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 18 genes and 6 clinical features across 210 patients, 9 significant findings detected with Q value < 0.25.

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

Results

Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 18 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	109 (52%)	101	0.149 (1.00)	9.69e-07 (9.79e-05)	0.211 (1.00)	0.461 (1.00)	2.41e-06 (0.000241)	0.00202 (0.19)
ATRX	91 (43%)	119	0.108 (1.00)	5.4e-06 (0.000535)	0.483 (1.00)	0.423 (1.00)	0.000209 (0.02)	0.0341 (1.00)
IDH1	158 (75%)	52	1.76e-05 (0.00173)	0.0117 (1.00)	0.00992 (0.893)	0.539 (1.00)	0.00454 (0.422)	0.419 (1.00)
CIC	38 (18%)	172	0.0549 (1.00)	0.703 (1.00)	0.718 (1.00)	0.573 (1.00)	8.82e-08 (9e-06)	0.364 (1.00)
FUBP1	21 (10%)	189	0.697 (1.00)	0.0107 (0.956)	1 (1.00)	0.72 (1.00)	0.000712 (0.0676)	1 (1.00)
NOTCH1	18 (9%)	192	0.834 (1.00)	0.0152 (1.00)	0.328 (1.00)	0.125 (1.00)	0.000119 (0.0116)	0.809 (1.00)
IL32	8 (4%)	202	0.539 (1.00)	0.555 (1.00)	0.732 (1.00)	0.191 (1.00)	1 (1.00)	0.022 (1.00)
IDH2	8 (4%)	202	0.48 (1.00)	0.124 (1.00)	0.732 (1.00)		0.265 (1.00)	1 (1.00)
PIK3CA	19 (9%)	191	0.872 (1.00)	0.458 (1.00)	0.631 (1.00)	0.346 (1.00)	0.104 (1.00)	0.808 (1.00)
PIK3R1	13 (6%)	197	0.2 (1.00)	0.125 (1.00)	0.397 (1.00)	0.662 (1.00)	0.254 (1.00)	0.778 (1.00)
PTEN	12 (6%)	198	0.158 (1.00)	0.0123 (1.00)	0.373 (1.00)	0.268 (1.00)	0.00517 (0.471)	1 (1.00)
CREBZF	4 (2%)	206	0.686 (1.00)	0.217 (1.00)	0.321 (1.00)	0.288 (1.00)	0.469 (1.00)	0.641 (1.00)
PCDHAC2	14 (7%)	196	0.148 (1.00)	0.302 (1.00)	0.162 (1.00)	0.273 (1.00)	0.0382 (1.00)	0.00501 (0.461)
NOX4	5 (2%)	205	0.457 (1.00)	0.491 (1.00)	1 (1.00)		0.736 (1.00)	0.652 (1.00)
ZNF57	6 (3%)	204	0.239 (1.00)	0.767 (1.00)	0.408 (1.00)		0.212 (1.00)	1 (1.00)
EIF1AX	3 (1%)	207	0.385 (1.00)	0.774 (1.00)	0.258 (1.00)		0.118 (1.00)	0.573 (1.00)
DCP1B	4 (2%)	206	0.666 (1.00)	0.0509 (1.00)	0.633 (1.00)		0.687 (1.00)	1 (1.00)
NAB2	4 (2%)	206	0.441 (1.00)	0.532 (1.00)	0.321 (1.00)		0.0673 (1.00)	0.641 (1.00)

'ATRX MUTATION STATUS' versus 'AGE'

P value = 5.4e-06 (t-test), Q value = 0.00053

Table S1. Gene #2: 'ATRX MUTATION STATUS' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	210	43.0 (13.3)
ATRX MUTATED	91	38.3 (12.0)
ATRX WILD-TYPE	119	46.5 (13.3)

Figure S1. Get High-res Image Gene #2: 'ATRX MUTATION STATUS' versus Clinical Feature #2: 'AGE'

'ATRX MUTATION STATUS' versus 'HISTOLOGICAL.TYPE'

P value = 0.000209 (Fisher's exact test), Q value = 0.02

Table S2. Gene #2: 'ATRX MUTATION STATUS' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	65	57	87
ATRX MUTATED	30	36	25
ATRX WILD-TYPE	35	21	62

Figure S2. Get High-res Image Gene #2: 'ATRX MUTATION STATUS' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'IDH1 MUTATION STATUS' versus 'Time to Death'

P value = 1.76e-05 (logrank test), Q value = 0.0017

Table S3. Gene #4: 'IDH1 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	210	53	0.0 - 211.2 (15.0)
IDH1 MUTATED	158	33	0.0 - 182.3 (17.4)
IDH1 WILD-TYPE	52	20	0.1 - 211.2 (10.0)

Figure S3. Get High-res Image Gene #4: 'IDH1 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'TP53 MUTATION STATUS' versus 'AGE'

P value = 9.69e-07 (t-test), Q value = 9.8e-05

Table S4. Gene #5: 'TP53 MUTATION STATUS' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	210	43.0 (13.3)
TP53 MUTATED	109	38.7 (11.8)
TP53 WILD-TYPE	101	47.6 (13.5)

Figure S4. Get High-res Image Gene #5: 'TP53 MUTATION STATUS' versus Clinical Feature #2: 'AGE'

'TP53 MUTATION STATUS' versus 'HISTOLOGICAL.TYPE'

P value = 2.41e-06 (Fisher's exact test), Q value = 0.00024

Table S5. Gene #5: 'TP53 MUTATION STATUS' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	65	57	87
TP53 MUTATED	42	39	27
TP53 WILD-TYPE	23	18	60

Figure S5. Get High-res Image Gene #5: 'TP53 MUTATION STATUS' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

P value = 0.00202 (Fisher's exact test), Q value = 0.19

Table S6. Gene #5: 'TP53 MUTATION STATUS' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	88	122
TP53 MUTATED	57	52
TP53 WILD-TYPE	31	70

Figure S6. Get High-res Image Gene #5: 'TP53 MUTATION STATUS' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'CIC MUTATION STATUS' versus 'HISTOLOGICAL.TYPE'

P value = 8.82e-08 (Fisher's exact test), Q value = 9e-06

Table S7. Gene #6: 'CIC MUTATION STATUS' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	65	57	87
CIC MUTATED	2	5	31
CIC WILD-TYPE	63	52	56

Figure S7. Get High-res Image Gene #6: 'CIC MUTATION STATUS' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'FUBP1 MUTATION STATUS' versus 'HISTOLOGICAL.TYPE'

P value = 0.000712 (Fisher's exact test), Q value = 0.068

Table S8. Gene #7: 'FUBP1 MUTATION STATUS' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	65	57	87
FUBP1 MUTATED	2	2	17
FUBP1 WILD-TYPE	63	55	70

Figure S8. Get High-res Image Gene #7: 'FUBP1 MUTATION STATUS' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'NOTCH1 MUTATION STATUS' versus 'HISTOLOGICAL.TYPE'

P value = 0.000119 (Fisher's exact test), Q value = 0.012

Table S9. Gene #8: 'NOTCH1 MUTATION STATUS' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	65	57	87
NOTCH1 MUTATED	1	1	16
NOTCH1 WILD-TYPE	64	56	71

Figure S9. Get High-res Image Gene #8: 'NOTCH1 MUTATION STATUS' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

Methods & Data

Input

Mutation data file = LGG-TP.mutsig.cluster.txt
Clinical data file = LGG-TP.clin.merged.picked.txt
Number of patients = 210
Number of significantly mutated genes = 18
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.

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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