Skin Cutaneous Melanoma: Correlation between copy number variations of arm-level result and selected clinical features
Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/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 72 arm-level results and 3 clinical features across 138 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 72 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 19 (14%) 119 0.148
(1.00) 		0.137
(1.00) 		1
(1.00)
1q gain 46 (33%) 92 0.214
(1.00) 		0.721
(1.00) 		1
(1.00)
2p gain 13 (9%) 125 0.219
(1.00)
2q gain 11 (8%) 127 0.336
(1.00)
3p gain 15 (11%) 123 0.52
(1.00) 		0.587
(1.00) 		0.572
(1.00)
3q gain 19 (14%) 119 0.33
(1.00) 		0.414
(1.00) 		0.435
(1.00)
4p gain 12 (9%) 126 0.297
(1.00) 		0.0972
(1.00) 		1
(1.00)
4q gain 10 (7%) 128 1
(1.00)
5p gain 15 (11%) 123 0.258
(1.00)
5q gain 4 (3%) 134 1
(1.00)
6p gain 47 (34%) 91 0.401
(1.00) 		0.586
(1.00) 		0.253
(1.00)
6q gain 9 (7%) 129 1
(1.00)
7p gain 65 (47%) 73 0.0973
(1.00) 		0.414
(1.00) 		1
(1.00)
7q gain 63 (46%) 75 0.28
(1.00) 		0.22
(1.00) 		0.721
(1.00)
8p gain 28 (20%) 110 0.58
(1.00) 		0.853
(1.00) 		0.372
(1.00)
8q gain 42 (30%) 96 0.155
(1.00) 		0.556
(1.00) 		0.556
(1.00)
11p gain 8 (6%) 130 0.269
(1.00)
11q gain 4 (3%) 134 0.301
(1.00)
12p gain 10 (7%) 128 0.496
(1.00)
12q gain 5 (4%) 133 0.665
(1.00)
13q gain 24 (17%) 114 0.474
(1.00) 		0.55
(1.00) 		1
(1.00)
14q gain 11 (8%) 127 1
(1.00)
15q gain 16 (12%) 122 0.78
(1.00)
16p gain 10 (7%) 128 0.731
(1.00)
16q gain 9 (7%) 129 1
(1.00)
17p gain 10 (7%) 128 0.731
(1.00)
17q gain 16 (12%) 122 0.401
(1.00)
18p gain 16 (12%) 122 0.541
(1.00) 		0.489
(1.00) 		0.78
(1.00)
18q gain 9 (7%) 129 0.867
(1.00) 		0.931
(1.00) 		1
(1.00)
19p gain 8 (6%) 130 1
(1.00)
19q gain 10 (7%) 128 0.0835
(1.00)
20p gain 41 (30%) 97 0.654
(1.00) 		0.432
(1.00) 		0.558
(1.00)
20q gain 49 (36%) 89 0.654
(1.00) 		0.432
(1.00) 		0.707
(1.00)
21q gain 15 (11%) 123 0.501
(1.00) 		0.62
(1.00) 		0.258
(1.00)
22q gain 37 (27%) 101 0.57
(1.00) 		0.34
(1.00) 		0.311
(1.00)
Xq gain 3 (2%) 135 0.551
(1.00)
1p loss 11 (8%) 127 1
(1.00)
1q loss 6 (4%) 132 1
(1.00)
2p loss 14 (10%) 124 0.774
(1.00)
2q loss 14 (10%) 124 1
(1.00)
3p loss 10 (7%) 128 1
(1.00)
3q loss 10 (7%) 128 0.528
(1.00) 		0.546
(1.00) 		0.301
(1.00)
4p loss 11 (8%) 127 0.18
(1.00)
4q loss 13 (9%) 125 0.125
(1.00)
5p loss 17 (12%) 121 0.914
(1.00) 		0.898
(1.00) 		1
(1.00)
5q loss 30 (22%) 108 0.497
(1.00) 		0.527
(1.00) 		0.827
(1.00)
6p loss 12 (9%) 126 0.214
(1.00)
6q loss 54 (39%) 84 0.0543
(1.00) 		0.411
(1.00) 		0.145
(1.00)
8p loss 17 (12%) 121 0.177
(1.00)
9p loss 80 (58%) 58 0.33
(1.00) 		0.812
(1.00) 		0.273
(1.00)
9q loss 59 (43%) 79 0.33
(1.00) 		0.575
(1.00) 		0.0104
(1.00)
10p loss 59 (43%) 79 0.32
(1.00) 		0.442
(1.00) 		0.274
(1.00)
10q loss 67 (49%) 71 0.964
(1.00) 		0.0336
(1.00) 		0.209
(1.00)
11p loss 35 (25%) 103 0.58
(1.00) 		0.105
(1.00) 		0.00351
(0.466)
11q loss 37 (27%) 101 0.891
(1.00) 		0.455
(1.00) 		0.0258
(1.00)
12p loss 10 (7%) 128 0.731
(1.00)
12q loss 16 (12%) 122 0.78
(1.00)
13q loss 19 (14%) 119 0.314
(1.00) 		0.453
(1.00) 		0.435
(1.00)
14q loss 34 (25%) 104 0.506
(1.00) 		0.623
(1.00) 		0.677
(1.00)
15q loss 11 (8%) 127 1
(1.00)
16p loss 11 (8%) 127 0.528
(1.00) 		0.546
(1.00) 		0.506
(1.00)
16q loss 26 (19%) 112 0.837
(1.00) 		0.63
(1.00) 		1
(1.00)
17p loss 30 (22%) 108 0.667
(1.00)
17q loss 14 (10%) 124 1
(1.00)
18p loss 26 (19%) 112 1
(1.00)
18q loss 25 (18%) 113 0.816
(1.00)
19p loss 10 (7%) 128 0.00242
(0.325)
19q loss 13 (9%) 125 0.0317
(1.00)
20p loss 6 (4%) 132 0.663
(1.00)
21q loss 19 (14%) 119 0.795
(1.00)
22q loss 8 (6%) 130 1
(1.00)
Xq loss 3 (2%) 135 0.258
(1.00)
Methods & Data
Input

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

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

  • Number of patients = 138

  • Number of significantly arm-level cnvs = 72

  • 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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