Correlation between gene mutation status and selected clinical features

Glioblastoma Multiforme (Primary solid tumor)

21 August 2015 | analyses__2015_08_21

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/C16W998M

Overview

Introduction

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

Summary

Testing the association between mutation status of 35 genes and 8 clinical features across 278 patients, 5 significant findings detected with Q value < 0.25.

TP53 mutation correlated to 'Time to Death'.
IDH1 mutation correlated to 'Time to Death' and 'YEARS_TO_BIRTH'.
ATRX mutation correlated to 'YEARS_TO_BIRTH'.
LZTR1 mutation correlated to 'ETHNICITY'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 35 genes and 8 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 5 significant findings detected.


		Clinical Features	Time to Death	YEARS TO BIRTH	GENDER	RADIATION THERAPY	KARNOFSKY PERFORMANCE SCORE	HISTOLOGICAL TYPE	RACE	ETHNICITY
	nMutated (%)	nWild-Type	logrank test	Wilcoxon-test	Fisher's exact test	Fisher's exact test	Wilcoxon-test	Fisher's exact test	Fisher's exact test	Fisher's exact test
IDH1	14 (5%)	264	0.000559 (0.0522)	3.92e-06 (0.0011)	0.392 (1.00)	0.139 (1.00)	0.129 (1.00)	0.499 (1.00)	0.0275 (0.641)	1 (1.00)
TP53	79 (28%)	199	0.00181 (0.127)	0.325 (1.00)	0.491 (1.00)	0.103 (1.00)	0.157 (1.00)	0.308 (1.00)	0.598 (1.00)	1 (1.00)
ATRX	16 (6%)	262	0.00818 (0.36)	1.49e-05 (0.00208)	0.593 (1.00)	0.746 (1.00)	0.233 (1.00)	0.548 (1.00)	0.0259 (0.641)	1 (1.00)
LZTR1	10 (4%)	268	0.69 (1.00)	0.234 (1.00)	0.101 (1.00)	0.148 (1.00)	0.296 (1.00)	0.169 (1.00)	1 (1.00)	0.00427 (0.239)
PIK3R1	32 (12%)	246	0.935 (1.00)	0.495 (1.00)	0.434 (1.00)	1 (1.00)	0.62 (1.00)	1 (1.00)	0.679 (1.00)	1 (1.00)
RB1	23 (8%)	255	0.287 (1.00)	0.949 (1.00)	0.654 (1.00)	1 (1.00)	0.0303 (0.641)	0.685 (1.00)	0.599 (1.00)	1 (1.00)
NF1	29 (10%)	249	0.283 (1.00)	0.134 (1.00)	0.84 (1.00)	0.591 (1.00)	0.0592 (0.842)	1 (1.00)	1 (1.00)	1 (1.00)
PTEN	85 (31%)	193	0.811 (1.00)	0.324 (1.00)	0.588 (1.00)	0.0811 (0.946)	0.983 (1.00)	0.0602 (0.842)	1 (1.00)	0.227 (1.00)
PIK3CA	26 (9%)	252	0.213 (1.00)	0.96 (1.00)	0.831 (1.00)	0.586 (1.00)	0.721 (1.00)	0.729 (1.00)	0.381 (1.00)	1 (1.00)
STAG2	12 (4%)	266	0.00899 (0.36)	0.849 (1.00)	0.761 (1.00)	0.233 (1.00)	0.107 (1.00)	1 (1.00)	0.615 (1.00)	1 (1.00)
SLC26A3	6 (2%)	272	0.722 (1.00)	0.279 (1.00)	0.424 (1.00)	0.594 (1.00)	0.98 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
SEMG1	8 (3%)	270	0.366 (1.00)	0.176 (1.00)	0.715 (1.00)	1 (1.00)	0.547 (1.00)	1 (1.00)	0.497 (1.00)	1 (1.00)
KDR	8 (3%)	270	0.661 (1.00)	0.499 (1.00)	0.266 (1.00)	0.632 (1.00)	0.634 (1.00)	0.321 (1.00)	1 (1.00)	1 (1.00)
RPL5	7 (3%)	271	0.82 (1.00)	0.803 (1.00)	0.256 (1.00)	0.61 (1.00)	0.444 (1.00)	1 (1.00)	0.448 (1.00)	1 (1.00)
MAP3K1	6 (2%)	272	0.691 (1.00)	0.295 (1.00)	0.424 (1.00)	1 (1.00)	0.767 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
BRAF	6 (2%)	272	0.244 (1.00)	0.918 (1.00)	1 (1.00)	0.594 (1.00)	0.742 (1.00)	0.0278 (0.641)	0.401 (1.00)	1 (1.00)
EGFR	73 (26%)	205	0.727 (1.00)	0.695 (1.00)	0.257 (1.00)	1 (1.00)	0.343 (1.00)	0.451 (1.00)	0.41 (1.00)	1 (1.00)
TMPRSS6	6 (2%)	272	0.963 (1.00)	0.274 (1.00)	0.67 (1.00)	1 (1.00)	0.293 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
PRKCD	3 (1%)	275	0.977 (1.00)	0.123 (1.00)	1 (1.00)	0.44 (1.00)		1 (1.00)	0.226 (1.00)	1 (1.00)
TP63	6 (2%)	272	0.334 (1.00)	0.994 (1.00)	0.67 (1.00)	1 (1.00)	0.697 (1.00)	1 (1.00)	1 (1.00)	0.0531 (0.842)
PDGFRA	11 (4%)	267	0.665 (1.00)	0.0539 (0.842)	1 (1.00)	0.413 (1.00)	0.121 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
CHD8	8 (3%)	270	0.885 (1.00)	0.0224 (0.641)	0.141 (1.00)	0.354 (1.00)	0.261 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
IL4R	8 (3%)	270	0.145 (1.00)	0.108 (1.00)	0.0273 (0.641)	0.354 (1.00)	0.394 (1.00)	1 (1.00)	0.496 (1.00)	1 (1.00)
REN	5 (2%)	273	0.766 (1.00)	0.617 (1.00)	0.657 (1.00)	1 (1.00)	0.357 (1.00)	0.0785 (0.946)	1 (1.00)	1 (1.00)
CD209	5 (2%)	273	0.671 (1.00)	0.717 (1.00)	0.0579 (0.842)	1 (1.00)	0.625 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
FBN3	11 (4%)	267	0.19 (1.00)	0.94 (1.00)	0.532 (1.00)	1 (1.00)	0.956 (1.00)	0.19 (1.00)	0.613 (1.00)	1 (1.00)
MMP13	5 (2%)	273	0.686 (1.00)	0.12 (1.00)	0.657 (1.00)	0.59 (1.00)	0.0727 (0.925)	1 (1.00)	1 (1.00)	1 (1.00)
TCF12	3 (1%)	275	0.957 (1.00)	0.94 (1.00)	0.555 (1.00)	1 (1.00)	0.625 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
IL18RAP	6 (2%)	272	0.708 (1.00)	0.158 (1.00)	0.67 (1.00)	0.594 (1.00)	0.188 (1.00)	1 (1.00)	0.401 (1.00)	1 (1.00)
ODF4	3 (1%)	275	0.328 (1.00)	0.568 (1.00)	1 (1.00)	0.44 (1.00)		1 (1.00)	1 (1.00)	1 (1.00)
KEL	15 (5%)	263	0.787 (1.00)	0.543 (1.00)	0.413 (1.00)	0.476 (1.00)	0.279 (1.00)	1 (1.00)	0.708 (1.00)	1 (1.00)
TESK1	3 (1%)	275	0.595 (1.00)	0.0671 (0.895)	1 (1.00)	0.44 (1.00)		1 (1.00)	1 (1.00)	1 (1.00)
MUC17	21 (8%)	257	0.281 (1.00)	0.5 (1.00)	0.817 (1.00)	0.76 (1.00)	0.643 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
FAM126B	4 (1%)	274	0.891 (1.00)	0.7 (1.00)	1 (1.00)	1 (1.00)	0.258 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
DDX5	3 (1%)	275	0.032 (0.641)	0.0388 (0.725)	0.555 (1.00)	0.44 (1.00)		0.135 (1.00)	1 (1.00)	1 (1.00)

'TP53 MUTATION STATUS' versus 'Time to Death'

P value = 0.00181 (logrank test), Q value = 0.13

Table S1. Gene #1: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	278	215	0.1 - 120.6 (11.3)
TP53 MUTATED	79	54	0.4 - 69.9 (12.9)
TP53 WILD-TYPE	199	161	0.1 - 120.6 (10.4)

Figure S1. Get High-res Image Gene #1: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'IDH1 MUTATION STATUS' versus 'Time to Death'

P value = 0.000559 (logrank test), Q value = 0.052

Table S2. Gene #6: 'IDH1 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	278	215	0.1 - 120.6 (11.3)
IDH1 MUTATED	14	4	3.4 - 50.5 (21.9)
IDH1 WILD-TYPE	264	211	0.1 - 120.6 (11.0)

Figure S2. Get High-res Image Gene #6: 'IDH1 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'IDH1 MUTATION STATUS' versus 'YEARS_TO_BIRTH'

P value = 3.92e-06 (Wilcoxon-test), Q value = 0.0011

Table S3. Gene #6: 'IDH1 MUTATION STATUS' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	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 S3. Get High-res Image Gene #6: 'IDH1 MUTATION STATUS' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'ATRX MUTATION STATUS' versus 'YEARS_TO_BIRTH'

P value = 1.49e-05 (Wilcoxon-test), Q value = 0.0021

Table S4. Gene #13: 'ATRX MUTATION STATUS' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	278	61.0 (13.0)
ATRX MUTATED	16	42.7 (16.4)
ATRX WILD-TYPE	262	62.2 (11.9)

Figure S4. Get High-res Image Gene #13: 'ATRX MUTATION STATUS' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'LZTR1 MUTATION STATUS' versus 'ETHNICITY'

P value = 0.00427 (Fisher's exact test), Q value = 0.24

Table S5. Gene #28: 'LZTR1 MUTATION STATUS' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	3	220
LZTR1 MUTATED	2	7
LZTR1 WILD-TYPE	1	213

Figure S5. Get High-res Image Gene #28: 'LZTR1 MUTATION STATUS' versus Clinical Feature #8: 'ETHNICITY'

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/GBM-TP/20063482/transformed.cor.cli.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/GBM-TP/19775175/GBM-TP.merged_data.txt
Number of patients = 278
Number of significantly mutated genes = 35
Number of selected clinical features = 8
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)

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