Correlation between copy number variations of arm-level result and selected clinical features
Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)
23 May 2013  |  analyses__2013_05_23
Maintainer Information
Citation Information
doi:10.7908/C1X0652P
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/C1X0652P
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 58 arm-level results and 8 clinical features across 104 patients, 7 significant findings detected with Q value < 0.25.

  • 1q gain cnv correlated to 'Time to Death'.

  • 6q gain cnv correlated to 'Time to Death'.

  • 7p gain cnv correlated to 'NEOPLASM.DISEASESTAGE'.

  • 17p gain cnv correlated to 'NEOPLASM.DISEASESTAGE'.

  • 3q loss cnv correlated to 'Time to Death'.

  • 5p loss cnv correlated to 'Time to Death'.

  • 17p 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 58 arm-level results and 8 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 7 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
1q gain 0 (0%) 96 8.75e-05
(0.0321) 		0.801
(1.00) 		0.433
(1.00) 		0.0788
(1.00) 		0.0313
(1.00) 		0.00604
(1.00)
6q gain 0 (0%) 101 4.2e-06
(0.00155) 		0.26
(1.00) 		0.549
(1.00) 		0.00644
(1.00) 		0.084
(1.00) 		0.0498
(1.00)
7p gain 0 (0%) 49 0.0751
(1.00) 		0.949
(1.00) 		0.0587
(1.00) 		0.184
(1.00) 		0.152
(1.00) 		0.00304
(1.00) 		0.000465
(0.169)
17p gain 0 (0%) 52 0.231
(1.00) 		0.0507
(1.00) 		0.0208
(1.00) 		0.182
(1.00) 		0.0715
(1.00) 		0.00208
(0.746) 		0.000279
(0.102)
3q loss 0 (0%) 101 2.4e-06
(0.000884) 		0.325
(1.00) 		1
(1.00) 		0.2
(1.00) 		0.0289
(1.00) 		0.0994
(1.00)
5p loss 0 (0%) 100 0.000193
(0.0705) 		0.0662
(1.00) 		0.101
(1.00) 		0.626
(1.00) 		0.0258
(1.00) 		0.0994
(1.00)
17p loss 0 (0%) 99 1.56e-07
(5.77e-05) 		0.491
(1.00) 		0.0383
(1.00) 		0.0916
(1.00) 		0.11
(1.00) 		0.0423
(1.00)
2p gain 0 (0%) 93 0.186
(1.00) 		0.357
(1.00) 		0.747
(1.00) 		0.541
(1.00) 		0.0429
(1.00) 		0.394
(1.00) 		0.0342
(1.00)
2q gain 0 (0%) 91 0.342
(1.00) 		0.847
(1.00) 		0.753
(1.00) 		0.501
(1.00) 		0.0615
(1.00) 		0.106
(1.00) 		0.00852
(1.00)
3p gain 0 (0%) 80 0.42
(1.00) 		0.92
(1.00) 		0.0815
(1.00) 		0.0792
(1.00) 		0.641
(1.00) 		0.102
(1.00) 		0.236
(1.00)
3q gain 0 (0%) 78 0.293
(1.00) 		0.859
(1.00) 		0.0322
(1.00) 		0.197
(1.00) 		0.614
(1.00) 		0.145
(1.00) 		0.399
(1.00)
4p gain 0 (0%) 100 0.0677
(1.00) 		0.0422
(1.00) 		1
(1.00) 		0.0285
(1.00) 		0.0513
(1.00) 		0.0498
(1.00)
4q gain 0 (0%) 101 0.696
(1.00) 		0.0416
(1.00) 		1
(1.00) 		0.114
(1.00)
5p gain 0 (0%) 94 0.754
(1.00) 		0.382
(1.00) 		0.725
(1.00) 		0.397
(1.00) 		0.389
(1.00) 		0.332
(1.00) 		0.176
(1.00)
5q gain 0 (0%) 94 0.302
(1.00) 		0.612
(1.00) 		0.725
(1.00) 		0.397
(1.00) 		0.485
(1.00) 		0.332
(1.00) 		0.404
(1.00)
6p gain 0 (0%) 100 0.0735
(1.00) 		0.365
(1.00) 		0.301
(1.00) 		0.00664
(1.00) 		0.0877
(1.00) 		0.0762
(1.00)
7q gain 0 (0%) 48 0.0751
(1.00) 		0.949
(1.00) 		0.0938
(1.00) 		0.184
(1.00) 		0.199
(1.00) 		0.00197
(0.713) 		0.00172
(0.622)
8p gain 0 (0%) 99 0.568
(1.00) 		0.241
(1.00) 		0.661
(1.00) 		0.73
(1.00) 		0.0484
(1.00) 		0.16
(1.00)
8q gain 0 (0%) 97 0.00461
(1.00) 		0.621
(1.00) 		0.68
(1.00) 		0.482
(1.00) 		0.00251
(0.897) 		0.00872
(1.00)
10p gain 0 (0%) 100 0.489
(1.00) 		0.801
(1.00) 		1
(1.00) 		1
(1.00) 		0.782
(1.00)
10q gain 0 (0%) 101 0.489
(1.00) 		0.801
(1.00) 		0.549
(1.00) 		1
(1.00) 		1
(1.00)
12p gain 0 (0%) 73 0.979
(1.00) 		0.658
(1.00) 		0.0225
(1.00) 		0.248
(1.00) 		0.412
(1.00) 		0.558
(1.00) 		0.347
(1.00)
12q gain 0 (0%) 73 0.979
(1.00) 		0.658
(1.00) 		0.0225
(1.00) 		0.248
(1.00) 		0.412
(1.00) 		0.558
(1.00) 		0.347
(1.00)
13q gain 0 (0%) 93 0.722
(1.00) 		0.237
(1.00) 		0.747
(1.00) 		0.197
(1.00) 		1
(1.00) 		0.47
(1.00) 		0.74
(1.00)
16p gain 0 (0%) 60 0.55
(1.00) 		0.438
(1.00) 		0.0106
(1.00) 		0.529
(1.00) 		0.657
(1.00) 		0.284
(1.00) 		0.861
(1.00)
16q gain 0 (0%) 63 0.107
(1.00) 		0.55
(1.00) 		0.0317
(1.00) 		0.25
(1.00) 		0.255
(1.00) 		0.0859
(1.00) 		0.819
(1.00)
17q gain 0 (0%) 42 0.791
(1.00) 		0.103
(1.00) 		0.203
(1.00) 		0.561
(1.00) 		0.176
(1.00) 		0.0482
(1.00) 		0.134
(1.00)
18p gain 0 (0%) 98 0.19
(1.00) 		0.598
(1.00) 		0.661
(1.00) 		0.541
(1.00) 		0.355
(1.00) 		0.632
(1.00) 		0.458
(1.00)
18q gain 0 (0%) 100 0.612
(1.00) 		0.572
(1.00) 		1
(1.00) 		1
(1.00) 		0.782
(1.00) 		1
(1.00)
20p gain 0 (0%) 73 0.446
(1.00) 		0.0192
(1.00) 		0.654
(1.00) 		0.636
(1.00) 		0.519
(1.00) 		0.686
(1.00) 		0.439
(1.00)
20q gain 0 (0%) 72 0.446
(1.00) 		0.046
(1.00) 		0.651
(1.00) 		0.636
(1.00) 		0.815
(1.00) 		0.616
(1.00) 		0.637
(1.00)
Xq gain 0 (0%) 100 0.413
(1.00) 		0.362
(1.00) 		0.101
(1.00) 		0.716
(1.00) 		0.217
(1.00) 		0.356
(1.00)
1p loss 0 (0%) 93 0.727
(1.00) 		0.414
(1.00) 		1
(1.00) 		0.342
(1.00) 		1
(1.00) 		0.714
(1.00) 		0.539
(1.00)
1q loss 0 (0%) 98 0.649
(1.00) 		0.825
(1.00) 		1
(1.00) 		0.342
(1.00) 		0.529
(1.00) 		1
(1.00) 		0.643
(1.00)
3p loss 0 (0%) 98 0.207
(1.00) 		0.0519
(1.00) 		0.661
(1.00) 		0.337
(1.00) 		0.00147
(0.535) 		0.0927
(1.00)
4p loss 0 (0%) 96 0.187
(1.00) 		0.104
(1.00) 		0.0141
(1.00) 		0.349
(1.00) 		0.0712
(1.00) 		0.051
(1.00)
4q loss 0 (0%) 95 0.8
(1.00) 		0.414
(1.00) 		0.0557
(1.00) 		0.682
(1.00) 		0.599
(1.00) 		0.14
(1.00)
5q loss 0 (0%) 100 0.0264
(1.00) 		0.23
(1.00) 		0.101
(1.00) 		0.626
(1.00) 		0.144
(1.00) 		0.565
(1.00)
6p loss 0 (0%) 95 0.0291
(1.00) 		0.997
(1.00) 		0.47
(1.00) 		0.248
(1.00) 		1
(1.00) 		0.401
(1.00) 		0.335
(1.00)
6q loss 0 (0%) 94 0.273
(1.00) 		0.361
(1.00) 		0.0756
(1.00) 		0.383
(1.00) 		0.583
(1.00) 		0.269
(1.00) 		0.483
(1.00)
8p loss 0 (0%) 101 0.75
(1.00) 		0.404
(1.00) 		0.0329
(1.00) 		1
(1.00) 		1
(1.00) 		0.268
(1.00)
9p loss 0 (0%) 91 0.0801
(1.00) 		0.475
(1.00) 		0.00846
(1.00) 		0.398
(1.00) 		0.386
(1.00) 		0.205
(1.00) 		0.176
(1.00)
9q loss 0 (0%) 91 0.127
(1.00) 		0.448
(1.00) 		0.0264
(1.00) 		0.398
(1.00) 		0.577
(1.00) 		0.205
(1.00) 		0.295
(1.00)
10p loss 0 (0%) 98 0.256
(1.00) 		0.987
(1.00) 		0.39
(1.00) 		0.397
(1.00) 		0.0916
(1.00) 		0.0553
(1.00) 		0.567
(1.00)
10q loss 0 (0%) 98 0.0212
(1.00) 		0.666
(1.00) 		1
(1.00) 		0.397
(1.00) 		0.181
(1.00) 		0.0412
(1.00) 		0.378
(1.00)
11p loss 0 (0%) 96 0.00469
(1.00) 		0.0515
(1.00) 		0.714
(1.00) 		0.523
(1.00) 		1
(1.00) 		0.174
(1.00)
11q loss 0 (0%) 95 0.0212
(1.00) 		0.0668
(1.00) 		1
(1.00) 		0.485
(1.00) 		0.468
(1.00) 		0.0641
(1.00)
13q loss 0 (0%) 95 0.0238
(1.00) 		0.428
(1.00) 		0.00521
(1.00) 		0.682
(1.00) 		0.156
(1.00) 		0.283
(1.00)
14q loss 0 (0%) 85 0.925
(1.00) 		0.379
(1.00) 		0.418
(1.00) 		0.377
(1.00) 		0.238
(1.00) 		0.232
(1.00) 		0.421
(1.00)
15q loss 0 (0%) 94 0.0623
(1.00) 		0.366
(1.00) 		0.725
(1.00) 		0.654
(1.00) 		1
(1.00) 		0.481
(1.00) 		0.237
(1.00)
16q loss 0 (0%) 101 0.0329
(1.00) 		0.626
(1.00) 		0.164
(1.00) 		0.356
(1.00)
18p loss 0 (0%) 89 0.00201
(0.724) 		0.86
(1.00) 		1
(1.00) 		0.342
(1.00) 		0.788
(1.00) 		0.49
(1.00) 		0.276
(1.00)
18q loss 0 (0%) 88 0.00201
(0.724) 		0.86
(1.00) 		0.773
(1.00) 		0.342
(1.00) 		0.801
(1.00) 		0.492
(1.00) 		0.138
(1.00)
19p loss 0 (0%) 100 0.649
(1.00) 		0.582
(1.00) 		1
(1.00) 		0.626
(1.00) 		0.782
(1.00) 		0.268
(1.00)
19q loss 0 (0%) 101 0.75
(1.00) 		0.877
(1.00) 		1
(1.00) 		1
(1.00)
21q loss 0 (0%) 93 0.152
(1.00) 		0.432
(1.00) 		0.497
(1.00) 		1
(1.00) 		1
(1.00) 		0.237
(1.00)
22q loss 0 (0%) 84 0.752
(1.00) 		0.699
(1.00) 		0.109
(1.00) 		0.445
(1.00) 		0.168
(1.00) 		0.481
(1.00) 		0.208
(1.00)
Xq loss 0 (0%) 101 0.75
(1.00) 		0.915
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00)
'1q gain' versus 'Time to Death'

P value = 8.75e-05 (logrank test), Q value = 0.032

Table S1.  Gene #1: '1q gain' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 97 14 0.0 - 182.7 (13.7)
1Q GAIN CNV 7 2 0.7 - 25.4 (7.6)
1Q GAIN WILD-TYPE 90 12 0.0 - 182.7 (14.6)

Figure S1.  Get High-res Image Gene #1: '1q gain' versus Clinical Feature #1: 'Time to Death'

'6q gain' versus 'Time to Death'

P value = 4.2e-06 (logrank test), Q value = 0.0015

Table S2.  Gene #11: '6q gain' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 97 14 0.0 - 182.7 (13.7)
6Q GAIN CNV 3 2 7.9 - 13.6 (9.6)
6Q GAIN WILD-TYPE 94 12 0.0 - 182.7 (14.4)

Figure S2.  Get High-res Image Gene #11: '6q gain' versus Clinical Feature #1: 'Time to Death'

'7p gain' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.000465 (Fisher's exact test), Q value = 0.17

Table S3.  Gene #12: '7p gain' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 53 7 24 9
7P GAIN CNV 36 2 5 4
7P GAIN WILD-TYPE 17 5 19 5

Figure S3.  Get High-res Image Gene #12: '7p gain' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

'17p gain' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.000279 (Fisher's exact test), Q value = 0.1

Table S4.  Gene #23: '17p gain' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 53 7 24 9
17P GAIN CNV 34 3 4 2
17P GAIN WILD-TYPE 19 4 20 7

Figure S4.  Get High-res Image Gene #23: '17p gain' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

'3q loss' versus 'Time to Death'

P value = 2.4e-06 (logrank test), Q value = 0.00088

Table S5.  Gene #33: '3q loss' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 97 14 0.0 - 182.7 (13.7)
3Q LOSS CNV 3 2 3.7 - 21.6 (8.8)
3Q LOSS WILD-TYPE 94 12 0.0 - 182.7 (13.9)

Figure S5.  Get High-res Image Gene #33: '3q loss' versus Clinical Feature #1: 'Time to Death'

'5p loss' versus 'Time to Death'

P value = 0.000193 (logrank test), Q value = 0.071

Table S6.  Gene #36: '5p loss' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 97 14 0.0 - 182.7 (13.7)
5P LOSS CNV 4 2 0.0 - 22.9 (7.4)
5P LOSS WILD-TYPE 93 12 0.0 - 182.7 (14.1)

Figure S6.  Get High-res Image Gene #36: '5p loss' versus Clinical Feature #1: 'Time to Death'

'17p loss' versus 'Time to Death'

P value = 1.56e-07 (logrank test), Q value = 5.8e-05

Table S7.  Gene #51: '17p loss' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 97 14 0.0 - 182.7 (13.7)
17P LOSS CNV 4 2 0.2 - 11.1 (5.2)
17P LOSS WILD-TYPE 93 12 0.0 - 182.7 (14.6)

Figure S7.  Get High-res Image Gene #51: '17p loss' versus Clinical Feature #1: 'Time to Death'

Methods & Data
Input

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

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

  • Number of patients = 104

  • Number of significantly arm-level cnvs = 58

  • 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)
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