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 | 12 (9%) | 126 |  |  | 0.337 (1.00)
2q gain | 10 (7%) | 128 |  |  | 0.496 (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 | 14 (10%) | 124 |  |  | 0.551 (1.00)
5q gain | 5 (4%) | 133 |  |  | 1 (1.00)
6p gain | 47 (34%) | 91 | 0.401 (1.00) | 0.586 (1.00) | 0.253 (1.00)
6q gain | 8 (6%) | 130 |  |  | 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 | 9 (7%) | 129 |  |  | 0.272 (1.00)
11q gain | 5 (4%) | 133 |  |  | 0.169 (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 | 11 (8%) | 127 |  |  | 0.506 (1.00)
17q gain | 18 (13%) | 120 |  |  | 0.117 (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 | 14 (10%) | 124 | 0.501 (1.00) | 0.62 (1.00) | 0.23 (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 | 12 (9%) | 126 |  |  | 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 | 18 (13%) | 120 | 0.914 (1.00) | 0.898 (1.00) | 0.6 (1.00)
5q loss | 32 (23%) | 106 | 0.497 (1.00) | 0.527 (1.00) | 1 (1.00)
6p loss | 12 (9%) | 126 |  |  | 0.214 (1.00)
6q loss | 56 (41%) | 82 | 0.142 (1.00) | 0.584 (1.00) | 0.271 (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 | 60 (43%) | 78 | 0.32 (1.00) | 0.442 (1.00) | 0.151 (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 (0.47)
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 | 29 (21%) | 109 |  |  | 1 (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 | 11 (8%) | 127 |  |  | 0.00657 (0.873)
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.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

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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