Correlation between copy number variations of arm-level result and selected clinical features
Uterine Carcinosarcoma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C15H7FD6
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/C15H7FD6
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 79 arm-level events and 3 clinical features across 56 patients, no significant finding detected with Q value < 0.25.

  • No arm-level cnvs related to clinical features.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between significant copy number variation of 79 arm-level events and 3 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, no significant finding detected.

Clinical
Features 		Time
to
Death 		YEARS
TO
BIRTH 		RACE
nCNV (%) nWild-Type logrank test Wilcoxon-test Fisher's exact test
1p gain 24 (43%) 32 0.649
(0.996) 		0.169
(0.996) 		0.274
(0.996)
1q gain 31 (55%) 25 0.239
(0.996) 		0.509
(0.996) 		0.149
(0.996)
2p gain 23 (41%) 33 0.0693
(0.996) 		0.526
(0.996) 		1
(1.00)
2q gain 21 (38%) 35 0.752
(0.996) 		0.635
(0.996) 		0.874
(1.00)
3p gain 12 (21%) 44 0.596
(0.996) 		0.322
(0.996) 		0.0949
(0.996)
3q gain 23 (41%) 33 0.595
(0.996) 		0.309
(0.996) 		0.676
(0.996)
4p gain 9 (16%) 47 0.431
(0.996) 		0.738
(0.996) 		0.162
(0.996)
4q gain 3 (5%) 53 0.912
(1.00) 		0.985
(1.00) 		1
(1.00)
5p gain 23 (41%) 33 0.726
(0.996) 		0.683
(0.996) 		0.876
(1.00)
5q gain 8 (14%) 48 0.813
(1.00) 		0.833
(1.00) 		0.242
(0.996)
6p gain 30 (54%) 26 1
(1.00) 		0.114
(0.996) 		0.451
(0.996)
6q gain 27 (48%) 29 0.865
(1.00) 		0.178
(0.996) 		0.6
(0.996)
7p gain 21 (38%) 35 0.761
(0.996) 		0.192
(0.996) 		0.237
(0.996)
7q gain 17 (30%) 39 0.671
(0.996) 		0.532
(0.996) 		0.866
(1.00)
8p gain 19 (34%) 37 0.107
(0.996) 		0.401
(0.996) 		0.308
(0.996)
8q gain 30 (54%) 26 0.15
(0.996) 		0.336
(0.996) 		0.45
(0.996)
9p gain 6 (11%) 50 0.987
(1.00) 		0.353
(0.996) 		0.702
(0.996)
10p gain 21 (38%) 35 0.429
(0.996) 		0.78
(1.00) 		0.666
(0.996)
10q gain 17 (30%) 39 0.315
(0.996) 		0.865
(1.00) 		0.353
(0.996)
11p gain 5 (9%) 51 0.924
(1.00) 		0.752
(0.996) 		1
(1.00)
11q gain 7 (12%) 49 0.989
(1.00) 		0.457
(0.996) 		1
(1.00)
12p gain 22 (39%) 34 0.151
(0.996) 		0.45
(0.996) 		0.284
(0.996)
12q gain 11 (20%) 45 0.999
(1.00) 		0.584
(0.996) 		0.393
(0.996)
13q gain 15 (27%) 41 0.293
(0.996) 		0.505
(0.996) 		0.73
(0.996)
14q gain 7 (12%) 49 0.0284
(0.996) 		0.766
(0.996) 		0.559
(0.996)
15q gain 4 (7%) 52 0.535
(0.996) 		0.166
(0.996) 		0.206
(0.996)
16p gain 10 (18%) 46 0.541
(0.996) 		0.416
(0.996) 		0.397
(0.996)
16q gain 6 (11%) 50 0.374
(0.996) 		0.853
(1.00) 		0.49
(0.996)
17p gain 9 (16%) 47 0.282
(0.996) 		0.396
(0.996) 		0.283
(0.996)
17q gain 18 (32%) 38 0.929
(1.00) 		0.765
(0.996) 		0.749
(0.996)
18p gain 18 (32%) 38 0.467
(0.996) 		0.854
(1.00) 		0.656
(0.996)
18q gain 14 (25%) 42 0.511
(0.996) 		0.985
(1.00) 		0.857
(1.00)
19p gain 24 (43%) 32 0.584
(0.996) 		0.637
(0.996) 		0.598
(0.996)
19q gain 28 (50%) 28 0.87
(1.00) 		0.522
(0.996) 		0.596
(0.996)
20p gain 37 (66%) 19 0.601
(0.996) 		0.209
(0.996) 		0.438
(0.996)
20q gain 44 (79%) 12 0.858
(1.00) 		0.697
(0.996) 		0.194
(0.996)
21q gain 18 (32%) 38 0.58
(0.996) 		0.752
(0.996) 		0.257
(0.996)
22q gain 8 (14%) 48 0.0923
(0.996) 		0.038
(0.996) 		0.275
(0.996)
xp gain 18 (32%) 38 0.905
(1.00) 		0.0074
(0.996) 		1
(1.00)
xq gain 15 (27%) 41 0.995
(1.00) 		0.604
(0.996) 		0.203
(0.996)
1p loss 9 (16%) 47 0.54
(0.996) 		0.584
(0.996) 		0.611
(0.996)
1q loss 9 (16%) 47 0.34
(0.996) 		0.454
(0.996) 		0.614
(0.996)
3p loss 20 (36%) 36 0.258
(0.996) 		0.784
(1.00) 		0.519
(0.996)
3q loss 14 (25%) 42 0.34
(0.996) 		0.302
(0.996) 		0.73
(0.996)
4p loss 31 (55%) 25 0.24
(0.996) 		0.98
(1.00) 		1
(1.00)
4q loss 33 (59%) 23 0.161
(0.996) 		0.537
(0.996) 		0.515
(0.996)
5p loss 9 (16%) 47 0.0935
(0.996) 		0.118
(0.996) 		0.61
(0.996)
5q loss 18 (32%) 38 0.577
(0.996) 		0.533
(0.996) 		0.749
(0.996)
6p loss 5 (9%) 51 0.0991
(0.996) 		0.508
(0.996) 		0.404
(0.996)
6q loss 7 (12%) 49 0.259
(0.996) 		0.682
(0.996) 		0.18
(0.996)
7p loss 13 (23%) 43 0.931
(1.00) 		0.734
(0.996) 		1
(1.00)
7q loss 13 (23%) 43 0.0863
(0.996) 		0.923
(1.00) 		0.716
(0.996)
8p loss 23 (41%) 33 0.834
(1.00) 		0.653
(0.996) 		0.219
(0.996)
8q loss 9 (16%) 47 0.902
(1.00) 		0.577
(0.996) 		0.283
(0.996)
9p loss 34 (61%) 22 0.514
(0.996) 		0.118
(0.996) 		0.524
(0.996)
9q loss 39 (70%) 17 0.253
(0.996) 		0.178
(0.996) 		0.413
(0.996)
10p loss 23 (41%) 33 0.692
(0.996) 		0.214
(0.996) 		0.262
(0.996)
10q loss 21 (38%) 35 0.215
(0.996) 		0.748
(0.996) 		0.284
(0.996)
11p loss 26 (46%) 30 0.0189
(0.996) 		0.23
(0.996) 		0.882
(1.00)
11q loss 24 (43%) 32 0.353
(0.996) 		0.573
(0.996) 		0.769
(0.996)
12p loss 13 (23%) 43 0.393
(0.996) 		0.907
(1.00) 		0.304
(0.996)
12q loss 14 (25%) 42 0.358
(0.996) 		0.191
(0.996) 		0.729
(0.996)
13q loss 26 (46%) 30 0.791
(1.00) 		0.831
(1.00) 		1
(1.00)
14q loss 27 (48%) 29 0.315
(0.996) 		0.533
(0.996) 		0.0849
(0.996)
15q loss 32 (57%) 24 0.618
(0.996) 		0.797
(1.00) 		0.356
(0.996)
16p loss 32 (57%) 24 0.413
(0.996) 		0.868
(1.00) 		0.516
(0.996)
16q loss 37 (66%) 19 0.0982
(0.996) 		0.965
(1.00) 		0.327
(0.996)
17p loss 34 (61%) 22 0.177
(0.996) 		0.574
(0.996) 		0.245
(0.996)
17q loss 17 (30%) 39 0.0409
(0.996) 		0.562
(0.996) 		0.738
(0.996)
18p loss 18 (32%) 38 0.73
(0.996) 		0.699
(0.996) 		0.257
(0.996)
18q loss 20 (36%) 36 0.325
(0.996) 		0.515
(0.996) 		0.0366
(0.996)
19p loss 15 (27%) 41 0.81
(1.00) 		0.505
(0.996) 		0.739
(0.996)
19q loss 13 (23%) 43 0.546
(0.996) 		0.437
(0.996) 		0.399
(0.996)
20p loss 7 (12%) 49 0.894
(1.00) 		0.823
(1.00) 		0.557
(0.996)
20q loss 4 (7%) 52 0.805
(1.00) 		0.886
(1.00) 		1
(1.00)
21q loss 17 (30%) 39 0.162
(0.996) 		0.175
(0.996) 		1
(1.00)
22q loss 33 (59%) 23 0.326
(0.996) 		0.683
(0.996) 		0.103
(0.996)
xp loss 15 (27%) 41 0.718
(0.996) 		0.247
(0.996) 		0.86
(1.00)
xq loss 18 (32%) 38 0.982
(1.00) 		0.635
(0.996) 		0.653
(0.996)
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/UCS-TP/15107631/transformed.cor.cli.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/UCS-TP/15096025/UCS-TP.merged_data.txt

  • Number of patients = 56

  • Number of significantly arm-level cnvs = 79

  • Number of selected clinical features = 3

  • 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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