Correlation between gene mutation status and selected clinical features
Cholangiocarcinoma (Primary solid tumor)
02 April 2015 | analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C16W993C
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between gene mutation status and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C16W993C
Overview
Introduction

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

Summary

Testing the association between mutation status of 16 genes and 8 clinical features across 35 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 16 genes and 8 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 | NEOPLASM DISEASESTAGE | PATHOLOGY T STAGE | PATHOLOGY N STAGE | PATHOLOGY M STAGE | GENDER | RACE
| 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 | Fisher's exact test
BAP1 | 8 (23%) | 27 | 0.52 (1.00) | 0.651 (1.00) | 0.721 (1.00) | 0.617 (1.00) | 1 (1.00) | 0.55 (1.00) | 0.424 (1.00) | 0.316 (1.00)
PBRM1 | 8 (23%) | 27 | 0.631 (1.00) | 0.271 (1.00) | 0.823 (1.00) | 0.854 (1.00) | 1 (1.00) | 0.55 (1.00) | 0.424 (1.00) | 0.74 (1.00)
MLL3 | 7 (20%) | 28 | 0.0578 (1.00) | 0.375 (1.00) | 0.827 (1.00) | 0.48 (1.00) | 1 (1.00) | 1 (1.00) | 0.415 (1.00) | 0.433 (1.00)
TP53 | 5 (14%) | 30 | 0.268 (1.00) | 0.869 (1.00) | 0.156 (1.00) | 0.0364 (1.00) | 0.183 (1.00) | 0.105 (1.00) | 0.642 (1.00) | 1 (1.00)
HLA-B | 5 (14%) | 30 | 0.9 (1.00) | 0.258 (1.00) | 0.207 (1.00) | 0.803 (1.00) | 1 (1.00) | 0.512 (1.00) | 0.642 (1.00) | 0.31 (1.00)
FTH1 | 3 (9%) | 32 | 0.98 (1.00) | 0.0338 (1.00) | 0.357 (1.00) | 0.69 (1.00) | 0.433 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00)
ARID1A | 5 (14%) | 30 | 0.00431 (0.552) | 0.724 (1.00) | 0.0903 (1.00) | 0.0882 (1.00) | 1 (1.00) | 0.431 (1.00) | 1 (1.00) | 0.563 (1.00)
DDHD1 | 4 (11%) | 31 | 0.509 (1.00) | 0.195 (1.00) | 0.833 (1.00) | 0.776 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00)
IDH1 | 4 (11%) | 31 | 0.773 (1.00) | 0.604 (1.00) | 0.209 (1.00) | 1 (1.00) | 1 (1.00) | 0.431 (1.00) | 1 (1.00) | 1 (1.00)
MUC2 | 7 (20%) | 28 | 0.0985 (1.00) | 0.918 (1.00) | 0.488 (1.00) | 0.172 (1.00) | 1 (1.00) | 1 (1.00) | 0.677 (1.00) | 0.13 (1.00)
MUC21 | 3 (9%) | 32 | 0.0678 (1.00) | 0.702 (1.00) | 0.763 (1.00) | 0.494 (1.00) | 1 (1.00) | 1 (1.00) | 0.0856 (1.00) | 0.379 (1.00)
PAXIP1 | 4 (11%) | 31 | 0.844 (1.00) | 0.161 (1.00) | 0.249 (1.00) | 0.777 (1.00) | 0.119 (1.00) | 0.431 (1.00) | 1 (1.00) | 1 (1.00)
EPHA2 | 4 (11%) | 31 | 0.687 (1.00) | 0.146 (1.00) | 0.333 (1.00) | 0.779 (1.00) | 1 (1.00) | 0.431 (1.00) | 1 (1.00) | 1 (1.00)
OTOP1 | 4 (11%) | 31 | 0.705 (1.00) | 0.114 (1.00) | 0.4 (1.00) | 0.23 (1.00) | 0.538 (1.00) | 1 (1.00) | 0.608 (1.00) | 1 (1.00)
CDC27 | 5 (14%) | 30 | 0.0311 (1.00) | 1 (1.00) | 0.103 (1.00) | 1 (1.00) | 0.538 (1.00) | 0.512 (1.00) | 0.642 (1.00) | 0.56 (1.00)
FAM35A | 3 (9%) | 32 | 0.2 (1.00) | 0.393 (1.00) | 0.515 (1.00) | 1 (1.00) | 0.31 (1.00) | 0.34 (1.00) | 0.582 (1.00) | 0.181 (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/CHOL-TP/15866308/transformed.cor.cli.txt

- Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/CHOL-TP/15076818/CHOL-TP.merged_data.txt

- Number of patients = 35

- Number of significantly mutated genes = 16

- Number of selected clinical features = 8

- 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.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

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)
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