Correlation between gene mutation status and selected clinical features

Bladder Urothelial Carcinoma (Primary solid tumor)

21 April 2013 | analyses__2013_04_21

Maintainer Information

Citation Information

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

Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Bladder Urothelial Carcinoma (Primary solid tumor cohort) - 21 April 2013: Correlation between gene mutation status and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1057CV6

Overview

Introduction

This pipeline computes the correlation between significantly recurrent gene mutations and selected clinical features.

Summary

Testing the association between mutation status of 3 genes and 5 clinical features across 28 patients, 2 significant findings detected with Q value < 0.25.

FBXW7 mutation correlated to 'NUMBER.OF.LYMPH.NODES'.
NFE2L2 mutation correlated to 'GENDER'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 3 genes and 5 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	GENDER	NUMBERPACKYEARSSMOKED	NUMBER OF LYMPH NODES
	nMutated (%)	nWild-Type	logrank test	t-test	Fisher's exact test	t-test	t-test
FBXW7	5 (18%)	23	0.894 (1.00)	0.233 (1.00)	1 (1.00)	0.324 (1.00)	0.0157 (0.188)
NFE2L2	4 (14%)	24	0.647 (1.00)	0.839 (1.00)	0.0103 (0.133)
TP53	11 (39%)	17	0.825 (1.00)	0.529 (1.00)	1 (1.00)	0.543 (1.00)	0.718 (1.00)

'FBXW7 MUTATION STATUS' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.0157 (t-test), Q value = 0.19

Table S1. Gene #2: 'FBXW7 MUTATION STATUS' versus Clinical Feature #5: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	20	1.9 (3.2)
FBXW7 MUTATED	3	0.0 (0.0)
FBXW7 WILD-TYPE	17	2.2 (3.3)

Figure S1. Get High-res Image Gene #2: 'FBXW7 MUTATION STATUS' versus Clinical Feature #5: 'NUMBER.OF.LYMPH.NODES'

'NFE2L2 MUTATION STATUS' versus 'GENDER'

P value = 0.0103 (Fisher's exact test), Q value = 0.13

Table S2. Gene #3: 'NFE2L2 MUTATION STATUS' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	10	18
NFE2L2 MUTATED	4	0
NFE2L2 WILD-TYPE	6	18

Figure S2. Get High-res Image Gene #3: 'NFE2L2 MUTATION STATUS' versus Clinical Feature #3: 'GENDER'

Methods & Data

Input

Mutation data file = BLCA-TP.mutsig.cluster.txt
Clinical data file = BLCA-TP.clin.merged.picked.txt
Number of patients = 28
Number of significantly mutated genes = 3
Number of selected clinical features = 5
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

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] 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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