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

Esophageal Carcinoma (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/C1X34VR6

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 54 arm-level events and 7 clinical features across 19 patients, 3 significant findings detected with Q value < 0.25.

19P GAIN MUTATION ANALYSIS cnv correlated to 'AGE'.
16P LOSS MUTATION ANALYSIS cnv correlated to 'AGE'.
16Q LOSS MUTATION ANALYSIS cnv correlated to 'AGE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between significant copy number variation of 54 arm-level events and 7 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 3 significant findings detected.


		Clinical Features	Time to Death	AGE	NEOPLASM DISEASESTAGE	PATHOLOGY T STAGE	PATHOLOGY N STAGE	GENDER	NUMBERPACKYEARSSMOKED
	nCNV (%)	nWild-Type	logrank test	t-test	Chi-square test	Fisher's exact test	Fisher's exact test	Fisher's exact test	t-test
19P GAIN MUTATION ANALYSIS	4 (21%)	15	0.0443 (1.00)	0.000534 (0.193)	0.132 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	0.684 (1.00)
16P LOSS MUTATION ANALYSIS	3 (16%)	16	0.719 (1.00)	4.56e-06 (0.00166)	0.412 (1.00)	1 (1.00)	1 (1.00)	0.422 (1.00)
16Q LOSS MUTATION ANALYSIS	3 (16%)	16	0.719 (1.00)	4.56e-06 (0.00166)	0.412 (1.00)	1 (1.00)	1 (1.00)	0.422 (1.00)
1P GAIN MUTATION ANALYSIS	6 (32%)	13	0.855 (1.00)	0.00631 (1.00)	0.34 (1.00)	0.321 (1.00)	1 (1.00)	1 (1.00)	0.426 (1.00)
1Q GAIN MUTATION ANALYSIS	8 (42%)	11	0.998 (1.00)	0.028 (1.00)	0.383 (1.00)	0.292 (1.00)	1 (1.00)	1 (1.00)	0.934 (1.00)
2P GAIN MUTATION ANALYSIS	7 (37%)	12	0.8 (1.00)	0.436 (1.00)	0.119 (1.00)	0.0223 (1.00)	1 (1.00)	1 (1.00)	0.128 (1.00)
2Q GAIN MUTATION ANALYSIS	3 (16%)	16	0.492 (1.00)	0.797 (1.00)	0.183 (1.00)	0.393 (1.00)	1 (1.00)	1 (1.00)
3P GAIN MUTATION ANALYSIS	4 (21%)	15	0.266 (1.00)	0.43 (1.00)	0.0801 (1.00)	0.0753 (1.00)	0.117 (1.00)	0.53 (1.00)	0.691 (1.00)
3Q GAIN MUTATION ANALYSIS	11 (58%)	8	0.192 (1.00)	0.201 (1.00)	0.688 (1.00)	1 (1.00)	0.633 (1.00)	1 (1.00)	0.896 (1.00)
5P GAIN MUTATION ANALYSIS	7 (37%)	12	0.954 (1.00)	0.801 (1.00)	0.238 (1.00)	0.844 (1.00)	1 (1.00)	0.523 (1.00)	0.592 (1.00)
6P GAIN MUTATION ANALYSIS	4 (21%)	15	0.589 (1.00)	0.961 (1.00)	0.495 (1.00)	0.798 (1.00)	1 (1.00)	0.00413 (1.00)
7P GAIN MUTATION ANALYSIS	13 (68%)	6	0.836 (1.00)	0.141 (1.00)	0.747 (1.00)	1 (1.00)	0.129 (1.00)	1 (1.00)	0.205 (1.00)
7Q GAIN MUTATION ANALYSIS	10 (53%)	9	0.366 (1.00)	0.881 (1.00)	0.19 (1.00)	0.607 (1.00)	1 (1.00)	1 (1.00)	0.316 (1.00)
8P GAIN MUTATION ANALYSIS	6 (32%)	13	0.237 (1.00)	0.308 (1.00)	0.18 (1.00)	0.0986 (1.00)	0.617 (1.00)	0.222 (1.00)	0.68 (1.00)
8Q GAIN MUTATION ANALYSIS	11 (58%)	8	0.195 (1.00)	0.236 (1.00)	0.688 (1.00)	0.844 (1.00)	1 (1.00)	1 (1.00)	0.257 (1.00)
9Q GAIN MUTATION ANALYSIS	3 (16%)	16	0.214 (1.00)	0.496 (1.00)	0.0157 (1.00)	0.769 (1.00)	0.523 (1.00)	1 (1.00)	0.176 (1.00)
11P GAIN MUTATION ANALYSIS	5 (26%)	14	0.535 (1.00)	0.365 (1.00)	0.833 (1.00)	0.663 (1.00)	1 (1.00)	1 (1.00)	0.146 (1.00)
11Q GAIN MUTATION ANALYSIS	3 (16%)	16	0.378 (1.00)	0.277 (1.00)	0.676 (1.00)	1 (1.00)	0.263 (1.00)	1 (1.00)
12P GAIN MUTATION ANALYSIS	5 (26%)	14	0.37 (1.00)	0.623 (1.00)	0.352 (1.00)	0.798 (1.00)	0.603 (1.00)	1 (1.00)	0.553 (1.00)
12Q GAIN MUTATION ANALYSIS	6 (32%)	13	0.37 (1.00)	0.405 (1.00)	0.571 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	0.316 (1.00)
14Q GAIN MUTATION ANALYSIS	5 (26%)	14	0.0515 (1.00)	0.512 (1.00)	0.638 (1.00)	1 (1.00)	0.0379 (1.00)	0.53 (1.00)	0.68 (1.00)
16P GAIN MUTATION ANALYSIS	4 (21%)	15	0.214 (1.00)	0.308 (1.00)	0.0628 (1.00)	0.473 (1.00)	0.117 (1.00)	1 (1.00)	0.631 (1.00)
16Q GAIN MUTATION ANALYSIS	4 (21%)	15	0.214 (1.00)	0.308 (1.00)	0.0628 (1.00)	0.473 (1.00)	0.117 (1.00)	1 (1.00)	0.631 (1.00)
17Q GAIN MUTATION ANALYSIS	5 (26%)	14	0.378 (1.00)	0.371 (1.00)	0.454 (1.00)	0.663 (1.00)	0.603 (1.00)	0.53 (1.00)	0.684 (1.00)
18P GAIN MUTATION ANALYSIS	5 (26%)	14	0.0427 (1.00)	0.971 (1.00)	0.17 (1.00)	0.0151 (1.00)	1 (1.00)	0.155 (1.00)	0.752 (1.00)
19Q GAIN MUTATION ANALYSIS	6 (32%)	13	0.101 (1.00)	0.0127 (1.00)	0.0996 (1.00)	0.552 (1.00)	0.333 (1.00)	0.517 (1.00)	0.684 (1.00)
20P GAIN MUTATION ANALYSIS	15 (79%)	4	0.266 (1.00)	0.512 (1.00)	0.718 (1.00)	0.372 (1.00)	0.603 (1.00)	1 (1.00)
20Q GAIN MUTATION ANALYSIS	16 (84%)	3		0.00626 (1.00)	0.863 (1.00)	0.567 (1.00)	0.523 (1.00)	1 (1.00)	0.572 (1.00)
22Q GAIN MUTATION ANALYSIS	5 (26%)	14	0.646 (1.00)	0.046 (1.00)	0.166 (1.00)	0.798 (1.00)	0.00181 (0.65)	1 (1.00)	0.195 (1.00)
XQ GAIN MUTATION ANALYSIS	6 (32%)	13	0.142 (1.00)	0.769 (1.00)	0.132 (1.00)	0.465 (1.00)	1 (1.00)	0.517 (1.00)	0.777 (1.00)
3P LOSS MUTATION ANALYSIS	9 (47%)	10	0.395 (1.00)	0.229 (1.00)	0.489 (1.00)	0.0611 (1.00)	1 (1.00)	1 (1.00)	0.662 (1.00)
4P LOSS MUTATION ANALYSIS	10 (53%)	9	0.147 (1.00)	0.748 (1.00)	0.634 (1.00)	0.247 (1.00)	1 (1.00)	0.582 (1.00)	0.631 (1.00)
4Q LOSS MUTATION ANALYSIS	7 (37%)	12	0.147 (1.00)	0.161 (1.00)	0.187 (1.00)	0.704 (1.00)	0.656 (1.00)	1 (1.00)	0.649 (1.00)
5P LOSS MUTATION ANALYSIS	5 (26%)	14	0.17 (1.00)	0.159 (1.00)	0.0708 (1.00)	1 (1.00)	0.106 (1.00)	1 (1.00)
5Q LOSS MUTATION ANALYSIS	10 (53%)	9	0.904 (1.00)	0.868 (1.00)	0.266 (1.00)	0.714 (1.00)	0.65 (1.00)	0.211 (1.00)	0.313 (1.00)
8P LOSS MUTATION ANALYSIS	8 (42%)	11	0.813 (1.00)	0.716 (1.00)	0.278 (1.00)	0.05 (1.00)	0.633 (1.00)	0.228 (1.00)	0.948 (1.00)
9P LOSS MUTATION ANALYSIS	10 (53%)	9	0.405 (1.00)	0.144 (1.00)	0.612 (1.00)	0.714 (1.00)	0.65 (1.00)	0.211 (1.00)	0.306 (1.00)
9Q LOSS MUTATION ANALYSIS	9 (47%)	10	0.481 (1.00)	0.135 (1.00)	0.921 (1.00)	1 (1.00)	0.65 (1.00)	0.0867 (1.00)	0.0729 (1.00)
10P LOSS MUTATION ANALYSIS	5 (26%)	14	0.763 (1.00)	0.657 (1.00)	0.454 (1.00)	0.663 (1.00)	1 (1.00)	1 (1.00)	0.89 (1.00)
10Q LOSS MUTATION ANALYSIS	6 (32%)	13	0.763 (1.00)	0.879 (1.00)	0.415 (1.00)	0.0583 (1.00)	1 (1.00)	1 (1.00)	0.676 (1.00)
11P LOSS MUTATION ANALYSIS	5 (26%)	14	0.266 (1.00)	0.47 (1.00)	0.166 (1.00)	0.122 (1.00)	0.305 (1.00)	0.53 (1.00)
11Q LOSS MUTATION ANALYSIS	7 (37%)	12	0.592 (1.00)	0.885 (1.00)	0.0754 (1.00)	0.844 (1.00)	0.326 (1.00)	1 (1.00)	0.357 (1.00)
12P LOSS MUTATION ANALYSIS	4 (21%)	15	0.588 (1.00)	0.355 (1.00)	0.718 (1.00)	0.285 (1.00)	0.245 (1.00)	0.097 (1.00)
12Q LOSS MUTATION ANALYSIS	3 (16%)	16	0.709 (1.00)	0.601 (1.00)	0.48 (1.00)	0.567 (1.00)	0.263 (1.00)	0.0506 (1.00)
13Q LOSS MUTATION ANALYSIS	10 (53%)	9	0.475 (1.00)	0.592 (1.00)	0.253 (1.00)	0.0399 (1.00)	0.0573 (1.00)	0.582 (1.00)	0.0195 (1.00)
14Q LOSS MUTATION ANALYSIS	4 (21%)	15	0.481 (1.00)	0.524 (1.00)	0.641 (1.00)	1 (1.00)	1 (1.00)	0.53 (1.00)	0.165 (1.00)
15Q LOSS MUTATION ANALYSIS	5 (26%)	14	0.17 (1.00)	0.919 (1.00)	0.128 (1.00)	0.798 (1.00)	1 (1.00)	1 (1.00)	0.592 (1.00)
17P LOSS MUTATION ANALYSIS	7 (37%)	12	0.681 (1.00)	0.66 (1.00)	0.0402 (1.00)	1 (1.00)	0.0174 (1.00)	1 (1.00)	0.504 (1.00)
18P LOSS MUTATION ANALYSIS	4 (21%)	15		0.541 (1.00)	0.439 (1.00)	0.213 (1.00)	1 (1.00)	1 (1.00)
18Q LOSS MUTATION ANALYSIS	8 (42%)	11	0.28 (1.00)	0.163 (1.00)	0.278 (1.00)	0.05 (1.00)	0.633 (1.00)	0.228 (1.00)	0.922 (1.00)
19P LOSS MUTATION ANALYSIS	8 (42%)	11	0.359 (1.00)	0.504 (1.00)	0.842 (1.00)	1 (1.00)	0.633 (1.00)	0.546 (1.00)
19Q LOSS MUTATION ANALYSIS	5 (26%)	14	0.446 (1.00)	0.574 (1.00)	0.883 (1.00)	1 (1.00)	1 (1.00)	0.155 (1.00)
21Q LOSS MUTATION ANALYSIS	11 (58%)	8	0.582 (1.00)	0.533 (1.00)	0.4 (1.00)	1 (1.00)	0.633 (1.00)	0.228 (1.00)	0.0686 (1.00)
22Q LOSS MUTATION ANALYSIS	8 (42%)	11	0.102 (1.00)	0.511 (1.00)	0.291 (1.00)	0.844 (1.00)	0.147 (1.00)	0.546 (1.00)	0.726 (1.00)

'19P GAIN MUTATION STATUS' versus 'AGE'

P value = 0.000534 (t-test), Q value = 0.19

Table S1. Gene #23: '19P GAIN MUTATION STATUS' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	19	66.4 (11.4)
19P GAIN MUTATED	4	78.5 (4.1)
19P GAIN WILD-TYPE	15	63.2 (10.5)

Figure S1. Get High-res Image Gene #23: '19P GAIN MUTATION STATUS' versus Clinical Feature #2: 'AGE'

'16P LOSS MUTATION STATUS' versus 'AGE'

P value = 4.56e-06 (t-test), Q value = 0.0017

Table S2. Gene #46: '16P LOSS MUTATION STATUS' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	19	66.4 (11.4)
16P LOSS MUTATED	3	83.3 (2.5)
16P LOSS WILD-TYPE	16	63.2 (9.3)

Figure S2. Get High-res Image Gene #46: '16P LOSS MUTATION STATUS' versus Clinical Feature #2: 'AGE'

'16Q LOSS MUTATION STATUS' versus 'AGE'

P value = 4.56e-06 (t-test), Q value = 0.0017

Table S3. Gene #47: '16Q LOSS MUTATION STATUS' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	19	66.4 (11.4)
16Q LOSS MUTATED	3	83.3 (2.5)
16Q LOSS WILD-TYPE	16	63.2 (9.3)

Figure S3. Get High-res Image Gene #47: '16Q LOSS MUTATION STATUS' versus Clinical Feature #2: 'AGE'

Methods & Data

Input

Copy number data file = transformed.cor.cli.txt
Clinical data file = ESCA-TP.clin.merged.picked.txt
Number of patients = 19
Number of significantly arm-level cnvs = 54
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

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.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] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)

[4] 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)

[5] 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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