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

Uterine Carcinosarcoma (Primary solid tumor)

23 September 2013 | analyses__2013_09_23

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 (2013): Correlation between copy number variations of arm-level result and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1GQ6W5X

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 56 arm-level events and 2 clinical features across 14 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 56 arm-level events and 2 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
	nCNV (%)	nWild-Type	logrank test	t-test
1P GAIN MUTATION ANALYSIS	7 (50%)	7	0.74 (1.00)	0.272 (1.00)
1Q GAIN MUTATION ANALYSIS	8 (57%)	6	0.282 (1.00)	0.116 (1.00)
2P GAIN MUTATION ANALYSIS	4 (29%)	10	0.0878 (1.00)	0.4 (1.00)
2Q GAIN MUTATION ANALYSIS	3 (21%)	11	0.364 (1.00)	0.141 (1.00)
3P GAIN MUTATION ANALYSIS	4 (29%)	10	0.436 (1.00)	0.93 (1.00)
3Q GAIN MUTATION ANALYSIS	5 (36%)	9	0.493 (1.00)	0.427 (1.00)
4P GAIN MUTATION ANALYSIS	3 (21%)	11	0.963 (1.00)	0.73 (1.00)
5P GAIN MUTATION ANALYSIS	6 (43%)	8	0.548 (1.00)	0.354 (1.00)
6P GAIN MUTATION ANALYSIS	6 (43%)	8	0.986 (1.00)	0.859 (1.00)
6Q GAIN MUTATION ANALYSIS	5 (36%)	9	0.46 (1.00)	0.581 (1.00)
8P GAIN MUTATION ANALYSIS	4 (29%)	10	0.115 (1.00)	0.742 (1.00)
8Q GAIN MUTATION ANALYSIS	6 (43%)	8	0.256 (1.00)	0.575 (1.00)
10P GAIN MUTATION ANALYSIS	6 (43%)	8	0.179 (1.00)	0.205 (1.00)
10Q GAIN MUTATION ANALYSIS	5 (36%)	9	0.887 (1.00)	0.841 (1.00)
11Q GAIN MUTATION ANALYSIS	3 (21%)	11	0.465 (1.00)	0.7 (1.00)
12P GAIN MUTATION ANALYSIS	6 (43%)	8	0.289 (1.00)	0.686 (1.00)
12Q GAIN MUTATION ANALYSIS	3 (21%)	11	0.177 (1.00)	0.356 (1.00)
13Q GAIN MUTATION ANALYSIS	4 (29%)	10	0.152 (1.00)	0.685 (1.00)
16P GAIN MUTATION ANALYSIS	3 (21%)	11	0.361 (1.00)	0.13 (1.00)
17P GAIN MUTATION ANALYSIS	4 (29%)	10	0.652 (1.00)	0.207 (1.00)
17Q GAIN MUTATION ANALYSIS	6 (43%)	8	0.844 (1.00)	0.686 (1.00)
18P GAIN MUTATION ANALYSIS	4 (29%)	10	0.367 (1.00)	0.0641 (1.00)
19P GAIN MUTATION ANALYSIS	6 (43%)	8	0.912 (1.00)	0.994 (1.00)
19Q GAIN MUTATION ANALYSIS	7 (50%)	7	0.586 (1.00)	0.678 (1.00)
20P GAIN MUTATION ANALYSIS	9 (64%)	5	0.942 (1.00)	0.169 (1.00)
20Q GAIN MUTATION ANALYSIS	11 (79%)	3	0.392 (1.00)	0.116 (1.00)
21Q GAIN MUTATION ANALYSIS	7 (50%)	7	0.71 (1.00)	0.00744 (0.833)
XQ GAIN MUTATION ANALYSIS	4 (29%)	10	0.768 (1.00)	0.893 (1.00)
3P LOSS MUTATION ANALYSIS	5 (36%)	9	0.364 (1.00)	0.588 (1.00)
3Q LOSS MUTATION ANALYSIS	4 (29%)	10	0.314 (1.00)	0.736 (1.00)
4P LOSS MUTATION ANALYSIS	6 (43%)	8	0.144 (1.00)	0.293 (1.00)
4Q LOSS MUTATION ANALYSIS	8 (57%)	6	0.0314 (1.00)	0.151 (1.00)
5Q LOSS MUTATION ANALYSIS	5 (36%)	9	0.202 (1.00)	0.531 (1.00)
7P LOSS MUTATION ANALYSIS	3 (21%)	11	0.338 (1.00)	0.354 (1.00)
7Q LOSS MUTATION ANALYSIS	4 (29%)	10	0.923 (1.00)	0.133 (1.00)
8P LOSS MUTATION ANALYSIS	4 (29%)	10	0.323 (1.00)	0.886 (1.00)
9P LOSS MUTATION ANALYSIS	7 (50%)	7	0.127 (1.00)	0.396 (1.00)
9Q LOSS MUTATION ANALYSIS	8 (57%)	6	0.434 (1.00)	0.159 (1.00)
10P LOSS MUTATION ANALYSIS	3 (21%)	11	0.136 (1.00)	0.0596 (1.00)
11P LOSS MUTATION ANALYSIS	7 (50%)	7	0.015 (1.00)	0.47 (1.00)
11Q LOSS MUTATION ANALYSIS	6 (43%)	8	0.144 (1.00)	0.774 (1.00)
12P LOSS MUTATION ANALYSIS	5 (36%)	9	0.903 (1.00)	0.791 (1.00)
12Q LOSS MUTATION ANALYSIS	3 (21%)	11	0.823 (1.00)	0.937 (1.00)
13Q LOSS MUTATION ANALYSIS	6 (43%)	8	0.822 (1.00)	0.894 (1.00)
14Q LOSS MUTATION ANALYSIS	9 (64%)	5	0.331 (1.00)	0.47 (1.00)
15Q LOSS MUTATION ANALYSIS	9 (64%)	5	0.463 (1.00)	0.223 (1.00)
16P LOSS MUTATION ANALYSIS	7 (50%)	7	0.434 (1.00)	0.435 (1.00)
16Q LOSS MUTATION ANALYSIS	9 (64%)	5	0.202 (1.00)	0.928 (1.00)
17P LOSS MUTATION ANALYSIS	6 (43%)	8	0.667 (1.00)	0.958 (1.00)
18P LOSS MUTATION ANALYSIS	4 (29%)	10	0.436 (1.00)	0.93 (1.00)
18Q LOSS MUTATION ANALYSIS	3 (21%)	11	0.436 (1.00)	0.983 (1.00)
19P LOSS MUTATION ANALYSIS	4 (29%)	10	0.56 (1.00)	0.971 (1.00)
19Q LOSS MUTATION ANALYSIS	4 (29%)	10	0.69 (1.00)	0.603 (1.00)
21Q LOSS MUTATION ANALYSIS	5 (36%)	9	0.765 (1.00)	0.0336 (1.00)
22Q LOSS MUTATION ANALYSIS	8 (57%)	6	0.84 (1.00)	0.554 (1.00)
XQ LOSS MUTATION ANALYSIS	3 (21%)	11	0.0679 (1.00)	0.247 (1.00)

Methods & Data

Input

Copy number data file = transformed.cor.cli.txt
Clinical data file = UCS-TP.clin.merged.picked.txt
Number of patients = 14
Number of significantly arm-level cnvs = 56
Number of selected clinical features = 2
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

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] 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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