Correlation between copy number variation genes (focal events) and selected clinical features
Mesothelioma (Primary solid tumor)
16 April 2014 | analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C1R78CV3
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/C1R78CV3
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 17 focal events and 7 clinical features across 13 patients, one significant finding detected with Q value < 0.25.

- del_9p21.3 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 focal events and 7 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 | NEOPLASM DISEASESTAGE | PATHOLOGY T STAGE | PATHOLOGY N STAGE | PATHOLOGY M STAGE | GENDER
| nCNV (%) | nWild-Type | logrank test | t-test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test
del 9p21 3 | 8 (62%) | 5 | 0.00163 (0.194) | 0.482 (1.00) | 0.728 (1.00) | 0.105 (1.00) | 0.51 (1.00) | 0.217 (1.00) | 1 (1.00)
amp 12p11 21 | 3 (23%) | 10 | 0.942 (1.00) | 0.0205 (1.00) | 0.388 (1.00) | 1 (1.00) | 0.528 (1.00) | 1 (1.00) | 1 (1.00)
amp 17q24 3 | 4 (31%) | 9 | 0.155 (1.00) | 0.18 (1.00) | 0.315 (1.00) | 1 (1.00) | 0.497 (1.00) | 0.53 (1.00) | 0.228 (1.00)
del 1p36 23 | 5 (38%) | 8 | 0.0272 (1.00) | 0.51 (1.00) | 0.728 (1.00) | 1 (1.00) | 0.231 (1.00) | 1 (1.00) | 1 (1.00)
del 1p22 1 | 7 (54%) | 6 | 0.557 (1.00) | 0.762 (1.00) | 0.266 (1.00) | 1 (1.00) | 0.559 (1.00) | 0.266 (1.00) | 1 (1.00)
del 3p21 1 | 9 (69%) | 4 | 0.317 (1.00) | 0.0715 (1.00) | 1 (1.00) | 0.228 (1.00) | 0.203 (1.00) | 0.53 (1.00) | 0.53 (1.00)
del 4q26 | 8 (62%) | 5 | 0.0372 (1.00) | 0.41 (1.00) | 0.728 (1.00) | 0.105 (1.00) | 0.51 (1.00) | 1 (1.00) | 1 (1.00)
del 6q22 31 | 9 (69%) | 4 | 0.646 (1.00) | 0.621 (1.00) | 0.119 (1.00) | 0.53 (1.00) | 0.203 (1.00) | 1 (1.00) | 1 (1.00)
del 10p15 1 | 5 (38%) | 8 | 0.00529 (0.625) | 0.845 (1.00) | 0.402 (1.00) | 0.00699 (0.818) | 0.231 (1.00) | 1 (1.00) | 1 (1.00)
del 10q24 1 | 3 (23%) | 10 | 0.253 (1.00) | 0.871 (1.00) | 1 (1.00) | 0.203 (1.00) | 0.528 (1.00) | 1 (1.00) | 0.497 (1.00)
del 13q13 3 | 10 (77%) | 3 | 0.138 (1.00) | 0.857 (1.00) | 1 (1.00) | 0.497 (1.00) | 0.108 (1.00) | 0.014 (1.00) | 0.203 (1.00)
del 14q11 2 | 4 (31%) | 9 | 0.313 (1.00) | 0.0441 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00) | 0.53 (1.00)
del 14q32 31 | 5 (38%) | 8 | 0.892 (1.00) | 0.0515 (1.00) | 0.728 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00)
del 15q15 1 | 5 (38%) | 8 | 0.351 (1.00) | 0.709 (1.00) | 0.402 (1.00) | 1 (1.00) | 0.51 (1.00) | 1 (1.00) | 1 (1.00)
del 16q21 | 6 (46%) | 7 | 0.0595 (1.00) | 0.921 (1.00) | 1 (1.00) | 0.266 (1.00) | 1 (1.00) | 0.559 (1.00) | 0.559 (1.00)
del 16q24 1 | 5 (38%) | 8 | 0.193 (1.00) | 0.578 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00)
del 22q12 2 | 10 (77%) | 3 | 0.548 (1.00) | 0.816 (1.00) | 0.388 (1.00) | 0.497 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00)

'del_9p21.3' versus 'Time to Death'

P value = 0.00163 (logrank test), Q value = 0.19

Table S1. Gene #8: 'del_9p21.3' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 13 | 10 | 1.9 - 91.7 (18.8)
DEL PEAK 8(9P21.3) MUTATED | 8 | 7 | 1.9 - 23.3 (12.3)
DEL PEAK 8(9P21.3) WILD-TYPE | 5 | 3 | 24.9 - 91.7 (51.8)

Figure S1. Get High-res Image Gene #8: 'del_9p21.3' versus Clinical Feature #1: 'Time to Death'

Methods & Data
Input

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

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

- Number of patients = 13

- Number of significantly focal cnvs = 17

- Number of selected clinical features = 7

- 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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