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
doi:10.7908/C1MW2F7W
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.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

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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