Correlation between copy number variations of arm-level result and molecular subtypes
Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)
21 April 2013  |  analyses__2013_04_21
Maintainer Information
Citation Information
doi:10.7908/C1MC8X0N
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Kidney Renal Papillary Cell Carcinoma (Primary solid tumor cohort) - 21 April 2013: Correlation between copy number variations of arm-level result and molecular subtypes. Broad Institute of MIT and Harvard. doi:10.7908/C1MC8X0N
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 59 arm-level results and 8 molecular subtypes across 127 patients, 40 significant findings detected with Q value < 0.25.

  • 1q gain cnv correlated to 'METHLYATION_CNMF'.

  • 3p gain cnv correlated to 'CN_CNMF',  'MRNASEQ_CNMF',  'MRNASEQ_CHIERARCHICAL', and 'MIRSEQ_CHIERARCHICAL'.

  • 3q gain cnv correlated to 'CN_CNMF',  'MRNASEQ_CNMF',  'MRNASEQ_CHIERARCHICAL', and 'MIRSEQ_CHIERARCHICAL'.

  • 5p gain cnv correlated to 'CN_CNMF'.

  • 5q gain cnv correlated to 'CN_CNMF'.

  • 7p gain cnv correlated to 'CN_CNMF',  'METHLYATION_CNMF',  'MRNASEQ_CNMF', and 'MRNASEQ_CHIERARCHICAL'.

  • 7q gain cnv correlated to 'CN_CNMF',  'METHLYATION_CNMF',  'MRNASEQ_CNMF', and 'MRNASEQ_CHIERARCHICAL'.

  • 12p gain cnv correlated to 'CN_CNMF'.

  • 12q gain cnv correlated to 'CN_CNMF'.

  • 16p gain cnv correlated to 'CN_CNMF' and 'MRNASEQ_CHIERARCHICAL'.

  • 16q gain cnv correlated to 'CN_CNMF',  'MRNASEQ_CHIERARCHICAL', and 'MIRSEQ_CHIERARCHICAL'.

  • 17p gain cnv correlated to 'CN_CNMF',  'METHLYATION_CNMF',  'MRNASEQ_CNMF', and 'MRNASEQ_CHIERARCHICAL'.

  • 17q gain cnv correlated to 'CN_CNMF'.

  • 4p loss cnv correlated to 'CN_CNMF'.

  • 5q loss cnv correlated to 'CN_CNMF'.

  • 6p loss cnv correlated to 'CN_CNMF'.

  • 9p loss cnv correlated to 'CN_CNMF' and 'METHLYATION_CNMF'.

  • 9q loss cnv correlated to 'CN_CNMF' and 'METHLYATION_CNMF'.

  • 14q 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 59 arm-level results and 8 molecular subtypes. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 40 significant findings detected.

Molecular
subtypes 		MRNA
CNMF 		MRNA
CHIERARCHICAL 		CN
CNMF 		METHLYATION
CNMF 		MRNASEQ
CNMF 		MRNASEQ
CHIERARCHICAL 		MIRSEQ
CNMF 		MIRSEQ
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
3p gain 0 (0%) 98 0.302
(1.00) 		0.0559
(1.00) 		7.99e-15
(3.22e-12) 		0.0132
(1.00) 		0.000268
(0.0993) 		3.64e-07
(0.000144) 		0.0375
(1.00) 		1.22e-05
(0.0047)
3q gain 0 (0%) 94 0.302
(1.00) 		0.0559
(1.00) 		1.95e-12
(7.83e-10) 		0.00739
(1.00) 		3.65e-05
(0.0139) 		6.94e-06
(0.0027) 		0.00911
(1.00) 		0.000246
(0.0914)
7p gain 0 (0%) 60 0.633
(1.00) 		0.432
(1.00) 		2.69e-07
(0.000107) 		2.62e-06
(0.00103) 		5.65e-05
(0.0214) 		2.01e-05
(0.00771) 		0.011
(1.00) 		0.00896
(1.00)
7q gain 0 (0%) 59 0.633
(1.00) 		0.432
(1.00) 		5.79e-08
(2.31e-05) 		9.37e-06
(0.00363) 		0.000181
(0.068) 		3.74e-05
(0.0142) 		0.00419
(1.00) 		0.0113
(1.00)
17p gain 0 (0%) 61 0.633
(1.00) 		0.432
(1.00) 		9.52e-08
(3.78e-05) 		1.3e-06
(0.000513) 		3.49e-05
(0.0133) 		2.21e-06
(0.000869) 		0.129
(1.00) 		0.00197
(0.692)
16q gain 0 (0%) 69 0.302
(1.00) 		0.0559
(1.00) 		9.17e-06
(0.00356) 		0.0521
(1.00) 		0.00419
(1.00) 		1.46e-05
(0.00563) 		0.00179
(0.629) 		0.000223
(0.0835)
16p gain 0 (0%) 67 0.126
(1.00) 		0.0699
(1.00) 		4.19e-06
(0.00164) 		0.221
(1.00) 		0.00874
(1.00) 		0.000182
(0.0684) 		0.00268
(0.934) 		0.000706
(0.256)
9p loss 0 (0%) 112 1
(1.00) 		0.75
(1.00) 		0.000169
(0.0639) 		0.000318
(0.117) 		0.00782
(1.00) 		0.0678
(1.00) 		0.232
(1.00) 		0.587
(1.00)
9q loss 0 (0%) 110 1
(1.00) 		0.000445
(0.163) 		3.17e-05
(0.0121) 		0.0256
(1.00) 		0.131
(1.00) 		0.37
(1.00) 		0.254
(1.00)
1q gain 0 (0%) 118 0.438
(1.00) 		0.00553
(1.00) 		0.000353
(0.13) 		0.00232
(0.813) 		0.00827
(1.00) 		0.433
(1.00) 		0.145
(1.00)
5p gain 0 (0%) 113 0.175
(1.00) 		1.96e-08
(7.8e-06) 		0.0521
(1.00) 		0.156
(1.00) 		0.8
(1.00) 		0.697
(1.00) 		0.731
(1.00)
5q gain 0 (0%) 113 0.175
(1.00) 		2.18e-10
(8.74e-08) 		0.0521
(1.00) 		0.579
(1.00) 		0.8
(1.00) 		0.919
(1.00) 		0.301
(1.00)
12p gain 0 (0%) 87 1
(1.00) 		0.0909
(1.00) 		0.00055
(0.201) 		0.833
(1.00) 		0.161
(1.00) 		0.0648
(1.00) 		0.146
(1.00) 		0.0968
(1.00)
12q gain 0 (0%) 87 1
(1.00) 		0.0909
(1.00) 		0.00055
(0.201) 		0.833
(1.00) 		0.161
(1.00) 		0.0648
(1.00) 		0.146
(1.00) 		0.0968
(1.00)
17q gain 0 (0%) 50 0.308
(1.00) 		0.808
(1.00) 		5.4e-06
(0.00211) 		0.00104
(0.372) 		0.0144
(1.00) 		0.000926
(0.333) 		0.362
(1.00) 		0.156
(1.00)
4p loss 0 (0%) 116 1
(1.00) 		0.000247
(0.0917) 		0.00449
(1.00) 		0.0129
(1.00) 		0.18
(1.00) 		0.535
(1.00) 		1
(1.00)
5q loss 0 (0%) 121 1.96e-05
(0.00754) 		0.23
(1.00) 		0.41
(1.00) 		0.211
(1.00) 		0.236
(1.00) 		0.916
(1.00)
6p loss 0 (0%) 118 1
(1.00) 		0.00043
(0.158) 		0.443
(1.00) 		0.468
(1.00) 		1
(1.00) 		0.331
(1.00) 		0.539
(1.00)
14q loss 0 (0%) 104 1
(1.00) 		0.385
(1.00) 		1.54e-11
(6.18e-09) 		0.267
(1.00) 		0.135
(1.00) 		0.0977
(1.00) 		0.457
(1.00) 		0.968
(1.00)
22q loss 0 (0%) 103 1
(1.00) 		0.000233
(0.087) 		0.00271
(0.944) 		0.673
(1.00) 		0.0477
(1.00) 		0.186
(1.00) 		0.433
(1.00)
2p gain 0 (0%) 114 0.55
(1.00) 		0.75
(1.00) 		0.0607
(1.00) 		0.132
(1.00) 		0.585
(1.00) 		0.338
(1.00) 		0.869
(1.00) 		0.0669
(1.00)
2q gain 0 (0%) 112 0.55
(1.00) 		0.75
(1.00) 		0.0309
(1.00) 		0.354
(1.00) 		0.329
(1.00) 		0.205
(1.00) 		0.856
(1.00) 		0.174
(1.00)
4p gain 0 (0%) 123 0.175
(1.00) 		0.382
(1.00) 		0.457
(1.00) 		1
(1.00) 		0.683
(1.00) 		0.881
(1.00)
4q gain 0 (0%) 124 0.438
(1.00) 		0.572
(1.00) 		0.645
(1.00) 		0.602
(1.00) 		0.767
(1.00) 		0.618
(1.00)
6p gain 0 (0%) 123 0.438
(1.00) 		0.503
(1.00) 		0.44
(1.00) 		0.114
(1.00) 		0.677
(1.00) 		0.165
(1.00) 		0.374
(1.00)
6q gain 0 (0%) 124 0.438
(1.00) 		0.213
(1.00) 		0.41
(1.00) 		1
(1.00) 		0.231
(1.00) 		0.397
(1.00)
8p gain 0 (0%) 118 1
(1.00) 		0.318
(1.00) 		0.087
(1.00) 		0.732
(1.00) 		0.232
(1.00) 		0.617
(1.00) 		0.854
(1.00)
8q gain 0 (0%) 116 1
(1.00) 		0.00446
(1.00) 		0.0131
(1.00) 		0.243
(1.00) 		0.0683
(1.00) 		0.187
(1.00) 		0.767
(1.00)
10p gain 0 (0%) 123 1
(1.00) 		0.184
(1.00) 		0.645
(1.00) 		0.602
(1.00) 		0.189
(1.00) 		0.881
(1.00)
10q gain 0 (0%) 124 1
(1.00) 		0.393
(1.00) 		0.454
(1.00) 		0.341
(1.00)
13q gain 0 (0%) 113 1
(1.00) 		0.00662
(1.00) 		0.179
(1.00) 		0.324
(1.00) 		0.0445
(1.00) 		0.747
(1.00) 		0.0852
(1.00)
18p gain 0 (0%) 121 0.55
(1.00) 		0.141
(1.00) 		0.16
(1.00) 		0.329
(1.00) 		0.863
(1.00) 		1
(1.00) 		0.845
(1.00) 		0.877
(1.00)
18q gain 0 (0%) 123 0.438
(1.00) 		0.0227
(1.00) 		0.329
(1.00) 		0.77
(1.00) 		0.432
(1.00) 		0.91
(1.00) 		0.331
(1.00)
20p gain 0 (0%) 88 0.126
(1.00) 		0.162
(1.00) 		0.0185
(1.00) 		0.0743
(1.00) 		0.000722
(0.261) 		0.0152
(1.00) 		0.305
(1.00) 		0.125
(1.00)
20q gain 0 (0%) 87 0.315
(1.00) 		0.119
(1.00) 		0.0107
(1.00) 		0.142
(1.00) 		0.000813
(0.294) 		0.0381
(1.00) 		0.179
(1.00) 		0.371
(1.00)
21q gain 0 (0%) 122 0.438
(1.00) 		0.637
(1.00) 		0.468
(1.00) 		0.11
(1.00) 		0.0492
(1.00)
Xq gain 0 (0%) 122 1
(1.00) 		0.121
(1.00) 		0.137
(1.00) 		0.457
(1.00) 		1
(1.00) 		0.57
(1.00) 		0.881
(1.00)
1p loss 0 (0%) 113 1
(1.00) 		0.0256
(1.00) 		0.328
(1.00) 		0.611
(1.00) 		0.589
(1.00) 		0.0114
(1.00) 		0.0873
(1.00)
1q loss 0 (0%) 120 1
(1.00) 		0.0171
(1.00) 		0.193
(1.00) 		0.103
(1.00) 		0.432
(1.00) 		0.0359
(1.00) 		0.0124
(1.00)
3p loss 0 (0%) 119 0.438
(1.00) 		0.00138
(0.493) 		0.00142
(0.504) 		0.058
(1.00) 		0.0149
(1.00) 		0.0591
(1.00) 		0.221
(1.00)
3q loss 0 (0%) 124 0.248
(1.00) 		0.0335
(1.00) 		0.0288
(1.00) 		0.0762
(1.00) 		0.521
(1.00) 		0.72
(1.00)
4q loss 0 (0%) 115 1
(1.00) 		0.00104
(0.372) 		0.00153
(0.543) 		0.0999
(1.00) 		0.0683
(1.00) 		0.853
(1.00) 		0.698
(1.00)
5p loss 0 (0%) 122 0.00932
(1.00) 		0.246
(1.00) 		0.114
(1.00) 		0.0626
(1.00) 		0.236
(1.00) 		0.916
(1.00)
6q loss 0 (0%) 115 1
(1.00) 		0.00107
(0.382) 		0.066
(1.00) 		0.374
(1.00) 		0.558
(1.00) 		0.783
(1.00) 		0.693
(1.00)
8p loss 0 (0%) 124 1
(1.00) 		0.846
(1.00) 		1
(1.00) 		0.618
(1.00)
10p loss 0 (0%) 120 1
(1.00) 		0.0171
(1.00) 		0.056
(1.00) 		0.269
(1.00) 		0.18
(1.00) 		0.265
(1.00) 		0.675
(1.00)
10q loss 0 (0%) 119 0.438
(1.00) 		0.00696
(1.00) 		0.0362
(1.00) 		0.269
(1.00) 		0.0871
(1.00) 		0.617
(1.00) 		0.221
(1.00)
11p loss 0 (0%) 118 1
(1.00) 		0.167
(1.00) 		0.113
(1.00) 		0.269
(1.00) 		0.145
(1.00) 		1
(1.00) 		0.447
(1.00)
11q loss 0 (0%) 116 0.00512
(1.00) 		0.00354
(1.00) 		0.296
(1.00) 		0.0563
(1.00) 		0.968
(1.00) 		0.9
(1.00)
13q loss 0 (0%) 115 1
(1.00) 		0.019
(1.00) 		0.00153
(0.543) 		0.091
(1.00) 		0.403
(1.00) 		0.355
(1.00) 		0.234
(1.00)
15q loss 0 (0%) 116 1
(1.00) 		0.00407
(1.00) 		0.119
(1.00) 		0.579
(1.00) 		0.487
(1.00) 		0.573
(1.00) 		0.816
(1.00)
16q loss 0 (0%) 124 0.632
(1.00) 		0.139
(1.00) 		0.862
(1.00) 		0.286
(1.00)
17p loss 0 (0%) 122 0.438
(1.00) 		0.756
(1.00) 		0.0436
(1.00) 		0.0288
(1.00) 		0.0762
(1.00) 		0.219
(1.00) 		0.251
(1.00)
18p loss 0 (0%) 108 1
(1.00) 		0.0863
(1.00) 		0.0306
(1.00) 		0.0772
(1.00) 		0.0114
(1.00) 		0.158
(1.00) 		0.445
(1.00)
18q loss 0 (0%) 107 1
(1.00) 		0.209
(1.00) 		0.0155
(1.00) 		0.0392
(1.00) 		0.00363
(1.00) 		0.322
(1.00) 		0.373
(1.00)
19p loss 0 (0%) 123 0.0513
(1.00) 		1
(1.00) 		0.746
(1.00) 		0.331
(1.00)
19q loss 0 (0%) 123 0.0513
(1.00) 		1
(1.00) 		0.279
(1.00) 		0.618
(1.00)
21q loss 0 (0%) 112 0.55
(1.00) 		0.413
(1.00) 		0.504
(1.00) 		0.231
(1.00) 		0.246
(1.00) 		0.728
(1.00) 		0.219
(1.00) 		0.284
(1.00)
Xq loss 0 (0%) 124 1
(1.00) 		0.632
(1.00) 		0.767
(1.00) 		0.1
(1.00)
'1q gain' versus 'METHLYATION_CNMF'

P value = 0.000353 (Fisher's exact test), Q value = 0.13

Table S1.  Gene #1: '1q gain' versus Molecular Subtype #4: 'METHLYATION_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 27 53 31
1Q GAIN CNV 1 0 7
1Q GAIN WILD-TYPE 26 53 24

Figure S1.  Get High-res Image Gene #1: '1q gain' versus Molecular Subtype #4: 'METHLYATION_CNMF'

'3p gain' versus 'CN_CNMF'

P value = 7.99e-15 (Chi-square test), Q value = 3.2e-12

Table S2.  Gene #4: '3p gain' versus Molecular Subtype #3: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5
ALL 24 44 13 26 20
3P GAIN CNV 1 3 1 5 19
3P GAIN WILD-TYPE 23 41 12 21 1

Figure S2.  Get High-res Image Gene #4: '3p gain' versus Molecular Subtype #3: 'CN_CNMF'

'3p gain' versus 'MRNASEQ_CNMF'

P value = 0.000268 (Fisher's exact test), Q value = 0.099

Table S3.  Gene #4: '3p gain' versus Molecular Subtype #5: 'MRNASEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 27 24 25
3P GAIN CNV 12 0 7
3P GAIN WILD-TYPE 15 24 18

Figure S3.  Get High-res Image Gene #4: '3p gain' versus Molecular Subtype #5: 'MRNASEQ_CNMF'

'3p gain' versus 'MRNASEQ_CHIERARCHICAL'

P value = 3.64e-07 (Fisher's exact test), Q value = 0.00014

Table S4.  Gene #4: '3p gain' versus Molecular Subtype #6: 'MRNASEQ_CHIERARCHICAL'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 18 37 21
3P GAIN CNV 0 19 0
3P GAIN WILD-TYPE 18 18 21

Figure S4.  Get High-res Image Gene #4: '3p gain' versus Molecular Subtype #6: 'MRNASEQ_CHIERARCHICAL'

'3p gain' versus 'MIRSEQ_CHIERARCHICAL'

P value = 1.22e-05 (Fisher's exact test), Q value = 0.0047

Table S5.  Gene #4: '3p gain' versus Molecular Subtype #8: 'MIRSEQ_CHIERARCHICAL'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 27 45 12 33
3P GAIN CNV 1 6 8 13
3P GAIN WILD-TYPE 26 39 4 20

Figure S5.  Get High-res Image Gene #4: '3p gain' versus Molecular Subtype #8: 'MIRSEQ_CHIERARCHICAL'

'3q gain' versus 'CN_CNMF'

P value = 1.95e-12 (Chi-square test), Q value = 7.8e-10

Table S6.  Gene #5: '3q gain' versus Molecular Subtype #3: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5
ALL 24 44 13 26 20
3Q GAIN CNV 2 4 2 6 19
3Q GAIN WILD-TYPE 22 40 11 20 1

Figure S6.  Get High-res Image Gene #5: '3q gain' versus Molecular Subtype #3: 'CN_CNMF'

'3q gain' versus 'MRNASEQ_CNMF'

P value = 3.65e-05 (Fisher's exact test), Q value = 0.014

Table S7.  Gene #5: '3q gain' versus Molecular Subtype #5: 'MRNASEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 27 24 25
3Q GAIN CNV 14 0 8
3Q GAIN WILD-TYPE 13 24 17

Figure S7.  Get High-res Image Gene #5: '3q gain' versus Molecular Subtype #5: 'MRNASEQ_CNMF'

'3q gain' versus 'MRNASEQ_CHIERARCHICAL'

P value = 6.94e-06 (Fisher's exact test), Q value = 0.0027

Table S8.  Gene #5: '3q gain' versus Molecular Subtype #6: 'MRNASEQ_CHIERARCHICAL'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 18 37 21
3Q GAIN CNV 1 20 1
3Q GAIN WILD-TYPE 17 17 20

Figure S8.  Get High-res Image Gene #5: '3q gain' versus Molecular Subtype #6: 'MRNASEQ_CHIERARCHICAL'

'3q gain' versus 'MIRSEQ_CHIERARCHICAL'

P value = 0.000246 (Fisher's exact test), Q value = 0.091

Table S9.  Gene #5: '3q gain' versus Molecular Subtype #8: 'MIRSEQ_CHIERARCHICAL'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 27 45 12 33
3Q GAIN CNV 4 6 8 14
3Q GAIN WILD-TYPE 23 39 4 19

Figure S9.  Get High-res Image Gene #5: '3q gain' versus Molecular Subtype #8: 'MIRSEQ_CHIERARCHICAL'

'5p gain' versus 'CN_CNMF'

P value = 1.96e-08 (Chi-square test), Q value = 7.8e-06

Table S10.  Gene #8: '5p gain' versus Molecular Subtype #3: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5
ALL 24 44 13 26 20
5P GAIN CNV 1 0 0 12 1
5P GAIN WILD-TYPE 23 44 13 14 19

Figure S10.  Get High-res Image Gene #8: '5p gain' versus Molecular Subtype #3: 'CN_CNMF'

'5q gain' versus 'CN_CNMF'

P value = 2.18e-10 (Chi-square test), Q value = 8.7e-08

Table S11.  Gene #9: '5q gain' versus Molecular Subtype #3: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5
ALL 24 44 13 26 20
5Q GAIN CNV 0 0 0 13 1
5Q GAIN WILD-TYPE 24 44 13 13 19

Figure S11.  Get High-res Image Gene #9: '5q gain' versus Molecular Subtype #3: 'CN_CNMF'

'7p gain' versus 'CN_CNMF'

P value = 2.69e-07 (Chi-square test), Q value = 0.00011

Table S12.  Gene #12: '7p gain' versus Molecular Subtype #3: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5
ALL 24 44 13 26 20
7P GAIN CNV 5 22 13 9 18
7P GAIN WILD-TYPE 19 22 0 17 2

Figure S12.  Get High-res Image Gene #12: '7p gain' versus Molecular Subtype #3: 'CN_CNMF'

'7p gain' versus 'METHLYATION_CNMF'

P value = 2.62e-06 (Fisher's exact test), Q value = 0.001

Table S13.  Gene #12: '7p gain' versus Molecular Subtype #4: 'METHLYATION_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 27 53 31
7P GAIN CNV 10 40 7
7P GAIN WILD-TYPE 17 13 24

Figure S13.  Get High-res Image Gene #12: '7p gain' versus Molecular Subtype #4: 'METHLYATION_CNMF'

'7p gain' versus 'MRNASEQ_CNMF'

P value = 5.65e-05 (Fisher's exact test), Q value = 0.021

Table S14.  Gene #12: '7p gain' versus Molecular Subtype #5: 'MRNASEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 27 24 25
7P GAIN CNV 22 5 12
7P GAIN WILD-TYPE 5 19 13

Figure S14.  Get High-res Image Gene #12: '7p gain' versus Molecular Subtype #5: 'MRNASEQ_CNMF'

'7p gain' versus 'MRNASEQ_CHIERARCHICAL'

P value = 2.01e-05 (Fisher's exact test), Q value = 0.0077

Table S15.  Gene #12: '7p gain' versus Molecular Subtype #6: 'MRNASEQ_CHIERARCHICAL'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 18 37 21
7P GAIN CNV 5 29 5
7P GAIN WILD-TYPE 13 8 16

Figure S15.  Get High-res Image Gene #12: '7p gain' versus Molecular Subtype #6: 'MRNASEQ_CHIERARCHICAL'

'7q gain' versus 'CN_CNMF'

P value = 5.79e-08 (Chi-square test), Q value = 2.3e-05

Table S16.  Gene #13: '7q gain' versus Molecular Subtype #3: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5
ALL 24 44 13 26 20
7Q GAIN CNV 5 22 13 9 19
7Q GAIN WILD-TYPE 19 22 0 17 1

Figure S16.  Get High-res Image Gene #13: '7q gain' versus Molecular Subtype #3: 'CN_CNMF'

'7q gain' versus 'METHLYATION_CNMF'

P value = 9.37e-06 (Fisher's exact test), Q value = 0.0036

Table S17.  Gene #13: '7q gain' versus Molecular Subtype #4: 'METHLYATION_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 27 53 31
7Q GAIN CNV 10 40 8
7Q GAIN WILD-TYPE 17 13 23

Figure S17.  Get High-res Image Gene #13: '7q gain' versus Molecular Subtype #4: 'METHLYATION_CNMF'

'7q gain' versus 'MRNASEQ_CNMF'

P value = 0.000181 (Fisher's exact test), Q value = 0.068

Table S18.  Gene #13: '7q gain' versus Molecular Subtype #5: 'MRNASEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 27 24 25
7Q GAIN CNV 22 6 12
7Q GAIN WILD-TYPE 5 18 13

Figure S18.  Get High-res Image Gene #13: '7q gain' versus Molecular Subtype #5: 'MRNASEQ_CNMF'

'7q gain' versus 'MRNASEQ_CHIERARCHICAL'

P value = 3.74e-05 (Fisher's exact test), Q value = 0.014

Table S19.  Gene #13: '7q gain' versus Molecular Subtype #6: 'MRNASEQ_CHIERARCHICAL'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 18 37 21
7Q GAIN CNV 6 29 5
7Q GAIN WILD-TYPE 12 8 16

Figure S19.  Get High-res Image Gene #13: '7q gain' versus Molecular Subtype #6: 'MRNASEQ_CHIERARCHICAL'

'12p gain' versus 'CN_CNMF'

P value = 0.00055 (Chi-square test), Q value = 0.2

Table S20.  Gene #18: '12p gain' versus Molecular Subtype #3: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5
ALL 24 44 13 26 20
12P GAIN CNV 7 6 10 9 8
12P GAIN WILD-TYPE 17 38 3 17 12

Figure S20.  Get High-res Image Gene #18: '12p gain' versus Molecular Subtype #3: 'CN_CNMF'

'12q gain' versus 'CN_CNMF'

P value = 0.00055 (Chi-square test), Q value = 0.2

Table S21.  Gene #19: '12q gain' versus Molecular Subtype #3: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5
ALL 24 44 13 26 20
12Q GAIN CNV 7 6 10 9 8
12Q GAIN WILD-TYPE 17 38 3 17 12

Figure S21.  Get High-res Image Gene #19: '12q gain' versus Molecular Subtype #3: 'CN_CNMF'

'16p gain' versus 'CN_CNMF'

P value = 4.19e-06 (Chi-square test), Q value = 0.0016

Table S22.  Gene #21: '16p gain' versus Molecular Subtype #3: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5
ALL 24 44 13 26 20
16P GAIN CNV 6 16 3 16 19
16P GAIN WILD-TYPE 18 28 10 10 1

Figure S22.  Get High-res Image Gene #21: '16p gain' versus Molecular Subtype #3: 'CN_CNMF'

'16p gain' versus 'MRNASEQ_CHIERARCHICAL'

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

Table S23.  Gene #21: '16p gain' versus Molecular Subtype #6: 'MRNASEQ_CHIERARCHICAL'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 18 37 21
16P GAIN CNV 2 24 6
16P GAIN WILD-TYPE 16 13 15

Figure S23.  Get High-res Image Gene #21: '16p gain' versus Molecular Subtype #6: 'MRNASEQ_CHIERARCHICAL'

'16q gain' versus 'CN_CNMF'

P value = 9.17e-06 (Chi-square test), Q value = 0.0036

Table S24.  Gene #22: '16q gain' versus Molecular Subtype #3: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5
ALL 24 44 13 26 20
16Q GAIN CNV 6 16 3 14 19
16Q GAIN WILD-TYPE 18 28 10 12 1

Figure S24.  Get High-res Image Gene #22: '16q gain' versus Molecular Subtype #3: 'CN_CNMF'

'16q gain' versus 'MRNASEQ_CHIERARCHICAL'

P value = 1.46e-05 (Fisher's exact test), Q value = 0.0056

Table S25.  Gene #22: '16q gain' versus Molecular Subtype #6: 'MRNASEQ_CHIERARCHICAL'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 18 37 21
16Q GAIN CNV 2 24 3
16Q GAIN WILD-TYPE 16 13 18

Figure S25.  Get High-res Image Gene #22: '16q gain' versus Molecular Subtype #6: 'MRNASEQ_CHIERARCHICAL'

'16q gain' versus 'MIRSEQ_CHIERARCHICAL'

P value = 0.000223 (Fisher's exact test), Q value = 0.083

Table S26.  Gene #22: '16q gain' versus Molecular Subtype #8: 'MIRSEQ_CHIERARCHICAL'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 27 45 12 33
16Q GAIN CNV 8 12 8 23
16Q GAIN WILD-TYPE 19 33 4 10

Figure S26.  Get High-res Image Gene #22: '16q gain' versus Molecular Subtype #8: 'MIRSEQ_CHIERARCHICAL'

'17p gain' versus 'CN_CNMF'

P value = 9.52e-08 (Chi-square test), Q value = 3.8e-05

Table S27.  Gene #23: '17p gain' versus Molecular Subtype #3: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5
ALL 24 44 13 26 20
17P GAIN CNV 6 19 13 9 19
17P GAIN WILD-TYPE 18 25 0 17 1

Figure S27.  Get High-res Image Gene #23: '17p gain' versus Molecular Subtype #3: 'CN_CNMF'

'17p gain' versus 'METHLYATION_CNMF'

P value = 1.3e-06 (Fisher's exact test), Q value = 0.00051

Table S28.  Gene #23: '17p gain' versus Molecular Subtype #4: 'METHLYATION_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 27 53 31
17P GAIN CNV 9 40 7
17P GAIN WILD-TYPE 18 13 24

Figure S28.  Get High-res Image Gene #23: '17p gain' versus Molecular Subtype #4: 'METHLYATION_CNMF'

'17p gain' versus 'MRNASEQ_CNMF'

P value = 3.49e-05 (Fisher's exact test), Q value = 0.013

Table S29.  Gene #23: '17p gain' versus Molecular Subtype #5: 'MRNASEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 27 24 25
17P GAIN CNV 20 3 10
17P GAIN WILD-TYPE 7 21 15

Figure S29.  Get High-res Image Gene #23: '17p gain' versus Molecular Subtype #5: 'MRNASEQ_CNMF'

'17p gain' versus 'MRNASEQ_CHIERARCHICAL'

P value = 2.21e-06 (Fisher's exact test), Q value = 0.00087

Table S30.  Gene #23: '17p gain' versus Molecular Subtype #6: 'MRNASEQ_CHIERARCHICAL'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 18 37 21
17P GAIN CNV 3 27 3
17P GAIN WILD-TYPE 15 10 18

Figure S30.  Get High-res Image Gene #23: '17p gain' versus Molecular Subtype #6: 'MRNASEQ_CHIERARCHICAL'

'17q gain' versus 'CN_CNMF'

P value = 5.4e-06 (Chi-square test), Q value = 0.0021

Table S31.  Gene #24: '17q gain' versus Molecular Subtype #3: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5
ALL 24 44 13 26 20
17Q GAIN CNV 7 25 13 13 19
17Q GAIN WILD-TYPE 17 19 0 13 1

Figure S31.  Get High-res Image Gene #24: '17q gain' versus Molecular Subtype #3: 'CN_CNMF'

'4p loss' versus 'CN_CNMF'

P value = 0.000247 (Chi-square test), Q value = 0.092

Table S32.  Gene #35: '4p loss' versus Molecular Subtype #3: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5
ALL 24 44 13 26 20
4P LOSS CNV 7 0 0 4 0
4P LOSS WILD-TYPE 17 44 13 22 20

Figure S32.  Get High-res Image Gene #35: '4p loss' versus Molecular Subtype #3: 'CN_CNMF'

'5q loss' versus 'CN_CNMF'

P value = 1.96e-05 (Chi-square test), Q value = 0.0075

Table S33.  Gene #38: '5q loss' versus Molecular Subtype #3: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5
ALL 24 44 13 26 20
5Q LOSS CNV 6 0 0 0 0
5Q LOSS WILD-TYPE 18 44 13 26 20

Figure S33.  Get High-res Image Gene #38: '5q loss' versus Molecular Subtype #3: 'CN_CNMF'

'6p loss' versus 'CN_CNMF'

P value = 0.00043 (Chi-square test), Q value = 0.16

Table S34.  Gene #39: '6p loss' versus Molecular Subtype #3: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5
ALL 24 44 13 26 20
6P LOSS CNV 1 0 0 7 1
6P LOSS WILD-TYPE 23 44 13 19 19

Figure S34.  Get High-res Image Gene #39: '6p loss' versus Molecular Subtype #3: 'CN_CNMF'

'9p loss' versus 'CN_CNMF'

P value = 0.000169 (Chi-square test), Q value = 0.064

Table S35.  Gene #42: '9p loss' versus Molecular Subtype #3: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5
ALL 24 44 13 26 20
9P LOSS CNV 8 0 0 6 1
9P LOSS WILD-TYPE 16 44 13 20 19

Figure S35.  Get High-res Image Gene #42: '9p loss' versus Molecular Subtype #3: 'CN_CNMF'

'9p loss' versus 'METHLYATION_CNMF'

P value = 0.000318 (Fisher's exact test), Q value = 0.12

Table S36.  Gene #42: '9p loss' versus Molecular Subtype #4: 'METHLYATION_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 27 53 31
9P LOSS CNV 5 0 7
9P LOSS WILD-TYPE 22 53 24

Figure S36.  Get High-res Image Gene #42: '9p loss' versus Molecular Subtype #4: 'METHLYATION_CNMF'

'9q loss' versus 'CN_CNMF'

P value = 0.000445 (Chi-square test), Q value = 0.16

Table S37.  Gene #43: '9q loss' versus Molecular Subtype #3: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5
ALL 24 44 13 26 20
9Q LOSS CNV 8 1 0 7 1
9Q LOSS WILD-TYPE 16 43 13 19 19

Figure S37.  Get High-res Image Gene #43: '9q loss' versus Molecular Subtype #3: 'CN_CNMF'

'9q loss' versus 'METHLYATION_CNMF'

P value = 3.17e-05 (Fisher's exact test), Q value = 0.012

Table S38.  Gene #43: '9q loss' versus Molecular Subtype #4: 'METHLYATION_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 27 53 31
9Q LOSS CNV 6 0 9
9Q LOSS WILD-TYPE 21 53 22

Figure S38.  Get High-res Image Gene #43: '9q loss' versus Molecular Subtype #4: 'METHLYATION_CNMF'

'14q loss' versus 'CN_CNMF'

P value = 1.54e-11 (Chi-square test), Q value = 6.2e-09

Table S39.  Gene #49: '14q loss' versus Molecular Subtype #3: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5
ALL 24 44 13 26 20
14Q LOSS CNV 16 0 0 7 0
14Q LOSS WILD-TYPE 8 44 13 19 20

Figure S39.  Get High-res Image Gene #49: '14q loss' versus Molecular Subtype #3: 'CN_CNMF'

'22q loss' versus 'CN_CNMF'

P value = 0.000233 (Chi-square test), Q value = 0.087

Table S40.  Gene #58: '22q loss' versus Molecular Subtype #3: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5
ALL 24 44 13 26 20
22Q LOSS CNV 6 6 0 12 0
22Q LOSS WILD-TYPE 18 38 13 14 20

Figure S40.  Get High-res Image Gene #58: '22q loss' versus Molecular Subtype #3: 'CN_CNMF'

Methods & Data
Input

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

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

  • Number of patients = 127

  • Number of significantly arm-level cnvs = 59

  • 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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