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

Skin Cutaneous Melanoma: Correlation between copy number variations of arm-level result and selected clinical features
(All_Primary 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 38 arm-level results and 7 clinical features across 29 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 38 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 (14%)	25	0.162 (1.00)	0.549 (1.00)	0.467 (1.00)	1 (1.00)	0.0369 (1.00)		0.3 (1.00)
1q gain	6 (21%)	23	0.929 (1.00)	0.725 (1.00)	0.18 (1.00)	1 (1.00)	0.0559 (1.00)		0.269 (1.00)
5p gain	4 (14%)	25	1 (1.00)	0.726 (1.00)	1 (1.00)	0.076 (1.00)	0.775 (1.00)		0.334 (1.00)
6p gain	8 (28%)	21	0.432 (1.00)	0.965 (1.00)	0.748 (1.00)	0.371 (1.00)	0.419 (1.00)		0.524 (1.00)
6q gain	3 (10%)	26	0.367 (1.00)	0.6 (1.00)	1 (1.00)	0.532 (1.00)	0.127 (1.00)		0.191 (1.00)
7p gain	13 (45%)	16	0.722 (1.00)	0.832 (1.00)	1 (1.00)	0.13 (1.00)	0.366 (1.00)		0.497 (1.00)
7q gain	13 (45%)	16	0.722 (1.00)	0.832 (1.00)	1 (1.00)	0.13 (1.00)	0.366 (1.00)		0.497 (1.00)
8p gain	5 (17%)	24	0.201 (1.00)	0.81 (1.00)	0.127 (1.00)	1 (1.00)	0.0839 (1.00)		0.202 (1.00)
8q gain	9 (31%)	20	0.193 (1.00)	0.301 (1.00)	0.364 (1.00)	0.396 (1.00)	0.613 (1.00)		0.73 (1.00)
13q gain	3 (10%)	26	1 (1.00)	0.948 (1.00)	1 (1.00)	0.532 (1.00)	0.419 (1.00)		0.865 (1.00)
18p gain	3 (10%)	26			0.371 (1.00)	1 (1.00)
18q gain	3 (10%)	26			0.371 (1.00)	1 (1.00)
20p gain	4 (14%)	25		0.0917 (1.00)	1 (1.00)	0.28 (1.00)	0.299 (1.00)		0.3 (1.00)
20q gain	5 (17%)	24		0.0173 (1.00)	1 (1.00)	1 (1.00)	0.419 (1.00)		0.121 (1.00)
22q gain	3 (10%)	26		0.133 (1.00)	1 (1.00)	1 (1.00)	0.893 (1.00)		0.191 (1.00)
2p loss	4 (14%)	25	0.705 (1.00)	0.633 (1.00)	0.08 (1.00)	1 (1.00)	0.0167 (1.00)		0.148 (1.00)
2q loss	3 (10%)	26	0.705 (1.00)	0.633 (1.00)	0.371 (1.00)	0.532 (1.00)	0.0501 (1.00)
4p loss	4 (14%)	25	1 (1.00)	0.786 (1.00)	1 (1.00)	0.28 (1.00)	0.889 (1.00)		0.334 (1.00)
4q loss	6 (21%)	23	1 (1.00)	0.985 (1.00)	1 (1.00)	0.633 (1.00)	0.47 (1.00)		0.358 (1.00)
5q loss	4 (14%)	25	0.682 (1.00)	0.486 (1.00)	0.467 (1.00)	0.28 (1.00)	0.219 (1.00)		0.191 (1.00)
6q loss	8 (28%)	21	0.395 (1.00)	0.569 (1.00)	0.748 (1.00)	1 (1.00)	0.975 (1.00)		0.739 (1.00)
8p loss	6 (21%)	23	0.395 (1.00)	0.932 (1.00)	1 (1.00)	0.633 (1.00)	0.47 (1.00)		0.848 (1.00)
8q loss	3 (10%)	26	1 (1.00)	0.312 (1.00)	1 (1.00)	1 (1.00)	0.893 (1.00)		0.911 (1.00)
9p loss	17 (59%)	12	0.231 (1.00)	0.412 (1.00)	0.147 (1.00)	0.422 (1.00)	0.582 (1.00)		0.637 (1.00)
9q loss	9 (31%)	20	0.682 (1.00)	0.679 (1.00)	1 (1.00)	0.675 (1.00)	0.94 (1.00)		0.0738 (1.00)
10p loss	13 (45%)	16	0.619 (1.00)	0.328 (1.00)	0.0301 (1.00)	1 (1.00)	0.346 (1.00)		0.269 (1.00)
10q loss	15 (52%)	14	0.619 (1.00)	0.259 (1.00)	0.0996 (1.00)	1 (1.00)	0.652 (1.00)		0.497 (1.00)
11p loss	7 (24%)	22	1 (1.00)	0.583 (1.00)	0.238 (1.00)	1 (1.00)	0.325 (1.00)		0.05 (1.00)
11q loss	8 (28%)	21	1 (1.00)	0.694 (1.00)	0.3 (1.00)	0.675 (1.00)	0.489 (1.00)		0.104 (1.00)
12p loss	3 (10%)	26	0.705 (1.00)	0.633 (1.00)	0.371 (1.00)	1 (1.00)	0.127 (1.00)
12q loss	5 (17%)	24	0.705 (1.00)	0.689 (1.00)	0.553 (1.00)	0.633 (1.00)	0.419 (1.00)		0.334 (1.00)
13q loss	5 (17%)	24	0.201 (1.00)	0.00425 (0.898)	1 (1.00)	0.633 (1.00)	0.419 (1.00)		0.525 (1.00)
14q loss	4 (14%)	25	1 (1.00)	0.56 (1.00)	1 (1.00)	0.568 (1.00)	0.889 (1.00)		0.958 (1.00)
16q loss	8 (28%)	21	0.00815 (1.00)	0.454 (1.00)	0.748 (1.00)	1 (1.00)	0.644 (1.00)		0.883 (1.00)
17p loss	4 (14%)	25	1 (1.00)	0.609 (1.00)	1 (1.00)	1 (1.00)	0.775 (1.00)		0.807 (1.00)
18p loss	3 (10%)	26		0.912 (1.00)	0.371 (1.00)	1 (1.00)	0.893 (1.00)
19q loss	3 (10%)	26			1 (1.00)	0.532 (1.00)	0.893 (1.00)		0.911 (1.00)
21q loss	3 (10%)	26	0.929 (1.00)	0.852 (1.00)	1 (1.00)	0.532 (1.00)	0.127 (1.00)		0.148 (1.00)

Methods & Data

Input

Mutation data file = broad_values_by_arm.mutsig.cluster.txt
Clinical data file = SKCM-All_Primary.clin.merged.picked.txt
Number of patients = 29
Number of significantly arm-level cnvs = 38
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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