Skin Cutaneous Melanoma: Correlation between copy number variations of arm-level result and selected clinical features
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
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 73 arm-level results and 3 clinical features across 144 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 73 arm-level results 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 		AGE GENDER
nCNV (%) nWild-Type logrank test t-test Fisher's exact test
1p gain 22 (15%) 122 0.159
(1.00) 		0.126
(1.00) 		0.335
(1.00)
1q gain 51 (35%) 93 0.575
(1.00) 		0.96
(1.00) 		1
(1.00)
2p gain 13 (9%) 131 0.999
(1.00) 		0.0375
(1.00) 		0.138
(1.00)
2q gain 11 (8%) 133 0.452
(1.00) 		0.145
(1.00) 		0.328
(1.00)
3p gain 14 (10%) 130 0.931
(1.00) 		0.813
(1.00) 		0.565
(1.00)
3q gain 19 (13%) 125 0.553
(1.00) 		0.534
(1.00) 		0.608
(1.00)
4p gain 15 (10%) 129 0.253
(1.00) 		0.0471
(1.00) 		0.778
(1.00)
4q gain 12 (8%) 132 0.382
(1.00) 		0.0848
(1.00) 		1
(1.00)
5p gain 16 (11%) 128 0.0382
(1.00) 		0.572
(1.00) 		0.58
(1.00)
5q gain 4 (3%) 140 0.674
(1.00) 		0.0236
(1.00) 		0.297
(1.00)
6p gain 50 (35%) 94 0.776
(1.00) 		0.364
(1.00) 		0.273
(1.00)
6q gain 9 (6%) 135 0.907
(1.00) 		0.94
(1.00) 		1
(1.00)
7p gain 65 (45%) 79 0.982
(1.00) 		0.896
(1.00) 		0.73
(1.00)
7q gain 64 (44%) 80 0.668
(1.00) 		0.561
(1.00) 		1
(1.00)
8p gain 29 (20%) 115 0.648
(1.00) 		0.559
(1.00) 		0.195
(1.00)
8q gain 44 (31%) 100 0.895
(1.00) 		0.203
(1.00) 		0.351
(1.00)
11p gain 8 (6%) 136 0.759
(1.00) 		0.51
(1.00) 		0.26
(1.00)
11q gain 5 (3%) 139 0.401
(1.00) 		0.604
(1.00) 		0.161
(1.00)
12p gain 14 (10%) 130 0.694
(1.00) 		0.183
(1.00) 		0.379
(1.00)
12q gain 5 (3%) 139 0.951
(1.00) 		0.575
(1.00) 		0.656
(1.00)
13q gain 25 (17%) 119 0.366
(1.00) 		0.353
(1.00) 		0.493
(1.00)
14q gain 13 (9%) 131 0.732
(1.00) 		0.982
(1.00) 		1
(1.00)
15q gain 18 (12%) 126 0.884
(1.00) 		0.387
(1.00) 		1
(1.00)
16p gain 11 (8%) 133 0.991
(1.00) 		0.703
(1.00) 		1
(1.00)
16q gain 10 (7%) 134 0.824
(1.00) 		0.953
(1.00) 		1
(1.00)
17p gain 11 (8%) 133 0.387
(1.00) 		0.81
(1.00) 		1
(1.00)
17q gain 19 (13%) 125 0.471
(1.00) 		0.066
(1.00) 		0.608
(1.00)
18p gain 17 (12%) 127 0.858
(1.00) 		0.166
(1.00) 		0.293
(1.00)
18q gain 9 (6%) 135 0.866
(1.00) 		0.494
(1.00) 		0.721
(1.00)
19p gain 10 (7%) 134 0.152
(1.00) 		0.287
(1.00) 		0.326
(1.00)
19q gain 12 (8%) 132 0.463
(1.00) 		0.308
(1.00) 		0.114
(1.00)
20p gain 46 (32%) 98 0.778
(1.00) 		0.815
(1.00) 		0.71
(1.00)
20q gain 57 (40%) 87 0.364
(1.00) 		0.895
(1.00) 		0.479
(1.00)
21q gain 19 (13%) 125 0.638
(1.00) 		0.544
(1.00) 		0.608
(1.00)
22q gain 40 (28%) 104 0.102
(1.00) 		0.647
(1.00) 		0.331
(1.00)
Xq gain 3 (2%) 141 0.624
(1.00) 		0.0653
(1.00) 		0.552
(1.00)
1p loss 11 (8%) 133 0.198
(1.00) 		0.612
(1.00) 		0.747
(1.00)
1q loss 5 (3%) 139 0.678
(1.00) 		0.818
(1.00) 		1
(1.00)
2p loss 13 (9%) 131 0.0594
(1.00) 		0.816
(1.00) 		1
(1.00)
2q loss 12 (8%) 132 0.1
(1.00) 		0.443
(1.00) 		0.754
(1.00)
3p loss 10 (7%) 134 0.302
(1.00) 		0.457
(1.00) 		0.743
(1.00)
3q loss 11 (8%) 133 0.52
(1.00) 		0.387
(1.00) 		0.197
(1.00)
4p loss 14 (10%) 130 0.974
(1.00) 		0.083
(1.00) 		0.0848
(1.00)
4q loss 14 (10%) 130 0.996
(1.00) 		0.28
(1.00) 		0.0848
(1.00)
5p loss 21 (15%) 123 0.926
(1.00) 		0.58
(1.00) 		1
(1.00)
5q loss 32 (22%) 112 0.955
(1.00) 		0.69
(1.00) 		0.677
(1.00)
6p loss 14 (10%) 130 0.917
(1.00) 		0.973
(1.00) 		0.565
(1.00)
6q loss 62 (43%) 82 0.619
(1.00) 		0.164
(1.00) 		0.297
(1.00)
8p loss 19 (13%) 125 0.923
(1.00) 		0.861
(1.00) 		0.801
(1.00)
8q loss 3 (2%) 141 0.268
(1.00) 		0.618
(1.00) 		0.0427
(1.00)
9p loss 83 (58%) 61 0.429
(1.00) 		0.55
(1.00) 		0.383
(1.00)
9q loss 65 (45%) 79 0.78
(1.00) 		0.0588
(1.00) 		0.0536
(1.00)
10p loss 66 (46%) 78 0.209
(1.00) 		0.452
(1.00) 		0.0238
(1.00)
10q loss 72 (50%) 72 0.769
(1.00) 		0.0134
(1.00) 		0.0809
(1.00)
11p loss 39 (27%) 105 0.155
(1.00) 		0.624
(1.00) 		0.00623
(1.00)
11q loss 41 (28%) 103 0.0674
(1.00) 		0.883
(1.00) 		0.0526
(1.00)
12p loss 9 (6%) 135 0.603
(1.00) 		0.998
(1.00) 		0.28
(1.00)
12q loss 15 (10%) 129 0.821
(1.00) 		0.858
(1.00) 		0.396
(1.00)
13q loss 25 (17%) 119 0.94
(1.00) 		0.428
(1.00) 		0.362
(1.00)
14q loss 36 (25%) 108 0.556
(1.00) 		0.213
(1.00) 		0.55
(1.00)
15q loss 10 (7%) 134 0.631
(1.00) 		0.318
(1.00) 		0.743
(1.00)
16p loss 11 (8%) 133 0.222
(1.00) 		0.68
(1.00) 		0.197
(1.00)
16q loss 26 (18%) 118 0.00454
(0.994) 		0.841
(1.00) 		0.498
(1.00)
17p loss 33 (23%) 111 0.505
(1.00) 		0.476
(1.00) 		1
(1.00)
17q loss 13 (9%) 131 0.111
(1.00) 		0.732
(1.00) 		0.772
(1.00)
18p loss 30 (21%) 114 0.43
(1.00) 		0.544
(1.00) 		1
(1.00)
18q loss 26 (18%) 118 0.244
(1.00) 		0.734
(1.00) 		1
(1.00)
19p loss 12 (8%) 132 0.751
(1.00) 		0.433
(1.00) 		0.0263
(1.00)
19q loss 14 (10%) 130 0.744
(1.00) 		0.892
(1.00) 		0.0848
(1.00)
20p loss 6 (4%) 138 0.301
(1.00) 		0.291
(1.00) 		0.423
(1.00)
21q loss 20 (14%) 124 0.799
(1.00) 		0.9
(1.00) 		0.45
(1.00)
22q loss 10 (7%) 134 0.67
(1.00) 		0.574
(1.00) 		0.326
(1.00)
Xq loss 4 (3%) 140 0.694
(1.00) 		0.641
(1.00) 		0.127
(1.00)
Methods & Data
Input

  • Mutation data file = broad_values_by_arm.mutsig.cluster.txt

  • Clinical data file = SKCM-TM.clin.merged.picked.txt

  • Number of patients = 144

  • Number of significantly arm-level cnvs = 73

  • Number of selected clinical features = 3

  • Exclude genes 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

Student's t-test analysis

For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between tumors with and without gene mutations using 't.test' 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

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

References
[1] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[2] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[3] 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)
[4] 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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