Correlation between gene mutation status and selected clinical features

Uterine Carcinosarcoma (Primary solid tumor)

15 January 2014 | analyses__2014_01_15

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 (2014): Correlation between gene mutation status and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1RX99MD

Overview

Introduction

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

Summary

Testing the association between mutation status of 9 genes and 2 clinical features across 32 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 9 genes and 2 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
	nMutated (%)	nWild-Type	logrank test	t-test
FBXW7	13 (41%)	19	0.583 (1.00)	0.311 (1.00)
KRAS	3 (9%)	29	0.87 (1.00)	0.663 (1.00)
TP53	28 (88%)	4	0.981 (1.00)	0.0281 (0.507)
PPP2R1A	10 (31%)	22	0.714 (1.00)	0.841 (1.00)
PIK3CA	11 (34%)	21	0.882 (1.00)	0.472 (1.00)
PTEN	5 (16%)	27	0.974 (1.00)	0.375 (1.00)
PCDHAC2	4 (12%)	28	0.221 (1.00)	0.688 (1.00)
PIK3R1	5 (16%)	27	0.415 (1.00)	0.246 (1.00)
ZBTB7B	4 (12%)	28	0.114 (1.00)	0.586 (1.00)

Methods & Data

Input

Mutation data file = transformed.cor.cli.txt
Clinical data file = UCS-TP.merged_data.txt
Number of patients = 32
Number of significantly mutated genes = 9
Number of selected clinical features = 2
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

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

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

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] 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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