Correlation between gene mutation status and selected clinical features

Kidney Renal Clear Cell Carcinoma (Primary solid tumor)

16 April 2014 | analyses__2014_04_16

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 (2014): Correlation between gene mutation status and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C11Z4317

Overview

Introduction

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

Summary

Testing the association between mutation status of 9 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 9 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	0.00101 (0.0635)	0.384 (1.00)	0.000133 (0.00878)	0.000223 (0.0145)	0.681 (1.00)	0.0126 (0.766)	0.000954 (0.061)
VHL	218 (52%)	199	0.896 (1.00)	0.343 (1.00)	0.0857 (1.00)	0.161 (1.00)	0.768 (1.00)	0.112 (1.00)	0.918 (1.00)	0.272 (1.00)
SETD2	48 (12%)	369	0.321 (1.00)	0.15 (1.00)	0.121 (1.00)	0.094 (1.00)	0.367 (1.00)	0.207 (1.00)	0.148 (1.00)
PBRM1	137 (33%)	280	0.401 (1.00)	0.393 (1.00)	0.966 (1.00)	0.219 (1.00)	0.515 (1.00)	1 (1.00)	0.229 (1.00)	0.387 (1.00)
KDM5C	27 (6%)	390	0.0536 (1.00)	0.0682 (1.00)	0.9 (1.00)	0.877 (1.00)	1 (1.00)	1 (1.00)	0.00612 (0.379)
PTEN	18 (4%)	399	0.363 (1.00)	0.654 (1.00)	0.0534 (1.00)	0.251 (1.00)	0.11 (1.00)	0.329 (1.00)	0.129 (1.00)	0.357 (1.00)
TSPAN19	5 (1%)	412	0.616 (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)
NEFH	6 (1%)	411	0.812 (1.00)	0.536 (1.00)	0.0638 (1.00)	0.184 (1.00)	1 (1.00)	0.248 (1.00)	0.67 (1.00)

'BAP1 MUTATION STATUS' versus 'Time to Death'

P value = 0.00101 (logrank test), Q value = 0.064

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	416	142	0.1 - 120.6 (37.2)
BAP1 MUTATED	42	24	0.1 - 93.3 (28.3)
BAP1 WILD-TYPE	374	118	0.4 - 120.6 (38.0)

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

'BAP1 MUTATION STATUS' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.000133 (Fisher's exact test), Q value = 0.0088

Table S2. Gene #1: '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 #1: 'BAP1 MUTATION STATUS' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.000223 (Fisher's exact test), Q value = 0.014

Table S3. Gene #1: '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 #1: '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 = 0.061

Table S4. Gene #1: '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 #1: 'BAP1 MUTATION STATUS' versus Clinical Feature #7: 'GENDER'

Methods & Data

Input

Mutation data file = transformed.cor.cli.txt
Clinical data file = KIRC-TP.merged_data.txt
Number of patients = 417
Number of significantly mutated genes = 9
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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