Correlation between gene mutation status and selected clinical features

Uveal Melanoma (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/C1668CQ0

Overview

Introduction

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

Summary

Testing the association between mutation status of 7 genes and 7 clinical features across 80 patients, 3 significant findings detected with Q value < 0.25.

GNAQ mutation correlated to 'PATHOLOGIC_STAGE'.
SF3B1 mutation correlated to 'Time to Death'.
BAP1 mutation correlated to 'Time to Death'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 7 genes and 7 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 3 significant findings detected.


		Clinical Features	Time to Death	YEARS TO BIRTH	PATHOLOGIC STAGE	PATHOLOGY T STAGE	PATHOLOGY M STAGE	GENDER	RADIATION THERAPY
	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
GNAQ	40 (50%)	40	0.0429 (0.487)	0.467 (1.00)	0.00158 (0.0774)	0.801 (1.00)	0.0515 (0.487)	1 (1.00)	0.615 (1.00)
SF3B1	18 (22%)	62	0.00713 (0.147)	0.231 (1.00)	0.879 (1.00)	0.881 (1.00)	0.573 (1.00)	0.596 (1.00)	0.545 (1.00)
BAP1	22 (28%)	58	0.00901 (0.147)	0.119 (0.73)	0.434 (1.00)	0.43 (1.00)	0.265 (1.00)	0.458 (1.00)	1 (1.00)
GNA11	36 (45%)	44	0.203 (1.00)	0.749 (1.00)	0.0597 (0.487)	0.837 (1.00)	0.307 (1.00)	0.653 (1.00)	0.0828 (0.579)
EIF1AX	10 (12%)	70	0.255 (1.00)	0.419 (1.00)	1 (1.00)	0.666 (1.00)	1 (1.00)	0.32 (1.00)	1 (1.00)
CYSLTR2	3 (4%)	77	0.676 (1.00)	0.425 (1.00)	0.684 (1.00)	0.78 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
SFRS2	3 (4%)	77	0.832 (1.00)	0.704 (1.00)	1 (1.00)	0.578 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)

'GNAQ MUTATION STATUS' versus 'PATHOLOGIC_STAGE'

P value = 0.00158 (Fisher's exact test), Q value = 0.077

Table S1. Gene #1: 'GNAQ MUTATION STATUS' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	12	27	25	10	1	4
GNAQ MUTATED	8	11	19	2	0	0
GNAQ WILD-TYPE	4	16	6	8	1	4

Figure S1. Get High-res Image Gene #1: 'GNAQ MUTATION STATUS' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'SF3B1 MUTATION STATUS' versus 'Time to Death'

P value = 0.00713 (logrank test), Q value = 0.15

Table S2. Gene #4: 'SF3B1 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	80	23	0.1 - 85.5 (25.8)
SF3B1 MUTATED	18	1	14.9 - 82.2 (38.4)
SF3B1 WILD-TYPE	62	22	0.1 - 85.5 (23.7)

Figure S2. Get High-res Image Gene #4: 'SF3B1 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'BAP1 MUTATION STATUS' versus 'Time to Death'

P value = 0.00901 (logrank test), Q value = 0.15

Table S3. Gene #5: 'BAP1 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	80	23	0.1 - 85.5 (25.8)
BAP1 MUTATED	22	11	1.6 - 61.2 (22.1)
BAP1 WILD-TYPE	58	12	0.1 - 85.5 (26.4)

Figure S3. Get High-res Image Gene #5: 'BAP1 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/UVM-TP/22572045/transformed.cor.cli.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/UVM-TP/22507229/UVM-TP.merged_data.txt
Number of patients = 80
Number of significantly mutated genes = 7
Number of selected clinical features = 7
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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