Prostate Adenocarcinoma: Correlation between copy number variations of arm-level result and selected clinical features
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Overview
Introduction

This pipeline computes the correlation between significant arm-level copy number variations (cnvs) and selected clinical features.

Summary

Testing the association between copy number variation 21 arm-level results and 3 clinical features across 146 patients, no significant finding detected with Q value < 0.25.

- No arm-level cnvs related to clinical features.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between significant copy number variation of 21 arm-level results 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 | Time to Death | AGE | RADIATIONS RADIATION REGIMENINDICATION
| nCNV (%) | nWild-Type | logrank test | t-test | Fisher's exact test
1q gain | 4 (3%) | 142 | 1 (1.00) | 0.299 (1.00) | 1 (1.00)
3p gain | 4 (3%) | 142 | 1 (1.00) | 0.646 (1.00) | 1 (1.00)
3q gain | 6 (4%) | 140 | 1 (1.00) | 0.329 (1.00) | 1 (1.00)
7p gain | 15 (10%) | 131 | 1 (1.00) | 0.0153 (0.963) | 1 (1.00)
7q gain | 12 (8%) | 134 | 1 (1.00) | 0.0682 (1.00) | 1 (1.00)
8p gain | 6 (4%) | 140 | 1 (1.00) | 0.537 (1.00) | 1 (1.00)
8q gain | 13 (9%) | 133 | 1 (1.00) | 0.603 (1.00) | 1 (1.00)
9q gain | 3 (2%) | 143 | 1 (1.00) | 0.276 (1.00) | 1 (1.00)
6q loss | 7 (5%) | 139 | 1 (1.00) | 0.258 (1.00) | 1 (1.00)
8p loss | 38 (26%) | 108 | 1 (1.00) | 0.0476 (1.00) | 0.327 (1.00)
8q loss | 4 (3%) | 142 | 1 (1.00) | 0.635 (1.00) | 1 (1.00)
10p loss | 5 (3%) | 141 | 1 (1.00) | 0.91 (1.00) | 1 (1.00)
10q loss | 4 (3%) | 142 | 1 (1.00) | 0.396 (1.00) | 1 (1.00)
12p loss | 7 (5%) | 139 | 1 (1.00) | 0.674 (1.00) | 1 (1.00)
13q loss | 11 (8%) | 135 | 1 (1.00) | 0.82 (1.00) | 1 (1.00)
16q loss | 18 (12%) | 128 | 1 (1.00) | 0.146 (1.00) | 1 (1.00)
17p loss | 17 (12%) | 129 | 1 (1.00) | 0.529 (1.00) | 1 (1.00)
18p loss | 14 (10%) | 132 | 1 (1.00) | 0.621 (1.00) | 1 (1.00)
18q loss | 19 (13%) | 127 | 1 (1.00) | 0.3 (1.00) | 1 (1.00)
20p loss | 4 (3%) | 142 | 1 (1.00) | 0.451 (1.00) | 0.131 (1.00)
22q loss | 5 (3%) | 141 | 1 (1.00) | 0.274 (1.00) | 0.162 (1.00)

Methods & Data
Input

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

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

- Number of patients = 146

- Number of significantly arm-level cnvs = 21

- Number of selected clinical features = 3

- 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

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] 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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