Correlation between copy number variation genes (focal events) and selected clinical features
Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (Primary solid tumor)
15 January 2014 | analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C1WQ027B
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 variation genes (focal events) and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1WQ027B
Overview
Introduction

This pipeline computes the correlation between significant copy number variation (cnv focal) genes and selected clinical features.

Summary

Testing the association between copy number variation 21 focal events and 3 clinical features across 21 patients, one significant finding detected with Q value < 0.25.

- del_17q24.1 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 21 focal 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
del 17q24 1 | 3 (14%) | 18 | 3.74e-05 (0.00235) | 0.844 (1.00) | 1 (1.00)
amp 1q31 1 | 7 (33%) | 14 | 0.222 (1.00) | 0.469 (1.00) | 1 (1.00)
amp 2p15 | 7 (33%) | 14 | 0.0511 (1.00) | 0.913 (1.00) | 1 (1.00)
amp 3q27 3 | 5 (24%) | 16 | 0.389 (1.00) | 0.808 (1.00) | 1 (1.00)
amp 8q24 12 | 5 (24%) | 16 | 0.477 (1.00) | 0.28 (1.00) | 1 (1.00)
amp 12q13 12 | 4 (19%) | 17 | 0.655 (1.00) | 0.353 (1.00) | 1 (1.00)
amp 16p11 2 | 5 (24%) | 16 | 0.469 (1.00) | 0.21 (1.00) | 1 (1.00)
amp xq27 3 | 4 (19%) | 17 | 0.655 (1.00) | 0.546 (1.00) | 1 (1.00)
del 1p13 1 | 4 (19%) | 17 | 0.799 (1.00) | 0.719 (1.00) | 0.253 (1.00)
del 1q43 | 4 (19%) | 17 | 0.569 (1.00) | 0.575 (1.00) | 0.131 (1.00)
del 2q23 1 | 4 (19%) | 17 | 0.609 (1.00) | 0.743 (1.00) | 0.131 (1.00)
del 6q14 1 | 6 (29%) | 15 | 0.27 (1.00) | 0.625 (1.00) | 0.631 (1.00)
del 6q23 3 | 5 (24%) | 16 | 0.79 (1.00) | 0.105 (1.00) | 0.325 (1.00)
del 9p21 3 | 7 (33%) | 14 | 0.27 (1.00) | 0.873 (1.00) | 1 (1.00)
del 10q23 31 | 4 (19%) | 17 | 0.201 (1.00) | 0.565 (1.00) | 1 (1.00)
del 13q14 2 | 3 (14%) | 18 | 0.0253 (1.00) | 0.173 (1.00) | 0.257 (1.00)
del 13q33 3 | 4 (19%) | 17 | 0.389 (1.00) | 0.139 (1.00) | 0.131 (1.00)
del 15q15 1 | 6 (29%) | 15 | 0.799 (1.00) | 0.799 (1.00) | 0.336 (1.00)
del 15q21 1 | 6 (29%) | 15 | 0.799 (1.00) | 0.799 (1.00) | 0.336 (1.00)
del 16p13 13 | 3 (14%) | 18 | 0.724 (1.00) | 0.881 (1.00) | 0.257 (1.00)
del 16q23 1 | 3 (14%) | 18 | 0.724 (1.00) | 0.91 (1.00) | 0.257 (1.00)

'del_17q24.1' versus 'Time to Death'

P value = 3.74e-05 (logrank test), Q value = 0.0024

Table S1. Gene #21: 'del_17q24.1' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 21 | 4 | 2.0 - 211.2 (31.7)
DEL PEAK 18(17Q24.1) MUTATED | 3 | 1 | 2.0 - 19.6 (4.6)
DEL PEAK 18(17Q24.1) WILD-TYPE | 18 | 3 | 4.1 - 211.2 (38.3)

Figure S1. Get High-res Image Gene #21: 'del_17q24.1' 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 focal cnvs = 21

- Number of selected clinical features = 3

- 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

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)
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