Kidney Renal Clear Cell Carcinoma: Correlation between gene mutation status and selected clinical features

Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/Harvard Medical School)

Overview

Introduction

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

Summary

Testing the association between mutation status of 11 genes and 8 clinical features across 293 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 11 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	AGE	GENDER	KARNOFSKY PERFORMANCE SCORE	PATHOLOGY T	PATHOLOGY N	PATHOLOGICSPREAD(M)	NEOADJUVANT THERAPY
	nMutated (%)	nWild-Type	logrank test	t-test	Fisher's exact test	t-test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test
CT47B1	3 (1%)	290		0.685 (1.00)	1 (1.00)		1 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
PCDHA10	5 (2%)	288	0.844 (1.00)	0.432 (1.00)	1 (1.00)		0.262 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
WDR46	3 (1%)	290	0.94 (1.00)	0.584 (1.00)	1 (1.00)		0.244 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
PCDHGA1	4 (1%)	289	0.681 (1.00)	0.737 (1.00)	1 (1.00)		0.606 (1.00)	0.0753 (1.00)	0.437 (1.00)	1 (1.00)
USP48	3 (1%)	290	0.674 (1.00)	0.607 (1.00)	1 (1.00)		1 (1.00)	0.145 (1.00)	0.35 (1.00)	1 (1.00)
ABCC10	3 (1%)	290	0.216 (1.00)	0.596 (1.00)	1 (1.00)		0.0917 (1.00)	1 (1.00)	0.0476 (1.00)	1 (1.00)
PCDHGA8	4 (1%)	289	0.251 (1.00)	0.207 (1.00)	1 (1.00)		1 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
KHK	3 (1%)	290	0.0253 (1.00)	0.18 (1.00)	1 (1.00)		0.718 (1.00)	1 (1.00)	0.35 (1.00)	1 (1.00)
PCDHB10	3 (1%)	290	0.389 (1.00)	0.254 (1.00)	0.278 (1.00)		1 (1.00)	1 (1.00)	0.35 (1.00)	1 (1.00)
PCDHA6	4 (1%)	289	0.0349 (1.00)	0.0761 (1.00)	0.612 (1.00)		0.0613 (1.00)	0.211 (1.00)	0.0871 (1.00)	1 (1.00)
PCDHB7	4 (1%)	289	0.24 (1.00)	0.543 (1.00)	1 (1.00)		1 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)

Methods & Data

Input

Mutation data file = KIRC.mutsig.cluster.txt
Clinical data file = KIRC.clin.merged.picked.txt
Number of patients = 293
Number of significantly mutated genes = 11
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

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

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

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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