Correlation between gene mutation status and selected clinical features

Glioblastoma Multiforme (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/C19W0CH3

Overview

Introduction

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

Summary

Testing the association between mutation status of 13 genes and 6 clinical features across 275 patients, 3 significant findings detected with Q value < 0.25.

IDH1 mutation correlated to 'Time to Death' and 'AGE'.
PRB2 mutation correlated to 'KARNOFSKY.PERFORMANCE.SCORE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 13 genes and 6 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 3 significant findings detected.


		Clinical Features	Time to Death	AGE	GENDER	KARNOFSKY PERFORMANCE SCORE	HISTOLOGICAL TYPE	RADIATIONS RADIATION REGIMENINDICATION
	nMutated (%)	nWild-Type	logrank test	t-test	Fisher's exact test	t-test	Fisher's exact test	Fisher's exact test
IDH1	13 (5%)	262	0.00223 (0.167)	0.000244 (0.0188)	0.387 (1.00)	0.0655 (1.00)	1 (1.00)	0.00585 (0.433)
PRB2	5 (2%)	270	0.917 (1.00)	0.166 (1.00)	1 (1.00)	0.000423 (0.0322)	1 (1.00)	1 (1.00)
EGFR	73 (27%)	202	0.7 (1.00)	0.628 (1.00)	0.205 (1.00)	0.512 (1.00)	0.723 (1.00)	0.565 (1.00)
PIK3R1	31 (11%)	244	0.74 (1.00)	0.607 (1.00)	0.432 (1.00)	0.872 (1.00)	1 (1.00)	0.227 (1.00)
BRAF	5 (2%)	270	0.107 (1.00)	0.784 (1.00)	1 (1.00)	0.158 (1.00)	0.17 (1.00)	0.667 (1.00)
TP53	77 (28%)	198	0.0314 (1.00)	0.158 (1.00)	0.58 (1.00)	0.00657 (0.48)	0.188 (1.00)	0.253 (1.00)
PTEN	85 (31%)	190	0.679 (1.00)	0.211 (1.00)	0.589 (1.00)	0.98 (1.00)	0.211 (1.00)	0.488 (1.00)
PIK3CA	29 (11%)	246	0.481 (1.00)	0.925 (1.00)	0.547 (1.00)	0.98 (1.00)	0.285 (1.00)	0.838 (1.00)
RB1	23 (8%)	252	0.17 (1.00)	0.859 (1.00)	0.653 (1.00)	0.0179 (1.00)	0.589 (1.00)	1 (1.00)
NF1	29 (11%)	246	0.697 (1.00)	0.191 (1.00)	0.841 (1.00)	0.18 (1.00)	0.678 (1.00)	0.54 (1.00)
CDC27	5 (2%)	270	0.224 (1.00)	0.686 (1.00)	0.357 (1.00)		1 (1.00)	0.667 (1.00)
STAG2	12 (4%)	263	0.0169 (1.00)	0.957 (1.00)	0.762 (1.00)	0.0956 (1.00)	1 (1.00)	0.22 (1.00)
TPTE2	8 (3%)	267	0.259 (1.00)	0.581 (1.00)	1 (1.00)	0.0232 (1.00)	1 (1.00)	0.446 (1.00)

'IDH1 MUTATION STATUS' versus 'Time to Death'

P value = 0.00223 (logrank test), Q value = 0.17

Table S1. Gene #4: 'IDH1 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	275	175	0.1 - 58.8 (8.3)
IDH1 MUTATED	13	3	3.4 - 40.9 (13.2)
IDH1 WILD-TYPE	262	172	0.1 - 58.8 (7.9)

Figure S1. Get High-res Image Gene #4: 'IDH1 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'IDH1 MUTATION STATUS' versus 'AGE'

P value = 0.000244 (t-test), Q value = 0.019

Table S2. Gene #4: 'IDH1 MUTATION STATUS' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	275	61.3 (12.8)
IDH1 MUTATED	13	41.5 (14.7)
IDH1 WILD-TYPE	262	62.2 (11.9)

Figure S2. Get High-res Image Gene #4: 'IDH1 MUTATION STATUS' versus Clinical Feature #2: 'AGE'

'PRB2 MUTATION STATUS' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.000423 (t-test), Q value = 0.032

Table S3. Gene #9: 'PRB2 MUTATION STATUS' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	194	75.9 (16.0)
PRB2 MUTATED	4	80.0 (0.0)
PRB2 WILD-TYPE	190	75.8 (16.2)

Figure S3. Get High-res Image Gene #9: 'PRB2 MUTATION STATUS' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

Methods & Data

Input

Mutation data file = GBM-TP.mutsig.cluster.txt
Clinical data file = GBM-TP.clin.merged.picked.txt
Number of patients = 275
Number of significantly mutated genes = 13
Number of selected clinical features = 6
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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