Correlation between copy number variations of arm-level result and selected clinical features
Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1JM28NT
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/C1JM28NT
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 52 arm-level events and 6 clinical features across 47 patients, one significant finding detected with Q value < 0.25.

  • 9p gain cnv correlated to 'HISTOLOGICAL_TYPE'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between significant copy number variation of 52 arm-level events and 6 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 HISTOLOGICAL
TYPE 		RACE ETHNICITY
nCNV (%) nWild-Type logrank test Wilcoxon-test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test
9p gain 7 (15%) 40 0.975
(1.00) 		0.16
(0.712) 		0.684
(1.00) 		0.00053
(0.165) 		0.494
(0.964) 		0.35
(0.84)
1q gain 5 (11%) 42 0.265
(0.785) 		0.277
(0.785) 		1
(1.00) 		1
(1.00) 		0.232
(0.785) 		1
(1.00)
2p gain 6 (13%) 41 0.984
(1.00) 		0.1
(0.631) 		1
(1.00) 		0.111
(0.631) 		0.71
(1.00) 		0.164
(0.712)
2q gain 6 (13%) 41 0.984
(1.00) 		0.1
(0.631) 		1
(1.00) 		0.111
(0.631) 		0.709
(1.00) 		0.164
(0.712)
3p gain 10 (21%) 37 0.0935
(0.631) 		0.979
(1.00) 		1
(1.00) 		0.786
(1.00) 		0.591
(1.00) 		0.251
(0.785)
3q gain 13 (28%) 34 0.0687
(0.596) 		0.568
(1.00) 		0.746
(1.00) 		1
(1.00) 		0.811
(1.00) 		0.269
(0.785)
5p gain 7 (15%) 40 0.636
(1.00) 		0.317
(0.84) 		1
(1.00) 		0.0132
(0.415) 		0.735
(1.00) 		0.0595
(0.573)
5q gain 6 (13%) 41 0.995
(1.00) 		0.156
(0.712) 		0.678
(1.00) 		0.0071
(0.313) 		1
(1.00) 		0.0301
(0.5)
6p gain 6 (13%) 41 0.275
(0.785) 		0.823
(1.00) 		0.204
(0.785) 		0.641
(1.00) 		0.709
(1.00) 		0.637
(1.00)
6q gain 4 (9%) 43 0.439
(0.961) 		0.954
(1.00) 		0.117
(0.631) 		1
(1.00) 		0.347
(0.84) 		1
(1.00)
7p gain 15 (32%) 32 0.131
(0.692) 		0.723
(1.00) 		0.758
(1.00) 		0.217
(0.785) 		1
(1.00) 		0.481
(0.962)
7q gain 13 (28%) 34 0.282
(0.792) 		0.695
(1.00) 		0.746
(1.00) 		0.318
(0.84) 		0.809
(1.00) 		0.713
(1.00)
8p gain 7 (15%) 40 0.757
(1.00) 		0.0391
(0.555) 		0.436
(0.961) 		0.15
(0.712) 		0.335
(0.84) 		0.00802
(0.313)
8q gain 8 (17%) 39 0.672
(1.00) 		0.0282
(0.5) 		0.715
(1.00) 		0.0209
(0.435) 		0.272
(0.785) 		0.00184
(0.287)
9q gain 7 (15%) 40 0.919
(1.00) 		0.139
(0.712) 		0.217
(0.785) 		0.0133
(0.415) 		0.494
(0.964) 		0.35
(0.84)
10p gain 4 (9%) 43 0.439
(0.961) 		0.954
(1.00) 		0.117
(0.631) 		1
(1.00) 		0.67
(1.00) 		1
(1.00)
10q gain 4 (9%) 43 0.439
(0.961) 		0.457
(0.961) 		0.617
(1.00) 		1
(1.00) 		0.668
(1.00) 		0.266
(0.785)
11p gain 9 (19%) 38 0.691
(1.00) 		0.695
(1.00) 		0.0305
(0.5) 		0.0663
(0.591) 		0.555
(1.00) 		0.0352
(0.523)
11q gain 13 (28%) 34 0.71
(1.00) 		0.475
(0.962) 		0.102
(0.631) 		0.232
(0.785) 		0.655
(1.00) 		0.269
(0.785)
12p gain 7 (15%) 40 0.23
(0.785) 		0.354
(0.843) 		1
(1.00) 		0.0575
(0.573) 		1
(1.00) 		0.35
(0.84)
12q gain 9 (19%) 38 0.0624
(0.573) 		0.57
(1.00) 		0.486
(0.964) 		0.0203
(0.435) 		0.774
(1.00) 		0.205
(0.785)
13q gain 5 (11%) 42 0.606
(1.00) 		0.342
(0.84) 		1
(1.00) 		1
(1.00) 		0.431
(0.961) 		0.59
(1.00)
16p gain 7 (15%) 40 0.248
(0.785) 		0.754
(1.00) 		1
(1.00) 		0.275
(0.785) 		1
(1.00) 		1
(1.00)
16q gain 7 (15%) 40 0.0943
(0.631) 		0.964
(1.00) 		0.684
(1.00) 		0.277
(0.785) 		1
(1.00) 		1
(1.00)
17q gain 3 (6%) 44 0.215
(0.785) 		0.24
(0.785) 		0.242
(0.785) 		0.39
(0.914) 		0.114
(0.631) 		0.56
(1.00)
18p gain 13 (28%) 34 0.0587
(0.573) 		0.295
(0.815) 		0.746
(1.00) 		0.804
(1.00) 		0.112
(0.631) 		0.713
(1.00)
18q gain 14 (30%) 33 0.218
(0.785) 		0.522
(1.00) 		1
(1.00) 		0.804
(1.00) 		0.0591
(0.573) 		0.731
(1.00)
19p gain 3 (6%) 44 0.459
(0.961) 		0.0895
(0.631) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		0.156
(0.712)
19q gain 3 (6%) 44 0.459
(0.961) 		0.0895
(0.631) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		0.156
(0.712)
20p gain 5 (11%) 42 0.791
(1.00) 		0.641
(1.00) 		0.0561
(0.573) 		0.573
(1.00) 		0.433
(0.961) 		0.59
(1.00)
20q gain 4 (9%) 43 0.6
(1.00) 		0.26
(0.785) 		0.117
(0.631) 		0.488
(0.964) 		0.669
(1.00) 		0.266
(0.785)
21q gain 10 (21%) 37 0.842
(1.00) 		0.317
(0.84) 		0.475
(0.962) 		0.285
(0.795) 		0.777
(1.00) 		0.7
(1.00)
xp gain 6 (13%) 41 0.985
(1.00) 		0.655
(1.00) 		1
(1.00) 		0.0189
(0.435) 		1
(1.00) 		0.0301
(0.5)
xq gain 6 (13%) 41 0.924
(1.00) 		0.632
(1.00) 		1
(1.00) 		0.0193
(0.435) 		0.708
(1.00) 		0.164
(0.712)
1p loss 3 (6%) 44 0.459
(0.961) 		0.384
(0.907) 		0.579
(1.00) 		0.198
(0.785) 		1
(1.00) 		1
(1.00)
3p loss 5 (11%) 42 0.444
(0.961) 		0.262
(0.785) 		1
(1.00) 		0.154
(0.712) 		0.23
(0.785) 		0.0971
(0.631)
3q loss 4 (9%) 43 0.444
(0.961) 		0.35
(0.84) 		1
(1.00) 		0.0985
(0.631) 		0.217
(0.785) 		0.0459
(0.573)
4p loss 3 (6%) 44 0.349
(0.84) 		0.242
(0.785) 		1
(1.00) 		1
(1.00) 		1
(1.00)
4q loss 4 (9%) 43 0.534
(1.00) 		0.469
(0.962) 		0.117
(0.631) 		1
(1.00) 		0.67
(1.00) 		1
(1.00)
6q loss 7 (15%) 40 0.769
(1.00) 		0.811
(1.00) 		1
(1.00) 		0.0884
(0.631) 		0.336
(0.84) 		0.0595
(0.573)
8p loss 8 (17%) 39 0.955
(1.00) 		0.799
(1.00) 		0.269
(0.785) 		0.342
(0.84) 		0.747
(1.00) 		0.403
(0.931)
8q loss 4 (9%) 43 0.459
(0.961) 		0.153
(0.712) 		0.617
(1.00) 		0.266
(0.785) 		1
(1.00) 		0.266
(0.785)
13q loss 3 (6%) 44 0.571
(1.00) 		0.879
(1.00) 		1
(1.00) 		0.0533
(0.573) 		0.313
(0.84) 		0.156
(0.712)
15q loss 7 (15%) 40 0.257
(0.785) 		0.765
(1.00) 		0.112
(0.631) 		1
(1.00) 		1
(1.00) 		0.659
(1.00)
16q loss 4 (9%) 43 0.478
(0.962) 		0.909
(1.00) 		0.117
(0.631) 		1
(1.00) 		0.346
(0.84) 		0.56
(1.00)
17p loss 9 (19%) 38 0.201
(0.785) 		0.871
(1.00) 		0.16
(0.712) 		0.0204
(0.435) 		0.554
(1.00) 		0.0048
(0.299)
17q loss 4 (9%) 43 0.473
(0.962) 		0.504
(0.977) 		1
(1.00) 		0.0039
(0.299) 		0.216
(0.785) 		0.00278
(0.289)
18p loss 5 (11%) 42 0.648
(1.00) 		0.863
(1.00) 		0.644
(1.00) 		0.332
(0.84) 		1
(1.00) 		0.59
(1.00)
18q loss 4 (9%) 43 0.399
(0.929) 		0.0327
(0.51) 		0.617
(1.00) 		0.266
(0.785) 		0.216
(0.785) 		0.0459
(0.573)
22q loss 3 (6%) 44 0.527
(1.00) 		0.794
(1.00) 		0.242
(0.785) 		1
(1.00) 		0.115
(0.631) 		0.56
(1.00)
xp loss 5 (11%) 42 0.0624
(0.573) 		0.468
(0.962) 		0.0561
(0.573) 		1
(1.00) 		0.434
(0.961) 		1
(1.00)
xq loss 4 (9%) 43 0.00594
(0.309) 		1
(1.00) 		0.117
(0.631) 		1
(1.00) 		0.345
(0.84) 		0.56
(1.00)
'9p gain' versus 'HISTOLOGICAL_TYPE'

P value = 0.00053 (Fisher's exact test), Q value = 0.17

Table S1.  Gene #14: '9p gain' versus Clinical Feature #4: 'HISTOLOGICAL_TYPE'

nPatients DIFFUSE LARGE B-CELL LYMPHOMA (DLBCL) NOS (ANY ANATOMIC SITE NODAL OR EXTRANODAL) PRIMARY DLBCL OF THE CNS PRIMARY MEDIASTINAL (THYMIC) DLBCL
ALL 40 3 4
9P GAIN MUTATED 3 0 4
9P GAIN WILD-TYPE 37 3 0

Figure S1.  Get High-res Image Gene #14: '9p gain' versus Clinical Feature #4: 'HISTOLOGICAL_TYPE'

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/DLBC-TP/15082594/transformed.cor.cli.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/DLBC-TP/15078461/DLBC-TP.merged_data.txt

  • Number of patients = 47

  • Number of significantly arm-level cnvs = 52

  • 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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