Glioblastoma Multiforme: Correlation between copy number variations of arm-level result and selected clinical features
(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 selected clinical features.

Summary

Testing the association between copy number variation 79 arm-level results and 5 clinical features across 544 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'.

  • 20p gain cnv correlated to 'AGE'.

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

  • 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 5 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 		RADIATIONS
RADIATION
REGIMENINDICATION
nCNV (%) nWild-Type logrank test t-test Fisher's exact test t-test Fisher's exact test
10p gain 11 (2%) 533 0.00302
(1.00) 		9.55e-05
(0.0371) 		0.0296
(1.00) 		0.000254
(0.098) 		1
(1.00)
10p loss 426 (78%) 118 0.000152
(0.0586) 		1.26e-09
(4.92e-07) 		0.395
(1.00) 		0.358
(1.00) 		0.316
(1.00)
6p gain 7 (1%) 537 0.1
(1.00) 		0.0989
(1.00) 		1
(1.00) 		0.000254
(0.098) 		0.438
(1.00)
6q gain 9 (2%) 535 0.134
(1.00) 		0.0449
(1.00) 		0.745
(1.00) 		0.000254
(0.098) 		0.725
(1.00)
7p gain 423 (78%) 121 0.00488
(1.00) 		5.79e-06
(0.00226) 		1
(1.00) 		0.53
(1.00) 		0.0976
(1.00)
7q gain 427 (78%) 117 0.00601
(1.00) 		7.78e-06
(0.00303) 		0.594
(1.00) 		0.584
(1.00) 		0.148
(1.00)
20p gain 174 (32%) 370 0.888
(1.00) 		0.000315
(0.121) 		0.707
(1.00) 		0.371
(1.00) 		0.279
(1.00)
10q loss 439 (81%) 105 0.00068
(0.259) 		2.4e-07
(9.39e-05) 		0.58
(1.00) 		0.0968
(1.00) 		0.562
(1.00)
1p gain 38 (7%) 506 0.607
(1.00) 		0.617
(1.00) 		0.606
(1.00) 		0.868
(1.00) 		0.476
(1.00)
1q gain 43 (8%) 501 0.987
(1.00) 		0.503
(1.00) 		1
(1.00) 		0.979
(1.00) 		0.612
(1.00)
2p gain 18 (3%) 526 0.577
(1.00) 		0.196
(1.00) 		0.219
(1.00) 		0.301
(1.00) 		0.448
(1.00)
2q gain 16 (3%) 528 0.772
(1.00) 		0.501
(1.00) 		0.44
(1.00) 		0.737
(1.00) 		0.29
(1.00)
3p gain 30 (6%) 514 0.124
(1.00) 		0.532
(1.00) 		1
(1.00) 		0.837
(1.00) 		0.322
(1.00)
3q gain 30 (6%) 514 0.126
(1.00) 		0.437
(1.00) 		1
(1.00) 		0.892
(1.00) 		0.322
(1.00)
4p gain 15 (3%) 529 0.0811
(1.00) 		0.0695
(1.00) 		0.791
(1.00) 		0.127
(1.00) 		0.407
(1.00)
4q gain 14 (3%) 530 0.031
(1.00) 		0.342
(1.00) 		0.789
(1.00) 		0.146
(1.00) 		0.245
(1.00)
5p gain 28 (5%) 516 0.916
(1.00) 		0.206
(1.00) 		0.697
(1.00) 		0.0435
(1.00) 		0.144
(1.00)
5q gain 21 (4%) 523 0.711
(1.00) 		0.52
(1.00) 		0.497
(1.00) 		0.301
(1.00) 		0.238
(1.00)
8p gain 26 (5%) 518 0.765
(1.00) 		0.553
(1.00) 		0.539
(1.00) 		0.402
(1.00) 		0.831
(1.00)
8q gain 31 (6%) 513 0.833
(1.00) 		0.205
(1.00) 		0.345
(1.00) 		0.389
(1.00) 		1
(1.00)
9p gain 17 (3%) 527 0.24
(1.00) 		0.0378
(1.00) 		0.314
(1.00) 		0.00622
(1.00) 		0.6
(1.00)
9q gain 32 (6%) 512 0.0339
(1.00) 		0.0482
(1.00) 		0.0612
(1.00) 		0.0256
(1.00) 		0.439
(1.00)
11p gain 4 (1%) 540 0.568
(1.00) 		0.383
(1.00) 		0.65
(1.00) 		0.841
(1.00) 		1
(1.00)
11q gain 7 (1%) 537 0.152
(1.00) 		0.211
(1.00) 		0.254
(1.00) 		0.0913
(1.00) 		0.438
(1.00)
12p gain 40 (7%) 504 0.928
(1.00) 		0.15
(1.00) 		1
(1.00) 		0.93
(1.00) 		0.6
(1.00)
12q gain 30 (6%) 514 0.498
(1.00) 		0.667
(1.00) 		0.445
(1.00) 		0.554
(1.00) 		0.322
(1.00)
13q gain 3 (1%) 541 0.991
(1.00) 		0.104
(1.00) 		0.0612
(1.00) 		0.0329
(1.00)
14q gain 6 (1%) 538 0.0771
(1.00) 		0.816
(1.00) 		0.685
(1.00) 		0.514
(1.00) 		1
(1.00)
15q gain 5 (1%) 539 0.427
(1.00) 		0.19
(1.00) 		1
(1.00) 		0.292
(1.00) 		0.659
(1.00)
16p gain 17 (3%) 527 0.044
(1.00) 		0.0244
(1.00) 		0.13
(1.00) 		0.71
(1.00) 		0.11
(1.00)
16q gain 17 (3%) 527 0.0449
(1.00) 		0.0676
(1.00) 		0.0422
(1.00) 		0.971
(1.00) 		0.291
(1.00)
17p gain 10 (2%) 534 0.0168
(1.00) 		0.0354
(1.00) 		0.747
(1.00) 		0.0913
(1.00) 		1
(1.00)
17q gain 18 (3%) 526 0.0339
(1.00) 		0.123
(1.00) 		1
(1.00) 		0.0318
(1.00) 		0.801
(1.00)
18p gain 26 (5%) 518 0.848
(1.00) 		0.929
(1.00) 		1
(1.00) 		0.0964
(1.00) 		0.831
(1.00)
18q gain 28 (5%) 516 0.82
(1.00) 		0.938
(1.00) 		0.697
(1.00) 		0.301
(1.00) 		0.836
(1.00)
19p gain 170 (31%) 374 0.197
(1.00) 		0.452
(1.00) 		0.85
(1.00) 		0.795
(1.00) 		0.277
(1.00)
19q gain 147 (27%) 397 0.141
(1.00) 		0.246
(1.00) 		0.694
(1.00) 		0.773
(1.00) 		0.837
(1.00)
20q gain 172 (32%) 372 0.651
(1.00) 		0.000657
(0.251) 		0.573
(1.00) 		0.406
(1.00) 		0.167
(1.00)
21q gain 32 (6%) 512 0.137
(1.00) 		0.856
(1.00) 		1
(1.00) 		0.634
(1.00) 		0.439
(1.00)
22q gain 11 (2%) 533 0.97
(1.00) 		0.391
(1.00) 		1
(1.00) 		0.968
(1.00) 		0.516
(1.00)
Xq gain 3 (1%) 541 0.128
(1.00) 		0.593
(1.00) 		1
(1.00) 		1
(1.00)
1p loss 5 (1%) 539 0.0316
(1.00) 		0.707
(1.00) 		0.653
(1.00) 		0.868
(1.00) 		1
(1.00)
1q loss 4 (1%) 540 0.721
(1.00) 		0.0658
(1.00) 		0.65
(1.00) 		0.0873
(1.00) 		1
(1.00)
2p loss 10 (2%) 534 0.179
(1.00) 		0.516
(1.00) 		0.204
(1.00) 		0.76
(1.00) 		1
(1.00)
2q loss 9 (2%) 535 0.235
(1.00) 		0.743
(1.00) 		0.328
(1.00) 		0.76
(1.00) 		0.725
(1.00)
3p loss 22 (4%) 522 0.293
(1.00) 		0.0224
(1.00) 		1
(1.00) 		0.846
(1.00) 		0.0184
(1.00)
3q loss 16 (3%) 528 0.774
(1.00) 		0.231
(1.00) 		0.44
(1.00) 		0.708
(1.00) 		0.29
(1.00)
4p loss 25 (5%) 519 0.14
(1.00) 		0.297
(1.00) 		0.142
(1.00) 		0.825
(1.00) 		0.083
(1.00)
4q loss 24 (4%) 520 0.21
(1.00) 		0.229
(1.00) 		0.67
(1.00) 		0.167
(1.00) 		0.51
(1.00)
5p loss 21 (4%) 523 0.0545
(1.00) 		0.277
(1.00) 		0.821
(1.00) 		0.503
(1.00) 		0.238
(1.00)
5q loss 20 (4%) 524 0.0597
(1.00) 		0.294
(1.00) 		0.645
(1.00) 		0.786
(1.00) 		0.33
(1.00)
6p loss 47 (9%) 497 0.008
(1.00) 		0.761
(1.00) 		0.878
(1.00) 		0.711
(1.00) 		0.747
(1.00)
6q loss 79 (15%) 465 0.277
(1.00) 		0.656
(1.00) 		0.534
(1.00) 		0.581
(1.00) 		0.897
(1.00)
7p loss 5 (1%) 539 0.514
(1.00) 		0.658
(1.00) 		1
(1.00) 		0.61
(1.00) 		1
(1.00)
7q loss 4 (1%) 540 0.617
(1.00) 		0.362
(1.00) 		1
(1.00) 		1
(1.00)
8p loss 34 (6%) 510 0.972
(1.00) 		0.422
(1.00) 		0.209
(1.00) 		0.541
(1.00) 		0.0233
(1.00)
8q loss 21 (4%) 523 0.995
(1.00) 		0.435
(1.00) 		0.497
(1.00) 		0.133
(1.00) 		0.00375
(1.00)
9p loss 160 (29%) 384 0.748
(1.00) 		0.41
(1.00) 		0.773
(1.00) 		0.427
(1.00) 		0.42
(1.00)
9q loss 63 (12%) 481 0.396
(1.00) 		0.575
(1.00) 		0.0139
(1.00) 		0.384
(1.00) 		0.776
(1.00)
11p loss 64 (12%) 480 0.312
(1.00) 		0.222
(1.00) 		0.221
(1.00) 		0.89
(1.00) 		0.254
(1.00)
11q loss 55 (10%) 489 0.96
(1.00) 		0.814
(1.00) 		0.772
(1.00) 		0.934
(1.00) 		0.0146
(1.00)
12p loss 37 (7%) 507 0.598
(1.00) 		0.913
(1.00) 		0.864
(1.00) 		0.89
(1.00) 		0.586
(1.00)
12q loss 34 (6%) 510 0.578
(1.00) 		0.992
(1.00) 		1
(1.00) 		0.61
(1.00) 		0.706
(1.00)
13q loss 134 (25%) 410 0.978
(1.00) 		0.39
(1.00) 		0.417
(1.00) 		0.538
(1.00) 		0.749
(1.00)
14q loss 120 (22%) 424 0.995
(1.00) 		0.891
(1.00) 		0.752
(1.00) 		0.769
(1.00) 		1
(1.00)
15q loss 62 (11%) 482 0.433
(1.00) 		0.181
(1.00) 		0.132
(1.00) 		0.486
(1.00) 		0.25
(1.00)
16p loss 23 (4%) 521 0.155
(1.00) 		0.119
(1.00) 		0.514
(1.00) 		0.0929
(1.00) 		1
(1.00)
16q loss 40 (7%) 504 0.0807
(1.00) 		0.927
(1.00) 		1
(1.00) 		0.204
(1.00) 		1
(1.00)
17p loss 35 (6%) 509 0.466
(1.00) 		0.285
(1.00) 		0.722
(1.00) 		0.643
(1.00) 		1
(1.00)
17q loss 19 (3%) 525 0.936
(1.00) 		0.315
(1.00) 		0.815
(1.00) 		0.62
(1.00) 		0.626
(1.00)
18p loss 43 (8%) 501 0.302
(1.00) 		0.768
(1.00) 		0.871
(1.00) 		0.625
(1.00) 		0.612
(1.00)
18q loss 38 (7%) 506 0.384
(1.00) 		0.432
(1.00) 		0.497
(1.00) 		0.349
(1.00) 		0.589
(1.00)
19p loss 13 (2%) 531 0.635
(1.00) 		0.187
(1.00) 		0.775
(1.00) 		0.58
(1.00) 		1
(1.00)
19q loss 18 (3%) 526 0.194
(1.00) 		0.667
(1.00) 		1
(1.00) 		0.276
(1.00) 		0.801
(1.00)
20p loss 11 (2%) 533 0.456
(1.00) 		0.14
(1.00) 		0.357
(1.00) 		0.7
(1.00) 		0.752
(1.00)
20q loss 10 (2%) 534 0.473
(1.00) 		0.468
(1.00) 		0.527
(1.00) 		0.612
(1.00) 		0.304
(1.00)
21q loss 25 (5%) 519 0.849
(1.00) 		0.235
(1.00) 		0.212
(1.00) 		0.629
(1.00) 		0.666
(1.00)
22q loss 138 (25%) 406 0.647
(1.00) 		0.0288
(1.00) 		0.764
(1.00) 		0.613
(1.00) 		1
(1.00)
Xq loss 11 (2%) 533 0.23
(1.00) 		0.0255
(1.00) 		0.759
(1.00) 		0.914
(1.00) 		0.516
(1.00)
'6p gain mutation analysis' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.000254 (t-test), Q value = 0.098

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

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

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

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

P value = 0.000254 (t-test), Q value = 0.098

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

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

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

'7p gain mutation analysis' versus 'AGE'

P value = 5.79e-06 (t-test), Q value = 0.0023

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

nPatients Mean (Std.Dev)
ALL 544 57.8 (14.3)
7P GAIN MUTATED 423 59.7 (12.2)
7P GAIN WILD-TYPE 121 51.3 (18.5)

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

'7q gain mutation analysis' versus 'AGE'

P value = 7.78e-06 (t-test), Q value = 0.003

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

nPatients Mean (Std.Dev)
ALL 544 57.8 (14.3)
7Q GAIN MUTATED 427 59.7 (12.1)
7Q GAIN WILD-TYPE 117 51.1 (19.1)

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

'10p gain mutation analysis' versus 'AGE'

P value = 9.55e-05 (t-test), Q value = 0.037

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

nPatients Mean (Std.Dev)
ALL 544 57.8 (14.3)
10P GAIN MUTATED 11 34.3 (12.9)
10P GAIN WILD-TYPE 533 58.3 (13.9)

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

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

P value = 0.000254 (t-test), Q value = 0.098

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

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

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

'20p gain mutation analysis' versus 'AGE'

P value = 0.000315 (t-test), Q value = 0.12

Table S7.  Gene #35: '20p gain mutation analysis' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 544 57.8 (14.3)
20P GAIN MUTATED 174 60.9 (13.1)
20P GAIN WILD-TYPE 370 56.4 (14.6)

Figure S7.  Get High-res Image Gene #35: '20p gain mutation analysis' versus Clinical Feature #2: 'AGE'

'10p loss mutation analysis' versus 'Time to Death'

P value = 0.000152 (logrank test), Q value = 0.059

Table S8.  Gene #58: '10p loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 544 411 0.1 - 127.6 (9.6)
10P LOSS MUTATED 426 323 0.1 - 127.6 (9.3)
10P LOSS WILD-TYPE 118 88 0.2 - 108.8 (10.8)

Figure S8.  Get High-res Image Gene #58: '10p loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

'10p loss mutation analysis' versus 'AGE'

P value = 1.26e-09 (t-test), Q value = 4.9e-07

Table S9.  Gene #58: '10p loss mutation analysis' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 544 57.8 (14.3)
10P LOSS MUTATED 426 60.3 (11.8)
10P LOSS WILD-TYPE 118 48.8 (18.3)

Figure S9.  Get High-res Image Gene #58: '10p loss mutation analysis' versus Clinical Feature #2: 'AGE'

'10q loss mutation analysis' versus 'AGE'

P value = 2.4e-07 (t-test), Q value = 9.4e-05

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

nPatients Mean (Std.Dev)
ALL 544 57.8 (14.3)
10Q LOSS MUTATED 439 59.8 (12.4)
10Q LOSS WILD-TYPE 105 49.6 (18.2)

Figure S10.  Get High-res Image Gene #59: '10q loss mutation analysis' 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 = 544

  • Number of significantly arm-level cnvs = 79

  • Number of selected clinical features = 5

  • 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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