Correlation between gene mutation status and selected clinical features

Kidney Chromophobe (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/C1S46RBF

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 12 clinical features across 66 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 8 genes and 12 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	PATHOLOGIC STAGE	PATHOLOGY T STAGE	PATHOLOGY N STAGE	PATHOLOGY M STAGE	GENDER	KARNOFSKY PERFORMANCE SCORE	NUMBER PACK YEARS SMOKED	YEAR OF TOBACCO SMOKING ONSET	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	Wilcoxon-test	Wilcoxon-test	Fisher's exact test	Fisher's exact test
TP53	22 (33%)	44	0.0218 (0.398)	0.935 (1.00)	0.307 (1.00)	0.368 (1.00)	0.036 (0.398)	0.524 (1.00)	1 (1.00)	1 (1.00)	0.394 (1.00)	0.885 (1.00)	0.194 (0.886)	1 (1.00)
PTEN	6 (9%)	60	0.00945 (0.326)	0.422 (1.00)	0.0497 (0.398)	0.0308 (0.398)	0.0874 (0.531)	1 (1.00)	0.217 (0.886)				0.215 (0.886)	0.213 (0.886)
ZNF814	3 (5%)	63	0.488 (1.00)	0.414 (1.00)	0.589 (1.00)	0.634 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)				1 (1.00)	1 (1.00)
CDC27	6 (9%)	60	0.945 (1.00)	0.0742 (0.531)	0.0405 (0.398)	0.0431 (0.398)	1 (1.00)	1 (1.00)	0.682 (1.00)	1 (1.00)			1 (1.00)	1 (1.00)
PABPC1	7 (11%)	59	0.0116 (0.326)	0.252 (0.928)	0.00426 (0.326)	0.0461 (0.398)	0.0874 (0.531)	1 (1.00)	0.226 (0.886)	1 (1.00)			0.0136 (0.326)	1 (1.00)
AMAC1L3	5 (8%)	61	0.418 (1.00)	0.799 (1.00)	0.902 (1.00)	0.615 (1.00)		1 (1.00)	0.641 (1.00)	0.231 (0.886)			0.331 (1.00)	1 (1.00)
GFM1	3 (5%)	63	0.0886 (0.531)	0.261 (0.928)	0.136 (0.724)	0.193 (0.886)		0.11 (0.618)	1 (1.00)				1 (1.00)	1 (1.00)
PABPC3	7 (11%)	59	0.658 (1.00)	0.0327 (0.398)	0.656 (1.00)	0.616 (1.00)	1 (1.00)	1 (1.00)	0.691 (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/KICH-TP/22569433/transformed.cor.cli.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/KICH-TP/22506450/KICH-TP.merged_data.txt
Number of patients = 66
Number of significantly mutated genes = 8
Number of selected clinical features = 12
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