Correlation between gene mutation status and selected clinical features

Glioblastoma Multiforme (Primary solid tumor)

16 April 2014 | analyses__2014_04_16

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/C1833QMG

Overview

Introduction

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

Summary

Testing the association between mutation status of 12 genes and 6 clinical features across 278 patients, 4 significant findings detected with Q value < 0.25.

IDH1 mutation correlated to 'Time to Death' and 'AGE'.
TP53 mutation correlated to 'Time to Death'.
STAG2 mutation correlated to 'Time to Death'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 12 genes and 6 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 4 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	14 (5%)	264	0.000147 (0.0105)	9.73e-05 (0.00701)	0.392 (1.00)	0.0754 (1.00)	0.497 (1.00)	0.04 (1.00)
TP53	78 (28%)	200	0.00149 (0.105)	0.114 (1.00)	0.677 (1.00)	0.0122 (0.817)	0.351 (1.00)	0.202 (1.00)
STAG2	12 (4%)	266	0.00259 (0.179)	0.901 (1.00)	0.761 (1.00)	0.0888 (1.00)	1 (1.00)	0.54 (1.00)
EGFR	73 (26%)	205	0.824 (1.00)	0.758 (1.00)	0.257 (1.00)	0.411 (1.00)	0.453 (1.00)	0.565 (1.00)
PIK3R1	32 (12%)	246	0.648 (1.00)	0.956 (1.00)	0.434 (1.00)	0.951 (1.00)	1 (1.00)	0.69 (1.00)
BRAF	6 (2%)	272	0.144 (1.00)	0.989 (1.00)	1 (1.00)	0.145 (1.00)	0.0273 (1.00)	0.401 (1.00)
PTEN	85 (31%)	193	0.452 (1.00)	0.153 (1.00)	0.588 (1.00)	0.833 (1.00)	0.0601 (1.00)	0.678 (1.00)
PIK3CA	29 (10%)	249	0.461 (1.00)	0.856 (1.00)	0.544 (1.00)	0.906 (1.00)	0.515 (1.00)	1 (1.00)
RB1	23 (8%)	255	0.221 (1.00)	0.799 (1.00)	0.654 (1.00)	0.011 (0.748)	0.683 (1.00)	1 (1.00)
NF1	29 (10%)	249	0.245 (1.00)	0.163 (1.00)	0.84 (1.00)	0.164 (1.00)	1 (1.00)	0.54 (1.00)
WNT2	5 (2%)	273	0.0995 (1.00)	0.232 (1.00)	0.0579 (1.00)	0.73 (1.00)	1 (1.00)	1 (1.00)
TPTE2	8 (3%)	270	0.408 (1.00)	0.613 (1.00)	1 (1.00)	0.0273 (1.00)	1 (1.00)	0.446 (1.00)

'IDH1 MUTATION STATUS' versus 'Time to Death'

P value = 0.000147 (logrank test), Q value = 0.01

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	278	206	0.1 - 73.8 (8.8)
IDH1 MUTATED	14	4	3.4 - 50.5 (18.8)
IDH1 WILD-TYPE	264	202	0.1 - 73.8 (8.5)

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 = 9.73e-05 (t-test), Q value = 0.007

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

	nPatients	Mean (Std.Dev)
ALL	278	61.0 (13.0)
IDH1 MUTATED	14	40.0 (15.1)
IDH1 WILD-TYPE	264	62.2 (11.9)

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

'TP53 MUTATION STATUS' versus 'Time to Death'

P value = 0.00149 (logrank test), Q value = 0.1

Table S3. Gene #5: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	278	206	0.1 - 73.8 (8.8)
TP53 MUTATED	78	48	0.4 - 50.5 (10.4)
TP53 WILD-TYPE	200	158	0.1 - 73.8 (8.3)

Figure S3. Get High-res Image Gene #5: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'STAG2 MUTATION STATUS' versus 'Time to Death'

P value = 0.00259 (logrank test), Q value = 0.18

Table S4. Gene #10: 'STAG2 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	278	206	0.1 - 73.8 (8.8)
STAG2 MUTATED	12	11	0.2 - 17.5 (4.1)
STAG2 WILD-TYPE	266	195	0.1 - 73.8 (8.9)

Figure S4. Get High-res Image Gene #10: 'STAG2 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

Methods & Data

Input

Mutation data file = transformed.cor.cli.txt
Clinical data file = GBM-TP.merged_data.txt
Number of patients = 278
Number of significantly mutated genes = 12
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

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] 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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