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

Mesothelioma (Primary solid tumor)

15 July 2014 | analyses__2014_07_15

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/C1R21056

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 36 arm-level events and 7 clinical features across 21 patients, one significant finding detected with Q value < 0.25.

11q 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 36 arm-level events and 7 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	NEOPLASM DISEASESTAGE	PATHOLOGY T STAGE	PATHOLOGY N STAGE	PATHOLOGY M STAGE	GENDER
	nCNV (%)	nWild-Type	logrank test	Wilcoxon-test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test
11q loss	3 (14%)	18	0.00035 (0.0868)	0.42 (1.00)	0.22 (1.00)	0.531 (1.00)		1 (1.00)	0.526 (1.00)
1q gain	5 (24%)	16	0.203 (1.00)	0.869 (1.00)	1 (1.00)	1 (1.00)	0.613 (1.00)	0.444 (1.00)	0.598 (1.00)
3p gain	4 (19%)	17	0.942 (1.00)	0.928 (1.00)	1 (1.00)	0.253 (1.00)	1 (1.00)	1 (1.00)	0.544 (1.00)
3q gain	5 (24%)	16	0.41 (1.00)	0.619 (1.00)	0.877 (1.00)	0.0475 (1.00)	1 (1.00)	1 (1.00)	0.115 (1.00)
5p gain	6 (29%)	15	0.568 (1.00)	0.149 (1.00)	0.816 (1.00)	0.146 (1.00)	1 (1.00)	0.723 (1.00)	0.291 (1.00)
5q gain	3 (14%)	18	0.469 (1.00)	0.174 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	0.26 (1.00)	0.184 (1.00)
7p gain	5 (24%)	16	0.187 (1.00)	0.282 (1.00)	0.879 (1.00)	0.0475 (1.00)	0.613 (1.00)	1 (1.00)	0.598 (1.00)
7q gain	5 (24%)	16	0.187 (1.00)	0.282 (1.00)	0.879 (1.00)	0.0475 (1.00)	0.613 (1.00)	1 (1.00)	0.598 (1.00)
8p gain	3 (14%)	18	0.0319 (1.00)	1 (1.00)	0.404 (1.00)	0.0421 (1.00)	1 (1.00)	0.605 (1.00)	1 (1.00)
8q gain	4 (19%)	17	0.0319 (1.00)	0.56 (1.00)	0.119 (1.00)	0.253 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
12p gain	5 (24%)	16	0.492 (1.00)	0.385 (1.00)	0.368 (1.00)	0.325 (1.00)	0.613 (1.00)	1 (1.00)	1 (1.00)
12q gain	5 (24%)	16	0.492 (1.00)	0.385 (1.00)	0.371 (1.00)	0.325 (1.00)	0.613 (1.00)	1 (1.00)	1 (1.00)
16p gain	5 (24%)	16	0.412 (1.00)	0.741 (1.00)	0.55 (1.00)	1 (1.00)	0.549 (1.00)	1 (1.00)	0.598 (1.00)
16q gain	6 (29%)	15	0.143 (1.00)	0.483 (1.00)	0.503 (1.00)	0.631 (1.00)	0.613 (1.00)	1 (1.00)	0.291 (1.00)
1p loss	3 (14%)	18	0.6 (1.00)	1 (1.00)	0.085 (1.00)	0.257 (1.00)	0.521 (1.00)	1 (1.00)	1 (1.00)
2q loss	3 (14%)	18	0.0342 (1.00)	0.481 (1.00)	1 (1.00)	0.531 (1.00)	0.521 (1.00)	0.603 (1.00)	0.015 (1.00)
4p loss	8 (38%)	13	0.555 (1.00)	0.856 (1.00)	0.769 (1.00)	0.646 (1.00)	0.325 (1.00)	1 (1.00)	0.146 (1.00)
4q loss	7 (33%)	14	0.416 (1.00)	0.477 (1.00)	0.916 (1.00)	0.346 (1.00)	0.354 (1.00)	0.744 (1.00)	0.12 (1.00)
6q loss	10 (48%)	11	0.721 (1.00)	0.916 (1.00)	0.0302 (1.00)	0.659 (1.00)	0.157 (1.00)	1 (1.00)	0.635 (1.00)
8p loss	3 (14%)	18	0.0754 (1.00)	0.131 (1.00)	0.0841 (1.00)	0.257 (1.00)	0.521 (1.00)	1 (1.00)	1 (1.00)
9p loss	6 (29%)	15	0.153 (1.00)	0.725 (1.00)	0.501 (1.00)	0.146 (1.00)	1 (1.00)	0.723 (1.00)	0.123 (1.00)
9q loss	6 (29%)	15	0.223 (1.00)	1 (1.00)	0.637 (1.00)	0.631 (1.00)	1 (1.00)	0.721 (1.00)	0.623 (1.00)
10p loss	6 (29%)	15	0.284 (1.00)	0.558 (1.00)	0.501 (1.00)	0.631 (1.00)	1 (1.00)	0.253 (1.00)	0.623 (1.00)
10q loss	5 (24%)	16	0.177 (1.00)	0.591 (1.00)	0.369 (1.00)	1 (1.00)	0.267 (1.00)	0.115 (1.00)	1 (1.00)
13q loss	13 (62%)	8	0.0333 (1.00)	0.384 (1.00)	0.623 (1.00)	0.085 (1.00)	0.613 (1.00)	0.111 (1.00)	1 (1.00)
14q loss	7 (33%)	14	0.354 (1.00)	0.167 (1.00)	0.914 (1.00)	0.346 (1.00)	1 (1.00)	0.74 (1.00)	0.12 (1.00)
15q loss	4 (19%)	17	0.0808 (1.00)	0.964 (1.00)	0.588 (1.00)	0.618 (1.00)	0.521 (1.00)	0.393 (1.00)	1 (1.00)
17p loss	4 (19%)	17	0.419 (1.00)	0.858 (1.00)	0.164 (1.00)	0.618 (1.00)	1 (1.00)	0.389 (1.00)	1 (1.00)
17q loss	3 (14%)	18	0.54 (1.00)	1 (1.00)	0.322 (1.00)	1 (1.00)		0.262 (1.00)	1 (1.00)
18p loss	4 (19%)	17	0.214 (1.00)	0.822 (1.00)	0.312 (1.00)	1 (1.00)	0.267 (1.00)	1 (1.00)	1 (1.00)
18q loss	4 (19%)	17	0.214 (1.00)	0.822 (1.00)	0.309 (1.00)	1 (1.00)	0.267 (1.00)	1 (1.00)	1 (1.00)
19q loss	3 (14%)	18	0.161 (1.00)	0.724 (1.00)	0.0703 (1.00)	0.257 (1.00)		0.0143 (1.00)	0.526 (1.00)
20p loss	4 (19%)	17	0.251 (1.00)	0.445 (1.00)	0.584 (1.00)	0.618 (1.00)	1 (1.00)	0.393 (1.00)	1 (1.00)
21q loss	3 (14%)	18	0.32 (1.00)	0.65 (1.00)	0.32 (1.00)	1 (1.00)		0.262 (1.00)	1 (1.00)
22q loss	15 (71%)	6	0.674 (1.00)	1 (1.00)	0.5 (1.00)	1 (1.00)	0.303 (1.00)	1 (1.00)	1 (1.00)
xq loss	4 (19%)	17	0.44 (1.00)	0.858 (1.00)	0.585 (1.00)	1 (1.00)	0.267 (1.00)	0.219 (1.00)	1 (1.00)

'11q loss' versus 'Time to Death'

P value = 0.00035 (logrank test), Q value = 0.087

Table S1. Gene #24: '11q loss' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	21	16	0.2 - 91.7 (17.3)
11Q LOSS MUTATED	3	2	0.2 - 5.2 (3.5)
11Q LOSS WILD-TYPE	18	14	1.9 - 91.7 (18.4)

Figure S1. Get High-res Image Gene #24: '11q loss' versus Clinical Feature #1: 'Time to Death'

Methods & Data

Input

Copy number data file = transformed.cor.cli.txt
Clinical data file = MESO-TP.merged_data.txt
Number of patients = 21
Number of significantly arm-level cnvs = 36
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

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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