Correlation between copy number variations of arm-level result and molecular subtypes
Liver Hepatocellular Carcinoma (Primary solid tumor)
23 May 2013  |  analyses__2013_05_23
Maintainer Information
Citation Information
doi:10.7908/C1VM49BR
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between copy number variations of arm-level result and molecular subtypes. Broad Institute of MIT and Harvard. doi:10.7908/C1VM49BR
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 74 arm-level results and 8 molecular subtypes across 108 patients, 6 significant findings detected with Q value < 0.25.

  • 8q gain cnv correlated to 'CN_CNMF'.

  • 4q loss cnv correlated to 'CN_CNMF'.

  • 7q loss cnv correlated to 'MRNASEQ_CNMF'.

  • 8p loss cnv correlated to 'CN_CNMF'.

  • 8q 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 74 arm-level results and 8 molecular subtypes. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 6 significant findings detected.

Molecular
subtypes 		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 Chi-square test Fisher's exact test
8q gain 0 (0%) 59 4.53e-10
(2.62e-07) 		0.478
(1.00) 		0.199
(1.00) 		0.631
(1.00) 		0.182
(1.00) 		0.218
(1.00) 		0.888
(1.00) 		0.634
(1.00)
4q loss 0 (0%) 88 0.000117
(0.0671) 		0.0138
(1.00) 		0.857
(1.00) 		0.771
(1.00) 		0.155
(1.00) 		0.108
(1.00) 		0.117
(1.00) 		0.216
(1.00)
7q loss 0 (0%) 100 0.0321
(1.00) 		1
(1.00) 		2.41e-05
(0.0139) 		1
(1.00) 		0.773
(1.00) 		0.189
(1.00) 		0.847
(1.00) 		0.647
(1.00)
8p loss 0 (0%) 63 5.13e-07
(0.000296) 		0.192
(1.00) 		0.196
(1.00) 		0.626
(1.00) 		0.708
(1.00) 		0.519
(1.00) 		0.263
(1.00) 		0.107
(1.00)
8q loss 0 (0%) 99 0.000421
(0.241) 		0.67
(1.00) 		0.00389
(1.00) 		0.634
(1.00) 		0.288
(1.00) 		1
(1.00) 		0.464
(1.00) 		0.354
(1.00)
14q loss 0 (0%) 78 0.000266
(0.153) 		0.274
(1.00) 		0.0101
(1.00) 		0.112
(1.00) 		0.566
(1.00) 		1
(1.00) 		0.0987
(1.00) 		0.367
(1.00)
1p gain 0 (0%) 97 0.527
(1.00) 		0.721
(1.00) 		0.146
(1.00) 		1
(1.00) 		0.624
(1.00) 		0.331
(1.00) 		0.629
(1.00) 		0.213
(1.00)
1q gain 0 (0%) 54 0.00224
(1.00) 		0.00126
(0.714) 		0.0575
(1.00) 		0.469
(1.00) 		0.741
(1.00) 		1
(1.00) 		0.383
(1.00) 		0.266
(1.00)
2p gain 0 (0%) 99 1
(1.00) 		0.286
(1.00) 		0.788
(1.00) 		0.443
(1.00) 		0.00574
(1.00) 		0.59
(1.00) 		0.404
(1.00) 		0.667
(1.00)
2q gain 0 (0%) 100 1
(1.00) 		0.103
(1.00) 		0.748
(1.00) 		0.23
(1.00) 		0.021
(1.00) 		1
(1.00) 		0.217
(1.00) 		0.784
(1.00)
3p gain 0 (0%) 101 0.623
(1.00) 		0.654
(1.00) 		0.109
(1.00) 		0.69
(1.00) 		0.248
(1.00) 		1
(1.00) 		0.258
(1.00) 		0.74
(1.00)
3q gain 0 (0%) 101 0.623
(1.00) 		0.654
(1.00) 		0.109
(1.00) 		0.69
(1.00) 		0.248
(1.00) 		1
(1.00) 		0.258
(1.00) 		0.74
(1.00)
4p gain 0 (0%) 102 1
(1.00) 		0.112
(1.00) 		0.00914
(1.00) 		0.0106
(1.00) 		0.288
(1.00) 		1
(1.00) 		0.0821
(1.00) 		0.74
(1.00)
5p gain 0 (0%) 75 0.00763
(1.00) 		0.0746
(1.00) 		0.526
(1.00) 		0.157
(1.00) 		0.797
(1.00) 		0.503
(1.00) 		0.169
(1.00) 		0.647
(1.00)
5q gain 0 (0%) 83 0.0983
(1.00) 		0.38
(1.00) 		0.504
(1.00) 		0.131
(1.00) 		0.308
(1.00) 		0.447
(1.00) 		0.183
(1.00) 		0.967
(1.00)
6p gain 0 (0%) 89 0.0399
(1.00) 		0.00475
(1.00) 		0.402
(1.00) 		0.777
(1.00) 		0.322
(1.00) 		1
(1.00) 		0.796
(1.00) 		0.435
(1.00)
6q gain 0 (0%) 96 0.144
(1.00) 		0.0539
(1.00) 		0.213
(1.00) 		0.732
(1.00) 		0.491
(1.00) 		1
(1.00) 		0.99
(1.00) 		0.347
(1.00)
7p gain 0 (0%) 80 0.127
(1.00) 		0.449
(1.00) 		0.299
(1.00) 		0.267
(1.00) 		0.0802
(1.00) 		0.723
(1.00) 		0.0437
(1.00) 		0.274
(1.00)
7q gain 0 (0%) 81 0.116
(1.00) 		0.0334
(1.00) 		0.205
(1.00) 		0.29
(1.00) 		0.196
(1.00) 		0.723
(1.00) 		0.0574
(1.00) 		0.359
(1.00)
8p gain 0 (0%) 93 0.266
(1.00) 		0.635
(1.00) 		0.0728
(1.00) 		0.302
(1.00) 		0.586
(1.00) 		1
(1.00) 		0.544
(1.00) 		0.283
(1.00)
9p gain 0 (0%) 104 0.116
(1.00) 		0.476
(1.00) 		0.849
(1.00) 		0.568
(1.00) 		0.136
(1.00) 		1
(1.00) 		0.418
(1.00) 		0.548
(1.00)
9q gain 0 (0%) 105 0.309
(1.00) 		0.79
(1.00) 		0.849
(1.00) 		0.568
(1.00) 		0.302
(1.00) 		1
(1.00) 		0.775
(1.00) 		0.867
(1.00)
10p gain 0 (0%) 99 0.292
(1.00) 		0.143
(1.00) 		0.052
(1.00) 		0.0363
(1.00) 		0.469
(1.00) 		1
(1.00) 		0.0475
(1.00) 		0.126
(1.00)
10q gain 0 (0%) 102 0.117
(1.00) 		0.0402
(1.00) 		0.0672
(1.00) 		0.568
(1.00) 		0.136
(1.00) 		1
(1.00) 		0.279
(1.00) 		0.391
(1.00)
11p gain 0 (0%) 105 0.531
(1.00) 		0.79
(1.00) 		0.42
(1.00) 		1
(1.00)
11q gain 0 (0%) 105 0.309
(1.00) 		0.251
(1.00) 		0.42
(1.00) 		0.784
(1.00) 		1
(1.00) 		0.14
(1.00) 		0.867
(1.00)
12p gain 0 (0%) 104 0.116
(1.00) 		0.81
(1.00) 		0.173
(1.00) 		0.82
(1.00) 		1
(1.00) 		0.0323
(1.00) 		1
(1.00)
12q gain 0 (0%) 102 0.0785
(1.00) 		0.763
(1.00) 		0.8
(1.00) 		0.568
(1.00) 		0.708
(1.00) 		1
(1.00) 		0.119
(1.00) 		1
(1.00)
13q gain 0 (0%) 103 0.0411
(1.00) 		0.517
(1.00) 		0.666
(1.00) 		0.302
(1.00) 		0.213
(1.00) 		1
(1.00) 		0.533
(1.00) 		0.441
(1.00)
14q gain 0 (0%) 102 0.686
(1.00) 		0.167
(1.00) 		0.506
(1.00) 		1
(1.00) 		0.471
(1.00) 		0.916
(1.00) 		0.922
(1.00)
15q gain 0 (0%) 102 1
(1.00) 		0.721
(1.00) 		0.48
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		0.478
(1.00) 		0.636
(1.00)
16p gain 0 (0%) 104 0.116
(1.00) 		0.476
(1.00) 		0.173
(1.00) 		0.673
(1.00) 		1
(1.00) 		0.331
(1.00) 		0.548
(1.00)
17p gain 0 (0%) 103 0.865
(1.00) 		0.119
(1.00) 		0.849
(1.00) 		0.568
(1.00) 		0.708
(1.00) 		1
(1.00) 		0.772
(1.00) 		0.511
(1.00)
17q gain 0 (0%) 85 0.0083
(1.00) 		0.953
(1.00) 		0.713
(1.00) 		0.566
(1.00) 		0.762
(1.00) 		0.447
(1.00) 		0.113
(1.00) 		0.872
(1.00)
18p gain 0 (0%) 104 0.215
(1.00) 		0.198
(1.00) 		0.173
(1.00) 		0.784
(1.00) 		1
(1.00) 		0.174
(1.00) 		0.867
(1.00)
18q gain 0 (0%) 103 0.275
(1.00) 		0.0402
(1.00) 		0.661
(1.00) 		0.0697
(1.00) 		0.673
(1.00) 		1
(1.00) 		0.113
(1.00) 		1
(1.00)
19p gain 0 (0%) 97 0.786
(1.00) 		0.0093
(1.00) 		0.0482
(1.00) 		0.0363
(1.00) 		0.00214
(1.00) 		0.592
(1.00) 		0.372
(1.00) 		0.545
(1.00)
19q gain 0 (0%) 95 0.372
(1.00) 		0.00104
(0.59) 		0.048
(1.00) 		0.00321
(1.00) 		0.0712
(1.00) 		0.6
(1.00) 		0.0365
(1.00) 		0.786
(1.00)
20p gain 0 (0%) 85 0.699
(1.00) 		0.191
(1.00) 		0.076
(1.00) 		0.144
(1.00) 		0.472
(1.00) 		1
(1.00) 		0.255
(1.00) 		0.573
(1.00)
20q gain 0 (0%) 84 0.592
(1.00) 		0.223
(1.00) 		0.0505
(1.00) 		0.25
(1.00) 		0.308
(1.00) 		1
(1.00) 		0.191
(1.00) 		0.728
(1.00)
21q gain 0 (0%) 102 0.518
(1.00) 		0.38
(1.00) 		0.895
(1.00) 		0.302
(1.00) 		1
(1.00) 		1
(1.00) 		0.186
(1.00) 		0.508
(1.00)
22q gain 0 (0%) 99 0.626
(1.00) 		0.724
(1.00) 		0.074
(1.00) 		0.122
(1.00) 		1
(1.00) 		0.59
(1.00) 		0.0703
(1.00) 		0.667
(1.00)
Xq gain 0 (0%) 103 0.275
(1.00) 		0.0867
(1.00) 		0.502
(1.00) 		0.258
(1.00) 		0.213
(1.00) 		0.471
(1.00) 		0.478
(1.00) 		0.0879
(1.00)
1p loss 0 (0%) 91 0.846
(1.00) 		0.00405
(1.00) 		0.072
(1.00) 		0.764
(1.00) 		0.141
(1.00) 		0.208
(1.00) 		0.339
(1.00) 		0.517
(1.00)
1q loss 0 (0%) 102 0.686
(1.00) 		0.323
(1.00) 		0.0166
(1.00) 		1
(1.00) 		0.351
(1.00) 		1
(1.00) 		0.395
(1.00) 		0.511
(1.00)
2p loss 0 (0%) 104 0.116
(1.00) 		0.58
(1.00) 		0.283
(1.00) 		0.258
(1.00) 		0.43
(1.00) 		1
(1.00) 		0.775
(1.00) 		0.15
(1.00)
2q loss 0 (0%) 103 0.0411
(1.00) 		0.323
(1.00) 		0.568
(1.00) 		0.634
(1.00) 		0.334
(1.00) 		1
(1.00) 		0.887
(1.00) 		0.146
(1.00)
3p loss 0 (0%) 98 0.137
(1.00) 		0.0903
(1.00) 		0.0128
(1.00) 		0.00396
(1.00) 		0.21
(1.00) 		0.59
(1.00) 		0.00461
(1.00) 		0.62
(1.00)
3q loss 0 (0%) 104 1
(1.00) 		0.0541
(1.00) 		0.239
(1.00) 		0.0697
(1.00) 		1
(1.00) 		1
(1.00) 		0.174
(1.00) 		0.479
(1.00)
4p loss 0 (0%) 95 0.00114
(0.646) 		0.00739
(1.00) 		0.625
(1.00) 		0.477
(1.00) 		0.589
(1.00) 		0.283
(1.00) 		0.109
(1.00) 		0.377
(1.00)
5p loss 0 (0%) 105 1
(1.00) 		0.506
(1.00) 		0.221
(1.00)
5q loss 0 (0%) 101 0.623
(1.00) 		0.0881
(1.00) 		0.58
(1.00) 		1
(1.00) 		0.279
(1.00) 		0.471
(1.00) 		0.296
(1.00) 		0.0717
(1.00)
6q loss 0 (0%) 86 0.478
(1.00) 		0.91
(1.00) 		0.73
(1.00) 		0.131
(1.00) 		0.841
(1.00) 		1
(1.00) 		0.00843
(1.00) 		0.768
(1.00)
7p loss 0 (0%) 103 0.0411
(1.00) 		0.517
(1.00) 		0.000514
(0.293) 		0.634
(1.00) 		0.845
(1.00) 		1
(1.00) 		0.867
(1.00) 		0.382
(1.00)
9p loss 0 (0%) 85 0.0423
(1.00) 		0.794
(1.00) 		0.674
(1.00) 		0.566
(1.00) 		0.573
(1.00) 		0.696
(1.00) 		0.72
(1.00) 		0.709
(1.00)
9q loss 0 (0%) 86 0.0635
(1.00) 		0.399
(1.00) 		0.555
(1.00) 		0.771
(1.00) 		0.787
(1.00) 		1
(1.00) 		0.537
(1.00) 		0.9
(1.00)
10p loss 0 (0%) 102 0.0785
(1.00) 		1
(1.00) 		0.696
(1.00) 		1
(1.00) 		0.857
(1.00) 		0.536
(1.00) 		0.437
(1.00) 		0.875
(1.00)
10q loss 0 (0%) 90 0.0247
(1.00) 		0.483
(1.00) 		0.16
(1.00) 		0.131
(1.00) 		0.358
(1.00) 		0.108
(1.00) 		0.00478
(1.00) 		0.12
(1.00)
11p loss 0 (0%) 100 0.588
(1.00) 		0.143
(1.00) 		0.624
(1.00) 		0.643
(1.00) 		0.857
(1.00) 		0.536
(1.00) 		0.0178
(1.00) 		0.875
(1.00)
11q loss 0 (0%) 98 0.413
(1.00) 		0.917
(1.00) 		0.00885
(1.00) 		0.0615
(1.00) 		0.55
(1.00) 		0.235
(1.00) 		0.019
(1.00) 		0.0735
(1.00)
12p loss 0 (0%) 97 0.15
(1.00) 		0.159
(1.00) 		0.11
(1.00) 		1
(1.00) 		0.244
(1.00) 		1
(1.00) 		0.00789
(1.00) 		0.221
(1.00)
12q loss 0 (0%) 104 0.215
(1.00) 		1
(1.00) 		0.239
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		0.174
(1.00) 		0.479
(1.00)
13q loss 0 (0%) 75 0.00267
(1.00) 		0.142
(1.00) 		0.0456
(1.00) 		0.436
(1.00) 		0.0552
(1.00) 		0.733
(1.00) 		0.00121
(0.683) 		0.443
(1.00)
15q loss 0 (0%) 97 0.0695
(1.00) 		0.0425
(1.00) 		0.987
(1.00) 		0.443
(1.00) 		0.589
(1.00) 		0.283
(1.00) 		0.186
(1.00) 		0.299
(1.00)
16p loss 0 (0%) 86 0.575
(1.00) 		0.0288
(1.00) 		0.00753
(1.00) 		0.0397
(1.00) 		0.0807
(1.00) 		0.452
(1.00) 		0.0229
(1.00) 		0.00286
(1.00)
16q loss 0 (0%) 78 0.101
(1.00) 		0.00105
(0.598) 		0.071
(1.00) 		0.068
(1.00) 		0.0366
(1.00) 		0.0312
(1.00) 		0.213
(1.00) 		0.0246
(1.00)
17p loss 0 (0%) 65 0.0018
(1.00) 		0.0155
(1.00) 		0.299
(1.00) 		0.634
(1.00) 		0.135
(1.00) 		0.009
(1.00) 		0.00472
(1.00) 		0.00193
(1.00)
17q loss 0 (0%) 102 0.518
(1.00) 		0.0881
(1.00) 		0.693
(1.00) 		0.634
(1.00) 		0.857
(1.00) 		0.536
(1.00) 		0.693
(1.00) 		0.779
(1.00)
18p loss 0 (0%) 95 0.494
(1.00) 		0.0151
(1.00) 		0.00493
(1.00) 		0.712
(1.00) 		0.826
(1.00) 		1
(1.00) 		0.314
(1.00) 		0.495
(1.00)
18q loss 0 (0%) 93 0.182
(1.00) 		0.0448
(1.00) 		0.0131
(1.00) 		0.477
(1.00) 		0.649
(1.00) 		1
(1.00) 		0.168
(1.00) 		1
(1.00)
19p loss 0 (0%) 101 0.792
(1.00) 		0.0352
(1.00) 		0.401
(1.00) 		1
(1.00) 		1
(1.00) 		0.471
(1.00) 		0.413
(1.00) 		0.922
(1.00)
20p loss 0 (0%) 103 0.275
(1.00) 		0.517
(1.00) 		1
(1.00) 		0.0618
(1.00) 		0.103
(1.00) 		0.616
(1.00) 		0.306
(1.00)
21q loss 0 (0%) 87 0.104
(1.00) 		0.905
(1.00) 		0.13
(1.00) 		0.477
(1.00) 		0.247
(1.00) 		0.0189
(1.00) 		0.0908
(1.00) 		0.13
(1.00)
22q loss 0 (0%) 96 0.144
(1.00) 		0.733
(1.00) 		0.0981
(1.00) 		0.152
(1.00) 		0.91
(1.00) 		0.331
(1.00) 		0.051
(1.00) 		0.393
(1.00)
'8q gain' versus 'CN_CNMF'

P value = 4.53e-10 (Fisher's exact test), Q value = 2.6e-07

Table S1.  Gene #15: '8q gain' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 37 37 34
8Q GAIN CNV 32 6 11
8Q GAIN WILD-TYPE 5 31 23

Figure S1.  Get High-res Image Gene #15: '8q gain' versus Molecular Subtype #1: 'CN_CNMF'

'4q loss' versus 'CN_CNMF'

P value = 0.000117 (Fisher's exact test), Q value = 0.067

Table S2.  Gene #46: '4q loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 37 37 34
4Q LOSS CNV 5 1 14
4Q LOSS WILD-TYPE 32 36 20

Figure S2.  Get High-res Image Gene #46: '4q loss' versus Molecular Subtype #1: 'CN_CNMF'

'7q loss' versus 'MRNASEQ_CNMF'

P value = 2.41e-05 (Chi-square test), Q value = 0.014

Table S3.  Gene #51: '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 16 8 6 14 11 3 6 1 4
7Q LOSS CNV 1 0 0 0 1 3 1 0 0
7Q LOSS WILD-TYPE 15 8 6 14 10 0 5 1 4

Figure S3.  Get High-res Image Gene #51: '7q loss' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

'8p loss' versus 'CN_CNMF'

P value = 5.13e-07 (Fisher's exact test), Q value = 3e-04

Table S4.  Gene #52: '8p loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 37 37 34
8P LOSS CNV 21 3 21
8P LOSS WILD-TYPE 16 34 13

Figure S4.  Get High-res Image Gene #52: '8p loss' versus Molecular Subtype #1: 'CN_CNMF'

'8q loss' versus 'CN_CNMF'

P value = 0.000421 (Fisher's exact test), Q value = 0.24

Table S5.  Gene #53: '8q loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 37 37 34
8Q LOSS CNV 0 1 8
8Q LOSS WILD-TYPE 37 36 26

Figure S5.  Get High-res Image Gene #53: '8q loss' versus Molecular Subtype #1: 'CN_CNMF'

'14q loss' versus 'CN_CNMF'

P value = 0.000266 (Fisher's exact test), Q value = 0.15

Table S6.  Gene #63: '14q loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 37 37 34
14Q LOSS CNV 8 4 18
14Q LOSS WILD-TYPE 29 33 16

Figure S6.  Get High-res Image Gene #63: '14q loss' versus Molecular Subtype #1: 'CN_CNMF'

Methods & Data
Input

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

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

  • Number of patients = 108

  • Number of significantly arm-level cnvs = 74

  • Number of molecular subtypes = 8

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

Fisher's exact test

For binary or multi-class clinical features (nominal or ordinal), two-tailed Fisher's exact tests (Fisher 1922) were used to estimate the P values using the 'fisher.test' function in R

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.test' function in R

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

Download Results

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

References
[1] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)
[2] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[3] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)
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