Correlation between copy number variations of arm-level result and selected clinical features
Prostate Adenocarcinoma (Primary solid tumor)
23 May 2013 | analyses__2013_05_23
Maintainer Information
Citation Information
doi:10.7908/C1DN434W
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between copy number variations of arm-level result and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1DN434W
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 6 clinical features across 155 patients, 5 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'.

- 13q loss cnv correlated to 'LYMPH.NODE.METASTASIS'.

- 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 6 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 5 significant findings detected.

|  | Clinical Features | Time to Death | AGE | LYMPH NODE METASTASIS | COMPLETENESS OF RESECTION | NUMBER OF LYMPH NODES | TUMOR STAGECODE
| nCNV (%) | nWild-Type | logrank test | t-test | Fisher's exact test | Fisher's exact test | t-test | t-test
1q gain | 0 (0%) | 151 | 1 (1.00) | 0.297 (1.00) | 1 (1.00) | 0.228 (1.00) | 0.00151 (0.186) |
3p gain | 0 (0%) | 151 | 1 (1.00) | 0.644 (1.00) | 1 (1.00) | 0.604 (1.00) | 0.00151 (0.186) |
5q loss | 0 (0%) | 152 | 1 (1.00) | 0.213 (1.00) | 1 (1.00) | 1 (1.00) | 0.00151 (0.186) |
13q loss | 0 (0%) | 144 | 1 (1.00) | 0.809 (1.00) | 0.000216 (0.0268) | 1 (1.00) | 0.1 (1.00) |
20p loss | 0 (0%) | 151 | 1 (1.00) | 0.447 (1.00) | 1 (1.00) | 0.228 (1.00) | 0.00151 (0.186) |
3q gain | 0 (0%) | 150 | 1 (1.00) | 0.358 (1.00) | 0.448 (1.00) | 1 (1.00) | 0.438 (1.00) |
7p gain | 0 (0%) | 141 | 1 (1.00) | 0.0184 (1.00) | 0.0284 (1.00) | 0.774 (1.00) | 0.169 (1.00) |
7q gain | 0 (0%) | 143 | 1 (1.00) | 0.0672 (1.00) | 0.304 (1.00) | 1 (1.00) | 0.967 (1.00) |
8p gain | 0 (0%) | 148 | 1 (1.00) | 0.628 (1.00) | 0.172 (1.00) | 0.405 (1.00) | 0.679 (1.00) |
8q gain | 0 (0%) | 141 | 1 (1.00) | 0.68 (1.00) | 0.157 (1.00) | 0.427 (1.00) | 0.846 (1.00) |
9p gain | 0 (0%) | 152 | 1 (1.00) | 0.72 (1.00) | 0.298 (1.00) |  | 0.553 (1.00) |
9q gain | 0 (0%) | 151 | 1 (1.00) | 0.694 (1.00) | 0.298 (1.00) | 1 (1.00) | 0.553 (1.00) |
10q gain | 0 (0%) | 152 | 1 (1.00) | 0.145 (1.00) | 0.298 (1.00) | 1 (1.00) | 0.456 (1.00) |
6q loss | 0 (0%) | 148 | 1 (1.00) | 0.257 (1.00) | 0.567 (1.00) | 1 (1.00) | 0.607 (1.00) |
8p loss | 0 (0%) | 116 | 1 (1.00) | 0.0835 (1.00) | 0.213 (1.00) | 0.495 (1.00) | 0.0925 (1.00) |
8q loss | 0 (0%) | 151 | 1 (1.00) | 0.644 (1.00) | 0.0601 (1.00) | 1 (1.00) | 0.247 (1.00) |
10p loss | 0 (0%) | 151 | 1 (1.00) | 0.679 (1.00) | 0.377 (1.00) | 1 (1.00) | 0.87 (1.00) |
10q loss | 0 (0%) | 151 | 1 (1.00) | 0.398 (1.00) | 0.0601 (1.00) | 0.604 (1.00) | 0.327 (1.00) |
12p loss | 0 (0%) | 148 | 1 (1.00) | 0.677 (1.00) | 0.00285 (0.339) | 0.405 (1.00) | 0.142 (1.00) |
16q loss | 0 (0%) | 135 | 1 (1.00) | 0.277 (1.00) | 0.0376 (1.00) | 0.638 (1.00) | 0.206 (1.00) |
17p loss | 0 (0%) | 136 | 1 (1.00) | 0.451 (1.00) | 0.0297 (1.00) | 0.517 (1.00) | 0.128 (1.00) |
18p loss | 0 (0%) | 141 | 1 (1.00) | 0.876 (1.00) | 0.637 (1.00) | 0.544 (1.00) | 0.483 (1.00) |
18q loss | 0 (0%) | 135 | 1 (1.00) | 0.702 (1.00) | 0.114 (1.00) | 0.517 (1.00) | 0.239 (1.00) |
21q loss | 0 (0%) | 152 | 1 (1.00) | 0.395 (1.00) | 0.298 (1.00) | 1 (1.00) | 0.502 (1.00) |
22q loss | 0 (0%) | 151 | 1 (1.00) | 0.462 (1.00) | 0.377 (1.00) | 0.228 (1.00) | 0.51 (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 | 136 | 0.2 (0.7)
1Q GAIN CNV | 4 | 0.0 (0.0)
1Q GAIN WILD-TYPE | 132 | 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 | 136 | 0.2 (0.7)
3P GAIN CNV | 4 | 0.0 (0.0)
3P GAIN WILD-TYPE | 132 | 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 | 136 | 0.2 (0.7)
5Q LOSS CNV | 3 | 0.0 (0.0)
5Q LOSS WILD-TYPE | 133 | 0.2 (0.7)

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

'13q loss' versus 'LYMPH.NODE.METASTASIS'

P value = 0.000216 (Fisher's exact test), Q value = 0.027

Table S4. Gene #18: '13q loss' versus Clinical Feature #3: 'LYMPH.NODE.METASTASIS'

nPatients | N0 | N1
--- | --- | ---
ALL | 121 | 15
13Q LOSS CNV | 5 | 6
13Q LOSS WILD-TYPE | 116 | 9

Figure S4. Get High-res Image Gene #18: '13q loss' versus Clinical Feature #3: 'LYMPH.NODE.METASTASIS'

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

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

Table S5. Gene #23: '20p loss' versus Clinical Feature #5: 'NUMBER.OF.LYMPH.NODES'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 136 | 0.2 (0.7)
20P LOSS CNV | 4 | 0.0 (0.0)
20P LOSS WILD-TYPE | 132 | 0.2 (0.8)

Figure S5. 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 = 155

- Number of significantly arm-level cnvs = 25

- Number of selected clinical features = 6

- 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)
Copyright © 2013 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle