Correlation between copy number variations of arm-level result and selected clinical features
Glioblastoma Multiforme (Primary solid tumor)
23 May 2013  |  analyses__2013_05_23
Maintainer Information
Citation Information
doi:10.7908/C1TT4NZQ
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/C1TT4NZQ
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 79 arm-level results and 6 clinical features across 552 patients, 10 significant findings detected with Q value < 0.25.

  • 6p gain cnv correlated to 'KARNOFSKY.PERFORMANCE.SCORE'.

  • 6q gain cnv correlated to 'KARNOFSKY.PERFORMANCE.SCORE'.

  • 7p gain cnv correlated to 'AGE'.

  • 7q gain cnv correlated to 'AGE'.

  • 10p gain cnv correlated to 'AGE' and 'KARNOFSKY.PERFORMANCE.SCORE'.

  • 10p loss cnv correlated to 'Time to Death',  'AGE', and 'HISTOLOGICAL.TYPE'.

  • 10q loss cnv correlated to 'AGE'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between significant copy number variation of 79 arm-level results and 6 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 10 significant findings detected.

Clinical
Features 		Time
to
Death 		AGE GENDER KARNOFSKY
PERFORMANCE
SCORE 		HISTOLOGICAL
TYPE 		RADIATIONS
RADIATION
REGIMENINDICATION
nCNV (%) nWild-Type logrank test t-test Fisher's exact test t-test Fisher's exact test Fisher's exact test
10p loss 0 (0%) 122 0.000393
(0.182) 		5.71e-09
(2.69e-06) 		0.345
(1.00) 		0.358
(1.00) 		0.000112
(0.0521) 		0.74
(1.00)
10p gain 0 (0%) 541 0.0028
(1.00) 		9.31e-05
(0.0435) 		0.0295
(1.00) 		0.000254
(0.118) 		0.415
(1.00) 		1
(1.00)
6p gain 0 (0%) 545 0.0981
(1.00) 		0.0975
(1.00) 		1
(1.00) 		0.000254
(0.118) 		1
(1.00) 		0.445
(1.00)
6q gain 0 (0%) 543 0.131
(1.00) 		0.0441
(1.00) 		0.744
(1.00) 		0.000254
(0.118) 		1
(1.00) 		0.728
(1.00)
7p gain 0 (0%) 125 0.00987
(1.00) 		1.61e-05
(0.00754) 		0.917
(1.00) 		0.53
(1.00) 		0.0426
(1.00) 		0.227
(1.00)
7q gain 0 (0%) 121 0.0122
(1.00) 		2.1e-05
(0.00981) 		0.528
(1.00) 		0.584
(1.00) 		0.0265
(1.00) 		0.266
(1.00)
10q loss 0 (0%) 109 0.00169
(0.776) 		9.25e-07
(0.000435) 		0.514
(1.00) 		0.0968
(1.00) 		0.00302
(1.00) 		1
(1.00)
1p gain 0 (0%) 514 0.626
(1.00) 		0.593
(1.00) 		0.607
(1.00) 		0.868
(1.00) 		1
(1.00) 		0.589
(1.00)
1q gain 0 (0%) 509 0.964
(1.00) 		0.483
(1.00) 		1
(1.00) 		0.979
(1.00) 		0.816
(1.00) 		0.733
(1.00)
2p gain 0 (0%) 533 0.385
(1.00) 		0.112
(1.00) 		0.101
(1.00) 		0.301
(1.00) 		0.144
(1.00) 		0.452
(1.00)
2q gain 0 (0%) 535 0.522
(1.00) 		0.318
(1.00) 		0.314
(1.00) 		0.737
(1.00) 		0.119
(1.00) 		0.293
(1.00)
3p gain 0 (0%) 522 0.118
(1.00) 		0.552
(1.00) 		1
(1.00) 		0.837
(1.00) 		1
(1.00) 		0.421
(1.00)
3q gain 0 (0%) 522 0.12
(1.00) 		0.456
(1.00) 		1
(1.00) 		0.892
(1.00) 		1
(1.00) 		0.421
(1.00)
4p gain 0 (0%) 537 0.0784
(1.00) 		0.0675
(1.00) 		0.791
(1.00) 		0.127
(1.00) 		0.52
(1.00) 		0.0457
(1.00)
4q gain 0 (0%) 538 0.03
(1.00) 		0.334
(1.00) 		0.789
(1.00) 		0.146
(1.00) 		0.495
(1.00) 		0.00709
(1.00)
5p gain 0 (0%) 523 0.681
(1.00) 		0.124
(1.00) 		0.563
(1.00) 		0.0435
(1.00) 		0.0132
(1.00) 		0.147
(1.00)
5q gain 0 (0%) 530 0.491
(1.00) 		0.883
(1.00) 		0.374
(1.00) 		0.301
(1.00) 		0.0172
(1.00) 		0.242
(1.00)
8p gain 0 (0%) 526 0.779
(1.00) 		0.536
(1.00) 		0.539
(1.00) 		0.402
(1.00) 		0.35
(1.00) 		0.392
(1.00)
8q gain 0 (0%) 521 0.82
(1.00) 		0.197
(1.00) 		0.345
(1.00) 		0.389
(1.00) 		1
(1.00) 		0.555
(1.00)
9p gain 0 (0%) 535 0.237
(1.00) 		0.0366
(1.00) 		0.314
(1.00) 		0.00622
(1.00) 		1
(1.00) 		0.603
(1.00)
9q gain 0 (0%) 520 0.0326
(1.00) 		0.0457
(1.00) 		0.0612
(1.00) 		0.0256
(1.00) 		0.559
(1.00) 		0.326
(1.00)
11p gain 0 (0%) 548 0.559
(1.00) 		0.38
(1.00) 		0.649
(1.00) 		0.841
(1.00) 		1
(1.00) 		1
(1.00)
11q gain 0 (0%) 545 0.147
(1.00) 		0.207
(1.00) 		0.254
(1.00) 		0.0913
(1.00) 		0.288
(1.00) 		0.445
(1.00)
12p gain 0 (0%) 511 0.707
(1.00) 		0.0935
(1.00) 		0.868
(1.00) 		0.93
(1.00) 		0.542
(1.00) 		0.603
(1.00)
12q gain 0 (0%) 521 0.77
(1.00) 		0.448
(1.00) 		0.345
(1.00) 		0.554
(1.00) 		0.303
(1.00) 		0.424
(1.00)
13q gain 0 (0%) 549 0.986
(1.00) 		0.105
(1.00) 		0.0611
(1.00) 		1
(1.00) 		0.0294
(1.00)
14q gain 0 (0%) 545 0.542
(1.00) 		0.807
(1.00) 		0.443
(1.00) 		0.514
(1.00) 		0.0219
(1.00) 		0.682
(1.00)
15q gain 0 (0%) 546 0.974
(1.00) 		0.0837
(1.00) 		0.685
(1.00) 		0.292
(1.00) 		0.0974
(1.00) 		0.38
(1.00)
16p gain 0 (0%) 535 0.0426
(1.00) 		0.0235
(1.00) 		0.13
(1.00) 		0.71
(1.00) 		0.565
(1.00) 		0.0294
(1.00)
16q gain 0 (0%) 535 0.0435
(1.00) 		0.0653
(1.00) 		0.0421
(1.00) 		0.971
(1.00) 		0.565
(1.00) 		0.109
(1.00)
17p gain 0 (0%) 541 0.201
(1.00) 		0.325
(1.00) 		1
(1.00) 		0.0913
(1.00) 		0.19
(1.00) 		0.745
(1.00)
17q gain 0 (0%) 533 0.157
(1.00) 		0.409
(1.00) 		0.815
(1.00) 		0.0318
(1.00) 		0.144
(1.00) 		1
(1.00)
18p gain 0 (0%) 526 0.872
(1.00) 		0.948
(1.00) 		1
(1.00) 		0.0964
(1.00) 		1
(1.00) 		0.669
(1.00)
18q gain 0 (0%) 524 0.8
(1.00) 		0.918
(1.00) 		0.697
(1.00) 		0.301
(1.00) 		0.747
(1.00) 		0.837
(1.00)
19p gain 0 (0%) 380 0.126
(1.00) 		0.708
(1.00) 		0.925
(1.00) 		0.795
(1.00) 		0.425
(1.00) 		0.427
(1.00)
19q gain 0 (0%) 403 0.0885
(1.00) 		0.445
(1.00) 		0.769
(1.00) 		0.773
(1.00) 		0.873
(1.00) 		0.836
(1.00)
20p gain 0 (0%) 376 0.694
(1.00) 		0.00136
(0.629) 		0.642
(1.00) 		0.371
(1.00) 		0.783
(1.00) 		0.323
(1.00)
20q gain 0 (0%) 378 0.485
(1.00) 		0.0026
(1.00) 		0.574
(1.00) 		0.406
(1.00) 		0.483
(1.00) 		0.276
(1.00)
21q gain 0 (0%) 518 0.126
(1.00) 		0.874
(1.00) 		0.858
(1.00) 		0.634
(1.00) 		0.123
(1.00) 		1
(1.00)
22q gain 0 (0%) 541 0.982
(1.00) 		0.384
(1.00) 		1
(1.00) 		0.968
(1.00) 		1
(1.00) 		0.516
(1.00)
Xq gain 0 (0%) 549 0.133
(1.00) 		0.59
(1.00) 		1
(1.00) 		0.135
(1.00) 		1
(1.00)
1p loss 0 (0%) 547 0.0304
(1.00) 		0.717
(1.00) 		0.653
(1.00) 		0.868
(1.00) 		0.215
(1.00) 		1
(1.00)
1q loss 0 (0%) 548 0.712
(1.00) 		0.0682
(1.00) 		0.649
(1.00) 		0.0873
(1.00) 		1
(1.00) 		1
(1.00)
2p loss 0 (0%) 542 0.174
(1.00) 		0.507
(1.00) 		0.204
(1.00) 		0.76
(1.00) 		1
(1.00) 		1
(1.00)
2q loss 0 (0%) 543 0.229
(1.00) 		0.733
(1.00) 		0.328
(1.00) 		0.76
(1.00) 		1
(1.00) 		0.728
(1.00)
3p loss 0 (0%) 530 0.279
(1.00) 		0.0214
(1.00) 		1
(1.00) 		0.846
(1.00) 		0.661
(1.00) 		0.0313
(1.00)
3q loss 0 (0%) 536 0.754
(1.00) 		0.225
(1.00) 		0.44
(1.00) 		0.708
(1.00) 		1
(1.00) 		0.412
(1.00)
4p loss 0 (0%) 527 0.134
(1.00) 		0.286
(1.00) 		0.142
(1.00) 		0.825
(1.00) 		1
(1.00) 		0.122
(1.00)
4q loss 0 (0%) 528 0.201
(1.00) 		0.22
(1.00) 		0.67
(1.00) 		0.167
(1.00) 		0.694
(1.00) 		0.654
(1.00)
5p loss 0 (0%) 531 0.053
(1.00) 		0.268
(1.00) 		0.821
(1.00) 		0.503
(1.00) 		0.644
(1.00) 		0.0303
(1.00)
5q loss 0 (0%) 532 0.0584
(1.00) 		0.286
(1.00) 		0.645
(1.00) 		0.786
(1.00) 		1
(1.00) 		0.143
(1.00)
6p loss 0 (0%) 505 0.009
(1.00) 		0.729
(1.00) 		0.878
(1.00) 		0.711
(1.00) 		0.475
(1.00) 		0.87
(1.00)
6q loss 0 (0%) 473 0.301
(1.00) 		0.616
(1.00) 		0.535
(1.00) 		0.581
(1.00) 		0.409
(1.00) 		0.793
(1.00)
7p loss 0 (0%) 547 0.505
(1.00) 		0.654
(1.00) 		1
(1.00) 		0.61
(1.00) 		1
(1.00) 		1
(1.00)
7q loss 0 (0%) 548 0.606
(1.00) 		0.36
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00)
8p loss 0 (0%) 518 0.945
(1.00) 		0.402
(1.00) 		0.209
(1.00) 		0.541
(1.00) 		1
(1.00) 		0.0354
(1.00)
8q loss 0 (0%) 531 0.975
(1.00) 		0.414
(1.00) 		0.497
(1.00) 		0.133
(1.00) 		0.644
(1.00) 		0.00663
(1.00)
9p loss 0 (0%) 391 0.665
(1.00) 		0.517
(1.00) 		0.848
(1.00) 		0.427
(1.00) 		0.159
(1.00) 		0.188
(1.00)
9q loss 0 (0%) 489 0.369
(1.00) 		0.543
(1.00) 		0.0139
(1.00) 		0.384
(1.00) 		0.885
(1.00) 		0.385
(1.00)
11p loss 0 (0%) 488 0.295
(1.00) 		0.208
(1.00) 		0.222
(1.00) 		0.89
(1.00) 		0.246
(1.00) 		0.389
(1.00)
11q loss 0 (0%) 497 0.986
(1.00) 		0.783
(1.00) 		0.772
(1.00) 		0.934
(1.00) 		0.872
(1.00) 		0.0447
(1.00)
12p loss 0 (0%) 514 0.655
(1.00) 		0.801
(1.00) 		0.864
(1.00) 		0.89
(1.00) 		0.507
(1.00) 		0.589
(1.00)
12q loss 0 (0%) 517 0.64
(1.00) 		0.873
(1.00) 		0.859
(1.00) 		0.61
(1.00) 		0.47
(1.00) 		0.706
(1.00)
13q loss 0 (0%) 416 0.895
(1.00) 		0.539
(1.00) 		0.545
(1.00) 		0.538
(1.00) 		0.448
(1.00) 		0.915
(1.00)
14q loss 0 (0%) 432 0.959
(1.00) 		0.947
(1.00) 		0.752
(1.00) 		0.769
(1.00) 		0.393
(1.00) 		0.657
(1.00)
15q loss 0 (0%) 490 0.458
(1.00) 		0.198
(1.00) 		0.132
(1.00) 		0.486
(1.00) 		0.272
(1.00) 		0.307
(1.00)
16p loss 0 (0%) 529 0.161
(1.00) 		0.126
(1.00) 		0.514
(1.00) 		0.0929
(1.00) 		1
(1.00) 		1
(1.00)
16q loss 0 (0%) 512 0.0879
(1.00) 		0.96
(1.00) 		1
(1.00) 		0.204
(1.00) 		1
(1.00) 		0.86
(1.00)
17p loss 0 (0%) 517 0.455
(1.00) 		0.299
(1.00) 		0.722
(1.00) 		0.643
(1.00) 		0.776
(1.00) 		0.851
(1.00)
17q loss 0 (0%) 533 0.92
(1.00) 		0.326
(1.00) 		0.815
(1.00) 		0.62
(1.00) 		1
(1.00) 		0.616
(1.00)
18p loss 0 (0%) 508 0.207
(1.00) 		0.552
(1.00) 		1
(1.00) 		0.625
(1.00) 		0.448
(1.00) 		0.402
(1.00)
18q loss 0 (0%) 513 0.265
(1.00) 		0.285
(1.00) 		0.399
(1.00) 		0.349
(1.00) 		0.391
(1.00) 		0.723
(1.00)
19p loss 0 (0%) 539 0.62
(1.00) 		0.194
(1.00) 		0.775
(1.00) 		0.58
(1.00) 		1
(1.00) 		0.763
(1.00)
19q loss 0 (0%) 534 0.185
(1.00) 		0.685
(1.00) 		1
(1.00) 		0.276
(1.00) 		0.206
(1.00) 		0.605
(1.00)
20p loss 0 (0%) 541 0.45
(1.00) 		0.136
(1.00) 		0.357
(1.00) 		0.7
(1.00) 		1
(1.00) 		1
(1.00)
20q loss 0 (0%) 542 0.468
(1.00) 		0.456
(1.00) 		0.526
(1.00) 		0.612
(1.00) 		1
(1.00) 		0.508
(1.00)
21q loss 0 (0%) 527 0.828
(1.00) 		0.228
(1.00) 		0.212
(1.00) 		0.629
(1.00) 		0.709
(1.00) 		0.658
(1.00)
22q loss 0 (0%) 412 0.571
(1.00) 		0.0172
(1.00) 		0.92
(1.00) 		0.613
(1.00) 		0.76
(1.00) 		0.673
(1.00)
Xq loss 0 (0%) 541 0.237
(1.00) 		0.0249
(1.00) 		0.759
(1.00) 		0.914
(1.00) 		1
(1.00) 		0.516
(1.00)
'6p gain' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.000254 (t-test), Q value = 0.12

Table S1.  Gene #11: '6p gain' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 409 77.3 (14.7)
6P GAIN CNV 4 80.0 (0.0)
6P GAIN WILD-TYPE 405 77.3 (14.8)

Figure S1.  Get High-res Image Gene #11: '6p gain' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'6q gain' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.000254 (t-test), Q value = 0.12

Table S2.  Gene #12: '6q gain' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 409 77.3 (14.7)
6Q GAIN CNV 5 80.0 (0.0)
6Q GAIN WILD-TYPE 404 77.3 (14.8)

Figure S2.  Get High-res Image Gene #12: '6q gain' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'7p gain' versus 'AGE'

P value = 1.61e-05 (t-test), Q value = 0.0075

Table S3.  Gene #13: '7p gain' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 552 57.9 (14.4)
7P GAIN CNV 427 59.7 (12.4)
7P GAIN WILD-TYPE 125 51.8 (18.6)

Figure S3.  Get High-res Image Gene #13: '7p gain' versus Clinical Feature #2: 'AGE'

'7q gain' versus 'AGE'

P value = 2.1e-05 (t-test), Q value = 0.0098

Table S4.  Gene #14: '7q gain' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 552 57.9 (14.4)
7Q GAIN CNV 431 59.7 (12.2)
7Q GAIN WILD-TYPE 121 51.6 (19.1)

Figure S4.  Get High-res Image Gene #14: '7q gain' versus Clinical Feature #2: 'AGE'

'10p gain' versus 'AGE'

P value = 9.31e-05 (t-test), Q value = 0.043

Table S5.  Gene #19: '10p gain' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 552 57.9 (14.4)
10P GAIN CNV 11 34.3 (12.9)
10P GAIN WILD-TYPE 541 58.4 (14.0)

Figure S5.  Get High-res Image Gene #19: '10p gain' versus Clinical Feature #2: 'AGE'

'10p gain' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.000254 (t-test), Q value = 0.12

Table S6.  Gene #19: '10p gain' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 409 77.3 (14.7)
10P GAIN CNV 11 80.0 (0.0)
10P GAIN WILD-TYPE 398 77.2 (14.9)

Figure S6.  Get High-res Image Gene #19: '10p gain' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'10p loss' versus 'Time to Death'

P value = 0.000393 (logrank test), Q value = 0.18

Table S7.  Gene #58: '10p loss' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 552 419 0.1 - 127.6 (9.6)
10P LOSS CNV 430 327 0.1 - 127.6 (9.4)
10P LOSS WILD-TYPE 122 92 0.2 - 108.8 (10.7)

Figure S7.  Get High-res Image Gene #58: '10p loss' versus Clinical Feature #1: 'Time to Death'

'10p loss' versus 'AGE'

P value = 5.71e-09 (t-test), Q value = 2.7e-06

Table S8.  Gene #58: '10p loss' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 552 57.9 (14.4)
10P LOSS CNV 430 60.3 (11.9)
10P LOSS WILD-TYPE 122 49.4 (18.5)

Figure S8.  Get High-res Image Gene #58: '10p loss' versus Clinical Feature #2: 'AGE'

'10p loss' versus 'HISTOLOGICAL.TYPE'

P value = 0.000112 (Fisher's exact test), Q value = 0.052

Table S9.  Gene #58: '10p loss' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients GLIOBLASTOMA MULTIFORME (GBM) TREATED PRIMARY GBM UNTREATED PRIMARY (DE NOVO) GBM
ALL 8 18 526
10P LOSS CNV 4 7 419
10P LOSS WILD-TYPE 4 11 107

Figure S9.  Get High-res Image Gene #58: '10p loss' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'10q loss' versus 'AGE'

P value = 9.25e-07 (t-test), Q value = 0.00043

Table S10.  Gene #59: '10q loss' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 552 57.9 (14.4)
10Q LOSS CNV 443 59.8 (12.5)
10Q LOSS WILD-TYPE 109 50.2 (18.4)

Figure S10.  Get High-res Image Gene #59: '10q loss' versus Clinical Feature #2: 'AGE'

Methods & Data
Input

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

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

  • Number of patients = 552

  • Number of significantly arm-level cnvs = 79

  • Number of selected clinical features = 6

  • 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)
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