Correlation between copy number variations of arm-level result and selected clinical features
Uveal Melanoma (Primary solid tumor)
13 July 2018  |  None
Maintainer Information
Maintained by Broad Institute GDAC (Broad Institute of MIT & Harvard)
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 7 clinical features across 80 patients, 10 significant findings detected with Q value < 0.25.

  • 4q gain cnv correlated to 'PATHOLOGIC_STAGE'.

  • 6q gain cnv correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

  • 8p gain cnv correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

  • 8q gain cnv correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

  • 3p loss cnv correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

  • 3q loss cnv correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

  • 6q loss cnv correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

  • 9p loss cnv correlated to 'YEARS_TO_BIRTH'.

  • 9q loss cnv correlated to 'DAYS_TO_DEATH_OR_LAST_FUP' and '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 7 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 10 significant findings detected.

Clinical
Features
DAYS
TO
DEATH
OR
LAST
FUP
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
9q loss 7 (9%) 73 0.00119
(0.0936)
0.00206
(0.0936)
0.772
(1.00)
0.762
(1.00)
1
(1.00)
0.456
(1.00)
0.249
(1.00)
4q gain 5 (6%) 75 0.567
(1.00)
0.0296
(0.662)
0.00324
(0.129)
0.0366
(0.662)
0.325
(1.00)
0.647
(1.00)
0.182
(0.917)
6q gain 20 (25%) 60 0.00494
(0.177)
0.0906
(0.789)
0.428
(1.00)
0.372
(1.00)
0.562
(1.00)
0.602
(1.00)
1
(1.00)
8p gain 32 (40%) 48 0.0021
(0.0936)
0.864
(1.00)
0.583
(1.00)
0.323
(1.00)
0.298
(1.00)
0.818
(1.00)
0.555
(1.00)
8q gain 54 (68%) 26 0.00153
(0.0936)
0.639
(1.00)
0.81
(1.00)
0.813
(1.00)
0.286
(1.00)
1
(1.00)
0.547
(1.00)
3p loss 42 (52%) 38 4.36e-06
(0.00156)
0.216
(0.976)
0.0408
(0.662)
0.237
(1.00)
0.113
(0.844)
0.823
(1.00)
1
(1.00)
3q loss 44 (55%) 36 1.89e-05
(0.00338)
0.287
(1.00)
0.0365
(0.662)
0.108
(0.844)
0.117
(0.856)
0.824
(1.00)
1
(1.00)
6q loss 14 (18%) 66 0.000324
(0.0386)
0.0701
(0.789)
0.0819
(0.789)
0.0775
(0.789)
0.204
(0.958)
0.373
(1.00)
0.452
(1.00)
9p loss 8 (10%) 72 0.0133
(0.397)
0.00185
(0.0936)
0.579
(1.00)
0.885
(1.00)
1
(1.00)
0.72
(1.00)
0.28
(1.00)
1q gain 10 (12%) 70 0.577
(1.00)
0.55
(1.00)
0.835
(1.00)
0.82
(1.00)
0.522
(1.00)
0.0903
(0.789)
1
(1.00)
2p gain 10 (12%) 70 0.72
(1.00)
0.353
(1.00)
0.374
(1.00)
0.661
(1.00)
1
(1.00)
0.313
(1.00)
0.341
(1.00)
2q gain 8 (10%) 72 0.806
(1.00)
0.113
(0.844)
0.595
(1.00)
0.489
(1.00)
1
(1.00)
0.28
(1.00)
1
(1.00)
4p gain 8 (10%) 72 0.848
(1.00)
0.407
(1.00)
0.0673
(0.789)
0.687
(1.00)
0.429
(1.00)
0.72
(1.00)
0.28
(1.00)
5p gain 4 (5%) 76 0.928
(1.00)
0.149
(0.892)
0.577
(1.00)
0.511
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
5q gain 4 (5%) 76 0.928
(1.00)
0.149
(0.892)
0.575
(1.00)
0.514
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
6p gain 40 (50%) 40 0.0339
(0.662)
0.388
(1.00)
0.537
(1.00)
0.722
(1.00)
0.617
(1.00)
0.821
(1.00)
1
(1.00)
7p gain 8 (10%) 72 0.142
(0.891)
0.505
(1.00)
0.692
(1.00)
0.554
(1.00)
0.325
(1.00)
0.72
(1.00)
1
(1.00)
7q gain 7 (9%) 73 0.0605
(0.789)
0.269
(1.00)
0.557
(1.00)
0.442
(1.00)
0.267
(1.00)
0.456
(1.00)
1
(1.00)
9p gain 4 (5%) 76 0.929
(1.00)
0.44
(1.00)
0.268
(1.00)
0.0634
(0.789)
1
(1.00)
0.309
(1.00)
1
(1.00)
9q gain 3 (4%) 77 0.288
(1.00)
0.457
(1.00)
0.194
(0.935)
0.0354
(0.662)
1
(1.00)
0.574
(1.00)
1
(1.00)
11p gain 9 (11%) 71 0.871
(1.00)
0.256
(1.00)
0.79
(1.00)
0.894
(1.00)
1
(1.00)
0.488
(1.00)
1
(1.00)
11q gain 9 (11%) 71 0.364
(1.00)
0.66
(1.00)
0.426
(1.00)
0.563
(1.00)
1
(1.00)
0.488
(1.00)
1
(1.00)
12p gain 3 (4%) 77 0.914
(1.00)
0.564
(1.00)
1
(1.00)
0.771
(1.00)
1
(1.00)
0.574
(1.00)
1
(1.00)
12q gain 3 (4%) 77 0.914
(1.00)
0.564
(1.00)
1
(1.00)
0.771
(1.00)
1
(1.00)
0.574
(1.00)
1
(1.00)
13q gain 6 (8%) 74 0.847
(1.00)
0.159
(0.892)
0.787
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
0.216
(0.976)
14q gain 3 (4%) 77 0.0817
(0.789)
0.608
(1.00)
0.885
(1.00)
1
(1.00)
1
(1.00)
0.255
(1.00)
1
(1.00)
16p gain 3 (4%) 77 0.534
(1.00)
0.158
(0.892)
1
(1.00)
0.773
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
17p gain 9 (11%) 71 0.427
(1.00)
0.787
(1.00)
0.867
(1.00)
1
(1.00)
1
(1.00)
0.488
(1.00)
1
(1.00)
17q gain 10 (12%) 70 0.598
(1.00)
0.785
(1.00)
0.792
(1.00)
0.819
(1.00)
1
(1.00)
0.313
(1.00)
1
(1.00)
20p gain 8 (10%) 72 0.16
(0.892)
0.333
(1.00)
0.732
(1.00)
0.552
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
20q gain 9 (11%) 71 0.0397
(0.662)
0.192
(0.935)
0.643
(1.00)
0.462
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
21q gain 14 (18%) 66 0.173
(0.899)
0.113
(0.844)
0.812
(1.00)
0.678
(1.00)
0.477
(1.00)
0.254
(1.00)
1
(1.00)
22q gain 6 (8%) 74 0.777
(1.00)
0.168
(0.894)
0.883
(1.00)
0.462
(1.00)
1
(1.00)
0.0792
(0.789)
1
(1.00)
xp gain 10 (12%) 70 0.125
(0.859)
0.161
(0.892)
0.947
(1.00)
0.663
(1.00)
1
(1.00)
0.0903
(0.789)
1
(1.00)
xq gain 9 (11%) 71 0.219
(0.978)
0.211
(0.976)
0.953
(1.00)
0.722
(1.00)
1
(1.00)
0.163
(0.892)
1
(1.00)
1p loss 18 (22%) 62 0.325
(1.00)
0.0569
(0.789)
0.707
(1.00)
1
(1.00)
0.265
(1.00)
0.785
(1.00)
0.503
(1.00)
1q loss 4 (5%) 76 0.288
(1.00)
0.0543
(0.789)
0.428
(1.00)
1
(1.00)
0.267
(1.00)
1
(1.00)
1
(1.00)
4q loss 3 (4%) 77 0.709
(1.00)
0.949
(1.00)
0.59
(1.00)
0.291
(1.00)
1
(1.00)
1
(1.00)
0.112
(0.844)
5q loss 3 (4%) 77 0.282
(1.00)
0.449
(1.00)
0.413
(1.00)
0.561
(1.00)
1
(1.00)
0.0757
(0.789)
1
(1.00)
8p loss 7 (9%) 73 0.515
(1.00)
0.0859
(0.789)
0.078
(0.789)
0.869
(1.00)
0.325
(1.00)
0.692
(1.00)
0.249
(1.00)
11p loss 3 (4%) 77 0.0192
(0.528)
0.305
(1.00)
1
(1.00)
0.132
(0.859)
1
(1.00)
0.574
(1.00)
1
(1.00)
12p loss 3 (4%) 77 0.715
(1.00)
0.768
(1.00)
0.372
(1.00)
0.135
(0.859)
1
(1.00)
0.574
(1.00)
1
(1.00)
13q loss 3 (4%) 77 0.274
(1.00)
0.768
(1.00)
1
(1.00)
0.134
(0.859)
1
(1.00)
0.0757
(0.789)
1
(1.00)
15q loss 5 (6%) 75 0.755
(1.00)
0.526
(1.00)
0.945
(1.00)
0.71
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
16p loss 7 (9%) 73 0.6
(1.00)
0.165
(0.892)
0.135
(0.859)
0.263
(1.00)
0.267
(1.00)
1
(1.00)
0.182
(0.917)
16q loss 16 (20%) 64 0.131
(0.859)
0.152
(0.892)
0.125
(0.859)
0.376
(1.00)
0.204
(0.958)
0.564
(1.00)
0.0813
(0.789)
17p loss 3 (4%) 77 0.0395
(0.662)
0.174
(0.899)
0.0709
(0.789)
1
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
19p loss 3 (4%) 77 0.632
(1.00)
0.868
(1.00)
0.334
(1.00)
0.382
(1.00)
1
(1.00)
1
(1.00)
0.112
(0.844)
19q loss 3 (4%) 77 0.632
(1.00)
0.868
(1.00)
0.333
(1.00)
0.379
(1.00)
1
(1.00)
1
(1.00)
0.112
(0.844)
xp loss 11 (14%) 69 0.911
(1.00)
0.0119
(0.386)
0.785
(1.00)
1
(1.00)
0.477
(1.00)
0.313
(1.00)
1
(1.00)
xq loss 12 (15%) 68 0.587
(1.00)
0.0266
(0.662)
0.88
(1.00)
0.913
(1.00)
0.522
(1.00)
0.192
(0.935)
1
(1.00)
'4q gain' versus 'PATHOLOGIC_STAGE'

P value = 0.00324 (Fisher's exact test), Q value = 0.13

Table S1.  Gene #5: '4q gain' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE IIA STAGE IIB STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV
ALL 11 27 25 10 1 4
4Q GAIN MUTATED 0 0 1 2 1 1
4Q GAIN WILD-TYPE 11 27 24 8 0 3

Figure S1.  Get High-res Image Gene #5: '4q gain' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'6q gain' versus 'DAYS_TO_DEATH_OR_LAST_FUP'

P value = 0.00494 (logrank test), Q value = 0.18

Table S2.  Gene #9: '6q gain' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

nPatients nDeath Duration Range (Median), Month
ALL 79 22 0.1 - 85.5 (26.1)
6Q GAIN MUTATED 20 0 0.6 - 82.2 (27.3)
6Q GAIN WILD-TYPE 59 22 0.1 - 85.5 (24.4)

Figure S2.  Get High-res Image Gene #9: '6q gain' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

'8p gain' versus 'DAYS_TO_DEATH_OR_LAST_FUP'

P value = 0.0021 (logrank test), Q value = 0.094

Table S3.  Gene #12: '8p gain' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

nPatients nDeath Duration Range (Median), Month
ALL 79 22 0.1 - 85.5 (26.1)
8P GAIN MUTATED 31 15 1.3 - 82.2 (24.0)
8P GAIN WILD-TYPE 48 7 0.1 - 85.5 (26.6)

Figure S3.  Get High-res Image Gene #12: '8p gain' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

'8q gain' versus 'DAYS_TO_DEATH_OR_LAST_FUP'

P value = 0.00153 (logrank test), Q value = 0.094

Table S4.  Gene #13: '8q gain' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

nPatients nDeath Duration Range (Median), Month
ALL 79 22 0.1 - 85.5 (26.1)
8Q GAIN MUTATED 53 20 0.1 - 82.2 (23.3)
8Q GAIN WILD-TYPE 26 2 0.2 - 85.5 (36.4)

Figure S4.  Get High-res Image Gene #13: '8q gain' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

'3p loss' versus 'DAYS_TO_DEATH_OR_LAST_FUP'

P value = 4.36e-06 (logrank test), Q value = 0.0016

Table S5.  Gene #33: '3p loss' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

nPatients nDeath Duration Range (Median), Month
ALL 79 22 0.1 - 85.5 (26.1)
3P LOSS MUTATED 41 20 0.1 - 61.2 (21.0)
3P LOSS WILD-TYPE 38 2 0.2 - 85.5 (27.5)

Figure S5.  Get High-res Image Gene #33: '3p loss' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

'3q loss' versus 'DAYS_TO_DEATH_OR_LAST_FUP'

P value = 1.89e-05 (logrank test), Q value = 0.0034

Table S6.  Gene #34: '3q loss' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

nPatients nDeath Duration Range (Median), Month
ALL 79 22 0.1 - 85.5 (26.1)
3Q LOSS MUTATED 43 20 0.1 - 61.2 (23.3)
3Q LOSS WILD-TYPE 36 2 0.2 - 85.5 (27.3)

Figure S6.  Get High-res Image Gene #34: '3q loss' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

'6q loss' versus 'DAYS_TO_DEATH_OR_LAST_FUP'

P value = 0.000324 (logrank test), Q value = 0.039

Table S7.  Gene #37: '6q loss' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

nPatients nDeath Duration Range (Median), Month
ALL 79 22 0.1 - 85.5 (26.1)
6Q LOSS MUTATED 14 8 1.4 - 36.6 (23.7)
6Q LOSS WILD-TYPE 65 14 0.1 - 85.5 (26.2)

Figure S7.  Get High-res Image Gene #37: '6q loss' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

'9p loss' versus 'YEARS_TO_BIRTH'

P value = 0.00185 (Wilcoxon-test), Q value = 0.094

Table S8.  Gene #39: '9p loss' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 79 61.5 (14.0)
9P LOSS MUTATED 8 75.4 (9.6)
9P LOSS WILD-TYPE 71 60.0 (13.6)

Figure S8.  Get High-res Image Gene #39: '9p loss' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'9q loss' versus 'DAYS_TO_DEATH_OR_LAST_FUP'

P value = 0.00119 (logrank test), Q value = 0.094

Table S9.  Gene #40: '9q loss' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

nPatients nDeath Duration Range (Median), Month
ALL 79 22 0.1 - 85.5 (26.1)
9Q LOSS MUTATED 7 4 1.6 - 24.0 (19.7)
9Q LOSS WILD-TYPE 72 18 0.1 - 85.5 (27.3)

Figure S9.  Get High-res Image Gene #40: '9q loss' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

'9q loss' versus 'YEARS_TO_BIRTH'

P value = 0.00206 (Wilcoxon-test), Q value = 0.094

Table S10.  Gene #40: '9q loss' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 79 61.5 (14.0)
9Q LOSS MUTATED 7 76.3 (10.0)
9Q LOSS WILD-TYPE 72 60.1 (13.5)

Figure S10.  Get High-res Image Gene #40: '9q 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 = /cromwell_root/fc-f5144117-2d5a-42c2-8998-5b38e52db5d9/bc5d8513-6b0c-49b6-b076-3d7f4a228ff8/correlate_genomic_events_all/46b76953-a313-4baa-a6bd-91b46d0a3069/call-preprocess_genomic_event/transformed.cor.cli.txt

  • Clinical data file = /cromwell_root/fc-2289d790-de74-4808-9b0a-cefafc34d859/0d7c7dcf-18e0-4b2d-afc0-a0b2ee1e45ff/preprocess_clinical_workflow/70152ac6-f707-4277-8d60-8770b1b366c6/call-preprocess_clinical/TCGA-UVM-TP.clin.merged.picked.txt

  • Number of patients = 80

  • Number of significantly arm-level cnvs = 51

  • 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.

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)