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

Cervical Squamous Cell Carcinoma (Primary solid tumor)

23 May 2013 | analyses__2013_05_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/C12Z13HP

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 49 arm-level results and 8 clinical features across 42 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 49 arm-level results and 8 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	RADIATIONS RADIATION REGIMENINDICATION	NUMBERPACKYEARSSMOKED	DISTANT METASTASIS	LYMPH NODE METASTASIS	NUMBER OF LYMPH NODES	TUMOR STAGECODE
	nCNV (%)	nWild-Type	logrank test	t-test	Fisher's exact test	t-test	Fisher's exact test	Fisher's exact test	t-test	t-test
1p gain	0 (0%)	33	0.774 (1.00)	0.0193 (1.00)	0.695 (1.00)		0.445 (1.00)	1 (1.00)	0.641 (1.00)
1q gain	0 (0%)	26	0.497 (1.00)	0.0574 (1.00)	0.187 (1.00)	0.8 (1.00)	0.495 (1.00)	0.728 (1.00)	0.28 (1.00)
2p gain	0 (0%)	37	0.116 (1.00)	0.639 (1.00)	0.637 (1.00)		0.625 (1.00)	1 (1.00)	0.565 (1.00)
3p gain	0 (0%)	37	0.144 (1.00)	0.764 (1.00)	0.637 (1.00)		0.346 (1.00)	0.337 (1.00)	0.214 (1.00)
3q gain	0 (0%)	17	0.244 (1.00)	0.144 (1.00)	1 (1.00)	0.291 (1.00)	0.732 (1.00)	1 (1.00)	0.405 (1.00)
5p gain	0 (0%)	31	0.191 (1.00)	0.458 (1.00)	0.713 (1.00)		0.705 (1.00)	0.699 (1.00)	0.138 (1.00)
6p gain	0 (0%)	34	0.306 (1.00)	0.251 (1.00)	0.686 (1.00)		0.687 (1.00)	1 (1.00)	0.374 (1.00)
6q gain	0 (0%)	39	0.28 (1.00)	0.187 (1.00)	1 (1.00)		0.275 (1.00)	1 (1.00)
7q gain	0 (0%)	38	0.256 (1.00)	0.378 (1.00)	0.0805 (1.00)		0.625 (1.00)	0.616 (1.00)	0.692 (1.00)
8p gain	0 (0%)	38	0.828 (1.00)	0.183 (1.00)	1 (1.00)		0.276 (1.00)	1 (1.00)	0.309 (1.00)
8q gain	0 (0%)	35	0.48 (1.00)	0.152 (1.00)	1 (1.00)		0.687 (1.00)	1 (1.00)	0.957 (1.00)
10p gain	0 (0%)	39	0.408 (1.00)	0.754 (1.00)	0.54 (1.00)		1 (1.00)	0.542 (1.00)	0.494 (1.00)
12p gain	0 (0%)	36	0.392 (1.00)	0.184 (1.00)	1 (1.00)		0.173 (1.00)	1 (1.00)	0.869 (1.00)
12q gain	0 (0%)	38	0.459 (1.00)	0.397 (1.00)	0.576 (1.00)		0.142 (1.00)	0.616 (1.00)	0.692 (1.00)
14q gain	0 (0%)	38	0.28 (1.00)	0.305 (1.00)	1 (1.00)		1 (1.00)	0.616 (1.00)	0.769 (1.00)
15q gain	0 (0%)	39	0.765 (1.00)	0.692 (1.00)	0.54 (1.00)		1 (1.00)	0.283 (1.00)	0.00246 (0.752)
16p gain	0 (0%)	37	0.765 (1.00)	0.482 (1.00)	1 (1.00)		1 (1.00)	0.616 (1.00)	0.494 (1.00)
16q gain	0 (0%)	38	0.665 (1.00)	0.398 (1.00)	1 (1.00)		0.275 (1.00)	1 (1.00)
18p gain	0 (0%)	38	0.355 (1.00)	0.158 (1.00)	0.576 (1.00)		1 (1.00)	0.616 (1.00)	0.769 (1.00)
18q gain	0 (0%)	39	0.665 (1.00)	0.318 (1.00)	0.222 (1.00)		1 (1.00)	0.542 (1.00)
19q gain	0 (0%)	34	0.142 (1.00)	0.993 (1.00)	0.00634 (1.00)	0.807 (1.00)	0.376 (1.00)	1 (1.00)	0.577 (1.00)
20p gain	0 (0%)	29	0.0395 (1.00)	0.78 (1.00)	0.495 (1.00)	0.0348 (1.00)	1 (1.00)	0.296 (1.00)	0.234 (1.00)
20q gain	0 (0%)	28	0.00142 (0.437)	0.232 (1.00)	0.729 (1.00)	0.0348 (1.00)	1 (1.00)	0.728 (1.00)	0.381 (1.00)
21q gain	0 (0%)	39	0.197 (1.00)	0.0776 (1.00)	0.222 (1.00)		1 (1.00)	0.542 (1.00)	0.769 (1.00)
22q gain	0 (0%)	37	0.335 (1.00)	0.264 (1.00)	0.637 (1.00)		1 (1.00)	0.616 (1.00)	0.692 (1.00)
3p loss	0 (0%)	31	0.681 (1.00)	0.1 (1.00)	0.713 (1.00)	0.0407 (1.00)	1 (1.00)	0.245 (1.00)	0.442 (1.00)
4p loss	0 (0%)	27	0.915 (1.00)	0.334 (1.00)	0.488 (1.00)	0.135 (1.00)	0.732 (1.00)	0.729 (1.00)	0.582 (1.00)
4q loss	0 (0%)	37	0.263 (1.00)	0.961 (1.00)	0.162 (1.00)	0.869 (1.00)	0.625 (1.00)	0.132 (1.00)	0.214 (1.00)
5q loss	0 (0%)	31	0.39 (1.00)	0.226 (1.00)	0.453 (1.00)	0.491 (1.00)	0.434 (1.00)	1 (1.00)	0.45 (1.00)
6p loss	0 (0%)	39	0.324 (1.00)	0.342 (1.00)	0.54 (1.00)		0.544 (1.00)	0.542 (1.00)	0.769 (1.00)
6q loss	0 (0%)	37	0.534 (1.00)	0.0699 (1.00)	1 (1.00)	0.0213 (1.00)	0.346 (1.00)	1 (1.00)	0.309 (1.00)
7p loss	0 (0%)	38	0.764 (1.00)	0.0461 (1.00)	0.0805 (1.00)	0.838 (1.00)	0.625 (1.00)	0.616 (1.00)	0.472 (1.00)
7q loss	0 (0%)	38	0.00693 (1.00)	0.472 (1.00)	0.0805 (1.00)	0.666 (1.00)	1 (1.00)	0.542 (1.00)	0.42 (1.00)
8p loss	0 (0%)	31	0.364 (1.00)	0.868 (1.00)	0.713 (1.00)		1 (1.00)	1 (1.00)	0.836 (1.00)
9p loss	0 (0%)	38	0.0714 (1.00)	0.518 (1.00)	0.576 (1.00)		0.275 (1.00)	1 (1.00)	0.565 (1.00)
9q loss	0 (0%)	39	0.0918 (1.00)	0.23 (1.00)	1 (1.00)		0.275 (1.00)	1 (1.00)	0.565 (1.00)
10p loss	0 (0%)	35	0.154 (1.00)	0.727 (1.00)	0.176 (1.00)	0.818 (1.00)	0.142 (1.00)	0.0519 (1.00)	0.225 (1.00)
10q loss	0 (0%)	35	0.0977 (1.00)	0.945 (1.00)	0.657 (1.00)	0.338 (1.00)	0.173 (1.00)	0.00598 (1.00)	0.0678 (1.00)
11p loss	0 (0%)	33	0.37 (1.00)	0.546 (1.00)	1 (1.00)	0.61 (1.00)	0.255 (1.00)	1 (1.00)	0.563 (1.00)
11q loss	0 (0%)	32	0.764 (1.00)	0.523 (1.00)	0.697 (1.00)	0.313 (1.00)	0.255 (1.00)	0.699 (1.00)	0.262 (1.00)
12p loss	0 (0%)	35	0.534 (1.00)	0.0198 (1.00)	0.405 (1.00)		0.63 (1.00)	0.383 (1.00)	0.0872 (1.00)
13q loss	0 (0%)	33	0.306 (1.00)	0.405 (1.00)	1 (1.00)		0.445 (1.00)	0.433 (1.00)	0.21 (1.00)
17p loss	0 (0%)	32	0.304 (1.00)	0.423 (1.00)	0.697 (1.00)	0.384 (1.00)	0.683 (1.00)	1 (1.00)	0.469 (1.00)
17q loss	0 (0%)	39	0.908 (1.00)	0.868 (1.00)	1 (1.00)		0.275 (1.00)	1 (1.00)	0.565 (1.00)
18p loss	0 (0%)	35	0.926 (1.00)	0.442 (1.00)	0.176 (1.00)	0.0348 (1.00)	1 (1.00)	0.383 (1.00)	0.0872 (1.00)
18q loss	0 (0%)	34	0.704 (1.00)	0.641 (1.00)	0.226 (1.00)	0.0348 (1.00)	0.687 (1.00)	0.227 (1.00)	0.0502 (1.00)
19p loss	0 (0%)	38	0.437 (1.00)	0.715 (1.00)	0.0805 (1.00)		0.517 (1.00)	1 (1.00)
20p loss	0 (0%)	39		0.29 (1.00)	0.54 (1.00)		1 (1.00)	0.522 (1.00)
21q loss	0 (0%)	36	0.35 (1.00)	0.787 (1.00)	0.153 (1.00)		1 (1.00)	0.383 (1.00)	0.508 (1.00)

Methods & Data

Input

Mutation data file = broad_values_by_arm.mutsig.cluster.txt
Clinical data file = CESC-TP.clin.merged.picked.txt
Number of patients = 42
Number of significantly arm-level cnvs = 49
Number of selected clinical features = 8
Exclude genes 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

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

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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