Correlation between copy number variations of arm-level result and selected clinical features
Prostate Adenocarcinoma (Primary solid tumor)
21 April 2013 | analyses__2013_04_21
Maintainer Information
Citation Information
doi:10.7908/C1JD4TRG
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 copy number variations of arm-level result and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1JD4TRG
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 25 arm-level results and 5 clinical features across 154 patients, 4 significant findings detected with Q value < 0.25.

- 1q gain cnv correlated to 'NUMBER.OF.LYMPH.NODES'.

- 3p gain cnv correlated to 'NUMBER.OF.LYMPH.NODES'.

- 5q loss cnv correlated to 'NUMBER.OF.LYMPH.NODES'.

- 20p loss cnv correlated to 'NUMBER.OF.LYMPH.NODES'.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between significant copy number variation of 25 arm-level results and 5 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 4 significant findings detected.

|  | Clinical Features | Time to Death | AGE | RADIATIONS RADIATION REGIMENINDICATION | COMPLETENESS OF RESECTION | NUMBER OF LYMPH NODES
| nCNV (%) | nWild-Type | logrank test | t-test | Fisher's exact test | Fisher's exact test | t-test
1q gain | 0 (0%) | 150 | 1 (1.00) | 0.302 (1.00) | 1 (1.00) | 0.228 (1.00) | 0.00151 (0.187)
3p gain | 0 (0%) | 150 | 1 (1.00) | 0.648 (1.00) | 1 (1.00) | 0.604 (1.00) | 0.00151 (0.187)
5q loss | 0 (0%) | 151 | 1 (1.00) | 0.217 (1.00) | 1 (1.00) | 1 (1.00) | 0.00151 (0.187)
20p loss | 0 (0%) | 150 | 1 (1.00) | 0.455 (1.00) | 0.125 (1.00) | 0.228 (1.00) | 0.00151 (0.187)
3q gain | 0 (0%) | 149 | 1 (1.00) | 0.362 (1.00) | 1 (1.00) | 1 (1.00) | 0.439 (1.00)
7p gain | 0 (0%) | 140 | 1 (1.00) | 0.0192 (1.00) | 1 (1.00) | 0.774 (1.00) | 0.17 (1.00)
7q gain | 0 (0%) | 142 | 1 (1.00) | 0.0695 (1.00) | 1 (1.00) | 1 (1.00) | 0.958 (1.00)
8p gain | 0 (0%) | 147 | 1 (1.00) | 0.636 (1.00) | 1 (1.00) | 0.405 (1.00) | 0.685 (1.00)
8q gain | 0 (0%) | 140 | 1 (1.00) | 0.694 (1.00) | 1 (1.00) | 0.427 (1.00) | 0.856 (1.00)
9p gain | 0 (0%) | 151 | 1 (1.00) | 0.725 (1.00) | 1 (1.00) |  | 0.555 (1.00)
9q gain | 0 (0%) | 150 | 1 (1.00) | 0.7 (1.00) | 1 (1.00) | 1 (1.00) | 0.555 (1.00)
10q gain | 0 (0%) | 151 | 1 (1.00) | 0.146 (1.00) | 1 (1.00) | 1 (1.00) | 0.456 (1.00)
6q loss | 0 (0%) | 147 | 1 (1.00) | 0.261 (1.00) | 1 (1.00) | 1 (1.00) | 0.609 (1.00)
8p loss | 0 (0%) | 115 | 1 (1.00) | 0.0895 (1.00) | 0.331 (1.00) | 0.495 (1.00) | 0.0935 (1.00)
8q loss | 0 (0%) | 150 | 1 (1.00) | 0.651 (1.00) | 1 (1.00) | 1 (1.00) | 0.248 (1.00)
10p loss | 0 (0%) | 150 | 1 (1.00) | 0.674 (1.00) | 1 (1.00) | 1 (1.00) | 0.875 (1.00)
10q loss | 0 (0%) | 150 | 1 (1.00) | 0.395 (1.00) | 1 (1.00) | 0.604 (1.00) | 0.328 (1.00)
12p loss | 0 (0%) | 147 | 1 (1.00) | 0.671 (1.00) | 1 (1.00) | 0.405 (1.00) | 0.142 (1.00)
13q loss | 0 (0%) | 143 | 1 (1.00) | 0.819 (1.00) | 1 (1.00) | 1 (1.00) | 0.1 (1.00)
16q loss | 0 (0%) | 134 | 1 (1.00) | 0.286 (1.00) | 1 (1.00) | 0.638 (1.00) | 0.207 (1.00)
17p loss | 0 (0%) | 135 | 1 (1.00) | 0.464 (1.00) | 1 (1.00) | 0.517 (1.00) | 0.129 (1.00)
18p loss | 0 (0%) | 140 | 1 (1.00) | 0.861 (1.00) | 1 (1.00) | 0.544 (1.00) | 0.487 (1.00)
18q loss | 0 (0%) | 134 | 1 (1.00) | 0.72 (1.00) | 1 (1.00) | 0.517 (1.00) | 0.242 (1.00)
21q loss | 0 (0%) | 151 | 1 (1.00) | 0.405 (1.00) | 1 (1.00) | 1 (1.00) | 0.503 (1.00)
22q loss | 0 (0%) | 150 | 1 (1.00) | 0.467 (1.00) | 0.125 (1.00) | 0.228 (1.00) | 0.511 (1.00)

'1q gain' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.00151 (t-test), Q value = 0.19

Table S1. Gene #1: '1q gain' versus Clinical Feature #5: 'NUMBER.OF.LYMPH.NODES'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 135 | 0.2 (0.7)
1Q GAIN CNV | 4 | 0.0 (0.0)
1Q GAIN WILD-TYPE | 131 | 0.2 (0.8)

Figure S1. Get High-res Image Gene #1: '1q gain' versus Clinical Feature #5: 'NUMBER.OF.LYMPH.NODES'

'3p gain' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.00151 (t-test), Q value = 0.19

Table S2. Gene #2: '3p gain' versus Clinical Feature #5: 'NUMBER.OF.LYMPH.NODES'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 135 | 0.2 (0.7)
3P GAIN CNV | 4 | 0.0 (0.0)
3P GAIN WILD-TYPE | 131 | 0.2 (0.8)

Figure S2. Get High-res Image Gene #2: '3p gain' versus Clinical Feature #5: 'NUMBER.OF.LYMPH.NODES'

'5q loss' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.00151 (t-test), Q value = 0.19

Table S3. Gene #11: '5q loss' versus Clinical Feature #5: 'NUMBER.OF.LYMPH.NODES'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 135 | 0.2 (0.7)
5Q LOSS CNV | 3 | 0.0 (0.0)
5Q LOSS WILD-TYPE | 132 | 0.2 (0.8)

Figure S3. Get High-res Image Gene #11: '5q loss' versus Clinical Feature #5: 'NUMBER.OF.LYMPH.NODES'

'20p loss' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.00151 (t-test), Q value = 0.19

Table S4. Gene #23: '20p loss' versus Clinical Feature #5: 'NUMBER.OF.LYMPH.NODES'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 135 | 0.2 (0.7)
20P LOSS CNV | 4 | 0.0 (0.0)
20P LOSS WILD-TYPE | 131 | 0.2 (0.8)

Figure S4. Get High-res Image Gene #23: '20p loss' versus Clinical Feature #5: 'NUMBER.OF.LYMPH.NODES'

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 = 154

- Number of significantly arm-level cnvs = 25

- Number of selected clinical features = 5

- 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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