Correlation between gene mutation status and selected clinical features

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.

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)

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