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
doi:10.7908/C11C1TVP
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.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

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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