Correlation between gene mutation status and selected clinical features

Uveal Melanoma (Primary solid tumor)

21 August 2015 | analyses__2015_08_21

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/C11J992X

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 7 clinical features across 80 patients, one significant finding detected with Q value < 0.25.

GNAQ mutation correlated to 'PATHOLOGIC_STAGE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 8 genes and 7 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, one significant finding 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	0.161 (1.00)	0.467 (1.00)	0.00166 (0.093)	0.801 (1.00)	0.0515 (0.823)	1 (1.00)	0.615 (1.00)
GNA11	36 (45%)	44	0.423 (1.00)	0.749 (1.00)	0.0588 (0.823)	0.836 (1.00)	0.307 (1.00)	0.653 (1.00)	0.0828 (0.907)
EIF1AX	10 (12%)	70	0.348 (1.00)	0.419 (1.00)	1 (1.00)	0.668 (1.00)	1 (1.00)	0.32 (1.00)	1 (1.00)
SF3B1	18 (22%)	62	0.0972 (0.907)	0.231 (1.00)	0.882 (1.00)	0.878 (1.00)	0.573 (1.00)	0.596 (1.00)	0.545 (1.00)
PRMT8	5 (6%)	75	0.375 (1.00)	0.133 (0.933)	0.115 (0.919)	0.391 (1.00)	1 (1.00)	0.649 (1.00)	0.18 (1.00)
CYSLTR2	3 (4%)	77	0.472 (1.00)	0.425 (1.00)	0.686 (1.00)	0.78 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
BAP1	12 (15%)	68	0.695 (1.00)	0.0556 (0.823)	0.232 (1.00)	0.916 (1.00)	1 (1.00)	1 (1.00)	0.394 (1.00)
SFRS2	3 (4%)	77	0.554 (1.00)	0.704 (1.00)	1 (1.00)	0.576 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)

'GNAQ MUTATION STATUS' versus 'PATHOLOGIC_STAGE'

P value = 0.00166 (Fisher's exact test), Q value = 0.093

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'

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/19894045/transformed.cor.cli.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/UVM-TP/19775655/UVM-TP.merged_data.txt
Number of patients = 80
Number of significantly mutated genes = 8
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)

Made with Nozzle