Prostate Adenocarcinoma: Correlation between copy number variations of arm-level result 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 significant arm-level copy number variations (cnvs) and selected clinical features.

Summary

Testing the association between copy number variation 20 arm-level results and 4 clinical features across 123 patients, 2 significant findings detected with Q value < 0.25.

- 7p gain cnv correlated to 'AGE'.

- 7q gain cnv correlated to 'AGE'.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between significant copy number variation of 20 arm-level results and 4 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 2 significant findings detected.

|  | Clinical Features | Time to Death | AGE | RADIATIONS RADIATION REGIMENINDICATION | NEOADJUVANT THERAPY
| nCNV (%) | nWild-Type | logrank test | t-test | Fisher's exact test | Fisher's exact test
7p gain | 14 (11%) | 109 | 1 (1.00) | 0.000303 (0.0242) | 1 (1.00) | 0.0635 (1.00)
7q gain | 11 (9%) | 112 | 1 (1.00) | 0.00208 (0.164) | 1 (1.00) | 0.316 (1.00)
1q gain | 4 (3%) | 119 | 1 (1.00) | 0.439 (1.00) | 1 (1.00) | 1 (1.00)
3p gain | 5 (4%) | 118 | 1 (1.00) | 0.681 (1.00) | 1 (1.00) | 1 (1.00)
3q gain | 7 (6%) | 116 | 1 (1.00) | 0.965 (1.00) | 1 (1.00) | 0.211 (1.00)
8p gain | 4 (3%) | 119 | 1 (1.00) | 0.501 (1.00) | 1 (1.00) | 1 (1.00)
8q gain | 11 (9%) | 112 | 1 (1.00) | 0.345 (1.00) | 1 (1.00) | 0.316 (1.00)
9q gain | 3 (2%) | 120 | 1 (1.00) | 0.352 (1.00) | 1 (1.00) | 1 (1.00)
6q loss | 5 (4%) | 118 | 1 (1.00) | 0.265 (1.00) | 1 (1.00) | 1 (1.00)
8p loss | 36 (29%) | 87 | 1 (1.00) | 0.215 (1.00) | 0.32 (1.00) | 0.58 (1.00)
8q loss | 4 (3%) | 119 | 1 (1.00) | 0.475 (1.00) | 1 (1.00) | 1 (1.00)
10p loss | 3 (2%) | 120 | 1 (1.00) | 0.0488 (1.00) | 1 (1.00) | 0.0952 (1.00)
12p loss | 5 (4%) | 118 | 1 (1.00) | 0.987 (1.00) | 1 (1.00) | 1 (1.00)
13q loss | 9 (7%) | 114 | 1 (1.00) | 0.853 (1.00) | 1 (1.00) | 0.265 (1.00)
16q loss | 14 (11%) | 109 | 1 (1.00) | 0.085 (1.00) | 1 (1.00) | 0.0635 (1.00)
17p loss | 14 (11%) | 109 | 1 (1.00) | 0.542 (1.00) | 1 (1.00) | 1 (1.00)
18p loss | 10 (8%) | 113 | 1 (1.00) | 0.928 (1.00) | 1 (1.00) | 0.291 (1.00)
18q loss | 15 (12%) | 108 | 1 (1.00) | 0.714 (1.00) | 1 (1.00) | 0.41 (1.00)
20p loss | 4 (3%) | 119 | 1 (1.00) | 0.676 (1.00) | 0.155 (1.00) | 1 (1.00)
22q loss | 5 (4%) | 118 | 1 (1.00) | 0.406 (1.00) | 0.19 (1.00) | 1 (1.00)

'7p gain mutation analysis' versus 'AGE'

P value = 0.000303 (t-test), Q value = 0.024

Table S1. Gene #4: '7p gain mutation analysis' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 123 | 61.1 (6.6)
7P GAIN MUTATED | 14 | 64.9 (3.0)
7P GAIN WILD-TYPE | 109 | 60.7 (6.8)

Figure S1. Get High-res Image Gene #4: '7p gain mutation analysis' versus Clinical Feature #2: 'AGE'

'7q gain mutation analysis' versus 'AGE'

P value = 0.00208 (t-test), Q value = 0.16

Table S2. Gene #5: '7q gain mutation analysis' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 123 | 61.1 (6.6)
7Q GAIN MUTATED | 11 | 64.7 (3.1)
7Q GAIN WILD-TYPE | 112 | 60.8 (6.8)

Figure S2. Get High-res Image Gene #5: '7q gain mutation analysis' versus Clinical Feature #2: 'AGE'

Methods & Data
Input

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

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

- Number of patients = 123

- Number of significantly arm-level cnvs = 20

- Number of selected clinical features = 4

- 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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