Correlation between gene mutation status and selected clinical features
Uterine Carcinosarcoma (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/C179445R
Overview
Introduction

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

Summary

Testing the association between mutation status of 11 genes and 5 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 11 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
YEARS
TO
BIRTH
RADIATION
THERAPY
HISTOLOGICAL
TYPE
RACE
nMutated (%) nWild-Type logrank test Wilcoxon-test Fisher's exact test Fisher's exact test Fisher's exact test
TP53 51 (89%) 6 0.271
(0.778)
0.0288
(0.711)
0.399
(0.778)
0.474
(0.809)
0.22
(0.755)
FBXW7 22 (39%) 35 0.517
(0.809)
0.407
(0.778)
0.579
(0.809)
0.165
(0.755)
1
(1.00)
PPP2R1A 16 (28%) 41 0.659
(0.809)
0.631
(0.809)
0.384
(0.778)
0.268
(0.778)
0.535
(0.809)
KRAS 7 (12%) 50 0.359
(0.778)
0.576
(0.809)
0.692
(0.809)
0.0824
(0.755)
0.557
(0.809)
PTEN 11 (19%) 46 0.389
(0.778)
0.504
(0.809)
0.736
(0.844)
0.686
(0.809)
0.678
(0.809)
RB1 6 (11%) 51 0.169
(0.755)
0.355
(0.778)
0.0849
(0.755)
0.0154
(0.711)
0.329
(0.778)
ZBTB7B 6 (11%) 51 0.134
(0.755)
0.649
(0.809)
0.399
(0.778)
1
(1.00)
0.218
(0.755)
PIK3R1 6 (11%) 51 0.6
(0.809)
0.207
(0.755)
0.653
(0.809)
0.863
(0.949)
0.219
(0.755)
ARHGAP35 6 (11%) 51 0.539
(0.809)
0.0983
(0.755)
0.675
(0.809)
0.41
(0.778)
0.0388
(0.711)
PIK3CA 20 (35%) 37 0.206
(0.755)
0.136
(0.755)
1
(1.00)
0.106
(0.755)
0.755
(0.848)
MAMLD1 4 (7%) 53 0.984
(1.00)
0.364
(0.778)
1
(1.00)
0.436
(0.799)
0.304
(0.778)
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/UCS-TP/22569432/transformed.cor.cli.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/UCS-TP/22507158/UCS-TP.merged_data.txt

  • Number of patients = 57

  • Number of significantly mutated genes = 11

  • 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)