Correlation between gene mutation status and selected clinical features

Cholangiocarcinoma (Primary solid tumor)

02 April 2015 | analyses__2015_04_02

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 (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.

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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