Correlation between gene mutation status and molecular subtypes

Ovarian Serous Cystadenocarcinoma (Primary solid tumor)

21 April 2013 | analyses__2013_04_21

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): Ovarian Serous Cystadenocarcinoma (Primary solid tumor cohort) - 21 April 2013: Correlation between gene mutation status and molecular subtypes. Broad Institute of MIT and Harvard. doi:10.7908/C1668B5D

Overview

Introduction

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

Summary

Testing the association between mutation status of 8 genes and 12 molecular subtypes across 316 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 8 genes and 12 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	MRNA CNMF	MRNA CHIERARCHICAL	MIR CNMF	MIR CHIERARCHICAL	CN CNMF	METHLYATION CNMF	RPPA CNMF	RPPA CHIERARCHICAL	MRNASEQ CNMF	MRNASEQ CHIERARCHICAL	MIRSEQ CNMF	MIRSEQ CHIERARCHICAL
	nMutated (%)	nWild-Type	Fisher's exact test	Fisher's exact test	Fisher's exact test	Chi-square 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	Fisher's exact test
TP53	276 (87%)	40	0.667 (1.00)	0.366 (1.00)	0.502 (1.00)	0.367 (1.00)	0.716 (1.00)	0.702 (1.00)	0.411 (1.00)	0.313 (1.00)	0.779 (1.00)	0.898 (1.00)	0.326 (1.00)	0.186 (1.00)
SRC	4 (1%)	312	0.836 (1.00)	0.683 (1.00)	0.566 (1.00)	0.16 (1.00)	0.127 (1.00)	0.462 (1.00)					0.483 (1.00)	0.325 (1.00)
TBP	4 (1%)	312	1 (1.00)	1 (1.00)	0.692 (1.00)	0.596 (1.00)	0.0357 (1.00)	0.0184 (1.00)	0.131 (1.00)	0.872 (1.00)	1 (1.00)	1 (1.00)	0.689 (1.00)	1 (1.00)
RB1	9 (3%)	307	0.758 (1.00)	0.118 (1.00)	0.402 (1.00)	0.712 (1.00)	0.776 (1.00)	0.199 (1.00)	0.892 (1.00)	0.818 (1.00)			0.358 (1.00)	0.557 (1.00)
GABRA6	6 (2%)	310	0.882 (1.00)	0.77 (1.00)	1 (1.00)	0.72 (1.00)	0.29 (1.00)	0.212 (1.00)	0.398 (1.00)	1 (1.00)	0.791 (1.00)	0.78 (1.00)	1 (1.00)	1 (1.00)
BRCA1	12 (4%)	304	0.173 (1.00)	0.814 (1.00)	0.256 (1.00)	0.172 (1.00)	0.506 (1.00)	0.298 (1.00)	0.488 (1.00)	0.184 (1.00)	1 (1.00)	0.893 (1.00)	0.718 (1.00)	0.784 (1.00)
CSMD3	18 (6%)	298	0.341 (1.00)	0.712 (1.00)	0.24 (1.00)	0.296 (1.00)	0.516 (1.00)	0.0567 (1.00)	0.0457 (1.00)	0.616 (1.00)	0.786 (1.00)	0.704 (1.00)	0.183 (1.00)	0.849 (1.00)
NF1	14 (4%)	302	0.425 (1.00)	0.45 (1.00)	0.836 (1.00)	0.468 (1.00)	0.7 (1.00)	0.188 (1.00)	0.112 (1.00)	0.0351 (1.00)	0.434 (1.00)	0.145 (1.00)	0.736 (1.00)	1 (1.00)

Methods & Data

Input

Mutation data file = OV-TP.mutsig.cluster.txt
Molecular subtypes file = OV-TP.transferedmergedcluster.txt
Number of patients = 316
Number of significantly mutated genes = 8
Number of Molecular subtypes = 12
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

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.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] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)

[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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