Correlation between gene mutation status and selected clinical features
Ovarian Serous Cystadenocarcinoma (Primary solid tumor)
15 January 2014  |  analyses__2014_01_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/C1C24TW8

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


Testing the association between mutation status of 3 genes and 3 clinical features across 316 patients, no significant finding detected with Q value < 0.25.

  • No gene mutations related to clinical features.

Overview of the results

Table 1.  Get Full Table Overview of the association between mutation status of 3 genes and 3 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, no significant finding detected.

nMutated (%) nWild-Type logrank test t-test t-test
TP53 276 (87%) 40 0.389
TBP 4 (1%) 312 0.323
SRC 4 (1%) 312 0.197
Methods & Data
  • Mutation data file = transformed.cor.cli.txt

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

  • Number of patients = 316

  • Number of significantly mutated genes = 3

  • Number of selected clinical features = 3

  • 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

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.

[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] 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)