Correlation between gene mutation status and selected clinical features

Uterine Carcinosarcoma (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/C1NK3D55

Overview

Introduction

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

Summary

Testing the association between mutation status of 13 genes and 3 clinical features across 57 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 13 genes and 3 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	RACE
	nMutated (%)	nWild-Type	logrank test	Wilcoxon-test	Fisher's exact test
TP53	51 (89%)	6	0.31 (0.748)	0.0288 (0.64)	0.219 (0.64)
FBXW7	22 (39%)	35	0.594 (0.773)	0.407 (0.755)	1 (1.00)
PPP2R1A	16 (28%)	41	0.579 (0.773)	0.631 (0.779)	0.534 (0.773)
PTEN	11 (19%)	46	0.442 (0.755)	0.504 (0.773)	0.679 (0.779)
KRAS	7 (12%)	50	0.445 (0.755)	0.576 (0.773)	0.556 (0.773)
ZBTB7B	6 (11%)	51	0.122 (0.64)	0.649 (0.779)	0.218 (0.64)
CHD4	10 (18%)	47	0.437 (0.755)	0.223 (0.64)	0.23 (0.64)
PIK3R1	6 (11%)	51	0.659 (0.779)	0.207 (0.64)	0.217 (0.64)
ARHGAP35	6 (11%)	51	0.584 (0.773)	0.0983 (0.64)	0.0398 (0.64)
PIK3CA	20 (35%)	37	0.188 (0.64)	0.136 (0.64)	0.755 (0.841)
MAMLD1	4 (7%)	53	0.976 (1.00)	0.364 (0.748)	0.306 (0.748)
RB1	6 (11%)	51	0.147 (0.64)	0.355 (0.748)	0.332 (0.748)
LYPLA2	3 (5%)	54	0.988 (1.00)	0.138 (0.64)	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/UCS-TP/15165005/transformed.cor.cli.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/UCS-TP/15096025/UCS-TP.merged_data.txt
Number of patients = 57
Number of significantly mutated genes = 13
Number of selected clinical features = 3
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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