Correlation between copy number variations of arm-level result and selected clinical features
Sarcoma (Primary solid tumor)
23 May 2013  |  analyses__2013_05_23
Maintainer Information
Citation Information
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/C1MW2F7W
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 47 arm-level results and 3 clinical features across 22 patients, one significant finding detected with Q value < 0.25.

  • 15q 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 47 arm-level results and 3 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, one significant finding detected.

Clinical
Features
Time
to
Death
AGE GENDER
nCNV (%) nWild-Type logrank test t-test Fisher's exact test
15q loss 0 (0%) 19 1.27e-05
(0.00179)
0.522
(1.00)
0.571
(1.00)
1p gain 0 (0%) 19 0.395
(1.00)
0.483
(1.00)
0.0779
(1.00)
4p gain 0 (0%) 19 0.335
(1.00)
0.955
(1.00)
0.221
(1.00)
5p gain 0 (0%) 17 0.811
(1.00)
0.0636
(1.00)
0.624
(1.00)
5q gain 0 (0%) 18 0.0456
(1.00)
0.124
(1.00)
1
(1.00)
6p gain 0 (0%) 17 0.428
(1.00)
0.427
(1.00)
1
(1.00)
6q gain 0 (0%) 16 0.254
(1.00)
0.664
(1.00)
0.646
(1.00)
7p gain 0 (0%) 15 0.563
(1.00)
0.00274
(0.384)
0.652
(1.00)
7q gain 0 (0%) 15 0.563
(1.00)
0.00274
(0.384)
0.652
(1.00)
8p gain 0 (0%) 18 0.00918
(1.00)
0.573
(1.00)
1
(1.00)
8q gain 0 (0%) 18 0.0114
(1.00)
0.929
(1.00)
1
(1.00)
9q gain 0 (0%) 19 0.452
(1.00)
0.99
(1.00)
1
(1.00)
15q gain 0 (0%) 18 0.959
(1.00)
0.968
(1.00)
0.293
(1.00)
16p gain 0 (0%) 19 0.327
(1.00)
0.532
(1.00)
0.571
(1.00)
17p gain 0 (0%) 16 0.558
(1.00)
0.164
(1.00)
0.162
(1.00)
17q gain 0 (0%) 19 0.452
(1.00)
0.303
(1.00)
1
(1.00)
18p gain 0 (0%) 18 0.926
(1.00)
0.968
(1.00)
0.293
(1.00)
18q gain 0 (0%) 17 0.677
(1.00)
0.702
(1.00)
0.135
(1.00)
19p gain 0 (0%) 16 0.608
(1.00)
0.37
(1.00)
1
(1.00)
19q gain 0 (0%) 18 0.743
(1.00)
0.867
(1.00)
0.594
(1.00)
20p gain 0 (0%) 15 0.5
(1.00)
0.227
(1.00)
0.652
(1.00)
20q gain 0 (0%) 13 0.0438
(1.00)
0.0666
(1.00)
1
(1.00)
21q gain 0 (0%) 17 0.275
(1.00)
0.724
(1.00)
0.0396
(1.00)
22q gain 0 (0%) 19 0.828
(1.00)
0.651
(1.00)
0.571
(1.00)
1p loss 0 (0%) 19 0.478
(1.00)
0.0998
(1.00)
0.571
(1.00)
1q loss 0 (0%) 18 0.444
(1.00)
0.767
(1.00)
0.594
(1.00)
2p loss 0 (0%) 19 0.585
(1.00)
0.437
(1.00)
1
(1.00)
2q loss 0 (0%) 19 0.585
(1.00)
0.437
(1.00)
1
(1.00)
3p loss 0 (0%) 18 0.335
(1.00)
0.972
(1.00)
1
(1.00)
3q loss 0 (0%) 17 0.335
(1.00)
0.904
(1.00)
1
(1.00)
4p loss 0 (0%) 19 0.118
(1.00)
0.996
(1.00)
0.571
(1.00)
4q loss 0 (0%) 19 0.118
(1.00)
0.996
(1.00)
0.571
(1.00)
8p loss 0 (0%) 16 0.263
(1.00)
0.691
(1.00)
1
(1.00)
9p loss 0 (0%) 18 0.0114
(1.00)
0.521
(1.00)
0.293
(1.00)
10p loss 0 (0%) 13 0.477
(1.00)
0.696
(1.00)
0.0274
(1.00)
10q loss 0 (0%) 13 0.683
(1.00)
0.644
(1.00)
0.0274
(1.00)
11p loss 0 (0%) 14 0.326
(1.00)
0.54
(1.00)
0.675
(1.00)
11q loss 0 (0%) 16 0.326
(1.00)
0.298
(1.00)
0.162
(1.00)
13q loss 0 (0%) 13 0.358
(1.00)
0.5
(1.00)
0.666
(1.00)
14q loss 0 (0%) 17 0.263
(1.00)
0.772
(1.00)
1
(1.00)
16p loss 0 (0%) 18 0.975
(1.00)
0.66
(1.00)
1
(1.00)
16q loss 0 (0%) 14 0.0926
(1.00)
0.497
(1.00)
0.675
(1.00)
18p loss 0 (0%) 18 0.0534
(1.00)
0.829
(1.00)
0.594
(1.00)
18q loss 0 (0%) 17 0.0534
(1.00)
0.414
(1.00)
1
(1.00)
19q loss 0 (0%) 19 0.11
(1.00)
0.00993
(1.00)
1
(1.00)
22q loss 0 (0%) 18 0.78
(1.00)
0.583
(1.00)
1
(1.00)
Xq loss 0 (0%) 18 0.892
(1.00)
0.456
(1.00)
0.594
(1.00)
'15q loss' versus 'Time to Death'

P value = 1.27e-05 (logrank test), Q value = 0.0018

Table S1.  Gene #40: '15q loss' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 22 6 0.1 - 53.3 (11.3)
15Q LOSS CNV 3 2 0.1 - 5.9 (4.5)
15Q LOSS WILD-TYPE 19 4 0.1 - 53.3 (13.6)

Figure S1.  Get High-res Image Gene #40: '15q loss' versus Clinical Feature #1: 'Time to Death'

Methods & Data
Input
  • Mutation data file = broad_values_by_arm.mutsig.cluster.txt

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

  • Number of patients = 22

  • Number of significantly arm-level cnvs = 47

  • Number of selected clinical features = 3

  • 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)