Correlation between copy number variations of arm-level result and selected clinical features
Sarcoma (Primary solid tumor)
16 April 2014  |  analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C1GT5KVT
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/C1GT5KVT
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 events and 3 clinical features across 103 patients, 10 significant findings detected with Q value < 0.25.

  • 6p gain cnv correlated to 'AGE'.

  • 7p gain cnv correlated to 'AGE'.

  • 7q gain cnv correlated to 'AGE'.

  • 8q gain cnv correlated to 'Time to Death'.

  • 9q gain cnv correlated to 'AGE'.

  • 10p gain cnv correlated to 'Time to Death'.

  • 10q gain cnv correlated to 'Time to Death'.

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

  • 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 80 arm-level events and 3 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 10 significant findings detected.

Clinical
Features 		Time
to
Death 		AGE GENDER
nCNV (%) nWild-Type logrank test t-test Fisher's exact test
12p gain 11 (11%) 92 3.85e-06
(0.000916) 		0.000183
(0.0428) 		1
(1.00)
6p gain 18 (17%) 85 0.359
(1.00) 		0.000174
(0.0409) 		0.449
(1.00)
7p gain 26 (25%) 77 0.338
(1.00) 		2.86e-06
(0.000682) 		0.173
(1.00)
7q gain 24 (23%) 79 0.516
(1.00) 		1.69e-06
(0.000406) 		0.642
(1.00)
8q gain 22 (21%) 81 0.000658
(0.152) 		0.186
(1.00) 		0.148
(1.00)
9q gain 26 (25%) 77 0.625
(1.00) 		0.000253
(0.0589) 		0.651
(1.00)
10p gain 10 (10%) 93 0.000334
(0.0775) 		0.0855
(1.00) 		0.325
(1.00)
10q gain 3 (3%) 100 9.56e-05
(0.0226) 		0.579
(1.00) 		0.604
(1.00)
10q loss 53 (51%) 50 0.275
(1.00) 		0.657
(1.00) 		7.18e-05
(0.017)
1p gain 17 (17%) 86 0.0675
(1.00) 		0.336
(1.00) 		0.605
(1.00)
1q gain 20 (19%) 83 0.369
(1.00) 		0.056
(1.00) 		0.469
(1.00)
2p gain 8 (8%) 95 0.575
(1.00) 		0.081
(1.00) 		0.718
(1.00)
2q gain 7 (7%) 96 0.499
(1.00) 		0.337
(1.00) 		1
(1.00)
3p gain 10 (10%) 93 0.805
(1.00) 		0.088
(1.00) 		0.744
(1.00)
3q gain 9 (9%) 94 0.799
(1.00) 		0.0616
(1.00) 		1
(1.00)
4p gain 18 (17%) 85 0.668
(1.00) 		0.397
(1.00) 		0.205
(1.00)
4q gain 14 (14%) 89 0.916
(1.00) 		0.171
(1.00) 		0.779
(1.00)
5p gain 32 (31%) 71 0.718
(1.00) 		0.0444
(1.00) 		0.397
(1.00)
5q gain 25 (24%) 78 0.727
(1.00) 		0.0852
(1.00) 		0.0373
(1.00)
6q gain 19 (18%) 84 0.268
(1.00) 		0.0015
(0.345) 		0.622
(1.00)
8p gain 20 (19%) 83 0.00673
(1.00) 		0.0899
(1.00) 		0.0882
(1.00)
9p gain 18 (17%) 85 0.181
(1.00) 		0.0038
(0.859) 		0.801
(1.00)
11p gain 6 (6%) 97 0.00431
(0.971) 		0.0196
(1.00) 		0.0278
(1.00)
11q gain 6 (6%) 97 0.133
(1.00) 		0.41
(1.00) 		0.0278
(1.00)
12q gain 5 (5%) 98 0.134
(1.00) 		0.011
(1.00) 		1
(1.00)
13q gain 5 (5%) 98 0.295
(1.00) 		0.179
(1.00) 		0.366
(1.00)
14q gain 18 (17%) 85 0.114
(1.00) 		0.177
(1.00) 		0.801
(1.00)
15q gain 25 (24%) 78 0.236
(1.00) 		0.0624
(1.00) 		0.107
(1.00)
16p gain 15 (15%) 88 0.0059
(1.00) 		0.113
(1.00) 		0.585
(1.00)
16q gain 4 (4%) 99 0.721
(1.00) 		0.697
(1.00) 		1
(1.00)
17p gain 22 (21%) 81 0.0735
(1.00) 		0.016
(1.00) 		1
(1.00)
17q gain 18 (17%) 85 0.0733
(1.00) 		0.0318
(1.00) 		0.0206
(1.00)
18p gain 17 (17%) 86 0.224
(1.00) 		0.0272
(1.00) 		0.3
(1.00)
18q gain 15 (15%) 88 0.321
(1.00) 		0.0358
(1.00) 		0.0979
(1.00)
19p gain 29 (28%) 74 0.0124
(1.00) 		0.00293
(0.668) 		0.664
(1.00)
19q gain 22 (21%) 81 0.501
(1.00) 		0.00892
(1.00) 		1
(1.00)
20p gain 25 (24%) 78 0.111
(1.00) 		0.0135
(1.00) 		0.819
(1.00)
20q gain 32 (31%) 71 0.239
(1.00) 		0.0233
(1.00) 		1
(1.00)
21q gain 20 (19%) 83 0.0345
(1.00) 		0.0241
(1.00) 		1
(1.00)
22q gain 22 (21%) 81 0.293
(1.00) 		0.0391
(1.00) 		0.0526
(1.00)
xq gain 9 (9%) 94 0.899
(1.00) 		0.132
(1.00) 		1
(1.00)
1p loss 15 (15%) 88 0.0785
(1.00) 		0.0934
(1.00) 		0.00462
(1.00)
1q loss 14 (14%) 89 0.873
(1.00) 		0.562
(1.00) 		0.156
(1.00)
2p loss 28 (27%) 75 0.51
(1.00) 		0.164
(1.00) 		0.184
(1.00)
2q loss 22 (21%) 81 0.0296
(1.00) 		0.00326
(0.74) 		1
(1.00)
3p loss 18 (17%) 85 0.0494
(1.00) 		0.357
(1.00) 		1
(1.00)
3q loss 21 (20%) 82 0.12
(1.00) 		0.321
(1.00) 		1
(1.00)
4p loss 18 (17%) 85 0.181
(1.00) 		0.635
(1.00) 		0.449
(1.00)
4q loss 20 (19%) 83 0.968
(1.00) 		0.337
(1.00) 		0.226
(1.00)
5p loss 8 (8%) 95 0.22
(1.00) 		0.702
(1.00) 		1
(1.00)
5q loss 13 (13%) 90 0.532
(1.00) 		0.75
(1.00) 		1
(1.00)
6p loss 25 (24%) 78 0.111
(1.00) 		0.753
(1.00) 		0.491
(1.00)
6q loss 13 (13%) 90 0.297
(1.00) 		0.204
(1.00) 		0.243
(1.00)
7p loss 14 (14%) 89 0.551
(1.00) 		0.937
(1.00) 		0.251
(1.00)
7q loss 13 (13%) 90 0.0601
(1.00) 		0.788
(1.00) 		0.769
(1.00)
8p loss 20 (19%) 83 0.986
(1.00) 		0.485
(1.00) 		1
(1.00)
8q loss 14 (14%) 89 0.682
(1.00) 		0.379
(1.00) 		0.156
(1.00)
9p loss 30 (29%) 73 0.892
(1.00) 		0.0169
(1.00) 		0.388
(1.00)
9q loss 18 (17%) 85 0.253
(1.00) 		0.0934
(1.00) 		0.801
(1.00)
10p loss 44 (43%) 59 0.858
(1.00) 		0.0795
(1.00) 		0.00259
(0.594)
11p loss 39 (38%) 64 0.456
(1.00) 		0.0134
(1.00) 		0.842
(1.00)
11q loss 32 (31%) 71 0.332
(1.00) 		0.00713
(1.00) 		0.525
(1.00)
12p loss 22 (21%) 81 0.688
(1.00) 		0.582
(1.00) 		1
(1.00)
12q loss 23 (22%) 80 0.789
(1.00) 		0.979
(1.00) 		0.236
(1.00)
13q loss 47 (46%) 56 0.78
(1.00) 		0.0208
(1.00) 		0.113
(1.00)
14q loss 28 (27%) 75 0.126
(1.00) 		0.776
(1.00) 		0.659
(1.00)
15q loss 16 (16%) 87 0.0771
(1.00) 		0.347
(1.00) 		0.426
(1.00)
16p loss 31 (30%) 72 0.79
(1.00) 		0.367
(1.00) 		0.0179
(1.00)
16q loss 54 (52%) 49 0.327
(1.00) 		0.243
(1.00) 		0.0305
(1.00)
17p loss 16 (16%) 87 0.822
(1.00) 		0.223
(1.00) 		1
(1.00)
17q loss 14 (14%) 89 0.705
(1.00) 		0.195
(1.00) 		0.779
(1.00)
18p loss 23 (22%) 80 0.674
(1.00) 		0.952
(1.00) 		0.00825
(1.00)
18q loss 25 (24%) 78 0.289
(1.00) 		0.0429
(1.00) 		0.107
(1.00)
19p loss 6 (6%) 97 0.392
(1.00) 		0.7
(1.00) 		0.208
(1.00)
19q loss 13 (13%) 90 0.687
(1.00) 		0.322
(1.00) 		0.769
(1.00)
20p loss 18 (17%) 85 0.893
(1.00) 		0.108
(1.00) 		0.449
(1.00)
20q loss 8 (8%) 95 0.307
(1.00) 		0.0384
(1.00) 		1
(1.00)
21q loss 18 (17%) 85 0.0716
(1.00) 		0.0345
(1.00) 		0.449
(1.00)
22q loss 30 (29%) 73 0.669
(1.00) 		0.339
(1.00) 		0.388
(1.00)
xq loss 41 (40%) 62 0.881
(1.00) 		0.257
(1.00) 		0.0747
(1.00)
'6p gain' versus 'AGE'

P value = 0.000174 (t-test), Q value = 0.041

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

nPatients Mean (Std.Dev)
ALL 103 62.4 (12.9)
6P GAIN MUTATED 18 73.4 (11.7)
6P GAIN WILD-TYPE 85 60.1 (12.0)

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

'7p gain' versus 'AGE'

P value = 2.86e-06 (t-test), Q value = 0.00068

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

nPatients Mean (Std.Dev)
ALL 103 62.4 (12.9)
7P GAIN MUTATED 26 71.8 (9.9)
7P GAIN WILD-TYPE 77 59.2 (12.3)

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

'7q gain' versus 'AGE'

P value = 1.69e-06 (t-test), Q value = 0.00041

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

nPatients Mean (Std.Dev)
ALL 103 62.4 (12.9)
7Q GAIN MUTATED 24 72.5 (9.7)
7Q GAIN WILD-TYPE 79 59.3 (12.2)

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

'8q gain' versus 'Time to Death'

P value = 0.000658 (logrank test), Q value = 0.15

Table S4.  Gene #16: '8q gain' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 103 31 0.1 - 143.4 (18.1)
8Q GAIN MUTATED 22 10 0.1 - 74.7 (9.8)
8Q GAIN WILD-TYPE 81 21 0.1 - 143.4 (21.0)

Figure S4.  Get High-res Image Gene #16: '8q gain' versus Clinical Feature #1: 'Time to Death'

'9q gain' versus 'AGE'

P value = 0.000253 (t-test), Q value = 0.059

Table S5.  Gene #18: '9q gain' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 103 62.4 (12.9)
9Q GAIN MUTATED 26 69.9 (10.7)
9Q GAIN WILD-TYPE 77 59.9 (12.7)

Figure S5.  Get High-res Image Gene #18: '9q gain' versus Clinical Feature #2: 'AGE'

'10p gain' versus 'Time to Death'

P value = 0.000334 (logrank test), Q value = 0.078

Table S6.  Gene #19: '10p gain' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 103 31 0.1 - 143.4 (18.1)
10P GAIN MUTATED 10 6 0.7 - 42.8 (10.4)
10P GAIN WILD-TYPE 93 25 0.1 - 143.4 (19.3)

Figure S6.  Get High-res Image Gene #19: '10p gain' versus Clinical Feature #1: 'Time to Death'

'10q gain' versus 'Time to Death'

P value = 9.56e-05 (logrank test), Q value = 0.023

Table S7.  Gene #20: '10q gain' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 103 31 0.1 - 143.4 (18.1)
10Q GAIN MUTATED 3 2 0.7 - 14.0 (4.5)
10Q GAIN WILD-TYPE 100 29 0.1 - 143.4 (18.1)

Figure S7.  Get High-res Image Gene #20: '10q gain' versus Clinical Feature #1: 'Time to Death'

'12p gain' versus 'Time to Death'

P value = 3.85e-06 (logrank test), Q value = 0.00092

Table S8.  Gene #23: '12p gain' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 103 31 0.1 - 143.4 (18.1)
12P GAIN MUTATED 11 7 0.1 - 27.2 (9.0)
12P GAIN WILD-TYPE 92 24 0.1 - 143.4 (18.9)

Figure S8.  Get High-res Image Gene #23: '12p gain' versus Clinical Feature #1: 'Time to Death'

'12p gain' versus 'AGE'

P value = 0.000183 (t-test), Q value = 0.043

Table S9.  Gene #23: '12p gain' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 103 62.4 (12.9)
12P GAIN MUTATED 11 76.3 (9.4)
12P GAIN WILD-TYPE 92 60.8 (12.3)

Figure S9.  Get High-res Image Gene #23: '12p gain' versus Clinical Feature #2: 'AGE'

'10q loss' versus 'GENDER'

P value = 7.18e-05 (Fisher's exact test), Q value = 0.017

Table S10.  Gene #60: '10q loss' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 54 49
10Q LOSS MUTATED 38 15
10Q LOSS WILD-TYPE 16 34

Figure S10.  Get High-res Image Gene #60: '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 = 103

  • Number of significantly arm-level cnvs = 80

  • 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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