Correlation between copy number variation genes (focal events) and selected clinical features
Uveal Melanoma (Primary solid tumor)
21 August 2015  |  analyses__2015_08_21
Maintainer Information
Citation Information
doi:10.7908/C1DV1J7J
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between copy number variation genes (focal events) and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1DV1J7J
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.

  • 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_16q12.1 cnv correlated to 'Time to Death'.

  • del_16q23.3 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 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
del 3p25 2 43 (54%) 37 0.000766
(0.0179) 		0.322
(0.654) 		0.0601
(0.484) 		0.267
(0.641) 		0.117
(0.484) 		1
(1.00) 		1
(1.00)
del 3p25 1 43 (54%) 37 0.000766
(0.0179) 		0.322
(0.654) 		0.0597
(0.484) 		0.267
(0.641) 		0.117
(0.484) 		1
(1.00) 		1
(1.00)
del 3p22 2 43 (54%) 37 0.000766
(0.0179) 		0.322
(0.654) 		0.0603
(0.484) 		0.265
(0.641) 		0.117
(0.484) 		1
(1.00) 		1
(1.00)
del 3p14 2 44 (55%) 36 0.000766
(0.0179) 		0.266
(0.641) 		0.0791
(0.484) 		0.182
(0.554) 		0.117
(0.484) 		1
(1.00) 		1
(1.00)
del 3q24 44 (55%) 36 0.000766
(0.0179) 		0.266
(0.641) 		0.0783
(0.484) 		0.181
(0.554) 		0.117
(0.484) 		1
(1.00) 		1
(1.00)
del 3q29 44 (55%) 36 0.000766
(0.0179) 		0.266
(0.641) 		0.0802
(0.484) 		0.179
(0.554) 		0.117
(0.484) 		1
(1.00) 		1
(1.00)
del 16q12 1 16 (20%) 64 0.0134
(0.244) 		0.125
(0.5) 		0.168
(0.554) 		0.376
(0.732) 		0.204
(0.571) 		0.779
(1.00) 		0.0907
(0.484)
del 16q23 3 17 (21%) 63 0.014
(0.244) 		0.14
(0.523) 		0.168
(0.554) 		0.279
(0.641) 		0.204
(0.571) 		1
(1.00) 		0.103
(0.484)
amp 6p24 3 45 (56%) 35 0.0308
(0.393) 		0.0832
(0.484) 		0.457
(0.81) 		0.481
(0.821) 		0.352
(0.693) 		1
(1.00) 		1
(1.00)
amp 8q24 22 61 (76%) 19 0.0625
(0.484) 		0.56
(0.907) 		0.898
(1.00) 		0.539
(0.907) 		0.562
(0.907) 		0.433
(0.797) 		1
(1.00)
amp 17q25 3 14 (18%) 66 0.83
(1.00) 		0.276
(0.641) 		0.469
(0.818) 		0.796
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00)
del 1p36 21 24 (30%) 56 0.0303
(0.393) 		0.0888
(0.484) 		0.918
(1.00) 		0.689
(0.98) 		0.602
(0.916) 		0.473
(0.818) 		1
(1.00)
del 2q37 2 3 (4%) 77 0.312
(0.654) 		0.63
(0.919) 		0.599
(0.916) 		0.296
(0.648) 		1
(1.00) 		0.578
(0.907) 		1
(1.00)
del 5q23 1 4 (5%) 76 0.397
(0.761) 		0.42
(0.784) 		0.739
(1.00) 		0.823
(1.00) 		1
(1.00) 		0.314
(0.654) 		1
(1.00)
del 6q22 31 24 (30%) 56 0.449
(0.81) 		0.416
(0.784) 		0.662
(0.955) 		0.141
(0.523) 		0.58
(0.907) 		0.623
(0.919) 		0.218
(0.586)
del 6q27 24 (30%) 56 0.154
(0.554) 		0.116
(0.484) 		0.704
(0.986) 		0.34
(0.681) 		0.58
(0.907) 		0.623
(0.919) 		0.218
(0.586)
del 8p11 22 19 (24%) 61 0.174
(0.554) 		0.0876
(0.484) 		0.0664
(0.484) 		0.29
(0.648) 		0.204
(0.571) 		0.292
(0.648) 		0.142
(0.523)
del 11q24 3 7 (9%) 73 0.742
(1.00) 		0.0683
(0.484) 		0.965
(1.00) 		0.868
(1.00) 		1
(1.00) 		0.693
(0.98) 		1
(1.00)
del 16q24 3 17 (21%) 63 0.0255
(0.393) 		0.0622
(0.484) 		0.169
(0.554) 		0.279
(0.641) 		0.204
(0.571) 		0.583
(0.907) 		0.103
(0.484)
del 17q12 3 (4%) 77 1
(1.00) 		0.612
(0.919) 		0.456
(0.81) 		0.576
(0.907) 		1
(1.00) 		1
(1.00) 		1
(1.00)
'del_3p25.2' versus 'Time to Death'

P value = 0.000766 (logrank test), Q value = 0.018

Table S1.  Gene #6: 'del_3p25.2' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 80 14 0.1 - 85.5 (19.1)
DEL PEAK 3(3P25.2) MUTATED 43 13 0.1 - 52.6 (15.4)
DEL PEAK 3(3P25.2) WILD-TYPE 37 1 0.1 - 85.5 (20.2)

Figure S1.  Get High-res Image Gene #6: 'del_3p25.2' versus Clinical Feature #1: 'Time to Death'

'del_3p25.1' versus 'Time to Death'

P value = 0.000766 (logrank test), Q value = 0.018

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

nPatients nDeath Duration Range (Median), Month
ALL 80 14 0.1 - 85.5 (19.1)
DEL PEAK 4(3P25.1) MUTATED 43 13 0.1 - 52.6 (15.4)
DEL PEAK 4(3P25.1) WILD-TYPE 37 1 0.1 - 85.5 (20.2)

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

'del_3p22.2' versus 'Time to Death'

P value = 0.000766 (logrank test), Q value = 0.018

Table S3.  Gene #8: 'del_3p22.2' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 80 14 0.1 - 85.5 (19.1)
DEL PEAK 5(3P22.2) MUTATED 43 13 0.1 - 52.6 (15.4)
DEL PEAK 5(3P22.2) WILD-TYPE 37 1 0.1 - 85.5 (20.2)

Figure S3.  Get High-res Image Gene #8: 'del_3p22.2' versus Clinical Feature #1: 'Time to Death'

'del_3p14.2' versus 'Time to Death'

P value = 0.000766 (logrank test), Q value = 0.018

Table S4.  Gene #9: 'del_3p14.2' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 80 14 0.1 - 85.5 (19.1)
DEL PEAK 6(3P14.2) MUTATED 44 13 0.1 - 52.6 (15.2)
DEL PEAK 6(3P14.2) WILD-TYPE 36 1 0.1 - 85.5 (21.1)

Figure S4.  Get High-res Image Gene #9: 'del_3p14.2' versus Clinical Feature #1: 'Time to Death'

'del_3q24' versus 'Time to Death'

P value = 0.000766 (logrank test), Q value = 0.018

Table S5.  Gene #10: 'del_3q24' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 80 14 0.1 - 85.5 (19.1)
DEL PEAK 7(3Q24) MUTATED 44 13 0.1 - 52.6 (15.2)
DEL PEAK 7(3Q24) WILD-TYPE 36 1 0.1 - 85.5 (21.1)

Figure S5.  Get High-res Image Gene #10: 'del_3q24' versus Clinical Feature #1: 'Time to Death'

'del_3q29' versus 'Time to Death'

P value = 0.000766 (logrank test), Q value = 0.018

Table S6.  Gene #11: 'del_3q29' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 80 14 0.1 - 85.5 (19.1)
DEL PEAK 8(3Q29) MUTATED 44 13 0.1 - 52.6 (15.2)
DEL PEAK 8(3Q29) WILD-TYPE 36 1 0.1 - 85.5 (21.1)

Figure S6.  Get High-res Image Gene #11: 'del_3q29' versus Clinical Feature #1: 'Time to Death'

'del_16q12.1' versus 'Time to Death'

P value = 0.0134 (logrank test), Q value = 0.24

Table S7.  Gene #17: 'del_16q12.1' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 80 14 0.1 - 85.5 (19.1)
DEL PEAK 16(16Q12.1) MUTATED 16 6 0.1 - 74.5 (14.5)
DEL PEAK 16(16Q12.1) WILD-TYPE 64 8 0.1 - 85.5 (20.6)

Figure S7.  Get High-res Image Gene #17: 'del_16q12.1' versus Clinical Feature #1: 'Time to Death'

'del_16q23.3' versus 'Time to Death'

P value = 0.014 (logrank test), Q value = 0.24

Table S8.  Gene #18: 'del_16q23.3' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 80 14 0.1 - 85.5 (19.1)
DEL PEAK 17(16Q23.3) MUTATED 17 6 0.1 - 74.5 (14.2)
DEL PEAK 17(16Q23.3) WILD-TYPE 63 8 0.1 - 85.5 (21.0)

Figure S8.  Get High-res Image Gene #18: 'del_16q23.3' 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/19783311/transformed.cor.cli.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/UVM-TP/19775655/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.

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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