Glioblastoma Multiforme: Correlation between copy number variations of arm-level result and selected clinical features
Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/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 77 arm-level results and 6 clinical features across 540 patients, 9 significant findings detected with Q value < 0.25.

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

  • 7p gain cnv correlated to 'AGE'.

  • 7q gain cnv correlated to 'AGE'.

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

  • 20p gain cnv correlated to 'AGE'.

  • 20q 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 77 arm-level results and 6 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 9 significant findings detected.

Clinical
Features 		Time
to
Death 		AGE GENDER KARNOFSKY
PERFORMANCE
SCORE 		RADIATIONS
RADIATION
REGIMENINDICATION 		NEOADJUVANT
THERAPY
nCNV (%) nWild-Type logrank test t-test Fisher's exact test t-test Fisher's exact test Fisher's exact test
10p loss 432 (80%) 108 0.000194
(0.0885) 		9.57e-09
(4.4e-06) 		0.189
(1.00) 		0.361
(1.00) 		0.645
(1.00) 		0.198
(1.00)
6p gain 11 (2%) 529 0.0971
(1.00) 		0.0389
(1.00) 		1
(1.00) 		0.00032
(0.145) 		0.516
(1.00) 		0.763
(1.00)
7p gain 421 (78%) 119 0.00853
(1.00) 		1.9e-06
(0.000868) 		0.459
(1.00) 		0.871
(1.00) 		0.0578
(1.00) 		0.755
(1.00)
7q gain 426 (79%) 114 0.019
(1.00) 		4.35e-05
(0.0199) 		0.452
(1.00) 		0.976
(1.00) 		0.175
(1.00) 		0.292
(1.00)
10p gain 8 (1%) 532 0.011
(1.00) 		0.00345
(1.00) 		0.064
(1.00) 		0.00032
(0.145) 		1
(1.00) 		0.485
(1.00)
20p gain 165 (31%) 375 0.959
(1.00) 		0.000227
(0.103) 		1
(1.00) 		0.482
(1.00) 		0.134
(1.00) 		1
(1.00)
20q gain 163 (30%) 377 0.864
(1.00) 		0.000475
(0.215) 		0.702
(1.00) 		0.524
(1.00) 		0.131
(1.00) 		0.778
(1.00)
10q loss 445 (82%) 95 0.00135
(0.609) 		6.89e-08
(3.16e-05) 		0.818
(1.00) 		0.184
(1.00) 		1
(1.00) 		0.259
(1.00)
1p gain 37 (7%) 503 0.963
(1.00) 		0.815
(1.00) 		0.863
(1.00) 		0.552
(1.00) 		0.856
(1.00) 		0.733
(1.00)
1q gain 42 (8%) 498 0.65
(1.00) 		0.529
(1.00) 		0.871
(1.00) 		0.998
(1.00) 		0.864
(1.00) 		0.749
(1.00)
2p gain 18 (3%) 522 0.823
(1.00) 		0.418
(1.00) 		0.807
(1.00) 		0.791
(1.00) 		0.0702
(1.00) 		0.242
(1.00)
2q gain 15 (3%) 525 0.161
(1.00) 		0.971
(1.00) 		0.79
(1.00) 		0.595
(1.00) 		0.0453
(1.00) 		0.794
(1.00)
3p gain 28 (5%) 512 0.27
(1.00) 		0.125
(1.00) 		0.843
(1.00) 		0.538
(1.00) 		0.534
(1.00) 		0.562
(1.00)
3q gain 32 (6%) 508 0.525
(1.00) 		0.128
(1.00) 		0.854
(1.00) 		0.359
(1.00) 		0.7
(1.00) 		0.362
(1.00)
4p gain 12 (2%) 528 0.125
(1.00) 		0.466
(1.00) 		0.771
(1.00) 		0.497
(1.00) 		0.115
(1.00) 		0.0399
(1.00)
4q gain 12 (2%) 528 0.00934
(1.00) 		0.33
(1.00) 		0.555
(1.00) 		0.195
(1.00) 		0.115
(1.00) 		0.391
(1.00)
5p gain 26 (5%) 514 0.577
(1.00) 		0.226
(1.00) 		0.541
(1.00) 		0.0543
(1.00) 		0.0831
(1.00) 		0.549
(1.00)
5q gain 21 (4%) 519 0.925
(1.00) 		0.815
(1.00) 		0.499
(1.00) 		0.211
(1.00) 		0.239
(1.00) 		0.661
(1.00)
6q gain 11 (2%) 529 0.21
(1.00) 		0.0341
(1.00) 		1
(1.00) 		0.849
(1.00) 		1
(1.00) 		0.364
(1.00)
8p gain 26 (5%) 514 0.189
(1.00) 		0.437
(1.00) 		1
(1.00) 		0.397
(1.00) 		0.518
(1.00) 		0.317
(1.00)
8q gain 34 (6%) 506 0.448
(1.00) 		0.0299
(1.00) 		0.858
(1.00) 		0.897
(1.00) 		0.851
(1.00) 		1
(1.00)
9p gain 16 (3%) 524 0.413
(1.00) 		0.0649
(1.00) 		0.443
(1.00) 		0.0955
(1.00) 		0.414
(1.00) 		0.613
(1.00)
9q gain 36 (7%) 504 0.0247
(1.00) 		0.0297
(1.00) 		0.219
(1.00) 		0.0236
(1.00) 		0.46
(1.00) 		0.734
(1.00)
11p gain 6 (1%) 534 0.221
(1.00) 		0.765
(1.00) 		0.222
(1.00) 		0.24
(1.00) 		1
(1.00) 		0.689
(1.00)
11q gain 7 (1%) 533 0.952
(1.00) 		0.989
(1.00) 		0.708
(1.00) 		0.651
(1.00) 		1
(1.00) 		0.016
(1.00)
12p gain 39 (7%) 501 0.46
(1.00) 		0.0976
(1.00) 		1
(1.00) 		0.363
(1.00) 		0.153
(1.00) 		0.74
(1.00)
12q gain 28 (5%) 512 0.25
(1.00) 		0.93
(1.00) 		0.843
(1.00) 		0.683
(1.00) 		0.298
(1.00) 		1
(1.00)
13q gain 3 (1%) 537 0.589
(1.00) 		0.886
(1.00) 		0.566
(1.00) 		0.247
(1.00) 		0.239
(1.00) 		0.605
(1.00)
14q gain 8 (1%) 532 0.0334
(1.00) 		0.737
(1.00) 		0.276
(1.00) 		0.353
(1.00) 		0.714
(1.00) 		1
(1.00)
15q gain 7 (1%) 533 0.289
(1.00) 		0.535
(1.00) 		0.708
(1.00) 		0.626
(1.00) 		0.685
(1.00) 		0.712
(1.00)
16p gain 18 (3%) 522 0.126
(1.00) 		0.0436
(1.00) 		0.464
(1.00) 		0.716
(1.00) 		0.203
(1.00) 		1
(1.00)
16q gain 17 (3%) 523 0.0362
(1.00) 		0.0294
(1.00) 		0.131
(1.00) 		0.781
(1.00) 		0.291
(1.00) 		1
(1.00)
17p gain 13 (2%) 527 0.58
(1.00) 		0.00347
(1.00) 		0.261
(1.00) 		0.0254
(1.00) 		1
(1.00) 		0.78
(1.00)
17q gain 24 (4%) 516 0.885
(1.00) 		0.508
(1.00) 		0.394
(1.00) 		0.00384
(1.00) 		0.271
(1.00) 		0.678
(1.00)
18p gain 27 (5%) 513 0.806
(1.00) 		0.798
(1.00) 		0.842
(1.00) 		0.2
(1.00) 		0.835
(1.00) 		1
(1.00)
18q gain 29 (5%) 511 0.547
(1.00) 		0.93
(1.00) 		0.848
(1.00) 		0.477
(1.00) 		0.687
(1.00) 		1
(1.00)
19p gain 171 (32%) 369 0.158
(1.00) 		0.374
(1.00) 		0.777
(1.00) 		0.862
(1.00) 		0.233
(1.00) 		0.853
(1.00)
19q gain 150 (28%) 390 0.251
(1.00) 		0.243
(1.00) 		1
(1.00) 		0.887
(1.00) 		0.354
(1.00) 		0.923
(1.00)
21q gain 29 (5%) 511 0.144
(1.00) 		0.762
(1.00) 		0.697
(1.00) 		0.495
(1.00) 		0.222
(1.00) 		1
(1.00)
22q gain 11 (2%) 529 0.871
(1.00) 		0.79
(1.00) 		1
(1.00) 		0.225
(1.00) 		0.516
(1.00) 		0.763
(1.00)
1p loss 7 (1%) 533 0.036
(1.00) 		0.77
(1.00) 		0.708
(1.00) 		0.678
(1.00) 		0.44
(1.00) 		1
(1.00)
1q loss 4 (1%) 536 0.947
(1.00) 		0.177
(1.00) 		1
(1.00) 		0.29
(1.00) 		1
(1.00) 		0.626
(1.00)
2p loss 10 (2%) 530 0.236
(1.00) 		0.534
(1.00) 		0.207
(1.00) 		0.383
(1.00) 		0.733
(1.00) 		0.755
(1.00)
2q loss 9 (2%) 531 0.139
(1.00) 		0.407
(1.00) 		0.166
(1.00) 		0.754
(1.00) 		1
(1.00) 		1
(1.00)
3p loss 31 (6%) 509 0.152
(1.00) 		0.0698
(1.00) 		0.851
(1.00) 		0.571
(1.00) 		0.164
(1.00) 		0.0156
(1.00)
3q loss 25 (5%) 515 0.843
(1.00) 		0.0959
(1.00) 		0.68
(1.00) 		0.559
(1.00) 		0.827
(1.00) 		0.541
(1.00)
4p loss 30 (6%) 510 0.115
(1.00) 		0.143
(1.00) 		0.338
(1.00) 		0.761
(1.00) 		0.42
(1.00) 		0.71
(1.00)
4q loss 25 (5%) 515 0.0533
(1.00) 		0.0657
(1.00) 		0.835
(1.00) 		0.0524
(1.00) 		0.511
(1.00) 		0.307
(1.00)
5p loss 20 (4%) 520 0.0532
(1.00) 		0.214
(1.00) 		0.647
(1.00) 		0.46
(1.00) 		0.331
(1.00) 		0.823
(1.00)
5q loss 18 (3%) 522 0.0635
(1.00) 		0.327
(1.00) 		0.22
(1.00) 		0.133
(1.00) 		0.203
(1.00) 		1
(1.00)
6p loss 54 (10%) 486 0.0185
(1.00) 		0.593
(1.00) 		0.309
(1.00) 		0.955
(1.00) 		1
(1.00) 		0.774
(1.00)
6q loss 92 (17%) 448 0.145
(1.00) 		0.446
(1.00) 		0.35
(1.00) 		0.904
(1.00) 		1
(1.00) 		1
(1.00)
7p loss 4 (1%) 536 0.563
(1.00) 		0.758
(1.00) 		0.652
(1.00) 		1
(1.00) 		1
(1.00)
7q loss 4 (1%) 536 0.355
(1.00) 		0.266
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00)
8p loss 36 (7%) 504 0.855
(1.00) 		0.375
(1.00) 		0.219
(1.00) 		0.947
(1.00) 		0.137
(1.00) 		0.388
(1.00)
8q loss 22 (4%) 518 0.221
(1.00) 		0.965
(1.00) 		0.376
(1.00) 		0.649
(1.00) 		0.0186
(1.00) 		0.384
(1.00)
9p loss 207 (38%) 333 0.568
(1.00) 		0.97
(1.00) 		0.417
(1.00) 		0.286
(1.00) 		0.776
(1.00) 		0.723
(1.00)
9q loss 76 (14%) 464 0.789
(1.00) 		0.955
(1.00) 		0.101
(1.00) 		0.209
(1.00) 		0.792
(1.00) 		1
(1.00)
11p loss 62 (11%) 478 0.351
(1.00) 		0.357
(1.00) 		0.0977
(1.00) 		0.702
(1.00) 		0.147
(1.00) 		0.686
(1.00)
11q loss 51 (9%) 489 0.657
(1.00) 		0.691
(1.00) 		0.454
(1.00) 		0.652
(1.00) 		0.0398
(1.00) 		0.184
(1.00)
12p loss 49 (9%) 491 0.482
(1.00) 		0.92
(1.00) 		0.649
(1.00) 		0.963
(1.00) 		0.265
(1.00) 		0.233
(1.00)
12q loss 45 (8%) 495 0.506
(1.00) 		0.837
(1.00) 		0.752
(1.00) 		0.865
(1.00) 		0.868
(1.00) 		0.877
(1.00)
13q loss 150 (28%) 390 0.878
(1.00) 		0.456
(1.00) 		0.17
(1.00) 		0.519
(1.00) 		0.918
(1.00) 		0.773
(1.00)
14q loss 135 (25%) 405 0.786
(1.00) 		0.167
(1.00) 		0.417
(1.00) 		0.904
(1.00) 		0.749
(1.00) 		0.371
(1.00)
15q loss 63 (12%) 477 0.656
(1.00) 		0.597
(1.00) 		0.338
(1.00) 		0.554
(1.00) 		0.568
(1.00) 		0.688
(1.00)
16p loss 31 (6%) 509 0.0334
(1.00) 		0.539
(1.00) 		0.573
(1.00) 		0.414
(1.00) 		0.844
(1.00) 		0.198
(1.00)
16q loss 45 (8%) 495 0.0804
(1.00) 		0.424
(1.00) 		1
(1.00) 		0.188
(1.00) 		0.868
(1.00) 		0.351
(1.00)
17p loss 35 (6%) 505 0.607
(1.00) 		0.363
(1.00) 		0.156
(1.00) 		0.844
(1.00) 		1
(1.00) 		0.227
(1.00)
17q loss 14 (3%) 526 0.22
(1.00) 		0.844
(1.00) 		0.268
(1.00) 		0.478
(1.00) 		1
(1.00) 		0.0595
(1.00)
18p loss 46 (9%) 494 0.566
(1.00) 		0.72
(1.00) 		0.433
(1.00) 		0.191
(1.00) 		0.624
(1.00) 		0.645
(1.00)
18q loss 40 (7%) 500 0.785
(1.00) 		0.83
(1.00) 		0.317
(1.00) 		0.0745
(1.00) 		0.481
(1.00) 		0.744
(1.00)
19p loss 14 (3%) 526 0.596
(1.00) 		0.215
(1.00) 		0.424
(1.00) 		0.889
(1.00) 		0.775
(1.00) 		0.59
(1.00)
19q loss 28 (5%) 512 0.271
(1.00) 		0.976
(1.00) 		0.697
(1.00) 		0.924
(1.00) 		0.836
(1.00) 		1
(1.00)
20p loss 10 (2%) 530 0.429
(1.00) 		0.218
(1.00) 		0.207
(1.00) 		0.225
(1.00) 		1
(1.00) 		1
(1.00)
20q loss 9 (2%) 531 0.446
(1.00) 		0.714
(1.00) 		0.494
(1.00) 		0.158
(1.00) 		0.476
(1.00) 		1
(1.00)
21q loss 27 (5%) 513 0.959
(1.00) 		0.34
(1.00) 		0.227
(1.00) 		0.382
(1.00) 		1
(1.00) 		0.694
(1.00)
22q loss 142 (26%) 398 0.524
(1.00) 		0.0121
(1.00) 		0.921
(1.00) 		0.569
(1.00) 		0.753
(1.00) 		0.559
(1.00)
'6p gain mutation analysis' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.00032 (t-test), Q value = 0.15

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

nPatients Mean (Std.Dev)
ALL 406 77.3 (14.8)
6P GAIN MUTATED 5 80.0 (0.0)
6P GAIN WILD-TYPE 401 77.3 (14.9)

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

'7p gain mutation analysis' versus 'AGE'

P value = 1.9e-06 (t-test), Q value = 0.00087

Table S2.  Gene #13: '7p gain mutation analysis' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 540 57.8 (14.3)
7P GAIN MUTATED 421 59.7 (12.4)
7P GAIN WILD-TYPE 119 50.9 (18.2)

Figure S2.  Get High-res Image Gene #13: '7p gain mutation analysis' versus Clinical Feature #2: 'AGE'

'7q gain mutation analysis' versus 'AGE'

P value = 4.35e-05 (t-test), Q value = 0.02

Table S3.  Gene #14: '7q gain mutation analysis' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 540 57.8 (14.3)
7Q GAIN MUTATED 426 59.4 (12.4)
7Q GAIN WILD-TYPE 114 51.6 (18.7)

Figure S3.  Get High-res Image Gene #14: '7q gain mutation analysis' versus Clinical Feature #2: 'AGE'

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

P value = 0.00032 (t-test), Q value = 0.15

Table S4.  Gene #19: '10p gain mutation analysis' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 406 77.3 (14.8)
10P GAIN MUTATED 8 80.0 (0.0)
10P GAIN WILD-TYPE 398 77.3 (14.9)

Figure S4.  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 = 0.000227 (t-test), Q value = 0.1

Table S5.  Gene #35: '20p gain mutation analysis' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 540 57.8 (14.3)
20P GAIN MUTATED 165 61.0 (12.4)
20P GAIN WILD-TYPE 375 56.4 (14.9)

Figure S5.  Get High-res Image Gene #35: '20p gain mutation analysis' versus Clinical Feature #2: 'AGE'

'20q gain mutation analysis' versus 'AGE'

P value = 0.000475 (t-test), Q value = 0.21

Table S6.  Gene #36: '20q gain mutation analysis' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 540 57.8 (14.3)
20Q GAIN MUTATED 163 60.9 (12.7)
20Q GAIN WILD-TYPE 377 56.4 (14.8)

Figure S6.  Get High-res Image Gene #36: '20q gain mutation analysis' versus Clinical Feature #2: 'AGE'

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

P value = 0.000194 (logrank test), Q value = 0.088

Table S7.  Gene #57: '10p loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 540 407 0.1 - 127.6 (9.6)
10P LOSS MUTATED 432 326 0.1 - 127.6 (9.3)
10P LOSS WILD-TYPE 108 81 0.2 - 108.8 (10.7)

Figure S7.  Get High-res Image Gene #57: '10p loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

'10p loss mutation analysis' versus 'AGE'

P value = 9.57e-09 (t-test), Q value = 4.4e-06

Table S8.  Gene #57: '10p loss mutation analysis' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 540 57.8 (14.3)
10P LOSS MUTATED 432 60.0 (12.1)
10P LOSS WILD-TYPE 108 48.7 (18.3)

Figure S8.  Get High-res Image Gene #57: '10p loss mutation analysis' versus Clinical Feature #2: 'AGE'

'10q loss mutation analysis' versus 'AGE'

P value = 6.89e-08 (t-test), Q value = 3.2e-05

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

nPatients Mean (Std.Dev)
ALL 540 57.8 (14.3)
10Q LOSS MUTATED 445 59.7 (12.6)
10Q LOSS WILD-TYPE 95 48.5 (18.0)

Figure S9.  Get High-res Image Gene #58: '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.clin.merged.picked.txt

  • Number of patients = 540

  • Number of significantly arm-level cnvs = 77

  • 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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