Correlation between gene mutation status and selected clinical features

Overview

Introduction

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

Summary

Testing the association between mutation status of 6 genes and 5 clinical features across 132 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 6 genes and 5 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, one significant finding detected.


		Clinical Features	Time to Death	AGE	GENDER	RACE	ETHNICITY
	nMutated (%)	nWild-Type	logrank test	Wilcoxon-test	Fisher's exact test	Fisher's exact test	Fisher's exact test
RET	6 (5%)	126	0.694 (1.00)	0.698 (1.00)	0.698 (1.00)	0.00607 (0.182)	1 (1.00)
HRAS	14 (11%)	118	0.491 (1.00)	0.258 (1.00)	0.776 (1.00)	0.211 (1.00)	1 (1.00)
EPAS1	7 (5%)	125	0.659 (1.00)	0.685 (1.00)	0.698 (1.00)	0.215 (1.00)	1 (1.00)
NF1	13 (10%)	119	0.574 (1.00)	0.0337 (0.977)	1 (1.00)	0.787 (1.00)	1 (1.00)
CSDE1	4 (3%)	128	0.699 (1.00)	0.222 (1.00)	0.315 (1.00)	1 (1.00)	1 (1.00)
AMMECR1	3 (2%)	129	0.812 (1.00)	0.376 (1.00)	1 (1.00)	0.312 (1.00)	1 (1.00)

'RET MUTATION STATUS' versus 'RACE'

P value = 0.00607 (Fisher's exact test), Q value = 0.18

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

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	5	9	114
RET MUTATED	1	1	1	3
RET WILD-TYPE	0	4	8	111

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

Methods & Data

Input

Mutation data file = transformed.cor.cli.txt
Clinical data file = PCPG-TP.merged_data.txt
Number of patients = 132
Number of significantly mutated genes = 6
Number of selected clinical features = 5
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.

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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