Skin Cutaneous Melanoma: Correlation between copy number variations of arm-level result and selected clinical features<BR>(NF1_Any

Skin Cutaneous Melanoma: Correlation between copy number variations of arm-level result and selected clinical features
(NF1_Any_Mutants 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 25 patients, 2 significant findings detected with Q value < 0.25.

1p gain cnv correlated to 'Time to Death'.
9p loss cnv correlated to 'AGE'.

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, 2 significant findings 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	Fisher's exact test	t-test	Chi-square test
1p gain	4 (16%)	21	0.000555 (0.125)	0.973 (1.00)	1 (1.00)	1 (1.00)	0.481 (1.00)
9p loss	16 (64%)	9	0.521 (1.00)	0.000199 (0.0451)	0.412 (1.00)	0.661 (1.00)	0.893 (1.00)		0.719 (1.00)
1q gain	9 (36%)	16	0.0169 (1.00)	0.433 (1.00)	0.353 (1.00)	0.394 (1.00)	0.1 (1.00)		0.349 (1.00)
3p gain	4 (16%)	21	0.528 (1.00)	0.61 (1.00)	1 (1.00)	1 (1.00)	0.481 (1.00)		0.151 (1.00)
3q gain	5 (20%)	20	0.167 (1.00)	0.963 (1.00)	0.812 (1.00)	1 (1.00)	0.352 (1.00)		0.117 (1.00)
5p gain	4 (16%)	21	0.0766 (1.00)	0.711 (1.00)	0.626 (1.00)	0.57 (1.00)	1 (1.00)		0.0803 (1.00)
5q gain	3 (12%)	22	0.0766 (1.00)	0.97 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)		0.188 (1.00)
6p gain	11 (44%)	14	0.734 (1.00)	0.327 (1.00)	0.367 (1.00)	1 (1.00)	0.323 (1.00)		0.316 (1.00)
7p gain	10 (40%)	15	0.96 (1.00)	0.756 (1.00)	0.196 (1.00)	0.194 (1.00)	0.533 (1.00)		0.812 (1.00)
7q gain	9 (36%)	16	0.719 (1.00)	0.827 (1.00)	0.547 (1.00)	0.087 (1.00)	0.406 (1.00)		0.512 (1.00)
8p gain	5 (20%)	20	0.223 (1.00)	0.951 (1.00)	0.812 (1.00)	0.283 (1.00)	0.838 (1.00)		0.17 (1.00)
8q gain	6 (24%)	19	0.452 (1.00)	0.949 (1.00)	0.576 (1.00)	0.344 (1.00)	0.654 (1.00)		0.349 (1.00)
14q gain	4 (16%)	21	0.516 (1.00)	0.504 (1.00)	0.496 (1.00)	0.57 (1.00)	0.605 (1.00)		0.788 (1.00)
15q gain	4 (16%)	21	0.219 (1.00)	0.957 (1.00)	0.383 (1.00)	0.0808 (1.00)	1 (1.00)		0.188 (1.00)
17q gain	5 (20%)	20	0.235 (1.00)	0.17 (1.00)	0.347 (1.00)	1 (1.00)	0.292 (1.00)		0.912 (1.00)
18p gain	3 (12%)	22	0.0455 (1.00)	0.25 (1.00)	0.565 (1.00)	1 (1.00)	0.0378 (1.00)		0.42 (1.00)
20p gain	6 (24%)	19	0.388 (1.00)	0.144 (1.00)	0.107 (1.00)	0.0593 (1.00)	0.401 (1.00)		0.349 (1.00)
20q gain	8 (32%)	17	0.309 (1.00)	0.487 (1.00)	0.621 (1.00)	0.359 (1.00)	0.144 (1.00)		0.487 (1.00)
22q gain	9 (36%)	16	0.73 (1.00)	0.462 (1.00)	1 (1.00)	0.394 (1.00)	0.202 (1.00)		0.577 (1.00)
1p loss	3 (12%)	22	0.0487 (1.00)	0.214 (1.00)	0.152 (1.00)	1 (1.00)	1 (1.00)		0.188 (1.00)
4p loss	4 (16%)	21	0.362 (1.00)	0.22 (1.00)	0.275 (1.00)	0.57 (1.00)	0.481 (1.00)		0.587 (1.00)
4q loss	4 (16%)	21	0.312 (1.00)	0.997 (1.00)	0.204 (1.00)	0.57 (1.00)	1 (1.00)		0.188 (1.00)
5p loss	4 (16%)	21	0.386 (1.00)	0.567 (1.00)	0.204 (1.00)	0.269 (1.00)	0.684 (1.00)		0.77 (1.00)
5q loss	6 (24%)	19	0.0542 (1.00)	0.959 (1.00)	0.138 (1.00)	0.129 (1.00)	0.133 (1.00)		0.738 (1.00)
6q loss	10 (40%)	15	0.654 (1.00)	0.0557 (1.00)	0.577 (1.00)	0.667 (1.00)	0.105 (1.00)		0.42 (1.00)
9q loss	13 (52%)	12	0.575 (1.00)	0.0565 (1.00)	0.249 (1.00)	0.673 (1.00)	0.784 (1.00)		0.294 (1.00)
10p loss	8 (32%)	17	0.594 (1.00)	0.601 (1.00)	1 (1.00)	0.359 (1.00)	0.401 (1.00)		0.986 (1.00)
10q loss	8 (32%)	17	0.594 (1.00)	0.205 (1.00)	1 (1.00)	0.359 (1.00)	0.401 (1.00)		0.752 (1.00)
11p loss	9 (36%)	16	0.99 (1.00)	0.377 (1.00)	0.152 (1.00)	0.394 (1.00)	0.0303 (1.00)		0.383 (1.00)
11q loss	9 (36%)	16	0.752 (1.00)	0.472 (1.00)	0.152 (1.00)	0.087 (1.00)	0.714 (1.00)		0.188 (1.00)
12q loss	3 (12%)	22	0.963 (1.00)	0.875 (1.00)	0.763 (1.00)	1 (1.00)	0.209 (1.00)		0.188 (1.00)
13q loss	6 (24%)	19	0.535 (1.00)	0.885 (1.00)	0.689 (1.00)	1 (1.00)	0.0898 (1.00)		0.236 (1.00)
14q loss	3 (12%)	22	0.619 (1.00)	0.996 (1.00)	1 (1.00)	0.231 (1.00)	0.481 (1.00)		0.587 (1.00)
16q loss	4 (16%)	21	0.249 (1.00)	0.944 (1.00)	0.626 (1.00)	1 (1.00)	1 (1.00)		0.326 (1.00)
17p loss	5 (20%)	20	0.661 (1.00)	0.755 (1.00)	0.812 (1.00)	1 (1.00)	0.135 (1.00)		0.278 (1.00)
18p loss	4 (16%)	21	0.303 (1.00)	0.559 (1.00)	1 (1.00)	0.57 (1.00)	0.605 (1.00)		0.716 (1.00)
18q loss	3 (12%)	22	0.872 (1.00)	0.61 (1.00)	0.763 (1.00)	1 (1.00)	0.684 (1.00)		0.854 (1.00)
21q loss	7 (28%)	18	0.215 (1.00)	0.978 (1.00)	0.719 (1.00)	0.64 (1.00)	0.87 (1.00)		0.31 (1.00)

'1p gain mutation analysis' versus 'Time to Death'

P value = 0.000555 (logrank test), Q value = 0.13

Table S1. Gene #1: '1p gain mutation analysis' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	25	16	0.2 - 204.6 (27.0)
1P GAIN MUTATED	4	3	0.2 - 26.4 (6.3)
1P GAIN WILD-TYPE	21	13	14.3 - 204.6 (29.8)

Figure S1. Get High-res Image Gene #1: '1p gain mutation analysis' versus Clinical Feature #1: 'Time to Death'

'9p loss mutation analysis' versus 'AGE'

P value = 0.000199 (t-test), Q value = 0.045

Table S2. Gene #25: '9p loss mutation analysis' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	25	66.3 (14.3)
9P LOSS MUTATED	16	73.2 (11.7)
9P LOSS WILD-TYPE	9	53.9 (9.3)

Figure S2. Get High-res Image Gene #25: '9p loss mutation analysis' versus Clinical Feature #2: 'AGE'

Methods & Data

Input

Mutation data file = broad_values_by_arm.mutsig.cluster.txt
Clinical data file = SKCM-NF1_Any_Mutants.clin.merged.picked.txt
Number of patients = 25
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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