Correlation between gene mutation status and molecular subtypes

Stomach Adenocarcinoma (Primary solid tumor)

21 April 2013 | analyses__2013_04_21

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): Stomach Adenocarcinoma (Primary solid tumor cohort) - 21 April 2013: Correlation between gene mutation status and molecular subtypes. Broad Institute of MIT and Harvard. doi:10.7908/C1B85643

Overview

Introduction

This pipeline computes the correlation between significantly recurrent gene mutations and molecular subtypes.

Summary

Testing the association between mutation status of 47 genes and 6 molecular subtypes across 116 patients, 7 significant findings detected with P value < 0.05 and Q value < 0.25.

TP53 mutation correlated to 'CN_CNMF' and 'MIRSEQ_CHIERARCHICAL'.
PIK3CA mutation correlated to 'METHLYATION_CNMF' and 'MRNASEQ_CNMF'.
ARID1A mutation correlated to 'CN_CNMF', 'MRNASEQ_CNMF', and 'MIRSEQ_CHIERARCHICAL'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 47 genes and 6 molecular subtypes. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 7 significant findings detected.


		Clinical Features	CN CNMF	METHLYATION CNMF	MRNASEQ CNMF	MRNASEQ CHIERARCHICAL	MIRSEQ CNMF	MIRSEQ CHIERARCHICAL
	nMutated (%)	nWild-Type	Fisher's exact test	Fisher's exact test	Chi-square test	Fisher's exact test	Fisher's exact test	Fisher's exact test
ARID1A	22 (19%)	94	7.42e-06 (0.00201)	0.00783 (1.00)	0.000768 (0.204)	0.154 (1.00)	0.0203 (1.00)	0.000551 (0.147)
TP53	52 (45%)	64	0.000278 (0.0748)	0.011 (1.00)	0.0949 (1.00)	0.0923 (1.00)	0.00228 (0.599)	9.14e-06 (0.00247)
PIK3CA	24 (21%)	92	0.00256 (0.669)	1.26e-06 (0.000344)	0.000322 (0.0862)	0.192 (1.00)	0.0282 (1.00)	0.0194 (1.00)
CBWD1	14 (12%)	102	0.0342 (1.00)	0.0585 (1.00)	0.00578 (1.00)	0.132 (1.00)	0.0482 (1.00)	0.216 (1.00)
KRAS	14 (12%)	102	0.0906 (1.00)	0.292 (1.00)	0.368 (1.00)	0.0849 (1.00)	0.685 (1.00)	0.578 (1.00)
PGM5	16 (14%)	100	0.0118 (1.00)	0.00266 (0.694)	0.192 (1.00)	0.306 (1.00)	0.158 (1.00)	0.222 (1.00)
RPL22	9 (8%)	107	0.254 (1.00)		0.123 (1.00)	0.091 (1.00)	0.299 (1.00)	0.152 (1.00)
TRIM48	10 (9%)	106	0.00622 (1.00)	0.174 (1.00)	0.0438 (1.00)	0.16 (1.00)	0.0839 (1.00)	0.0155 (1.00)
XPOT	6 (5%)	110	0.0703 (1.00)		0.0277 (1.00)	0.373 (1.00)	0.34 (1.00)	0.128 (1.00)
ACVR2A	13 (11%)	103	0.00214 (0.564)	0.0128 (1.00)	0.00158 (0.418)	0.0808 (1.00)	0.0253 (1.00)	0.0203 (1.00)
RHOA	7 (6%)	109	0.354 (1.00)	0.639 (1.00)	0.476 (1.00)	1 (1.00)	0.0881 (1.00)	1 (1.00)
OR8H3	10 (9%)	106	0.0238 (1.00)	0.0884 (1.00)	0.0274 (1.00)	0.572 (1.00)	0.221 (1.00)	0.474 (1.00)
EDNRB	12 (10%)	104	0.112 (1.00)	0.543 (1.00)	0.868 (1.00)	0.324 (1.00)	0.475 (1.00)	0.588 (1.00)
ZNF804B	18 (16%)	98	0.236 (1.00)	0.047 (1.00)	0.0795 (1.00)	0.00277 (0.719)	0.0127 (1.00)	0.274 (1.00)
IRF2	8 (7%)	108	0.0405 (1.00)	0.0511 (1.00)	0.0225 (1.00)	0.886 (1.00)	0.176 (1.00)	0.321 (1.00)
IAPP	4 (3%)	112	0.859 (1.00)		0.736 (1.00)	0.455 (1.00)	0.147 (1.00)	1 (1.00)
PCDH15	22 (19%)	94	0.634 (1.00)	0.863 (1.00)	0.928 (1.00)	0.439 (1.00)	0.594 (1.00)	0.947 (1.00)
SPRYD5	8 (7%)	108	0.843 (1.00)	0.0343 (1.00)	0.441 (1.00)	0.717 (1.00)	0.263 (1.00)	0.687 (1.00)
TUSC3	9 (8%)	107	0.666 (1.00)	0.88 (1.00)	0.968 (1.00)	1 (1.00)	1 (1.00)	0.712 (1.00)
FGF22	3 (3%)	113	0.0257 (1.00)		0.028 (1.00)	0.327 (1.00)	1 (1.00)	0.573 (1.00)
HLA-B	9 (8%)	107	0.13 (1.00)	0.00377 (0.977)	0.123 (1.00)	0.117 (1.00)	0.299 (1.00)	0.636 (1.00)
PTH2	3 (3%)	113	0.812 (1.00)	0.302 (1.00)	0.171 (1.00)	0.161 (1.00)	0.155 (1.00)	0.384 (1.00)
C17ORF63	3 (3%)	113	0.435 (1.00)		0.736 (1.00)	1 (1.00)	1 (1.00)	0.773 (1.00)
SMAD4	7 (6%)	109	1 (1.00)	0.859 (1.00)	0.186 (1.00)	0.477 (1.00)	1 (1.00)	0.753 (1.00)
POTEG	6 (5%)	110	0.294 (1.00)	0.48 (1.00)	0.469 (1.00)	0.854 (1.00)	1 (1.00)	0.375 (1.00)
RNF43	13 (11%)	103	0.00576 (1.00)	0.0168 (1.00)	0.0478 (1.00)	0.0789 (1.00)	0.0253 (1.00)	0.0592 (1.00)
WBSCR17	12 (10%)	104	0.288 (1.00)	0.00419 (1.00)	0.0813 (1.00)	0.903 (1.00)	0.0289 (1.00)	0.412 (1.00)
PHF2	12 (10%)	104	0.006 (1.00)	0.0128 (1.00)	0.00746 (1.00)	0.0311 (1.00)	0.0289 (1.00)	0.00602 (1.00)
TPTE	14 (12%)	102	0.563 (1.00)	1 (1.00)	0.464 (1.00)	0.698 (1.00)	0.0419 (1.00)	0.856 (1.00)
CDH1	11 (9%)	105	0.14 (1.00)	0.769 (1.00)	0.0161 (1.00)	0.121 (1.00)	0.0919 (1.00)	0.0862 (1.00)
CPS1	13 (11%)	103	0.731 (1.00)	0.223 (1.00)	0.518 (1.00)	0.856 (1.00)	0.906 (1.00)	0.335 (1.00)
INO80E	5 (4%)	111	0.534 (1.00)		0.798 (1.00)	0.713 (1.00)	0.369 (1.00)	1 (1.00)
ELF3	5 (4%)	111	0.118 (1.00)	0.163 (1.00)	0.263 (1.00)	0.358 (1.00)	0.493 (1.00)	0.221 (1.00)
PARK2	9 (8%)	107	0.235 (1.00)	0.0548 (1.00)	0.306 (1.00)	0.0219 (1.00)	0.869 (1.00)	0.636 (1.00)
LARP4B	5 (4%)	111	0.0339 (1.00)	0.174 (1.00)	0.108 (1.00)	0.358 (1.00)	0.493 (1.00)	0.221 (1.00)
OR6K3	6 (5%)	110	0.347 (1.00)	0.358 (1.00)	0.0808 (1.00)	0.867 (1.00)	0.434 (1.00)	1 (1.00)
TM7SF4	7 (6%)	109	0.931 (1.00)	0.223 (1.00)	0.642 (1.00)	0.782 (1.00)	0.852 (1.00)	0.376 (1.00)
UPF3A	6 (5%)	110	0.347 (1.00)	0.174 (1.00)	0.0905 (1.00)	0.231 (1.00)	0.434 (1.00)	0.619 (1.00)
C7ORF63	5 (4%)	111	0.118 (1.00)		0.108 (1.00)	0.569 (1.00)	0.493 (1.00)	0.697 (1.00)
KDM4B	10 (9%)	106	0.0222 (1.00)	0.0343 (1.00)	0.211 (1.00)	0.607 (1.00)	0.221 (1.00)	0.53 (1.00)
KIAA0748	7 (6%)	109	0.776 (1.00)	0.545 (1.00)	0.279 (1.00)	0.473 (1.00)	0.852 (1.00)	0.432 (1.00)
OR8B4	3 (3%)	113	0.155 (1.00)	0.00894 (1.00)			1 (1.00)	0.573 (1.00)
POM121L12	7 (6%)	109	0.0617 (1.00)	0.122 (1.00)	0.0988 (1.00)	0.209 (1.00)	0.852 (1.00)	0.376 (1.00)
RASA1	9 (8%)	107	0.448 (1.00)	0.551 (1.00)	0.467 (1.00)	0.0148 (1.00)	0.066 (1.00)	0.314 (1.00)
SLITRK6	10 (9%)	106	0.258 (1.00)	0.88 (1.00)	0.522 (1.00)	0.717 (1.00)	0.14 (1.00)	0.307 (1.00)
TP53TG5	4 (3%)	112	0.024 (1.00)		0.736 (1.00)	1 (1.00)	1 (1.00)	0.641 (1.00)
LHCGR	7 (6%)	109	1 (1.00)	0.813 (1.00)	0.999 (1.00)	0.713 (1.00)	0.597 (1.00)	0.275 (1.00)

'TP53 MUTATION STATUS' versus 'CN_CNMF'

P value = 0.000278 (Fisher's exact test), Q value = 0.075

Table S1. Gene #5: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3	CLUS_4
ALL	47	15	10	43
TP53 MUTATED	11	9	3	28
TP53 WILD-TYPE	36	6	7	15

Figure S1. Get High-res Image Gene #5: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'CN_CNMF'

'TP53 MUTATION STATUS' versus 'MIRSEQ_CHIERARCHICAL'

P value = 9.14e-06 (Fisher's exact test), Q value = 0.0025

Table S2. Gene #5: 'TP53 MUTATION STATUS' versus Clinical Feature #6: 'MIRSEQ_CHIERARCHICAL'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	26	23	67
TP53 MUTATED	22	6	24
TP53 WILD-TYPE	4	17	43

Figure S2. Get High-res Image Gene #5: 'TP53 MUTATION STATUS' versus Clinical Feature #6: 'MIRSEQ_CHIERARCHICAL'

'PIK3CA MUTATION STATUS' versus 'METHLYATION_CNMF'

P value = 1.26e-06 (Fisher's exact test), Q value = 0.00034

Table S3. Gene #8: 'PIK3CA MUTATION STATUS' versus Clinical Feature #2: 'METHLYATION_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3	CLUS_4
ALL	9	14	22	23
PIK3CA MUTATED	9	4	3	2
PIK3CA WILD-TYPE	0	10	19	21

Figure S3. Get High-res Image Gene #8: 'PIK3CA MUTATION STATUS' versus Clinical Feature #2: 'METHLYATION_CNMF'

'PIK3CA MUTATION STATUS' versus 'MRNASEQ_CNMF'

P value = 0.000322 (Chi-square test), Q value = 0.086

Table S4. Gene #8: 'PIK3CA MUTATION STATUS' versus Clinical Feature #3: 'MRNASEQ_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3	CLUS_4	CLUS_5
ALL	23	18	17	23	23
PIK3CA MUTATED	12	2	1	6	1
PIK3CA WILD-TYPE	11	16	16	17	22

Figure S4. Get High-res Image Gene #8: 'PIK3CA MUTATION STATUS' versus Clinical Feature #3: 'MRNASEQ_CNMF'

'ARID1A MUTATION STATUS' versus 'CN_CNMF'

P value = 7.42e-06 (Fisher's exact test), Q value = 0.002

Table S5. Gene #11: 'ARID1A MUTATION STATUS' versus Clinical Feature #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3	CLUS_4
ALL	47	15	10	43
ARID1A MUTATED	15	2	5	0
ARID1A WILD-TYPE	32	13	5	43

Figure S5. Get High-res Image Gene #11: 'ARID1A MUTATION STATUS' versus Clinical Feature #1: 'CN_CNMF'

'ARID1A MUTATION STATUS' versus 'MRNASEQ_CNMF'

P value = 0.000768 (Chi-square test), Q value = 0.2

Table S6. Gene #11: 'ARID1A MUTATION STATUS' versus Clinical Feature #3: 'MRNASEQ_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3	CLUS_4	CLUS_5
ALL	23	18	17	23	23
ARID1A MUTATED	11	2	2	5	0
ARID1A WILD-TYPE	12	16	15	18	23

Figure S6. Get High-res Image Gene #11: 'ARID1A MUTATION STATUS' versus Clinical Feature #3: 'MRNASEQ_CNMF'

'ARID1A MUTATION STATUS' versus 'MIRSEQ_CHIERARCHICAL'

P value = 0.000551 (Fisher's exact test), Q value = 0.15

Table S7. Gene #11: 'ARID1A MUTATION STATUS' versus Clinical Feature #6: 'MIRSEQ_CHIERARCHICAL'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	26	23	67
ARID1A MUTATED	0	2	20
ARID1A WILD-TYPE	26	21	47

Figure S7. Get High-res Image Gene #11: 'ARID1A MUTATION STATUS' versus Clinical Feature #6: 'MIRSEQ_CHIERARCHICAL'

Methods & Data

Input

Mutation data file = STAD-TP.mutsig.cluster.txt
Molecular subtypes file = STAD-TP.transferedmergedcluster.txt
Number of patients = 116
Number of significantly mutated genes = 47
Number of Molecular subtypes = 6
Exclude genes that fewer than K tumors have mutations, K = 3

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] 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)

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

[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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