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 72 arm-level results and 3 clinical features across 126 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 (15%) 107 0.0672
(1.00) 		0.341
(1.00) 		0.612
(1.00)
1q gain 43 (34%) 83 0.0198
(1.00) 		0.672
(1.00) 		0.847
(1.00)
2p gain 12 (10%) 114 0.208
(1.00)
2q gain 10 (8%) 116 0.324
(1.00)
3p gain 14 (11%) 112 0.769
(1.00)
3q gain 18 (14%) 108 0.262
(1.00) 		0.928
(1.00) 		0.443
(1.00)
4p gain 12 (10%) 114 0.471
(1.00) 		0.0842
(1.00) 		1
(1.00)
4q gain 10 (8%) 116 0.745
(1.00)
5p gain 14 (11%) 112 0.377
(1.00)
5q gain 3 (2%) 123 0.299
(1.00)
6p gain 43 (34%) 83 0.372
(1.00) 		0.862
(1.00) 		0.243
(1.00)
6q gain 6 (5%) 120 0.668
(1.00)
7p gain 58 (46%) 68 0.15
(1.00) 		0.879
(1.00) 		0.853
(1.00)
7q gain 56 (44%) 70 0.49
(1.00) 		0.774
(1.00) 		0.582
(1.00)
8p gain 26 (21%) 100 0.701
(1.00) 		0.362
(1.00) 		0.361
(1.00)
8q gain 39 (31%) 87 0.41
(1.00) 		0.508
(1.00) 		0.427
(1.00)
11p gain 7 (6%) 119 0.421
(1.00)
11q gain 4 (3%) 122 0.296
(1.00)
12p gain 10 (8%) 116 0.324
(1.00)
12q gain 5 (4%) 121 0.652
(1.00)
13q gain 21 (17%) 105 0.818
(1.00) 		0.53
(1.00) 		0.808
(1.00)
14q gain 11 (9%) 115 1
(1.00)
15q gain 16 (13%) 110 1
(1.00)
16p gain 9 (7%) 117 0.723
(1.00)
16q gain 8 (6%) 118 1
(1.00)
17p gain 10 (8%) 116 1
(1.00)
17q gain 16 (13%) 110 0.583
(1.00)
18p gain 15 (12%) 111 0.436
(1.00) 		0.925
(1.00) 		0.781
(1.00)
18q gain 8 (6%) 118 0.436
(1.00) 		0.925
(1.00) 		1
(1.00)
19p gain 7 (6%) 119 0.705
(1.00)
19q gain 10 (8%) 116 0.168
(1.00)
20p gain 38 (30%) 88 0.686
(1.00) 		0.845
(1.00) 		0.547
(1.00)
20q gain 46 (37%) 80 0.686
(1.00) 		0.845
(1.00) 		0.566
(1.00)
21q gain 15 (12%) 111 0.705
(1.00) 		0.603
(1.00) 		0.403
(1.00)
22q gain 34 (27%) 92 0.795
(1.00) 		0.671
(1.00) 		0.303
(1.00)
Xq gain 3 (2%) 123 0.299
(1.00)
1p loss 10 (8%) 116 0.745
(1.00)
1q loss 5 (4%) 121 1
(1.00)
2p loss 11 (9%) 115 1
(1.00)
2q loss 11 (9%) 115 0.528
(1.00)
3p loss 10 (8%) 116 1
(1.00)
3q loss 10 (8%) 116 0.802
(1.00) 		0.529
(1.00) 		0.168
(1.00)
4p loss 10 (8%) 116 0.168
(1.00)
4q loss 11 (9%) 115 0.0965
(1.00)
5p loss 17 (13%) 109 0.598
(1.00) 		0.892
(1.00) 		1
(1.00)
5q loss 28 (22%) 98 0.598
(1.00) 		0.892
(1.00) 		0.661
(1.00)
6p loss 12 (10%) 114 0.353
(1.00)
6q loss 53 (42%) 73 0.00289
(0.376) 		0.332
(1.00) 		0.353
(1.00)
8p loss 14 (11%) 112 0.254
(1.00)
9p loss 73 (58%) 53 0.19
(1.00) 		0.146
(1.00) 		0.262
(1.00)
9q loss 56 (44%) 70 0.19
(1.00) 		0.0632
(1.00) 		0.0166
(1.00)
10p loss 55 (44%) 71 0.128
(1.00) 		0.496
(1.00) 		0.0928
(1.00)
10q loss 61 (48%) 65 0.368
(1.00) 		0.0147
(1.00) 		0.197
(1.00)
11p loss 32 (25%) 94 0.891
(1.00) 		0.0674
(1.00) 		0.0104
(1.00)
11q loss 35 (28%) 91 0.554
(1.00) 		0.4
(1.00) 		0.0996
(1.00)
12p loss 8 (6%) 118 0.462
(1.00)
12q loss 13 (10%) 113 0.546
(1.00)
13q loss 19 (15%) 107 0.31
(1.00)
14q loss 33 (26%) 93 0.166
(1.00) 		0.599
(1.00) 		0.528
(1.00)
15q loss 10 (8%) 116 1
(1.00)
16p loss 10 (8%) 116 0.802
(1.00) 		0.529
(1.00) 		0.495
(1.00)
16q loss 23 (18%) 103 0.928
(1.00) 		0.916
(1.00) 		0.813
(1.00)
17p loss 29 (23%) 97 1
(1.00)
17q loss 13 (10%) 113 1
(1.00)
18p loss 24 (19%) 102 1
(1.00)
18q loss 23 (18%) 103 0.813
(1.00)
19p loss 10 (8%) 116 0.00476
(0.614)
19q loss 12 (10%) 114 0.0297
(1.00)
20p loss 5 (4%) 121 0.652
(1.00)
21q loss 17 (13%) 109 0.788
(1.00)
22q loss 8 (6%) 118 1
(1.00)
Xq loss 3 (2%) 123 0.553
(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 = 126

  • 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)
Copyright © 2013 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle