Correlation between copy number variations of arm-level result and selected clinical features
Kidney Renal Clear Cell Carcinoma (Primary solid tumor)
23 May 2013  |  analyses__2013_05_23
Maintainer Information
Citation Information
doi:10.7908/C1Z60M25
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 selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1Z60M25
Overview
Introduction

This pipeline computes the correlation between significant arm-level copy number variations (cnvs) and selected clinical features.

Summary

Testing the association between copy number variation 71 arm-level results and 8 clinical features across 493 patients, 4 significant findings detected with Q value < 0.25.

  • 5p gain cnv correlated to 'AGE'.

  • 5q gain cnv correlated to 'AGE'.

  • 11p loss cnv correlated to 'Time to Death'.

  • 19p loss cnv correlated to 'Time to Death'.

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 8 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 4 significant findings detected.

Clinical
Features 		Time
to
Death 		AGE GENDER KARNOFSKY
PERFORMANCE
SCORE 		DISTANT
METASTASIS 		LYMPH
NODE
METASTASIS 		TUMOR
STAGECODE 		NEOPLASM
DISEASESTAGE
nCNV (%) nWild-Type logrank test t-test Fisher's exact test t-test Fisher's exact test Fisher's exact test t-test Fisher's exact test
5p gain 0 (0%) 352 0.124
(1.00) 		0.000246
(0.114) 		0.116
(1.00) 		0.563
(1.00) 		0.783
(1.00) 		0.383
(1.00) 		0.246
(1.00)
5q gain 0 (0%) 337 0.297
(1.00) 		0.00037
(0.171) 		0.156
(1.00) 		0.482
(1.00) 		0.594
(1.00) 		0.302
(1.00) 		0.368
(1.00)
11p loss 0 (0%) 488 3.9e-05
(0.0182) 		0.943
(1.00) 		0.663
(1.00) 		0.569
(1.00) 		0.162
(1.00) 		0.838
(1.00)
19p loss 0 (0%) 489 0.000514
(0.238) 		0.89
(1.00) 		0.612
(1.00) 		1
(1.00) 		1
(1.00) 		0.0681
(1.00)
1p gain 0 (0%) 488 0.498
(1.00) 		0.942
(1.00) 		1
(1.00) 		1
(1.00) 		0.162
(1.00) 		0.148
(1.00)
1q gain 0 (0%) 461 0.96
(1.00) 		0.185
(1.00) 		0.565
(1.00) 		0.115
(1.00) 		0.803
(1.00) 		0.598
(1.00) 		0.0358
(1.00)
2p gain 0 (0%) 445 0.58
(1.00) 		0.14
(1.00) 		0.201
(1.00) 		0.563
(1.00) 		0.206
(1.00) 		0.64
(1.00) 		0.209
(1.00)
2q gain 0 (0%) 444 0.411
(1.00) 		0.205
(1.00) 		0.0387
(1.00) 		0.159
(1.00) 		0.208
(1.00) 		0.76
(1.00) 		0.274
(1.00)
3p gain 0 (0%) 486 0.95
(1.00) 		0.429
(1.00) 		0.243
(1.00) 		0.295
(1.00) 		0.326
(1.00) 		0.12
(1.00)
3q gain 0 (0%) 465 0.372
(1.00) 		0.941
(1.00) 		1
(1.00) 		0.0261
(1.00) 		0.174
(1.00) 		0.164
(1.00) 		0.256
(1.00)
4p gain 0 (0%) 485 0.235
(1.00) 		0.875
(1.00) 		0.72
(1.00) 		0.616
(1.00) 		0.144
(1.00) 		0.107
(1.00)
4q gain 0 (0%) 485 0.653
(1.00) 		0.389
(1.00) 		1
(1.00) 		0.616
(1.00) 		0.144
(1.00) 		0.28
(1.00)
6p gain 0 (0%) 487 0.224
(1.00) 		0.236
(1.00) 		1
(1.00) 		1
(1.00) 		0.547
(1.00) 		0.709
(1.00)
6q gain 0 (0%) 488 0.183
(1.00) 		0.442
(1.00) 		0.663
(1.00) 		0.569
(1.00) 		0.336
(1.00) 		0.367
(1.00)
7p gain 0 (0%) 366 0.862
(1.00) 		0.314
(1.00) 		0.0522
(1.00) 		0.893
(1.00) 		0.0451
(1.00) 		0.165
(1.00) 		0.192
(1.00)
7q gain 0 (0%) 364 0.517
(1.00) 		0.38
(1.00) 		0.0673
(1.00) 		0.699
(1.00) 		0.00661
(1.00) 		0.503
(1.00) 		0.0464
(1.00)
8p gain 0 (0%) 479 0.574
(1.00) 		0.761
(1.00) 		0.0417
(1.00) 		0.757
(1.00) 		0.462
(1.00) 		0.759
(1.00) 		0.00133
(0.614)
8q gain 0 (0%) 459 0.856
(1.00) 		0.427
(1.00) 		0.192
(1.00) 		0.471
(1.00) 		1
(1.00) 		0.248
(1.00) 		0.00273
(1.00)
9p gain 0 (0%) 485 0.123
(1.00) 		0.0377
(1.00) 		0.0232
(1.00) 		0.616
(1.00) 		1
(1.00) 		0.41
(1.00)
9q gain 0 (0%) 485 0.769
(1.00) 		0.97
(1.00) 		0.457
(1.00) 		0.356
(1.00) 		1
(1.00) 		0.0841
(1.00)
10p gain 0 (0%) 487 0.72
(1.00) 		0.511
(1.00) 		0.422
(1.00) 		0.233
(1.00) 		1
(1.00) 		0.269
(1.00)
10q gain 0 (0%) 489 0.511
(1.00) 		0.169
(1.00) 		0.123
(1.00) 		0.489
(1.00) 		0.677
(1.00) 		0.328
(1.00)
11p gain 0 (0%) 476 0.974
(1.00) 		0.8
(1.00) 		0.608
(1.00) 		0.115
(1.00) 		0.16
(1.00) 		0.271
(1.00) 		0.0189
(1.00)
11q gain 0 (0%) 478 0.317
(1.00) 		0.811
(1.00) 		0.409
(1.00) 		0.713
(1.00) 		0.467
(1.00) 		0.0475
(1.00)
12p gain 0 (0%) 415 0.111
(1.00) 		0.115
(1.00) 		0.364
(1.00) 		0.404
(1.00) 		0.122
(1.00) 		0.288
(1.00) 		0.0369
(1.00)
12q gain 0 (0%) 414 0.146
(1.00) 		0.179
(1.00) 		0.302
(1.00) 		0.404
(1.00) 		0.125
(1.00) 		0.362
(1.00) 		0.0282
(1.00)
13q gain 0 (0%) 479 0.291
(1.00) 		0.851
(1.00) 		0.779
(1.00) 		1
(1.00) 		0.134
(1.00) 		0.262
(1.00)
14q gain 0 (0%) 488 0.438
(1.00) 		0.524
(1.00) 		1
(1.00) 		0.569
(1.00) 		0.0522
(1.00) 		0.131
(1.00)
15q gain 0 (0%) 479 0.868
(1.00) 		0.939
(1.00) 		0.779
(1.00) 		0.462
(1.00) 		0.505
(1.00) 		0.431
(1.00)
16p gain 0 (0%) 428 0.611
(1.00) 		0.0654
(1.00) 		0.212
(1.00) 		0.4
(1.00) 		0.713
(1.00) 		0.103
(1.00) 		0.774
(1.00)
16q gain 0 (0%) 435 0.487
(1.00) 		0.0864
(1.00) 		0.106
(1.00) 		0.191
(1.00) 		1
(1.00) 		0.0982
(1.00) 		0.796
(1.00)
17p gain 0 (0%) 477 0.456
(1.00) 		0.533
(1.00) 		0.795
(1.00) 		0.149
(1.00) 		0.553
(1.00) 		0.0825
(1.00)
17q gain 0 (0%) 471 0.143
(1.00) 		0.539
(1.00) 		0.647
(1.00) 		0.161
(1.00) 		0.553
(1.00) 		0.726
(1.00) 		0.483
(1.00)
18p gain 0 (0%) 475 0.0884
(1.00) 		0.782
(1.00) 		0.0223
(1.00) 		0.333
(1.00) 		0.472
(1.00) 		0.709
(1.00)
18q gain 0 (0%) 475 0.0884
(1.00) 		0.782
(1.00) 		0.0223
(1.00) 		0.333
(1.00) 		0.472
(1.00) 		0.709
(1.00)
19p gain 0 (0%) 468 0.935
(1.00) 		0.703
(1.00) 		1
(1.00) 		0.161
(1.00) 		0.251
(1.00) 		0.24
(1.00) 		0.432
(1.00)
19q gain 0 (0%) 465 0.884
(1.00) 		0.709
(1.00) 		0.683
(1.00) 		0.161
(1.00) 		0.174
(1.00) 		0.242
(1.00) 		0.4
(1.00)
20p gain 0 (0%) 427 0.274
(1.00) 		0.268
(1.00) 		0.0127
(1.00) 		0.315
(1.00) 		0.0429
(1.00) 		0.428
(1.00) 		0.0101
(1.00)
20q gain 0 (0%) 425 0.428
(1.00) 		0.22
(1.00) 		0.0195
(1.00) 		0.315
(1.00) 		0.106
(1.00) 		0.555
(1.00) 		0.0166
(1.00)
21q gain 0 (0%) 460 0.339
(1.00) 		0.324
(1.00) 		0.851
(1.00) 		0.161
(1.00) 		1
(1.00) 		0.72
(1.00) 		0.844
(1.00)
22q gain 0 (0%) 468 0.964
(1.00) 		0.83
(1.00) 		0.526
(1.00) 		0.251
(1.00) 		0.813
(1.00) 		0.432
(1.00)
Xq gain 0 (0%) 484 0.06
(1.00) 		0.791
(1.00) 		0.0705
(1.00) 		0.0357
(1.00) 		1
(1.00) 		0.0184
(1.00)
1p loss 0 (0%) 461 0.877
(1.00) 		0.0984
(1.00) 		0.565
(1.00) 		0.115
(1.00) 		0.45
(1.00) 		0.501
(1.00) 		0.0661
(1.00)
1q loss 0 (0%) 473 0.504
(1.00) 		0.775
(1.00) 		0.474
(1.00) 		0.753
(1.00) 		0.917
(1.00) 		0.364
(1.00)
2p loss 0 (0%) 483 0.471
(1.00) 		0.932
(1.00) 		0.506
(1.00) 		1
(1.00) 		0.548
(1.00) 		0.498
(1.00)
2q loss 0 (0%) 482 0.856
(1.00) 		0.983
(1.00) 		0.344
(1.00) 		1
(1.00) 		0.701
(1.00) 		0.786
(1.00)
3p loss 0 (0%) 189 0.605
(1.00) 		0.263
(1.00) 		0.0797
(1.00) 		0.906
(1.00) 		0.0145
(1.00) 		0.327
(1.00) 		0.0343
(1.00)
3q loss 0 (0%) 420 0.932
(1.00) 		0.00515
(1.00) 		0.00215
(0.986) 		0.573
(1.00) 		0.729
(1.00) 		0.0453
(1.00) 		0.462
(1.00)
4p loss 0 (0%) 458 0.0602
(1.00) 		0.558
(1.00) 		0.854
(1.00) 		0.0176
(1.00) 		0.808
(1.00) 		1
(1.00) 		0.125
(1.00)
4q loss 0 (0%) 465 0.0183
(1.00) 		0.992
(1.00) 		0.841
(1.00) 		0.0261
(1.00) 		0.174
(1.00) 		0.679
(1.00) 		0.0235
(1.00)
6p loss 0 (0%) 435 0.235
(1.00) 		0.187
(1.00) 		0.00303
(1.00) 		0.499
(1.00) 		0.439
(1.00) 		0.034
(1.00) 		0.776
(1.00)
6q loss 0 (0%) 411 0.548
(1.00) 		0.719
(1.00) 		0.127
(1.00) 		0.851
(1.00) 		0.868
(1.00) 		0.00727
(1.00) 		0.707
(1.00)
8p loss 0 (0%) 396 0.528
(1.00) 		0.0663
(1.00) 		0.192
(1.00) 		0.0817
(1.00) 		0.531
(1.00) 		0.132
(1.00) 		0.801
(1.00)
8q loss 0 (0%) 454 0.347
(1.00) 		0.592
(1.00) 		0.0226
(1.00) 		0.00419
(1.00) 		0.489
(1.00) 		0.261
(1.00) 		0.812
(1.00)
9p loss 0 (0%) 406 0.0111
(1.00) 		0.0624
(1.00) 		0.0061
(1.00) 		0.0261
(1.00) 		0.0209
(1.00) 		0.665
(1.00) 		0.0496
(1.00)
9q loss 0 (0%) 401 0.00542
(1.00) 		0.034
(1.00) 		0.00764
(1.00) 		0.0473
(1.00) 		0.0035
(1.00) 		0.933
(1.00) 		0.00169
(0.775)
10p loss 0 (0%) 461 0.507
(1.00) 		0.49
(1.00) 		0.565
(1.00) 		0.0261
(1.00) 		0.803
(1.00) 		0.198
(1.00) 		0.884
(1.00)
10q loss 0 (0%) 445 0.742
(1.00) 		0.258
(1.00) 		0.268
(1.00) 		0.0535
(1.00) 		0.677
(1.00) 		0.0712
(1.00) 		0.68
(1.00)
11q loss 0 (0%) 486 0.148
(1.00) 		0.638
(1.00) 		0.43
(1.00) 		1
(1.00) 		0.326
(1.00) 		1
(1.00)
13q loss 0 (0%) 462 0.0012
(0.555) 		0.0513
(1.00) 		0.334
(1.00) 		0.00419
(1.00) 		0.0394
(1.00) 		0.838
(1.00) 		0.00477
(1.00)
14q loss 0 (0%) 335 0.251
(1.00) 		0.203
(1.00) 		0.188
(1.00) 		0.508
(1.00) 		0.142
(1.00) 		0.0814
(1.00) 		0.0231
(1.00)
15q loss 0 (0%) 481 0.257
(1.00) 		0.13
(1.00) 		1
(1.00) 		0.408
(1.00) 		0.386
(1.00) 		0.272
(1.00)
16q loss 0 (0%) 488 0.0864
(1.00) 		0.0696
(1.00) 		0.663
(1.00) 		0.569
(1.00) 		0.73
(1.00) 		0.0627
(1.00)
17p loss 0 (0%) 468 0.0387
(1.00) 		0.658
(1.00) 		0.288
(1.00) 		0.0473
(1.00) 		0.0396
(1.00) 		0.592
(1.00) 		0.189
(1.00)
17q loss 0 (0%) 481 0.697
(1.00) 		0.831
(1.00) 		0.232
(1.00) 		1
(1.00) 		0.609
(1.00) 		0.471
(1.00)
18p loss 0 (0%) 447 0.317
(1.00) 		0.106
(1.00) 		0.626
(1.00) 		0.518
(1.00) 		0.204
(1.00) 		0.814
(1.00) 		0.333
(1.00)
18q loss 0 (0%) 445 0.699
(1.00) 		0.22
(1.00) 		0.268
(1.00) 		0.518
(1.00) 		0.292
(1.00) 		0.856
(1.00) 		0.664
(1.00)
20p loss 0 (0%) 487 0.751
(1.00) 		0.967
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		0.931
(1.00)
21q loss 0 (0%) 461 0.779
(1.00) 		0.944
(1.00) 		1
(1.00) 		0.45
(1.00) 		0.565
(1.00) 		0.169
(1.00)
22q loss 0 (0%) 482 0.228
(1.00) 		0.0446
(1.00) 		0.344
(1.00) 		1
(1.00) 		1
(1.00) 		0.196
(1.00)
Xq loss 0 (0%) 484 0.886
(1.00) 		0.739
(1.00) 		0.0308
(1.00) 		0.367
(1.00) 		0.346
(1.00) 		0.343
(1.00)
'5p gain' versus 'AGE'

P value = 0.000246 (t-test), Q value = 0.11

Table S1.  Gene #9: '5p gain' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 492 60.6 (12.2)
5P GAIN CNV 141 63.7 (12.0)
5P GAIN WILD-TYPE 351 59.3 (12.1)

Figure S1.  Get High-res Image Gene #9: '5p gain' versus Clinical Feature #2: 'AGE'

'5q gain' versus 'AGE'

P value = 0.00037 (t-test), Q value = 0.17

Table S2.  Gene #10: '5q gain' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 492 60.6 (12.2)
5Q GAIN CNV 155 63.5 (12.3)
5Q GAIN WILD-TYPE 337 59.2 (11.9)

Figure S2.  Get High-res Image Gene #10: '5q gain' versus Clinical Feature #2: 'AGE'

'11p loss' versus 'Time to Death'

P value = 3.9e-05 (logrank test), Q value = 0.018

Table S3.  Gene #57: '11p loss' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 490 158 0.1 - 111.0 (35.2)
11P LOSS CNV 5 4 1.8 - 28.5 (12.1)
11P LOSS WILD-TYPE 485 154 0.1 - 111.0 (35.5)

Figure S3.  Get High-res Image Gene #57: '11p loss' versus Clinical Feature #1: 'Time to Death'

'19p loss' versus 'Time to Death'

P value = 0.000514 (logrank test), Q value = 0.24

Table S4.  Gene #67: '19p loss' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 490 158 0.1 - 111.0 (35.2)
19P LOSS CNV 4 4 10.8 - 32.6 (24.1)
19P LOSS WILD-TYPE 486 154 0.1 - 111.0 (35.5)

Figure S4.  Get High-res Image Gene #67: '19p loss' versus Clinical Feature #1: 'Time to Death'

Methods & Data
Input

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

  • Clinical data file = KIRC-TP.clin.merged.picked.txt

  • Number of patients = 493

  • Number of significantly arm-level cnvs = 71

  • Number of selected clinical features = 8

  • Exclude genes that fewer than K tumors have mutations, K = 3

Survival analysis

For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R

Student's t-test analysis

For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between tumors with and without gene mutations using 't.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

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] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[2] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[3] 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)
[4] 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