Correlation between copy number variations of arm-level result and molecular subtypes
Liver Hepatocellular Carcinoma (Primary solid tumor)
21 April 2013  |  analyses__2013_04_21
Maintainer Information
Citation Information
doi:10.7908/C13X84M2
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Liver Hepatocellular 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/C13X84M2
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 71 arm-level results and 6 molecular subtypes across 97 patients, 4 significant findings detected with Q value < 0.25.

  • 8q gain cnv correlated to 'CN_CNMF'.

  • 7p loss cnv correlated to 'MRNASEQ_CNMF'.

  • 7q loss cnv correlated to 'MRNASEQ_CNMF'.

  • 16q 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 71 arm-level results and 6 molecular subtypes. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 4 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 Chi-square test Fisher's exact test Fisher's exact test Fisher's exact test
8q gain 0 (0%) 52 4.7e-09
(1.97e-06) 		0.75
(1.00) 		0.199
(1.00) 		0.631
(1.00) 		0.182
(1.00) 		0.218
(1.00)
7p loss 0 (0%) 92 0.0508
(1.00) 		0.85
(1.00) 		0.000514
(0.214) 		0.634
(1.00) 		0.845
(1.00) 		1
(1.00)
7q loss 0 (0%) 90 0.0337
(1.00) 		1
(1.00) 		2.41e-05
(0.0101) 		1
(1.00) 		0.773
(1.00) 		0.189
(1.00)
16q loss 0 (0%) 70 0.00429
(1.00) 		0.000233
(0.0973) 		0.071
(1.00) 		0.068
(1.00) 		0.0366
(1.00) 		0.0312
(1.00)
1p gain 0 (0%) 87 0.54
(1.00) 		0.392
(1.00) 		0.146
(1.00) 		1
(1.00) 		0.624
(1.00) 		0.331
(1.00)
1q gain 0 (0%) 46 0.00386
(1.00) 		0.0464
(1.00) 		0.0575
(1.00) 		0.469
(1.00) 		0.741
(1.00) 		1
(1.00)
2p gain 0 (0%) 89 0.586
(1.00) 		0.137
(1.00) 		0.788
(1.00) 		0.443
(1.00) 		0.00574
(1.00) 		0.59
(1.00)
2q gain 0 (0%) 90 0.618
(1.00) 		0.0561
(1.00) 		0.748
(1.00) 		0.23
(1.00) 		0.021
(1.00) 		1
(1.00)
3p gain 0 (0%) 90 0.377
(1.00) 		0.758
(1.00) 		0.109
(1.00) 		0.69
(1.00) 		0.248
(1.00) 		1
(1.00)
3q gain 0 (0%) 90 0.377
(1.00) 		0.758
(1.00) 		0.109
(1.00) 		0.69
(1.00) 		0.248
(1.00) 		1
(1.00)
4p gain 0 (0%) 91 0.766
(1.00) 		0.101
(1.00) 		0.00914
(1.00) 		0.0106
(1.00) 		0.288
(1.00) 		1
(1.00)
5p gain 0 (0%) 69 0.0381
(1.00) 		0.247
(1.00) 		0.526
(1.00) 		0.157
(1.00) 		0.797
(1.00) 		0.503
(1.00)
5q gain 0 (0%) 77 0.129
(1.00) 		0.523
(1.00) 		0.912
(1.00) 		0.335
(1.00) 		0.23
(1.00) 		0.452
(1.00)
6p gain 0 (0%) 79 0.00474
(1.00) 		0.00692
(1.00) 		0.402
(1.00) 		0.777
(1.00) 		0.322
(1.00) 		1
(1.00)
6q gain 0 (0%) 86 0.0806
(1.00) 		0.0922
(1.00) 		0.213
(1.00) 		0.732
(1.00) 		0.491
(1.00) 		1
(1.00)
7p gain 0 (0%) 71 0.0589
(1.00) 		0.195
(1.00) 		0.267
(1.00) 		0.112
(1.00) 		0.107
(1.00) 		1
(1.00)
7q gain 0 (0%) 71 0.103
(1.00) 		0.00982
(1.00) 		0.16
(1.00) 		0.188
(1.00) 		0.311
(1.00) 		1
(1.00)
8p gain 0 (0%) 83 0.338
(1.00) 		0.458
(1.00) 		0.0728
(1.00) 		0.302
(1.00) 		0.586
(1.00) 		1
(1.00)
9p gain 0 (0%) 93 0.201
(1.00) 		0.571
(1.00) 		0.849
(1.00) 		0.568
(1.00) 		0.136
(1.00) 		1
(1.00)
9q gain 0 (0%) 94 0.633
(1.00) 		0.797
(1.00) 		0.849
(1.00) 		0.568
(1.00) 		0.302
(1.00) 		1
(1.00)
10p gain 0 (0%) 90 0.433
(1.00) 		0.553
(1.00) 		0.052
(1.00) 		0.0363
(1.00) 		0.469
(1.00) 		1
(1.00)
10q gain 0 (0%) 93 0.258
(1.00) 		0.444
(1.00) 		0.0672
(1.00) 		0.568
(1.00) 		0.136
(1.00) 		1
(1.00)
11q gain 0 (0%) 94 0.192
(1.00) 		0.325
(1.00) 		0.42
(1.00) 		0.784
(1.00) 		1
(1.00)
12p gain 0 (0%) 93 0.201
(1.00) 		0.688
(1.00) 		0.173
(1.00) 		0.82
(1.00) 		1
(1.00)
12q gain 0 (0%) 92 0.0736
(1.00) 		0.85
(1.00) 		0.8
(1.00) 		0.568
(1.00) 		0.708
(1.00) 		1
(1.00)
13q gain 0 (0%) 92 0.0508
(1.00) 		0.595
(1.00) 		0.666
(1.00) 		0.302
(1.00) 		0.213
(1.00) 		1
(1.00)
14q gain 0 (0%) 92 0.374
(1.00) 		0.36
(1.00) 		0.506
(1.00) 		1
(1.00) 		0.471
(1.00)
15q gain 0 (0%) 92 0.315
(1.00) 		0.264
(1.00) 		0.48
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00)
16p gain 0 (0%) 93 0.201
(1.00) 		0.571
(1.00) 		0.173
(1.00) 		0.673
(1.00) 		1
(1.00)
17p gain 0 (0%) 92 0.315
(1.00) 		0.0992
(1.00) 		0.849
(1.00) 		0.568
(1.00) 		0.708
(1.00) 		1
(1.00)
17q gain 0 (0%) 77 0.0406
(1.00) 		0.947
(1.00) 		0.713
(1.00) 		0.566
(1.00) 		0.762
(1.00) 		0.447
(1.00)
18p gain 0 (0%) 94 0.192
(1.00) 		0.444
(1.00) 		0.173
(1.00) 		0.784
(1.00) 		1
(1.00)
18q gain 0 (0%) 93 0.258
(1.00) 		0.144
(1.00) 		0.661
(1.00) 		0.0697
(1.00) 		0.673
(1.00) 		1
(1.00)
19p gain 0 (0%) 88 0.223
(1.00) 		0.0144
(1.00) 		0.0482
(1.00) 		0.0363
(1.00) 		0.00214
(0.884) 		0.592
(1.00)
19q gain 0 (0%) 87 0.489
(1.00) 		0.00543
(1.00) 		0.048
(1.00) 		0.00321
(1.00) 		0.0712
(1.00) 		0.6
(1.00)
20p gain 0 (0%) 77 0.376
(1.00) 		0.427
(1.00) 		0.076
(1.00) 		0.144
(1.00) 		0.472
(1.00) 		1
(1.00)
20q gain 0 (0%) 76 0.263
(1.00) 		0.399
(1.00) 		0.0505
(1.00) 		0.25
(1.00) 		0.308
(1.00) 		1
(1.00)
21q gain 0 (0%) 91 0.316
(1.00) 		0.254
(1.00) 		0.895
(1.00) 		0.302
(1.00) 		1
(1.00) 		1
(1.00)
22q gain 0 (0%) 89 0.238
(1.00) 		0.35
(1.00) 		0.074
(1.00) 		0.122
(1.00) 		1
(1.00) 		0.59
(1.00)
Xq gain 0 (0%) 92 0.126
(1.00) 		0.08
(1.00) 		0.502
(1.00) 		0.258
(1.00) 		0.213
(1.00) 		0.471
(1.00)
1p loss 0 (0%) 82 0.496
(1.00) 		0.0422
(1.00) 		0.103
(1.00) 		1
(1.00) 		0.247
(1.00) 		1
(1.00)
1q loss 0 (0%) 92 0.315
(1.00) 		0.188
(1.00) 		0.0166
(1.00) 		1
(1.00) 		0.351
(1.00) 		1
(1.00)
2p loss 0 (0%) 94 0.385
(1.00) 		0.603
(1.00) 		0.283
(1.00) 		0.258
(1.00) 		0.43
(1.00) 		1
(1.00)
2q loss 0 (0%) 93 0.144
(1.00) 		0.458
(1.00) 		0.568
(1.00) 		0.634
(1.00) 		0.334
(1.00) 		1
(1.00)
3p loss 0 (0%) 89 0.181
(1.00) 		0.147
(1.00) 		0.0128
(1.00) 		0.00396
(1.00) 		0.21
(1.00) 		0.59
(1.00)
3q loss 0 (0%) 94 0.192
(1.00) 		0.0353
(1.00) 		0.239
(1.00) 		0.0697
(1.00) 		1
(1.00) 		1
(1.00)
4p loss 0 (0%) 88 0.021
(1.00) 		0.492
(1.00) 		0.625
(1.00) 		0.477
(1.00) 		0.589
(1.00) 		0.283
(1.00)
4q loss 0 (0%) 79 0.00105
(0.437) 		0.591
(1.00) 		0.914
(1.00) 		1
(1.00) 		0.28
(1.00) 		0.213
(1.00)
5q loss 0 (0%) 92 0.0736
(1.00) 		0.571
(1.00) 		0.58
(1.00) 		1
(1.00) 		0.279
(1.00) 		0.471
(1.00)
6q loss 0 (0%) 77 0.513
(1.00) 		1
(1.00) 		0.73
(1.00) 		0.131
(1.00) 		0.841
(1.00) 		1
(1.00)
8p loss 0 (0%) 56 0.00184
(0.761) 		0.641
(1.00) 		0.196
(1.00) 		0.626
(1.00) 		0.708
(1.00) 		0.519
(1.00)
8q loss 0 (0%) 91 0.0015
(0.623) 		0.472
(1.00) 		0.00389
(1.00) 		0.634
(1.00) 		0.288
(1.00) 		1
(1.00)
9p loss 0 (0%) 77 0.465
(1.00) 		0.85
(1.00) 		0.674
(1.00) 		0.566
(1.00) 		0.573
(1.00) 		0.696
(1.00)
9q loss 0 (0%) 78 0.499
(1.00) 		0.427
(1.00) 		0.555
(1.00) 		0.771
(1.00) 		0.787
(1.00) 		1
(1.00)
10p loss 0 (0%) 91 0.204
(1.00) 		1
(1.00) 		0.696
(1.00) 		1
(1.00) 		0.857
(1.00) 		0.536
(1.00)
10q loss 0 (0%) 80 0.0534
(1.00) 		0.255
(1.00) 		0.16
(1.00) 		0.131
(1.00) 		0.358
(1.00) 		0.108
(1.00)
11p loss 0 (0%) 91 0.316
(1.00) 		0.553
(1.00) 		0.624
(1.00) 		0.643
(1.00) 		0.857
(1.00) 		0.536
(1.00)
11q loss 0 (0%) 89 0.238
(1.00) 		1
(1.00) 		0.00885
(1.00) 		0.0615
(1.00) 		0.55
(1.00) 		0.235
(1.00)
12p loss 0 (0%) 89 0.337
(1.00) 		0.00863
(1.00) 		0.0374
(1.00) 		0.69
(1.00) 		0.41
(1.00) 		1
(1.00)
13q loss 0 (0%) 67 0.0219
(1.00) 		0.115
(1.00) 		0.0456
(1.00) 		0.436
(1.00) 		0.0552
(1.00) 		0.733
(1.00)
14q loss 0 (0%) 71 0.0114
(1.00) 		0.508
(1.00) 		0.0101
(1.00) 		0.112
(1.00) 		0.566
(1.00) 		1
(1.00)
15q loss 0 (0%) 88 0.00523
(1.00) 		0.492
(1.00) 		0.987
(1.00) 		0.443
(1.00) 		0.589
(1.00) 		0.283
(1.00)
16p loss 0 (0%) 77 0.0776
(1.00) 		0.0149
(1.00) 		0.00753
(1.00) 		0.0397
(1.00) 		0.0807
(1.00) 		0.452
(1.00)
17p loss 0 (0%) 56 0.00899
(1.00) 		0.018
(1.00) 		0.299
(1.00) 		0.634
(1.00) 		0.135
(1.00) 		0.009
(1.00)
17q loss 0 (0%) 91 0.766
(1.00) 		0.078
(1.00) 		0.693
(1.00) 		0.634
(1.00) 		0.857
(1.00) 		0.536
(1.00)
18p loss 0 (0%) 87 0.191
(1.00) 		0.029
(1.00) 		0.00493
(1.00) 		0.712
(1.00) 		0.826
(1.00) 		1
(1.00)
18q loss 0 (0%) 86 0.0245
(1.00) 		0.0938
(1.00) 		0.0131
(1.00) 		0.477
(1.00) 		0.649
(1.00) 		1
(1.00)
19p loss 0 (0%) 92 0.52
(1.00) 		0.198
(1.00) 		0.401
(1.00) 		1
(1.00) 		1
(1.00) 		0.471
(1.00)
20p loss 0 (0%) 92 0.734
(1.00) 		0.595
(1.00) 		1
(1.00) 		0.0618
(1.00) 		0.103
(1.00)
21q loss 0 (0%) 81 0.357
(1.00) 		0.563
(1.00) 		0.13
(1.00) 		0.477
(1.00) 		0.247
(1.00) 		0.0189
(1.00)
22q loss 0 (0%) 87 0.374
(1.00) 		0.473
(1.00) 		0.0981
(1.00) 		0.152
(1.00) 		0.91
(1.00) 		0.331
(1.00)
'8q gain' versus 'CN_CNMF'

P value = 4.7e-09 (Fisher's exact test), Q value = 2e-06

Table S1.  Gene #15: '8q gain' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 28 36 33
8Q GAIN CNV 26 8 11
8Q GAIN WILD-TYPE 2 28 22

Figure S1.  Get High-res Image Gene #15: '8q gain' versus Molecular Subtype #1: 'CN_CNMF'

'7p loss' versus 'MRNASEQ_CNMF'

P value = 0.000514 (Chi-square test), Q value = 0.21

Table S2.  Gene #48: '7p loss' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6 CLUS_7 CLUS_8 CLUS_9
ALL 16 8 6 14 11 3 6 1 4
7P LOSS CNV 0 0 0 0 0 2 1 0 1
7P LOSS WILD-TYPE 16 8 6 14 11 1 5 1 3

Figure S2.  Get High-res Image Gene #48: '7p loss' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

'7q loss' versus 'MRNASEQ_CNMF'

P value = 2.41e-05 (Chi-square test), Q value = 0.01

Table S3.  Gene #49: '7q loss' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6 CLUS_7 CLUS_8 CLUS_9
ALL 16 8 6 14 11 3 6 1 4
7Q LOSS CNV 1 0 0 0 1 3 1 0 0
7Q LOSS WILD-TYPE 15 8 6 14 10 0 5 1 4

Figure S3.  Get High-res Image Gene #49: '7q loss' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

'16q loss' versus 'METHLYATION_CNMF'

P value = 0.000233 (Fisher's exact test), Q value = 0.097

Table S4.  Gene #63: '16q loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 23 28 45
16Q LOSS CNV 1 15 10
16Q LOSS WILD-TYPE 22 13 35

Figure S4.  Get High-res Image Gene #63: '16q loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'

Methods & Data
Input

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

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

  • Number of patients = 97

  • Number of significantly arm-level cnvs = 71

  • 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

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)
Copyright © 2013 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle