Correlation between gene mutation status and molecular subtypes

Prostate Adenocarcinoma (Primary solid tumor)

22 February 2013 | analyses__2013_02_22

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 (2013): Correlation between gene mutation status and molecular subtypes. Broad Institute of MIT and Harvard. doi:10.7908/C12B8W72

Overview

Introduction

This pipeline computes the correlation between significantly recurrent gene mutations and molecular subtypes.

Summary

Testing the association between mutation status of 13 genes and 6 molecular subtypes across 83 patients, one significant finding detected with P value < 0.05 and Q value < 0.25.

SPOP mutation correlated to 'CN_CNMF'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 13 genes and 6 molecular subtypes. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, one significant finding detected.


		Clinical Features	CN CNMF	METHLYATION CNMF	MRNASEQ CNMF	MRNASEQ CHIERARCHICAL	MIRSEQ CNMF	MIRSEQ CHIERARCHICAL
	nMutated (%)	nWild-Type	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test
SPOP	4 (5%)	79	0.00264 (0.195)	0.0499 (1.00)	0.1 (1.00)	0.226 (1.00)	0.565 (1.00)	1 (1.00)
NKX3-1	5 (6%)	78	0.351 (1.00)	0.248 (1.00)	0.546 (1.00)	0.166 (1.00)	0.118 (1.00)	0.14 (1.00)
TP53	5 (6%)	78	0.00525 (0.383)	0.248 (1.00)	0.376 (1.00)	0.121 (1.00)	0.494 (1.00)	1 (1.00)
FRG1	4 (5%)	79	0.262 (1.00)	0.2 (1.00)	0.494 (1.00)	0.781 (1.00)	0.565 (1.00)	1 (1.00)
YBX1	3 (4%)	80	0.709 (1.00)	0.0945 (1.00)	0.277 (1.00)	0.252 (1.00)	1 (1.00)	0.594 (1.00)
CCNF	3 (4%)	80	1 (1.00)	0.791 (1.00)	0.773 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
CLSTN1	3 (4%)	80	1 (1.00)	0.457 (1.00)	0.494 (1.00)	0.781 (1.00)	0.707 (1.00)	0.325 (1.00)
PRR21	4 (5%)	79	0.709 (1.00)	0.353 (1.00)	0.494 (1.00)	0.178 (1.00)	0.194 (1.00)	1 (1.00)
AGT	3 (4%)	80	0.709 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	0.16 (1.00)
CTNNB1	3 (4%)	80	1 (1.00)	0.602 (1.00)	0.773 (1.00)	0.343 (1.00)	0.322 (1.00)	0.325 (1.00)
DUSP27	3 (4%)	80	0.544 (1.00)	0.457 (1.00)			0.707 (1.00)	0.325 (1.00)
OR4D5	3 (4%)	80	0.239 (1.00)	1 (1.00)			0.707 (1.00)	1 (1.00)
OR6N1	3 (4%)	80	0.384 (1.00)	1 (1.00)	1 (1.00)	0.465 (1.00)	0.0778 (1.00)	0.594 (1.00)

'SPOP MUTATION STATUS' versus 'CN_CNMF'

P value = 0.00264 (Fisher's exact test), Q value = 0.2

Table S1. Gene #4: 'SPOP MUTATION STATUS' versus Clinical Feature #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	11	45	26
SPOP MUTATED	3	0	1
SPOP WILD-TYPE	8	45	25

Figure S1. Get High-res Image Gene #4: 'SPOP MUTATION STATUS' versus Clinical Feature #1: 'CN_CNMF'

Methods & Data

Input

Mutation data file = PRAD-TP.mutsig.cluster.txt
Molecular subtypes file = PRAD-TP.transferedmergedcluster.txt
Number of patients = 83
Number of significantly mutated genes = 13
Number of Molecular subtypes = 6
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

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] 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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