Correlation between gene mutation status and molecular subtypes

Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)

Due to issues with GAF 3.0, we strongly advise caution in the use of these results.

23 September 2013 | analyses__2013_09_23

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/C1057D80

Overview

Introduction

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

Summary

Testing the association between mutation status of 4 genes and 8 molecular subtypes across 112 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 4 genes and 8 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	MIRSEQ MATURE CNMF	MIRSEQ MATURE 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	Fisher's exact test	Fisher's exact test
CDC27	4 (4%)	108	0.517 (1.00)	0.555 (1.00)	1 (1.00)	1 (1.00)	0.469 (1.00)	0.355 (1.00)	1 (1.00)	1 (1.00)
IL32	4 (4%)	108	1 (1.00)		0.299 (1.00)	0.589 (1.00)	0.311 (1.00)	0.83 (1.00)
NF2	7 (6%)	105	0.0123 (0.307)	0.0669 (1.00)	0.234 (1.00)	0.261 (1.00)	0.53 (1.00)	0.787 (1.00)	1 (1.00)	1 (1.00)
PPARGC1B	3 (3%)	109	0.0703 (1.00)		0.197 (1.00)	0.304 (1.00)	0.38 (1.00)	0.0114 (0.297)

Methods & Data

Input

Mutation data file = KIRP-TP.mutsig.cluster.txt
Molecular subtypes file = KIRP-TP.transferedmergedcluster.txt
Number of patients = 112
Number of significantly mutated genes = 4
Number of Molecular subtypes = 8
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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