Correlation between copy number variations of arm-level result and molecular subtypes
Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)
22 February 2013  |  analyses__2013_02_22
Maintainer Information
Citation Information
doi:10.7908/C1T43R8Q
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/C1T43R8Q
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 117 patients, 28 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'.

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

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

  • 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'.

  • 10p 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 59 arm-level results and 8 molecular subtypes. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 28 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 Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test
3p gain 28 (24%) 89 0.302
(1.00) 		0.0559
(1.00) 		2.1e-14
(8.44e-12) 		0.0297
(1.00) 		0.000268
(0.101) 		3.64e-07
(0.000144) 		0.0375
(1.00) 		1.22e-05
(0.00477)
3q gain 32 (27%) 85 0.302
(1.00) 		0.0559
(1.00) 		3.79e-10
(1.5e-07) 		0.0245
(1.00) 		3.65e-05
(0.0141) 		6.94e-06
(0.00273) 		0.00911
(1.00) 		0.000246
(0.0928)
7p gain 64 (55%) 53 0.633
(1.00) 		0.432
(1.00) 		3.47e-10
(1.38e-07) 		0.000334
(0.125) 		5.65e-05
(0.0217) 		2.01e-05
(0.00785) 		0.011
(1.00) 		0.00896
(1.00)
17p gain 61 (52%) 56 0.633
(1.00) 		0.432
(1.00) 		1.02e-14
(4.08e-12) 		5.33e-05
(0.0205) 		3.49e-05
(0.0135) 		2.21e-06
(0.000871) 		0.129
(1.00) 		0.00197
(0.713)
7q gain 65 (56%) 52 0.633
(1.00) 		0.432
(1.00) 		7.18e-11
(2.86e-08) 		0.0011
(0.405) 		0.000181
(0.0691) 		3.74e-05
(0.0144) 		0.00419
(1.00) 		0.0113
(1.00)
16q gain 51 (44%) 66 0.302
(1.00) 		0.0559
(1.00) 		2.25e-05
(0.00875) 		0.117
(1.00) 		0.00419
(1.00) 		1.46e-05
(0.00571) 		0.00179
(0.65) 		0.000223
(0.0846)
16p gain 54 (46%) 63 0.126
(1.00) 		0.0699
(1.00) 		0.000205
(0.0778) 		0.352
(1.00) 		0.00874
(1.00) 		0.000182
(0.0695) 		0.00268
(0.96) 		0.000706
(0.264)
1q gain 8 (7%) 109 0.438
(1.00) 		0.207
(1.00) 		0.000172
(0.0658) 		0.00232
(0.836) 		0.00827
(1.00) 		0.433
(1.00) 		0.145
(1.00)
17q gain 71 (61%) 46 0.308
(1.00) 		0.808
(1.00) 		1.32e-09
(5.23e-07) 		0.0143
(1.00) 		0.0144
(1.00) 		0.000926
(0.344) 		0.362
(1.00) 		0.156
(1.00)
10p loss 6 (5%) 111 1
(1.00) 		0.000295
(0.111) 		0.199
(1.00) 		0.269
(1.00) 		0.18
(1.00) 		0.265
(1.00) 		0.675
(1.00)
14q loss 20 (17%) 97 1
(1.00) 		0.385
(1.00) 		2.83e-11
(1.13e-08) 		0.39
(1.00) 		0.135
(1.00) 		0.0977
(1.00) 		0.457
(1.00) 		0.968
(1.00)
2p gain 11 (9%) 106 0.55
(1.00) 		0.75
(1.00) 		0.00302
(1.00) 		0.149
(1.00) 		0.585
(1.00) 		0.338
(1.00) 		0.869
(1.00) 		0.0669
(1.00)
2q gain 13 (11%) 104 0.55
(1.00) 		0.75
(1.00) 		0.00178
(0.65) 		0.286
(1.00) 		0.329
(1.00) 		0.205
(1.00) 		0.856
(1.00) 		0.174
(1.00)
4p gain 4 (3%) 113 0.175
(1.00) 		0.438
(1.00) 		0.457
(1.00) 		1
(1.00) 		0.683
(1.00) 		0.881
(1.00)
4q gain 3 (3%) 114 0.438
(1.00) 		0.777
(1.00) 		0.645
(1.00) 		0.602
(1.00) 		0.767
(1.00) 		0.618
(1.00)
5p gain 11 (9%) 106 0.175
(1.00) 		0.23
(1.00) 		0.18
(1.00) 		0.156
(1.00) 		0.8
(1.00) 		0.697
(1.00) 		0.731
(1.00)
5q gain 11 (9%) 106 0.175
(1.00) 		0.23
(1.00) 		0.18
(1.00) 		0.579
(1.00) 		0.8
(1.00) 		0.919
(1.00) 		0.301
(1.00)
6p gain 4 (3%) 113 0.438
(1.00) 		0.438
(1.00) 		0.456
(1.00) 		0.114
(1.00) 		0.677
(1.00) 		0.165
(1.00) 		0.374
(1.00)
6q gain 3 (3%) 114 0.438
(1.00) 		0.113
(1.00) 		0.41
(1.00) 		1
(1.00) 		0.231
(1.00) 		0.397
(1.00)
8p gain 7 (6%) 110 1
(1.00) 		0.72
(1.00) 		0.0776
(1.00) 		0.732
(1.00) 		0.232
(1.00) 		0.617
(1.00) 		0.854
(1.00)
8q gain 9 (8%) 108 1
(1.00) 		0.371
(1.00) 		0.0234
(1.00) 		0.243
(1.00) 		0.0683
(1.00) 		0.187
(1.00) 		0.767
(1.00)
10p gain 4 (3%) 113 1
(1.00) 		0.173
(1.00) 		0.645
(1.00) 		0.602
(1.00) 		0.189
(1.00) 		0.881
(1.00)
10q gain 3 (3%) 114 1
(1.00) 		0.31
(1.00) 		0.454
(1.00) 		0.341
(1.00)
12p gain 36 (31%) 81 1
(1.00) 		0.0909
(1.00) 		0.0345
(1.00) 		0.276
(1.00) 		0.161
(1.00) 		0.0648
(1.00) 		0.146
(1.00) 		0.0968
(1.00)
12q gain 36 (31%) 81 1
(1.00) 		0.0909
(1.00) 		0.0345
(1.00) 		0.276
(1.00) 		0.161
(1.00) 		0.0648
(1.00) 		0.146
(1.00) 		0.0968
(1.00)
13q gain 14 (12%) 103 1
(1.00) 		0.0454
(1.00) 		0.455
(1.00) 		0.324
(1.00) 		0.0445
(1.00) 		0.747
(1.00) 		0.0852
(1.00)
18p gain 6 (5%) 111 0.55
(1.00) 		0.141
(1.00) 		0.0489
(1.00) 		0.327
(1.00) 		0.863
(1.00) 		1
(1.00) 		0.845
(1.00) 		0.877
(1.00)
18q gain 4 (3%) 113 0.438
(1.00) 		0.00209
(0.753) 		0.327
(1.00) 		0.77
(1.00) 		0.432
(1.00) 		0.91
(1.00) 		0.331
(1.00)
20p gain 37 (32%) 80 0.126
(1.00) 		0.162
(1.00) 		0.00294
(1.00) 		0.106
(1.00) 		0.000722
(0.269) 		0.0152
(1.00) 		0.305
(1.00) 		0.125
(1.00)
20q gain 38 (32%) 79 0.315
(1.00) 		0.119
(1.00) 		0.00595
(1.00) 		0.293
(1.00) 		0.000813
(0.303) 		0.0381
(1.00) 		0.179
(1.00) 		0.371
(1.00)
21q gain 4 (3%) 113 0.438
(1.00) 		0.317
(1.00) 		0.11
(1.00) 		0.0492
(1.00)
Xq gain 4 (3%) 113 1
(1.00) 		0.113
(1.00) 		0.456
(1.00) 		0.457
(1.00) 		1
(1.00) 		0.57
(1.00) 		0.881
(1.00)
1p loss 12 (10%) 105 1
(1.00) 		0.119
(1.00) 		1
(1.00) 		0.611
(1.00) 		0.589
(1.00) 		0.0114
(1.00) 		0.0873
(1.00)
1q loss 7 (6%) 110 1
(1.00) 		0.0998
(1.00) 		0.198
(1.00) 		0.103
(1.00) 		0.432
(1.00) 		0.0359
(1.00) 		0.0124
(1.00)
3p loss 7 (6%) 110 0.438
(1.00) 		0.00335
(1.00) 		0.00274
(0.979) 		0.058
(1.00) 		0.0149
(1.00) 		0.0591
(1.00) 		0.221
(1.00)
3q loss 3 (3%) 114 0.0248
(1.00) 		0.0401
(1.00) 		0.0288
(1.00) 		0.0762
(1.00) 		0.521
(1.00) 		0.72
(1.00)
4p loss 10 (9%) 107 1
(1.00) 		0.0826
(1.00) 		0.00141
(0.518) 		0.00399
(1.00) 		0.0866
(1.00) 		0.853
(1.00) 		0.9
(1.00)
4q loss 10 (9%) 107 1
(1.00) 		0.0112
(1.00) 		0.00141
(0.518) 		0.0999
(1.00) 		0.0683
(1.00) 		0.853
(1.00) 		0.698
(1.00)
5p loss 5 (4%) 112 0.286
(1.00) 		0.434
(1.00) 		0.114
(1.00) 		0.0626
(1.00) 		0.236
(1.00) 		0.916
(1.00)
5q loss 5 (4%) 112 0.411
(1.00) 		0.434
(1.00) 		0.41
(1.00) 		0.211
(1.00) 		0.236
(1.00) 		0.916
(1.00)
6p loss 9 (8%) 108 1
(1.00) 		0.107
(1.00) 		0.568
(1.00) 		0.468
(1.00) 		1
(1.00) 		0.331
(1.00) 		0.539
(1.00)
6q loss 11 (9%) 106 1
(1.00) 		0.0822
(1.00) 		0.205
(1.00) 		0.374
(1.00) 		0.558
(1.00) 		0.783
(1.00) 		0.693
(1.00)
8p loss 3 (3%) 114 1
(1.00) 		0.143
(1.00) 		1
(1.00) 		0.618
(1.00)
9p loss 14 (12%) 103 1
(1.00) 		0.75
(1.00) 		0.0123
(1.00) 		0.00135
(0.495) 		0.00782
(1.00) 		0.0678
(1.00) 		0.232
(1.00) 		0.587
(1.00)
9q loss 14 (12%) 103 1
(1.00) 		0.0531
(1.00) 		0.00125
(0.463) 		0.0256
(1.00) 		0.131
(1.00) 		0.37
(1.00) 		0.254
(1.00)
10q loss 7 (6%) 110 0.438
(1.00) 		0.00335
(1.00) 		0.0868
(1.00) 		0.269
(1.00) 		0.0871
(1.00) 		0.617
(1.00) 		0.221
(1.00)
11p loss 8 (7%) 109 1
(1.00) 		0.0664
(1.00) 		0.0434
(1.00) 		0.269
(1.00) 		0.145
(1.00) 		1
(1.00) 		0.447
(1.00)
11q loss 10 (9%) 107 0.0362
(1.00) 		0.00652
(1.00) 		0.296
(1.00) 		0.0563
(1.00) 		0.968
(1.00) 		0.9
(1.00)
13q loss 10 (9%) 107 1
(1.00) 		0.178
(1.00) 		0.00141
(0.518) 		0.091
(1.00) 		0.403
(1.00) 		0.355
(1.00) 		0.234
(1.00)
15q loss 10 (9%) 107 1
(1.00) 		0.0362
(1.00) 		0.32
(1.00) 		0.579
(1.00) 		0.487
(1.00) 		0.573
(1.00) 		0.816
(1.00)
16q loss 3 (3%) 114 0.143
(1.00) 		0.0401
(1.00) 		0.862
(1.00) 		0.286
(1.00)
17p loss 5 (4%) 112 0.438
(1.00) 		0.411
(1.00) 		0.0106
(1.00) 		0.0288
(1.00) 		0.0762
(1.00) 		0.219
(1.00) 		0.251
(1.00)
18p loss 17 (15%) 100 1
(1.00) 		0.905
(1.00) 		0.0675
(1.00) 		0.0772
(1.00) 		0.0114
(1.00) 		0.158
(1.00) 		0.445
(1.00)
18q loss 18 (15%) 99 1
(1.00) 		0.91
(1.00) 		0.0318
(1.00) 		0.0392
(1.00) 		0.00363
(1.00) 		0.322
(1.00) 		0.373
(1.00)
19p loss 4 (3%) 113 0.0954
(1.00) 		1
(1.00) 		0.746
(1.00) 		0.331
(1.00)
19q loss 3 (3%) 114 0.143
(1.00) 		0.603
(1.00) 		0.279
(1.00) 		0.618
(1.00)
21q loss 12 (10%) 105 0.55
(1.00) 		0.413
(1.00) 		0.5
(1.00) 		0.355
(1.00) 		0.246
(1.00) 		0.728
(1.00) 		0.219
(1.00) 		0.284
(1.00)
22q loss 21 (18%) 96 1
(1.00) 		0.00916
(1.00) 		0.0971
(1.00) 		0.673
(1.00) 		0.0477
(1.00) 		0.186
(1.00) 		0.433
(1.00)
Xq loss 3 (3%) 114 1
(1.00) 		0.31
(1.00) 		0.767
(1.00) 		0.1
(1.00)
'1q gain mutation analysis' versus 'METHLYATION_CNMF'

P value = 0.000172 (Fisher's exact test), Q value = 0.066

Table S1.  Gene #1: '1q gain mutation analysis' versus Clinical Feature #4: 'METHLYATION_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 21 27 39
1Q GAIN MUTATED 0 7 0
1Q GAIN WILD-TYPE 21 20 39

Figure S1.  Get High-res Image Gene #1: '1q gain mutation analysis' versus Clinical Feature #4: 'METHLYATION_CNMF'

'3p gain mutation analysis' versus 'CN_CNMF'

P value = 2.1e-14 (Fisher's exact test), Q value = 8.4e-12

Table S2.  Gene #4: '3p gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 24 20 28 45
3P GAIN MUTATED 21 2 0 5
3P GAIN WILD-TYPE 3 18 28 40

Figure S2.  Get High-res Image Gene #4: '3p gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

'3p gain mutation analysis' versus 'MRNASEQ_CNMF'

P value = 0.000268 (Fisher's exact test), Q value = 0.1

Table S3.  Gene #4: '3p gain mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

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

Figure S3.  Get High-res Image Gene #4: '3p gain mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

'3p gain mutation analysis' versus 'MRNASEQ_CHIERARCHICAL'

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

Table S4.  Gene #4: '3p gain mutation analysis' versus Clinical Feature #6: 'MRNASEQ_CHIERARCHICAL'

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

Figure S4.  Get High-res Image Gene #4: '3p gain mutation analysis' versus Clinical Feature #6: 'MRNASEQ_CHIERARCHICAL'

'3p gain mutation analysis' versus 'MIRSEQ_CHIERARCHICAL'

P value = 1.22e-05 (Fisher's exact test), Q value = 0.0048

Table S5.  Gene #4: '3p gain mutation analysis' versus Clinical Feature #8: 'MIRSEQ_CHIERARCHICAL'

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

Figure S5.  Get High-res Image Gene #4: '3p gain mutation analysis' versus Clinical Feature #8: 'MIRSEQ_CHIERARCHICAL'

'3q gain mutation analysis' versus 'CN_CNMF'

P value = 3.79e-10 (Fisher's exact test), Q value = 1.5e-07

Table S6.  Gene #5: '3q gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 24 20 28 45
3Q GAIN MUTATED 20 4 2 6
3Q GAIN WILD-TYPE 4 16 26 39

Figure S6.  Get High-res Image Gene #5: '3q gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

'3q gain mutation analysis' versus 'MRNASEQ_CNMF'

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

Table S7.  Gene #5: '3q gain mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

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

Figure S7.  Get High-res Image Gene #5: '3q gain mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

'3q gain mutation analysis' versus 'MRNASEQ_CHIERARCHICAL'

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

Table S8.  Gene #5: '3q gain mutation analysis' versus Clinical Feature #6: 'MRNASEQ_CHIERARCHICAL'

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

Figure S8.  Get High-res Image Gene #5: '3q gain mutation analysis' versus Clinical Feature #6: 'MRNASEQ_CHIERARCHICAL'

'3q gain mutation analysis' versus 'MIRSEQ_CHIERARCHICAL'

P value = 0.000246 (Fisher's exact test), Q value = 0.093

Table S9.  Gene #5: '3q gain mutation analysis' versus Clinical Feature #8: 'MIRSEQ_CHIERARCHICAL'

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

Figure S9.  Get High-res Image Gene #5: '3q gain mutation analysis' versus Clinical Feature #8: 'MIRSEQ_CHIERARCHICAL'

'7p gain mutation analysis' versus 'CN_CNMF'

P value = 3.47e-10 (Fisher's exact test), Q value = 1.4e-07

Table S10.  Gene #12: '7p gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 24 20 28 45
7P GAIN MUTATED 20 20 6 18
7P GAIN WILD-TYPE 4 0 22 27

Figure S10.  Get High-res Image Gene #12: '7p gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

'7p gain mutation analysis' versus 'METHLYATION_CNMF'

P value = 0.000334 (Fisher's exact test), Q value = 0.13

Table S11.  Gene #12: '7p gain mutation analysis' versus Clinical Feature #4: 'METHLYATION_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 21 27 39
7P GAIN MUTATED 11 6 28
7P GAIN WILD-TYPE 10 21 11

Figure S11.  Get High-res Image Gene #12: '7p gain mutation analysis' versus Clinical Feature #4: 'METHLYATION_CNMF'

'7p gain mutation analysis' versus 'MRNASEQ_CNMF'

P value = 5.65e-05 (Fisher's exact test), Q value = 0.022

Table S12.  Gene #12: '7p gain mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

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

Figure S12.  Get High-res Image Gene #12: '7p gain mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

'7p gain mutation analysis' versus 'MRNASEQ_CHIERARCHICAL'

P value = 2.01e-05 (Fisher's exact test), Q value = 0.0079

Table S13.  Gene #12: '7p gain mutation analysis' versus Clinical Feature #6: 'MRNASEQ_CHIERARCHICAL'

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

Figure S13.  Get High-res Image Gene #12: '7p gain mutation analysis' versus Clinical Feature #6: 'MRNASEQ_CHIERARCHICAL'

'7q gain mutation analysis' versus 'CN_CNMF'

P value = 7.18e-11 (Fisher's exact test), Q value = 2.9e-08

Table S14.  Gene #13: '7q gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 24 20 28 45
7Q GAIN MUTATED 21 20 6 18
7Q GAIN WILD-TYPE 3 0 22 27

Figure S14.  Get High-res Image Gene #13: '7q gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

'7q gain mutation analysis' versus 'MRNASEQ_CNMF'

P value = 0.000181 (Fisher's exact test), Q value = 0.069

Table S15.  Gene #13: '7q gain mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

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

Figure S15.  Get High-res Image Gene #13: '7q gain mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

'7q gain mutation analysis' versus 'MRNASEQ_CHIERARCHICAL'

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

Table S16.  Gene #13: '7q gain mutation analysis' versus Clinical Feature #6: 'MRNASEQ_CHIERARCHICAL'

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

Figure S16.  Get High-res Image Gene #13: '7q gain mutation analysis' versus Clinical Feature #6: 'MRNASEQ_CHIERARCHICAL'

'16p gain mutation analysis' versus 'CN_CNMF'

P value = 0.000205 (Fisher's exact test), Q value = 0.078

Table S17.  Gene #21: '16p gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 24 20 28 45
16P GAIN MUTATED 20 8 7 19
16P GAIN WILD-TYPE 4 12 21 26

Figure S17.  Get High-res Image Gene #21: '16p gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

'16p gain mutation analysis' versus 'MRNASEQ_CHIERARCHICAL'

P value = 0.000182 (Fisher's exact test), Q value = 0.069

Table S18.  Gene #21: '16p gain mutation analysis' versus Clinical Feature #6: 'MRNASEQ_CHIERARCHICAL'

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

Figure S18.  Get High-res Image Gene #21: '16p gain mutation analysis' versus Clinical Feature #6: 'MRNASEQ_CHIERARCHICAL'

'16q gain mutation analysis' versus 'CN_CNMF'

P value = 2.25e-05 (Fisher's exact test), Q value = 0.0088

Table S19.  Gene #22: '16q gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 24 20 28 45
16Q GAIN MUTATED 20 8 5 18
16Q GAIN WILD-TYPE 4 12 23 27

Figure S19.  Get High-res Image Gene #22: '16q gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

'16q gain mutation analysis' versus 'MRNASEQ_CHIERARCHICAL'

P value = 1.46e-05 (Fisher's exact test), Q value = 0.0057

Table S20.  Gene #22: '16q gain mutation analysis' versus Clinical Feature #6: 'MRNASEQ_CHIERARCHICAL'

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

Figure S20.  Get High-res Image Gene #22: '16q gain mutation analysis' versus Clinical Feature #6: 'MRNASEQ_CHIERARCHICAL'

'16q gain mutation analysis' versus 'MIRSEQ_CHIERARCHICAL'

P value = 0.000223 (Fisher's exact test), Q value = 0.085

Table S21.  Gene #22: '16q gain mutation analysis' versus Clinical Feature #8: 'MIRSEQ_CHIERARCHICAL'

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

Figure S21.  Get High-res Image Gene #22: '16q gain mutation analysis' versus Clinical Feature #8: 'MIRSEQ_CHIERARCHICAL'

'17p gain mutation analysis' versus 'CN_CNMF'

P value = 1.02e-14 (Fisher's exact test), Q value = 4.1e-12

Table S22.  Gene #23: '17p gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 24 20 28 45
17P GAIN MUTATED 22 20 3 16
17P GAIN WILD-TYPE 2 0 25 29

Figure S22.  Get High-res Image Gene #23: '17p gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

'17p gain mutation analysis' versus 'METHLYATION_CNMF'

P value = 5.33e-05 (Fisher's exact test), Q value = 0.021

Table S23.  Gene #23: '17p gain mutation analysis' versus Clinical Feature #4: 'METHLYATION_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 21 27 39
17P GAIN MUTATED 8 5 28
17P GAIN WILD-TYPE 13 22 11

Figure S23.  Get High-res Image Gene #23: '17p gain mutation analysis' versus Clinical Feature #4: 'METHLYATION_CNMF'

'17p gain mutation analysis' versus 'MRNASEQ_CNMF'

P value = 3.49e-05 (Fisher's exact test), Q value = 0.014

Table S24.  Gene #23: '17p gain mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

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

Figure S24.  Get High-res Image Gene #23: '17p gain mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

'17p gain mutation analysis' versus 'MRNASEQ_CHIERARCHICAL'

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

Table S25.  Gene #23: '17p gain mutation analysis' versus Clinical Feature #6: 'MRNASEQ_CHIERARCHICAL'

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

Figure S25.  Get High-res Image Gene #23: '17p gain mutation analysis' versus Clinical Feature #6: 'MRNASEQ_CHIERARCHICAL'

'17q gain mutation analysis' versus 'CN_CNMF'

P value = 1.32e-09 (Fisher's exact test), Q value = 5.2e-07

Table S26.  Gene #24: '17q gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 24 20 28 45
17Q GAIN MUTATED 22 20 8 21
17Q GAIN WILD-TYPE 2 0 20 24

Figure S26.  Get High-res Image Gene #24: '17q gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

'10p loss mutation analysis' versus 'CN_CNMF'

P value = 0.000295 (Fisher's exact test), Q value = 0.11

Table S27.  Gene #44: '10p loss mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 24 20 28 45
10P LOSS MUTATED 0 0 6 0
10P LOSS WILD-TYPE 24 20 22 45

Figure S27.  Get High-res Image Gene #44: '10p loss mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

'14q loss mutation analysis' versus 'CN_CNMF'

P value = 2.83e-11 (Fisher's exact test), Q value = 1.1e-08

Table S28.  Gene #49: '14q loss mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 24 20 28 45
14Q LOSS MUTATED 3 0 17 0
14Q LOSS WILD-TYPE 21 20 11 45

Figure S28.  Get High-res Image Gene #49: '14q loss mutation analysis' versus Clinical Feature #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 = 117

  • 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

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] 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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