Skin Cutaneous Melanoma: Correlation between gene mutation status and selected clinical features<BR>(All

Skin Cutaneous Melanoma: Correlation between gene mutation status 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 significantly recurrent gene mutations and selected clinical features.

Summary

Testing the association between mutation status of 15 genes and 7 clinical features across 23 patients, no significant finding detected with Q value < 0.25.

No gene mutations related to clinical features.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 15 genes 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
	nMutated (%)	nWild-Type	logrank test	t-test	Fisher's exact test	Fisher's exact test	Chi-square test	t-test	Chi-square test
BRAF	16 (70%)	7	0.547 (1.00)	0.447 (1.00)	0.352 (1.00)	0.626 (1.00)	0.302 (1.00)		0.681 (1.00)
NRAS	3 (13%)	20			0.453 (1.00)	1 (1.00)
PRB2	4 (17%)	19		0.368 (1.00)	1 (1.00)	0.557 (1.00)	0.343 (1.00)		0.156 (1.00)
FAM135B	6 (26%)	17	0.722 (1.00)	0.00415 (0.311)	1 (1.00)	0.621 (1.00)	0.697 (1.00)		0.469 (1.00)
PTEN	4 (17%)	19	0.0828 (1.00)	0.696 (1.00)	0.125 (1.00)	1 (1.00)	0.343 (1.00)		0.961 (1.00)
TLL1	6 (26%)	17	0.0828 (1.00)	0.0736 (1.00)	1 (1.00)	1 (1.00)	0.302 (1.00)		0.469 (1.00)
ARID2	4 (17%)	19	0.0828 (1.00)	0.37 (1.00)	1 (1.00)	0.273 (1.00)	0.343 (1.00)		0.961 (1.00)
ZFHX4	8 (35%)	15	0.547 (1.00)	0.734 (1.00)	0.779 (1.00)	0.345 (1.00)	0.404 (1.00)		0.148 (1.00)
CFHR1	5 (22%)	18	0.404 (1.00)	0.302 (1.00)	1 (1.00)	0.272 (1.00)	0.959 (1.00)		0.29 (1.00)
OR52M1	3 (13%)	20		0.319 (1.00)	1 (1.00)	1 (1.00)	0.804 (1.00)		0.339 (1.00)
PPP6C	3 (13%)	20			0.453 (1.00)	1 (1.00)
RUNDC3B	3 (13%)	20	0.322 (1.00)	0.154 (1.00)	0.453 (1.00)	0.526 (1.00)	0.191 (1.00)
ADCYAP1R1	3 (13%)	20	0.0143 (1.00)	0.224 (1.00)	1 (1.00)	1 (1.00)	0.839 (1.00)		0.928 (1.00)
KCNC2	3 (13%)	20			0.453 (1.00)	0.209 (1.00)
PIK3R1	4 (17%)	19	0.913 (1.00)	0.754 (1.00)	1 (1.00)	0.557 (1.00)	0.0811 (1.00)		0.505 (1.00)

Methods & Data

Input

Mutation data file = SKCM-All_Primary.mutsig.cluster.txt
Clinical data file = SKCM-All_Primary.clin.merged.picked.txt
Number of patients = 23
Number of significantly mutated genes = 15
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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