Correlation between gene mutation status and selected clinical features
Kidney Chromophobe (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/C1PN94CT
Overview
Introduction

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

Summary

Testing the association between mutation status of 4 genes and 11 clinical features across 66 patients, one significant finding detected with Q value < 0.25.

  • PABPC1 mutation correlated to 'NEOPLASM.DISEASESTAGE'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between mutation status of 4 genes and 11 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, one significant finding detected.

Clinical
Features
Time
to
Death
AGE NEOPLASM
DISEASESTAGE
PATHOLOGY
T
STAGE
PATHOLOGY
N
STAGE
PATHOLOGY
M
STAGE
GENDER KARNOFSKY
PERFORMANCE
SCORE
NUMBERPACKYEARSSMOKED RACE ETHNICITY
nMutated (%) nWild-Type logrank test Wilcoxon-test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Wilcoxon-test Wilcoxon-test Fisher's exact test Fisher's exact test
PABPC1 7 (11%) 59 100
(1.00)
0.252
(1.00)
0.00471
(0.165)
0.0462
(1.00)
0.0874
(1.00)
1
(1.00)
0.226
(1.00)
0.0142
(0.483)
1
(1.00)
TP53 22 (33%) 44 100
(1.00)
0.935
(1.00)
0.306
(1.00)
0.37
(1.00)
0.036
(1.00)
0.108
(1.00)
1
(1.00)
0.394
(1.00)
0.195
(1.00)
1
(1.00)
PTEN 6 (9%) 60 100
(1.00)
0.422
(1.00)
0.0493
(1.00)
0.0321
(1.00)
0.0874
(1.00)
0.66
(1.00)
0.217
(1.00)
0.216
(1.00)
0.213
(1.00)
URGCP 3 (5%) 63 0.841
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
'PABPC1 MUTATION STATUS' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00471 (Fisher's exact test), Q value = 0.16

Table S1.  Gene #3: 'PABPC1 MUTATION STATUS' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 21 25 14 6
PABPC1 MUTATED 3 0 1 3
PABPC1 WILD-TYPE 18 25 13 3

Figure S1.  Get High-res Image Gene #3: 'PABPC1 MUTATION STATUS' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

Methods & Data
Input
  • Mutation data file = transformed.cor.cli.txt

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

  • Number of patients = 66

  • Number of significantly mutated genes = 4

  • Number of selected clinical features = 11

  • 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)