Correlation between gene mutation status and selected clinical features

Kidney Renal Clear Cell Carcinoma (Primary solid tumor)

23 May 2013 | analyses__2013_05_23

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/C1F47M4C

Overview

Introduction

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

Summary

Testing the association between mutation status of 10 genes and 8 clinical features across 293 patients, 5 significant findings detected with Q value < 0.25.

BAP1 mutation correlated to 'GENDER', 'DISTANT.METASTASIS', and 'NEOPLASM.DISEASESTAGE'.
TP53 mutation correlated to 'Time to Death'.
EBPL mutation correlated to 'GENDER'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 10 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	AGE	GENDER	KARNOFSKY PERFORMANCE SCORE	DISTANT METASTASIS	LYMPH NODE METASTASIS	TUMOR STAGECODE	NEOPLASM DISEASESTAGE
	nMutated (%)	nWild-Type	logrank test	t-test	Fisher's exact test	t-test	Fisher's exact test	Fisher's exact test	t-test	Fisher's exact test
BAP1	27 (9%)	266	0.0136 (0.76)	0.742 (1.00)	0.00238 (0.14)		0.00411 (0.238)	0.108 (1.00)		0.000291 (0.018)
TP53	6 (2%)	287	0.000354 (0.0216)	0.259 (1.00)	0.188 (1.00)		0.579 (1.00)	0.169 (1.00)		0.0221 (1.00)
EBPL	6 (2%)	287	0.48 (1.00)	0.527 (1.00)	0.00161 (0.0968)		1 (1.00)	0.0384 (1.00)		0.0758 (1.00)
PBRM1	107 (37%)	186	0.533 (1.00)	0.813 (1.00)	0.309 (1.00)	0.429 (1.00)	0.593 (1.00)	0.413 (1.00)		0.939 (1.00)
SV2C	3 (1%)	290	0.731 (1.00)	0.0205 (1.00)	1 (1.00)		1 (1.00)	0.205 (1.00)		1 (1.00)
VHL	138 (47%)	155	0.668 (1.00)	0.0301 (1.00)	0.176 (1.00)	0.359 (1.00)	1 (1.00)	0.491 (1.00)		0.145 (1.00)
SETD2	34 (12%)	259	0.762 (1.00)	0.0994 (1.00)	0.181 (1.00)		0.101 (1.00)	0.72 (1.00)		0.0591 (1.00)
KDM5C	18 (6%)	275	0.0803 (1.00)	0.206 (1.00)	0.00486 (0.277)		1 (1.00)	0.741 (1.00)		0.857 (1.00)
PTEN	9 (3%)	284	0.769 (1.00)	0.219 (1.00)	0.503 (1.00)		0.613 (1.00)	0.0299 (1.00)		0.226 (1.00)
TOR1A	3 (1%)	290	0.0532 (1.00)	0.704 (1.00)	1 (1.00)		1 (1.00)	0.0562 (1.00)		1 (1.00)

'BAP1 MUTATION STATUS' versus 'GENDER'

P value = 0.00238 (Fisher's exact test), Q value = 0.14

Table S1. Gene #4: 'BAP1 MUTATION STATUS' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	102	191
BAP1 MUTATED	17	10
BAP1 WILD-TYPE	85	181

Figure S1. Get High-res Image Gene #4: 'BAP1 MUTATION STATUS' versus Clinical Feature #3: 'GENDER'

'BAP1 MUTATION STATUS' versus 'DISTANT.METASTASIS'

P value = 0.00411 (Fisher's exact test), Q value = 0.24

Table S2. Gene #4: 'BAP1 MUTATION STATUS' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients	M0	M1
ALL	254	39
BAP1 MUTATED	18	9
BAP1 WILD-TYPE	236	30

Figure S2. Get High-res Image Gene #4: 'BAP1 MUTATION STATUS' versus Clinical Feature #5: 'DISTANT.METASTASIS'

'BAP1 MUTATION STATUS' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.000291 (Fisher's exact test), Q value = 0.018

Table S3. Gene #4: 'BAP1 MUTATION STATUS' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	144	32	77	40
BAP1 MUTATED	5	5	7	10
BAP1 WILD-TYPE	139	27	70	30

Figure S3. Get High-res Image Gene #4: 'BAP1 MUTATION STATUS' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

'TP53 MUTATION STATUS' versus 'Time to Death'

P value = 0.000354 (logrank test), Q value = 0.022

Table S4. Gene #7: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	291	77	0.1 - 109.6 (34.3)
TP53 MUTATED	6	3	0.2 - 25.7 (9.8)
TP53 WILD-TYPE	285	74	0.1 - 109.6 (35.2)

Figure S4. Get High-res Image Gene #7: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'EBPL MUTATION STATUS' versus 'GENDER'

P value = 0.00161 (Fisher's exact test), Q value = 0.097

Table S5. Gene #9: 'EBPL MUTATION STATUS' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	102	191
EBPL MUTATED	6	0
EBPL WILD-TYPE	96	191

Figure S5. Get High-res Image Gene #9: 'EBPL MUTATION STATUS' versus Clinical Feature #3: 'GENDER'

Methods & Data

Input

Mutation data file = KIRC-TP.mutsig.cluster.txt
Clinical data file = KIRC-TP.clin.merged.picked.txt
Number of patients = 293
Number of significantly mutated genes = 10
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

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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