Ovarian Serous Cystadenocarcinoma: Correlation between copy number variations of arm-level result and selected clinical features
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 80 arm-level results and 6 clinical features across 552 patients, 8 significant findings detected with Q value < 0.25.

  • 2p gain cnv correlated to 'AGE'.

  • 3q gain cnv correlated to 'AGE'.

  • 10p gain cnv correlated to 'AGE'.

  • 12p gain cnv correlated to 'AGE'.

  • 12q gain cnv correlated to 'AGE'.

  • 20p gain cnv correlated to 'AGE'.

  • 20q gain cnv correlated to 'AGE'.

  • 9q 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 80 arm-level results and 6 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 8 significant findings detected.

Clinical
Features 		Time
to
Death 		AGE PRIMARY
SITE
OF
DISEASE 		KARNOFSKY
PERFORMANCE
SCORE 		TUMOR
STAGE 		RADIATIONS
RADIATION
REGIMENINDICATION
nCNV (%) nWild-Type logrank test t-test Fisher's exact test t-test Fisher's exact test Fisher's exact test
2p gain 124 (22%) 428 0.0293
(1.00) 		2.59e-07
(0.000123) 		1
(1.00) 		0.956
(1.00) 		0.966
(1.00) 		0.535
(1.00)
3q gain 203 (37%) 349 0.759
(1.00) 		1.36e-06
(0.000642) 		0.126
(1.00) 		0.671
(1.00) 		0.504
(1.00) 		0.301
(1.00)
10p gain 131 (24%) 421 0.216
(1.00) 		3.24e-05
(0.0152) 		0.109
(1.00) 		0.152
(1.00) 		0.847
(1.00) 		1
(1.00)
12p gain 201 (36%) 351 0.26
(1.00) 		3.85e-08
(1.83e-05) 		0.125
(1.00) 		0.0302
(1.00) 		0.00442
(1.00) 		1
(1.00)
12q gain 116 (21%) 436 0.131
(1.00) 		3.77e-06
(0.00177) 		0.612
(1.00) 		0.0312
(1.00) 		0.0674
(1.00) 		0.508
(1.00)
20p gain 230 (42%) 322 0.112
(1.00) 		1.44e-06
(0.00068) 		1
(1.00) 		0.105
(1.00) 		0.19
(1.00) 		0.574
(1.00)
20q gain 267 (48%) 285 0.0787
(1.00) 		4.38e-07
(0.000207) 		1
(1.00) 		0.229
(1.00) 		0.0814
(1.00) 		0.612
(1.00)
9q loss 235 (43%) 317 0.492
(1.00) 		3.28e-06
(0.00155) 		0.152
(1.00) 		0.779
(1.00) 		0.245
(1.00) 		0.578
(1.00)
1p gain 101 (18%) 451 0.153
(1.00) 		0.672
(1.00) 		0.155
(1.00) 		0.126
(1.00) 		0.24
(1.00) 		0.0877
(1.00)
1q gain 153 (28%) 399 0.249
(1.00) 		0.0229
(1.00) 		0.0668
(1.00) 		0.0331
(1.00) 		0.221
(1.00) 		1
(1.00)
2q gain 98 (18%) 454 0.104
(1.00) 		0.000536
(0.251) 		1
(1.00) 		0.69
(1.00) 		0.664
(1.00) 		0.0829
(1.00)
3p gain 106 (19%) 446 0.426
(1.00) 		0.0356
(1.00) 		0.575
(1.00) 		0.73
(1.00) 		0.814
(1.00) 		1
(1.00)
4p gain 30 (5%) 522 0.272
(1.00) 		0.579
(1.00) 		1
(1.00) 		0.921
(1.00) 		0.782
(1.00) 		1
(1.00)
4q gain 17 (3%) 535 0.522
(1.00) 		0.0845
(1.00) 		1
(1.00) 		0.921
(1.00) 		0.391
(1.00) 		1
(1.00)
5p gain 139 (25%) 413 0.403
(1.00) 		0.313
(1.00) 		0.122
(1.00) 		0.791
(1.00) 		0.871
(1.00) 		1
(1.00)
5q gain 31 (6%) 521 0.314
(1.00) 		0.939
(1.00) 		0.207
(1.00) 		0.137
(1.00) 		0.874
(1.00) 		1
(1.00)
6p gain 121 (22%) 431 0.403
(1.00) 		0.00618
(1.00) 		0.629
(1.00) 		0.0337
(1.00) 		0.0703
(1.00) 		1
(1.00)
6q gain 61 (11%) 491 0.515
(1.00) 		0.084
(1.00) 		1
(1.00) 		0.195
(1.00) 		0.0138
(1.00) 		1
(1.00)
7p gain 121 (22%) 431 0.166
(1.00) 		0.00106
(0.497) 		1
(1.00) 		0.292
(1.00) 		0.414
(1.00) 		1
(1.00)
7q gain 144 (26%) 408 0.129
(1.00) 		0.00887
(1.00) 		0.703
(1.00) 		0.276
(1.00) 		0.297
(1.00) 		0.571
(1.00)
8p gain 86 (16%) 466 0.0775
(1.00) 		0.678
(1.00) 		1
(1.00) 		0.894
(1.00) 		0.429
(1.00) 		0.399
(1.00)
8q gain 202 (37%) 350 0.175
(1.00) 		0.547
(1.00) 		0.249
(1.00) 		0.88
(1.00) 		0.224
(1.00) 		1
(1.00)
9p gain 59 (11%) 493 0.754
(1.00) 		0.77
(1.00) 		1
(1.00) 		0.95
(1.00) 		0.694
(1.00) 		1
(1.00)
9q gain 26 (5%) 526 0.937
(1.00) 		0.73
(1.00) 		1
(1.00) 		0.446
(1.00) 		1
(1.00)
10q gain 67 (12%) 485 0.573
(1.00) 		0.0505
(1.00) 		0.405
(1.00) 		0.32
(1.00) 		0.393
(1.00) 		1
(1.00)
11p gain 41 (7%) 511 0.622
(1.00) 		0.918
(1.00) 		1
(1.00) 		0.852
(1.00) 		0.151
(1.00) 		1
(1.00)
11q gain 59 (11%) 493 0.363
(1.00) 		0.294
(1.00) 		1
(1.00) 		0.566
(1.00) 		0.249
(1.00) 		1
(1.00)
13q gain 39 (7%) 513 0.719
(1.00) 		0.0197
(1.00) 		1
(1.00) 		0.476
(1.00) 		0.323
(1.00) 		0.198
(1.00)
14q gain 33 (6%) 519 0.302
(1.00) 		0.792
(1.00) 		1
(1.00) 		0.497
(1.00) 		0.538
(1.00) 		0.169
(1.00)
15q gain 24 (4%) 528 0.865
(1.00) 		0.356
(1.00) 		1
(1.00) 		0.032
(1.00) 		1
(1.00)
16p gain 32 (6%) 520 0.621
(1.00) 		0.521
(1.00) 		1
(1.00) 		0.232
(1.00) 		0.196
(1.00) 		1
(1.00)
16q gain 16 (3%) 536 0.672
(1.00) 		0.748
(1.00) 		1
(1.00) 		0.359
(1.00) 		1
(1.00)
17p gain 10 (2%) 542 0.162
(1.00) 		0.0789
(1.00) 		1
(1.00) 		0.767
(1.00) 		0.564
(1.00) 		1
(1.00)
17q gain 23 (4%) 529 0.0971
(1.00) 		0.177
(1.00) 		1
(1.00) 		0.614
(1.00) 		0.575
(1.00) 		1
(1.00)
18p gain 73 (13%) 479 0.193
(1.00) 		0.166
(1.00) 		1
(1.00) 		0.341
(1.00) 		0.0086
(1.00) 		1
(1.00)
18q gain 41 (7%) 511 0.255
(1.00) 		0.341
(1.00) 		1
(1.00) 		0.252
(1.00) 		0.0717
(1.00) 		0.207
(1.00)
19p gain 98 (18%) 454 0.309
(1.00) 		0.0212
(1.00) 		1
(1.00) 		0.996
(1.00) 		0.258
(1.00) 		0.444
(1.00)
19q gain 90 (16%) 462 0.139
(1.00) 		0.015
(1.00) 		1
(1.00) 		0.649
(1.00) 		0.286
(1.00) 		0.414
(1.00)
21q gain 67 (12%) 485 0.00569
(1.00) 		0.0138
(1.00) 		0.405
(1.00) 		0.566
(1.00) 		0.868
(1.00) 		1
(1.00)
22q gain 9 (2%) 543 0.593
(1.00) 		0.294
(1.00) 		1
(1.00) 		0.391
(1.00) 		1
(1.00)
Xq gain 33 (6%) 519 0.0927
(1.00) 		0.358
(1.00) 		1
(1.00) 		0.137
(1.00) 		0.975
(1.00) 		1
(1.00)
1p loss 34 (6%) 518 0.85
(1.00) 		0.418
(1.00) 		1
(1.00) 		0.296
(1.00) 		0.709
(1.00) 		0.174
(1.00)
1q loss 22 (4%) 530 0.338
(1.00) 		0.0896
(1.00) 		1
(1.00) 		0.22
(1.00) 		0.361
(1.00) 		0.115
(1.00)
2p loss 26 (5%) 526 0.587
(1.00) 		0.835
(1.00) 		1
(1.00) 		0.0731
(1.00) 		0.474
(1.00) 		1
(1.00)
2q loss 30 (5%) 522 0.729
(1.00) 		0.461
(1.00) 		0.201
(1.00) 		0.0731
(1.00) 		0.344
(1.00) 		1
(1.00)
3p loss 58 (11%) 494 0.34
(1.00) 		0.104
(1.00) 		0.359
(1.00) 		0.321
(1.00) 		0.683
(1.00) 		0.284
(1.00)
3q loss 23 (4%) 529 0.139
(1.00) 		0.837
(1.00) 		0.157
(1.00) 		0.607
(1.00) 		0.173
(1.00) 		0.12
(1.00)
4p loss 251 (45%) 301 0.248
(1.00) 		0.325
(1.00) 		0.166
(1.00) 		0.106
(1.00) 		0.472
(1.00) 		0.594
(1.00)
4q loss 292 (53%) 260 0.394
(1.00) 		0.182
(1.00) 		0.751
(1.00) 		0.952
(1.00) 		0.323
(1.00) 		1
(1.00)
5p loss 82 (15%) 470 0.222
(1.00) 		0.0164
(1.00) 		0.475
(1.00) 		0.227
(1.00) 		0.332
(1.00) 		1
(1.00)
5q loss 167 (30%) 385 0.579
(1.00) 		0.0923
(1.00) 		0.353
(1.00) 		0.42
(1.00) 		0.279
(1.00) 		1
(1.00)
6p loss 120 (22%) 432 0.105
(1.00) 		0.00286
(1.00) 		0.626
(1.00) 		0.647
(1.00) 		0.453
(1.00) 		0.521
(1.00)
6q loss 188 (34%) 364 0.42
(1.00) 		0.0083
(1.00) 		0.114
(1.00) 		0.309
(1.00) 		0.374
(1.00) 		1
(1.00)
7p loss 90 (16%) 462 0.0921
(1.00) 		0.977
(1.00) 		0.51
(1.00) 		0.0336
(1.00) 		0.268
(1.00) 		1
(1.00)
7q loss 48 (9%) 504 0.0369
(1.00) 		0.244
(1.00) 		0.306
(1.00) 		0.00339
(1.00) 		0.939
(1.00) 		0.239
(1.00)
8p loss 226 (41%) 326 0.346
(1.00) 		0.0251
(1.00) 		0.145
(1.00) 		0.0388
(1.00) 		0.442
(1.00) 		1
(1.00)
8q loss 61 (11%) 491 0.625
(1.00) 		0.0197
(1.00) 		1
(1.00) 		0.331
(1.00) 		0.0179
(1.00) 		1
(1.00)
9p loss 207 (38%) 345 0.613
(1.00) 		0.0371
(1.00) 		0.13
(1.00) 		0.479
(1.00) 		0.485
(1.00) 		0.559
(1.00)
10p loss 62 (11%) 490 0.768
(1.00) 		0.119
(1.00) 		1
(1.00) 		0.753
(1.00) 		0.915
(1.00) 		0.301
(1.00)
10q loss 87 (16%) 465 0.757
(1.00) 		0.236
(1.00) 		1
(1.00) 		0.72
(1.00) 		0.46
(1.00) 		0.0038
(1.00)
11p loss 140 (25%) 412 0.635
(1.00) 		0.0358
(1.00) 		1
(1.00) 		0.741
(1.00) 		0.458
(1.00) 		1
(1.00)
11q loss 105 (19%) 447 0.912
(1.00) 		0.136
(1.00) 		0.571
(1.00) 		0.243
(1.00) 		0.356
(1.00) 		0.47
(1.00)
12p loss 49 (9%) 503 0.8
(1.00) 		0.115
(1.00) 		1
(1.00) 		0.113
(1.00) 		0.0223
(1.00) 		1
(1.00)
12q loss 72 (13%) 480 0.304
(1.00) 		0.429
(1.00) 		1
(1.00) 		0.695
(1.00) 		0.00584
(1.00) 		1
(1.00)
13q loss 251 (45%) 301 0.326
(1.00) 		0.865
(1.00) 		0.458
(1.00) 		0.683
(1.00) 		0.167
(1.00) 		1
(1.00)
14q loss 160 (29%) 392 0.392
(1.00) 		0.105
(1.00) 		1
(1.00) 		0.706
(1.00) 		0.0424
(1.00) 		0.56
(1.00)
15q loss 214 (39%) 338 0.834
(1.00) 		0.046
(1.00) 		0.278
(1.00) 		0.65
(1.00) 		0.11
(1.00) 		0.563
(1.00)
16p loss 260 (47%) 292 0.0794
(1.00) 		0.999
(1.00) 		0.472
(1.00) 		0.295
(1.00) 		0.239
(1.00) 		0.604
(1.00)
16q loss 346 (63%) 206 0.208
(1.00) 		0.00297
(1.00) 		0.78
(1.00) 		0.0536
(1.00) 		0.372
(1.00) 		1
(1.00)
17p loss 415 (75%) 137 0.988
(1.00) 		0.0143
(1.00) 		0.258
(1.00) 		0.883
(1.00) 		0.149
(1.00) 		0.576
(1.00)
17q loss 319 (58%) 233 0.386
(1.00) 		0.0527
(1.00) 		1
(1.00) 		0.0547
(1.00) 		0.672
(1.00) 		0.267
(1.00)
18p loss 178 (32%) 374 0.514
(1.00) 		0.772
(1.00) 		0.79
(1.00) 		0.752
(1.00) 		0.107
(1.00) 		0.244
(1.00)
18q loss 225 (41%) 327 0.54
(1.00) 		0.906
(1.00) 		0.766
(1.00) 		0.65
(1.00) 		0.395
(1.00) 		0.57
(1.00)
19p loss 138 (25%) 414 0.941
(1.00) 		0.082
(1.00) 		0.685
(1.00) 		0.485
(1.00) 		0.513
(1.00) 		1
(1.00)
19q loss 138 (25%) 414 0.699
(1.00) 		0.613
(1.00) 		0.685
(1.00) 		0.407
(1.00) 		0.662
(1.00) 		0.577
(1.00)
20p loss 36 (7%) 516 0.0307
(1.00) 		0.203
(1.00) 		1
(1.00) 		0.00339
(1.00) 		0.806
(1.00) 		0.183
(1.00)
20q loss 21 (4%) 531 0.0406
(1.00) 		0.786
(1.00) 		1
(1.00) 		0.00339
(1.00) 		0.632
(1.00) 		1
(1.00)
21q loss 145 (26%) 407 0.68
(1.00) 		0.921
(1.00) 		1
(1.00) 		0.508
(1.00) 		0.519
(1.00) 		1
(1.00)
22q loss 376 (68%) 176 0.246
(1.00) 		0.211
(1.00) 		0.382
(1.00) 		0.954
(1.00) 		0.176
(1.00) 		0.555
(1.00)
Xq loss 101 (18%) 451 0.228
(1.00) 		0.752
(1.00) 		0.555
(1.00) 		0.485
(1.00) 		0.879
(1.00) 		1
(1.00)
'2p gain mutation analysis' versus 'AGE'

P value = 2.59e-07 (t-test), Q value = 0.00012

Table S1.  Gene #3: '2p gain mutation analysis' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 541 59.8 (11.6)
2P GAIN MUTATED 122 64.5 (11.0)
2P GAIN WILD-TYPE 419 58.4 (11.4)

Figure S1.  Get High-res Image Gene #3: '2p gain mutation analysis' versus Clinical Feature #2: 'AGE'

'3q gain mutation analysis' versus 'AGE'

P value = 1.36e-06 (t-test), Q value = 0.00064

Table S2.  Gene #6: '3q gain mutation analysis' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 541 59.8 (11.6)
3Q GAIN MUTATED 200 62.9 (11.1)
3Q GAIN WILD-TYPE 341 58.0 (11.5)

Figure S2.  Get High-res Image Gene #6: '3q gain mutation analysis' versus Clinical Feature #2: 'AGE'

'10p gain mutation analysis' versus 'AGE'

P value = 3.24e-05 (t-test), Q value = 0.015

Table S3.  Gene #19: '10p gain mutation analysis' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 541 59.8 (11.6)
10P GAIN MUTATED 127 63.4 (10.7)
10P GAIN WILD-TYPE 414 58.7 (11.7)

Figure S3.  Get High-res Image Gene #19: '10p gain mutation analysis' versus Clinical Feature #2: 'AGE'

'12p gain mutation analysis' versus 'AGE'

P value = 3.85e-08 (t-test), Q value = 1.8e-05

Table S4.  Gene #23: '12p gain mutation analysis' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 541 59.8 (11.6)
12P GAIN MUTATED 196 63.4 (11.2)
12P GAIN WILD-TYPE 345 57.8 (11.4)

Figure S4.  Get High-res Image Gene #23: '12p gain mutation analysis' versus Clinical Feature #2: 'AGE'

'12q gain mutation analysis' versus 'AGE'

P value = 3.77e-06 (t-test), Q value = 0.0018

Table S5.  Gene #24: '12q gain mutation analysis' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 541 59.8 (11.6)
12Q GAIN MUTATED 112 64.1 (10.5)
12Q GAIN WILD-TYPE 429 58.7 (11.6)

Figure S5.  Get High-res Image Gene #24: '12q gain mutation analysis' versus Clinical Feature #2: 'AGE'

'20p gain mutation analysis' versus 'AGE'

P value = 1.44e-06 (t-test), Q value = 0.00068

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

nPatients Mean (Std.Dev)
ALL 541 59.8 (11.6)
20P GAIN MUTATED 226 62.6 (11.4)
20P GAIN WILD-TYPE 315 57.8 (11.3)

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

'20q gain mutation analysis' versus 'AGE'

P value = 4.38e-07 (t-test), Q value = 0.00021

Table S7.  Gene #37: '20q gain mutation analysis' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 541 59.8 (11.6)
20Q GAIN MUTATED 262 62.4 (11.8)
20Q GAIN WILD-TYPE 279 57.4 (10.9)

Figure S7.  Get High-res Image Gene #37: '20q gain mutation analysis' versus Clinical Feature #2: 'AGE'

'9q loss mutation analysis' versus 'AGE'

P value = 3.28e-06 (t-test), Q value = 0.0015

Table S8.  Gene #58: '9q loss mutation analysis' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 541 59.8 (11.6)
9Q LOSS MUTATED 229 62.5 (11.3)
9Q LOSS WILD-TYPE 312 57.8 (11.4)

Figure S8.  Get High-res Image Gene #58: '9q loss mutation analysis' versus Clinical Feature #2: 'AGE'

Methods & Data
Input

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

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

  • Number of patients = 552

  • Number of significantly arm-level cnvs = 80

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