Correlation between gene mutation status and selected clinical features

Ovarian Serous Cystadenocarcinoma (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/C1736PZC

Overview

Introduction

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

Summary

Testing the association between mutation status of 23 genes and 8 clinical features across 465 patients, no significant finding detected with Q value < 0.25.

No gene mutations related to clinical features.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 23 genes and 8 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, no significant finding detected.


		Clinical Features	Time to Death	YEARS TO BIRTH	PRIMARY SITE OF DISEASE	KARNOFSKY PERFORMANCE SCORE	RADIATIONS RADIATION REGIMENINDICATION	COMPLETENESS OF RESECTION	RACE	ETHNICITY
	nMutated (%)	nWild-Type	logrank test	Wilcoxon-test	Fisher's exact test	Wilcoxon-test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test
TP53	382 (82%)	83	0.286 (1.00)	0.535 (1.00)	0.446 (1.00)	0.908 (1.00)	0.446 (1.00)	0.283 (1.00)	0.12 (1.00)	0.0615 (1.00)
BRCA1	19 (4%)	446	0.741 (1.00)	0.704 (1.00)	1 (1.00)		1 (1.00)		0.785 (1.00)	1 (1.00)
NF1	24 (5%)	441	0.389 (1.00)	0.219 (1.00)	1 (1.00)		0.147 (1.00)	1 (1.00)	1 (1.00)	0.396 (1.00)
RB1	15 (3%)	450	0.273 (1.00)	0.238 (1.00)	1 (1.00)		1 (1.00)		0.272 (1.00)	1 (1.00)
BRCA2	13 (3%)	452	0.00699 (0.812)	0.588 (1.00)	1 (1.00)		1 (1.00)		0.713 (1.00)	1 (1.00)
IL21R	8 (2%)	457	0.134 (1.00)	0.815 (1.00)	1 (1.00)		1 (1.00)		0.266 (1.00)	1 (1.00)
KRAS	5 (1%)	460	0.706 (1.00)	0.576 (1.00)	1 (1.00)		1 (1.00)		1 (1.00)	1 (1.00)
YSK4	10 (2%)	455	0.629 (1.00)	0.302 (1.00)	1 (1.00)		1 (1.00)		1 (1.00)	0.00882 (0.812)
ANKRD35	9 (2%)	456	0.518 (1.00)	0.984 (1.00)	1 (1.00)		1 (1.00)		0.384 (1.00)	1 (1.00)
C9ORF171	5 (1%)	460	0.78 (1.00)	0.0961 (1.00)	1 (1.00)	0.705 (1.00)	1 (1.00)		1 (1.00)	1 (1.00)
MTA2	4 (1%)	461	0.0758 (1.00)	0.126 (1.00)	1 (1.00)		1 (1.00)		1 (1.00)	1 (1.00)
CYP11B1	8 (2%)	457	0.923 (1.00)	0.263 (1.00)	1 (1.00)		0.0508 (1.00)		0.535 (1.00)	1 (1.00)
NRAS	4 (1%)	461	0.941 (1.00)	0.124 (1.00)	1 (1.00)		1 (1.00)		0.181 (1.00)	1 (1.00)
ACBD4	3 (1%)	462	0.846 (1.00)	0.483 (1.00)	1 (1.00)		1 (1.00)		1 (1.00)	1 (1.00)
EFEMP1	7 (2%)	458	0.462 (1.00)	0.0712 (1.00)	1 (1.00)		1 (1.00)		0.306 (1.00)	1 (1.00)
PODN	6 (1%)	459	0.0722 (1.00)	0.0671 (1.00)	1 (1.00)		1 (1.00)		1 (1.00)	0.124 (1.00)
TOP2A	8 (2%)	457	0.949 (1.00)	0.211 (1.00)	1 (1.00)		1 (1.00)		1 (1.00)	0.18 (1.00)
NCOA3	5 (1%)	460	0.0175 (1.00)	0.628 (1.00)	1 (1.00)		1 (1.00)		1 (1.00)	1 (1.00)
TP53TG5	3 (1%)	462	0.0417 (1.00)	0.436 (1.00)	1 (1.00)		1 (1.00)		1 (1.00)
RPGRIP1	7 (2%)	458	0.345 (1.00)	0.657 (1.00)	1 (1.00)		1 (1.00)		1 (1.00)	1 (1.00)
PTEN	5 (1%)	460	0.662 (1.00)	0.916 (1.00)	1 (1.00)		1 (1.00)		0.375 (1.00)	1 (1.00)
RB1CC1	9 (2%)	456	0.348 (1.00)	0.178 (1.00)	1 (1.00)		1 (1.00)		0.577 (1.00)	1 (1.00)
LPAR3	4 (1%)	461	0.172 (1.00)	0.17 (1.00)	1 (1.00)		1 (1.00)		1 (1.00)	1 (1.00)

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/OV-TP/15650402/transformed.cor.cli.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/OV-TP/15085780/OV-TP.merged_data.txt
Number of patients = 465
Number of significantly mutated genes = 23
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)

Made with Nozzle