Correlation between gene mutation status and selected clinical features

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

15 July 2014 | analyses__2014_07_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/C14748MM

Overview

Introduction

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

Summary

Testing the association between mutation status of 25 genes and 5 clinical features across 195 patients, 4 significant findings detected with Q value < 0.25.

DNMT3A mutation correlated to 'Time to Death'.
IDH2 mutation correlated to 'AGE'.
TP53 mutation correlated to 'Time to Death' and 'AGE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 25 genes and 5 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	RACE	ETHNICITY
	nMutated (%)	nWild-Type	logrank test	Wilcoxon-test	Fisher's exact test	Fisher's exact test	Fisher's exact test
TP53	15 (8%)	180	1.18e-05 (0.00144)	0.00204 (0.243)	0.177 (1.00)	1 (1.00)	1 (1.00)
DNMT3A	49 (25%)	146	0.000206 (0.0249)	0.266 (1.00)	0.188 (1.00)	0.387 (1.00)	0.161 (1.00)
IDH2	20 (10%)	175	0.481 (1.00)	0.0016 (0.192)	0.815 (1.00)	0.356 (1.00)	0.282 (1.00)
FLT3	53 (27%)	142	0.106 (1.00)	0.328 (1.00)	0.52 (1.00)	0.0698 (1.00)	0.562 (1.00)
NPM1	52 (27%)	143	0.0954 (1.00)	0.774 (1.00)	0.145 (1.00)	0.232 (1.00)	0.564 (1.00)
IDH1	18 (9%)	177	0.947 (1.00)	0.322 (1.00)	0.466 (1.00)	0.702 (1.00)	1 (1.00)
RUNX1	18 (9%)	177	0.0571 (1.00)	0.0168 (1.00)	0.622 (1.00)	1 (1.00)	1 (1.00)
TET2	17 (9%)	178	0.71 (1.00)	0.128 (1.00)	0.319 (1.00)	1 (1.00)	1 (1.00)
NRAS	15 (8%)	180	0.826 (1.00)	0.257 (1.00)	1 (1.00)	0.671 (1.00)	1 (1.00)
WT1	12 (6%)	183	0.7 (1.00)	0.168 (1.00)	0.774 (1.00)	1 (1.00)	0.163 (1.00)
U2AF1	8 (4%)	187	0.642 (1.00)	0.00419 (0.494)	0.0695 (1.00)	1 (1.00)	1 (1.00)
CEBPA	13 (7%)	182	0.95 (1.00)	0.0116 (1.00)	0.58 (1.00)	1 (1.00)	1 (1.00)
PHF6	6 (3%)	189	0.874 (1.00)	0.158 (1.00)	0.0309 (1.00)	1 (1.00)	1 (1.00)
KRAS	8 (4%)	187	0.436 (1.00)	0.101 (1.00)	0.149 (1.00)	1 (1.00)	0.12 (1.00)
SMC3	7 (4%)	188	0.061 (1.00)	0.782 (1.00)	0.707 (1.00)	1 (1.00)	1 (1.00)
KIT	8 (4%)	187	0.589 (1.00)	0.68 (1.00)	0.477 (1.00)	1 (1.00)	1 (1.00)
RAD21	5 (3%)	190	0.999 (1.00)	0.231 (1.00)	1 (1.00)	0.0178 (1.00)	1 (1.00)
EZH2	3 (2%)	192		0.214 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
STAG2	6 (3%)	189	0.409 (1.00)	0.214 (1.00)	0.42 (1.00)	0.13 (1.00)	1 (1.00)
PTPN11	9 (5%)	186	0.415 (1.00)	0.294 (1.00)	1 (1.00)	0.575 (1.00)	1 (1.00)
ASXL1	5 (3%)	190	0.197 (1.00)	0.0653 (1.00)	0.666 (1.00)	1 (1.00)	0.0765 (1.00)
SUZ12	3 (2%)	192		0.947 (1.00)	0.249 (1.00)		1 (1.00)
PHACTR1	3 (2%)	192	0.355 (1.00)	0.963 (1.00)	0.599 (1.00)	1 (1.00)	0.0464 (1.00)
SMC1A	6 (3%)	189	0.222 (1.00)	0.265 (1.00)	0.218 (1.00)	0.43 (1.00)	1 (1.00)
KDM6A	3 (2%)	192	0.875 (1.00)	0.893 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)

'DNMT3A MUTATION STATUS' versus 'Time to Death'

P value = 0.000206 (logrank test), Q value = 0.025

Table S1. Gene #3: 'DNMT3A MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	171	107	0.9 - 94.1 (12.0)
DNMT3A MUTATED	44	34	0.9 - 34.0 (9.0)
DNMT3A WILD-TYPE	127	73	0.9 - 94.1 (15.0)

Figure S1. Get High-res Image Gene #3: 'DNMT3A MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'IDH2 MUTATION STATUS' versus 'AGE'

P value = 0.0016 (Wilcoxon-test), Q value = 0.19

Table S2. Gene #4: 'IDH2 MUTATION STATUS' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	195	54.9 (16.1)
IDH2 MUTATED	20	64.9 (8.0)
IDH2 WILD-TYPE	175	53.8 (16.5)

Figure S2. Get High-res Image Gene #4: 'IDH2 MUTATION STATUS' versus Clinical Feature #2: 'AGE'

'TP53 MUTATION STATUS' versus 'Time to Death'

P value = 1.18e-05 (logrank test), Q value = 0.0014

Table S3. Gene #11: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	171	107	0.9 - 94.1 (12.0)
TP53 MUTATED	12	12	1.0 - 17.0 (8.0)
TP53 WILD-TYPE	159	95	0.9 - 94.1 (13.0)

Figure S3. Get High-res Image Gene #11: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'TP53 MUTATION STATUS' versus 'AGE'

P value = 0.00204 (Wilcoxon-test), Q value = 0.24

Table S4. Gene #11: 'TP53 MUTATION STATUS' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	195	54.9 (16.1)
TP53 MUTATED	15	66.9 (10.7)
TP53 WILD-TYPE	180	53.9 (16.1)

Figure S4. Get High-res Image Gene #11: 'TP53 MUTATION STATUS' versus Clinical Feature #2: 'AGE'

Methods & Data

Input

Mutation data file = transformed.cor.cli.txt
Clinical data file = LAML-TB.merged_data.txt
Number of patients = 195
Number of significantly mutated genes = 25
Number of selected clinical features = 5
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

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] 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)

[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)

Made with Nozzle