Correlation between copy number variations of arm-level result and selected clinical features
Uveal Melanoma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C10P0Z4B
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 variations of arm-level result and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C10P0Z4B
Overview
Introduction

This pipeline computes the correlation between significant arm-level copy number variations (cnvs) and selected clinical features.

Summary

Testing the association between copy number variation 51 arm-level events and 6 clinical features across 80 patients, 3 significant findings detected with Q value < 0.25.

  • 3p loss cnv correlated to 'Time to Death'.

  • 3q loss cnv correlated to 'Time to Death'.

  • 9p loss cnv correlated to 'YEARS_TO_BIRTH'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between significant copy number variation of 51 arm-level events and 6 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 3 significant findings detected.

Clinical
Features 		Time
to
Death 		YEARS
TO
BIRTH 		NEOPLASM
DISEASESTAGE 		PATHOLOGY
T
STAGE 		PATHOLOGY
M
STAGE 		GENDER
nCNV (%) nWild-Type logrank test Wilcoxon-test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test
3p loss 43 (54%) 37 0.00165
(0.194) 		0.322
(0.989) 		0.0601
(0.968) 		0.267
(0.989) 		0.117
(0.989) 		1
(1.00)
3q loss 43 (54%) 37 0.00165
(0.194) 		0.322
(0.989) 		0.0585
(0.968) 		0.266
(0.989) 		0.117
(0.989) 		1
(1.00)
9p loss 8 (10%) 72 0.017
(0.523) 		0.0019
(0.194) 		0.6
(1.00) 		0.887
(1.00) 		1
(1.00) 		0.724
(1.00)
1q gain 8 (10%) 72 0.239
(0.989) 		0.34
(0.989) 		0.362
(0.989) 		0.49
(0.989) 		0.477
(0.989) 		0.288
(0.989)
2p gain 10 (12%) 70 0.819
(1.00) 		0.344
(0.989) 		0.36
(0.989) 		0.667
(1.00) 		1
(1.00) 		0.32
(0.989)
2q gain 8 (10%) 72 0.624
(1.00) 		0.11
(0.989) 		0.621
(1.00) 		0.551
(1.00) 		1
(1.00) 		0.288
(0.989)
4p gain 7 (9%) 73 0.408
(0.989) 		0.17
(0.989) 		0.0333
(0.783) 		0.288
(0.989) 		0.379
(0.989) 		1
(1.00)
4q gain 4 (5%) 76 0.914
(1.00) 		0.0849
(0.988) 		0.00544
(0.333) 		0.0949
(0.989) 		0.267
(0.989) 		0.314
(0.989)
5p gain 3 (4%) 77 0.412
(0.989) 		0.447
(0.989) 		0.889
(1.00) 		1
(1.00) 		1
(1.00) 		0.578
(1.00)
5q gain 3 (4%) 77 0.412
(0.989) 		0.447
(0.989) 		0.89
(1.00) 		1
(1.00) 		1
(1.00) 		0.578
(1.00)
6p gain 39 (49%) 41 0.303
(0.989) 		0.256
(0.989) 		0.892
(1.00) 		0.873
(1.00) 		0.624
(1.00) 		0.822
(1.00)
6q gain 16 (20%) 64 0.0542
(0.968) 		0.279
(0.989) 		0.473
(0.989) 		0.301
(0.989) 		1
(1.00) 		0.587
(1.00)
7p gain 9 (11%) 71 0.42
(0.989) 		0.415
(0.989) 		0.518
(1.00) 		0.423
(0.989) 		0.325
(0.989) 		1
(1.00)
7q gain 8 (10%) 72 0.231
(0.989) 		0.217
(0.989) 		0.394
(0.989) 		0.491
(0.989) 		0.267
(0.989) 		0.724
(1.00)
8p gain 33 (41%) 47 0.05
(0.963) 		0.725
(1.00) 		0.426
(0.989) 		0.575
(1.00) 		0.307
(0.989) 		1
(1.00)
8q gain 53 (66%) 27 0.0188
(0.523) 		0.768
(1.00) 		0.91
(1.00) 		0.779
(1.00) 		1
(1.00) 		1
(1.00)
9p gain 6 (8%) 74 0.237
(0.989) 		0.136
(0.989) 		0.763
(1.00) 		0.223
(0.989) 		1
(1.00) 		0.0811
(0.988)
9q gain 5 (6%) 75 0.302
(0.989) 		0.249
(0.989) 		0.575
(1.00) 		0.32
(0.989) 		1
(1.00) 		0.162
(0.989)
11p gain 9 (11%) 71 0.571
(1.00) 		0.26
(0.989) 		0.779
(1.00) 		0.724
(1.00) 		1
(1.00) 		0.494
(0.989)
11q gain 10 (12%) 70 0.478
(0.989) 		0.436
(0.989) 		0.777
(1.00) 		0.743
(1.00) 		1
(1.00) 		0.741
(1.00)
12p gain 3 (4%) 77 0.412
(0.989) 		0.56
(1.00) 		1
(1.00) 		0.78
(1.00) 		1
(1.00) 		0.578
(1.00)
12q gain 3 (4%) 77 0.412
(0.989) 		0.56
(1.00) 		1
(1.00) 		0.783
(1.00) 		1
(1.00) 		0.578
(1.00)
13q gain 6 (8%) 74 0.968
(1.00) 		0.165
(0.989) 		0.789
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00)
14q gain 3 (4%) 77 0.488
(0.989) 		0.603
(1.00) 		0.889
(1.00) 		1
(1.00) 		1
(1.00) 		0.252
(0.989)
16p gain 4 (5%) 76 0.536
(1.00) 		0.707
(1.00) 		0.795
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00)
17p gain 8 (10%) 72 0.677
(1.00) 		0.365
(0.989) 		0.97
(1.00) 		0.888
(1.00) 		1
(1.00) 		0.724
(1.00)
17q gain 9 (11%) 71 0.582
(1.00) 		0.772
(1.00) 		0.868
(1.00) 		0.643
(1.00) 		1
(1.00) 		0.494
(0.989)
20p gain 8 (10%) 72 0.827
(1.00) 		0.332
(0.989) 		0.696
(1.00) 		0.488
(0.989) 		1
(1.00) 		1
(1.00)
20q gain 9 (11%) 71 0.521
(1.00) 		0.19
(0.989) 		0.599
(1.00) 		0.381
(0.989) 		1
(1.00) 		1
(1.00)
21q gain 14 (18%) 66 0.219
(0.989) 		0.115
(0.989) 		0.779
(1.00) 		0.629
(1.00) 		0.477
(0.989) 		0.375
(0.989)
22q gain 6 (8%) 74 0.871
(1.00) 		0.168
(0.989) 		0.881
(1.00) 		0.405
(0.989) 		1
(1.00) 		0.0811
(0.988)
xp gain 10 (12%) 70 0.122
(0.989) 		0.158
(0.989) 		0.962
(1.00) 		0.666
(1.00) 		1
(1.00) 		0.0949
(0.989)
xq gain 9 (11%) 71 0.154
(0.989) 		0.209
(0.989) 		0.977
(1.00) 		0.807
(1.00) 		1
(1.00) 		0.169
(0.989)
1p loss 19 (24%) 61 0.188
(0.989) 		0.0503
(0.963) 		0.915
(1.00) 		0.833
(1.00) 		1
(1.00) 		0.433
(0.989)
1q loss 3 (4%) 77 0.329
(0.989) 		0.494
(0.989) 		1
(1.00) 		0.137
(0.989) 		1
(1.00) 		0.578
(1.00)
4q loss 3 (4%) 77 0.526
(1.00) 		0.939
(1.00) 		0.599
(1.00) 		0.297
(0.989) 		1
(1.00) 		1
(1.00)
5q loss 3 (4%) 77 0.479
(0.989) 		0.462
(0.989) 		0.456
(0.989) 		0.58
(1.00) 		1
(1.00) 		0.0797
(0.988)
6q loss 17 (21%) 63 0.0871
(0.988) 		0.167
(0.989) 		0.0668
(0.988) 		0.0372
(0.812) 		0.298
(0.989) 		0.583
(1.00)
8p loss 9 (11%) 71 0.794
(1.00) 		0.61
(1.00) 		0.171
(0.989) 		0.199
(0.989) 		0.379
(0.989) 		1
(1.00)
8q loss 3 (4%) 77 0.464
(0.989) 		0.751
(1.00) 		0.3
(0.989) 		1
(1.00) 		0.141
(0.989) 		1
(1.00)
9q loss 7 (9%) 73 0.017
(0.523) 		0.00729
(0.372) 		0.375
(0.989) 		0.441
(0.989) 		1
(1.00) 		1
(1.00)
11p loss 3 (4%) 77 0.0176
(0.523) 		0.305
(0.989) 		1
(1.00) 		0.138
(0.989) 		1
(1.00) 		0.578
(1.00)
12p loss 3 (4%) 77 0.485
(0.989) 		0.761
(1.00) 		0.337
(0.989) 		0.137
(0.989) 		1
(1.00) 		0.578
(1.00)
13q loss 3 (4%) 77 0.412
(0.989) 		0.761
(1.00) 		1
(1.00) 		0.137
(0.989) 		1
(1.00) 		0.0797
(0.988)
15q loss 4 (5%) 76 0.464
(0.989) 		0.208
(0.989) 		0.793
(1.00) 		0.394
(0.989) 		1
(1.00) 		1
(1.00)
16p loss 3 (4%) 77 0.133
(0.989) 		0.254
(0.989) 		0.0869
(0.988) 		0.209
(0.989) 		0.206
(0.989) 		0.578
(1.00)
16q loss 16 (20%) 64 0.00361
(0.277) 		0.125
(0.989) 		0.167
(0.989) 		0.379
(0.989) 		0.204
(0.989) 		0.779
(1.00)
19p loss 3 (4%) 77 0.585
(1.00) 		0.879
(1.00) 		0.418
(0.989) 		0.389
(0.989) 		1
(1.00) 		1
(1.00)
19q loss 3 (4%) 77 0.585
(1.00) 		0.879
(1.00) 		0.41
(0.989) 		0.39
(0.989) 		1
(1.00) 		1
(1.00)
xp loss 12 (15%) 68 0.473
(0.989) 		0.0119
(0.521) 		0.171
(0.989) 		0.223
(0.989) 		0.522
(1.00) 		0.349
(0.989)
xq loss 13 (16%) 67 0.671
(1.00) 		0.0248
(0.632) 		0.393
(0.989) 		0.201
(0.989) 		0.563
(1.00) 		0.223
(0.989)
'3p loss' versus 'Time to Death'

P value = 0.00165 (logrank test), Q value = 0.19

Table S1.  Gene #33: '3p loss' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 80 13 0.1 - 74.5 (19.1)
3P LOSS MUTATED 43 12 0.1 - 52.6 (15.0)
3P LOSS WILD-TYPE 37 1 0.1 - 74.5 (20.2)

Figure S1.  Get High-res Image Gene #33: '3p loss' versus Clinical Feature #1: 'Time to Death'

'3q loss' versus 'Time to Death'

P value = 0.00165 (logrank test), Q value = 0.19

Table S2.  Gene #34: '3q loss' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 80 13 0.1 - 74.5 (19.1)
3Q LOSS MUTATED 43 12 0.1 - 52.6 (15.0)
3Q LOSS WILD-TYPE 37 1 0.1 - 74.5 (20.2)

Figure S2.  Get High-res Image Gene #34: '3q loss' versus Clinical Feature #1: 'Time to Death'

'9p loss' versus 'YEARS_TO_BIRTH'

P value = 0.0019 (Wilcoxon-test), Q value = 0.19

Table S3.  Gene #40: '9p loss' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 80 61.6 (13.9)
9P LOSS MUTATED 8 75.4 (9.6)
9P LOSS WILD-TYPE 72 60.1 (13.6)

Figure S3.  Get High-res Image Gene #40: '9p loss' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

Methods & Data
Input

  • Copy number data file = broad_values_by_arm.txt from GISTIC pipeline

  • Processed Copy number data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_Correlate_Genomic_Events_Preprocess/UVM-TP/15908355/transformed.cor.cli.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/UVM-TP/15096076/UVM-TP.merged_data.txt

  • Number of patients = 80

  • Number of significantly arm-level cnvs = 51

  • Number of selected clinical features = 6

  • Exclude regions 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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