Correlation between gene mutation status and selected clinical features
Pheochromocytoma and Paraganglioma (Primary solid tumor)
02 April 2015 | analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1G44PDZ
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between gene mutation status and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1G44PDZ
Overview
Introduction

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

Summary

Testing the association between mutation status of 9 genes and 5 clinical features across 178 patients, 2 significant findings detected with Q value < 0.25.

- NF1 mutation correlated to 'YEARS_TO_BIRTH'.

- RET mutation correlated to 'RACE'.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 9 genes and 5 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 2 significant findings detected.

|  | Clinical Features | Time to Death | YEARS TO BIRTH | GENDER | RACE | ETHNICITY
| nMutated (%) | nWild-Type | logrank test | Wilcoxon-test | Fisher's exact test | Fisher's exact test | Fisher's exact test
NF1 | 15 (8%) | 163 | 0.592 (1.00) | 0.00156 (0.0701) | 1 (1.00) | 0.383 (1.00) | 0.283 (1.00)
RET | 6 (3%) | 172 | 0.701 (1.00) | 0.441 (1.00) | 0.7 (1.00) | 0.00591 (0.133) | 1 (1.00)
HRAS | 18 (10%) | 160 | 0.452 (1.00) | 0.157 (1.00) | 0.804 (1.00) | 0.0984 (1.00) | 1 (1.00)
EPAS1 | 8 (4%) | 170 | 0.482 (1.00) | 0.966 (1.00) | 0.469 (1.00) | 0.153 (1.00) | 1 (1.00)
CSDE1 | 4 (2%) | 174 | 0.714 (1.00) | 0.136 (1.00) | 0.317 (1.00) | 1 (1.00) | 1 (1.00)
GPR128 | 4 (2%) | 174 | 0.716 (1.00) | 0.891 (1.00) | 0.635 (1.00) | 0.494 (1.00) | 1 (1.00)
AMMECR1 | 3 (2%) | 175 | 0.799 (1.00) | 0.296 (1.00) | 1 (1.00) | 0.401 (1.00) | 1 (1.00)
SHROOM4 | 3 (2%) | 175 | 0.749 (1.00) | 0.233 (1.00) | 0.579 (1.00) | 1 (1.00) | 0.0694 (1.00)
FAM83D | 3 (2%) | 175 | 0.871 (1.00) | 0.786 (1.00) | 0.579 (1.00) |  | 1 (1.00)

'NF1 MUTATION STATUS' versus 'YEARS_TO_BIRTH'

P value = 0.00156 (Wilcoxon-test), Q value = 0.07

Table S1. Gene #3: 'NF1 MUTATION STATUS' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 178 | 47.2 (15.1)
NF1 MUTATED | 15 | 59.4 (13.4)
NF1 WILD-TYPE | 163 | 46.1 (14.8)

Figure S1. Get High-res Image Gene #3: 'NF1 MUTATION STATUS' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RET MUTATION STATUS' versus 'RACE'

P value = 0.00591 (Fisher's exact test), Q value = 0.13

Table S2. Gene #4: 'RET MUTATION STATUS' versus Clinical Feature #4: 'RACE'

nPatients | AMERICAN INDIAN OR ALASKA NATIVE | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE
--- | --- | --- | --- | ---
ALL | 1 | 6 | 20 | 147
RET MUTATED | 1 | 1 | 1 | 3
RET WILD-TYPE | 0 | 5 | 19 | 144

Figure S2. Get High-res Image Gene #4: 'RET MUTATION STATUS' versus Clinical Feature #4: 'RACE'

Methods & Data
Input

- Mutation data file = sample_sig_gene_table.txt from Mutsig_2CV pipeline

- Processed Mutation data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_Correlate_Genomic_Events_Preprocess/PCPG-TP/15165019/transformed.cor.cli.txt

- Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/PCPG-TP/15087180/PCPG-TP.merged_data.txt

- Number of patients = 178

- Number of significantly mutated genes = 9

- 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)
Copyright © 2015 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle