Correlation between copy number variations of arm-level result and molecular subtypes
Pheochromocytoma and Paraganglioma (Primary solid tumor)
16 April 2014  |  analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C16T0K98
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/C16T0K98
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 67 arm-level events and 6 molecular subtypes across 159 patients, 21 significant findings detected with P value < 0.05 and Q value < 0.25.

  • 1q gain cnv correlated to 'CN_CNMF'.

  • 12p gain cnv correlated to 'MIRSEQ_MATURE_CNMF'.

  • 13q gain cnv correlated to 'CN_CNMF'.

  • 1p loss cnv correlated to 'CN_CNMF'.

  • 3p loss cnv correlated to 'CN_CNMF',  'MIRSEQ_CNMF', and 'MIRSEQ_CHIERARCHICAL'.

  • 3q loss cnv correlated to 'CN_CNMF'.

  • 6q loss cnv correlated to 'CN_CNMF',  'METHLYATION_CNMF',  'MIRSEQ_CNMF',  'MIRSEQ_CHIERARCHICAL',  'MIRSEQ_MATURE_CNMF', and 'MIRSEQ_MATURE_CHIERARCHICAL'.

  • 11p loss cnv correlated to 'CN_CNMF',  'MIRSEQ_CHIERARCHICAL',  'MIRSEQ_MATURE_CNMF', and 'MIRSEQ_MATURE_CHIERARCHICAL'.

  • 11q loss cnv correlated to 'CN_CNMF' and 'MIRSEQ_MATURE_CNMF'.

  • 16q 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 67 arm-level events and 6 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 		MIRSEQ
CNMF 		MIRSEQ
CHIERARCHICAL 		MIRSEQ
MATURE
CNMF 		MIRSEQ
MATURE
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
6q loss 19 (12%) 140 0.000412
(0.152) 		0.000536
(0.196) 		0.00044
(0.162) 		9.39e-06
(0.00355) 		0.000367
(0.136) 		4.75e-06
(0.0018)
11p loss 52 (33%) 107 5.84e-12
(2.25e-09) 		0.00106
(0.381) 		0.00206
(0.732) 		7.08e-06
(0.00268) 		0.000203
(0.0757) 		8.26e-05
(0.0311)
3p loss 61 (38%) 98 2.4e-08
(9.18e-06) 		0.0305
(1.00) 		6.9e-05
(0.026) 		0.00053
(0.194) 		0.000918
(0.332) 		0.0112
(1.00)
11q loss 39 (25%) 120 4.13e-11
(1.59e-08) 		0.049
(1.00) 		0.0301
(1.00) 		0.00644
(1.00) 		0.000182
(0.0681) 		0.000879
(0.32)
1q gain 16 (10%) 143 1.33e-12
(5.15e-10) 		0.698
(1.00) 		0.525
(1.00) 		0.447
(1.00) 		0.638
(1.00) 		0.801
(1.00)
12p gain 11 (7%) 148 0.00724
(1.00) 		0.00844
(1.00) 		0.182
(1.00) 		0.0743
(1.00) 		0.000209
(0.0779) 		0.0325
(1.00)
13q gain 9 (6%) 150 0.000279
(0.103) 		0.0213
(1.00) 		0.142
(1.00) 		0.096
(1.00) 		0.00779
(1.00) 		0.0694
(1.00)
1p loss 96 (60%) 63 1.46e-09
(5.58e-07) 		0.0482
(1.00) 		0.0103
(1.00) 		0.0137
(1.00) 		0.000883
(0.321) 		0.0248
(1.00)
3q loss 89 (56%) 70 3.15e-07
(0.00012) 		0.522
(1.00) 		0.0522
(1.00) 		0.000727
(0.265) 		0.244
(1.00) 		0.0118
(1.00)
16q loss 4 (3%) 155 0.000193
(0.0723) 		0.274
(1.00) 		1
(1.00) 		0.441
(1.00) 		0.348
(1.00) 		0.471
(1.00)
1p gain 5 (3%) 154 0.00188
(0.671) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		0.292
(1.00) 		0.681
(1.00)
2p gain 6 (4%) 153 0.134
(1.00) 		0.00595
(1.00) 		0.184
(1.00) 		0.285
(1.00) 		0.00537
(1.00) 		0.138
(1.00)
4p gain 4 (3%) 155 0.806
(1.00) 		0.471
(1.00) 		0.218
(1.00) 		0.376
(1.00) 		0.348
(1.00) 		0.181
(1.00)
4q gain 3 (2%) 156 0.361
(1.00) 		0.803
(1.00)
5p gain 11 (7%) 148 0.08
(1.00) 		0.367
(1.00) 		0.567
(1.00) 		0.569
(1.00) 		1
(1.00) 		0.902
(1.00)
5q gain 7 (4%) 152 0.218
(1.00) 		0.00526
(1.00) 		0.157
(1.00) 		0.227
(1.00) 		0.61
(1.00) 		0.166
(1.00)
6p gain 13 (8%) 146 0.49
(1.00) 		0.293
(1.00) 		0.911
(1.00) 		0.609
(1.00) 		0.547
(1.00) 		0.645
(1.00)
6q gain 7 (4%) 152 0.0835
(1.00) 		0.000935
(0.337) 		0.157
(1.00) 		0.191
(1.00) 		0.0607
(1.00) 		0.166
(1.00)
7p gain 26 (16%) 133 0.0376
(1.00) 		0.118
(1.00) 		0.41
(1.00) 		0.288
(1.00) 		0.201
(1.00) 		0.179
(1.00)
7q gain 21 (13%) 138 0.00702
(1.00) 		0.0113
(1.00) 		0.375
(1.00) 		0.487
(1.00) 		0.0345
(1.00) 		0.246
(1.00)
8p gain 10 (6%) 149 0.0256
(1.00) 		0.334
(1.00) 		0.361
(1.00) 		0.286
(1.00) 		0.109
(1.00) 		0.217
(1.00)
8q gain 13 (8%) 146 0.152
(1.00) 		0.753
(1.00) 		0.309
(1.00) 		0.253
(1.00) 		0.144
(1.00) 		0.268
(1.00)
9p gain 3 (2%) 156 0.185
(1.00) 		0.135
(1.00) 		0.402
(1.00) 		0.441
(1.00) 		0.112
(1.00) 		0.471
(1.00)
9q gain 3 (2%) 156 0.185
(1.00) 		0.135
(1.00) 		0.402
(1.00) 		0.441
(1.00) 		0.112
(1.00) 		0.471
(1.00)
10p gain 12 (8%) 147 0.816
(1.00) 		0.0487
(1.00) 		0.819
(1.00) 		1
(1.00) 		0.907
(1.00) 		0.905
(1.00)
10q gain 10 (6%) 149 0.542
(1.00) 		0.691
(1.00) 		0.507
(1.00) 		0.718
(1.00) 		0.423
(1.00) 		0.807
(1.00)
11p gain 4 (3%) 155 0.534
(1.00) 		0.808
(1.00) 		0.15
(1.00) 		0.139
(1.00) 		0.184
(1.00) 		0.0473
(1.00)
12q gain 13 (8%) 146 0.0617
(1.00) 		0.102
(1.00) 		0.263
(1.00) 		0.171
(1.00) 		0.00933
(1.00) 		0.0912
(1.00)
14q gain 4 (3%) 155 0.468
(1.00) 		0.471
(1.00) 		0.832
(1.00) 		0.731
(1.00) 		0.0631
(1.00) 		0.46
(1.00)
15q gain 14 (9%) 145 0.214
(1.00) 		0.0266
(1.00) 		1
(1.00) 		1
(1.00) 		0.595
(1.00) 		1
(1.00)
16p gain 6 (4%) 153 0.152
(1.00) 		0.551
(1.00) 		0.412
(1.00) 		1
(1.00) 		0.167
(1.00) 		0.681
(1.00)
16q gain 6 (4%) 153 0.191
(1.00) 		1
(1.00) 		0.507
(1.00) 		0.567
(1.00) 		0.61
(1.00) 		0.369
(1.00)
17q gain 4 (3%) 155 0.369
(1.00) 		0.185
(1.00) 		0.15
(1.00) 		0.619
(1.00) 		0.184
(1.00) 		0.336
(1.00)
18p gain 8 (5%) 151 0.1
(1.00) 		0.257
(1.00) 		1
(1.00) 		0.569
(1.00) 		0.0751
(1.00) 		0.651
(1.00)
18q gain 10 (6%) 149 0.0892
(1.00) 		0.585
(1.00) 		1
(1.00) 		0.672
(1.00) 		0.146
(1.00) 		0.619
(1.00)
19p gain 20 (13%) 139 0.0221
(1.00) 		0.0746
(1.00) 		0.274
(1.00) 		0.63
(1.00) 		0.254
(1.00) 		0.654
(1.00)
19q gain 14 (9%) 145 0.0715
(1.00) 		0.00761
(1.00) 		0.151
(1.00) 		0.263
(1.00) 		0.0849
(1.00) 		0.232
(1.00)
20p gain 11 (7%) 148 0.166
(1.00) 		0.19
(1.00) 		0.735
(1.00) 		0.392
(1.00) 		0.023
(1.00) 		0.454
(1.00)
20q gain 9 (6%) 150 0.218
(1.00) 		0.0358
(1.00) 		0.236
(1.00) 		0.0625
(1.00) 		0.00702
(1.00) 		0.0487
(1.00)
21q gain 3 (2%) 156 0.551
(1.00) 		0.135
(1.00)
22q gain 3 (2%) 156 0.185
(1.00) 		0.0823
(1.00) 		0.218
(1.00) 		0.376
(1.00) 		0.0163
(1.00) 		0.181
(1.00)
xq gain 5 (3%) 154 0.541
(1.00) 		0.054
(1.00) 		0.832
(1.00) 		0.289
(1.00) 		0.167
(1.00) 		0.359
(1.00)
1q loss 28 (18%) 131 0.217
(1.00) 		0.0741
(1.00) 		0.918
(1.00) 		0.387
(1.00) 		0.351
(1.00) 		0.125
(1.00)
2p loss 10 (6%) 149 0.463
(1.00) 		0.0815
(1.00) 		0.419
(1.00) 		0.00286
(1.00) 		0.454
(1.00) 		0.00121
(0.433)
2q loss 13 (8%) 146 0.559
(1.00) 		0.448
(1.00) 		0.645
(1.00) 		0.0974
(1.00) 		0.933
(1.00) 		0.327
(1.00)
4p loss 10 (6%) 149 0.0134
(1.00) 		0.239
(1.00) 		0.396
(1.00) 		0.569
(1.00) 		0.0238
(1.00) 		0.651
(1.00)
4q loss 10 (6%) 149 0.103
(1.00) 		0.239
(1.00) 		0.396
(1.00) 		0.569
(1.00) 		0.0238
(1.00) 		0.651
(1.00)
5p loss 5 (3%) 154 0.0266
(1.00) 		0.307
(1.00) 		1
(1.00) 		0.441
(1.00) 		0.348
(1.00) 		0.471
(1.00)
5q loss 7 (4%) 152 0.0747
(1.00) 		0.785
(1.00) 		0.457
(1.00) 		0.557
(1.00) 		0.108
(1.00) 		1
(1.00)
7q loss 6 (4%) 153 0.221
(1.00) 		0.426
(1.00) 		0.131
(1.00) 		0.0588
(1.00) 		0.0306
(1.00) 		0.042
(1.00)
8p loss 19 (12%) 140 0.218
(1.00) 		0.575
(1.00) 		0.753
(1.00) 		0.645
(1.00) 		0.34
(1.00) 		0.414
(1.00)
8q loss 12 (8%) 147 0.564
(1.00) 		0.803
(1.00) 		0.396
(1.00) 		0.26
(1.00) 		0.902
(1.00) 		0.266
(1.00)
9p loss 12 (8%) 147 0.0468
(1.00) 		0.686
(1.00) 		1
(1.00) 		0.186
(1.00) 		0.249
(1.00) 		0.306
(1.00)
9q loss 11 (7%) 148 0.0439
(1.00) 		0.663
(1.00) 		0.439
(1.00) 		0.476
(1.00) 		0.417
(1.00) 		0.833
(1.00)
13q loss 7 (4%) 152 0.757
(1.00) 		0.611
(1.00) 		0.49
(1.00) 		0.203
(1.00) 		0.571
(1.00) 		0.489
(1.00)
14q loss 20 (13%) 139 0.00984
(1.00) 		0.0474
(1.00) 		0.36
(1.00) 		0.125
(1.00) 		0.722
(1.00) 		0.279
(1.00)
15q loss 3 (2%) 156 0.159
(1.00) 		0.327
(1.00)
16p loss 8 (5%) 151 0.0118
(1.00) 		0.0244
(1.00) 		0.312
(1.00) 		0.341
(1.00) 		0.479
(1.00) 		0.791
(1.00)
17p loss 61 (38%) 98 0.0351
(1.00) 		0.00258
(0.913) 		0.00496
(1.00) 		0.0178
(1.00) 		0.00802
(1.00) 		0.0234
(1.00)
17q loss 20 (13%) 139 0.0102
(1.00) 		0.919
(1.00) 		0.204
(1.00) 		0.0421
(1.00) 		0.0241
(1.00) 		0.0682
(1.00)
18p loss 16 (10%) 143 0.783
(1.00) 		0.744
(1.00) 		0.302
(1.00) 		0.328
(1.00) 		0.496
(1.00) 		0.707
(1.00)
18q loss 6 (4%) 153 0.413
(1.00) 		0.876
(1.00) 		0.157
(1.00) 		0.227
(1.00) 		0.218
(1.00) 		0.166
(1.00)
19q loss 6 (4%) 153 0.0178
(1.00) 		1
(1.00) 		0.236
(1.00) 		0.814
(1.00) 		0.819
(1.00) 		1
(1.00)
20q loss 3 (2%) 156 0.763
(1.00) 		1
(1.00)
21q loss 34 (21%) 125 0.0985
(1.00) 		0.0639
(1.00) 		0.763
(1.00) 		0.491
(1.00) 		0.631
(1.00) 		0.126
(1.00)
22q loss 56 (35%) 103 0.027
(1.00) 		0.0358
(1.00) 		0.0404
(1.00) 		0.102
(1.00) 		0.0399
(1.00) 		0.0075
(1.00)
xq loss 49 (31%) 110 0.00477
(1.00) 		0.00139
(0.496) 		0.00869
(1.00) 		0.0176
(1.00) 		0.00184
(0.657) 		0.00276
(0.974)
'1q gain' versus 'CN_CNMF'

P value = 1.33e-12 (Chi-square test), Q value = 5.2e-10

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

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6
ALL 23 24 57 46 7 2
1Q GAIN MUTATED 13 1 2 0 0 0
1Q GAIN WILD-TYPE 10 23 55 46 7 2

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

'12p gain' versus 'MIRSEQ_MATURE_CNMF'

P value = 0.000209 (Fisher's exact test), Q value = 0.078

Table S2.  Gene #19: '12p gain' versus Molecular Subtype #5: 'MIRSEQ_MATURE_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 34 55 42
12P GAIN MUTATED 7 0 1
12P GAIN WILD-TYPE 27 55 41

Figure S2.  Get High-res Image Gene #19: '12p gain' versus Molecular Subtype #5: 'MIRSEQ_MATURE_CNMF'

'13q gain' versus 'CN_CNMF'

P value = 0.000279 (Chi-square test), Q value = 0.1

Table S3.  Gene #21: '13q gain' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6
ALL 23 24 57 46 7 2
13Q GAIN MUTATED 0 0 0 9 0 0
13Q GAIN WILD-TYPE 23 24 57 37 7 2

Figure S3.  Get High-res Image Gene #21: '13q gain' versus Molecular Subtype #1: 'CN_CNMF'

'1p loss' versus 'CN_CNMF'

P value = 1.46e-09 (Chi-square test), Q value = 5.6e-07

Table S4.  Gene #36: '1p loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6
ALL 23 24 57 46 7 2
1P LOSS MUTATED 17 4 49 18 7 1
1P LOSS WILD-TYPE 6 20 8 28 0 1

Figure S4.  Get High-res Image Gene #36: '1p loss' versus Molecular Subtype #1: 'CN_CNMF'

'3p loss' versus 'CN_CNMF'

P value = 2.4e-08 (Chi-square test), Q value = 9.2e-06

Table S5.  Gene #40: '3p loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6
ALL 23 24 57 46 7 2
3P LOSS MUTATED 12 1 12 33 3 0
3P LOSS WILD-TYPE 11 23 45 13 4 2

Figure S5.  Get High-res Image Gene #40: '3p loss' versus Molecular Subtype #1: 'CN_CNMF'

'3p loss' versus 'MIRSEQ_CNMF'

P value = 6.9e-05 (Fisher's exact test), Q value = 0.026

Table S6.  Gene #40: '3p loss' versus Molecular Subtype #3: 'MIRSEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 54 53 24
3P LOSS MUTATED 32 13 4
3P LOSS WILD-TYPE 22 40 20

Figure S6.  Get High-res Image Gene #40: '3p loss' versus Molecular Subtype #3: 'MIRSEQ_CNMF'

'3p loss' versus 'MIRSEQ_CHIERARCHICAL'

P value = 0.00053 (Fisher's exact test), Q value = 0.19

Table S7.  Gene #40: '3p loss' versus Molecular Subtype #4: 'MIRSEQ_CHIERARCHICAL'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 20 41 18 52
3P LOSS MUTATED 2 12 5 30
3P LOSS WILD-TYPE 18 29 13 22

Figure S7.  Get High-res Image Gene #40: '3p loss' versus Molecular Subtype #4: 'MIRSEQ_CHIERARCHICAL'

'3q loss' versus 'CN_CNMF'

P value = 3.15e-07 (Chi-square test), Q value = 0.00012

Table S8.  Gene #41: '3q loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6
ALL 23 24 57 46 7 2
3Q LOSS MUTATED 12 0 39 32 5 1
3Q LOSS WILD-TYPE 11 24 18 14 2 1

Figure S8.  Get High-res Image Gene #41: '3q loss' versus Molecular Subtype #1: 'CN_CNMF'

'6q loss' versus 'CN_CNMF'

P value = 0.000412 (Chi-square test), Q value = 0.15

Table S9.  Gene #46: '6q loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6
ALL 23 24 57 46 7 2
6Q LOSS MUTATED 0 0 15 3 0 1
6Q LOSS WILD-TYPE 23 24 42 43 7 1

Figure S9.  Get High-res Image Gene #46: '6q loss' versus Molecular Subtype #1: 'CN_CNMF'

'6q loss' versus 'METHLYATION_CNMF'

P value = 0.000536 (Fisher's exact test), Q value = 0.2

Table S10.  Gene #46: '6q loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 40 77 42
6Q LOSS MUTATED 1 17 1
6Q LOSS WILD-TYPE 39 60 41

Figure S10.  Get High-res Image Gene #46: '6q loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'

'6q loss' versus 'MIRSEQ_CNMF'

P value = 0.00044 (Fisher's exact test), Q value = 0.16

Table S11.  Gene #46: '6q loss' versus Molecular Subtype #3: 'MIRSEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 54 53 24
6Q LOSS MUTATED 0 11 2
6Q LOSS WILD-TYPE 54 42 22

Figure S11.  Get High-res Image Gene #46: '6q loss' versus Molecular Subtype #3: 'MIRSEQ_CNMF'

'6q loss' versus 'MIRSEQ_CHIERARCHICAL'

P value = 9.39e-06 (Fisher's exact test), Q value = 0.0035

Table S12.  Gene #46: '6q loss' versus Molecular Subtype #4: 'MIRSEQ_CHIERARCHICAL'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 20 41 18 52
6Q LOSS MUTATED 1 12 0 0
6Q LOSS WILD-TYPE 19 29 18 52

Figure S12.  Get High-res Image Gene #46: '6q loss' versus Molecular Subtype #4: 'MIRSEQ_CHIERARCHICAL'

'6q loss' versus 'MIRSEQ_MATURE_CNMF'

P value = 0.000367 (Fisher's exact test), Q value = 0.14

Table S13.  Gene #46: '6q loss' versus Molecular Subtype #5: 'MIRSEQ_MATURE_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 34 55 42
6Q LOSS MUTATED 0 12 1
6Q LOSS WILD-TYPE 34 43 41

Figure S13.  Get High-res Image Gene #46: '6q loss' versus Molecular Subtype #5: 'MIRSEQ_MATURE_CNMF'

'6q loss' versus 'MIRSEQ_MATURE_CHIERARCHICAL'

P value = 4.75e-06 (Fisher's exact test), Q value = 0.0018

Table S14.  Gene #46: '6q loss' versus Molecular Subtype #6: 'MIRSEQ_MATURE_CHIERARCHICAL'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 32 43 56
6Q LOSS MUTATED 1 12 0
6Q LOSS WILD-TYPE 31 31 56

Figure S14.  Get High-res Image Gene #46: '6q loss' versus Molecular Subtype #6: 'MIRSEQ_MATURE_CHIERARCHICAL'

'11p loss' versus 'CN_CNMF'

P value = 5.84e-12 (Chi-square test), Q value = 2.3e-09

Table S15.  Gene #52: '11p loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6
ALL 23 24 57 46 7 2
11P LOSS MUTATED 8 5 1 30 6 2
11P LOSS WILD-TYPE 15 19 56 16 1 0

Figure S15.  Get High-res Image Gene #52: '11p loss' versus Molecular Subtype #1: 'CN_CNMF'

'11p loss' versus 'MIRSEQ_CHIERARCHICAL'

P value = 7.08e-06 (Fisher's exact test), Q value = 0.0027

Table S16.  Gene #52: '11p loss' versus Molecular Subtype #4: 'MIRSEQ_CHIERARCHICAL'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 20 41 18 52
11P LOSS MUTATED 6 3 5 29
11P LOSS WILD-TYPE 14 38 13 23

Figure S16.  Get High-res Image Gene #52: '11p loss' versus Molecular Subtype #4: 'MIRSEQ_CHIERARCHICAL'

'11p loss' versus 'MIRSEQ_MATURE_CNMF'

P value = 0.000203 (Fisher's exact test), Q value = 0.076

Table S17.  Gene #52: '11p loss' versus Molecular Subtype #5: 'MIRSEQ_MATURE_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 34 55 42
11P LOSS MUTATED 20 9 14
11P LOSS WILD-TYPE 14 46 28

Figure S17.  Get High-res Image Gene #52: '11p loss' versus Molecular Subtype #5: 'MIRSEQ_MATURE_CNMF'

'11p loss' versus 'MIRSEQ_MATURE_CHIERARCHICAL'

P value = 8.26e-05 (Fisher's exact test), Q value = 0.031

Table S18.  Gene #52: '11p loss' versus Molecular Subtype #6: 'MIRSEQ_MATURE_CHIERARCHICAL'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 32 43 56
11P LOSS MUTATED 9 5 29
11P LOSS WILD-TYPE 23 38 27

Figure S18.  Get High-res Image Gene #52: '11p loss' versus Molecular Subtype #6: 'MIRSEQ_MATURE_CHIERARCHICAL'

'11q loss' versus 'CN_CNMF'

P value = 4.13e-11 (Chi-square test), Q value = 1.6e-08

Table S19.  Gene #53: '11q loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6
ALL 23 24 57 46 7 2
11Q LOSS MUTATED 5 2 1 29 1 1
11Q LOSS WILD-TYPE 18 22 56 17 6 1

Figure S19.  Get High-res Image Gene #53: '11q loss' versus Molecular Subtype #1: 'CN_CNMF'

'11q loss' versus 'MIRSEQ_MATURE_CNMF'

P value = 0.000182 (Fisher's exact test), Q value = 0.068

Table S20.  Gene #53: '11q loss' versus Molecular Subtype #5: 'MIRSEQ_MATURE_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 34 55 42
11Q LOSS MUTATED 18 8 7
11Q LOSS WILD-TYPE 16 47 35

Figure S20.  Get High-res Image Gene #53: '11q loss' versus Molecular Subtype #5: 'MIRSEQ_MATURE_CNMF'

'16q loss' versus 'CN_CNMF'

P value = 0.000193 (Chi-square test), Q value = 0.072

Table S21.  Gene #58: '16q loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6
ALL 23 24 57 46 7 2
16Q LOSS MUTATED 0 0 0 3 0 1
16Q LOSS WILD-TYPE 23 24 57 43 7 1

Figure S21.  Get High-res Image Gene #58: '16q loss' versus Molecular Subtype #1: 'CN_CNMF'

Methods & Data
Input

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

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

  • Number of patients = 159

  • Number of significantly arm-level cnvs = 67

  • Number of molecular subtypes = 6

  • 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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