Correlation between copy number variations of arm-level result and selected clinical features
Sarcoma (Primary solid tumor)
15 January 2014  |  analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C17H1H1F
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/C17H1H1F
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 events and 3 clinical features across 82 patients, 8 significant findings detected with Q value < 0.25.

  • 6p gain cnv correlated to 'AGE'.

  • 6q gain cnv correlated to 'AGE'.

  • 7p gain cnv correlated to 'AGE'.

  • 7q gain cnv correlated to 'AGE'.

  • 12p gain cnv correlated to 'Time to Death' and 'AGE'.

  • 10p loss cnv correlated to 'GENDER'.

  • 10q loss cnv correlated to 'GENDER'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between significant copy number variation of 79 arm-level events and 3 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 GENDER
nCNV (%) nWild-Type logrank test t-test Fisher's exact test
12p gain 8 (10%) 74 3.93e-05
(0.00925) 		3.66e-05
(0.00864) 		1
(1.00)
6p gain 10 (12%) 72 0.736
(1.00) 		4.44e-05
(0.0104) 		0.514
(1.00)
6q gain 13 (16%) 69 0.208
(1.00) 		0.000414
(0.0956) 		1
(1.00)
7p gain 21 (26%) 61 0.488
(1.00) 		0.00013
(0.0303) 		0.0773
(1.00)
7q gain 20 (24%) 62 0.313
(1.00) 		2.87e-05
(0.00681) 		0.308
(1.00)
10p loss 35 (43%) 47 0.447
(1.00) 		0.0104
(1.00) 		0.000705
(0.162)
10q loss 41 (50%) 41 0.23
(1.00) 		0.337
(1.00) 		0.00014
(0.0324)
1p gain 14 (17%) 68 0.0378
(1.00) 		0.019
(1.00) 		0.565
(1.00)
1q gain 15 (18%) 67 0.206
(1.00) 		0.0186
(1.00) 		0.399
(1.00)
2p gain 8 (10%) 74 0.573
(1.00) 		0.0673
(1.00) 		0.477
(1.00)
2q gain 7 (9%) 75 0.543
(1.00) 		0.302
(1.00) 		0.709
(1.00)
3p gain 6 (7%) 76 0.927
(1.00) 		0.358
(1.00) 		1
(1.00)
3q gain 6 (7%) 76 0.927
(1.00) 		0.358
(1.00) 		1
(1.00)
4p gain 11 (13%) 71 0.586
(1.00) 		0.416
(1.00) 		0.753
(1.00)
4q gain 10 (12%) 72 0.972
(1.00) 		0.0626
(1.00) 		0.738
(1.00)
5p gain 22 (27%) 60 0.893
(1.00) 		0.0855
(1.00) 		1
(1.00)
5q gain 20 (24%) 62 0.457
(1.00) 		0.0741
(1.00) 		0.125
(1.00)
8p gain 16 (20%) 66 0.00319
(0.721) 		0.0224
(1.00) 		0.271
(1.00)
8q gain 18 (22%) 64 0.00485
(1.00) 		0.0887
(1.00) 		0.598
(1.00)
9p gain 15 (18%) 67 0.442
(1.00) 		0.0116
(1.00) 		0.779
(1.00)
9q gain 21 (26%) 61 0.403
(1.00) 		0.00208
(0.476) 		0.452
(1.00)
10p gain 7 (9%) 75 0.017
(1.00) 		0.289
(1.00) 		1
(1.00)
10q gain 3 (4%) 79 0.052
(1.00) 		0.986
(1.00) 		0.241
(1.00)
11p gain 4 (5%) 78 0.0923
(1.00) 		0.0124
(1.00) 		0.0523
(1.00)
11q gain 4 (5%) 78 0.822
(1.00) 		0.308
(1.00) 		0.0523
(1.00)
12q gain 4 (5%) 78 0.15
(1.00) 		0.00879
(1.00) 		1
(1.00)
14q gain 14 (17%) 68 0.0595
(1.00) 		0.104
(1.00) 		1
(1.00)
15q gain 18 (22%) 64 0.376
(1.00) 		0.0331
(1.00) 		0.291
(1.00)
16p gain 15 (18%) 67 0.0421
(1.00) 		0.191
(1.00) 		0.158
(1.00)
16q gain 4 (5%) 78 0.865
(1.00) 		0.658
(1.00) 		1
(1.00)
17p gain 16 (20%) 66 0.208
(1.00) 		0.0276
(1.00) 		1
(1.00)
17q gain 13 (16%) 69 0.185
(1.00) 		0.103
(1.00) 		0.137
(1.00)
18p gain 11 (13%) 71 0.662
(1.00) 		0.15
(1.00) 		0.024
(1.00)
18q gain 11 (13%) 71 0.655
(1.00) 		0.034
(1.00) 		0.024
(1.00)
19p gain 21 (26%) 61 0.0128
(1.00) 		0.00306
(0.695) 		0.616
(1.00)
19q gain 15 (18%) 67 0.373
(1.00) 		0.00354
(0.796) 		1
(1.00)
20p gain 21 (26%) 61 0.0769
(1.00) 		0.0243
(1.00) 		0.452
(1.00)
20q gain 25 (30%) 57 0.191
(1.00) 		0.0455
(1.00) 		0.474
(1.00)
21q gain 13 (16%) 69 0.0483
(1.00) 		0.0244
(1.00) 		0.228
(1.00)
22q gain 16 (20%) 66 0.613
(1.00) 		0.0348
(1.00) 		0.0259
(1.00)
xq gain 8 (10%) 74 0.755
(1.00) 		0.337
(1.00) 		1
(1.00)
1p loss 14 (17%) 68 0.142
(1.00) 		0.0983
(1.00) 		0.00292
(0.665)
1q loss 11 (13%) 71 0.769
(1.00) 		0.889
(1.00) 		0.344
(1.00)
2p loss 21 (26%) 61 0.887
(1.00) 		0.269
(1.00) 		0.452
(1.00)
2q loss 17 (21%) 65 0.121
(1.00) 		0.00807
(1.00) 		1
(1.00)
3p loss 16 (20%) 66 0.0732
(1.00) 		0.408
(1.00) 		0.783
(1.00)
3q loss 18 (22%) 64 0.162
(1.00) 		0.298
(1.00) 		0.792
(1.00)
4p loss 16 (20%) 66 0.105
(1.00) 		0.58
(1.00) 		0.271
(1.00)
4q loss 18 (22%) 64 0.929
(1.00) 		0.223
(1.00) 		0.112
(1.00)
5p loss 8 (10%) 74 0.272
(1.00) 		0.642
(1.00) 		1
(1.00)
5q loss 11 (13%) 71 0.579
(1.00) 		0.686
(1.00) 		0.753
(1.00)
6p loss 21 (26%) 61 0.356
(1.00) 		0.896
(1.00) 		0.0773
(1.00)
6q loss 10 (12%) 72 0.498
(1.00) 		0.201
(1.00) 		0.0457
(1.00)
7p loss 11 (13%) 71 0.228
(1.00) 		0.638
(1.00) 		0.52
(1.00)
7q loss 9 (11%) 73 0.477
(1.00) 		0.998
(1.00) 		0.483
(1.00)
8p loss 19 (23%) 63 0.864
(1.00) 		0.449
(1.00) 		0.796
(1.00)
8q loss 12 (15%) 70 0.502
(1.00) 		0.209
(1.00) 		0.221
(1.00)
9p loss 24 (29%) 58 0.903
(1.00) 		0.0355
(1.00) 		0.629
(1.00)
9q loss 15 (18%) 67 0.129
(1.00) 		0.115
(1.00) 		1
(1.00)
11p loss 33 (40%) 49 0.344
(1.00) 		0.0105
(1.00) 		1
(1.00)
11q loss 29 (35%) 53 0.297
(1.00) 		0.00877
(1.00) 		0.649
(1.00)
12p loss 17 (21%) 65 0.686
(1.00) 		0.207
(1.00) 		0.589
(1.00)
12q loss 18 (22%) 64 0.848
(1.00) 		0.647
(1.00) 		0.598
(1.00)
13q loss 39 (48%) 43 0.477
(1.00) 		0.0157
(1.00) 		0.121
(1.00)
14q loss 25 (30%) 57 0.0321
(1.00) 		0.613
(1.00) 		0.474
(1.00)
15q loss 14 (17%) 68 0.171
(1.00) 		0.324
(1.00) 		0.565
(1.00)
16p loss 19 (23%) 63 0.81
(1.00) 		0.154
(1.00) 		0.437
(1.00)
16q loss 40 (49%) 42 0.312
(1.00) 		0.363
(1.00) 		0.185
(1.00)
17p loss 14 (17%) 68 0.986
(1.00) 		0.193
(1.00) 		1
(1.00)
17q loss 13 (16%) 69 0.887
(1.00) 		0.248
(1.00) 		0.375
(1.00)
18p loss 21 (26%) 61 0.694
(1.00) 		0.709
(1.00) 		0.0225
(1.00)
18q loss 23 (28%) 59 0.342
(1.00) 		0.0299
(1.00) 		0.0858
(1.00)
19p loss 5 (6%) 77 0.273
(1.00) 		0.809
(1.00) 		0.196
(1.00)
19q loss 12 (15%) 70 0.761
(1.00) 		0.411
(1.00) 		0.757
(1.00)
20p loss 13 (16%) 69 0.928
(1.00) 		0.0582
(1.00) 		1
(1.00)
20q loss 7 (9%) 75 0.349
(1.00) 		0.0401
(1.00) 		1
(1.00)
21q loss 17 (21%) 65 0.291
(1.00) 		0.00855
(1.00) 		0.42
(1.00)
22q loss 27 (33%) 55 0.373
(1.00) 		0.257
(1.00) 		0.482
(1.00)
xq loss 31 (38%) 51 0.991
(1.00) 		0.142
(1.00) 		0.495
(1.00)
'6p gain' versus 'AGE'

P value = 4.44e-05 (t-test), Q value = 0.01

Table S1.  Gene #11: '6p gain' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 82 62.1 (12.8)
6P GAIN MUTATED 10 74.7 (7.1)
6P GAIN WILD-TYPE 72 60.4 (12.5)

Figure S1.  Get High-res Image Gene #11: '6p gain' versus Clinical Feature #2: 'AGE'

'6q gain' versus 'AGE'

P value = 0.000414 (t-test), Q value = 0.096

Table S2.  Gene #12: '6q gain' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 82 62.1 (12.8)
6Q GAIN MUTATED 13 71.9 (8.6)
6Q GAIN WILD-TYPE 69 60.3 (12.7)

Figure S2.  Get High-res Image Gene #12: '6q gain' versus Clinical Feature #2: 'AGE'

'7p gain' versus 'AGE'

P value = 0.00013 (t-test), Q value = 0.03

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

nPatients Mean (Std.Dev)
ALL 82 62.1 (12.8)
7P GAIN MUTATED 21 70.1 (9.1)
7P GAIN WILD-TYPE 61 59.4 (12.8)

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

'7q gain' versus 'AGE'

P value = 2.87e-05 (t-test), Q value = 0.0068

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

nPatients Mean (Std.Dev)
ALL 82 62.1 (12.8)
7Q GAIN MUTATED 20 71.0 (8.8)
7Q GAIN WILD-TYPE 62 59.2 (12.6)

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

'12p gain' versus 'Time to Death'

P value = 3.93e-05 (logrank test), Q value = 0.0092

Table S5.  Gene #23: '12p gain' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 82 24 0.1 - 143.4 (18.1)
12P GAIN MUTATED 8 6 0.1 - 27.2 (10.9)
12P GAIN WILD-TYPE 74 18 0.1 - 143.4 (19.7)

Figure S5.  Get High-res Image Gene #23: '12p gain' versus Clinical Feature #1: 'Time to Death'

'12p gain' versus 'AGE'

P value = 3.66e-05 (t-test), Q value = 0.0086

Table S6.  Gene #23: '12p gain' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 82 62.1 (12.8)
12P GAIN MUTATED 8 74.9 (5.7)
12P GAIN WILD-TYPE 74 60.7 (12.6)

Figure S6.  Get High-res Image Gene #23: '12p gain' versus Clinical Feature #2: 'AGE'

'10p loss' versus 'GENDER'

P value = 0.000705 (Fisher's exact test), Q value = 0.16

Table S7.  Gene #58: '10p loss' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 40 42
10P LOSS MUTATED 25 10
10P LOSS WILD-TYPE 15 32

Figure S7.  Get High-res Image Gene #58: '10p loss' versus Clinical Feature #3: 'GENDER'

'10q loss' versus 'GENDER'

P value = 0.00014 (Fisher's exact test), Q value = 0.032

Table S8.  Gene #59: '10q loss' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 40 42
10Q LOSS MUTATED 29 12
10Q LOSS WILD-TYPE 11 30

Figure S8.  Get High-res Image Gene #59: '10q loss' versus Clinical Feature #3: 'GENDER'

Methods & Data
Input

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

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

  • Number of patients = 82

  • Number of significantly arm-level cnvs = 79

  • Number of selected clinical features = 3

  • 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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