Correlation between gene mutation status and selected clinical features

Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)

16 April 2014 | analyses__2014_04_16

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 (2014): Correlation between gene mutation status and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1RB737C

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 10 clinical features across 155 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 7 genes and 10 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	AGE	NEOPLASM DISEASESTAGE	PATHOLOGY T STAGE	PATHOLOGY N STAGE	PATHOLOGY M STAGE	GENDER	KARNOFSKY PERFORMANCE SCORE	NUMBERPACKYEARSSMOKED	YEAROFTOBACCOSMOKINGONSET
	nMutated (%)	nWild-Type	logrank test	t-test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	t-test	t-test	t-test
SKI	6 (4%)	149	0.657 (1.00)	0.714 (1.00)	0.739 (1.00)	0.678 (1.00)		1 (1.00)	0.668 (1.00)
NEFH	9 (6%)	146	0.397 (1.00)	0.427 (1.00)	0.262 (1.00)	0.0671 (1.00)		0.196 (1.00)	1 (1.00)
HNRNPM	11 (7%)	144	0.192 (1.00)	0.307 (1.00)	0.853 (1.00)	0.718 (1.00)		0.838 (1.00)	0.506 (1.00)	0.289 (1.00)
NF2	11 (7%)	144	0.174 (1.00)	0.332 (1.00)	0.04 (1.00)	0.0984 (1.00)	0.0166 (0.813)	0.108 (1.00)	0.31 (1.00)
ZNF598	12 (8%)	143	0.336 (1.00)	0.302 (1.00)	0.358 (1.00)	0.488 (1.00)	0.598 (1.00)	0.126 (1.00)	0.755 (1.00)	0.604 (1.00)
BMS1	13 (8%)	142	0.129 (1.00)	0.211 (1.00)	0.853 (1.00)	0.0902 (1.00)		0.198 (1.00)	0.755 (1.00)	0.504 (1.00)
MUC2	25 (16%)	130	0.0641 (1.00)	0.144 (1.00)	0.182 (1.00)	0.823 (1.00)	0.361 (1.00)	0.131 (1.00)	0.635 (1.00)	0.025 (1.00)

Methods & Data

Input

Mutation data file = transformed.cor.cli.txt
Clinical data file = KIRP-TP.merged_data.txt
Number of patients = 155
Number of significantly mutated genes = 7
Number of selected clinical features = 10
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

Student's t-test analysis

For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between tumors with and without gene mutations using 't.test' 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] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)

[3] 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)

[4] 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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