Correlation between copy number variations of arm-level result and selected clinical features
Pheochromocytoma and Paraganglioma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1057F18
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/C1057F18
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 69 arm-level events and 4 clinical features across 162 patients, one significant finding detected with Q value < 0.25.

  • 16p loss 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 69 arm-level events and 4 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, one significant finding detected.

Clinical
Features 		Time
to
Death 		YEARS
TO
BIRTH 		GENDER RACE
nCNV (%) nWild-Type logrank test Wilcoxon-test Fisher's exact test Fisher's exact test
16p loss 9 (6%) 153 9.55e-05
(0.0264) 		0.124
(1.00) 		0.733
(1.00) 		0.721
(1.00)
1p gain 7 (4%) 155 0.424
(1.00) 		0.419
(1.00) 		0.048
(1.00) 		1
(1.00)
1q gain 19 (12%) 143 0.489
(1.00) 		0.0891
(1.00) 		0.141
(1.00) 		0.857
(1.00)
2p gain 6 (4%) 156 0.62
(1.00) 		0.968
(1.00) 		1
(1.00) 		0.227
(1.00)
4p gain 4 (2%) 158 0.657
(1.00) 		0.339
(1.00) 		0.332
(1.00) 		0.208
(1.00)
4q gain 3 (2%) 159 0.756
(1.00) 		0.305
(1.00) 		0.593
(1.00) 		1
(1.00)
5p gain 11 (7%) 151 0.529
(1.00) 		0.225
(1.00) 		0.756
(1.00) 		1
(1.00)
5q gain 7 (4%) 155 0.594
(1.00) 		0.863
(1.00) 		0.127
(1.00) 		1
(1.00)
6p gain 15 (9%) 147 0.483
(1.00) 		0.0416
(1.00) 		1
(1.00) 		1
(1.00)
6q gain 8 (5%) 154 0.603
(1.00) 		0.071
(1.00) 		1
(1.00) 		0.46
(1.00)
7p gain 27 (17%) 135 0.337
(1.00) 		0.694
(1.00) 		0.53
(1.00) 		0.617
(1.00)
7q gain 22 (14%) 140 0.391
(1.00) 		0.845
(1.00) 		0.106
(1.00) 		1
(1.00)
8p gain 10 (6%) 152 0.554
(1.00) 		0.211
(1.00) 		1
(1.00) 		0.571
(1.00)
8q gain 13 (8%) 149 0.328
(1.00) 		0.851
(1.00) 		0.259
(1.00) 		0.345
(1.00)
9p gain 4 (2%) 158 0.729
(1.00) 		0.742
(1.00) 		1
(1.00) 		0.498
(1.00)
9q gain 3 (2%) 159 0.756
(1.00) 		0.374
(1.00) 		1
(1.00) 		0.401
(1.00)
10p gain 12 (7%) 150 0.571
(1.00) 		0.972
(1.00) 		1
(1.00) 		0.763
(1.00)
10q gain 10 (6%) 152 0.612
(1.00) 		0.646
(1.00) 		0.514
(1.00) 		1
(1.00)
11p gain 5 (3%) 157 0.772
(1.00) 		0.611
(1.00) 		0.377
(1.00) 		1
(1.00)
12p gain 11 (7%) 151 0.526
(1.00) 		0.266
(1.00) 		1
(1.00) 		0.759
(1.00)
12q gain 14 (9%) 148 0.474
(1.00) 		0.661
(1.00) 		1
(1.00) 		0.821
(1.00)
13q gain 9 (6%) 153 0.603
(1.00) 		0.829
(1.00) 		0.733
(1.00) 		1
(1.00)
14q gain 4 (2%) 158 0.00491
(0.453) 		0.901
(1.00) 		0.332
(1.00) 		0.5
(1.00)
15q gain 14 (9%) 148 0.481
(1.00) 		0.406
(1.00) 		0.784
(1.00) 		0.245
(1.00)
16p gain 6 (4%) 156 0.653
(1.00) 		0.327
(1.00) 		0.689
(1.00) 		0.651
(1.00)
16q gain 6 (4%) 156 0.0509
(1.00) 		0.0753
(1.00) 		1
(1.00) 		0.647
(1.00)
17q gain 5 (3%) 157 0.7
(1.00) 		0.591
(1.00) 		0.179
(1.00) 		0.579
(1.00)
18p gain 8 (5%) 154 0.56
(1.00) 		0.954
(1.00) 		0.471
(1.00) 		1
(1.00)
18q gain 10 (6%) 152 0.529
(1.00) 		0.611
(1.00) 		1
(1.00) 		1
(1.00)
19p gain 20 (12%) 142 0.725
(1.00) 		0.019
(0.874) 		0.638
(1.00) 		1
(1.00)
19q gain 14 (9%) 148 0.467
(1.00) 		0.0235
(0.925) 		1
(1.00) 		0.821
(1.00)
20p gain 12 (7%) 150 0.497
(1.00) 		0.294
(1.00) 		1
(1.00) 		0.603
(1.00)
20q gain 10 (6%) 152 0.511
(1.00) 		0.333
(1.00) 		0.756
(1.00) 		1
(1.00)
21q gain 3 (2%) 159 0.759
(1.00) 		0.0182
(0.874) 		0.593
(1.00) 		0.404
(1.00)
22q gain 3 (2%) 159 0.756
(1.00) 		0.543
(1.00) 		0.593
(1.00) 		0.406
(1.00)
xp gain 6 (4%) 156 0.622
(1.00) 		0.842
(1.00) 		0.221
(1.00) 		0.338
(1.00)
xq gain 5 (3%) 157 0.661
(1.00) 		0.958
(1.00) 		0.377
(1.00) 		0.579
(1.00)
1p loss 98 (60%) 64 0.208
(1.00) 		0.894
(1.00) 		0.748
(1.00) 		0.231
(1.00)
1q loss 27 (17%) 135 0.94
(1.00) 		0.927
(1.00) 		0.53
(1.00) 		0.219
(1.00)
2p loss 10 (6%) 152 0.646
(1.00) 		0.636
(1.00) 		1
(1.00) 		0.402
(1.00)
2q loss 13 (8%) 149 0.497
(1.00) 		0.995
(1.00) 		0.259
(1.00) 		0.529
(1.00)
3p loss 62 (38%) 100 0.535
(1.00) 		0.307
(1.00) 		0.195
(1.00) 		0.0687
(1.00)
3q loss 88 (54%) 74 0.323
(1.00) 		0.145
(1.00) 		0.875
(1.00) 		0.935
(1.00)
4p loss 11 (7%) 151 0.601
(1.00) 		0.823
(1.00) 		0.551
(1.00) 		1
(1.00)
4q loss 10 (6%) 152 0.587
(1.00) 		0.989
(1.00) 		0.514
(1.00) 		1
(1.00)
5p loss 5 (3%) 157 0.772
(1.00) 		0.981
(1.00) 		0.661
(1.00) 		0.579
(1.00)
5q loss 7 (4%) 155 0.684
(1.00) 		0.751
(1.00) 		0.703
(1.00) 		0.4
(1.00)
6q loss 19 (12%) 143 0.61
(1.00) 		0.859
(1.00) 		0.469
(1.00) 		0.856
(1.00)
7q loss 6 (4%) 156 0.0957
(1.00) 		0.683
(1.00) 		1
(1.00) 		0.0179
(0.874)
8p loss 20 (12%) 142 0.188
(1.00) 		0.249
(1.00) 		0.346
(1.00) 		0.507
(1.00)
8q loss 12 (7%) 150 0.292
(1.00) 		0.567
(1.00) 		1
(1.00) 		1
(1.00)
9p loss 11 (7%) 151 0.028
(0.965) 		0.704
(1.00) 		1
(1.00) 		1
(1.00)
9q loss 11 (7%) 151 0.568
(1.00) 		0.316
(1.00) 		1
(1.00) 		0.759
(1.00)
11p loss 54 (33%) 108 0.56
(1.00) 		0.919
(1.00) 		0.316
(1.00) 		0.598
(1.00)
11q loss 40 (25%) 122 0.925
(1.00) 		0.712
(1.00) 		0.856
(1.00) 		1
(1.00)
13q loss 8 (5%) 154 0.61
(1.00) 		0.951
(1.00) 		1
(1.00) 		0.315
(1.00)
14q loss 20 (12%) 142 0.316
(1.00) 		0.311
(1.00) 		0.811
(1.00) 		0.671
(1.00)
15q loss 3 (2%) 159 0.545
(1.00) 		0.452
(1.00) 		0.251
(1.00) 		0.0425
(1.00)
16q loss 5 (3%) 157 0.693
(1.00) 		0.131
(1.00) 		1
(1.00) 		1
(1.00)
17p loss 61 (38%) 101 0.958
(1.00) 		0.761
(1.00) 		0.332
(1.00) 		0.081
(1.00)
17q loss 19 (12%) 143 0.11
(1.00) 		0.585
(1.00) 		0.328
(1.00) 		0.758
(1.00)
18p loss 16 (10%) 146 0.478
(1.00) 		0.511
(1.00) 		0.19
(1.00) 		0.177
(1.00)
18q loss 6 (4%) 156 0.69
(1.00) 		0.435
(1.00) 		0.413
(1.00) 		0.148
(1.00)
19q loss 6 (4%) 156 0.0901
(1.00) 		0.723
(1.00) 		1
(1.00) 		0.23
(1.00)
20q loss 3 (2%) 159 0.83
(1.00) 		0.751
(1.00) 		1
(1.00) 		1
(1.00)
21q loss 36 (22%) 126 0.2
(1.00) 		0.588
(1.00) 		0.705
(1.00) 		0.207
(1.00)
22q loss 58 (36%) 104 0.00492
(0.453) 		0.388
(1.00) 		0.742
(1.00) 		0.669
(1.00)
xp loss 48 (30%) 114 0.847
(1.00) 		0.958
(1.00) 		0.733
(1.00) 		0.563
(1.00)
xq loss 49 (30%) 113 0.872
(1.00) 		0.926
(1.00) 		0.865
(1.00) 		0.614
(1.00)
'16p loss' versus 'Time to Death'

P value = 9.55e-05 (logrank test), Q value = 0.026

Table S1.  Gene #58: '16p loss' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 162 6 0.1 - 316.7 (21.4)
16P LOSS MUTATED 9 3 0.8 - 117.1 (23.8)
16P LOSS WILD-TYPE 153 3 0.1 - 316.7 (20.6)

Figure S1.  Get High-res Image Gene #58: '16p loss' versus Clinical Feature #1: 'Time to Death'

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/PCPG-TP/15096455/transformed.cor.cli.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/PCPG-TP/15087180/PCPG-TP.merged_data.txt

  • Number of patients = 162

  • Number of significantly arm-level cnvs = 69

  • Number of selected clinical features = 4

  • 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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