Correlation between gene mutation status and selected clinical features
Pheochromocytoma and Paraganglioma (Primary solid tumor)
17 October 2014 | analyses__2014_10_17
Maintainer Information
Citation Information
doi:10.7908/C1M32TPB
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between gene mutation status and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1M32TPB
Overview
Introduction

This pipeline computes the correlation between significantly recurrent gene mutations and selected clinical features.

Summary

Testing the association between mutation status of 6 genes and 5 clinical features across 132 patients, one significant finding detected with Q value < 0.25.

- RET mutation correlated to 'RACE'.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 6 genes and 5 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 | RACE | ETHNICITY
| nMutated (%) | nWild-Type | logrank test | Wilcoxon-test | Fisher's exact test | Fisher's exact test | Fisher's exact test
RET | 6 (5%) | 126 | 0.694 (1.00) | 0.698 (1.00) | 0.698 (1.00) | 0.00607 (0.182) | 1 (1.00)
HRAS | 14 (11%) | 118 | 0.491 (1.00) | 0.258 (1.00) | 0.776 (1.00) | 0.211 (1.00) | 1 (1.00)
EPAS1 | 7 (5%) | 125 | 0.659 (1.00) | 0.685 (1.00) | 0.698 (1.00) | 0.215 (1.00) | 1 (1.00)
NF1 | 13 (10%) | 119 | 0.574 (1.00) | 0.0337 (0.977) | 1 (1.00) | 0.787 (1.00) | 1 (1.00)
CSDE1 | 4 (3%) | 128 | 0.699 (1.00) | 0.222 (1.00) | 0.315 (1.00) | 1 (1.00) | 1 (1.00)
AMMECR1 | 3 (2%) | 129 | 0.812 (1.00) | 0.376 (1.00) | 1 (1.00) | 0.312 (1.00) | 1 (1.00)

'RET MUTATION STATUS' versus 'RACE'

P value = 0.00607 (Fisher's exact test), Q value = 0.18

Table S1. Gene #4: 'RET MUTATION STATUS' versus Clinical Feature #4: 'RACE'

nPatients | AMERICAN INDIAN OR ALASKA NATIVE | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE
--- | --- | --- | --- | ---
ALL | 1 | 5 | 9 | 114
RET MUTATED | 1 | 1 | 1 | 3
RET WILD-TYPE | 0 | 4 | 8 | 111

Figure S1. Get High-res Image Gene #4: 'RET MUTATION STATUS' versus Clinical Feature #4: 'RACE'

Methods & Data
Input

- Mutation data file = transformed.cor.cli.txt

- Clinical data file = PCPG-TP.merged_data.txt

- Number of patients = 132

- Number of significantly mutated genes = 6

- Number of selected clinical features = 5

- 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

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

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

- 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] 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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