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

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

Summary

Testing the association between mutation status of 5 genes and 7 clinical features across 22 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 5 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
ADAMTS9	5 (23%)	17	0.937 (1.00)	0.627 (1.00)	0.151 (1.00)	0.0351 (0.947)	0.765 (1.00)		0.451 (1.00)
RAC1	4 (18%)	18		0.0851 (1.00)	0.083 (1.00)	0.586 (1.00)	0.745 (1.00)		0.345 (1.00)
SERPINI2	3 (14%)	19		0.126 (1.00)	0.51 (1.00)	1 (1.00)	0.58 (1.00)		0.801 (1.00)
ESRP1	4 (18%)	18		0.208 (1.00)	0.414 (1.00)	0.0902 (1.00)	0.586 (1.00)		0.487 (1.00)
PLCB4	4 (18%)	18	0.494 (1.00)	0.208 (1.00)	1 (1.00)	0.586 (1.00)	0.555 (1.00)		0.651 (1.00)

Methods & Data

Input

Mutation data file = SKCM-WT.mutsig.cluster.txt
Clinical data file = SKCM-WT.clin.merged.picked.txt
Number of patients = 22
Number of significantly mutated genes = 5
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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