Correlation between gene mutation status and selected clinical features

Lung Squamous Cell Carcinoma (Primary solid tumor)

23 May 2013 | analyses__2013_05_23

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 (2013): Correlation between gene mutation status and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1H1301N

Overview

Introduction

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

Summary

Testing the association between mutation status of 17 genes and 13 clinical features across 177 patients, one significant finding detected with Q value < 0.25.

ASCL4 mutation correlated to 'NUMBERPACKYEARSSMOKED'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 17 genes and 13 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	GENDER	KARNOFSKY PERFORMANCE SCORE	HISTOLOGICAL TYPE	RADIATIONS RADIATION REGIMENINDICATION	NUMBERPACKYEARSSMOKED	YEAROFTOBACCOSMOKINGONSET	DISTANT METASTASIS	LYMPH NODE METASTASIS	COMPLETENESS OF RESECTION	TUMOR STAGECODE	NEOPLASM DISEASESTAGE
	nMutated (%)	nWild-Type	logrank test	t-test	Fisher's exact test	t-test	Fisher's exact test	Fisher's exact test	t-test	t-test	Fisher's exact test	Fisher's exact test	Fisher's exact test	t-test	Chi-square test
ASCL4	6 (3%)	171	0.972 (1.00)	0.316 (1.00)	0.651 (1.00)		1 (1.00)	1 (1.00)	8.63e-07 (0.000171)	0.193 (1.00)	1 (1.00)	0.132 (1.00)	0.154 (1.00)		0.613 (1.00)
CDKN2A	26 (15%)	151	0.251 (1.00)	0.663 (1.00)	0.812 (1.00)	0.937 (1.00)	0.62 (1.00)	0.473 (1.00)	0.378 (1.00)	0.638 (1.00)	0.746 (1.00)	0.494 (1.00)	0.879 (1.00)		0.892 (1.00)
TP53	141 (80%)	36	0.143 (1.00)	0.843 (1.00)	1 (1.00)	0.134 (1.00)	0.669 (1.00)	0.583 (1.00)	0.316 (1.00)	0.915 (1.00)	0.119 (1.00)	0.537 (1.00)	0.341 (1.00)		0.943 (1.00)
NFE2L2	27 (15%)	150	0.599 (1.00)	0.804 (1.00)	0.232 (1.00)	0.925 (1.00)	0.635 (1.00)	1 (1.00)	0.0966 (1.00)	0.88 (1.00)	0.27 (1.00)	0.832 (1.00)	0.5 (1.00)		0.704 (1.00)
PIK3CA	27 (15%)	150	0.114 (1.00)	0.345 (1.00)	0.232 (1.00)	0.482 (1.00)	1 (1.00)	1 (1.00)	0.148 (1.00)	0.967 (1.00)	0.395 (1.00)	0.714 (1.00)	1 (1.00)		0.0271 (1.00)
PTEN	14 (8%)	163	0.023 (1.00)	0.805 (1.00)	1 (1.00)		1 (1.00)	1 (1.00)	0.585 (1.00)	0.797 (1.00)	0.0376 (1.00)	0.271 (1.00)	0.297 (1.00)		0.181 (1.00)
KEAP1	22 (12%)	155	0.247 (1.00)	0.751 (1.00)	0.801 (1.00)	0.388 (1.00)	0.228 (1.00)	1 (1.00)	0.727 (1.00)	0.502 (1.00)	1 (1.00)	0.883 (1.00)	0.839 (1.00)		0.964 (1.00)
CSMD3	81 (46%)	96	0.0466 (1.00)	0.453 (1.00)	0.497 (1.00)	0.679 (1.00)	0.817 (1.00)	1 (1.00)	0.573 (1.00)	0.64 (1.00)	0.0609 (1.00)	0.112 (1.00)	0.723 (1.00)		0.0661 (1.00)
MLL2	35 (20%)	142	0.0409 (1.00)	0.884 (1.00)	0.83 (1.00)	0.868 (1.00)	0.667 (1.00)	0.586 (1.00)	0.305 (1.00)	0.834 (1.00)	0.794 (1.00)	0.754 (1.00)	0.695 (1.00)		0.961 (1.00)
FBXW7	11 (6%)	166	0.0651 (1.00)	0.995 (1.00)	0.293 (1.00)	0.00639 (1.00)	0.323 (1.00)	1 (1.00)	0.508 (1.00)	0.00663 (1.00)	1 (1.00)	0.299 (1.00)	0.694 (1.00)		0.12 (1.00)
RB1	12 (7%)	165	0.724 (1.00)	0.491 (1.00)	0.303 (1.00)		0.0544 (1.00)	1 (1.00)	0.206 (1.00)	0.233 (1.00)	1 (1.00)	0.324 (1.00)	0.265 (1.00)		0.928 (1.00)
HS6ST1	5 (3%)	172	0.526 (1.00)	0.902 (1.00)	1 (1.00)		0.16 (1.00)	1 (1.00)	0.176 (1.00)	0.0959 (1.00)	1 (1.00)	0.658 (1.00)	1 (1.00)		0.766 (1.00)
ZNF567	3 (2%)	174	0.73 (1.00)	0.269 (1.00)	0.166 (1.00)		1 (1.00)	1 (1.00)		0.608 (1.00)	1 (1.00)	0.00212 (0.418)	1 (1.00)		0.00383 (0.751)
ELTD1	18 (10%)	159	0.77 (1.00)	0.408 (1.00)	0.0452 (1.00)	0.684 (1.00)	0.48 (1.00)	1 (1.00)	0.306 (1.00)	0.683 (1.00)	0.637 (1.00)	0.704 (1.00)	0.584 (1.00)		0.278 (1.00)
OR2G6	16 (9%)	161	0.237 (1.00)	0.597 (1.00)	0.246 (1.00)	0.746 (1.00)	0.0929 (1.00)	1 (1.00)	0.31 (1.00)	0.884 (1.00)	1 (1.00)	1 (1.00)	0.138 (1.00)		0.913 (1.00)
HLA-A	6 (3%)	171	0.805 (1.00)	0.57 (1.00)	0.182 (1.00)	0.746 (1.00)	1 (1.00)	1 (1.00)	0.877 (1.00)	0.482 (1.00)	1 (1.00)	0.111 (1.00)	0.47 (1.00)		0.536 (1.00)
NOTCH1	14 (8%)	163	0.26 (1.00)	0.993 (1.00)	0.361 (1.00)	0.00631 (1.00)	1 (1.00)	1 (1.00)	0.683 (1.00)	0.278 (1.00)	1 (1.00)	0.442 (1.00)	0.77 (1.00)		0.273 (1.00)

'ASCL4 MUTATION STATUS' versus 'NUMBERPACKYEARSSMOKED'

P value = 8.63e-07 (t-test), Q value = 0.00017

Table S1. Gene #14: 'ASCL4 MUTATION STATUS' versus Clinical Feature #7: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	147	52.4 (31.3)
ASCL4 MUTATED	4	23.8 (4.8)
ASCL4 WILD-TYPE	143	53.2 (31.3)

Figure S1. Get High-res Image Gene #14: 'ASCL4 MUTATION STATUS' versus Clinical Feature #7: 'NUMBERPACKYEARSSMOKED'

Methods & Data

Input

Mutation data file = LUSC-TP.mutsig.cluster.txt
Clinical data file = LUSC-TP.clin.merged.picked.txt
Number of patients = 177
Number of significantly mutated genes = 17
Number of selected clinical features = 13
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

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.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

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

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] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)

[5] 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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