Skin Cutaneous Melanoma: Correlation between copy number variations of arm-level result and selected clinical features<BR>(WT cohort)

Skin Cutaneous Melanoma: Correlation between copy number variations of arm-level result and selected clinical features
(WT cohort)

Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)

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 40 arm-level results and 7 clinical features across 23 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 40 arm-level results and 7 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	PRIMARY SITE OF DISEASE	GENDER	LYMPH NODE METASTASIS	TUMOR STAGECODE	NEOPLASM DISEASESTAGE
	nCNV (%)	nWild-Type	logrank test	t-test	Fisher's exact test	Fisher's exact test	Chi-square test	t-test	Chi-square test
1p gain	4 (17%)	19	0.32 (1.00)	0.0779 (1.00)	0.178 (1.00)	0.59 (1.00)	0.0559 (1.00)		0.194 (1.00)
1q gain	8 (35%)	15	0.0635 (1.00)	0.275 (1.00)	0.0424 (1.00)	0.667 (1.00)	0.194 (1.00)		0.27 (1.00)
3p gain	3 (13%)	20	0.89 (1.00)	0.682 (1.00)	0.0107 (1.00)	1 (1.00)	0.543 (1.00)		0.144 (1.00)
3q gain	4 (17%)	19	0.588 (1.00)	0.732 (1.00)	0.00903 (1.00)	0.59 (1.00)	0.0559 (1.00)		0.0314 (1.00)
4p gain	7 (30%)	16	0.993 (1.00)	0.268 (1.00)	0.376 (1.00)	1 (1.00)	0.394 (1.00)		0.209 (1.00)
4q gain	3 (13%)	20	0.911 (1.00)	0.113 (1.00)	0.486 (1.00)	1 (1.00)
6p gain	8 (35%)	15	0.269 (1.00)	0.705 (1.00)	0.0424 (1.00)	0.667 (1.00)	0.508 (1.00)		0.288 (1.00)
7p gain	7 (30%)	16	0.951 (1.00)	0.959 (1.00)	1 (1.00)	1 (1.00)	0.0615 (1.00)		0.525 (1.00)
7q gain	6 (26%)	17	0.636 (1.00)	0.444 (1.00)	0.822 (1.00)	0.64 (1.00)	0.353 (1.00)		0.295 (1.00)
8p gain	6 (26%)	17	0.204 (1.00)	0.625 (1.00)	0.195 (1.00)	0.64 (1.00)	0.11 (1.00)		0.0364 (1.00)
8q gain	9 (39%)	14	0.989 (1.00)	0.349 (1.00)	0.502 (1.00)	1 (1.00)	0.0929 (1.00)		0.0103 (1.00)
12p gain	4 (17%)	19	0.935 (1.00)	0.326 (1.00)	0.0443 (1.00)	0.59 (1.00)	0.543 (1.00)		0.544 (1.00)
12q gain	3 (13%)	20	0.981 (1.00)	0.696 (1.00)	0.332 (1.00)	0.59 (1.00)
15q gain	3 (13%)	20	0.907 (1.00)	0.255 (1.00)	1 (1.00)	0.59 (1.00)
17q gain	3 (13%)	20	0.643 (1.00)	0.912 (1.00)	0.0457 (1.00)	0.217 (1.00)	0.822 (1.00)		0.21 (1.00)
18p gain	6 (26%)	17	0.365 (1.00)	0.00125 (0.282)	0.141 (1.00)	0.155 (1.00)	0.394 (1.00)		0.0805 (1.00)
18q gain	4 (17%)	19	0.946 (1.00)	0.00787 (1.00)	0.178 (1.00)	0.0932 (1.00)	0.607 (1.00)		0.0314 (1.00)
20p gain	9 (39%)	14	0.543 (1.00)	0.828 (1.00)	0.355 (1.00)	1 (1.00)	0.517 (1.00)		0.407 (1.00)
20q gain	10 (43%)	13	0.459 (1.00)	0.551 (1.00)	0.0826 (1.00)	0.68 (1.00)	0.796 (1.00)		0.558 (1.00)
21q gain	3 (13%)	20	0.289 (1.00)	0.923 (1.00)	0.692 (1.00)	1 (1.00)
22q gain	6 (26%)	17	0.894 (1.00)	0.886 (1.00)	0.141 (1.00)	0.155 (1.00)	0.14 (1.00)		0.0877 (1.00)
1p loss	3 (13%)	20	0.0653 (1.00)	0.948 (1.00)	1 (1.00)	1 (1.00)
2p loss	3 (13%)	20	0.946 (1.00)	0.303 (1.00)	0.692 (1.00)	1 (1.00)
2q loss	4 (17%)	19	0.552 (1.00)	0.132 (1.00)	0.291 (1.00)	1 (1.00)	0.543 (1.00)		0.544 (1.00)
4p loss	3 (13%)	20	0.582 (1.00)	0.121 (1.00)	1 (1.00)	0.59 (1.00)	0.822 (1.00)		0.761 (1.00)
6q loss	6 (26%)	17	0.00227 (0.512)	0.0158 (1.00)	1 (1.00)	0.64 (1.00)	0.829 (1.00)		0.881 (1.00)
9p loss	9 (39%)	14	0.844 (1.00)	0.669 (1.00)	1 (1.00)	1 (1.00)	0.186 (1.00)		0.638 (1.00)
9q loss	6 (26%)	17	0.715 (1.00)	0.973 (1.00)	0.822 (1.00)	0.64 (1.00)	0.0615 (1.00)		0.525 (1.00)
10p loss	11 (48%)	12	0.326 (1.00)	0.243 (1.00)	0.714 (1.00)	0.414 (1.00)	0.578 (1.00)		0.625 (1.00)
10q loss	9 (39%)	14	0.312 (1.00)	0.21 (1.00)	0.355 (1.00)	1 (1.00)	0.843 (1.00)		0.868 (1.00)
11p loss	6 (26%)	17	0.757 (1.00)	0.43 (1.00)	0.511 (1.00)	0.371 (1.00)	0.727 (1.00)		0.822 (1.00)
11q loss	8 (35%)	15	0.651 (1.00)	0.129 (1.00)	0.694 (1.00)	1 (1.00)	0.729 (1.00)		0.766 (1.00)
12q loss	3 (13%)	20	0.56 (1.00)	0.0115 (1.00)	1 (1.00)	1 (1.00)	0.672 (1.00)		0.21 (1.00)
13q loss	4 (17%)	19	0.867 (1.00)	0.0161 (1.00)	1 (1.00)	0.59 (1.00)	0.3 (1.00)		0.934 (1.00)
14q loss	5 (22%)	18	0.658 (1.00)	0.104 (1.00)	0.194 (1.00)	1 (1.00)	0.727 (1.00)		0.544 (1.00)
16q loss	4 (17%)	19	0.0525 (1.00)	0.142 (1.00)	0.753 (1.00)	1 (1.00)	0.3 (1.00)		0.761 (1.00)
17p loss	3 (13%)	20	0.487 (1.00)	0.983 (1.00)	0.692 (1.00)	0.59 (1.00)	0.672 (1.00)		0.252 (1.00)
17q loss	3 (13%)	20	0.402 (1.00)	0.246 (1.00)	0.692 (1.00)	0.0932 (1.00)
18q loss	3 (13%)	20	0.298 (1.00)	0.698 (1.00)	0.486 (1.00)	0.59 (1.00)	0.149 (1.00)		0.252 (1.00)
21q loss	3 (13%)	20	0.0328 (1.00)	0.74 (1.00)	0.486 (1.00)	1 (1.00)	0.615 (1.00)		0.352 (1.00)

Methods & Data

Input

Mutation data file = broad_values_by_arm.mutsig.cluster.txt
Clinical data file = SKCM-WT.clin.merged.picked.txt
Number of patients = 23
Number of significantly arm-level cnvs = 40
Number of selected clinical features = 7
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

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

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] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)

[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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