Correlation between copy number variations of arm-level result and selected clinical features
Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (Primary solid tumor)
16 April 2014 | analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C1V69H66
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/C1V69H66
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 17 arm-level events and 3 clinical features across 21 patients, one significant finding detected with Q value < 0.25.

- xq loss cnv correlated to 'Time to Death'.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between significant copy number variation of 17 arm-level events and 3 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, one significant finding detected.

|  | Clinical Features | Time to Death | AGE | GENDER
| nCNV (%) | nWild-Type | logrank test | t-test | Fisher's exact test
xq loss | 3 (14%) | 18 | 0.00468 (0.239) | 0.91 (1.00) | 0.257 (1.00)
1q gain | 4 (19%) | 17 | 0.405 (1.00) | 0.42 (1.00) | 0.618 (1.00)
3p gain | 3 (14%) | 18 | 0.418 (1.00) | 0.264 (1.00) | 1 (1.00)
3q gain | 4 (19%) | 17 | 0.418 (1.00) | 0.993 (1.00) | 1 (1.00)
7p gain | 7 (33%) | 14 | 0.317 (1.00) | 0.905 (1.00) | 1 (1.00)
7q gain | 6 (29%) | 15 | 0.522 (1.00) | 0.717 (1.00) | 1 (1.00)
11p gain | 3 (14%) | 18 | 0.569 (1.00) | 0.407 (1.00) | 0.257 (1.00)
11q gain | 7 (33%) | 14 | 0.249 (1.00) | 0.273 (1.00) | 0.656 (1.00)
12p gain | 3 (14%) | 18 | 0.724 (1.00) | 0.0499 (1.00) | 1 (1.00)
12q gain | 3 (14%) | 18 | 0.724 (1.00) | 0.0499 (1.00) | 1 (1.00)
16p gain | 3 (14%) | 18 | 0.808 (1.00) | 0.844 (1.00) | 1 (1.00)
16q gain | 3 (14%) | 18 | 0.316 (1.00) | 0.606 (1.00) | 0.531 (1.00)
18p gain | 4 (19%) | 17 | 0.389 (1.00) | 0.0811 (1.00) | 1 (1.00)
18q gain | 4 (19%) | 17 | 0.389 (1.00) | 0.0811 (1.00) | 1 (1.00)
21q gain | 5 (24%) | 16 | 0.892 (1.00) | 0.718 (1.00) | 1 (1.00)
15q loss | 4 (19%) | 17 | 0.522 (1.00) | 0.964 (1.00) | 1 (1.00)
16q loss | 3 (14%) | 18 | 0.724 (1.00) | 0.91 (1.00) | 0.257 (1.00)

'xq loss' versus 'Time to Death'

P value = 0.00468 (logrank test), Q value = 0.24

Table S1. Gene #17: 'xq loss' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 21 | 4 | 2.0 - 211.2 (31.7)
XQ LOSS MUTATED | 3 | 1 | 4.1 - 31.7 (19.6)
XQ LOSS WILD-TYPE | 18 | 3 | 2.0 - 211.2 (38.3)

Figure S1. Get High-res Image Gene #17: 'xq loss' versus Clinical Feature #1: 'Time to Death'

Methods & Data
Input

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

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

- Number of patients = 21

- Number of significantly arm-level cnvs = 17

- Number of selected clinical features = 3

- 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

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

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] 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 © 2014 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle