Bladder Urothelial Carcinoma: Correlation between gene mutation status and selected clinical features
(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 selected clinical features.

Summary

Testing the association between mutation status of 7 genes and 6 clinical features across 28 patients, one significant finding detected with Q value < 0.25.

- KDM6A mutation correlated to 'STOPPEDSMOKINGYEAR'.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 7 genes and 6 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, one significant finding detected.

|  | Clinical Features | Time to Death | AGE | GENDER | NUMBERPACKYEARSSMOKED | STOPPEDSMOKINGYEAR | TOBACCOSMOKINGHISTORYINDICATOR
| nMutated (%) | nWild-Type | logrank test | t-test | Fisher's exact test | t-test | t-test | t-test
KDM6A | 6 (21%) | 22 | 0.675 (1.00) | 0.503 (1.00) | 0.634 (1.00) |  | 0.00162 (0.0535) | 0.428 (1.00)
TP53 | 11 (39%) | 17 | 0.825 (1.00) | 0.529 (1.00) | 1 (1.00) | 0.543 (1.00) | 0.395 (1.00) | 0.78 (1.00)
FBXW7 | 5 (18%) | 23 | 0.894 (1.00) | 0.233 (1.00) | 1 (1.00) | 0.324 (1.00) |  | 0.31 (1.00)
GPS2 | 3 (11%) | 25 | 0.803 (1.00) | 0.115 (1.00) | 1 (1.00) |  |  | 0.671 (1.00)
NFE2L2 | 4 (14%) | 24 | 0.647 (1.00) | 0.839 (1.00) | 0.0103 (0.328) |  |  | 0.112 (1.00)
HCN1 | 4 (14%) | 24 | 0.295 (1.00) | 0.637 (1.00) | 0.601 (1.00) | 0.0795 (1.00) |  | 0.815 (1.00)
ARID1A | 6 (21%) | 22 | 0.534 (1.00) | 0.788 (1.00) | 1 (1.00) |  |  | 0.557 (1.00)

'KDM6A MUTATION STATUS' versus 'STOPPEDSMOKINGYEAR'

P value = 0.00162 (t-test), Q value = 0.054

Table S1. Gene #2: 'KDM6A MUTATION STATUS' versus Clinical Feature #5: 'STOPPEDSMOKINGYEAR'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 9 | 1974.0 (17.2)
KDM6A MUTATED | 4 | 1990.2 (3.9)
KDM6A WILD-TYPE | 5 | 1961.0 (10.3)

Figure S1. Get High-res Image Gene #2: 'KDM6A MUTATION STATUS' versus Clinical Feature #5: 'STOPPEDSMOKINGYEAR'

Methods & Data
Input

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

- Clinical data file = BLCA-TP.clin.merged.picked.txt

- Number of patients = 28

- Number of significantly mutated genes = 7

- Number of selected clinical features = 6

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

Survival analysis

For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R

Student's t-test analysis

For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between tumors with and without gene mutations using 't.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] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[2] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[3] 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)
[4] 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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