Correlation between gene mutation status and selected clinical features
Prostate Adenocarcinoma (Primary solid tumor)
15 January 2014 | analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C1WM1BV2
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between gene mutation status and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1WM1BV2
Overview
Introduction

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

Summary

Testing the association between mutation status of 6 genes and 14 clinical features across 171 patients, 3 significant findings detected with Q value < 0.25.

- CTNNB1 mutation correlated to 'NUMBER.OF.LYMPH.NODES'.

- PTEN mutation correlated to 'NUMBER.OF.LYMPH.NODES'.

- FOXA1 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 6 genes and 14 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 | PATHOLOGY T STAGE | PATHOLOGY N STAGE | COMPLETENESS OF RESECTION | NUMBER OF LYMPH NODES | GLEASON SCORE COMBINED | GLEASON SCORE PRIMARY | GLEASON SCORE SECONDARY | GLEASON SCORE | PSA RESULT PREOP | DAYS TO PREOP PSA | PSA VALUE | DAYS TO PSA
| nMutated (%) | nWild-Type | logrank test | t-test | Fisher's exact test | Fisher's exact test | Fisher's exact test | t-test | t-test | t-test | t-test | t-test | t-test | t-test | t-test | t-test
CTNNB1 | 4 (2%) | 167 | 100 (1.00) | 0.892 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00) | 0.00109 (0.0915) | 0.366 (1.00) | 0.902 (1.00) | 0.155 (1.00) | 0.401 (1.00) | 0.0552 (1.00) | 0.726 (1.00) | 0.769 (1.00) | 0.798 (1.00)
PTEN | 9 (5%) | 162 | 100 (1.00) | 0.411 (1.00) | 0.385 (1.00) | 1 (1.00) | 1 (1.00) | 0.00109 (0.0915) | 0.896 (1.00) | 0.451 (1.00) | 0.623 (1.00) | 0.756 (1.00) | 0.821 (1.00) | 0.611 (1.00) | 0.492 (1.00) | 0.106 (1.00)
FOXA1 | 6 (4%) | 165 | 100 (1.00) | 0.621 (1.00) | 0.745 (1.00) | 1 (1.00) | 0.633 (1.00) | 0.00109 (0.0915) | 0.637 (1.00) | 0.869 (1.00) | 0.72 (1.00) | 0.49 (1.00) | 0.392 (1.00) | 0.428 (1.00) | 0.565 (1.00) | 0.434 (1.00)
SPOP | 12 (7%) | 159 | 100 (1.00) | 0.0444 (1.00) | 0.757 (1.00) | 1 (1.00) | 1 (1.00) | 0.52 (1.00) | 0.664 (1.00) | 0.163 (1.00) | 0.509 (1.00) | 0.815 (1.00) | 0.468 (1.00) | 0.374 (1.00) | 0.0305 (1.00) | 0.107 (1.00)
PCDHAC2 | 19 (11%) | 152 | 100 (1.00) | 0.417 (1.00) | 0.384 (1.00) | 0.00323 (0.262) | 0.129 (1.00) | 0.0918 (1.00) | 0.231 (1.00) | 0.918 (1.00) | 0.155 (1.00) | 0.0504 (1.00) | 0.219 (1.00) | 0.443 (1.00) | 0.664 (1.00) | 0.853 (1.00)
TP53 | 15 (9%) | 156 | 100 (1.00) | 0.927 (1.00) | 0.281 (1.00) | 0.66 (1.00) | 0.785 (1.00) | 0.963 (1.00) | 0.0138 (1.00) | 0.0195 (1.00) | 0.208 (1.00) | 0.00439 (0.351) | 0.145 (1.00) | 0.364 (1.00) | 0.983 (1.00) | 0.155 (1.00)

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

P value = 0.00109 (t-test), Q value = 0.092

Table S1. Gene #1: 'CTNNB1 MUTATION STATUS' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 150 | 0.2 (0.7)
CTNNB1 MUTATED | 4 | 0.0 (0.0)
CTNNB1 WILD-TYPE | 146 | 0.2 (0.7)

Figure S1. Get High-res Image Gene #1: 'CTNNB1 MUTATION STATUS' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

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

P value = 0.00109 (t-test), Q value = 0.092

Table S2. Gene #3: 'PTEN MUTATION STATUS' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 150 | 0.2 (0.7)
PTEN MUTATED | 8 | 0.0 (0.0)
PTEN WILD-TYPE | 142 | 0.2 (0.7)

Figure S2. Get High-res Image Gene #3: 'PTEN MUTATION STATUS' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

'FOXA1 MUTATION STATUS' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.00109 (t-test), Q value = 0.092

Table S3. Gene #5: 'FOXA1 MUTATION STATUS' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 150 | 0.2 (0.7)
FOXA1 MUTATED | 5 | 0.0 (0.0)
FOXA1 WILD-TYPE | 145 | 0.2 (0.7)

Figure S3. Get High-res Image Gene #5: 'FOXA1 MUTATION STATUS' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

Methods & Data
Input

- Mutation data file = transformed.cor.cli.txt

- Clinical data file = PRAD-TP.merged_data.txt

- Number of patients = 171

- Number of significantly mutated genes = 6

- Number of selected clinical features = 14

- 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

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

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

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] 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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