Correlation between gene mutation status and selected clinical features

Glioblastoma Multiforme (Primary solid tumor)

28 January 2016 | analyses__2016_01_28

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 (2016): Correlation between gene mutation status and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1VQ322X

Overview

Introduction

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

Summary

Testing the association between mutation status of 36 genes and 8 clinical features across 280 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 36 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%)	266	0.000917 (0.088)	3.67e-06 (0.00106)	0.392 (1.00)	0.139 (1.00)	0.12 (1.00)	0.547 (1.00)	0.0268 (0.614)	1 (1.00)
TP53	80 (29%)	200	0.00259 (0.186)	0.351 (1.00)	0.41 (1.00)	0.103 (1.00)	0.2 (1.00)	0.463 (1.00)	0.603 (1.00)	1 (1.00)
ATRX	16 (6%)	264	0.012 (0.43)	1.39e-05 (0.00201)	0.594 (1.00)	0.746 (1.00)	0.216 (1.00)	0.596 (1.00)	0.0334 (0.686)	1 (1.00)
LZTR1	10 (4%)	270	0.707 (1.00)	0.244 (1.00)	0.101 (1.00)	0.148 (1.00)	0.323 (1.00)	0.182 (1.00)	1 (1.00)	0.0042 (0.242)
PIK3R1	32 (11%)	248	0.783 (1.00)	0.52 (1.00)	0.434 (1.00)	1 (1.00)	0.693 (1.00)	1 (1.00)	0.572 (1.00)	1 (1.00)
RB1	24 (9%)	256	0.35 (1.00)	0.831 (1.00)	0.828 (1.00)	1 (1.00)	0.0943 (1.00)	0.473 (1.00)	0.611 (1.00)	1 (1.00)
NF1	29 (10%)	251	0.303 (1.00)	0.145 (1.00)	0.84 (1.00)	0.591 (1.00)	0.0747 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
PTEN	86 (31%)	194	0.851 (1.00)	0.308 (1.00)	0.422 (1.00)	0.0811 (1.00)	0.895 (1.00)	0.194 (1.00)	1 (1.00)	0.229 (1.00)
PIK3CA	26 (9%)	254	0.227 (1.00)	0.987 (1.00)	0.832 (1.00)	0.586 (1.00)	0.666 (1.00)	1 (1.00)	0.375 (1.00)	1 (1.00)
STAG2	12 (4%)	268	0.0101 (0.417)	0.827 (1.00)	0.761 (1.00)	0.233 (1.00)	0.123 (1.00)	1 (1.00)	0.608 (1.00)	1 (1.00)
SLC26A3	7 (2%)	273	0.997 (1.00)	0.528 (1.00)	0.428 (1.00)	0.594 (1.00)	0.459 (1.00)	0.324 (1.00)	1 (1.00)	1 (1.00)
SEMG1	8 (3%)	272	0.357 (1.00)	0.181 (1.00)	0.715 (1.00)	1 (1.00)	0.525 (1.00)	1 (1.00)	0.492 (1.00)	1 (1.00)
KDR	8 (3%)	272	0.645 (1.00)	0.518 (1.00)	0.266 (1.00)	0.632 (1.00)	0.604 (1.00)	0.358 (1.00)	1 (1.00)	1 (1.00)
RPL5	7 (2%)	273	0.835 (1.00)	0.822 (1.00)	0.257 (1.00)	0.61 (1.00)	0.479 (1.00)	1 (1.00)	0.446 (1.00)	1 (1.00)
MAP3K1	6 (2%)	274	0.677 (1.00)	0.308 (1.00)	0.424 (1.00)	1 (1.00)	0.736 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
BRAF	6 (2%)	274	0.24 (1.00)	0.925 (1.00)	1 (1.00)	0.594 (1.00)	0.711 (1.00)	0.0359 (0.689)	0.397 (1.00)	1 (1.00)
EGFR	73 (26%)	207	0.865 (1.00)	0.64 (1.00)	0.258 (1.00)	1 (1.00)	0.434 (1.00)	0.281 (1.00)	0.31 (1.00)	1 (1.00)
TMPRSS6	6 (2%)	274	0.975 (1.00)	0.282 (1.00)	0.671 (1.00)	1 (1.00)	0.28 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
PRKCD	3 (1%)	277	0.973 (1.00)	0.121 (1.00)	1 (1.00)	0.44 (1.00)		1 (1.00)	0.223 (1.00)	1 (1.00)
TP63	6 (2%)	274	0.328 (1.00)	0.992 (1.00)	0.671 (1.00)	1 (1.00)	0.671 (1.00)	1 (1.00)	1 (1.00)	0.0526 (0.937)
PDGFRA	11 (4%)	269	0.681 (1.00)	0.0572 (0.937)	1 (1.00)	0.413 (1.00)	0.14 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
CHD8	8 (3%)	272	0.904 (1.00)	0.0231 (0.606)	0.142 (1.00)	0.354 (1.00)	0.288 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
IL4R	8 (3%)	272	0.153 (1.00)	0.112 (1.00)	0.0277 (0.614)	0.354 (1.00)	0.423 (1.00)	1 (1.00)	0.492 (1.00)	1 (1.00)
REN	5 (2%)	275	0.778 (1.00)	0.608 (1.00)	0.657 (1.00)	1 (1.00)	0.347 (1.00)	0.0833 (1.00)	1 (1.00)	1 (1.00)
CD209	5 (2%)	275	0.686 (1.00)	0.732 (1.00)	0.0585 (0.937)	1 (1.00)	0.651 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
FBN3	11 (4%)	269	0.0915 (1.00)	0.964 (1.00)	0.533 (1.00)	1 (1.00)	0.915 (1.00)	0.205 (1.00)	0.605 (1.00)	1 (1.00)
MMP13	5 (2%)	275	0.674 (1.00)	0.121 (1.00)	0.657 (1.00)	0.59 (1.00)	0.0703 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
TCF12	4 (1%)	276	0.697 (1.00)	0.739 (1.00)	1 (1.00)	1 (1.00)	0.189 (1.00)	0.2 (1.00)	1 (1.00)	1 (1.00)
ZDHHC4	3 (1%)	277	0.758 (1.00)	0.813 (1.00)	0.296 (1.00)	1 (1.00)		0.153 (1.00)	0.221 (1.00)	1 (1.00)
IL18RAP	6 (2%)	274	0.727 (1.00)	0.164 (1.00)	0.671 (1.00)	0.594 (1.00)	0.205 (1.00)	1 (1.00)	0.396 (1.00)	1 (1.00)
ODF4	3 (1%)	277	0.34 (1.00)	0.576 (1.00)	1 (1.00)	0.44 (1.00)		1 (1.00)	1 (1.00)	1 (1.00)
KEL	15 (5%)	265	0.805 (1.00)	0.562 (1.00)	0.414 (1.00)	0.476 (1.00)	0.258 (1.00)	1 (1.00)	0.707 (1.00)	1 (1.00)
TESK1	3 (1%)	277		0.068 (1.00)	1 (1.00)	0.44 (1.00)		1 (1.00)	1 (1.00)	1 (1.00)
MUC17	22 (8%)	258	0.213 (1.00)	0.391 (1.00)	1 (1.00)	0.76 (1.00)	0.387 (1.00)	0.717 (1.00)	1 (1.00)	1 (1.00)
FAM126B	4 (1%)	276	0.905 (1.00)	0.693 (1.00)	1 (1.00)	1 (1.00)	0.251 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
DDX5	4 (1%)	276	0.00657 (0.315)	0.0215 (0.606)	0.3 (1.00)	0.44 (1.00)	0.242 (1.00)	0.0145 (0.464)	1 (1.00)	1 (1.00)

'TP53 MUTATION STATUS' versus 'Time to Death'

P value = 0.00259 (logrank test), Q value = 0.19

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	277	218	0.1 - 120.6 (11.5)
TP53 MUTATED	80	56	0.4 - 69.9 (13.0)
TP53 WILD-TYPE	197	162	0.1 - 120.6 (10.7)

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 = 0.000917 (logrank test), Q value = 0.088

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	277	218	0.1 - 120.6 (11.5)
IDH1 MUTATED	14	5	3.4 - 50.5 (21.9)
IDH1 WILD-TYPE	263	213	0.1 - 120.6 (11.3)

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 = 3.67e-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	280	61.1 (13.0)
IDH1 MUTATED	14	40.0 (15.1)
IDH1 WILD-TYPE	266	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 = 1.39e-05 (Wilcoxon-test), Q value = 0.002

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

	nPatients	Mean (Std.Dev)
ALL	280	61.1 (13.0)
ATRX MUTATED	16	42.7 (16.4)
ATRX WILD-TYPE	264	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 = 0.0042 (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	222
LZTR1 MUTATED	2	7
LZTR1 WILD-TYPE	1	215

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/22811914/transformed.cor.cli.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/GBM-TP/22506586/GBM-TP.merged_data.txt
Number of patients = 280
Number of significantly mutated genes = 36
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)

Made with Nozzle