Correlation between gene mutation status and selected clinical features

Kidney Renal Clear Cell Carcinoma (Primary solid tumor)

23 September 2013 | analyses__2013_09_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/C1XG9PG7

Overview

Introduction

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

Summary

Testing the association between mutation status of 15 genes and 8 clinical features across 417 patients, 4 significant findings detected with Q value < 0.25.

BAP1 mutation correlated to 'Time to Death', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', and 'GENDER'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 15 genes and 8 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 4 significant findings detected.


		Clinical Features	Time to Death	AGE	NEOPLASM DISEASESTAGE	PATHOLOGY T STAGE	PATHOLOGY N STAGE	PATHOLOGY M STAGE	GENDER	KARNOFSKY PERFORMANCE SCORE
	nMutated (%)	nWild-Type	logrank test	t-test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	t-test
BAP1	42 (10%)	375	0.00143 (0.149)	0.385 (1.00)	0.000107 (0.0115)	0.000185 (0.0196)	0.681 (1.00)	0.0126 (1.00)	0.000954 (0.1)
TPSD1	4 (1%)	413	0.454 (1.00)	0.691 (1.00)	0.326 (1.00)	0.225 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
NEFH	12 (3%)	405	0.707 (1.00)	0.287 (1.00)	0.486 (1.00)	0.372 (1.00)	1 (1.00)	0.419 (1.00)	0.553 (1.00)
VHL	218 (52%)	199	0.802 (1.00)	0.346 (1.00)	0.0852 (1.00)	0.163 (1.00)	0.768 (1.00)	0.112 (1.00)	0.918 (1.00)	0.272 (1.00)
SETD2	48 (12%)	369	0.246 (1.00)	0.146 (1.00)	0.118 (1.00)	0.0941 (1.00)	0.367 (1.00)	0.207 (1.00)	0.148 (1.00)
PBRM1	137 (33%)	280	0.421 (1.00)	0.39 (1.00)	0.969 (1.00)	0.217 (1.00)	0.515 (1.00)	1 (1.00)	0.229 (1.00)	0.387 (1.00)
KDM5C	27 (6%)	390	0.0591 (1.00)	0.0684 (1.00)	0.916 (1.00)	0.924 (1.00)	1 (1.00)	1 (1.00)	0.00612 (0.63)
PTEN	18 (4%)	399	0.347 (1.00)	0.653 (1.00)	0.0477 (1.00)	0.244 (1.00)	0.11 (1.00)	0.329 (1.00)	0.129 (1.00)	0.357 (1.00)
TSPAN19	5 (1%)	412	0.641 (1.00)	0.278 (1.00)	0.476 (1.00)	0.7 (1.00)	1 (1.00)	0.585 (1.00)	0.661 (1.00)
TCEB1	3 (1%)	414	0.278 (1.00)	0.246 (1.00)	0.586 (1.00)	0.383 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
FAM22F	4 (1%)	413	0.772 (1.00)	0.746 (1.00)	0.168 (1.00)	1 (1.00)	1 (1.00)	0.123 (1.00)	0.614 (1.00)
C15ORF40	3 (1%)	414	0.797 (1.00)	0.283 (1.00)	0.586 (1.00)	0.383 (1.00)		1 (1.00)	1 (1.00)
PLA2G4E	8 (2%)	409	0.413 (1.00)	0.282 (1.00)	0.434 (1.00)	0.811 (1.00)	1 (1.00)	0.365 (1.00)	0.135 (1.00)
UQCRFS1	3 (1%)	414	0.233 (1.00)	0.55 (1.00)	0.0367 (1.00)	0.0797 (1.00)	1 (1.00)	1 (1.00)	0.281 (1.00)
GPRIN1	8 (2%)	409	0.786 (1.00)	0.828 (1.00)	0.136 (1.00)	0.646 (1.00)	1 (1.00)	0.122 (1.00)	0.135 (1.00)

'BAP1 MUTATION STATUS' versus 'Time to Death'

P value = 0.00143 (logrank test), Q value = 0.15

Table S1. Gene #6: 'BAP1 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	416	141	0.1 - 120.6 (37.0)
BAP1 MUTATED	42	24	0.1 - 93.3 (28.3)
BAP1 WILD-TYPE	374	117	0.1 - 120.6 (37.2)

Figure S1. Get High-res Image Gene #6: 'BAP1 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'BAP1 MUTATION STATUS' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.000107 (Fisher's exact test), Q value = 0.011

Table S2. Gene #6: 'BAP1 MUTATION STATUS' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	197	40	113	67
BAP1 MUTATED	7	7	16	12
BAP1 WILD-TYPE	190	33	97	55

Figure S2. Get High-res Image Gene #6: 'BAP1 MUTATION STATUS' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'BAP1 MUTATION STATUS' versus 'PATHOLOGY.T.STAGE'

P value = 0.000185 (Fisher's exact test), Q value = 0.02

Table S3. Gene #6: 'BAP1 MUTATION STATUS' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	202	49	160	6
BAP1 MUTATED	8	10	24	0
BAP1 WILD-TYPE	194	39	136	6

Figure S3. Get High-res Image Gene #6: 'BAP1 MUTATION STATUS' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'BAP1 MUTATION STATUS' versus 'GENDER'

P value = 0.000954 (Fisher's exact test), Q value = 0.1

Table S4. Gene #6: 'BAP1 MUTATION STATUS' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	146	271
BAP1 MUTATED	25	17
BAP1 WILD-TYPE	121	254

Figure S4. Get High-res Image Gene #6: 'BAP1 MUTATION STATUS' versus Clinical Feature #7: 'GENDER'

Methods & Data

Input

Mutation data file = KIRC-TP.mutsig.cluster.txt
Clinical data file = KIRC-TP.clin.merged.picked.txt
Number of patients = 417
Number of significantly mutated genes = 15
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

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] 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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