Correlation between copy number variations of arm-level result and selected clinical features
Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (Primary solid tumor)
17 October 2014 | analyses__2014_10_17
Maintainer Information
Citation Information
doi:10.7908/C1PR7TT6
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between copy number variations of arm-level result and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1PR7TT6
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 37 arm-level events and 4 clinical features across 35 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 37 arm-level events and 4 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 | GENDER | RACE
| nCNV (%) | nWild-Type | logrank test | Wilcoxon-test | Fisher's exact test | Fisher's exact test
1q gain | 4 (11%) | 31 | 0.412 (1.00) | 0.468 (1.00) | 1 (1.00) | 0.0611 (1.00)
2p gain | 3 (9%) | 32 | 0.711 (1.00) | 0.0198 (1.00) | 1 (1.00) | 0.293 (1.00)
2q gain | 3 (9%) | 32 | 0.711 (1.00) | 0.0198 (1.00) | 1 (1.00) | 0.296 (1.00)
3p gain | 7 (20%) | 28 | 0.291 (1.00) | 0.635 (1.00) | 1 (1.00) | 0.526 (1.00)
3q gain | 9 (26%) | 26 | 0.291 (1.00) | 0.925 (1.00) | 0.443 (1.00) | 0.587 (1.00)
5p gain | 3 (9%) | 32 | 0.461 (1.00) | 0.813 (1.00) | 1 (1.00) | 1 (1.00)
6p gain | 4 (11%) | 31 | 0.589 (1.00) | 1 (1.00) | 0.603 (1.00) | 0.65 (1.00)
6q gain | 3 (9%) | 32 | 0.762 (1.00) | 0.555 (1.00) | 0.229 (1.00) | 0.298 (1.00)
7p gain | 10 (29%) | 25 | 0.221 (1.00) | 0.661 (1.00) | 0.711 (1.00) | 0.798 (1.00)
7q gain | 9 (26%) | 26 | 0.357 (1.00) | 0.428 (1.00) | 0.443 (1.00) | 0.591 (1.00)
9p gain | 4 (11%) | 31 | 0.623 (1.00) | 0.979 (1.00) | 0.338 (1.00) | 0.21 (1.00)
9q gain | 4 (11%) | 31 | 0.623 (1.00) | 0.979 (1.00) | 0.338 (1.00) | 0.209 (1.00)
10p gain | 3 (9%) | 32 | 0.762 (1.00) | 0.555 (1.00) | 0.229 (1.00) | 1 (1.00)
11p gain | 5 (14%) | 30 | 0.421 (1.00) | 0.832 (1.00) | 0.338 (1.00) | 0.696 (1.00)
11q gain | 9 (26%) | 26 | 0.172 (1.00) | 0.0856 (1.00) | 0.443 (1.00) | 0.782 (1.00)
12p gain | 4 (11%) | 31 | 0.559 (1.00) | 0.0516 (1.00) | 0.603 (1.00) | 0.65 (1.00)
12q gain | 6 (17%) | 29 | 0.349 (1.00) | 0.204 (1.00) | 1 (1.00) | 0.717 (1.00)
13q gain | 3 (9%) | 32 | 0.711 (1.00) | 0.302 (1.00) | 0.603 (1.00) | 0.296 (1.00)
16p gain | 6 (17%) | 29 | 0.623 (1.00) | 0.878 (1.00) | 0.658 (1.00) | 1 (1.00)
16q gain | 6 (17%) | 29 | 0.514 (1.00) | 0.776 (1.00) | 1 (1.00) | 1 (1.00)
17q gain | 3 (9%) | 32 | 0.762 (1.00) | 0.194 (1.00) | 0.229 (1.00) | 0.296 (1.00)
18p gain | 9 (26%) | 26 | 0.277 (1.00) | 0.265 (1.00) | 0.443 (1.00) | 0.0256 (1.00)
18q gain | 10 (29%) | 25 | 0.263 (1.00) | 0.571 (1.00) | 0.711 (1.00) | 0.0112 (1.00)
20p gain | 3 (9%) | 32 | 0.661 (1.00) | 0.443 (1.00) | 0.229 (1.00) | 0.293 (1.00)
21q gain | 7 (20%) | 28 | 0.812 (1.00) | 0.536 (1.00) | 1 (1.00) | 0.746 (1.00)
xq gain | 4 (11%) | 31 | 0.55 (1.00) | 0.551 (1.00) | 0.338 (1.00) | 0.648 (1.00)
1p loss | 3 (9%) | 32 | 0.589 (1.00) | 0.345 (1.00) | 0.603 (1.00) | 0.627 (1.00)
4q loss | 3 (9%) | 32 |  | 0.86 (1.00) | 0.229 (1.00) | 1 (1.00)
6q loss | 4 (11%) | 31 | 0.34 (1.00) | 0.436 (1.00) | 0.338 (1.00) | 0.4 (1.00)
8p loss | 6 (17%) | 29 | 0.589 (1.00) | 0.827 (1.00) | 0.177 (1.00) | 0.719 (1.00)
8q loss | 3 (9%) | 32 |  | 0.516 (1.00) | 1 (1.00) | 0.623 (1.00)
15q loss | 6 (17%) | 29 | 0.421 (1.00) | 0.861 (1.00) | 0.177 (1.00) | 1 (1.00)
16q loss | 4 (11%) | 31 | 0.661 (1.00) | 0.856 (1.00) | 0.104 (1.00) | 0.651 (1.00)
17p loss | 4 (11%) | 31 | 0.661 (1.00) | 0.795 (1.00) | 0.603 (1.00) | 1 (1.00)
18p loss | 3 (9%) | 32 | 0.593 (1.00) | 0.443 (1.00) | 0.104 (1.00) | 1 (1.00)
22q loss | 3 (9%) | 32 | 0.762 (1.00) | 0.836 (1.00) | 0.229 (1.00) | 0.298 (1.00)
xq loss | 4 (11%) | 31 | 0.0391 (1.00) | 0.979 (1.00) | 0.104 (1.00) | 0.65 (1.00)

Methods & Data
Input

- Copy number data file = transformed.cor.cli.txt

- Clinical data file = DLBC-TP.merged_data.txt

- Number of patients = 35

- Number of significantly arm-level cnvs = 37

- Number of selected clinical features = 4

- Exclude regions 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

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

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

- 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] 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)
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