Correlation between gene mutation status and selected clinical features

Bladder Urothelial Carcinoma (Primary solid tumor)

16 April 2014 | analyses__2014_04_16

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/C1D21W69

Overview

Introduction

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

Summary

Testing the association between mutation status of 23 genes and 11 clinical features across 128 patients, 3 significant findings detected with Q value < 0.25.

FOXQ1 mutation correlated to 'NUMBER.OF.LYMPH.NODES'.
TXNIP mutation correlated to 'KARNOFSKY.PERFORMANCE.SCORE'.
RXRA mutation correlated to 'KARNOFSKY.PERFORMANCE.SCORE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 23 genes and 11 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 3 significant findings detected.


		Clinical Features	Time to Death	AGE	NEOPLASM DISEASESTAGE	PATHOLOGY T STAGE	PATHOLOGY N STAGE	PATHOLOGY M STAGE	GENDER	KARNOFSKY PERFORMANCE SCORE	NUMBERPACKYEARSSMOKED	NUMBER OF LYMPH NODES	GLEASON SCORE
	nMutated (%)	nWild-Type	logrank test	t-test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	t-test	t-test	t-test	t-test
FOXQ1	7 (5%)	121	0.04 (1.00)	0.748 (1.00)	0.0756 (1.00)	0.284 (1.00)	0.361 (1.00)	0.0636 (1.00)	0.679 (1.00)		0.122 (1.00)	1.07e-05 (0.00249)
TXNIP	9 (7%)	119	0.915 (1.00)	0.563 (1.00)	0.843 (1.00)	1 (1.00)	0.178 (1.00)	0.488 (1.00)	1 (1.00)	0.000106 (0.0246)	0.798 (1.00)	0.997 (1.00)
RXRA	12 (9%)	116	0.908 (1.00)	0.0635 (1.00)	0.564 (1.00)	0.604 (1.00)	0.928 (1.00)	0.401 (1.00)	1 (1.00)	0.000106 (0.0246)	0.733 (1.00)	0.972 (1.00)
PIK3CA	26 (20%)	102	0.0798 (1.00)	0.859 (1.00)	0.734 (1.00)	0.766 (1.00)	0.0775 (1.00)	0.228 (1.00)	0.803 (1.00)	0.978 (1.00)	0.518 (1.00)	0.00426 (0.979)	0.854 (1.00)
TP53	64 (50%)	64	0.88 (1.00)	0.899 (1.00)	0.226 (1.00)	0.572 (1.00)	0.196 (1.00)	0.44 (1.00)	0.839 (1.00)	0.111 (1.00)	0.653 (1.00)	0.191 (1.00)	0.183 (1.00)
CDKN1A	17 (13%)	111	0.619 (1.00)	0.147 (1.00)	0.731 (1.00)	0.367 (1.00)	0.171 (1.00)	0.634 (1.00)	0.236 (1.00)	0.201 (1.00)	0.999 (1.00)	0.192 (1.00)	0.672 (1.00)
RB1	17 (13%)	111	0.0536 (1.00)	0.559 (1.00)	1 (1.00)	1 (1.00)	0.583 (1.00)	0.299 (1.00)	0.559 (1.00)	0.114 (1.00)	0.0257 (1.00)	0.802 (1.00)	0.269 (1.00)
ARID1A	32 (25%)	96	0.435 (1.00)	0.663 (1.00)	0.19 (1.00)	0.288 (1.00)	0.564 (1.00)	0.56 (1.00)	0.643 (1.00)	0.433 (1.00)	0.894 (1.00)	0.954 (1.00)	0.158 (1.00)
MLL2	34 (27%)	94	0.0145 (1.00)	0.648 (1.00)	0.0234 (1.00)	0.155 (1.00)	0.339 (1.00)	0.0667 (1.00)	1 (1.00)	0.713 (1.00)	0.281 (1.00)	0.294 (1.00)	0.697 (1.00)
FBXW7	13 (10%)	115	0.124 (1.00)	0.0865 (1.00)	0.0822 (1.00)	0.602 (1.00)	0.271 (1.00)	0.16 (1.00)	0.309 (1.00)	0.926 (1.00)	0.664 (1.00)	0.963 (1.00)	0.764 (1.00)
KDM6A	31 (24%)	97	0.784 (1.00)	0.554 (1.00)	0.0876 (1.00)	0.485 (1.00)	0.0752 (1.00)	0.774 (1.00)	0.635 (1.00)	0.462 (1.00)	0.709 (1.00)	0.265 (1.00)	0.252 (1.00)
ELF3	11 (9%)	117	0.481 (1.00)	0.559 (1.00)	0.0562 (1.00)	0.812 (1.00)	0.0842 (1.00)	0.826 (1.00)	0.465 (1.00)	0.943 (1.00)	0.455 (1.00)	0.289 (1.00)
FGFR3	15 (12%)	113	0.09 (1.00)	0.233 (1.00)	0.85 (1.00)	0.646 (1.00)	0.416 (1.00)	0.00434 (0.994)	0.354 (1.00)	0.824 (1.00)	0.954 (1.00)	0.149 (1.00)	0.945 (1.00)
STAG2	14 (11%)	114	0.181 (1.00)	0.168 (1.00)	0.0787 (1.00)	0.3 (1.00)	0.356 (1.00)	0.00763 (1.00)	1 (1.00)	0.604 (1.00)	0.378 (1.00)	0.388 (1.00)	0.62 (1.00)
NFE2L2	10 (8%)	118	0.389 (1.00)	0.772 (1.00)	0.171 (1.00)	0.193 (1.00)	0.92 (1.00)	0.345 (1.00)	0.12 (1.00)		0.806 (1.00)	0.107 (1.00)
CDKN2A	7 (5%)	121	0.475 (1.00)	0.437 (1.00)	0.0597 (1.00)	1 (1.00)	0.784 (1.00)	1 (1.00)	1 (1.00)	0.215 (1.00)	0.548 (1.00)
KLF5	9 (7%)	119	0.734 (1.00)	0.92 (1.00)	0.552 (1.00)	0.458 (1.00)	0.441 (1.00)	0.614 (1.00)	0.0427 (1.00)		0.995 (1.00)	0.51 (1.00)
ASXL2	9 (7%)	119	0.932 (1.00)	0.203 (1.00)	0.561 (1.00)	0.445 (1.00)	0.0991 (1.00)	0.803 (1.00)	1 (1.00)		0.307 (1.00)	0.502 (1.00)	0.945 (1.00)
TSC1	10 (8%)	118	0.542 (1.00)	0.541 (1.00)	0.392 (1.00)	0.118 (1.00)	0.364 (1.00)	0.345 (1.00)	1 (1.00)	0.515 (1.00)	0.773 (1.00)	0.252 (1.00)
ERCC2	16 (12%)	112	0.581 (1.00)	0.355 (1.00)	0.764 (1.00)	0.448 (1.00)	0.583 (1.00)	0.608 (1.00)	0.759 (1.00)	0.0875 (1.00)	0.14 (1.00)	0.577 (1.00)	0.227 (1.00)
EP300	21 (16%)	107	0.953 (1.00)	0.961 (1.00)	0.851 (1.00)	0.815 (1.00)	0.943 (1.00)	0.625 (1.00)	0.591 (1.00)	0.00503 (1.00)	0.863 (1.00)	0.251 (1.00)	0.508 (1.00)
FOXA1	7 (5%)	121	0.759 (1.00)	0.647 (1.00)	0.0851 (1.00)	0.412 (1.00)	0.0109 (1.00)	0.0163 (1.00)	0.065 (1.00)			0.299 (1.00)
PHLDA3	4 (3%)	124	0.36 (1.00)	0.971 (1.00)	0.691 (1.00)	1 (1.00)	0.517 (1.00)	0.258 (1.00)	0.26 (1.00)

'FOXQ1 MUTATION STATUS' versus 'NUMBER.OF.LYMPH.NODES'

P value = 1.07e-05 (t-test), Q value = 0.0025

Table S1. Gene #11: 'FOXQ1 MUTATION STATUS' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	93	1.8 (3.8)
FOXQ1 MUTATED	5	0.0 (0.0)
FOXQ1 WILD-TYPE	88	1.9 (3.9)

Figure S1. Get High-res Image Gene #11: 'FOXQ1 MUTATION STATUS' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

'TXNIP MUTATION STATUS' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.000106 (t-test), Q value = 0.025

Table S2. Gene #19: 'TXNIP MUTATION STATUS' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	37	78.4 (16.4)
TXNIP MUTATED	3	90.0 (0.0)
TXNIP WILD-TYPE	34	77.4 (16.8)

Figure S2. Get High-res Image Gene #19: 'TXNIP MUTATION STATUS' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'RXRA MUTATION STATUS' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.000106 (t-test), Q value = 0.025

Table S3. Gene #21: 'RXRA MUTATION STATUS' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	37	78.4 (16.4)
RXRA MUTATED	3	90.0 (0.0)
RXRA WILD-TYPE	34	77.4 (16.8)

Figure S3. Get High-res Image Gene #21: 'RXRA MUTATION STATUS' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

Methods & Data

Input

Mutation data file = transformed.cor.cli.txt
Clinical data file = BLCA-TP.merged_data.txt
Number of patients = 128
Number of significantly mutated genes = 23
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

Student's t-test analysis

For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between tumors with and without gene mutations using 't.test' 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] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)

[3] 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)

[4] 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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