Bladder Urothelial Carcinoma: 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 3 genes and 8 molecular subtypes across 28 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 3 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 | RPPA CNMF | RPPA CHIERARCHICAL | MRNASEQ CNMF | MRNASEQ CHIERARCHICAL | MIRSEQ CNMF | MIRSEQ CHIERARCHICAL
| nMutated (%) | nWild-Type | 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
TP53 | 11 (39%) | 17 | 0.0685 (1.00) | 0.179 (1.00) | 0.553 (1.00) | 0.143 (1.00) | 0.0438 (0.92) | 0.743 (1.00) | 0.873 (1.00) | 1 (1.00)
FBXW7 | 5 (18%) | 23 | 0.025 (0.579) | 0.0241 (0.579) | 0.651 (1.00) | 0.834 (1.00) | 0.123 (1.00) | 0.0372 (0.817) | 1 (1.00) | 1 (1.00)
NFE2L2 | 4 (14%) | 24 | 0.267 (1.00) | 0.435 (1.00) | 0.212 (1.00) | 1 (1.00) | 0.791 (1.00) | 0.36 (1.00) | 0.659 (1.00) | 1 (1.00)

Methods & Data
Input

- Mutation data file = BLCA-TP.mutsig.cluster.txt

- Molecular subtypes file = BLCA-TP.transferedmergedcluster.txt

- Number of patients = 28

- Number of significantly mutated genes = 3

- Number of Molecular subtypes = 8

- Exclude genes that fewer than K tumors have mutations, K = 3

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

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] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[2] 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)
[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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