Correlation between gene mutation status and selected clinical features

Kidney Renal Clear Cell Carcinoma (Primary solid tumor)

02 April 2015 | analyses__2015_04_02

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 (2015): Correlation between gene mutation status and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1416W31

Overview

Introduction

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

Summary

Testing the association between mutation status of 30 genes and 10 clinical features across 441 patients, 4 significant findings detected with Q value < 0.25.

BAP1 mutation correlated to 'Time to Death', 'NEOPLASM_DISEASESTAGE', and 'PATHOLOGY_T_STAGE'.
TRIM6 mutation correlated to 'Time to Death'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 30 genes and 10 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	YEARS TO BIRTH	NEOPLASM DISEASESTAGE	PATHOLOGY T STAGE	PATHOLOGY N STAGE	PATHOLOGY M STAGE	GENDER	KARNOFSKY PERFORMANCE SCORE	RACE	ETHNICITY
	nMutated (%)	nWild-Type	logrank test	Wilcoxon-test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Wilcoxon-test	Fisher's exact test	Fisher's exact test
BAP1	44 (10%)	397	0.00182 (0.182)	0.916 (1.00)	7e-05 (0.0105)	6e-05 (0.0105)	0.245 (1.00)	0.0152 (0.364)	0.00713 (0.267)		1 (1.00)	0.724 (1.00)
TRIM6	5 (1%)	436	0.00322 (0.242)	0.196 (0.944)	0.634 (1.00)	0.599 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)		1 (1.00)	1 (1.00)
SETD2	46 (10%)	395	0.1 (0.916)	0.241 (1.00)	0.299 (1.00)	0.191 (0.944)	0.379 (1.00)	0.285 (1.00)	0.332 (1.00)		0.732 (1.00)	0.71 (1.00)
PBRM1	139 (32%)	302	0.505 (1.00)	0.185 (0.944)	0.986 (1.00)	0.186 (0.944)	0.163 (0.944)	0.781 (1.00)	0.592 (1.00)	0.547 (1.00)	0.255 (1.00)	0.234 (1.00)
KDM5C	27 (6%)	414	0.0591 (0.771)	0.0331 (0.584)	0.98 (1.00)	0.975 (1.00)	1 (1.00)	1 (1.00)	0.00615 (0.267)		1 (1.00)	0.379 (1.00)
VHL	171 (39%)	270	0.898 (1.00)	0.726 (1.00)	0.137 (0.916)	0.111 (0.916)	0.791 (1.00)	0.287 (1.00)	0.474 (1.00)	0.278 (1.00)	0.0156 (0.364)	0.821 (1.00)
PTEN	17 (4%)	424	0.855 (1.00)	0.575 (1.00)	0.0871 (0.916)	0.328 (1.00)	0.158 (0.944)	0.494 (1.00)	0.0405 (0.675)	0.427 (1.00)	0.658 (1.00)	0.61 (1.00)
MTOR	28 (6%)	413	0.138 (0.916)	0.12 (0.916)	0.276 (1.00)	0.378 (1.00)	0.356 (1.00)	0.182 (0.944)	0.103 (0.916)	0.0256 (0.511)	0.179 (0.944)	1 (1.00)
TP53	12 (3%)	429	0.00989 (0.33)	0.875 (1.00)	0.507 (1.00)	0.262 (1.00)	1 (1.00)	0.416 (1.00)	0.76 (1.00)		0.529 (1.00)	1 (1.00)
FAM200A	5 (1%)	436	0.495 (1.00)	0.685 (1.00)	0.759 (1.00)	0.486 (1.00)	1 (1.00)	1 (1.00)	0.0538 (0.74)		1 (1.00)	1 (1.00)
NEFH	5 (1%)	436	0.669 (1.00)	0.543 (1.00)	0.128 (0.916)	0.197 (0.944)	1 (1.00)	0.58 (1.00)	0.661 (1.00)		1 (1.00)	1 (1.00)
PTCH1	7 (2%)	434	0.0543 (0.74)	0.245 (1.00)	0.366 (1.00)	0.0758 (0.843)	1 (1.00)	1 (1.00)	0.701 (1.00)		1 (1.00)	1 (1.00)
NF2	6 (1%)	435	0.139 (0.916)	0.322 (1.00)	0.457 (1.00)	0.471 (1.00)	0.0143 (0.364)	0.244 (1.00)	1 (1.00)		0.31 (1.00)	0.358 (1.00)
CCDC120	4 (1%)	437	0.134 (0.916)	0.856 (1.00)	0.105 (0.916)	0.122 (0.916)	1 (1.00)	1 (1.00)	1 (1.00)		1 (1.00)	0.198 (0.944)
PIK3CA	12 (3%)	429	0.618 (1.00)	0.83 (1.00)	0.57 (1.00)	0.593 (1.00)	1 (1.00)	1 (1.00)	0.0296 (0.556)		1 (1.00)	0.56 (1.00)
ATM	12 (3%)	429	0.96 (1.00)	0.332 (1.00)	0.136 (0.916)	0.00711 (0.267)	0.132 (0.916)	0.416 (1.00)	0.358 (1.00)		1 (1.00)	0.405 (1.00)
KIAA1751	6 (1%)	435	0.9 (1.00)	0.186 (0.944)	0.497 (1.00)	0.878 (1.00)	0.317 (1.00)	0.596 (1.00)	0.67 (1.00)		1 (1.00)	1 (1.00)
GUSB	4 (1%)	437	0.826 (1.00)	0.0707 (0.839)	0.168 (0.944)	0.396 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)		1 (1.00)	1 (1.00)
ARID1A	11 (2%)	430	0.866 (1.00)	0.735 (1.00)	0.0518 (0.74)	0.112 (0.916)	1 (1.00)	1 (1.00)	0.528 (1.00)		1 (1.00)	0.127 (0.916)
GPR50	3 (1%)	438	0.272 (1.00)	0.9 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	0.283 (1.00)		0.168 (0.944)	1 (1.00)
PCK1	5 (1%)	436	0.187 (0.944)	0.345 (1.00)	0.18 (0.944)	0.278 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)		1 (1.00)	1 (1.00)
DPCR1	6 (1%)	435	0.384 (1.00)	0.517 (1.00)	0.103 (0.916)	0.877 (1.00)	1 (1.00)	0.0536 (0.74)	1 (1.00)		0.31 (1.00)	1 (1.00)
NFAT5	6 (1%)	435	0.88 (1.00)	0.628 (1.00)	0.708 (1.00)	1 (1.00)	1 (1.00)	0.596 (1.00)	0.67 (1.00)		1 (1.00)	1 (1.00)
EGFR	7 (2%)	434	0.017 (0.364)	0.595 (1.00)	0.0161 (0.364)	0.00624 (0.267)	0.262 (1.00)	0.308 (1.00)	0.43 (1.00)		1 (1.00)	1 (1.00)
RBMX	4 (1%)	437	0.701 (1.00)	0.217 (1.00)	1 (1.00)	1 (1.00)	0.14 (0.916)	0.5 (1.00)	0.302 (1.00)		1 (1.00)	1 (1.00)
GPR172B	4 (1%)	437	0.576 (1.00)	0.798 (1.00)	0.885 (1.00)	0.805 (1.00)	0.204 (0.954)	1 (1.00)	0.127 (0.916)		1 (1.00)	0.0707 (0.839)
FGFR3	4 (1%)	437	0.973 (1.00)	0.666 (1.00)	0.283 (1.00)	0.496 (1.00)	0.0727 (0.839)	1 (1.00)	0.302 (1.00)		1 (1.00)	1 (1.00)
ARAP3	3 (1%)	438	0.733 (1.00)	0.544 (1.00)	0.819 (1.00)	0.735 (1.00)		0.405 (1.00)	0.283 (1.00)		0.169 (0.944)	1 (1.00)
OPTC	4 (1%)	437	0.946 (1.00)	0.636 (1.00)	0.884 (1.00)	0.808 (1.00)	1 (1.00)	1 (1.00)	0.615 (1.00)		1 (1.00)	1 (1.00)
GOLGA5	5 (1%)	436	0.44 (1.00)	0.725 (1.00)	0.181 (0.944)	0.276 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)		1 (1.00)	1 (1.00)

'BAP1 MUTATION STATUS' versus 'Time to Death'

P value = 0.00182 (logrank test), Q value = 0.18

Table S1. Gene #2: 'BAP1 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	441	146	0.1 - 120.6 (37.6)
BAP1 MUTATED	44	24	0.1 - 93.3 (28.3)
BAP1 WILD-TYPE	397	122	0.2 - 120.6 (39.2)

Figure S1. Get High-res Image Gene #2: 'BAP1 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'BAP1 MUTATION STATUS' versus 'NEOPLASM_DISEASESTAGE'

P value = 7e-05 (Fisher's exact test), Q value = 0.01

Table S2. Gene #2: 'BAP1 MUTATION STATUS' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	211	45	115	70
BAP1 MUTATED	7	7	18	12
BAP1 WILD-TYPE	204	38	97	58

Figure S2. Get High-res Image Gene #2: 'BAP1 MUTATION STATUS' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'BAP1 MUTATION STATUS' versus 'PATHOLOGY_T_STAGE'

P value = 6e-05 (Fisher's exact test), Q value = 0.01

Table S3. Gene #2: 'BAP1 MUTATION STATUS' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	216	56	162	7
BAP1 MUTATED	8	11	25	0
BAP1 WILD-TYPE	208	45	137	7

Figure S3. Get High-res Image Gene #2: 'BAP1 MUTATION STATUS' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'TRIM6 MUTATION STATUS' versus 'Time to Death'

P value = 0.00322 (logrank test), Q value = 0.24

Table S4. Gene #24: 'TRIM6 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	441	146	0.1 - 120.6 (37.6)
TRIM6 MUTATED	5	3	0.2 - 34.4 (12.3)
TRIM6 WILD-TYPE	436	143	0.1 - 120.6 (38.5)

Figure S4. Get High-res Image Gene #24: 'TRIM6 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

Methods & Data

Input

Mutation data file = sample_sig_gene_table.txt from Mutsig_2CV pipeline
Processed Mutation data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_Correlate_Genomic_Events_Preprocess/KIRC-TP/15182434/transformed.cor.cli.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/KIRC-TP/15081616/KIRC-TP.merged_data.txt
Number of patients = 441
Number of significantly mutated genes = 30
Number of selected clinical features = 10
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

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] 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)

[3] 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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