Correlation between gene mutation status and selected clinical features

Rectum Adenocarcinoma (Primary solid tumor)

23 May 2013 | analyses__2013_05_23

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): Correlation between gene mutation status and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1513W9W

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 10 clinical features across 69 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 3 genes and 10 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	GENDER	HISTOLOGICAL TYPE	DISTANT METASTASIS	LYMPH NODE METASTASIS	COMPLETENESS OF RESECTION	NUMBER OF LYMPH NODES	TUMOR STAGECODE	NEOPLASM DISEASESTAGE
	nMutated (%)	nWild-Type	logrank test	t-test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	t-test	t-test	Chi-square test
APC	57 (83%)	12	0.535 (1.00)	0.897 (1.00)	1 (1.00)	0.634 (1.00)	0.362 (1.00)	0.846 (1.00)	0.693 (1.00)	0.536 (1.00)		0.202 (1.00)
KRAS	38 (55%)	31	0.0233 (0.628)	0.534 (1.00)	0.329 (1.00)	0.27 (1.00)	0.327 (1.00)	0.8 (1.00)	0.0405 (1.00)	0.363 (1.00)		0.7 (1.00)
TP53	45 (65%)	24	0.886 (1.00)	0.976 (1.00)	0.803 (1.00)	0.238 (1.00)	0.73 (1.00)	0.958 (1.00)	0.624 (1.00)	0.51 (1.00)		0.488 (1.00)

Methods & Data

Input

Mutation data file = READ-TP.mutsig.cluster.txt
Clinical data file = READ-TP.clin.merged.picked.txt
Number of patients = 69
Number of significantly mutated genes = 3
Number of selected clinical features = 10
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)

Made with Nozzle