Correlation between copy number variation genes (focal events) and selected clinical features
Sarcoma (Primary solid tumor)
15 January 2014 | analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C13R0RB5
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/C13R0RB5
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 54 focal events and 3 clinical features across 82 patients, 2 significant findings detected with Q value < 0.25.

- del_10q23.31 cnv correlated to 'GENDER'.

- del_19p13.3 cnv correlated to 'GENDER'.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between significant copy number variation of 54 focal events and 3 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 2 significant findings detected.

|  | Clinical Features | Time to Death | AGE | GENDER
| nCNV (%) | nWild-Type | logrank test | t-test | Fisher's exact test
del 10q23 31 | 44 (54%) | 38 | 0.232 (1.00) | 0.474 (1.00) | 0.000999 (0.161)
del 19p13 3 | 21 (26%) | 61 | 0.536 (1.00) | 0.563 (1.00) | 0.00083 (0.134)
amp 1p32 1 | 27 (33%) | 55 | 0.101 (1.00) | 0.142 (1.00) | 0.643 (1.00)
amp 1q24 3 | 32 (39%) | 50 | 0.967 (1.00) | 0.819 (1.00) | 0.824 (1.00)
amp 3p11 2 | 18 (22%) | 64 | 0.406 (1.00) | 0.0466 (1.00) | 1 (1.00)
amp 5p15 33 | 36 (44%) | 46 | 0.0573 (1.00) | 0.00631 (0.965) | 0.657 (1.00)
amp 6p21 1 | 17 (21%) | 65 | 0.161 (1.00) | 0.00398 (0.62) | 0.42 (1.00)
amp 6q24 3 | 26 (32%) | 56 | 0.489 (1.00) | 0.15 (1.00) | 0.482 (1.00)
amp 7p21 3 | 25 (30%) | 57 | 0.984 (1.00) | 0.00974 (1.00) | 0.474 (1.00)
amp 8q21 12 | 28 (34%) | 54 | 0.00565 (0.87) | 0.0849 (1.00) | 0.818 (1.00)
amp 11q22 2 | 14 (17%) | 68 | 0.532 (1.00) | 0.501 (1.00) | 0.771 (1.00)
amp 12p13 32 | 17 (21%) | 65 | 0.112 (1.00) | 0.178 (1.00) | 1 (1.00)
amp 12q15 | 37 (45%) | 45 | 0.998 (1.00) | 0.392 (1.00) | 0.0021 (0.333)
amp 17p12 | 27 (33%) | 55 | 0.581 (1.00) | 0.376 (1.00) | 0.241 (1.00)
amp 19p13 2 | 29 (35%) | 53 | 0.797 (1.00) | 0.276 (1.00) | 0.171 (1.00)
amp 19q12 | 28 (34%) | 54 | 0.2 (1.00) | 0.0964 (1.00) | 1 (1.00)
amp 20q12 | 32 (39%) | 50 | 0.153 (1.00) | 0.0095 (1.00) | 0.651 (1.00)
amp xp21 1 | 22 (27%) | 60 | 0.9 (1.00) | 0.716 (1.00) | 0.322 (1.00)
amp xq21 1 | 12 (15%) | 70 | 0.154 (1.00) | 0.0324 (1.00) | 1 (1.00)
del 1p36 32 | 30 (37%) | 52 | 0.0271 (1.00) | 0.0631 (1.00) | 0.0662 (1.00)
del 1q43 | 22 (27%) | 60 | 0.999 (1.00) | 0.356 (1.00) | 0.805 (1.00)
del 2p25 3 | 26 (32%) | 56 | 0.456 (1.00) | 0.179 (1.00) | 0.00408 (0.633)
del 2q37 3 | 30 (37%) | 52 | 0.216 (1.00) | 0.266 (1.00) | 0.647 (1.00)
del 2q37 3 | 30 (37%) | 52 | 0.621 (1.00) | 0.0387 (1.00) | 0.647 (1.00)
del 3p21 31 | 17 (21%) | 65 | 0.153 (1.00) | 0.00794 (1.00) | 0.42 (1.00)
del 3q26 31 | 19 (23%) | 63 | 0.472 (1.00) | 0.236 (1.00) | 1 (1.00)
del 4q35 1 | 32 (39%) | 50 | 0.211 (1.00) | 0.365 (1.00) | 0.174 (1.00)
del 5q31 1 | 9 (11%) | 73 | 0.938 (1.00) | 0.998 (1.00) | 0.307 (1.00)
del 6p24 3 | 31 (38%) | 51 | 0.745 (1.00) | 0.56 (1.00) | 0.111 (1.00)
del 6q14 1 | 18 (22%) | 64 | 0.365 (1.00) | 0.217 (1.00) | 0.598 (1.00)
del 7q36 3 | 19 (23%) | 63 | 0.495 (1.00) | 0.683 (1.00) | 0.299 (1.00)
del 8p23 3 | 24 (29%) | 58 | 0.82 (1.00) | 0.983 (1.00) | 0.229 (1.00)
del 9p24 3 | 32 (39%) | 50 | 0.934 (1.00) | 0.0017 (0.272) | 0.651 (1.00)
del 9p21 3 | 37 (45%) | 45 | 0.825 (1.00) | 0.073 (1.00) | 0.506 (1.00)
del 9q34 3 | 18 (22%) | 64 | 0.204 (1.00) | 0.501 (1.00) | 0.598 (1.00)
del 10p15 3 | 38 (46%) | 44 | 0.558 (1.00) | 0.179 (1.00) | 0.0758 (1.00)
del 11p15 5 | 34 (41%) | 48 | 0.172 (1.00) | 0.00966 (1.00) | 1 (1.00)
del 11q24 3 | 37 (45%) | 45 | 0.272 (1.00) | 0.00373 (0.589) | 1 (1.00)
del 12p13 1 | 21 (26%) | 61 | 0.702 (1.00) | 0.498 (1.00) | 0.0432 (1.00)
del 12q12 | 17 (21%) | 65 | 0.587 (1.00) | 0.768 (1.00) | 1 (1.00)
del 13q14 2 | 55 (67%) | 27 | 0.373 (1.00) | 0.0252 (1.00) | 0.0621 (1.00)
del 14q24 1 | 32 (39%) | 50 | 0.161 (1.00) | 0.441 (1.00) | 0.174 (1.00)
del 16q12 1 | 44 (54%) | 38 | 0.395 (1.00) | 0.322 (1.00) | 0.128 (1.00)
del 17p13 3 | 27 (33%) | 55 | 0.141 (1.00) | 0.615 (1.00) | 0.815 (1.00)
del 17p13 1 | 33 (40%) | 49 | 0.724 (1.00) | 0.823 (1.00) | 0.115 (1.00)
del 17q11 2 | 18 (22%) | 64 | 0.333 (1.00) | 0.0967 (1.00) | 1 (1.00)
del 17q25 3 | 23 (28%) | 59 | 0.385 (1.00) | 0.191 (1.00) | 0.807 (1.00)
del 19q13 43 | 29 (35%) | 53 | 0.747 (1.00) | 0.71 (1.00) | 0.249 (1.00)
del 21q11 2 | 16 (20%) | 66 | 0.8 (1.00) | 0.34 (1.00) | 1 (1.00)
del 21q22 3 | 25 (30%) | 57 | 0.841 (1.00) | 0.00385 (0.604) | 0.474 (1.00)
del 22q13 31 | 32 (39%) | 50 | 0.27 (1.00) | 0.19 (1.00) | 0.174 (1.00)
del xp22 32 | 27 (33%) | 55 | 0.334 (1.00) | 0.0656 (1.00) | 0.482 (1.00)
del xq21 1 | 41 (50%) | 41 | 0.156 (1.00) | 0.139 (1.00) | 0.269 (1.00)
del xq27 1 | 39 (48%) | 43 | 0.822 (1.00) | 0.322 (1.00) | 0.269 (1.00)

'del_10q23.31' versus 'GENDER'

P value = 0.000999 (Fisher's exact test), Q value = 0.16

Table S1. Gene #35: 'del_10q23.31' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 40 | 42
DEL PEAK 18(10Q23.31) MUTATED | 29 | 15
DEL PEAK 18(10Q23.31) WILD-TYPE | 11 | 27

Figure S1. Get High-res Image Gene #35: 'del_10q23.31' versus Clinical Feature #3: 'GENDER'

'del_19p13.3' versus 'GENDER'

P value = 0.00083 (Fisher's exact test), Q value = 0.13

Table S2. Gene #47: 'del_19p13.3' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 40 | 42
DEL PEAK 30(19P13.3) MUTATED | 17 | 4
DEL PEAK 30(19P13.3) WILD-TYPE | 23 | 38

Figure S2. Get High-res Image Gene #47: 'del_19p13.3' versus Clinical Feature #3: 'GENDER'

Methods & Data
Input

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

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

- Number of patients = 82

- Number of significantly focal cnvs = 54

- 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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