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

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.

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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