Correlation between gene mutation status and molecular subtypes

Rectum Adenocarcinoma (Primary solid tumor)

23 May 2013 | analyses__2013_05_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/C1VH5KWK

Overview

Introduction

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

Summary

Testing the association between mutation status of 3 genes and 7 molecular subtypes across 69 patients, 4 significant findings detected with P value < 0.05 and Q value < 0.25.

KRAS mutation correlated to 'MRNA_CHIERARCHICAL' and 'CN_CNMF'.
TP53 mutation correlated to 'MRNA_CNMF' and 'MRNA_CHIERARCHICAL'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 3 genes and 7 molecular subtypes. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 4 significant findings detected.


		Clinical Features	MRNA CNMF	MRNA CHIERARCHICAL	CN CNMF	RPPA CNMF	RPPA CHIERARCHICAL	MIRSEQ CNMF	MIRSEQ CHIERARCHICAL
	nMutated (%)	nWild-Type	Fisher's exact test	Fisher's exact test	Chi-square test	Fisher's exact test	Chi-square test	Fisher's exact test	Fisher's exact test
KRAS	38 (55%)	31	0.249 (1.00)	0.00641 (0.115)	0.00233 (0.0467)	0.83 (1.00)	0.817 (1.00)	0.292 (1.00)	0.0465 (0.79)
TP53	45 (65%)	24	0.000428 (0.00899)	0.00307 (0.0584)	0.21 (1.00)	0.16 (1.00)	0.603 (1.00)	0.812 (1.00)	0.288 (1.00)
APC	57 (83%)	12	0.568 (1.00)	0.849 (1.00)	0.0561 (0.897)	0.362 (1.00)	0.598 (1.00)	0.337 (1.00)	1 (1.00)

'KRAS MUTATION STATUS' versus 'MRNA_CHIERARCHICAL'

P value = 0.00641 (Fisher's exact test), Q value = 0.12

Table S1. Gene #2: 'KRAS MUTATION STATUS' versus Clinical Feature #2: 'MRNA_CHIERARCHICAL'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	23	26	15
KRAS MUTATED	16	8	11
KRAS WILD-TYPE	7	18	4

Figure S1. Get High-res Image Gene #2: 'KRAS MUTATION STATUS' versus Clinical Feature #2: 'MRNA_CHIERARCHICAL'

'KRAS MUTATION STATUS' versus 'CN_CNMF'

P value = 0.00233 (Chi-square test), Q value = 0.047

Table S2. Gene #2: 'KRAS MUTATION STATUS' versus Clinical Feature #3: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3	CLUS_4	CLUS_5
ALL	1	15	17	25	10
KRAS MUTATED	1	3	7	19	8
KRAS WILD-TYPE	0	12	10	6	2

Figure S2. Get High-res Image Gene #2: 'KRAS MUTATION STATUS' versus Clinical Feature #3: 'CN_CNMF'

'TP53 MUTATION STATUS' versus 'MRNA_CNMF'

P value = 0.000428 (Fisher's exact test), Q value = 0.009

Table S3. Gene #3: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'MRNA_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	24	21	19
TP53 MUTATED	8	15	17
TP53 WILD-TYPE	16	6	2

Figure S3. Get High-res Image Gene #3: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'MRNA_CNMF'

'TP53 MUTATION STATUS' versus 'MRNA_CHIERARCHICAL'

P value = 0.00307 (Fisher's exact test), Q value = 0.058

Table S4. Gene #3: 'TP53 MUTATION STATUS' versus Clinical Feature #2: 'MRNA_CHIERARCHICAL'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	23	26	15
TP53 MUTATED	8	20	12
TP53 WILD-TYPE	15	6	3

Figure S4. Get High-res Image Gene #3: 'TP53 MUTATION STATUS' versus Clinical Feature #2: 'MRNA_CHIERARCHICAL'

Methods & Data

Input

Mutation data file = READ-TP.mutsig.cluster.txt
Molecular subtypes file = READ-TP.transferedmergedcluster.txt
Number of patients = 69
Number of significantly mutated genes = 3
Number of Molecular subtypes = 7
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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