Correlation between copy number variations of arm-level result and selected clinical features
Mesothelioma (Primary solid tumor)
16 April 2014 | analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C1W094KT
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/C1W094KT
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 15 arm-level events and 7 clinical features across 13 patients, no significant finding detected with Q value < 0.25.

- No arm-level cnvs related to clinical features.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between significant copy number variation of 15 arm-level events and 7 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, no significant finding detected.

|  | Clinical Features | Time to Death | AGE | NEOPLASM DISEASESTAGE | PATHOLOGY T STAGE | PATHOLOGY N STAGE | PATHOLOGY M STAGE | GENDER
| 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
5p gain | 4 (31%) | 9 | 0.926 (1.00) | 0.35 (1.00) | 0.706 (1.00) | 0.53 (1.00) | 1 (1.00) | 0.53 (1.00) | 0.53 (1.00)
16q gain | 3 (23%) | 10 | 0.158 (1.00) | 0.1 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00) | 0.497 (1.00) | 0.203 (1.00)
1p loss | 3 (23%) | 10 | 0.498 (1.00) | 0.613 (1.00) | 0.0699 (1.00) | 0.497 (1.00) | 0.528 (1.00) | 1 (1.00) | 1 (1.00)
4p loss | 6 (46%) | 7 | 0.463 (1.00) | 0.31 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00)
4q loss | 5 (38%) | 8 | 0.313 (1.00) | 0.166 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00)
6q loss | 7 (54%) | 6 | 0.856 (1.00) | 0.976 (1.00) | 0.164 (1.00) | 1 (1.00) | 0.0699 (1.00) | 0.266 (1.00) | 1 (1.00)
8p loss | 3 (23%) | 10 | 0.136 (1.00) | 0.169 (1.00) | 0.0699 (1.00) | 0.497 (1.00) | 0.528 (1.00) | 1 (1.00) | 1 (1.00)
9p loss | 4 (31%) | 9 | 0.0638 (1.00) | 0.517 (1.00) | 0.706 (1.00) | 0.0517 (1.00) | 0.497 (1.00) | 1 (1.00) | 0.228 (1.00)
9q loss | 4 (31%) | 9 | 0.11 (1.00) | 0.951 (1.00) | 1 (1.00) | 0.53 (1.00) | 0.497 (1.00) | 1 (1.00) | 1 (1.00)
13q loss | 8 (62%) | 5 | 0.0434 (1.00) | 0.914 (1.00) | 1 (1.00) | 1 (1.00) | 0.51 (1.00) | 0.217 (1.00) | 1 (1.00)
14q loss | 4 (31%) | 9 | 0.313 (1.00) | 0.0441 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00) | 0.53 (1.00)
18p loss | 4 (31%) | 9 | 0.175 (1.00) | 0.356 (1.00) | 0.315 (1.00) | 1 (1.00) | 0.497 (1.00) | 1 (1.00) | 1 (1.00)
18q loss | 4 (31%) | 9 | 0.175 (1.00) | 0.356 (1.00) | 0.315 (1.00) | 1 (1.00) | 0.497 (1.00) | 1 (1.00) | 1 (1.00)
22q loss | 10 (77%) | 3 | 0.548 (1.00) | 0.816 (1.00) | 0.388 (1.00) | 0.497 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00)
xq loss | 3 (23%) | 10 | 0.595 (1.00) | 0.909 (1.00) | 0.633 (1.00) | 1 (1.00) | 0.528 (1.00) | 1 (1.00) | 1 (1.00)

Methods & Data
Input

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

- Clinical data file = MESO-TP.merged_data.txt

- Number of patients = 13

- Number of significantly arm-level cnvs = 15

- Number of selected clinical features = 7

- 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.

- 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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