Stomach Adenocarcinoma: Correlation between gene mutation status and selected clinical features

Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/Harvard Medical School)

Overview

Introduction

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

Summary

Testing the association between mutation status of 37 genes and 8 clinical features across 132 patients, one significant finding detected with Q value < 0.25.

GPR146 mutation correlated to 'AGE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 37 genes and 8 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	HISTOLOGICAL TYPE	PATHOLOGY T	PATHOLOGY N	PATHOLOGICSPREAD(M)	NEOADJUVANT THERAPY
	nMutated (%)	nWild-Type	logrank test	t-test	Fisher's exact test	Chi-square test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test
GPR146	4 (3%)	128	0.748 (1.00)	1.09e-14 (2.82e-12)	1 (1.00)	0.805 (1.00)	0.821 (1.00)	0.136 (1.00)	1 (1.00)	1 (1.00)
CIC	11 (8%)	121	0.142 (1.00)	0.00885 (1.00)	0.116 (1.00)	0.619 (1.00)	0.429 (1.00)	0.398 (1.00)	0.00152 (0.393)	1 (1.00)
HIST1H2AD	3 (2%)	129		0.841 (1.00)	0.564 (1.00)	0.953 (1.00)	0.428 (1.00)		0.0196 (1.00)	1 (1.00)
MXRA5	17 (13%)	115	0.645 (1.00)	0.0262 (1.00)	0.294 (1.00)	0.433 (1.00)	0.506 (1.00)	0.396 (1.00)	0.25 (1.00)	0.594 (1.00)
OCEL1	4 (3%)	128	0.614 (1.00)	0.648 (1.00)	0.302 (1.00)	0.937 (1.00)	0.0317 (1.00)	0.79 (1.00)	0.0381 (1.00)	1 (1.00)
OR10J1	4 (3%)	128	0.361 (1.00)	0.413 (1.00)	0.649 (1.00)	0.239 (1.00)	0.097 (1.00)	0.395 (1.00)	0.162 (1.00)	0.198 (1.00)
BACH2	10 (8%)	122	0.364 (1.00)	0.295 (1.00)	0.522 (1.00)	0.4 (1.00)	0.429 (1.00)	0.213 (1.00)	0.222 (1.00)	1 (1.00)
NPFF	3 (2%)	129	0.386 (1.00)	0.248 (1.00)	1 (1.00)	0.599 (1.00)	0.288 (1.00)	0.296 (1.00)	1 (1.00)	1 (1.00)
OR6C70	4 (3%)	128	0.541 (1.00)	0.00322 (0.829)	0.649 (1.00)	0.805 (1.00)	0.151 (1.00)	0.263 (1.00)	0.162 (1.00)	1 (1.00)
CCS	3 (2%)	129		0.588 (1.00)	1 (1.00)	0.447 (1.00)		0.706 (1.00)	1 (1.00)	1 (1.00)
TNNI2	5 (4%)	127		0.216 (1.00)	0.648 (1.00)	0.89 (1.00)	0.349 (1.00)	0.553 (1.00)	0.504 (1.00)	1 (1.00)
PRKRA	3 (2%)	129		0.114 (1.00)	1 (1.00)	0.81 (1.00)			0.114 (1.00)	1 (1.00)
MS4A6A	3 (2%)	129		0.757 (1.00)	0.564 (1.00)	0.81 (1.00)	0.428 (1.00)	0.843 (1.00)	1 (1.00)	1 (1.00)
CCDC97	3 (2%)	129		0.0562 (1.00)	0.0625 (1.00)	0.953 (1.00)	0.151 (1.00)		1 (1.00)	1 (1.00)
ZNF223	4 (3%)	128	0.532 (1.00)	0.252 (1.00)	0.302 (1.00)	0.937 (1.00)	0.428 (1.00)	0.79 (1.00)	0.428 (1.00)	1 (1.00)
ZNF284	3 (2%)	129		0.103 (1.00)	0.274 (1.00)	0.81 (1.00)	1 (1.00)		0.114 (1.00)	1 (1.00)
PCDHA6	14 (11%)	118	0.255 (1.00)	0.29 (1.00)	0.0188 (1.00)	0.775 (1.00)	0.704 (1.00)	1 (1.00)	0.768 (1.00)	0.553 (1.00)
ZDHHC23	3 (2%)	129			0.274 (1.00)	0.81 (1.00)	0.288 (1.00)	0.418 (1.00)	0.114 (1.00)	1 (1.00)
DSCR4	3 (2%)	129		0.252 (1.00)	0.564 (1.00)	0.81 (1.00)	0.428 (1.00)	0.706 (1.00)	1 (1.00)	1 (1.00)
GLT6D1	3 (2%)	129		0.343 (1.00)	0.564 (1.00)	0.81 (1.00)		0.706 (1.00)	1 (1.00)	1 (1.00)
MAML1	4 (3%)	128	0.916 (1.00)	0.257 (1.00)	0.302 (1.00)	0.68 (1.00)	1 (1.00)	0.296 (1.00)	0.162 (1.00)	1 (1.00)
NME5	3 (2%)	129		0.898 (1.00)	1 (1.00)	0.953 (1.00)	0.428 (1.00)	1 (1.00)	0.114 (1.00)	1 (1.00)
RRAS	3 (2%)	129		0.512 (1.00)	1 (1.00)	0.81 (1.00)		1 (1.00)	1 (1.00)	1 (1.00)
USP48	5 (4%)	127		0.528 (1.00)	0.648 (1.00)	0.89 (1.00)	1 (1.00)	0.466 (1.00)	0.0616 (1.00)	1 (1.00)
LRRC55	3 (2%)	129		0.712 (1.00)	0.564 (1.00)		0.113 (1.00)	0.706 (1.00)	1 (1.00)	1 (1.00)
MAPK15	5 (4%)	127		0.0304 (1.00)	1 (1.00)	0.89 (1.00)	1 (1.00)	1 (1.00)	0.504 (1.00)	1 (1.00)
RNF167	4 (3%)	128		0.549 (1.00)	0.302 (1.00)	0.937 (1.00)	0.571 (1.00)	1 (1.00)	0.428 (1.00)	1 (1.00)
MOSC1	3 (2%)	129		0.41 (1.00)	0.274 (1.00)	0.953 (1.00)	0.0691 (1.00)	1 (1.00)	0.00366 (0.936)	1 (1.00)
HOMEZ	4 (3%)	128		0.419 (1.00)	0.302 (1.00)	0.691 (1.00)	0.04 (1.00)	0.843 (1.00)	0.428 (1.00)	1 (1.00)
MSH5	5 (4%)	127		0.697 (1.00)	0.39 (1.00)	0.571 (1.00)	0.173 (1.00)	0.912 (1.00)	0.213 (1.00)	1 (1.00)
FBXO24	5 (4%)	127	0.436 (1.00)	0.62 (1.00)	1 (1.00)	0.782 (1.00)	0.466 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
UBE2Q1	3 (2%)	129		0.264 (1.00)	0.274 (1.00)	0.447 (1.00)	0.113 (1.00)	0.59 (1.00)	0.114 (1.00)	1 (1.00)
WNT10A	3 (2%)	129		0.938 (1.00)	0.564 (1.00)	0.447 (1.00)	0.00686 (1.00)	0.59 (1.00)	0.341 (1.00)	1 (1.00)
PRSS1	3 (2%)	129		0.241 (1.00)	0.564 (1.00)			0.0431 (1.00)	0.341 (1.00)	1 (1.00)
PCDHB9	10 (8%)	122	0.487 (1.00)	0.0765 (1.00)	0.739 (1.00)	0.613 (1.00)	0.446 (1.00)	0.825 (1.00)	0.32 (1.00)	1 (1.00)
EDF1	3 (2%)	129		0.0471 (1.00)	1 (1.00)	0.81 (1.00)		0.843 (1.00)	0.341 (1.00)	1 (1.00)
CLEC4F	4 (3%)	128		0.128 (1.00)	1 (1.00)	0.671 (1.00)	0.236 (1.00)	1 (1.00)	0.162 (1.00)	1 (1.00)

'GPR146 MUTATION STATUS' versus 'AGE'

P value = 1.09e-14 (t-test), Q value = 2.8e-12

Table S1. Gene #37: 'GPR146 MUTATION STATUS' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	130	67.8 (10.8)
GPR146 MUTATED	4	78.8 (1.0)
GPR146 WILD-TYPE	126	67.5 (10.8)

Figure S1. Get High-res Image Gene #37: 'GPR146 MUTATION STATUS' versus Clinical Feature #2: 'AGE'

Methods & Data

Input

Mutation data file = STAD.mutsig.cluster.txt
Clinical data file = STAD.clin.merged.picked.txt
Number of patients = 132
Number of significantly mutated genes = 37
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

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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