Cervical Squamous Cell Carcinoma: Correlation between copy number variations of arm-level result and molecular subtypes
(primary solid tumor cohort)
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
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 73 arm-level results and 6 molecular subtypes across 126 patients, 6 significant findings detected with Q value < 0.25.

  • 3q gain cnv correlated to 'CN_CNMF'.

  • 20q gain cnv correlated to 'CN_CNMF'.

  • 3p loss cnv correlated to 'MRNASEQ_CHIERARCHICAL'.

  • 4q loss cnv correlated to 'CN_CNMF'.

  • 16q loss cnv correlated to 'MRNASEQ_CHIERARCHICAL'.

  • 18q loss cnv correlated to 'MRNASEQ_CNMF'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between significant copy number variation of 73 arm-level results and 6 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
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
3q gain 62 (49%) 64 1.3e-05
(0.0057) 		0.362
(1.00) 		0.0191
(1.00) 		0.0478
(1.00) 		0.73
(1.00) 		0.03
(1.00)
20q gain 38 (30%) 88 0.000534
(0.231) 		0.362
(1.00) 		0.512
(1.00) 		0.167
(1.00) 		0.548
(1.00) 		0.432
(1.00)
3p loss 27 (21%) 99 0.32
(1.00) 		0.0016
(0.685) 		0.198
(1.00) 		0.000436
(0.19) 		0.0127
(1.00) 		0.00406
(1.00)
4q loss 24 (19%) 102 6.4e-06
(0.0028) 		0.773
(1.00) 		0.0976
(1.00) 		0.297
(1.00) 		0.474
(1.00) 		0.501
(1.00)
16q loss 14 (11%) 112 0.821
(1.00) 		0.0237
(1.00) 		0.00107
(0.46) 		0.000523
(0.227) 		0.614
(1.00) 		0.00792
(1.00)
18q loss 24 (19%) 102 0.112
(1.00) 		0.0214
(1.00) 		0.000428
(0.187) 		0.00334
(1.00) 		0.103
(1.00) 		0.00237
(1.00)
1p gain 34 (27%) 92 0.415
(1.00) 		0.764
(1.00) 		0.274
(1.00) 		0.904
(1.00) 		0.668
(1.00) 		0.655
(1.00)
1q gain 48 (38%) 78 0.0208
(1.00) 		0.479
(1.00) 		0.345
(1.00) 		0.568
(1.00) 		0.107
(1.00) 		0.435
(1.00)
2p gain 17 (13%) 109 0.0833
(1.00) 		0.303
(1.00) 		0.1
(1.00) 		0.0951
(1.00) 		0.0943
(1.00) 		0.0418
(1.00)
2q gain 5 (4%) 121 0.0843
(1.00) 		0.611
(1.00) 		0.0694
(1.00) 		0.153
(1.00) 		1
(1.00) 		1
(1.00)
3p gain 21 (17%) 105 0.568
(1.00) 		0.685
(1.00) 		0.717
(1.00) 		0.0367
(1.00) 		0.748
(1.00) 		0.667
(1.00)
4q gain 3 (2%) 123 0.115
(1.00) 		0.11
(1.00) 		0.453
(1.00) 		0.485
(1.00) 		0.203
(1.00) 		0.343
(1.00)
5p gain 41 (33%) 85 0.0204
(1.00) 		1
(1.00) 		0.935
(1.00) 		0.728
(1.00) 		0.0792
(1.00) 		0.221
(1.00)
5q gain 13 (10%) 113 0.572
(1.00) 		0.394
(1.00) 		1
(1.00) 		1
(1.00) 		0.534
(1.00) 		0.383
(1.00)
6p gain 18 (14%) 108 0.108
(1.00) 		0.00399
(1.00) 		0.0262
(1.00) 		0.0987
(1.00) 		0.0237
(1.00) 		0.0619
(1.00)
6q gain 9 (7%) 117 0.501
(1.00) 		0.00852
(1.00) 		0.0536
(1.00) 		0.00601
(1.00) 		0.309
(1.00) 		0.0708
(1.00)
7p gain 7 (6%) 119 0.48
(1.00) 		0.195
(1.00) 		0.198
(1.00) 		0.41
(1.00) 		0.215
(1.00) 		0.315
(1.00)
7q gain 11 (9%) 115 0.109
(1.00) 		0.34
(1.00) 		0.285
(1.00) 		1
(1.00) 		0.576
(1.00) 		1
(1.00)
8p gain 10 (8%) 116 0.644
(1.00) 		0.41
(1.00) 		0.12
(1.00) 		0.167
(1.00) 		0.416
(1.00) 		0.761
(1.00)
8q gain 22 (17%) 104 0.0349
(1.00) 		0.0253
(1.00) 		0.104
(1.00) 		0.383
(1.00) 		0.48
(1.00) 		0.441
(1.00)
9p gain 12 (10%) 114 0.32
(1.00) 		0.748
(1.00) 		0.47
(1.00) 		0.0148
(1.00) 		0.926
(1.00) 		0.312
(1.00)
9q gain 11 (9%) 115 0.669
(1.00) 		0.239
(1.00) 		0.434
(1.00) 		0.0361
(1.00) 		0.772
(1.00) 		0.272
(1.00)
10p gain 8 (6%) 118 0.653
(1.00) 		0.901
(1.00) 		0.883
(1.00) 		0.725
(1.00) 		0.296
(1.00) 		1
(1.00)
10q gain 4 (3%) 122 0.457
(1.00) 		0.568
(1.00) 		0.81
(1.00) 		0.145
(1.00) 		0.387
(1.00) 		0.693
(1.00)
12p gain 14 (11%) 112 0.0103
(1.00) 		0.418
(1.00) 		0.385
(1.00) 		0.00322
(1.00) 		0.163
(1.00) 		0.334
(1.00)
12q gain 10 (8%) 116 0.0213
(1.00) 		0.841
(1.00) 		0.637
(1.00) 		0.0515
(1.00) 		0.545
(1.00) 		0.838
(1.00)
13q gain 6 (5%) 120 0.0397
(1.00) 		0.491
(1.00) 		0.168
(1.00) 		0.488
(1.00) 		0.464
(1.00) 		0.693
(1.00)
14q gain 10 (8%) 116 0.305
(1.00) 		1
(1.00) 		0.201
(1.00) 		0.124
(1.00) 		0.289
(1.00) 		0.739
(1.00)
15q gain 13 (10%) 113 0.00184
(0.785) 		1
(1.00) 		0.313
(1.00) 		0.794
(1.00) 		0.933
(1.00) 		0.931
(1.00)
16p gain 14 (11%) 112 0.0456
(1.00) 		0.171
(1.00) 		0.506
(1.00) 		0.0615
(1.00) 		0.38
(1.00) 		0.455
(1.00)
16q gain 10 (8%) 116 0.92
(1.00) 		0.546
(1.00) 		0.364
(1.00) 		0.0122
(1.00) 		0.387
(1.00) 		0.361
(1.00)
17p gain 5 (4%) 121 0.616
(1.00) 		0.214
(1.00) 		0.269
(1.00) 		0.0126
(1.00) 		0.159
(1.00) 		0.029
(1.00)
17q gain 13 (10%) 113 0.0157
(1.00) 		0.0131
(1.00) 		0.0253
(1.00) 		0.0148
(1.00) 		0.348
(1.00) 		0.099
(1.00)
18p gain 12 (10%) 114 0.408
(1.00) 		0.692
(1.00) 		0.606
(1.00) 		0.0271
(1.00) 		0.23
(1.00) 		0.19
(1.00)
18q gain 7 (6%) 119 0.0757
(1.00) 		1
(1.00) 		0.486
(1.00) 		0.00832
(1.00) 		0.316
(1.00) 		0.136
(1.00)
19p gain 8 (6%) 118 0.53
(1.00) 		0.727
(1.00) 		0.547
(1.00) 		0.331
(1.00) 		0.146
(1.00) 		0.894
(1.00)
19q gain 23 (18%) 103 0.218
(1.00) 		0.672
(1.00) 		0.409
(1.00) 		0.0309
(1.00) 		0.0068
(1.00) 		0.094
(1.00)
20p gain 32 (25%) 94 0.000689
(0.297) 		0.478
(1.00) 		0.856
(1.00) 		0.083
(1.00) 		0.413
(1.00) 		0.562
(1.00)
21q gain 14 (11%) 112 0.556
(1.00) 		0.938
(1.00) 		0.266
(1.00) 		0.905
(1.00) 		0.163
(1.00) 		0.504
(1.00)
22q gain 8 (6%) 118 0.0403
(1.00) 		0.901
(1.00) 		0.326
(1.00) 		0.00377
(1.00) 		0.0298
(1.00) 		0.884
(1.00)
Xq gain 6 (5%) 120 0.435
(1.00) 		0.199
(1.00) 		0.763
(1.00) 		0.332
(1.00) 		0.322
(1.00) 		0.721
(1.00)
1q loss 3 (2%) 123 0.262
(1.00) 		0.786
(1.00) 		1
(1.00) 		0.485
(1.00) 		0.647
(1.00) 		1
(1.00)
2p loss 3 (2%) 123 0.115
(1.00) 		0.182
(1.00) 		0.251
(1.00) 		0.673
(1.00) 		0.773
(1.00) 		0.792
(1.00)
2q loss 5 (4%) 121 0.0843
(1.00) 		0.0295
(1.00) 		0.0859
(1.00) 		0.267
(1.00) 		1
(1.00) 		1
(1.00)
4p loss 41 (33%) 85 0.00186
(0.79) 		0.0459
(1.00) 		0.279
(1.00) 		0.956
(1.00) 		0.11
(1.00) 		0.763
(1.00)
5p loss 3 (2%) 123 0.346
(1.00) 		0.786
(1.00) 		0.79
(1.00) 		1
(1.00) 		1
(1.00) 		0.616
(1.00)
5q loss 20 (16%) 106 0.0316
(1.00) 		0.0535
(1.00) 		0.527
(1.00) 		0.0345
(1.00) 		0.0572
(1.00) 		0.0239
(1.00)
6p loss 12 (10%) 114 0.926
(1.00) 		0.0536
(1.00) 		0.384
(1.00) 		0.432
(1.00) 		0.478
(1.00) 		0.926
(1.00)
6q loss 23 (18%) 103 0.208
(1.00) 		0.587
(1.00) 		0.287
(1.00) 		0.777
(1.00) 		0.366
(1.00) 		0.652
(1.00)
7p loss 6 (5%) 120 0.0625
(1.00) 		0.875
(1.00) 		0.558
(1.00) 		0.332
(1.00) 		0.0958
(1.00) 		0.483
(1.00)
7q loss 13 (10%) 113 0.0129
(1.00) 		0.707
(1.00) 		0.0352
(1.00) 		0.188
(1.00) 		0.0136
(1.00) 		0.0909
(1.00)
8p loss 27 (21%) 99 0.435
(1.00) 		0.259
(1.00) 		0.445
(1.00) 		0.795
(1.00) 		0.925
(1.00) 		0.921
(1.00)
8q loss 6 (5%) 120 1
(1.00) 		0.491
(1.00) 		0.326
(1.00) 		0.332
(1.00) 		0.596
(1.00) 		0.0163
(1.00)
9p loss 12 (10%) 114 0.32
(1.00) 		0.864
(1.00) 		0.74
(1.00) 		0.776
(1.00) 		0.289
(1.00) 		0.287
(1.00)
9q loss 11 (9%) 115 0.669
(1.00) 		0.288
(1.00) 		0.0145
(1.00) 		0.275
(1.00) 		0.844
(1.00) 		0.0534
(1.00)
10p loss 22 (17%) 104 0.0152
(1.00) 		0.795
(1.00) 		0.139
(1.00) 		0.102
(1.00) 		0.583
(1.00) 		0.397
(1.00)
10q loss 24 (19%) 102 0.0129
(1.00) 		0.958
(1.00) 		0.79
(1.00) 		0.181
(1.00) 		0.279
(1.00) 		1
(1.00)
11p loss 27 (21%) 99 0.0822
(1.00) 		0.332
(1.00) 		0.874
(1.00) 		0.941
(1.00) 		0.312
(1.00) 		0.66
(1.00)
11q loss 30 (24%) 96 0.00965
(1.00) 		0.748
(1.00) 		0.771
(1.00) 		0.177
(1.00) 		0.345
(1.00) 		0.53
(1.00)
12p loss 19 (15%) 107 0.00218
(0.924) 		0.209
(1.00) 		0.739
(1.00) 		0.72
(1.00) 		0.326
(1.00) 		0.355
(1.00)
12q loss 4 (3%) 122 0.0356
(1.00) 		0.46
(1.00) 		0.286
(1.00) 		0.298
(1.00) 		1
(1.00) 		0.809
(1.00)
13q loss 23 (18%) 103 0.43
(1.00) 		0.0341
(1.00) 		0.489
(1.00) 		0.0659
(1.00) 		0.911
(1.00) 		0.191
(1.00)
14q loss 7 (6%) 119 0.424
(1.00) 		0.143
(1.00) 		0.0234
(1.00) 		0.00355
(1.00) 		0.0373
(1.00) 		0.00487
(1.00)
15q loss 8 (6%) 118 0.00157
(0.674) 		1
(1.00) 		0.316
(1.00) 		0.116
(1.00) 		0.905
(1.00) 		0.807
(1.00)
16p loss 8 (6%) 118 0.415
(1.00) 		0.144
(1.00) 		0.129
(1.00) 		0.00638
(1.00) 		0.794
(1.00) 		0.169
(1.00)
17p loss 27 (21%) 99 0.0148
(1.00) 		0.00444
(1.00) 		0.0411
(1.00) 		0.0596
(1.00) 		0.0282
(1.00) 		0.111
(1.00)
17q loss 5 (4%) 121 0.616
(1.00) 		0.0295
(1.00) 		0.0859
(1.00) 		0.267
(1.00) 		0.37
(1.00) 		0.519
(1.00)
18p loss 16 (13%) 110 0.55
(1.00) 		0.465
(1.00) 		0.0389
(1.00) 		0.0988
(1.00) 		0.367
(1.00) 		0.0642
(1.00)
19p loss 12 (10%) 114 0.44
(1.00) 		0.375
(1.00) 		0.258
(1.00) 		0.397
(1.00) 		0.195
(1.00) 		0.00108
(0.465)
19q loss 6 (5%) 120 0.495
(1.00) 		0.569
(1.00) 		0.486
(1.00) 		0.173
(1.00) 		0.596
(1.00) 		0.0298
(1.00)
20p loss 8 (6%) 118 0.53
(1.00) 		0.418
(1.00) 		0.596
(1.00) 		0.376
(1.00) 		1
(1.00) 		0.595
(1.00)
21q loss 13 (10%) 113 0.000976
(0.421) 		0.53
(1.00) 		0.858
(1.00) 		0.81
(1.00) 		1
(1.00) 		0.868
(1.00)
22q loss 13 (10%) 113 0.87
(1.00) 		1
(1.00) 		0.867
(1.00) 		0.512
(1.00) 		1
(1.00) 		0.926
(1.00)
'3q gain mutation analysis' versus 'CN_CNMF'

P value = 1.3e-05 (Fisher's exact test), Q value = 0.0057

Table S1.  Gene #6: '3q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 42 32 52
3Q GAIN MUTATED 33 12 17
3Q GAIN WILD-TYPE 9 20 35

Figure S1.  Get High-res Image Gene #6: '3q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'20q gain mutation analysis' versus 'CN_CNMF'

P value = 0.000534 (Fisher's exact test), Q value = 0.23

Table S2.  Gene #34: '20q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 42 32 52
20Q GAIN MUTATED 21 10 7
20Q GAIN WILD-TYPE 21 22 45

Figure S2.  Get High-res Image Gene #34: '20q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'3p loss mutation analysis' versus 'MRNASEQ_CHIERARCHICAL'

P value = 0.000436 (Fisher's exact test), Q value = 0.19

Table S3.  Gene #41: '3p loss mutation analysis' versus Clinical Feature #4: 'MRNASEQ_CHIERARCHICAL'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 11 64 36
3P LOSS MUTATED 1 24 2
3P LOSS WILD-TYPE 10 40 34

Figure S3.  Get High-res Image Gene #41: '3p loss mutation analysis' versus Clinical Feature #4: 'MRNASEQ_CHIERARCHICAL'

'4q loss mutation analysis' versus 'CN_CNMF'

P value = 6.4e-06 (Fisher's exact test), Q value = 0.0028

Table S4.  Gene #43: '4q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 42 32 52
4Q LOSS MUTATED 7 15 2
4Q LOSS WILD-TYPE 35 17 50

Figure S4.  Get High-res Image Gene #43: '4q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'16q loss mutation analysis' versus 'MRNASEQ_CHIERARCHICAL'

P value = 0.000523 (Fisher's exact test), Q value = 0.23

Table S5.  Gene #64: '16q loss mutation analysis' versus Clinical Feature #4: 'MRNASEQ_CHIERARCHICAL'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 11 64 36
16Q LOSS MUTATED 4 2 8
16Q LOSS WILD-TYPE 7 62 28

Figure S5.  Get High-res Image Gene #64: '16q loss mutation analysis' versus Clinical Feature #4: 'MRNASEQ_CHIERARCHICAL'

'18q loss mutation analysis' versus 'MRNASEQ_CNMF'

P value = 0.000428 (Fisher's exact test), Q value = 0.19

Table S6.  Gene #68: '18q loss mutation analysis' versus Clinical Feature #3: 'MRNASEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 50 30 31
18Q LOSS MUTATED 3 6 13
18Q LOSS WILD-TYPE 47 24 18

Figure S6.  Get High-res Image Gene #68: '18q loss mutation analysis' versus Clinical Feature #3: 'MRNASEQ_CNMF'

Methods & Data
Input

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

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

  • Number of patients = 126

  • Number of significantly arm-level cnvs = 73

  • Number of molecular subtypes = 6

  • 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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