Correlation between gene mutation status and selected clinical features

Acute Myeloid Leukemia (Primary blood derived cancer - Peripheral blood)

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/C1CV4FS9

Overview

Introduction

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

Summary

Testing the association between mutation status of 7 genes and 3 clinical features across 196 patients, 3 significant findings detected with Q value < 0.25.

DNMT3A mutation correlated to 'Time to Death'.
U2AF1 mutation correlated to 'AGE'.
IDH2 mutation correlated to 'AGE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 7 genes and 3 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
	nMutated (%)	nWild-Type	logrank test	t-test	Fisher's exact test
DNMT3A	51 (26%)	145	0.000405 (0.00811)	0.0653 (1.00)	0.328 (1.00)
U2AF1	8 (4%)	188	0.614 (1.00)	0.00137 (0.026)	0.0703 (1.00)
IDH2	20 (10%)	176	0.448 (1.00)	1.11e-05 (0.000233)	0.815 (1.00)
FLT3	56 (29%)	140	0.085 (1.00)	0.563 (1.00)	0.754 (1.00)
IDH1	19 (10%)	177	0.81 (1.00)	0.304 (1.00)	0.633 (1.00)
NPM1	54 (28%)	142	0.112 (1.00)	0.968 (1.00)	0.262 (1.00)
NRAS	15 (8%)	181	0.851 (1.00)	0.287 (1.00)	1 (1.00)

'DNMT3A MUTATION STATUS' versus 'Time to Death'

P value = 0.000405 (logrank test), Q value = 0.0081

Table S1. Gene #1: 'DNMT3A MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	172	107	0.9 - 94.1 (12.0)
DNMT3A MUTATED	46	35	0.9 - 37.0 (9.0)
DNMT3A WILD-TYPE	126	72	0.9 - 94.1 (15.0)

Figure S1. Get High-res Image Gene #1: 'DNMT3A MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'U2AF1 MUTATION STATUS' versus 'AGE'

P value = 0.00137 (t-test), Q value = 0.026

Table S2. Gene #2: 'U2AF1 MUTATION STATUS' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	196	55.1 (16.2)
U2AF1 MUTATED	8	69.9 (9.0)
U2AF1 WILD-TYPE	188	54.5 (16.1)

Figure S2. Get High-res Image Gene #2: 'U2AF1 MUTATION STATUS' versus Clinical Feature #2: 'AGE'

'IDH2 MUTATION STATUS' versus 'AGE'

P value = 1.11e-05 (t-test), Q value = 0.00023

Table S3. Gene #4: 'IDH2 MUTATION STATUS' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	196	55.1 (16.2)
IDH2 MUTATED	20	64.8 (8.0)
IDH2 WILD-TYPE	176	54.0 (16.5)

Figure S3. Get High-res Image Gene #4: 'IDH2 MUTATION STATUS' versus Clinical Feature #2: 'AGE'

Methods & Data

Input

Mutation data file = LAML-TB.mutsig.cluster.txt
Clinical data file = LAML-TB.clin.merged.picked.txt
Number of patients = 196
Number of significantly mutated genes = 7
Number of selected clinical features = 3
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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