Correlation between copy number variation genes (focal events) and selected clinical features
Uveal Melanoma (Primary solid tumor)
28 January 2016 | analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C1KH0MTQ
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Correlation between copy number variation genes (focal events) and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1KH0MTQ
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 20 focal events and 7 clinical features across 80 patients, 8 significant findings detected with Q value < 0.25.

- amp_6p24.3 cnv correlated to 'Time to Death'.

- del_3p25.2 cnv correlated to 'Time to Death'.

- del_3p25.1 cnv correlated to 'Time to Death'.

- del_3p22.2 cnv correlated to 'Time to Death'.

- del_3p14.2 cnv correlated to 'Time to Death'.

- del_3q24 cnv correlated to 'Time to Death'.

- del_3q29 cnv correlated to 'Time to Death'.

- del_8p11.22 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 20 focal events and 7 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 8 significant findings detected.

|  | Clinical Features | Time to Death | YEARS TO BIRTH | PATHOLOGIC STAGE | PATHOLOGY T STAGE | PATHOLOGY M STAGE | GENDER | RADIATION THERAPY
| nCNV (%) | nWild-Type | logrank test | Wilcoxon-test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test
amp 6p24 3 | 45 (56%) | 35 | 0.00113 (0.0227) | 0.0832 (0.433) | 0.457 (0.772) | 0.48 (0.791) | 0.352 (0.631) | 1 (1.00) | 1 (1.00)
del 3p25 2 | 43 (54%) | 37 | 5.85e-06 (0.000273) | 0.322 (0.594) | 0.0586 (0.433) | 0.265 (0.571) | 0.117 (0.433) | 1 (1.00) | 1 (1.00)
del 3p25 1 | 43 (54%) | 37 | 5.85e-06 (0.000273) | 0.322 (0.594) | 0.0583 (0.433) | 0.265 (0.571) | 0.117 (0.433) | 1 (1.00) | 1 (1.00)
del 3p22 2 | 43 (54%) | 37 | 5.85e-06 (0.000273) | 0.322 (0.594) | 0.0589 (0.433) | 0.266 (0.571) | 0.117 (0.433) | 1 (1.00) | 1 (1.00)
del 3p14 2 | 44 (55%) | 36 | 1.22e-05 (0.000284) | 0.266 (0.571) | 0.0787 (0.433) | 0.182 (0.494) | 0.117 (0.433) | 1 (1.00) | 1 (1.00)
del 3q24 | 44 (55%) | 36 | 1.22e-05 (0.000284) | 0.266 (0.571) | 0.0785 (0.433) | 0.182 (0.494) | 0.117 (0.433) | 1 (1.00) | 1 (1.00)
del 3q29 | 44 (55%) | 36 | 1.22e-05 (0.000284) | 0.266 (0.571) | 0.0776 (0.433) | 0.183 (0.494) | 0.117 (0.433) | 1 (1.00) | 1 (1.00)
del 8p11 22 | 19 (24%) | 61 | 0.00628 (0.11) | 0.0876 (0.433) | 0.0668 (0.433) | 0.289 (0.575) | 0.204 (0.501) | 0.292 (0.575) | 0.142 (0.462)
amp 8q24 22 | 61 (76%) | 19 | 0.0196 (0.305) | 0.56 (0.85) | 0.899 (1.00) | 0.54 (0.85) | 0.562 (0.85) | 0.433 (0.748) | 1 (1.00)
amp 17q25 3 | 14 (18%) | 66 | 0.712 (0.931) | 0.276 (0.571) | 0.466 (0.777) | 0.795 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00)
del 1p36 12 | 25 (31%) | 55 | 0.0477 (0.433) | 0.0638 (0.433) | 0.544 (0.85) | 0.627 (0.862) | 0.131 (0.459) | 0.634 (0.862) | 1 (1.00)
del 1p12 | 18 (22%) | 62 | 0.151 (0.48) | 0.179 (0.494) | 0.709 (0.931) | 0.882 (1.00) | 0.265 (0.571) | 0.111 (0.433) | 0.522 (0.849)
del 2q37 2 | 3 (4%) | 77 | 0.138 (0.462) | 0.63 (0.862) | 0.599 (0.862) | 0.296 (0.575) | 1 (1.00) | 0.578 (0.85) | 1 (1.00)
del 6q25 2 | 23 (29%) | 57 | 0.293 (0.575) | 0.312 (0.594) | 0.55 (0.85) | 0.0708 (0.433) | 0.58 (0.85) | 0.628 (0.862) | 0.202 (0.501)
del 6q27 | 24 (30%) | 56 | 0.165 (0.494) | 0.116 (0.433) | 0.703 (0.931) | 0.343 (0.623) | 0.58 (0.85) | 0.623 (0.862) | 0.218 (0.525)
del 11q24 3 | 7 (9%) | 73 | 0.373 (0.657) | 0.0683 (0.433) | 0.963 (1.00) | 0.87 (1.00) | 1 (1.00) | 0.693 (0.931) | 1 (1.00)
del 16q12 1 | 16 (20%) | 64 | 0.0544 (0.433) | 0.125 (0.448) | 0.169 (0.494) | 0.375 (0.657) | 0.204 (0.501) | 0.779 (1.00) | 0.0907 (0.433)
del 16q23 3 | 17 (21%) | 63 | 0.0559 (0.433) | 0.14 (0.462) | 0.17 (0.494) | 0.277 (0.571) | 0.204 (0.501) | 1 (1.00) | 0.103 (0.433)
del 16q24 3 | 17 (21%) | 63 | 0.0251 (0.352) | 0.0622 (0.433) | 0.166 (0.494) | 0.277 (0.571) | 0.204 (0.501) | 0.583 (0.85) | 0.103 (0.433)
del 17q12 | 3 (4%) | 77 | 0.0836 (0.433) | 0.612 (0.862) | 0.457 (0.772) | 0.577 (0.85) | 1 (1.00) | 1 (1.00) | 1 (1.00)

'amp_6p24.3' versus 'Time to Death'

P value = 0.00113 (logrank test), Q value = 0.023

Table S1. Gene #1: 'amp_6p24.3' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 80 | 23 | 0.1 - 85.5 (25.8)
AMP PEAK 1(6P24.3) MUTATED | 45 | 7 | 0.1 - 85.5 (27.0)
AMP PEAK 1(6P24.3) WILD-TYPE | 35 | 16 | 0.2 - 61.2 (19.9)

Figure S1. Get High-res Image Gene #1: 'amp_6p24.3' versus Clinical Feature #1: 'Time to Death'

'del_3p25.2' versus 'Time to Death'

P value = 5.85e-06 (logrank test), Q value = 0.00027

Table S2. Gene #7: 'del_3p25.2' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 80 | 23 | 0.1 - 85.5 (25.8)
DEL PEAK 4(3P25.2) MUTATED | 43 | 21 | 0.1 - 61.2 (21.0)
DEL PEAK 4(3P25.2) WILD-TYPE | 37 | 2 | 0.2 - 85.5 (27.5)

Figure S2. Get High-res Image Gene #7: 'del_3p25.2' versus Clinical Feature #1: 'Time to Death'

'del_3p25.1' versus 'Time to Death'

P value = 5.85e-06 (logrank test), Q value = 0.00027

Table S3. Gene #8: 'del_3p25.1' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 80 | 23 | 0.1 - 85.5 (25.8)
DEL PEAK 5(3P25.1) MUTATED | 43 | 21 | 0.1 - 61.2 (21.0)
DEL PEAK 5(3P25.1) WILD-TYPE | 37 | 2 | 0.2 - 85.5 (27.5)

Figure S3. Get High-res Image Gene #8: 'del_3p25.1' versus Clinical Feature #1: 'Time to Death'

'del_3p22.2' versus 'Time to Death'

P value = 5.85e-06 (logrank test), Q value = 0.00027

Table S4. Gene #9: 'del_3p22.2' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 80 | 23 | 0.1 - 85.5 (25.8)
DEL PEAK 6(3P22.2) MUTATED | 43 | 21 | 0.1 - 61.2 (21.0)
DEL PEAK 6(3P22.2) WILD-TYPE | 37 | 2 | 0.2 - 85.5 (27.5)

Figure S4. Get High-res Image Gene #9: 'del_3p22.2' versus Clinical Feature #1: 'Time to Death'

'del_3p14.2' versus 'Time to Death'

P value = 1.22e-05 (logrank test), Q value = 0.00028

Table S5. Gene #10: 'del_3p14.2' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 80 | 23 | 0.1 - 85.5 (25.8)
DEL PEAK 7(3P14.2) MUTATED | 44 | 21 | 0.1 - 61.2 (22.1)
DEL PEAK 7(3P14.2) WILD-TYPE | 36 | 2 | 0.2 - 85.5 (27.3)

Figure S5. Get High-res Image Gene #10: 'del_3p14.2' versus Clinical Feature #1: 'Time to Death'

'del_3q24' versus 'Time to Death'

P value = 1.22e-05 (logrank test), Q value = 0.00028

Table S6. Gene #11: 'del_3q24' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 80 | 23 | 0.1 - 85.5 (25.8)
DEL PEAK 8(3Q24) MUTATED | 44 | 21 | 0.1 - 61.2 (22.1)
DEL PEAK 8(3Q24) WILD-TYPE | 36 | 2 | 0.2 - 85.5 (27.3)

Figure S6. Get High-res Image Gene #11: 'del_3q24' versus Clinical Feature #1: 'Time to Death'

'del_3q29' versus 'Time to Death'

P value = 1.22e-05 (logrank test), Q value = 0.00028

Table S7. Gene #12: 'del_3q29' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 80 | 23 | 0.1 - 85.5 (25.8)
DEL PEAK 9(3Q29) MUTATED | 44 | 21 | 0.1 - 61.2 (22.1)
DEL PEAK 9(3Q29) WILD-TYPE | 36 | 2 | 0.2 - 85.5 (27.3)

Figure S7. Get High-res Image Gene #12: 'del_3q29' versus Clinical Feature #1: 'Time to Death'

'del_8p11.22' versus 'Time to Death'

P value = 0.00628 (logrank test), Q value = 0.11

Table S8. Gene #15: 'del_8p11.22' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 80 | 23 | 0.1 - 85.5 (25.8)
DEL PEAK 12(8P11.22) MUTATED | 19 | 10 | 0.1 - 61.2 (23.3)
DEL PEAK 12(8P11.22) WILD-TYPE | 61 | 13 | 0.2 - 85.5 (26.2)

Figure S8. Get High-res Image Gene #15: 'del_8p11.22' versus Clinical Feature #1: 'Time to Death'

Methods & Data
Input

- Copy number data file = all_lesions.txt from GISTIC pipeline

- Processed Copy number data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_Correlate_Genomic_Events_Preprocess/UVM-TP/22534464/transformed.cor.cli.txt

- Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/UVM-TP/22507229/UVM-TP.merged_data.txt

- Number of patients = 80

- Number of significantly focal cnvs = 20

- 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

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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