Stomach Adenocarcinoma: Correlation between gene mutation status and selected clinical features<BR>(primary solid tumor cohort)

Stomach Adenocarcinoma: Correlation between gene mutation status and selected clinical features
(primary solid tumor cohort)

Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/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 40 genes and 8 clinical features across 116 patients, 3 significant findings detected with Q value < 0.25.

CBWD1 mutation correlated to 'PATHOLOGY.T'.
OR4Q3 mutation correlated to 'AGE'.
INO80E mutation correlated to 'PATHOLOGY.T'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 40 genes and 8 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	GENDER	HISTOLOGICAL TYPE	PATHOLOGY T	PATHOLOGY N	PATHOLOGICSPREAD(M)	TUMOR STAGE
	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
CBWD1	14 (12%)	102	0.303 (1.00)	0.0229 (1.00)	0.243 (1.00)	0.312 (1.00)	0.000583 (0.182)	0.692 (1.00)	0.505 (1.00)	0.264 (1.00)
OR4Q3	6 (5%)	110	0.344 (1.00)	0.00067 (0.208)	0.4 (1.00)	0.812 (1.00)	0.224 (1.00)	0.0842 (1.00)	0.114 (1.00)	0.263 (1.00)
INO80E	5 (4%)	111		0.936 (1.00)	0.0798 (1.00)	0.805 (1.00)	0.000484 (0.151)	0.913 (1.00)	0.554 (1.00)	0.0587 (1.00)
TP53	52 (45%)	64	0.0826 (1.00)	0.519 (1.00)	0.345 (1.00)	0.0205 (1.00)	0.754 (1.00)	0.975 (1.00)	0.123 (1.00)	0.747 (1.00)
KRAS	14 (12%)	102	0.353 (1.00)	0.496 (1.00)	0.243 (1.00)	0.701 (1.00)	0.8 (1.00)	0.348 (1.00)	0.623 (1.00)	0.41 (1.00)
PIK3CA	24 (21%)	92	0.407 (1.00)	0.457 (1.00)	0.253 (1.00)	0.789 (1.00)	0.669 (1.00)	0.655 (1.00)	1 (1.00)	0.375 (1.00)
ARID1A	22 (19%)	94	0.225 (1.00)	0.814 (1.00)	0.0525 (1.00)	0.298 (1.00)	0.114 (1.00)	0.422 (1.00)	0.0193 (1.00)	0.168 (1.00)
PGM5	16 (14%)	100	0.962 (1.00)	0.00184 (0.566)	0.0138 (1.00)	0.495 (1.00)	0.00445 (1.00)	0.898 (1.00)	0.555 (1.00)	0.381 (1.00)
ACVR2A	13 (11%)	103	0.00155 (0.478)	0.218 (1.00)	0.131 (1.00)	0.263 (1.00)	0.128 (1.00)	0.881 (1.00)	0.371 (1.00)	0.414 (1.00)
RPL22	9 (8%)	107	0.478 (1.00)	0.0449 (1.00)	0.48 (1.00)	0.591 (1.00)	0.0528 (1.00)	0.303 (1.00)	0.000925 (0.287)	0.099 (1.00)
OR8H3	10 (9%)	106	0.626 (1.00)	0.424 (1.00)	0.738 (1.00)	0.0783 (1.00)	0.774 (1.00)	0.113 (1.00)	0.724 (1.00)	0.688 (1.00)
RHOA	7 (6%)	109	0.0968 (1.00)	0.0615 (1.00)	0.701 (1.00)	0.451 (1.00)	0.559 (1.00)	0.765 (1.00)	0.681 (1.00)	0.157 (1.00)
TRIM48	10 (9%)	106	0.322 (1.00)	0.157 (1.00)	0.191 (1.00)	0.448 (1.00)	0.0924 (1.00)	0.698 (1.00)	0.0506 (1.00)	0.241 (1.00)
ZNF804B	18 (16%)	98	0.909 (1.00)	0.267 (1.00)	0.61 (1.00)	0.416 (1.00)	1 (1.00)	0.13 (1.00)	0.382 (1.00)	0.0243 (1.00)
EDNRB	12 (10%)	104	0.877 (1.00)	0.831 (1.00)	0.537 (1.00)	0.365 (1.00)	0.233 (1.00)	0.339 (1.00)	0.451 (1.00)	0.885 (1.00)
SPRYD5	8 (7%)	108	0.143 (1.00)	0.135 (1.00)	1 (1.00)	0.904 (1.00)	0.0528 (1.00)	0.765 (1.00)	0.103 (1.00)	0.0411 (1.00)
PCDH15	22 (19%)	94	0.855 (1.00)	0.534 (1.00)	0.63 (1.00)	0.878 (1.00)	0.407 (1.00)	0.757 (1.00)	0.649 (1.00)	0.222 (1.00)
HLA-B	9 (8%)	107	0.963 (1.00)	0.717 (1.00)	0.028 (1.00)	0.556 (1.00)	0.126 (1.00)	0.111 (1.00)	0.329 (1.00)	0.303 (1.00)
TUSC3	9 (8%)	107	0.76 (1.00)	0.28 (1.00)	1 (1.00)	0.783 (1.00)	0.46 (1.00)	0.488 (1.00)	0.329 (1.00)	0.551 (1.00)
TPTE	14 (12%)	102	0.382 (1.00)	0.92 (1.00)	0.401 (1.00)	0.00396 (1.00)	0.283 (1.00)	0.263 (1.00)	0.505 (1.00)	0.974 (1.00)
C17ORF63	3 (3%)	113		0.524 (1.00)	0.562 (1.00)	0.836 (1.00)	0.121 (1.00)	0.437 (1.00)	1 (1.00)	0.532 (1.00)
SLITRK6	10 (9%)	106	0.231 (1.00)	0.693 (1.00)	0.738 (1.00)	0.742 (1.00)	0.925 (1.00)	0.619 (1.00)	0.26 (1.00)	0.327 (1.00)
RNF43	13 (11%)	103	0.0257 (1.00)	0.0612 (1.00)	0.368 (1.00)	0.151 (1.00)	0.062 (1.00)	0.881 (1.00)	0.484 (1.00)	0.185 (1.00)
TM7SF4	7 (6%)	109	0.915 (1.00)	0.553 (1.00)	0.701 (1.00)	0.855 (1.00)	0.619 (1.00)	0.847 (1.00)	1 (1.00)	0.676 (1.00)
WBSCR17	12 (10%)	104	0.116 (1.00)	0.185 (1.00)	0.537 (1.00)	0.281 (1.00)	0.419 (1.00)	0.965 (1.00)	0.333 (1.00)	0.835 (1.00)
OR4C16	8 (7%)	108	0.614 (1.00)	0.415 (1.00)	0.475 (1.00)	0.245 (1.00)	0.346 (1.00)	0.798 (1.00)	0.695 (1.00)	0.788 (1.00)
SMAD4	7 (6%)	109	0.103 (1.00)	0.051 (1.00)	0.701 (1.00)	0.663 (1.00)	0.9 (1.00)	1 (1.00)	0.153 (1.00)	1 (1.00)
ASTN2	14 (12%)	102	0.525 (1.00)	0.266 (1.00)	0.401 (1.00)	0.169 (1.00)	0.119 (1.00)	0.933 (1.00)	0.224 (1.00)	0.747 (1.00)
IAPP	4 (3%)	112		0.485 (1.00)	1 (1.00)	0.937 (1.00)	1 (1.00)	0.897 (1.00)	0.189 (1.00)	0.107 (1.00)
IRF2	8 (7%)	108	0.74 (1.00)	0.234 (1.00)	0.0568 (1.00)	0.762 (1.00)	0.0166 (1.00)	0.873 (1.00)	0.288 (1.00)	0.928 (1.00)
CDH20	14 (12%)	102	0.525 (1.00)	0.129 (1.00)	0.0172 (1.00)	0.698 (1.00)	0.393 (1.00)	0.845 (1.00)	0.623 (1.00)	0.824 (1.00)
OR2T6	6 (5%)	110	0.996 (1.00)	0.988 (1.00)	0.68 (1.00)	0.542 (1.00)	0.0647 (1.00)	0.442 (1.00)	0.622 (1.00)	0.105 (1.00)
PARK2	9 (8%)	107	0.73 (1.00)	0.00874 (1.00)	1 (1.00)	0.109 (1.00)	0.292 (1.00)	0.351 (1.00)	0.329 (1.00)	0.199 (1.00)
PDZRN4	11 (9%)	105	0.699 (1.00)	0.61 (1.00)	0.522 (1.00)	0.952 (1.00)	0.172 (1.00)	0.766 (1.00)	0.0645 (1.00)	0.93 (1.00)
CYP7B1	7 (6%)	109	1 (1.00)	0.333 (1.00)	1 (1.00)	0.0234 (1.00)	1 (1.00)	0.0797 (1.00)	0.681 (1.00)	0.378 (1.00)
CHRM2	6 (5%)	110		0.373 (1.00)	0.4 (1.00)	0.0979 (1.00)	0.478 (1.00)	1 (1.00)	1 (1.00)	0.186 (1.00)
KIAA0748	7 (6%)	109	0.337 (1.00)	0.375 (1.00)	1 (1.00)	0.735 (1.00)	0.699 (1.00)	0.568 (1.00)	1 (1.00)	0.199 (1.00)
FMOD	8 (7%)	108	0.911 (1.00)	0.753 (1.00)	0.711 (1.00)	0.434 (1.00)	0.559 (1.00)	0.021 (1.00)	1 (1.00)	0.863 (1.00)
BMPR2	9 (8%)	107	0.621 (1.00)	0.281 (1.00)	0.48 (1.00)	0.801 (1.00)	0.781 (1.00)	0.596 (1.00)	1 (1.00)	0.885 (1.00)
CNBD1	5 (4%)	111		0.136 (1.00)	1 (1.00)	0.769 (1.00)		1 (1.00)	1 (1.00)

'CBWD1 MUTATION STATUS' versus 'PATHOLOGY.T'

P value = 0.000583 (Fisher's exact test), Q value = 0.18

Table S1. Gene #4: 'CBWD1 MUTATION STATUS' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	4	46	41	15
CBWD1 MUTATED	3	2	3	4
CBWD1 WILD-TYPE	1	44	38	11

Figure S1. Get High-res Image Gene #4: 'CBWD1 MUTATION STATUS' versus Clinical Feature #5: 'PATHOLOGY.T'

'OR4Q3 MUTATION STATUS' versus 'AGE'

P value = 0.00067 (t-test), Q value = 0.21

Table S2. Gene #27: 'OR4Q3 MUTATION STATUS' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	114	68.1 (11.1)
OR4Q3 MUTATED	6	82.8 (6.0)
OR4Q3 WILD-TYPE	108	67.3 (10.7)

Figure S2. Get High-res Image Gene #27: 'OR4Q3 MUTATION STATUS' versus Clinical Feature #2: 'AGE'

'INO80E MUTATION STATUS' versus 'PATHOLOGY.T'

P value = 0.000484 (Fisher's exact test), Q value = 0.15

Table S3. Gene #40: 'INO80E MUTATION STATUS' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	4	46	41	15
INO80E MUTATED	0	0	0	4
INO80E WILD-TYPE	4	46	41	11

Figure S3. Get High-res Image Gene #40: 'INO80E MUTATION STATUS' versus Clinical Feature #5: 'PATHOLOGY.T'

Methods & Data

Input

Mutation data file = STAD-TP.mutsig.cluster.txt
Clinical data file = STAD-TP.clin.merged.picked.txt
Number of patients = 116
Number of significantly mutated genes = 40
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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