus 'CN_CNMF'

P value = 9.88e-09 (Fisher's exact test), Q value = 6.3e-06

Table S11.  Gene #48: '4q loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 46 63 56
4Q LOSS MUTATED 14 6 34
4Q LOSS WILD-TYPE 32 57 22

Figure S11.  Get High-res Image Gene #48: '4q loss' versus Molecular Subtype #1: 'CN_CNMF'

'7q loss' versus 'MRNASEQ_CNMF'

P value = 0.000175 (Chi-square test), Q value = 0.11

Table S12.  Gene #54: '7q loss' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6 CLUS_7 CLUS_8 CLUS_9
ALL 17 14 14 29 21 6 17 16 9
7Q LOSS MUTATED 1 1 1 0 2 4 3 0 1
7Q LOSS WILD-TYPE 16 13 13 29 19 2 14 16 8

Figure S12.  Get High-res Image Gene #54: '7q loss' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

'8q loss' versus 'CN_CNMF'

P value = 0.000229 (Fisher's exact test), Q value = 0.14

Table S13.  Gene #56: '8q loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 46 63 56
8Q LOSS MUTATED 1 2 13
8Q LOSS WILD-TYPE 45 61 43

Figure S13.  Get High-res Image Gene #56: '8q loss' versus Molecular Subtype #1: 'CN_CNMF'

'10q loss' versus 'MRNASEQ_CNMF'

P value = 2.12e-05 (Chi-square test), Q value = 0.013

Table S14.  Gene #60: '10q loss' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6 CLUS_7 CLUS_8 CLUS_9
ALL 17 14 14 29 21 6 17 16 9
10Q LOSS MUTATED 11 6 0 8 2 4 0 3 2
10Q LOSS WILD-TYPE 6 8 14 21 19 2 17 13 7

Figure S14.  Get High-res Image Gene #60: '10q loss' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

'13q loss' versus 'MRNASEQ_CNMF'

P value = 2.56e-05 (Chi-square test), Q value = 0.016

Table S15.  Gene #65: '13q loss' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6 CLUS_7 CLUS_8 CLUS_9
ALL 17 14 14 29 21 6 17 16 9
13Q LOSS MUTATED 6 11 1 9 2 6 7 4 3
13Q LOSS WILD-TYPE 11 3 13 20 19 0 10 12 6

Figure S15.  Get High-res Image Gene #65: '13q loss' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

'14q loss' versus 'MRNASEQ_CNMF'

P value = 0.000309 (Chi-square test), Q value = 0.19

Table S16.  Gene #66: '14q loss' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6 CLUS_7 CLUS_8 CLUS_9
ALL 17 14 14 29 21 6 17 16 9
14Q LOSS MUTATED 8 7 0 7 2 5 6 4 6
14Q LOSS WILD-TYPE 9 7 14 22 19 1 11 12 3

Figure S16.  Get High-res Image Gene #66: '14q loss' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

'16p loss' versus 'MRNASEQ_CNMF'

P value = 8.06e-09 (Chi-square test), Q value = 5.1e-06

Table S17.  Gene #68: '16p loss' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6 CLUS_7 CLUS_8 CLUS_9
ALL 17 14 14 29 21 6 17 16 9
16P LOSS MUTATED 5 6 1 5 1 3 17 4 2
16P LOSS WILD-TYPE 12 8 13 24 20 3 0 12 7

Figure S17.  Get High-res Image Gene #68: '16p loss' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

'16p loss' versus 'MRNASEQ_CHIERARCHICAL'

P value = 0.000323 (Fisher's exact test), Q value = 0.2

Table S18.  Gene #68: '16p loss' versus Molecular Subtype #4: 'MRNASEQ_CHIERARCHICAL'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 22 27 37 57
16P LOSS MUTATED 2 6 7 29
16P LOSS WILD-TYPE 20 21 30 28

Figure S18.  Get High-res Image Gene #68: '16p loss' versus Molecular Subtype #4: 'MRNASEQ_CHIERARCHICAL'

'16q loss' versus 'MRNASEQ_CNMF'

P value = 1.77e-07 (Chi-square test), Q value = 0.00011

Table S19.  Gene #69: '16q loss' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6 CLUS_7 CLUS_8 CLUS_9
ALL 17 14 14 29 21 6 17 16 9
16Q LOSS MUTATED 9 5 2 8 3 5 17 3 3
16Q LOSS WILD-TYPE 8 9 12 21 18 1 0 13 6

Figure S19.  Get High-res Image Gene #69: '16q loss' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

'16q loss' versus 'MRNASEQ_CHIERARCHICAL'

P value = 0.000208 (Fisher's exact test), Q value = 0.13

Table S20.  Gene #69: '16q loss' versus Molecular Subtype #4: 'MRNASEQ_CHIERARCHICAL'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 22 27 37 57
16Q LOSS MUTATED 4 5 12 34
16Q LOSS WILD-TYPE 18 22 25 23

Figure S20.  Get High-res Image Gene #69: '16q loss' versus Molecular Subtype #4: 'MRNASEQ_CHIERARCHICAL'

'17p loss' versus 'CN_CNMF'

P value = 2.53e-06 (Fisher's exact test), Q value = 0.0016

Table S21.  Gene #70: '17p loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 46 63 56
17P LOSS MUTATED 15 27 44
17P LOSS WILD-TYPE 31 36 12

Figure S21.  Get High-res Image Gene #70: '17p loss' versus Molecular Subtype #1: 'CN_CNMF'

Methods & Data
Input

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

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

  • Number of patients = 165

  • Number of significantly arm-level cnvs = 80

  • 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

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] 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)
Copyright © 2014 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle