Correlation between gene mutation status and selected clinical features

Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (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/C1FX78T4

Overview

Introduction

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

Summary

Testing the association between mutation status of 36 genes and 8 clinical features across 48 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 36 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	TUMOR TISSUE SITE	GENDER	RADIATION THERAPY	HISTOLOGICAL TYPE	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	Fisher's exact test
B2M	10 (21%)	38	0.519 (1.00)	0.223 (1.00)	0.383 (1.00)	0.0134 (0.708)	0.117 (1.00)	0.018 (0.708)	0.78 (1.00)	1 (1.00)
MYD88	3 (6%)	45	0.0211 (0.708)	0.932 (1.00)	0.984 (1.00)	0.587 (1.00)	1 (1.00)	0.193 (1.00)	0.579 (1.00)	0.563 (1.00)
MLL2	14 (29%)	34	0.296 (1.00)	0.0446 (0.755)	0.0393 (0.708)	0.526 (1.00)	0.0864 (0.986)	0.252 (1.00)	0.527 (1.00)	0.465 (1.00)
HLA-C	7 (15%)	41	0.155 (1.00)	0.286 (1.00)	0.0315 (0.708)	0.223 (1.00)	0.573 (1.00)	1 (1.00)	0.227 (1.00)	0.169 (1.00)
ZNF814	7 (15%)	41	0.684 (1.00)	0.965 (1.00)	0.551 (1.00)	1 (1.00)	0.0566 (0.777)	0.694 (1.00)	0.223 (1.00)	0.0552 (0.777)
CD79B	5 (10%)	43	0.378 (1.00)	0.188 (1.00)	0.0322 (0.708)	0.649 (1.00)	1 (1.00)	0.0355 (0.708)	0.426 (1.00)	0.587 (1.00)
TP53	5 (10%)	43	0.379 (1.00)	0.244 (1.00)	1 (1.00)	0.0538 (0.777)	1 (1.00)	0.326 (1.00)	1 (1.00)	0.587 (1.00)
TNFAIP3	7 (15%)	41	0.28 (1.00)	0.381 (1.00)	0.753 (1.00)	0.687 (1.00)	1 (1.00)	0.144 (1.00)	0.126 (1.00)	0.662 (1.00)
TMSB4X	6 (12%)	42	0.163 (1.00)	0.512 (1.00)		0.199 (1.00)	0.214 (1.00)	0.106 (0.992)	0.704 (1.00)	0.631 (1.00)
APLF	3 (6%)	45	0.778 (1.00)	0.717 (1.00)		0.587 (1.00)	0.391 (1.00)	1 (1.00)	0.321 (1.00)	0.563 (1.00)
RHPN2	7 (15%)	41	0.947 (1.00)	0.102 (0.992)	0.485 (1.00)	0.687 (1.00)	0.276 (1.00)	1 (1.00)	0.223 (1.00)	0.662 (1.00)
CIITA	5 (10%)	43	0.997 (1.00)	0.879 (1.00)	0.537 (1.00)	0.357 (1.00)	0.154 (1.00)	0.56 (1.00)	1 (1.00)	1 (1.00)
KDR	4 (8%)	44	0.986 (1.00)	0.985 (1.00)	0.984 (1.00)	0.32 (1.00)	1 (1.00)	0.262 (1.00)	0.343 (1.00)	1 (1.00)
CARD11	10 (21%)	38	0.337 (1.00)	0.684 (1.00)	0.861 (1.00)	0.735 (1.00)	1 (1.00)	1 (1.00)	0.108 (0.992)	0.414 (1.00)
EZH2	3 (6%)	45	0.472 (1.00)	0.225 (1.00)		1 (1.00)	1 (1.00)	1 (1.00)	0.322 (1.00)	0.563 (1.00)
NFKBIE	4 (8%)	44	0.986 (1.00)	0.896 (1.00)		0.32 (1.00)	1 (1.00)	1 (1.00)	0.66 (1.00)	0.56 (1.00)
MLH1	3 (6%)	45	0.245 (1.00)	0.639 (1.00)		0.587 (1.00)	0.391 (1.00)	1 (1.00)	0.32 (1.00)	0.563 (1.00)
FAS	5 (10%)	43	0.0368 (0.708)	0.244 (1.00)		0.357 (1.00)	1 (1.00)	1 (1.00)	0.672 (1.00)	1 (1.00)
ENOX1	4 (8%)	44	0.963 (1.00)	0.38 (1.00)		1 (1.00)	0.488 (1.00)	0.479 (1.00)	0.0999 (0.992)	1 (1.00)
SGK1	4 (8%)	44	0.99 (1.00)	0.24 (1.00)	0.0772 (0.926)	1 (1.00)	0.488 (1.00)	1 (1.00)	1 (1.00)	0.56 (1.00)
IFITM3	3 (6%)	45	0.513 (1.00)	0.782 (1.00)		1 (1.00)	1 (1.00)	1 (1.00)	0.00889 (0.708)	0.563 (1.00)
APOB	3 (6%)	45	0.868 (1.00)	0.268 (1.00)		1 (1.00)	0.391 (1.00)	0.382 (1.00)	0.0326 (0.708)	1 (1.00)
MCM8	3 (6%)	45	0.868 (1.00)	0.382 (1.00)	0.0173 (0.708)	0.587 (1.00)	1 (1.00)	1 (1.00)	0.0335 (0.708)	0.563 (1.00)
ARID1A	5 (10%)	43	0.924 (1.00)	0.543 (1.00)	0.549 (1.00)	0.0538 (0.777)	1 (1.00)	1 (1.00)	0.0345 (0.708)	0.312 (1.00)
PCDHA10	6 (12%)	42	0.691 (1.00)	0.815 (1.00)		0.392 (1.00)	0.571 (1.00)	1 (1.00)	0.458 (1.00)	1 (1.00)
LRRC16B	4 (8%)	44	0.848 (1.00)	0.614 (1.00)		1 (1.00)	0.488 (1.00)	1 (1.00)	0.345 (1.00)	0.56 (1.00)
PNPLA7	3 (6%)	45	0.245 (1.00)	0.268 (1.00)		0.089 (0.986)	1 (1.00)	1 (1.00)	0.322 (1.00)	0.563 (1.00)
SLC16A8	3 (6%)	45	0.547 (1.00)	0.749 (1.00)		0.587 (1.00)	1 (1.00)	1 (1.00)	0.321 (1.00)	0.563 (1.00)
PKHD1L1	5 (10%)	43	0.205 (1.00)	0.0247 (0.708)		1 (1.00)	0.571 (1.00)	0.0744 (0.926)	0.11 (0.992)	1 (1.00)
HRCT1	4 (8%)	44	0.963 (1.00)	0.162 (1.00)	0.413 (1.00)	0.32 (1.00)	1 (1.00)	1 (1.00)	0.06 (0.785)	0.56 (1.00)
TYRO3	4 (8%)	44	0.231 (1.00)	0.601 (1.00)		0.0376 (0.708)	1 (1.00)	1 (1.00)	1 (1.00)	0.56 (1.00)
GSTZ1	3 (6%)	45	0.777 (1.00)	0.317 (1.00)		0.587 (1.00)	1 (1.00)	1 (1.00)	0.322 (1.00)	0.563 (1.00)
STAT3	7 (15%)	41	0.771 (1.00)	0.661 (1.00)	0.961 (1.00)	0.687 (1.00)	1 (1.00)	0.00228 (0.657)	0.737 (1.00)	1 (1.00)
IRF8	5 (10%)	43	0.825 (1.00)	0.28 (1.00)		0.649 (1.00)	0.488 (1.00)	0.149 (1.00)	0.231 (1.00)	0.587 (1.00)
TMEM30A	4 (8%)	44	0.295 (1.00)	0.287 (1.00)		0.32 (1.00)	0.488 (1.00)	0.476 (1.00)	1 (1.00)	1 (1.00)
UBE2A	4 (8%)	44	0.664 (1.00)	0.167 (1.00)		0.614 (1.00)	1 (1.00)	0.0969 (0.992)	0.66 (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/DLBC-TP/22573828/transformed.cor.cli.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/DLBC-TP/22506396/DLBC-TP.merged_data.txt
Number of patients = 48
Number of significantly mutated genes = 36
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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