Correlation between gene mutation status and selected clinical features
Pheochromocytoma and Paraganglioma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Correlation between gene mutation status and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1NV9HQM
Overview
Introduction

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

Summary

Testing the association between mutation status of 8 genes and 10 clinical features across 179 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 8 genes and 10 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
TUMOR
TISSUE
SITE
GENDER RADIATION
THERAPY
KARNOFSKY
PERFORMANCE
SCORE
HISTOLOGICAL
TYPE
NUMBER
OF
LYMPH
NODES
RACE ETHNICITY
nMutated (%) nWild-Type logrank test Wilcoxon-test Fisher's exact test Fisher's exact test Fisher's exact test Wilcoxon-test Fisher's exact test Wilcoxon-test Fisher's exact test Fisher's exact test
NF1 15 (8%) 164 0.574
(1.00)
0.0017
(0.136)
0.478
(1.00)
1
(1.00)
1
(1.00)
0.94
(1.00)
0.753
(1.00)
0.552
(1.00)
0.281
(1.00)
RET 6 (3%) 173 0.71
(1.00)
0.458
(1.00)
0.593
(1.00)
0.698
(1.00)
1
(1.00)
1
(1.00)
0.00576
(0.23)
1
(1.00)
HRAS 18 (10%) 161 0.435
(1.00)
0.169
(1.00)
0.204
(1.00)
0.804
(1.00)
1
(1.00)
0.35
(1.00)
0.475
(1.00)
0.0984
(0.875)
1
(1.00)
EPAS1 8 (4%) 171 0.488
(1.00)
0.955
(1.00)
0.0352
(0.705)
0.469
(1.00)
0.209
(1.00)
0.0615
(0.828)
0.0121
(0.323)
0.152
(1.00)
1
(1.00)
NUDT11 5 (3%) 174 0.645
(1.00)
0.186
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
0.619
(1.00)
0.185
(1.00)
1
(1.00)
SHROOM4 3 (2%) 176 0.757
(1.00)
0.229
(1.00)
0.448
(1.00)
0.581
(1.00)
1
(1.00)
0.228
(1.00)
1
(1.00)
0.0689
(0.828)
AMMECR1 3 (2%) 176 0.804
(1.00)
0.301
(1.00)
0.0828
(0.828)
1
(1.00)
1
(1.00)
0.078
(0.828)
0.399
(1.00)
1
(1.00)
GPR128 4 (2%) 175 0.724
(1.00)
0.907
(1.00)
1
(1.00)
0.633
(1.00)
1
(1.00)
1
(1.00)
0.49
(1.00)
1
(1.00)
'NF1 MUTATION STATUS' versus 'YEARS_TO_BIRTH'

P value = 0.0017 (Wilcoxon-test), Q value = 0.14

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

nPatients Mean (Std.Dev)
ALL 179 47.3 (15.1)
NF1 MUTATED 15 59.4 (13.4)
NF1 WILD-TYPE 164 46.2 (14.8)

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

'RET MUTATION STATUS' versus 'RACE'

P value = 0.00576 (Fisher's exact test), Q value = 0.23

Table S2.  Gene #5: 'RET MUTATION STATUS' versus Clinical Feature #9: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 6 20 148
RET MUTATED 1 1 1 3
RET WILD-TYPE 0 5 19 145

Figure S2.  Get High-res Image Gene #5: 'RET MUTATION STATUS' versus Clinical Feature #9: '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/22569699/transformed.cor.cli.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/PCPG-TP/22506925/PCPG-TP.merged_data.txt

  • Number of patients = 179

  • Number of significantly mutated genes = 8

  • Number of selected clinical features = 10

  • 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.

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)