Correlation between gene mutation status and selected clinical features

Pheochromocytoma and Paraganglioma (Primary solid tumor)

15 July 2014 | analyses__2014_07_15

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/C1PN94D8

Overview

Introduction

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

Summary

Testing the association between mutation status of 4 genes and 3 clinical features across 61 patients, one significant finding detected with Q value < 0.25.

RET mutation correlated to 'RACE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 4 genes and 3 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, one significant finding detected.


		Clinical Features	AGE	GENDER	RACE
	nMutated (%)	nWild-Type	Wilcoxon-test	Fisher's exact test	Fisher's exact test
RET	4 (7%)	57	0.726 (1.00)	1 (1.00)	0.00801 (0.0961)
HRAS	5 (8%)	56	0.572 (1.00)	0.154 (1.00)	1 (1.00)
EPAS1	4 (7%)	57	0.0778 (0.855)	0.602 (1.00)	0.307 (1.00)
NF1	7 (11%)	54	0.138 (1.00)	1 (1.00)	0.747 (1.00)

'RET MUTATION STATUS' versus 'RACE'

P value = 0.00801 (Fisher's exact test), Q value = 0.096

Table S1. Gene #4: 'RET MUTATION STATUS' versus Clinical Feature #3: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	3	7	48
RET MUTATED	1	1	1	1
RET WILD-TYPE	0	2	6	47

Figure S1. Get High-res Image Gene #4: 'RET MUTATION STATUS' versus Clinical Feature #3: 'RACE'

Methods & Data

Input

Mutation data file = transformed.cor.cli.txt
Clinical data file = PCPG-TP.merged_data.txt
Number of patients = 61
Number of significantly mutated genes = 4
Number of selected clinical features = 3
Exclude genes that fewer than K tumors have mutations, K = 3

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] 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)

[2] 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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