Correlation between gene mutation status and selected clinical features
Ovarian Serous Cystadenocarcinoma (Primary solid tumor)
15 July 2014  |  analyses__2014_07_15
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 (2014): Correlation between gene mutation status and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1CN72PG
Overview
Introduction

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

Summary

Testing the association between mutation status of 19 genes and 5 clinical features across 316 patients, no significant finding detected with Q value < 0.25.

  • No gene mutations related to clinical features.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between mutation status of 19 genes and 5 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, no significant finding detected.

Clinical
Features
Time
to
Death
AGE KARNOFSKY
PERFORMANCE
SCORE
RACE ETHNICITY
nMutated (%) nWild-Type logrank test Wilcoxon-test Wilcoxon-test Fisher's exact test Fisher's exact test
TP53 273 (86%) 43 0.178
(1.00)
0.7
(1.00)
0.667
(1.00)
0.307
(1.00)
0.0668
(1.00)
BRCA1 12 (4%) 304 0.957
(1.00)
0.597
(1.00)
1
(1.00)
1
(1.00)
FAM86B2 6 (2%) 310 0.867
(1.00)
0.161
(1.00)
1
(1.00)
0.185
(1.00)
TBP 4 (1%) 312 0.321
(1.00)
0.4
(1.00)
1
(1.00)
1
(1.00)
NBPF10 4 (1%) 312 0.871
(1.00)
0.457
(1.00)
1
(1.00)
1
(1.00)
NF1 14 (4%) 302 0.2
(1.00)
0.604
(1.00)
0.655
(1.00)
0.34
(1.00)
C10ORF140 5 (2%) 311 0.916
(1.00)
0.458
(1.00)
0.0658
(1.00)
1
(1.00)
BRCA2 11 (3%) 305 0.085
(1.00)
0.423
(1.00)
0.565
(1.00)
1
(1.00)
CDK12 8 (3%) 308 0.481
(1.00)
0.188
(1.00)
0.452
(1.00)
1
(1.00)
OR4F21 3 (1%) 313 0.199
(1.00)
1
(1.00)
1
(1.00)
C9ORF171 5 (2%) 311 0.895
(1.00)
0.0718
(1.00)
0.7
(1.00)
1
(1.00)
1
(1.00)
HYDIN 11 (3%) 305 0.908
(1.00)
0.839
(1.00)
1
(1.00)
1
(1.00)
RB1 8 (3%) 308 0.248
(1.00)
0.176
(1.00)
1
(1.00)
1
(1.00)
GART 3 (1%) 313 0.196
(1.00)
0.019
(1.00)
1
(1.00)
1
(1.00)
SRC 4 (1%) 312 0.197
(1.00)
0.48
(1.00)
1
(1.00)
1
(1.00)
ZNF236 7 (2%) 309 0.613
(1.00)
0.254
(1.00)
0.362
(1.00)
1
(1.00)
NBPF16 4 (1%) 312 0.972
(1.00)
0.589
(1.00)
1
(1.00)
1
(1.00)
CYP11B1 7 (2%) 309 0.0926
(1.00)
0.864
(1.00)
0.406
(1.00)
1
(1.00)
SON 8 (3%) 308 0.483
(1.00)
0.0746
(1.00)
1
(1.00)
0.157
(1.00)
Methods & Data
Input
  • Mutation data file = transformed.cor.cli.txt

  • Clinical data file = OV-TP.merged_data.txt

  • Number of patients = 316

  • Number of significantly mutated genes = 19

  • 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.

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)