Correlation between copy number variations of arm-level result and selected clinical features
Uveal Melanoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C1Q52P5K
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 variations of arm-level result and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1Q52P5K
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 50 arm-level events and 7 clinical features across 80 patients, 4 significant findings detected with Q value < 0.25.

  • 8q gain cnv correlated to 'Time to Death'.

  • 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 50 arm-level events and 7 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 4 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
8q gain 53 (66%) 27 0.00199
(0.174) 		0.768
(1.00) 		0.91
(1.00) 		0.778
(1.00) 		1
(1.00) 		1
(1.00) 		0.547
(1.00)
3p loss 43 (54%) 37 5.85e-06
(0.00102) 		0.322
(1.00) 		0.0583
(0.905) 		0.268
(1.00) 		0.117
(1.00) 		1
(1.00) 		1
(1.00)
3q loss 43 (54%) 37 5.85e-06
(0.00102) 		0.322
(1.00) 		0.0595
(0.905) 		0.269
(1.00) 		0.117
(1.00) 		1
(1.00) 		1
(1.00)
9p loss 8 (10%) 72 0.0191
(0.476) 		0.0019
(0.174) 		0.603
(1.00) 		0.889
(1.00) 		1
(1.00) 		0.724
(1.00) 		0.277
(1.00)
1q gain 8 (10%) 72 0.231
(1.00) 		0.34
(1.00) 		0.367
(1.00) 		0.488
(1.00) 		0.477
(1.00) 		0.288
(1.00) 		1
(1.00)
2p gain 10 (12%) 70 0.776
(1.00) 		0.344
(1.00) 		0.361
(1.00) 		0.667
(1.00) 		1
(1.00) 		0.32
(1.00) 		0.337
(1.00)
2q gain 8 (10%) 72 0.757
(1.00) 		0.11
(1.00) 		0.619
(1.00) 		0.552
(1.00) 		1
(1.00) 		0.288
(1.00) 		1
(1.00)
4p gain 7 (9%) 73 0.674
(1.00) 		0.17
(1.00) 		0.0343
(0.745) 		0.288
(1.00) 		0.379
(1.00) 		1
(1.00) 		0.246
(1.00)
4q gain 4 (5%) 76 0.785
(1.00) 		0.0849
(1.00) 		0.00512
(0.32) 		0.0965
(1.00) 		0.267
(1.00) 		0.314
(1.00) 		1
(1.00)
5p gain 3 (4%) 77 0.475
(1.00) 		0.447
(1.00) 		0.888
(1.00) 		1
(1.00) 		1
(1.00) 		0.578
(1.00) 		1
(1.00)
5q gain 3 (4%) 77 0.475
(1.00) 		0.447
(1.00) 		0.889
(1.00) 		1
(1.00) 		1
(1.00) 		0.578
(1.00) 		1
(1.00)
6p gain 38 (48%) 42 0.00822
(0.32) 		0.15
(1.00) 		0.837
(1.00) 		0.798
(1.00) 		0.632
(1.00) 		0.653
(1.00) 		1
(1.00)
6q gain 16 (20%) 64 0.00726
(0.32) 		0.279
(1.00) 		0.472
(1.00) 		0.302
(1.00) 		1
(1.00) 		0.587
(1.00) 		0.498
(1.00)
7p gain 9 (11%) 71 0.3
(1.00) 		0.415
(1.00) 		0.518
(1.00) 		0.424
(1.00) 		0.325
(1.00) 		1
(1.00) 		1
(1.00)
7q gain 8 (10%) 72 0.17
(1.00) 		0.217
(1.00) 		0.397
(1.00) 		0.488
(1.00) 		0.267
(1.00) 		0.724
(1.00) 		1
(1.00)
8p gain 39 (49%) 41 0.0075
(0.32) 		0.441
(1.00) 		0.714
(1.00) 		0.58
(1.00) 		0.611
(1.00) 		0.822
(1.00) 		0.107
(1.00)
9p gain 5 (6%) 75 0.684
(1.00) 		0.183
(1.00) 		0.6
(1.00) 		0.32
(1.00) 		1
(1.00) 		0.162
(1.00) 		1
(1.00)
9q gain 4 (5%) 76 0.212
(1.00) 		0.178
(1.00) 		0.26
(1.00) 		0.289
(1.00) 		1
(1.00) 		0.314
(1.00) 		1
(1.00)
11p gain 10 (12%) 70 0.973
(1.00) 		0.462
(1.00) 		0.915
(1.00) 		0.909
(1.00) 		1
(1.00) 		0.32
(1.00) 		1
(1.00)
11q gain 10 (12%) 70 0.699
(1.00) 		0.436
(1.00) 		0.775
(1.00) 		0.743
(1.00) 		1
(1.00) 		0.741
(1.00) 		1
(1.00)
12p gain 3 (4%) 77 0.882
(1.00) 		0.56
(1.00) 		1
(1.00) 		0.782
(1.00) 		1
(1.00) 		0.578
(1.00) 		1
(1.00)
12q gain 3 (4%) 77 0.882
(1.00) 		0.56
(1.00) 		1
(1.00) 		0.784
(1.00) 		1
(1.00) 		0.578
(1.00) 		1
(1.00)
13q gain 6 (8%) 74 0.789
(1.00) 		0.165
(1.00) 		0.791
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		0.213
(1.00)
14q gain 3 (4%) 77 0.103
(1.00) 		0.603
(1.00) 		0.887
(1.00) 		1
(1.00) 		1
(1.00) 		0.252
(1.00) 		1
(1.00)
16p gain 4 (5%) 76 0.518
(1.00) 		0.707
(1.00) 		0.795
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00)
17p gain 8 (10%) 72 0.484
(1.00) 		0.365
(1.00) 		0.97
(1.00) 		0.888
(1.00) 		1
(1.00) 		0.724
(1.00) 		1
(1.00)
17q gain 9 (11%) 71 0.665
(1.00) 		0.772
(1.00) 		0.869
(1.00) 		0.64
(1.00) 		1
(1.00) 		0.494
(1.00) 		1
(1.00)
20p gain 8 (10%) 72 0.199
(1.00) 		0.332
(1.00) 		0.696
(1.00) 		0.491
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00)
20q gain 9 (11%) 71 0.0549
(0.905) 		0.19
(1.00) 		0.602
(1.00) 		0.381
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00)
21q gain 14 (18%) 66 0.211
(1.00) 		0.115
(1.00) 		0.777
(1.00) 		0.631
(1.00) 		0.477
(1.00) 		0.375
(1.00) 		1
(1.00)
22q gain 5 (6%) 75 0.682
(1.00) 		0.0435
(0.846) 		0.664
(1.00) 		0.195
(1.00) 		1
(1.00) 		0.0135
(0.394) 		1
(1.00)
xp gain 10 (12%) 70 0.115
(1.00) 		0.158
(1.00) 		0.962
(1.00) 		0.666
(1.00) 		1
(1.00) 		0.0949
(1.00) 		1
(1.00)
xq gain 9 (11%) 71 0.204
(1.00) 		0.209
(1.00) 		0.976
(1.00) 		0.81
(1.00) 		1
(1.00) 		0.169
(1.00) 		1
(1.00)
1p loss 19 (24%) 61 0.55
(1.00) 		0.0503
(0.905) 		0.916
(1.00) 		0.831
(1.00) 		1
(1.00) 		0.433
(1.00) 		0.567
(1.00)
1q loss 3 (4%) 77 0.859
(1.00) 		0.494
(1.00) 		1
(1.00) 		0.137
(1.00) 		1
(1.00) 		0.578
(1.00) 		1
(1.00)
4q loss 3 (4%) 77 0.757
(1.00) 		0.939
(1.00) 		0.598
(1.00) 		0.296
(1.00) 		1
(1.00) 		1
(1.00) 		0.111
(1.00)
5q loss 3 (4%) 77 0.276
(1.00) 		0.462
(1.00) 		0.457
(1.00) 		0.577
(1.00) 		1
(1.00) 		0.0797
(1.00) 		1
(1.00)
6q loss 17 (21%) 63 0.0112
(0.379) 		0.167
(1.00) 		0.0676
(0.986) 		0.0362
(0.745) 		0.298
(1.00) 		0.583
(1.00) 		0.522
(1.00)
8p loss 4 (5%) 76 0.626
(1.00) 		0.965
(1.00) 		0.309
(1.00) 		0.513
(1.00) 		0.141
(1.00) 		0.628
(1.00) 		1
(1.00)
8q loss 3 (4%) 77 0.427
(1.00) 		0.751
(1.00) 		0.302
(1.00) 		1
(1.00) 		0.141
(1.00) 		1
(1.00) 		1
(1.00)
9q loss 7 (9%) 73 0.018
(0.476) 		0.00729
(0.32) 		0.378
(1.00) 		0.439
(1.00) 		1
(1.00) 		1
(1.00) 		0.246
(1.00)
12p loss 3 (4%) 77 0.763
(1.00) 		0.761
(1.00) 		0.341
(1.00) 		0.139
(1.00) 		1
(1.00) 		0.578
(1.00) 		1
(1.00)
13q loss 3 (4%) 77 0.264
(1.00) 		0.761
(1.00) 		1
(1.00) 		0.138
(1.00) 		1
(1.00) 		0.0797
(1.00) 		1
(1.00)
15q loss 4 (5%) 76 0.707
(1.00) 		0.208
(1.00) 		0.795
(1.00) 		0.395
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00)
16p loss 3 (4%) 77 0.386
(1.00) 		0.254
(1.00) 		0.0863
(1.00) 		0.211
(1.00) 		0.206
(1.00) 		0.578
(1.00) 		1
(1.00)
16q loss 16 (20%) 64 0.0544
(0.905) 		0.125
(1.00) 		0.169
(1.00) 		0.375
(1.00) 		0.204
(1.00) 		0.779
(1.00) 		0.0907
(1.00)
19p loss 3 (4%) 77 0.684
(1.00) 		0.879
(1.00) 		0.412
(1.00) 		0.391
(1.00) 		1
(1.00) 		1
(1.00) 		0.111
(1.00)
19q loss 3 (4%) 77 0.684
(1.00) 		0.879
(1.00) 		0.415
(1.00) 		0.395
(1.00) 		1
(1.00) 		1
(1.00) 		0.111
(1.00)
xp loss 12 (15%) 68 0.765
(1.00) 		0.0119
(0.379) 		0.169
(1.00) 		0.224
(1.00) 		0.522
(1.00) 		0.349
(1.00) 		0.394
(1.00)
xq loss 13 (16%) 67 0.876
(1.00) 		0.0248
(0.579) 		0.394
(1.00) 		0.203
(1.00) 		0.563
(1.00) 		0.223
(1.00) 		0.421
(1.00)
'8q gain' versus 'Time to Death'

P value = 0.00199 (logrank test), Q value = 0.17

Table S1.  Gene #13: '8q gain' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 80 23 0.1 - 85.5 (25.8)
8Q GAIN MUTATED 53 21 0.1 - 82.2 (24.1)
8Q GAIN WILD-TYPE 27 2 0.2 - 85.5 (27.0)

Figure S1.  Get High-res Image Gene #13: '8q gain' versus Clinical Feature #1: 'Time to Death'

'3p loss' versus 'Time to Death'

P value = 5.85e-06 (logrank test), Q value = 0.001

Table S2.  Gene #33: '3p loss' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 80 23 0.1 - 85.5 (25.8)
3P LOSS MUTATED 43 21 0.1 - 61.2 (21.0)
3P LOSS WILD-TYPE 37 2 0.2 - 85.5 (27.5)

Figure S2.  Get High-res Image Gene #33: '3p loss' versus Clinical Feature #1: 'Time to Death'

'3q loss' versus 'Time to Death'

P value = 5.85e-06 (logrank test), Q value = 0.001

Table S3.  Gene #34: '3q loss' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 80 23 0.1 - 85.5 (25.8)
3Q LOSS MUTATED 43 21 0.1 - 61.2 (21.0)
3Q LOSS WILD-TYPE 37 2 0.2 - 85.5 (27.5)

Figure S3.  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 = 0.17

Table S4.  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 S4.  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/22534465/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 arm-level cnvs = 50

  • Number of selected clinical features = 7

  • 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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