Correlation between copy number variations of arm-level result and molecular subtypes
Mesothelioma (Primary solid tumor)
17 October 2014  |  analyses__2014_10_17
Maintainer Information
Citation Information
doi:10.7908/C15H7F6H
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/C15H7F6H
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 72 arm-level events and 8 molecular subtypes across 87 patients, 12 significant findings detected with P value < 0.05 and Q value < 0.25.

  • 3q gain cnv correlated to 'CN_CNMF'.

  • 12p gain cnv correlated to 'CN_CNMF'.

  • 12q gain cnv correlated to 'CN_CNMF'.

  • 4p loss cnv correlated to 'CN_CNMF'.

  • 4q loss cnv correlated to 'CN_CNMF'.

  • 9p loss cnv correlated to 'CN_CNMF'.

  • 14q loss cnv correlated to 'CN_CNMF',  'MRNASEQ_CNMF',  'MRNASEQ_CHIERARCHICAL', and 'MIRSEQ_MATURE_CHIERARCHICAL'.

  • 22q loss cnv correlated to 'METHLYATION_CNMF'.

  • xq loss cnv correlated to 'METHLYATION_CNMF'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between significant copy number variation of 72 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, 12 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 Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test
14q loss 35 (40%) 52 0.00035
(0.192) 		0.36
(1.00) 		5e-05
(0.0278) 		1e-05
(0.0056) 		0.00348
(1.00) 		0.0005
(0.273) 		0.00255
(1.00) 		0.00028
(0.155)
3q gain 19 (22%) 68 0.00019
(0.105) 		0.767
(1.00) 		0.746
(1.00) 		0.528
(1.00) 		0.651
(1.00) 		0.936
(1.00) 		0.519
(1.00) 		0.762
(1.00)
12p gain 18 (21%) 69 0.00038
(0.209) 		0.814
(1.00) 		0.835
(1.00) 		0.946
(1.00) 		0.479
(1.00) 		0.927
(1.00) 		0.935
(1.00) 		0.86
(1.00)
12q gain 18 (21%) 69 0.00028
(0.155) 		0.813
(1.00) 		0.832
(1.00) 		0.946
(1.00) 		0.48
(1.00) 		0.926
(1.00) 		0.936
(1.00) 		0.858
(1.00)
4p loss 32 (37%) 55 3e-05
(0.0167) 		0.325
(1.00) 		0.22
(1.00) 		0.00456
(1.00) 		0.175
(1.00) 		0.716
(1.00) 		0.207
(1.00) 		0.664
(1.00)
4q loss 33 (38%) 54 6e-05
(0.0333) 		0.1
(1.00) 		0.00388
(1.00) 		0.0005
(0.273) 		0.118
(1.00) 		0.124
(1.00) 		0.656
(1.00) 		0.227
(1.00)
9p loss 28 (32%) 59 1e-05
(0.0056) 		0.0141
(1.00) 		0.0557
(1.00) 		0.00687
(1.00) 		0.822
(1.00) 		0.322
(1.00) 		0.629
(1.00) 		0.359
(1.00)
22q loss 67 (77%) 20 0.00511
(1.00) 		5e-05
(0.0278) 		0.0301
(1.00) 		0.00368
(1.00) 		1
(1.00) 		0.801
(1.00) 		0.0692
(1.00) 		0.0968
(1.00)
xq loss 28 (32%) 59 0.00195
(1.00) 		0.00015
(0.0831) 		0.0151
(1.00) 		0.00304
(1.00) 		0.335
(1.00) 		0.576
(1.00) 		0.0147
(1.00) 		0.0575
(1.00)
1p gain 10 (11%) 77 0.0148
(1.00) 		0.783
(1.00) 		0.961
(1.00) 		0.61
(1.00) 		0.74
(1.00) 		0.398
(1.00) 		0.355
(1.00) 		0.755
(1.00)
1q gain 21 (24%) 66 0.00898
(1.00) 		0.03
(1.00) 		0.0513
(1.00) 		0.225
(1.00) 		0.711
(1.00) 		0.412
(1.00) 		0.695
(1.00) 		0.248
(1.00)
2p gain 4 (5%) 83 0.103
(1.00) 		0.376
(1.00) 		0.618
(1.00) 		0.902
(1.00) 		0.053
(1.00) 		0.614
(1.00) 		0.256
(1.00) 		0.481
(1.00)
2q gain 7 (8%) 80 0.516
(1.00) 		0.461
(1.00) 		0.317
(1.00) 		0.537
(1.00) 		0.269
(1.00) 		0.806
(1.00) 		0.0129
(1.00) 		0.168
(1.00)
3p gain 17 (20%) 70 0.00049
(0.269) 		0.931
(1.00) 		0.703
(1.00) 		0.727
(1.00) 		1
(1.00) 		1
(1.00) 		0.566
(1.00) 		0.819
(1.00)
5p gain 24 (28%) 63 0.195
(1.00) 		0.329
(1.00) 		0.659
(1.00) 		0.583
(1.00) 		0.0221
(1.00) 		0.0937
(1.00) 		0.0449
(1.00) 		0.386
(1.00)
5q gain 15 (17%) 72 0.0612
(1.00) 		0.354
(1.00) 		0.528
(1.00) 		0.955
(1.00) 		0.12
(1.00) 		0.124
(1.00) 		0.0477
(1.00) 		0.637
(1.00)
6p gain 7 (8%) 80 0.0762
(1.00) 		0.0958
(1.00) 		0.316
(1.00) 		0.333
(1.00) 		0.0836
(1.00) 		0.357
(1.00) 		0.88
(1.00) 		0.935
(1.00)
6q gain 4 (5%) 83 0.102
(1.00) 		0.636
(1.00) 		0.837
(1.00) 		0.745
(1.00) 		0.56
(1.00) 		0.45
(1.00) 		0.569
(1.00) 		0.912
(1.00)
7p gain 25 (29%) 62 0.0146
(1.00) 		0.392
(1.00) 		0.241
(1.00) 		0.0399
(1.00) 		0.0324
(1.00) 		0.0354
(1.00) 		0.186
(1.00) 		0.135
(1.00)
7q gain 22 (25%) 65 0.0242
(1.00) 		0.447
(1.00) 		0.444
(1.00) 		0.0203
(1.00) 		0.0668
(1.00) 		0.17
(1.00) 		0.404
(1.00) 		0.38
(1.00)
8p gain 12 (14%) 75 0.0455
(1.00) 		0.522
(1.00) 		0.755
(1.00) 		0.488
(1.00) 		0.176
(1.00) 		0.254
(1.00) 		0.216
(1.00) 		0.368
(1.00)
8q gain 14 (16%) 73 0.0493
(1.00) 		0.933
(1.00) 		0.737
(1.00) 		0.532
(1.00) 		0.296
(1.00) 		0.663
(1.00) 		0.399
(1.00) 		0.485
(1.00)
9p gain 3 (3%) 84 0.703
(1.00) 		0.534
(1.00) 		0.329
(1.00) 		0.872
(1.00) 		0.183
(1.00) 		0.774
(1.00) 		0.336
(1.00) 		0.373
(1.00)
9q gain 4 (5%) 83 0.502
(1.00) 		0.0709
(1.00) 		0.441
(1.00) 		0.615
(1.00) 		0.0607
(1.00) 		0.854
(1.00) 		1
(1.00) 		0.716
(1.00)
10p gain 3 (3%) 84 0.08
(1.00) 		0.776
(1.00) 		0.239
(1.00) 		0.754
(1.00)
10q gain 3 (3%) 84 0.0795
(1.00) 		0.775
(1.00) 		0.237
(1.00) 		0.755
(1.00)
11p gain 11 (13%) 76 0.00814
(1.00) 		0.662
(1.00) 		0.517
(1.00) 		0.0621
(1.00) 		0.123
(1.00) 		0.827
(1.00) 		0.599
(1.00) 		0.618
(1.00)
11q gain 12 (14%) 75 0.0098
(1.00) 		0.38
(1.00) 		0.199
(1.00) 		0.034
(1.00) 		0.114
(1.00) 		0.44
(1.00) 		0.315
(1.00) 		0.304
(1.00)
13q gain 5 (6%) 82 0.423
(1.00) 		0.0964
(1.00) 		0.0239
(1.00) 		0.0961
(1.00) 		0.115
(1.00) 		0.893
(1.00) 		0.259
(1.00) 		0.716
(1.00)
15q gain 11 (13%) 76 0.0253
(1.00) 		0.00406
(1.00) 		0.0141
(1.00) 		0.126
(1.00) 		0.0111
(1.00) 		0.18
(1.00) 		0.753
(1.00) 		0.671
(1.00)
16p gain 17 (20%) 70 0.00696
(1.00) 		0.568
(1.00) 		0.0383
(1.00) 		0.148
(1.00) 		0.0323
(1.00) 		0.172
(1.00) 		0.578
(1.00) 		0.113
(1.00)
16q gain 16 (18%) 71 0.00228
(1.00) 		0.803
(1.00) 		0.048
(1.00) 		0.116
(1.00) 		0.169
(1.00) 		0.513
(1.00) 		0.747
(1.00) 		0.51
(1.00)
17p gain 7 (8%) 80 0.219
(1.00) 		1
(1.00) 		0.482
(1.00) 		0.779
(1.00) 		0.434
(1.00) 		0.0421
(1.00) 		0.85
(1.00) 		0.0691
(1.00)
17q gain 17 (20%) 70 0.0951
(1.00) 		0.318
(1.00) 		0.00991
(1.00) 		0.0494
(1.00) 		0.0887
(1.00) 		0.375
(1.00) 		0.747
(1.00) 		0.705
(1.00)
18p gain 8 (9%) 79 0.0676
(1.00) 		0.317
(1.00) 		0.113
(1.00) 		0.184
(1.00) 		0.05
(1.00) 		0.542
(1.00) 		0.137
(1.00) 		0.255
(1.00)
18q gain 4 (5%) 83 0.0887
(1.00) 		1
(1.00) 		0.366
(1.00) 		0.619
(1.00) 		0.181
(1.00) 		0.845
(1.00)
19p gain 14 (16%) 73 0.273
(1.00) 		0.0332
(1.00) 		0.153
(1.00) 		0.282
(1.00) 		0.397
(1.00) 		0.922
(1.00) 		0.789
(1.00) 		0.252
(1.00)
19q gain 10 (11%) 77 0.805
(1.00) 		0.119
(1.00) 		0.449
(1.00) 		0.608
(1.00) 		0.55
(1.00) 		1
(1.00) 		0.717
(1.00) 		0.402
(1.00)
20p gain 8 (9%) 79 0.69
(1.00) 		0.727
(1.00) 		0.244
(1.00) 		0.77
(1.00) 		0.609
(1.00) 		0.876
(1.00) 		0.881
(1.00) 		0.332
(1.00)
20q gain 10 (11%) 77 0.204
(1.00) 		0.908
(1.00) 		0.464
(1.00) 		0.87
(1.00) 		0.356
(1.00) 		0.795
(1.00) 		0.624
(1.00) 		0.113
(1.00)
21q gain 6 (7%) 81 0.252
(1.00) 		0.611
(1.00) 		0.231
(1.00) 		0.233
(1.00) 		0.114
(1.00) 		0.804
(1.00) 		0.0402
(1.00) 		0.0866
(1.00)
xq gain 6 (7%) 81 0.232
(1.00) 		0.496
(1.00) 		0.648
(1.00) 		0.875
(1.00) 		0.564
(1.00) 		0.913
(1.00) 		1
(1.00) 		0.847
(1.00)
1p loss 8 (9%) 79 0.0248
(1.00) 		0.225
(1.00) 		0.41
(1.00) 		0.871
(1.00) 		0.119
(1.00) 		0.163
(1.00) 		0.136
(1.00) 		0.706
(1.00)
2p loss 6 (7%) 81 0.685
(1.00) 		0.386
(1.00) 		0.104
(1.00) 		0.285
(1.00) 		0.114
(1.00) 		0.735
(1.00) 		0.258
(1.00) 		0.496
(1.00)
2q loss 4 (5%) 83 0.637
(1.00) 		0.343
(1.00) 		0.697
(1.00) 		0.658
(1.00) 		0.671
(1.00) 		0.899
(1.00) 		0.783
(1.00) 		0.952
(1.00)
3p loss 8 (9%) 79 0.369
(1.00) 		0.09
(1.00) 		0.151
(1.00) 		0.296
(1.00) 		0.722
(1.00) 		0.131
(1.00) 		0.194
(1.00) 		0.206
(1.00)
3q loss 6 (7%) 81 0.719
(1.00) 		0.0923
(1.00) 		0.234
(1.00) 		0.555
(1.00) 		0.878
(1.00) 		0.217
(1.00) 		0.314
(1.00) 		0.846
(1.00)
5p loss 4 (5%) 83 0.5
(1.00) 		0.691
(1.00) 		0.0313
(1.00) 		0.616
(1.00) 		0.479
(1.00) 		0.312
(1.00)
5q loss 9 (10%) 78 0.025
(1.00) 		0.838
(1.00) 		0.28
(1.00) 		0.228
(1.00) 		0.723
(1.00) 		0.273
(1.00) 		0.351
(1.00) 		0.541
(1.00)
6p loss 9 (10%) 78 0.834
(1.00) 		0.323
(1.00) 		0.275
(1.00) 		0.4
(1.00) 		0.887
(1.00) 		0.502
(1.00) 		0.61
(1.00) 		0.0365
(1.00)
6q loss 29 (33%) 58 0.00327
(1.00) 		0.214
(1.00) 		0.332
(1.00) 		0.518
(1.00) 		1
(1.00) 		0.835
(1.00) 		0.103
(1.00) 		0.363
(1.00)
8p loss 13 (15%) 74 0.87
(1.00) 		0.69
(1.00) 		0.975
(1.00) 		0.9
(1.00) 		0.402
(1.00) 		0.355
(1.00) 		0.399
(1.00) 		0.86
(1.00)
8q loss 4 (5%) 83 0.104
(1.00) 		0.374
(1.00) 		0.442
(1.00) 		0.617
(1.00) 		0.357
(1.00) 		0.844
(1.00) 		0.61
(1.00) 		0.635
(1.00)
9q loss 21 (24%) 66 0.00194
(1.00) 		0.207
(1.00) 		0.173
(1.00) 		0.152
(1.00) 		1
(1.00) 		0.282
(1.00) 		0.302
(1.00) 		0.138
(1.00)
10p loss 24 (28%) 63 0.00428
(1.00) 		0.00063
(0.343) 		0.122
(1.00) 		0.141
(1.00) 		0.819
(1.00) 		0.658
(1.00) 		0.653
(1.00) 		0.89
(1.00)
10q loss 19 (22%) 68 0.0237
(1.00) 		0.0163
(1.00) 		0.513
(1.00) 		0.378
(1.00) 		0.195
(1.00) 		0.866
(1.00) 		0.644
(1.00) 		0.805
(1.00)
11p loss 4 (5%) 83 0.683
(1.00) 		0.831
(1.00) 		0.364
(1.00) 		0.151
(1.00) 		0.618
(1.00) 		0.565
(1.00) 		1
(1.00) 		0.953
(1.00)
11q loss 5 (6%) 82 0.579
(1.00) 		0.615
(1.00) 		0.00722
(1.00) 		0.00304
(1.00) 		0.447
(1.00) 		0.183
(1.00) 		1
(1.00) 		0.339
(1.00)
12p loss 4 (5%) 83 0.261
(1.00) 		0.0706
(1.00) 		0.442
(1.00) 		0.248
(1.00) 		0.36
(1.00) 		0.245
(1.00) 		0.464
(1.00) 		0.908
(1.00)
13q loss 33 (38%) 54 0.103
(1.00) 		0.736
(1.00) 		0.246
(1.00) 		0.445
(1.00) 		0.83
(1.00) 		1
(1.00) 		0.00646
(1.00) 		0.309
(1.00)
15q loss 11 (13%) 76 0.114
(1.00) 		0.759
(1.00) 		0.322
(1.00) 		0.198
(1.00) 		0.256
(1.00) 		0.0775
(1.00) 		0.786
(1.00) 		0.876
(1.00)
16p loss 7 (8%) 80 0.00972
(1.00) 		0.195
(1.00) 		0.598
(1.00) 		0.123
(1.00) 		0.651
(1.00) 		0.595
(1.00) 		1
(1.00) 		0.939
(1.00)
16q loss 10 (11%) 77 0.0498
(1.00) 		0.905
(1.00) 		0.255
(1.00) 		0.249
(1.00) 		0.67
(1.00) 		0.966
(1.00) 		0.599
(1.00) 		0.768
(1.00)
17p loss 23 (26%) 64 0.348
(1.00) 		0.383
(1.00) 		0.764
(1.00) 		0.759
(1.00) 		0.0479
(1.00) 		0.171
(1.00) 		0.237
(1.00) 		0.0296
(1.00)
17q loss 7 (8%) 80 0.557
(1.00) 		0.155
(1.00) 		0.67
(1.00) 		0.896
(1.00) 		0.317
(1.00) 		0.688
(1.00) 		0.361
(1.00) 		0.706
(1.00)
18p loss 9 (10%) 78 0.14
(1.00) 		0.782
(1.00) 		0.92
(1.00) 		0.546
(1.00) 		0.361
(1.00) 		0.387
(1.00) 		0.435
(1.00) 		0.155
(1.00)
18q loss 14 (16%) 73 0.403
(1.00) 		0.686
(1.00) 		0.764
(1.00) 		0.928
(1.00) 		0.0514
(1.00) 		0.111
(1.00) 		0.475
(1.00) 		0.222
(1.00)
19p loss 4 (5%) 83 0.685
(1.00) 		0.202
(1.00) 		0.615
(1.00) 		0.107
(1.00) 		1
(1.00) 		0.474
(1.00) 		0.204
(1.00) 		0.28
(1.00)
19q loss 7 (8%) 80 0.559
(1.00) 		0.0497
(1.00) 		1
(1.00) 		0.853
(1.00) 		1
(1.00) 		0.597
(1.00) 		0.315
(1.00) 		0.939
(1.00)
20p loss 14 (16%) 73 0.0259
(1.00) 		0.11
(1.00) 		0.108
(1.00) 		0.645
(1.00) 		0.251
(1.00) 		0.086
(1.00) 		0.563
(1.00) 		0.0622
(1.00)
20q loss 3 (3%) 84 0.239
(1.00) 		0.337
(1.00) 		0.89
(1.00) 		0.411
(1.00)
21q loss 11 (13%) 76 0.11
(1.00) 		0.0131
(1.00) 		0.239
(1.00) 		0.0754
(1.00) 		0.76
(1.00) 		0.929
(1.00) 		0.0996
(1.00) 		0.949
(1.00)
'3q gain' versus 'CN_CNMF'

P value = 0.00019 (Fisher's exact test), Q value = 0.11

Table S1.  Gene #6: '3q gain' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 23 26 24 14
3Q GAIN MUTATED 1 12 1 5
3Q GAIN WILD-TYPE 22 14 23 9

Figure S1.  Get High-res Image Gene #6: '3q gain' versus Molecular Subtype #1: 'CN_CNMF'

'12p gain' versus 'CN_CNMF'

P value = 0.00038 (Fisher's exact test), Q value = 0.21

Table S2.  Gene #21: '12p gain' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 23 26 24 14
12P GAIN MUTATED 1 13 3 1
12P GAIN WILD-TYPE 22 13 21 13

Figure S2.  Get High-res Image Gene #21: '12p gain' versus Molecular Subtype #1: 'CN_CNMF'

'12q gain' versus 'CN_CNMF'

P value = 0.00028 (Fisher's exact test), Q value = 0.15

Table S3.  Gene #22: '12q gain' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 23 26 24 14
12Q GAIN MUTATED 1 13 3 1
12Q GAIN WILD-TYPE 22 13 21 13

Figure S3.  Get High-res Image Gene #22: '12q gain' versus Molecular Subtype #1: 'CN_CNMF'

'4p loss' versus 'CN_CNMF'

P value = 3e-05 (Fisher's exact test), Q value = 0.017

Table S4.  Gene #42: '4p loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 23 26 24 14
4P LOSS MUTATED 3 18 4 7
4P LOSS WILD-TYPE 20 8 20 7

Figure S4.  Get High-res Image Gene #42: '4p loss' versus Molecular Subtype #1: 'CN_CNMF'

'4q loss' versus 'CN_CNMF'

P value = 6e-05 (Fisher's exact test), Q value = 0.033

Table S5.  Gene #43: '4q loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 23 26 24 14
4Q LOSS MUTATED 3 17 4 9
4Q LOSS WILD-TYPE 20 9 20 5

Figure S5.  Get High-res Image Gene #43: '4q loss' versus Molecular Subtype #1: 'CN_CNMF'

'9p loss' versus 'CN_CNMF'

P value = 1e-05 (Fisher's exact test), Q value = 0.0056

Table S6.  Gene #50: '9p loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 23 26 24 14
9P LOSS MUTATED 0 16 9 3
9P LOSS WILD-TYPE 23 10 15 11

Figure S6.  Get High-res Image Gene #50: '9p loss' versus Molecular Subtype #1: 'CN_CNMF'

'14q loss' versus 'CN_CNMF'

P value = 0.00035 (Fisher's exact test), Q value = 0.19

Table S7.  Gene #58: '14q loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 23 26 24 14
14Q LOSS MUTATED 3 15 7 10
14Q LOSS WILD-TYPE 20 11 17 4

Figure S7.  Get High-res Image Gene #58: '14q loss' versus Molecular Subtype #1: 'CN_CNMF'

'14q loss' versus 'MRNASEQ_CNMF'

P value = 5e-05 (Fisher's exact test), Q value = 0.028

Table S8.  Gene #58: '14q loss' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 25 21 18 21
14Q LOSS MUTATED 4 17 4 9
14Q LOSS WILD-TYPE 21 4 14 12

Figure S8.  Get High-res Image Gene #58: '14q loss' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

'14q loss' versus 'MRNASEQ_CHIERARCHICAL'

P value = 1e-05 (Fisher's exact test), Q value = 0.0056

Table S9.  Gene #58: '14q loss' versus Molecular Subtype #4: 'MRNASEQ_CHIERARCHICAL'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 21 35 13 16
14Q LOSS MUTATED 3 25 0 6
14Q LOSS WILD-TYPE 18 10 13 10

Figure S9.  Get High-res Image Gene #58: '14q loss' versus Molecular Subtype #4: 'MRNASEQ_CHIERARCHICAL'

'14q loss' versus 'MIRSEQ_MATURE_CHIERARCHICAL'

P value = 0.00028 (Fisher's exact test), Q value = 0.15

Table S10.  Gene #58: '14q loss' versus Molecular Subtype #8: 'MIRSEQ_MATURE_CHIERARCHICAL'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6
ALL 11 17 7 14 12 13
14Q LOSS MUTATED 2 14 1 9 4 2
14Q LOSS WILD-TYPE 9 3 6 5 8 11

Figure S10.  Get High-res Image Gene #58: '14q loss' versus Molecular Subtype #8: 'MIRSEQ_MATURE_CHIERARCHICAL'

'22q loss' versus 'METHLYATION_CNMF'

P value = 5e-05 (Fisher's exact test), Q value = 0.028

Table S11.  Gene #71: '22q loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 14 20 26 27
22Q LOSS MUTATED 10 19 25 13
22Q LOSS WILD-TYPE 4 1 1 14

Figure S11.  Get High-res Image Gene #71: '22q loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'

'xq loss' versus 'METHLYATION_CNMF'

P value = 0.00015 (Fisher's exact test), Q value = 0.083

Table S12.  Gene #72: 'xq loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 14 20 26 27
XQ LOSS MUTATED 4 9 14 1
XQ LOSS WILD-TYPE 10 11 12 26

Figure S12.  Get High-res Image Gene #72: 'xq loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'

Methods & Data
Input

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

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

  • Number of patients = 87

  • Number of significantly arm-level cnvs = 72

  • 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

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] 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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