Correlation between gene mutation status and selected clinical features

Thymoma (Primary solid tumor)

28 January 2016 | analyses__2016_01_28

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 (2016): Correlation between gene mutation status and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1QR4WNZ

Overview

Introduction

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

Summary

Testing the association between mutation status of 8 genes and 8 clinical features across 120 patients, 6 significant findings detected with Q value < 0.25.

GTF2I mutation correlated to 'YEARS_TO_BIRTH', 'RADIATION_THERAPY', and 'HISTOLOGICAL_TYPE'.
HRAS mutation correlated to 'HISTOLOGICAL_TYPE'.
TP53 mutation correlated to 'Time to Death'.
PLEKHG4B mutation correlated to 'Time to Death'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 8 genes and 8 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 6 significant findings detected.


		Clinical Features	Time to Death	YEARS TO BIRTH	TUMOR TISSUE SITE	GENDER	RADIATION THERAPY	HISTOLOGICAL TYPE	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	Fisher's exact test
GTF2I	49 (41%)	71	0.521 (1.00)	0.00247 (0.0395)	0.652 (1.00)	0.714 (1.00)	0.00643 (0.0823)	1e-05 (0.00064)	0.537 (1.00)	0.736 (1.00)
HRAS	10 (8%)	110	0.709 (1.00)	0.0432 (0.345)	0.686 (1.00)	0.323 (0.97)	1 (1.00)	0.00026 (0.00555)	1 (1.00)	1 (1.00)
TP53	4 (3%)	116	0.000107 (0.00341)	0.439 (1.00)	0.579 (1.00)	0.619 (1.00)	0.123 (0.604)	0.0351 (0.321)	1 (1.00)	0.303 (0.97)
PLEKHG4B	3 (2%)	117	0.0138 (0.147)	0.067 (0.42)	1 (1.00)	1 (1.00)	1 (1.00)	0.312 (0.97)		1 (1.00)
CAPNS1	3 (2%)	117	0.705 (1.00)	0.0722 (0.42)	1 (1.00)	0.116 (0.604)	1 (1.00)	0.623 (1.00)	1 (1.00)	1 (1.00)
ATRN	3 (2%)	117	0.57 (1.00)	0.973 (1.00)	0.507 (1.00)	1 (1.00)	0.551 (1.00)	0.443 (1.00)	0.413 (1.00)	1 (1.00)
NRAS	3 (2%)	117	0.421 (1.00)	0.832 (1.00)	0.507 (1.00)	0.244 (0.97)	0.28 (0.97)	0.31 (0.97)	0.0677 (0.42)	1 (1.00)
UNC93B1	5 (4%)	115	0.652 (1.00)	0.333 (0.97)	1 (1.00)	0.677 (1.00)	0.161 (0.736)	0.383 (1.00)	1 (1.00)	0.236 (0.97)

'GTF2I MUTATION STATUS' versus 'YEARS_TO_BIRTH'

P value = 0.00247 (Wilcoxon-test), Q value = 0.04

Table S1. Gene #1: 'GTF2I MUTATION STATUS' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	119	58.5 (12.7)
GTF2I MUTATED	48	63.0 (11.0)
GTF2I WILD-TYPE	71	55.5 (13.0)

Figure S1. Get High-res Image Gene #1: 'GTF2I MUTATION STATUS' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'GTF2I MUTATION STATUS' versus 'RADIATION_THERAPY'

P value = 0.00643 (Fisher's exact test), Q value = 0.082

Table S2. Gene #1: 'GTF2I MUTATION STATUS' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	78	42
GTF2I MUTATED	39	10
GTF2I WILD-TYPE	39	32

Figure S2. Get High-res Image Gene #1: 'GTF2I MUTATION STATUS' versus Clinical Feature #5: 'RADIATION_THERAPY'

'GTF2I MUTATION STATUS' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00064

Table S3. Gene #1: 'GTF2I MUTATION STATUS' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients	THYMOMA; TYPE A	THYMOMA; TYPE AB	THYMOMA; TYPE B1	THYMOMA; TYPE B2	THYMOMA; TYPE B3	THYMOMA; TYPE C
ALL	17	38	13	29	12	11
GTF2I MUTATED	14	27	0	5	2	1
GTF2I WILD-TYPE	3	11	13	24	10	10

Figure S3. Get High-res Image Gene #1: 'GTF2I MUTATION STATUS' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'HRAS MUTATION STATUS' versus 'HISTOLOGICAL_TYPE'

P value = 0.00026 (Fisher's exact test), Q value = 0.0055

Table S4. Gene #2: 'HRAS MUTATION STATUS' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients	THYMOMA; TYPE A	THYMOMA; TYPE AB	THYMOMA; TYPE B1	THYMOMA; TYPE B2	THYMOMA; TYPE B3	THYMOMA; TYPE C
ALL	17	38	13	29	12	11
HRAS MUTATED	7	3	0	0	0	0
HRAS WILD-TYPE	10	35	13	29	12	11

Figure S4. Get High-res Image Gene #2: 'HRAS MUTATION STATUS' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'TP53 MUTATION STATUS' versus 'Time to Death'

P value = 0.000107 (logrank test), Q value = 0.0034

Table S5. Gene #4: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	119	9	0.5 - 150.4 (40.1)
TP53 MUTATED	4	2	12.5 - 93.7 (23.3)
TP53 WILD-TYPE	115	7	0.5 - 150.4 (41.2)

Figure S5. Get High-res Image Gene #4: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'PLEKHG4B MUTATION STATUS' versus 'Time to Death'

P value = 0.0138 (logrank test), Q value = 0.15

Table S6. Gene #7: 'PLEKHG4B MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	119	9	0.5 - 150.4 (40.1)
PLEKHG4B MUTATED	3	1	12.5 - 59.8 (12.7)
PLEKHG4B WILD-TYPE	116	8	0.5 - 150.4 (40.6)

Figure S6. Get High-res Image Gene #7: 'PLEKHG4B 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/THYM-TP/22571831/transformed.cor.cli.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/THYM-TP/22507339/THYM-TP.merged_data.txt
Number of patients = 120
Number of significantly mutated genes = 8
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

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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