Correlation between copy number variations of arm-level result and selected clinical features

Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (Primary solid tumor)

17 October 2014 | analyses__2014_10_17

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/C1PR7TT6

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 37 arm-level events and 4 clinical features across 35 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 37 arm-level events and 4 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	GENDER	RACE
	nCNV (%)	nWild-Type	logrank test	Wilcoxon-test	Fisher's exact test	Fisher's exact test
1q gain	4 (11%)	31	0.412 (1.00)	0.468 (1.00)	1 (1.00)	0.0611 (1.00)
2p gain	3 (9%)	32	0.711 (1.00)	0.0198 (1.00)	1 (1.00)	0.293 (1.00)
2q gain	3 (9%)	32	0.711 (1.00)	0.0198 (1.00)	1 (1.00)	0.296 (1.00)
3p gain	7 (20%)	28	0.291 (1.00)	0.635 (1.00)	1 (1.00)	0.526 (1.00)
3q gain	9 (26%)	26	0.291 (1.00)	0.925 (1.00)	0.443 (1.00)	0.587 (1.00)
5p gain	3 (9%)	32	0.461 (1.00)	0.813 (1.00)	1 (1.00)	1 (1.00)
6p gain	4 (11%)	31	0.589 (1.00)	1 (1.00)	0.603 (1.00)	0.65 (1.00)
6q gain	3 (9%)	32	0.762 (1.00)	0.555 (1.00)	0.229 (1.00)	0.298 (1.00)
7p gain	10 (29%)	25	0.221 (1.00)	0.661 (1.00)	0.711 (1.00)	0.798 (1.00)
7q gain	9 (26%)	26	0.357 (1.00)	0.428 (1.00)	0.443 (1.00)	0.591 (1.00)
9p gain	4 (11%)	31	0.623 (1.00)	0.979 (1.00)	0.338 (1.00)	0.21 (1.00)
9q gain	4 (11%)	31	0.623 (1.00)	0.979 (1.00)	0.338 (1.00)	0.209 (1.00)
10p gain	3 (9%)	32	0.762 (1.00)	0.555 (1.00)	0.229 (1.00)	1 (1.00)
11p gain	5 (14%)	30	0.421 (1.00)	0.832 (1.00)	0.338 (1.00)	0.696 (1.00)
11q gain	9 (26%)	26	0.172 (1.00)	0.0856 (1.00)	0.443 (1.00)	0.782 (1.00)
12p gain	4 (11%)	31	0.559 (1.00)	0.0516 (1.00)	0.603 (1.00)	0.65 (1.00)
12q gain	6 (17%)	29	0.349 (1.00)	0.204 (1.00)	1 (1.00)	0.717 (1.00)
13q gain	3 (9%)	32	0.711 (1.00)	0.302 (1.00)	0.603 (1.00)	0.296 (1.00)
16p gain	6 (17%)	29	0.623 (1.00)	0.878 (1.00)	0.658 (1.00)	1 (1.00)
16q gain	6 (17%)	29	0.514 (1.00)	0.776 (1.00)	1 (1.00)	1 (1.00)
17q gain	3 (9%)	32	0.762 (1.00)	0.194 (1.00)	0.229 (1.00)	0.296 (1.00)
18p gain	9 (26%)	26	0.277 (1.00)	0.265 (1.00)	0.443 (1.00)	0.0256 (1.00)
18q gain	10 (29%)	25	0.263 (1.00)	0.571 (1.00)	0.711 (1.00)	0.0112 (1.00)
20p gain	3 (9%)	32	0.661 (1.00)	0.443 (1.00)	0.229 (1.00)	0.293 (1.00)
21q gain	7 (20%)	28	0.812 (1.00)	0.536 (1.00)	1 (1.00)	0.746 (1.00)
xq gain	4 (11%)	31	0.55 (1.00)	0.551 (1.00)	0.338 (1.00)	0.648 (1.00)
1p loss	3 (9%)	32	0.589 (1.00)	0.345 (1.00)	0.603 (1.00)	0.627 (1.00)
4q loss	3 (9%)	32		0.86 (1.00)	0.229 (1.00)	1 (1.00)
6q loss	4 (11%)	31	0.34 (1.00)	0.436 (1.00)	0.338 (1.00)	0.4 (1.00)
8p loss	6 (17%)	29	0.589 (1.00)	0.827 (1.00)	0.177 (1.00)	0.719 (1.00)
8q loss	3 (9%)	32		0.516 (1.00)	1 (1.00)	0.623 (1.00)
15q loss	6 (17%)	29	0.421 (1.00)	0.861 (1.00)	0.177 (1.00)	1 (1.00)
16q loss	4 (11%)	31	0.661 (1.00)	0.856 (1.00)	0.104 (1.00)	0.651 (1.00)
17p loss	4 (11%)	31	0.661 (1.00)	0.795 (1.00)	0.603 (1.00)	1 (1.00)
18p loss	3 (9%)	32	0.593 (1.00)	0.443 (1.00)	0.104 (1.00)	1 (1.00)
22q loss	3 (9%)	32	0.762 (1.00)	0.836 (1.00)	0.229 (1.00)	0.298 (1.00)
xq loss	4 (11%)	31	0.0391 (1.00)	0.979 (1.00)	0.104 (1.00)	0.65 (1.00)

Methods & Data

Input

Copy number data file = transformed.cor.cli.txt
Clinical data file = DLBC-TP.merged_data.txt
Number of patients = 35
Number of significantly arm-level cnvs = 37
Number of selected clinical features = 4
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

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] 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)

[3] 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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