Prostate Adenocarcinoma: Correlation between gene mutation status and selected clinical features
Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/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 46 genes and 3 clinical features across 83 patients, no significant finding detected with Q value < 0.25.

- No gene mutations related to clinical features.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 46 genes and 3 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, no significant finding detected.

|  | Clinical Features | AGE | RADIATIONS RADIATION REGIMENINDICATION | NEOADJUVANT THERAPY
| nMutated (%) | nWild-Type | t-test | Fisher's exact test | Fisher's exact test
POM121 | 3 (4%) | 80 | 0.0611 (1.00) | 0.172 (1.00) | 1 (1.00)
ZNF285 | 3 (4%) | 80 | 0.51 (1.00) | 1 (1.00) | 1 (1.00)
MUC4 | 13 (16%) | 70 | 0.974 (1.00) | 1 (1.00) | 1 (1.00)
C9ORF150 | 3 (4%) | 80 | 0.76 (1.00) | 1 (1.00) | 0.139 (1.00)
NKX3-1 | 5 (6%) | 78 | 0.463 (1.00) | 1 (1.00) | 1 (1.00)
FIP1L1 | 3 (4%) | 80 | 0.168 (1.00) | 1 (1.00) | 1 (1.00)
NDUFS4 | 3 (4%) | 80 | 0.093 (1.00) | 0.0086 (1.00) | 1 (1.00)
AGT | 3 (4%) | 80 | 0.6 (1.00) | 1 (1.00) | 1 (1.00)
CCNF | 3 (4%) | 80 | 0.643 (1.00) | 0.172 (1.00) | 0.0052 (0.713)
DUSP27 | 3 (4%) | 80 | 0.824 (1.00) | 1 (1.00) | 1 (1.00)
CLSTN1 | 3 (4%) | 80 | 0.093 (1.00) | 0.0086 (1.00) | 1 (1.00)
TP53 | 5 (6%) | 78 | 0.64 (1.00) | 1 (1.00) | 1 (1.00)
TPTE2 | 6 (7%) | 77 | 0.561 (1.00) | 0.0398 (1.00) | 0.0247 (1.00)
FRG1 | 5 (6%) | 78 | 0.19 (1.00) | 0.0272 (1.00) | 0.0168 (1.00)
ZNF492 | 4 (5%) | 79 | 0.188 (1.00) | 0.224 (1.00) | 1 (1.00)
YBX1 | 3 (4%) | 80 | 0.784 (1.00) | 1 (1.00) | 1 (1.00)
SPOP | 4 (5%) | 79 | 0.481 (1.00) | 1 (1.00) | 0.182 (1.00)
ARHGAP11B | 4 (5%) | 79 | 0.838 (1.00) | 0.224 (1.00) | 0.182 (1.00)
SCAI | 5 (6%) | 78 | 0.299 (1.00) | 1 (1.00) | 1 (1.00)
LILRB3 | 4 (5%) | 79 | 0.512 (1.00) | 1 (1.00) | 1 (1.00)
ZNF814 | 4 (5%) | 79 | 0.527 (1.00) | 0.224 (1.00) | 0.182 (1.00)
CTNNB1 | 3 (4%) | 80 | 0.992 (1.00) | 0.172 (1.00) | 0.139 (1.00)
PDE4DIP | 6 (7%) | 77 | 0.605 (1.00) | 1 (1.00) | 1 (1.00)
PRR21 | 4 (5%) | 79 | 0.00302 (0.417) | 0.224 (1.00) | 0.0102 (1.00)
NOTCH2NL | 3 (4%) | 80 | 0.562 (1.00) | 0.172 (1.00) | 1 (1.00)
ANKRD36 | 4 (5%) | 79 | 0.602 (1.00) | 1 (1.00) | 1 (1.00)
OR6N1 | 3 (4%) | 80 | 0.367 (1.00) | 0.172 (1.00) | 1 (1.00)
PRIM2 | 4 (5%) | 79 | 0.381 (1.00) | 1 (1.00) | 0.182 (1.00)
POTEC | 3 (4%) | 80 | 0.647 (1.00) | 1 (1.00) | 1 (1.00)
LRIT2 | 4 (5%) | 79 | 0.491 (1.00) | 1 (1.00) | 1 (1.00)
OR4D5 | 3 (4%) | 80 | 0.092 (1.00) | 1 (1.00) | 1 (1.00)
ZNF98 | 4 (5%) | 79 | 0.964 (1.00) | 0.224 (1.00) | 0.182 (1.00)
CROCC | 5 (6%) | 78 | 0.885 (1.00) | 1 (1.00) | 1 (1.00)
CYP2D6 | 4 (5%) | 79 | 0.848 (1.00) | 1 (1.00) | 1 (1.00)
ATM | 5 (6%) | 78 | 0.882 (1.00) | 0.273 (1.00) | 1 (1.00)
MLL3 | 7 (8%) | 76 | 0.618 (1.00) | 0.364 (1.00) | 0.302 (1.00)
TAF1L | 3 (4%) | 80 | 0.142 (1.00) | 1 (1.00) | 1 (1.00)
PRB1 | 3 (4%) | 80 | 0.353 (1.00) | 1 (1.00) | 1 (1.00)
CNTNAP5 | 5 (6%) | 78 | 0.488 (1.00) | 1 (1.00) | 1 (1.00)
BCL6 | 3 (4%) | 80 | 0.0299 (1.00) | 1 (1.00) | 1 (1.00)
OR5L2 | 3 (4%) | 80 | 0.0989 (1.00) | 1 (1.00) | 1 (1.00)
TTLL11 | 3 (4%) | 80 | 0.419 (1.00) | 1 (1.00) | 1 (1.00)
TRIOBP | 3 (4%) | 80 | 0.544 (1.00) | 1 (1.00) | 1 (1.00)
UGT2B10 | 3 (4%) | 80 | 0.949 (1.00) | 0.172 (1.00) | 1 (1.00)
SLITRK4 | 3 (4%) | 80 | 0.726 (1.00) | 1 (1.00) | 1 (1.00)
PRG4 | 4 (5%) | 79 | 0.523 (1.00) | 1 (1.00) | 0.182 (1.00)

Methods & Data
Input

- Mutation data file = PRAD.mutsig.cluster.txt

- Clinical data file = PRAD.clin.merged.picked.txt

- Number of patients = 83

- Number of significantly mutated genes = 46

- Number of selected clinical features = 3

- 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)
Copyright © 2012 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle