Correlation between gene mutation status and selected clinical features

Brain Lower Grade Glioma (Primary solid tumor)

22 February 2013 | analyses__2013_02_22

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 (2013): Correlation between gene mutation status and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1707ZN0

Overview

Introduction

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

Summary

Testing the association between mutation status of 33 genes and 6 clinical features across 168 patients, 9 significant findings detected with Q value < 0.25.

IDH1 mutation correlated to 'Time to Death'.
TP53 mutation correlated to 'AGE', 'HISTOLOGICAL.TYPE', and 'RADIATIONS.RADIATION.REGIMENINDICATION'.
ATRX mutation correlated to 'AGE'.
CIC mutation correlated to 'HISTOLOGICAL.TYPE'.
FUBP1 mutation correlated to 'HISTOLOGICAL.TYPE'.
NOTCH1 mutation correlated to 'HISTOLOGICAL.TYPE'.
EGFR mutation correlated to 'KARNOFSKY.PERFORMANCE.SCORE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 33 genes and 6 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 9 significant findings detected.


		Clinical Features	Time to Death	AGE	GENDER	KARNOFSKY PERFORMANCE SCORE	HISTOLOGICAL TYPE	RADIATIONS RADIATION REGIMENINDICATION
	nMutated (%)	nWild-Type	logrank test	t-test	Fisher's exact test	t-test	Fisher's exact test	Fisher's exact test
TP53	88 (52%)	80	0.101 (1.00)	6.07e-06 (0.00109)	0.276 (1.00)	0.592 (1.00)	3.08e-05 (0.00549)	0.000344 (0.0608)
IDH1	131 (78%)	37	2.4e-05 (0.0043)	0.113 (1.00)	0.0246 (1.00)	0.892 (1.00)	0.0133 (1.00)	1 (1.00)
ATRX	73 (43%)	95	0.124 (1.00)	0.000623 (0.108)	0.53 (1.00)	0.69 (1.00)	0.00168 (0.29)	0.019 (1.00)
CIC	35 (21%)	133	0.0537 (1.00)	0.437 (1.00)	0.706 (1.00)	0.535 (1.00)	1.43e-09 (2.59e-07)	0.087 (1.00)
FUBP1	19 (11%)	149	0.775 (1.00)	0.00573 (0.974)	0.806 (1.00)	0.694 (1.00)	0.000607 (0.106)	0.334 (1.00)
NOTCH1	16 (10%)	152	0.859 (1.00)	0.0196 (1.00)	0.115 (1.00)	0.111 (1.00)	0.000574 (0.101)	0.44 (1.00)
EGFR	8 (5%)	160	0.0261 (1.00)	0.00243 (0.415)	0.29 (1.00)	6.32e-11 (1.15e-08)	0.0689 (1.00)	1 (1.00)
IDH2	6 (4%)	162	0.684 (1.00)	0.13 (1.00)	0.701 (1.00)		0.175 (1.00)	1 (1.00)
PIK3CA	15 (9%)	153	0.915 (1.00)	0.558 (1.00)	1 (1.00)	0.36 (1.00)	0.337 (1.00)	0.292 (1.00)
PIK3R1	12 (7%)	156	0.239 (1.00)	0.0323 (1.00)	0.559 (1.00)	0.696 (1.00)	0.745 (1.00)	0.773 (1.00)
ZNF844	4 (2%)	164	0.35 (1.00)	0.997 (1.00)	1 (1.00)		0.693 (1.00)	1 (1.00)
IL32	4 (2%)	164	0.015 (1.00)	0.254 (1.00)	1 (1.00)	0.132 (1.00)	1 (1.00)	0.0445 (1.00)
PTEN	7 (4%)	161	0.194 (1.00)	0.365 (1.00)	0.463 (1.00)		0.0355 (1.00)	0.452 (1.00)
TIMD4	5 (3%)	163	0.1 (1.00)	0.173 (1.00)	0.652 (1.00)	0.132 (1.00)	1 (1.00)	0.184 (1.00)
CREBZF	4 (2%)	164	0.722 (1.00)	0.22 (1.00)	0.315 (1.00)	0.297 (1.00)	0.482 (1.00)	1 (1.00)
ZNF57	6 (4%)	162	0.247 (1.00)	0.756 (1.00)	0.404 (1.00)		0.151 (1.00)	1 (1.00)
ARID1A	11 (7%)	157	0.058 (1.00)	0.794 (1.00)	0.118 (1.00)	0.353 (1.00)	0.622 (1.00)	0.547 (1.00)
NF1	11 (7%)	157	0.0464 (1.00)	0.725 (1.00)	1 (1.00)	0.776 (1.00)	0.0514 (1.00)	0.0641 (1.00)
TCF12	6 (4%)	162	0.972 (1.00)	0.554 (1.00)	0.085 (1.00)	0.671 (1.00)	1 (1.00)	0.418 (1.00)
NOX4	5 (3%)	163	0.483 (1.00)	0.493 (1.00)	1 (1.00)		0.735 (1.00)	1 (1.00)
ZBTB20	7 (4%)	161	0.163 (1.00)	0.64 (1.00)	0.7 (1.00)		0.488 (1.00)	0.0503 (1.00)
MUC7	4 (2%)	164	0.147 (1.00)	0.46 (1.00)	1 (1.00)		0.816 (1.00)	1 (1.00)
ZNF845	6 (4%)	162	0.347 (1.00)	0.796 (1.00)	1 (1.00)		0.885 (1.00)	0.218 (1.00)
ANKRD30A	7 (4%)	161	0.274 (1.00)	0.0454 (1.00)	0.241 (1.00)		0.0647 (1.00)	1 (1.00)
SPDYE5	3 (2%)	165	0.00205 (0.352)	0.226 (1.00)	0.577 (1.00)	0.671 (1.00)	0.362 (1.00)	0.098 (1.00)
SCAF1	4 (2%)	164	0.134 (1.00)	0.477 (1.00)	0.0321 (1.00)		0.816 (1.00)	0.624 (1.00)
PRDM9	5 (3%)	163	0.259 (1.00)	0.0461 (1.00)	0.652 (1.00)		0.389 (1.00)	0.374 (1.00)
PRAMEF11	5 (3%)	163	0.129 (1.00)	0.981 (1.00)	1 (1.00)		1 (1.00)	1 (1.00)
ZCCHC12	3 (2%)	165	0.539 (1.00)	0.758 (1.00)	0.577 (1.00)		0.26 (1.00)	1 (1.00)
ZNF91	5 (3%)	163	0.968 (1.00)	0.793 (1.00)	0.652 (1.00)		0.519 (1.00)	0.184 (1.00)
C3ORF35	3 (2%)	165	0.495 (1.00)	0.724 (1.00)	1 (1.00)		1 (1.00)	0.249 (1.00)
RPTN	5 (3%)	163	0.169 (1.00)	0.969 (1.00)	1 (1.00)		0.735 (1.00)	1 (1.00)
DDX5	5 (3%)	163	0.157 (1.00)	0.965 (1.00)	1 (1.00)	0.398 (1.00)	0.605 (1.00)	0.664 (1.00)

'IDH1 MUTATION STATUS' versus 'Time to Death'

P value = 2.4e-05 (logrank test), Q value = 0.0043

Table S1. Gene #1: 'IDH1 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	167	46	0.0 - 211.2 (15.1)
IDH1 MUTATED	130	29	0.0 - 182.3 (17.4)
IDH1 WILD-TYPE	37	17	0.1 - 211.2 (8.4)

Figure S1. Get High-res Image Gene #1: 'IDH1 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'TP53 MUTATION STATUS' versus 'AGE'

P value = 6.07e-06 (t-test), Q value = 0.0011

Table S2. Gene #3: 'TP53 MUTATION STATUS' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	168	43.0 (13.4)
TP53 MUTATED	88	38.7 (12.2)
TP53 WILD-TYPE	80	47.8 (13.1)

Figure S2. Get High-res Image Gene #3: 'TP53 MUTATION STATUS' versus Clinical Feature #2: 'AGE'

'TP53 MUTATION STATUS' versus 'HISTOLOGICAL.TYPE'

P value = 3.08e-05 (Fisher's exact test), Q value = 0.0055

Table S3. Gene #3: 'TP53 MUTATION STATUS' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	48	47	72
TP53 MUTATED	32	32	23
TP53 WILD-TYPE	16	15	49

Figure S3. Get High-res Image Gene #3: 'TP53 MUTATION STATUS' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'TP53 MUTATION STATUS' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.000344 (Fisher's exact test), Q value = 0.061

Table S4. Gene #3: 'TP53 MUTATION STATUS' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	90	78
TP53 MUTATED	59	29
TP53 WILD-TYPE	31	49

Figure S4. Get High-res Image Gene #3: 'TP53 MUTATION STATUS' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'ATRX MUTATION STATUS' versus 'AGE'

P value = 0.000623 (t-test), Q value = 0.11

Table S5. Gene #5: 'ATRX MUTATION STATUS' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	168	43.0 (13.4)
ATRX MUTATED	73	39.1 (12.6)
ATRX WILD-TYPE	95	46.1 (13.3)

Figure S5. Get High-res Image Gene #5: 'ATRX MUTATION STATUS' versus Clinical Feature #2: 'AGE'

'CIC MUTATION STATUS' versus 'HISTOLOGICAL.TYPE'

P value = 1.43e-09 (Fisher's exact test), Q value = 2.6e-07

Table S6. Gene #6: 'CIC MUTATION STATUS' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	48	47	72
CIC MUTATED	1	3	31
CIC WILD-TYPE	47	44	41

Figure S6. Get High-res Image Gene #6: 'CIC MUTATION STATUS' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'FUBP1 MUTATION STATUS' versus 'HISTOLOGICAL.TYPE'

P value = 0.000607 (Fisher's exact test), Q value = 0.11

Table S7. Gene #7: 'FUBP1 MUTATION STATUS' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	48	47	72
FUBP1 MUTATED	1	2	16
FUBP1 WILD-TYPE	47	45	56

Figure S7. Get High-res Image Gene #7: 'FUBP1 MUTATION STATUS' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'NOTCH1 MUTATION STATUS' versus 'HISTOLOGICAL.TYPE'

P value = 0.000574 (Fisher's exact test), Q value = 0.1

Table S8. Gene #9: 'NOTCH1 MUTATION STATUS' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	48	47	72
NOTCH1 MUTATED	1	1	14
NOTCH1 WILD-TYPE	47	46	58

Figure S8. Get High-res Image Gene #9: 'NOTCH1 MUTATION STATUS' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'EGFR MUTATION STATUS' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 6.32e-11 (t-test), Q value = 1.2e-08

Table S9. Gene #20: 'EGFR MUTATION STATUS' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	88	88.3 (10.5)
EGFR MUTATED	4	80.0 (0.0)
EGFR WILD-TYPE	84	88.7 (10.6)

Figure S9. Get High-res Image Gene #20: 'EGFR MUTATION STATUS' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

Methods & Data

Input

Mutation data file = LGG-TP.mutsig.cluster.txt
Clinical data file = LGG-TP.clin.merged.picked.txt
Number of patients = 168
Number of significantly mutated genes = 33
Number of selected clinical features = 6
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

Student's t-test analysis

For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between tumors with and without gene mutations using 't.test' 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

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

References

[1] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)

[2] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)

[3] 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)

[4] 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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