Correlation between gene mutation status and selected clinical features

Prostate 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): Prostate Adenocarcinoma (Primary solid tumor cohort) - 21 April 2013: Correlation between gene mutation status and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C11C1TVP

Overview

Introduction

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

Summary

Testing the association between mutation status of 13 genes and 4 clinical features across 83 patients, 6 significant findings detected with Q value < 0.25.

NKX3-1 mutation correlated to 'NUMBER.OF.LYMPH.NODES'.
CLSTN1 mutation correlated to 'NUMBER.OF.LYMPH.NODES'.
PRR21 mutation correlated to 'AGE'.
CTNNB1 mutation correlated to 'NUMBER.OF.LYMPH.NODES'.
DUSP27 mutation correlated to 'NUMBER.OF.LYMPH.NODES'.
OR4D5 mutation correlated to 'NUMBER.OF.LYMPH.NODES'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 13 genes and 4 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 6 significant findings detected.


		Clinical Features	AGE	RADIATIONS RADIATION REGIMENINDICATION	COMPLETENESS OF RESECTION	NUMBER OF LYMPH NODES
	nMutated (%)	nWild-Type	t-test	Fisher's exact test	Fisher's exact test	t-test
NKX3-1	5 (6%)	78	0.463 (1.00)	1 (1.00)	0.368 (1.00)	0.00452 (0.23)
CLSTN1	3 (4%)	80	0.093 (1.00)	0.0086 (0.396)	0.177 (1.00)	0.00454 (0.23)
PRR21	4 (5%)	79	0.00302 (0.157)	0.224 (1.00)	0.588 (1.00)	0.249 (1.00)
CTNNB1	3 (4%)	80	0.992 (1.00)	0.172 (1.00)	1 (1.00)	0.00454 (0.23)
DUSP27	3 (4%)	80	0.824 (1.00)	1 (1.00)	1 (1.00)	0.00454 (0.23)
OR4D5	3 (4%)	80	0.092 (1.00)	1 (1.00)	1 (1.00)	0.00454 (0.23)
TP53	5 (6%)	78	0.64 (1.00)	1 (1.00)	0.368 (1.00)	0.822 (1.00)
FRG1	4 (5%)	79	0.0586 (1.00)	0.224 (1.00)	0.285 (1.00)	0.834 (1.00)
SPOP	4 (5%)	79	0.481 (1.00)	1 (1.00)	0.588 (1.00)	0.464 (1.00)
YBX1	3 (4%)	80	0.784 (1.00)	1 (1.00)	1 (1.00)	0.547 (1.00)
CCNF	3 (4%)	80	0.643 (1.00)	0.172 (1.00)	1 (1.00)	0.374 (1.00)
AGT	3 (4%)	80	0.6 (1.00)	1 (1.00)	1 (1.00)	0.945 (1.00)
OR6N1	3 (4%)	80	0.367 (1.00)	0.172 (1.00)	0.0145 (0.651)	0.945 (1.00)

'NKX3-1 MUTATION STATUS' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.00452 (t-test), Q value = 0.23

Table S1. Gene #1: 'NKX3-1 MUTATION STATUS' versus Clinical Feature #4: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	78	0.3 (0.9)
NKX3-1 MUTATED	5	0.0 (0.0)
NKX3-1 WILD-TYPE	73	0.3 (1.0)

Figure S1. Get High-res Image Gene #1: 'NKX3-1 MUTATION STATUS' versus Clinical Feature #4: 'NUMBER.OF.LYMPH.NODES'

'CLSTN1 MUTATION STATUS' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.00454 (t-test), Q value = 0.23

Table S2. Gene #7: 'CLSTN1 MUTATION STATUS' versus Clinical Feature #4: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	78	0.3 (0.9)
CLSTN1 MUTATED	3	0.0 (0.0)
CLSTN1 WILD-TYPE	75	0.3 (0.9)

Figure S2. Get High-res Image Gene #7: 'CLSTN1 MUTATION STATUS' versus Clinical Feature #4: 'NUMBER.OF.LYMPH.NODES'

'PRR21 MUTATION STATUS' versus 'AGE'

P value = 0.00302 (t-test), Q value = 0.16

Table S3. Gene #8: 'PRR21 MUTATION STATUS' versus Clinical Feature #1: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	83	61.1 (6.8)
PRR21 MUTATED	4	66.5 (2.1)
PRR21 WILD-TYPE	79	60.8 (6.8)

Figure S3. Get High-res Image Gene #8: 'PRR21 MUTATION STATUS' versus Clinical Feature #1: 'AGE'

'CTNNB1 MUTATION STATUS' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.00454 (t-test), Q value = 0.23

Table S4. Gene #10: 'CTNNB1 MUTATION STATUS' versus Clinical Feature #4: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	78	0.3 (0.9)
CTNNB1 MUTATED	3	0.0 (0.0)
CTNNB1 WILD-TYPE	75	0.3 (0.9)

Figure S4. Get High-res Image Gene #10: 'CTNNB1 MUTATION STATUS' versus Clinical Feature #4: 'NUMBER.OF.LYMPH.NODES'

'DUSP27 MUTATION STATUS' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.00454 (t-test), Q value = 0.23

Table S5. Gene #11: 'DUSP27 MUTATION STATUS' versus Clinical Feature #4: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	78	0.3 (0.9)
DUSP27 MUTATED	3	0.0 (0.0)
DUSP27 WILD-TYPE	75	0.3 (0.9)

Figure S5. Get High-res Image Gene #11: 'DUSP27 MUTATION STATUS' versus Clinical Feature #4: 'NUMBER.OF.LYMPH.NODES'

'OR4D5 MUTATION STATUS' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.00454 (t-test), Q value = 0.23

Table S6. Gene #12: 'OR4D5 MUTATION STATUS' versus Clinical Feature #4: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	78	0.3 (0.9)
OR4D5 MUTATED	3	0.0 (0.0)
OR4D5 WILD-TYPE	75	0.3 (0.9)

Figure S6. Get High-res Image Gene #12: 'OR4D5 MUTATION STATUS' versus Clinical Feature #4: 'NUMBER.OF.LYMPH.NODES'

Methods & Data

Input

Mutation data file = PRAD-TP.mutsig.cluster.txt
Clinical data file = PRAD-TP.clin.merged.picked.txt
Number of patients = 83
Number of significantly mutated genes = 13
Number of selected clinical features = 4
Exclude genes that fewer than K tumors have mutations, K = 3

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

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] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)

[2] 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)

[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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