Correlation between copy number variations of arm-level result and selected clinical features
Kidney Chromophobe (Primary solid tumor)
15 January 2014  |  analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C10000HS
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between copy number variations of arm-level result and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C10000HS
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 61 arm-level events and 10 clinical features across 66 patients, 5 significant findings detected with Q value < 0.25.

  • 16p loss cnv correlated to 'Time to Death',  'NEOPLASM.DISEASESTAGE', and 'PATHOLOGY.N.STAGE'.

  • 16q loss cnv correlated to 'Time to Death' and 'PATHOLOGY.N.STAGE'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between significant copy number variation of 61 arm-level events and 10 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 5 significant findings detected.

Clinical
Features 		Time
to
Death 		AGE NEOPLASM
DISEASESTAGE 		PATHOLOGY
T
STAGE 		PATHOLOGY
N
STAGE 		PATHOLOGY
M
STAGE 		GENDER KARNOFSKY
PERFORMANCE
SCORE 		NUMBERPACKYEARSSMOKED YEAROFTOBACCOSMOKINGONSET
nCNV (%) nWild-Type logrank test t-test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test t-test t-test t-test
16p loss 4 (6%) 62 6.42e-09
(3.2e-06) 		0.503
(1.00) 		0.000409
(0.203) 		0.00672
(1.00) 		3.36e-05
(0.0167) 		0.639
(1.00)
16q loss 5 (8%) 61 4.22e-09
(2.11e-06) 		0.218
(1.00) 		0.00439
(1.00) 		0.0483
(1.00) 		0.000165
(0.0816) 		0.641
(1.00)
3p gain 8 (12%) 58 0.00672
(1.00) 		0.0756
(1.00) 		0.0158
(1.00) 		0.465
(1.00) 		0.0327
(1.00) 		0.254
(1.00) 		0.128
(1.00)
3q gain 8 (12%) 58 0.00672
(1.00) 		0.0756
(1.00) 		0.0158
(1.00) 		0.465
(1.00) 		0.0327
(1.00) 		0.254
(1.00) 		0.128
(1.00)
4p gain 24 (36%) 42 0.674
(1.00) 		0.204
(1.00) 		0.141
(1.00) 		0.809
(1.00) 		1
(1.00) 		0.112
(1.00) 		1
(1.00) 		0.863
(1.00) 		0.0352
(1.00) 		0.0084
(1.00)
4q gain 24 (36%) 42 0.674
(1.00) 		0.204
(1.00) 		0.141
(1.00) 		0.809
(1.00) 		1
(1.00) 		0.112
(1.00) 		1
(1.00) 		0.863
(1.00) 		0.0352
(1.00) 		0.0084
(1.00)
5p gain 8 (12%) 58 0.00174
(0.857) 		0.0627
(1.00) 		0.00152
(0.751) 		0.364
(1.00) 		0.0874
(1.00) 		0.00803
(1.00) 		0.455
(1.00)
5q gain 8 (12%) 58 0.00174
(0.857) 		0.0627
(1.00) 		0.00152
(0.751) 		0.364
(1.00) 		0.0874
(1.00) 		0.00803
(1.00) 		0.455
(1.00)
7p gain 24 (36%) 42 0.0663
(1.00) 		0.162
(1.00) 		0.361
(1.00) 		0.691
(1.00) 		0.636
(1.00) 		0.112
(1.00) 		0.195
(1.00) 		0.863
(1.00) 		0.776
(1.00) 		0.0341
(1.00)
7q gain 24 (36%) 42 0.0663
(1.00) 		0.162
(1.00) 		0.361
(1.00) 		0.691
(1.00) 		0.636
(1.00) 		0.112
(1.00) 		0.195
(1.00) 		0.863
(1.00) 		0.776
(1.00) 		0.0341
(1.00)
8p gain 17 (26%) 49 0.959
(1.00) 		0.151
(1.00) 		0.389
(1.00) 		0.408
(1.00) 		1
(1.00) 		0.664
(1.00) 		0.776
(1.00) 		0.863
(1.00) 		0.516
(1.00)
8q gain 18 (27%) 48 0.959
(1.00) 		0.161
(1.00) 		0.228
(1.00) 		0.447
(1.00) 		1
(1.00) 		0.123
(1.00) 		0.577
(1.00) 		0.863
(1.00) 		0.516
(1.00)
9p gain 10 (15%) 56 0.511
(1.00) 		0.893
(1.00) 		0.122
(1.00) 		0.194
(1.00) 		0.306
(1.00) 		1
(1.00) 		1
(1.00) 		0.708
(1.00)
9q gain 10 (15%) 56 0.511
(1.00) 		0.893
(1.00) 		0.122
(1.00) 		0.194
(1.00) 		0.306
(1.00) 		1
(1.00) 		1
(1.00) 		0.708
(1.00)
10p gain 4 (6%) 62 0.641
(1.00) 		0.286
(1.00) 		0.269
(1.00) 		0.454
(1.00) 		1
(1.00)
11p gain 15 (23%) 51 0.82
(1.00) 		0.676
(1.00) 		0.741
(1.00) 		0.811
(1.00) 		0.617
(1.00) 		0.384
(1.00) 		1
(1.00) 		0.0352
(1.00) 		0.0084
(1.00)
11q gain 15 (23%) 51 0.573
(1.00) 		0.945
(1.00) 		0.741
(1.00) 		0.811
(1.00) 		1
(1.00) 		0.384
(1.00) 		1
(1.00) 		0.0352
(1.00) 		0.0084
(1.00)
12p gain 19 (29%) 47 0.926
(1.00) 		0.01
(1.00) 		0.0768
(1.00) 		0.317
(1.00) 		1
(1.00) 		0.135
(1.00) 		0.412
(1.00) 		0.863
(1.00) 		0.576
(1.00)
12q gain 20 (30%) 46 0.437
(1.00) 		0.0506
(1.00) 		0.111
(1.00) 		0.709
(1.00) 		0.33
(1.00) 		0.135
(1.00) 		0.593
(1.00) 		0.863
(1.00) 		0.291
(1.00) 		0.0084
(1.00)
14q gain 21 (32%) 45 0.622
(1.00) 		0.112
(1.00) 		0.0969
(1.00) 		0.381
(1.00) 		1
(1.00) 		0.155
(1.00) 		0.794
(1.00) 		0.863
(1.00) 		0.291
(1.00) 		0.0084
(1.00)
15q gain 21 (32%) 45 0.206
(1.00) 		0.349
(1.00) 		0.412
(1.00) 		0.479
(1.00) 		0.35
(1.00) 		0.155
(1.00) 		0.794
(1.00) 		0.863
(1.00) 		0.555
(1.00) 		0.161
(1.00)
16p gain 21 (32%) 45 0.296
(1.00) 		0.957
(1.00) 		0.476
(1.00) 		0.403
(1.00) 		0.635
(1.00) 		0.724
(1.00) 		1
(1.00) 		0.863
(1.00) 		0.0352
(1.00) 		0.0084
(1.00)
16q gain 21 (32%) 45 0.296
(1.00) 		0.957
(1.00) 		0.476
(1.00) 		0.403
(1.00) 		0.635
(1.00) 		0.724
(1.00) 		1
(1.00) 		0.863
(1.00) 		0.0352
(1.00) 		0.0084
(1.00)
18p gain 17 (26%) 49 0.968
(1.00) 		0.299
(1.00) 		0.291
(1.00) 		0.433
(1.00) 		0.303
(1.00) 		0.505
(1.00) 		0.776
(1.00) 		0.449
(1.00)
18q gain 16 (24%) 50 0.44
(1.00) 		0.56
(1.00) 		0.31
(1.00) 		0.2
(1.00) 		0.303
(1.00) 		0.505
(1.00) 		1
(1.00) 		0.449
(1.00)
19p gain 19 (29%) 47 0.00754
(1.00) 		0.0226
(1.00) 		0.0157
(1.00) 		0.521
(1.00) 		0.00246
(1.00) 		0.0824
(1.00) 		0.412
(1.00) 		0.776
(1.00) 		0.0341
(1.00)
19q gain 17 (26%) 49 0.0557
(1.00) 		0.0104
(1.00) 		0.295
(1.00) 		0.644
(1.00) 		0.136
(1.00) 		0.0617
(1.00) 		0.776
(1.00) 		0.555
(1.00) 		0.161
(1.00)
20p gain 20 (30%) 46 0.602
(1.00) 		0.135
(1.00) 		0.368
(1.00) 		0.793
(1.00) 		0.35
(1.00) 		0.708
(1.00) 		0.593
(1.00) 		0.863
(1.00) 		0.291
(1.00) 		0.0084
(1.00)
20q gain 21 (32%) 45 0.199
(1.00) 		0.0536
(1.00) 		0.137
(1.00) 		0.846
(1.00) 		0.35
(1.00) 		0.708
(1.00) 		0.433
(1.00) 		0.863
(1.00) 		0.291
(1.00) 		0.0084
(1.00)
21q gain 4 (6%) 62 0.561
(1.00) 		0.565
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00)
22q gain 19 (29%) 47 0.0929
(1.00) 		0.449
(1.00) 		0.492
(1.00) 		0.521
(1.00) 		0.166
(1.00) 		0.356
(1.00) 		0.412
(1.00) 		0.776
(1.00) 		0.0341
(1.00)
xq gain 6 (9%) 60 0.425
(1.00) 		0.746
(1.00) 		0.381
(1.00) 		0.661
(1.00) 		1
(1.00) 		0.192
(1.00) 		0.388
(1.00)
1p loss 53 (80%) 13 0.317
(1.00) 		0.426
(1.00) 		0.0285
(1.00) 		0.0411
(1.00) 		1
(1.00) 		0.00606
(1.00) 		0.534
(1.00) 		0.374
(1.00) 		0.556
(1.00)
1q loss 52 (79%) 14 0.961
(1.00) 		0.414
(1.00) 		0.00616
(1.00) 		0.0133
(1.00) 		0.529
(1.00) 		0.0203
(1.00) 		0.367
(1.00) 		0.374
(1.00)
2p loss 46 (70%) 20 0.474
(1.00) 		0.277
(1.00) 		0.144
(1.00) 		0.114
(1.00) 		1
(1.00) 		0.194
(1.00) 		0.284
(1.00) 		0.374
(1.00)
2q loss 46 (70%) 20 0.474
(1.00) 		0.277
(1.00) 		0.144
(1.00) 		0.114
(1.00) 		1
(1.00) 		0.194
(1.00) 		0.284
(1.00) 		0.374
(1.00)
3p loss 9 (14%) 57 0.243
(1.00) 		0.05
(1.00) 		0.0761
(1.00) 		0.0228
(1.00) 		1
(1.00) 		0.658
(1.00) 		0.469
(1.00)
3q loss 8 (12%) 58 0.284
(1.00) 		0.111
(1.00) 		0.141
(1.00) 		0.0528
(1.00) 		1
(1.00) 		0.639
(1.00) 		0.256
(1.00)
5p loss 10 (15%) 56 0.854
(1.00) 		0.831
(1.00) 		0.133
(1.00) 		0.0843
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		0.117
(1.00) 		0.0496
(1.00)
5q loss 10 (15%) 56 0.854
(1.00) 		0.831
(1.00) 		0.133
(1.00) 		0.0843
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		0.117
(1.00) 		0.0496
(1.00)
6p loss 51 (77%) 15 0.184
(1.00) 		0.383
(1.00) 		0.0823
(1.00) 		0.0557
(1.00) 		0.568
(1.00) 		0.17
(1.00) 		0.244
(1.00) 		0.374
(1.00)
6q loss 51 (77%) 15 0.184
(1.00) 		0.383
(1.00) 		0.0823
(1.00) 		0.0557
(1.00) 		0.568
(1.00) 		0.17
(1.00) 		0.244
(1.00) 		0.374
(1.00)
8p loss 9 (14%) 57 0.249
(1.00) 		0.865
(1.00) 		0.741
(1.00) 		0.439
(1.00) 		1
(1.00) 		1
(1.00) 		0.727
(1.00)
8q loss 8 (12%) 58 0.292
(1.00) 		0.753
(1.00) 		0.701
(1.00) 		0.412
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00)
9p loss 10 (15%) 56 0.0238
(1.00) 		0.00998
(1.00) 		1
(1.00) 		1
(1.00) 		0.529
(1.00) 		0.488
(1.00) 		0.508
(1.00)
9q loss 10 (15%) 56 0.0238
(1.00) 		0.00998
(1.00) 		1
(1.00) 		1
(1.00) 		0.529
(1.00) 		0.488
(1.00) 		0.508
(1.00)
10p loss 48 (73%) 18 0.171
(1.00) 		0.758
(1.00) 		0.544
(1.00) 		0.394
(1.00) 		1
(1.00) 		0.0194
(1.00) 		1
(1.00) 		0.107
(1.00) 		0.249
(1.00)
10q loss 49 (74%) 17 0.178
(1.00) 		0.928
(1.00) 		0.347
(1.00) 		0.21
(1.00) 		1
(1.00) 		0.0194
(1.00) 		1
(1.00) 		0.107
(1.00) 		0.249
(1.00)
11p loss 7 (11%) 59 0.366
(1.00) 		0.0268
(1.00) 		0.0966
(1.00) 		0.079
(1.00) 		0.461
(1.00) 		1
(1.00) 		0.691
(1.00)
11q loss 7 (11%) 59 0.366
(1.00) 		0.0268
(1.00) 		0.0966
(1.00) 		0.079
(1.00) 		0.461
(1.00) 		1
(1.00) 		0.691
(1.00)
13q loss 43 (65%) 23 0.266
(1.00) 		0.0705
(1.00) 		0.447
(1.00) 		0.357
(1.00) 		0.301
(1.00) 		0.534
(1.00) 		0.294
(1.00) 		0.81
(1.00) 		0.293
(1.00) 		0.362
(1.00)
17p loss 50 (76%) 16 0.673
(1.00) 		0.146
(1.00) 		0.074
(1.00) 		0.0419
(1.00) 		0.568
(1.00) 		0.17
(1.00) 		0.158
(1.00) 		0.374
(1.00)
17q loss 50 (76%) 16 0.673
(1.00) 		0.146
(1.00) 		0.074
(1.00) 		0.0419
(1.00) 		0.568
(1.00) 		0.17
(1.00) 		0.158
(1.00) 		0.374
(1.00)
18p loss 8 (12%) 58 0.928
(1.00) 		0.499
(1.00) 		0.444
(1.00) 		0.899
(1.00) 		0.211
(1.00) 		0.658
(1.00) 		0.256
(1.00)
18q loss 10 (15%) 56 0.352
(1.00) 		0.342
(1.00) 		0.137
(1.00) 		0.835
(1.00) 		0.258
(1.00) 		0.658
(1.00) 		0.295
(1.00)
19q loss 3 (5%) 63 0.106
(1.00) 		0.3
(1.00) 		0.0119
(1.00) 		0.191
(1.00) 		0.0289
(1.00) 		1
(1.00)
20p loss 4 (6%) 62 0.524
(1.00) 		0.188
(1.00) 		0.138
(1.00) 		0.142
(1.00) 		0.304
(1.00) 		1
(1.00) 		1
(1.00)
20q loss 3 (5%) 63 0.378
(1.00) 		0.448
(1.00) 		0.04
(1.00) 		0.0249
(1.00) 		0.304
(1.00) 		1
(1.00) 		1
(1.00)
21q loss 35 (53%) 31 0.0674
(1.00) 		0.485
(1.00) 		0.906
(1.00) 		0.781
(1.00) 		0.0731
(1.00) 		0.0787
(1.00) 		0.805
(1.00) 		0.278
(1.00) 		0.435
(1.00) 		0.101
(1.00)
22q loss 8 (12%) 58 0.23
(1.00) 		0.8
(1.00) 		0.178
(1.00) 		1
(1.00) 		0.211
(1.00) 		0.322
(1.00) 		0.0553
(1.00)
xq loss 39 (59%) 27 0.514
(1.00) 		0.113
(1.00) 		0.303
(1.00) 		0.362
(1.00) 		1
(1.00) 		0.147
(1.00) 		0.136
(1.00) 		0.0791
(1.00) 		0.242
(1.00) 		0.277
(1.00)
'16p loss' versus 'Time to Death'

P value = 6.42e-09 (logrank test), Q value = 3.2e-06

Table S1.  Gene #50: '16p loss' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 65 8 0.6 - 151.9 (63.9)
16P LOSS MUTATED 4 3 0.6 - 30.2 (22.4)
16P LOSS WILD-TYPE 61 5 2.5 - 151.9 (66.5)

Figure S1.  Get High-res Image Gene #50: '16p loss' versus Clinical Feature #1: 'Time to Death'

'16p loss' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.000409 (Fisher's exact test), Q value = 0.2

Table S2.  Gene #50: '16p loss' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 21 25 14 6
16P LOSS MUTATED 0 0 1 3
16P LOSS WILD-TYPE 21 25 13 3

Figure S2.  Get High-res Image Gene #50: '16p loss' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'16p loss' versus 'PATHOLOGY.N.STAGE'

P value = 3.36e-05 (Fisher's exact test), Q value = 0.017

Table S3.  Gene #50: '16p loss' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1+N2
ALL 40 5
16P LOSS MUTATED 0 4
16P LOSS WILD-TYPE 40 1

Figure S3.  Get High-res Image Gene #50: '16p loss' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'16q loss' versus 'Time to Death'

P value = 4.22e-09 (logrank test), Q value = 2.1e-06

Table S4.  Gene #51: '16q loss' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 65 8 0.6 - 151.9 (63.9)
16Q LOSS MUTATED 5 4 0.6 - 52.3 (28.1)
16Q LOSS WILD-TYPE 60 4 2.5 - 151.9 (67.2)

Figure S4.  Get High-res Image Gene #51: '16q loss' versus Clinical Feature #1: 'Time to Death'

'16q loss' versus 'PATHOLOGY.N.STAGE'

P value = 0.000165 (Fisher's exact test), Q value = 0.082

Table S5.  Gene #51: '16q loss' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1+N2
ALL 40 5
16Q LOSS MUTATED 1 4
16Q LOSS WILD-TYPE 39 1

Figure S5.  Get High-res Image Gene #51: '16q loss' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

Methods & Data
Input

  • Copy number data file = transformed.cor.cli.txt

  • Clinical data file = KICH-TP.merged_data.txt

  • Number of patients = 66

  • Number of significantly arm-level cnvs = 61

  • Number of selected clinical features = 10

  • Exclude regions 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

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

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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