Prostate Adenocarcinoma: Correlation between gene mutation status and molecular subtypes<BR>(primary solid tumor cohort)

Prostate Adenocarcinoma: Correlation between gene mutation status and molecular subtypes
(primary solid tumor cohort)

Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)

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, no significant finding detected with P value < 0.05 and Q value < 0.25.

No gene mutations related to molecuar subtypes.

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, no 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
NKX3-1	5 (6%)	78	0.177 (1.00)	0.366 (1.00)	0.845 (1.00)	0.583 (1.00)	0.118 (1.00)	0.14 (1.00)
TP53	5 (6%)	78	0.00583 (0.455)	0.366 (1.00)	0.21 (1.00)	0.117 (1.00)	0.494 (1.00)	1 (1.00)
FRG1	4 (5%)	79	0.255 (1.00)	0.57 (1.00)	0.363 (1.00)	0.668 (1.00)	0.565 (1.00)	1 (1.00)
SPOP	4 (5%)	79	0.0148 (1.00)	0.0695 (1.00)	0.0596 (1.00)	0.00937 (0.721)	0.565 (1.00)	1 (1.00)
YBX1	3 (4%)	80	0.701 (1.00)	0.225 (1.00)	1 (1.00)	0.295 (1.00)	1 (1.00)	0.594 (1.00)
CCNF	3 (4%)	80	1 (1.00)	0.796 (1.00)	0.471 (1.00)	0.436 (1.00)	1 (1.00)	1 (1.00)
CLSTN1	3 (4%)	80	1 (1.00)	0.441 (1.00)	0.618 (1.00)	1 (1.00)	0.707 (1.00)	0.325 (1.00)
PRR21	4 (5%)	79	0.701 (1.00)	0.459 (1.00)	1 (1.00)	1 (1.00)	0.194 (1.00)	1 (1.00)
AGT	3 (4%)	80	0.701 (1.00)	1 (1.00)	1 (1.00)	0.771 (1.00)	1 (1.00)	0.16 (1.00)
CTNNB1	3 (4%)	80	1 (1.00)	0.603 (1.00)	0.471 (1.00)	0.436 (1.00)	0.322 (1.00)	0.325 (1.00)
DUSP27	3 (4%)	80	0.513 (1.00)	0.441 (1.00)	0.618 (1.00)	0.583 (1.00)	0.707 (1.00)	0.325 (1.00)
OR4D5	3 (4%)	80	0.224 (1.00)	1 (1.00)	1 (1.00)	0.771 (1.00)	0.707 (1.00)	1 (1.00)
OR6N1	3 (4%)	80	0.364 (1.00)	1 (1.00)	1 (1.00)	0.771 (1.00)	0.0778 (1.00)	0.594 (1.00)

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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