Correlation between gene mutation status and selected clinical features
Ovarian Serous Cystadenocarcinoma (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/C11J9975
Overview
Introduction

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

Summary

Testing the association between mutation status of 21 genes and 7 clinical features across 465 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 21 genes and 7 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
TUMOR
TISSUE
SITE
RADIATION
THERAPY
KARNOFSKY
PERFORMANCE
SCORE
RESIDUAL
TUMOR
ETHNICITY
nMutated (%) nWild-Type logrank test Wilcoxon-test Fisher's exact test Fisher's exact test Wilcoxon-test Fisher's exact test Fisher's exact test
TP53 384 (83%) 81 0.18
(1.00)
0.419
(1.00)
0.437
(1.00)
0.524
(1.00)
0.33
(1.00)
0.284
(1.00)
0.0539
(1.00)
RB1 15 (3%) 450 0.399
(1.00)
0.238
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
BRCA1 18 (4%) 447 0.442
(1.00)
0.555
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
NF1 24 (5%) 441 0.615
(1.00)
0.219
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
0.396
(1.00)
CDK12 15 (3%) 450 0.761
(1.00)
0.461
(1.00)
1
(1.00)
1
(1.00)
0.508
(1.00)
1
(1.00)
KRAS 5 (1%) 460 0.582
(1.00)
0.576
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
HNF1B 5 (1%) 460 0.378
(1.00)
0.141
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
PTEN 5 (1%) 460 0.876
(1.00)
0.916
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
LARP1 4 (1%) 461 0.222
(1.00)
0.688
(1.00)
1
(1.00)
0.0352
(1.00)
1
(1.00)
BRCA2 13 (3%) 452 0.00596
(0.876)
0.588
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
EFEMP1 7 (2%) 458 0.623
(1.00)
0.0712
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
NRAS 4 (1%) 461 0.977
(1.00)
0.124
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
MTA2 4 (1%) 461 0.063
(1.00)
0.126
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
ERCC6 4 (1%) 461 0.49
(1.00)
0.41
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
IL21R 8 (2%) 457 0.119
(1.00)
0.815
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
PKD1L1 6 (1%) 459 0.503
(1.00)
0.0761
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
SAMD9L 9 (2%) 456 0.313
(1.00)
0.203
(1.00)
1
(1.00)
1
(1.00)
0.152
(1.00)
AQP2 3 (1%) 462 0.315
(1.00)
0.217
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
CREBBP 11 (2%) 454 0.0801
(1.00)
0.296
(1.00)
1
(1.00)
1
(1.00)
0.207
(1.00)
C9ORF171 5 (1%) 460 0.405
(1.00)
0.0961
(1.00)
1
(1.00)
1
(1.00)
0.705
(1.00)
1
(1.00)
NCOA3 5 (1%) 460 0.0425
(1.00)
0.628
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
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/OV-TP/22572516/transformed.cor.cli.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/OV-TP/22507289/OV-TP.merged_data.txt

  • Number of patients = 465

  • Number of significantly mutated genes = 21

  • Number of selected clinical features = 7

  • 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)