Correlation between gene mutation status and selected clinical features

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

02 April 2015 | analyses__2015_04_02

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 (2015): Correlation between gene mutation status and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1RB73NR

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, 6 significant findings detected with Q value < 0.25.

DNMT3A mutation correlated to 'Time to Death'.
IDH2 mutation correlated to 'YEARS_TO_BIRTH'.
U2AF1 mutation correlated to 'YEARS_TO_BIRTH'.
TP53 mutation correlated to 'Time to Death' and 'YEARS_TO_BIRTH'.
CEBPA mutation correlated to 'YEARS_TO_BIRTH'.

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, 6 significant findings detected.


		Clinical Features	Time to Death	YEARS TO BIRTH	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	6.22e-06 (0.000778)	0.00204 (0.0638)	0.177 (0.761)	1 (1.00)	1 (1.00)
DNMT3A	49 (25%)	146	0.00185 (0.0638)	0.266 (0.844)	0.188 (0.785)	0.389 (0.952)	0.161 (0.75)
IDH2	20 (10%)	175	0.724 (1.00)	0.0016 (0.0638)	0.815 (1.00)	0.355 (0.888)	0.282 (0.844)
U2AF1	8 (4%)	187	0.496 (1.00)	0.00419 (0.105)	0.0695 (0.595)	1 (1.00)	1 (1.00)
CEBPA	13 (7%)	182	0.737 (1.00)	0.0116 (0.242)	0.58 (1.00)	1 (1.00)	1 (1.00)
FLT3	53 (27%)	142	0.135 (0.75)	0.328 (0.844)	0.52 (1.00)	0.0713 (0.595)	0.562 (1.00)
NPM1	52 (27%)	143	0.044 (0.527)	0.774 (1.00)	0.145 (0.75)	0.231 (0.827)	0.564 (1.00)
IDH1	18 (9%)	177	0.609 (1.00)	0.322 (0.844)	0.466 (1.00)	0.7 (1.00)	1 (1.00)
RUNX1	18 (9%)	177	0.0714 (0.595)	0.0168 (0.288)	0.622 (1.00)	1 (1.00)	1 (1.00)
TET2	17 (9%)	178	0.805 (1.00)	0.128 (0.75)	0.319 (0.844)	1 (1.00)	1 (1.00)
NRAS	15 (8%)	180	0.637 (1.00)	0.257 (0.844)	1 (1.00)	0.674 (1.00)	1 (1.00)
WT1	12 (6%)	183	0.55 (1.00)	0.168 (0.75)	0.774 (1.00)	1 (1.00)	0.163 (0.75)
PHF6	6 (3%)	189	0.998 (1.00)	0.158 (0.75)	0.0309 (0.429)	1 (1.00)	1 (1.00)
KRAS	8 (4%)	187	0.319 (0.844)	0.101 (0.745)	0.149 (0.75)	1 (1.00)	0.12 (0.75)
SMC3	7 (4%)	188	0.129 (0.75)	0.782 (1.00)	0.707 (1.00)	1 (1.00)	1 (1.00)
KIT	8 (4%)	187	0.468 (1.00)	0.68 (1.00)	0.477 (1.00)	1 (1.00)	1 (1.00)
RAD21	5 (3%)	190	0.869 (1.00)	0.231 (0.827)	1 (1.00)	0.0184 (0.288)	1 (1.00)
EZH2	3 (2%)	192		0.214 (0.825)	1 (1.00)	1 (1.00)	1 (1.00)
STAG2	6 (3%)	189	0.331 (0.844)	0.214 (0.825)	0.42 (1.00)	0.128 (0.75)	1 (1.00)
PTPN11	9 (5%)	186	0.582 (1.00)	0.294 (0.844)	1 (1.00)	0.571 (1.00)	1 (1.00)
ASXL1	5 (3%)	190	0.281 (0.844)	0.0653 (0.595)	0.666 (1.00)	1 (1.00)	0.0765 (0.598)
SUZ12	3 (2%)	192	0.897 (1.00)	0.947 (1.00)	0.249 (0.844)		1 (1.00)
PHACTR1	3 (2%)	192	0.316 (0.844)	0.963 (1.00)	0.599 (1.00)	1 (1.00)	0.0464 (0.527)
SMC1A	6 (3%)	189	0.321 (0.844)	0.265 (0.844)	0.218 (0.825)	0.43 (1.00)	1 (1.00)
KDM6A	3 (2%)	192	0.793 (1.00)	0.893 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)

'DNMT3A MUTATION STATUS' versus 'Time to Death'

P value = 0.00185 (logrank test), Q value = 0.064

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	181	117	0.0 - 94.1 (12.0)
DNMT3A MUTATED	45	35	0.0 - 34.0 (9.0)
DNMT3A WILD-TYPE	136	82	0.0 - 94.1 (12.5)

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

'IDH2 MUTATION STATUS' versus 'YEARS_TO_BIRTH'

P value = 0.0016 (Wilcoxon-test), Q value = 0.064

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

	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: 'YEARS_TO_BIRTH'

'U2AF1 MUTATION STATUS' versus 'YEARS_TO_BIRTH'

P value = 0.00419 (Wilcoxon-test), Q value = 0.1

Table S3. Gene #10: 'U2AF1 MUTATION STATUS' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	195	54.9 (16.1)
U2AF1 MUTATED	8	69.9 (9.0)
U2AF1 WILD-TYPE	187	54.3 (16.1)

Figure S3. Get High-res Image Gene #10: 'U2AF1 MUTATION STATUS' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'TP53 MUTATION STATUS' versus 'Time to Death'

P value = 6.22e-06 (logrank test), Q value = 0.00078

Table S4. Gene #11: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	181	117	0.0 - 94.1 (12.0)
TP53 MUTATED	14	14	0.0 - 17.0 (6.0)
TP53 WILD-TYPE	167	103	0.0 - 94.1 (12.0)

Figure S4. Get High-res Image Gene #11: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'TP53 MUTATION STATUS' versus 'YEARS_TO_BIRTH'

P value = 0.00204 (Wilcoxon-test), Q value = 0.064

Table S5. Gene #11: 'TP53 MUTATION STATUS' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	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 S5. Get High-res Image Gene #11: 'TP53 MUTATION STATUS' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'CEBPA MUTATION STATUS' versus 'YEARS_TO_BIRTH'

P value = 0.0116 (Wilcoxon-test), Q value = 0.24

Table S6. Gene #12: 'CEBPA MUTATION STATUS' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	195	54.9 (16.1)
CEBPA MUTATED	13	42.7 (17.6)
CEBPA WILD-TYPE	182	55.8 (15.7)

Figure S6. Get High-res Image Gene #12: 'CEBPA MUTATION STATUS' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

Methods & Data

Input

Mutation data file = sample_sig_gene_table.txt from Mutsig_2CV pipeline
Processed Mutation data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_Correlate_Genomic_Events_Preprocess/LAML-TB/15166008/transformed.cor.cli.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/LAML-TB/15082450/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