Correlation between gene mutation status and selected clinical features
Uterine Carcinosarcoma (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/C1S181BG
Overview
Introduction

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

Summary

Testing the association between mutation status of 13 genes and 3 clinical features across 57 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 13 genes and 3 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 RACE
nMutated (%) nWild-Type logrank test Wilcoxon-test Fisher's exact test
TP53 51 (89%) 6 0.522
(1.00)
0.0288
(1.00)
0.22
(1.00)
FBXW7 22 (39%) 35 0.607
(1.00)
0.407
(1.00)
1
(1.00)
PPP2R1A 16 (28%) 41 0.759
(1.00)
0.631
(1.00)
0.534
(1.00)
PTEN 11 (19%) 46 0.279
(1.00)
0.504
(1.00)
0.677
(1.00)
KRAS 7 (12%) 50 0.403
(1.00)
0.576
(1.00)
0.558
(1.00)
ZBTB7B 6 (11%) 51 0.0829
(1.00)
0.649
(1.00)
0.221
(1.00)
CHD4 10 (18%) 47 0.486
(1.00)
0.223
(1.00)
0.23
(1.00)
PIK3R1 6 (11%) 51 0.691
(1.00)
0.207
(1.00)
0.219
(1.00)
ARHGAP35 6 (11%) 51 0.469
(1.00)
0.0983
(1.00)
0.0403
(1.00)
PIK3CA 20 (35%) 37 0.192
(1.00)
0.136
(1.00)
0.755
(1.00)
MAMLD1 4 (7%) 53 0.848
(1.00)
0.364
(1.00)
0.306
(1.00)
RB1 6 (11%) 51 0.173
(1.00)
0.355
(1.00)
0.33
(1.00)
LYPLA2 3 (5%) 54 0.951
(1.00)
0.138
(1.00)
1
(1.00)
Methods & Data
Input
  • Mutation data file = transformed.cor.cli.txt

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

  • Number of patients = 57

  • Number of significantly mutated genes = 13

  • 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

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)