Correlation between gene mutation status and molecular subtypes
Prostate Adenocarcinoma (Primary solid tumor)
22 February 2013 | analyses__2013_02_22
Maintainer Information
Citation Information
doi:10.7908/C12B8W72
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.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

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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