Skin Cutaneous Melanoma: Correlation between copy number variations of arm-level result and molecular subtypes<BR>(All

Skin Cutaneous Melanoma: Correlation between copy number variations of arm-level result and molecular subtypes
(All_Primary cohort)

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 subtypes.

Summary

Testing the association between copy number variation 47 arm-level results and 8 molecular subtypes across 46 patients, 3 significant findings detected with Q value < 0.25.

8q gain cnv correlated to 'CN_CNMF'.
10p loss cnv correlated to 'CN_CNMF'.
10q loss cnv correlated to 'CN_CNMF'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between significant copy number variation of 47 arm-level results and 8 molecular subtypes. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 3 significant findings detected.


		Molecular subtypes	CN CNMF	METHLYATION CNMF	RPPA CNMF	RPPA CHIERARCHICAL	MRNASEQ CNMF	MRNASEQ CHIERARCHICAL	MIRSEQ CNMF	MIRSEQ CHIERARCHICAL
	nCNV (%)	nWild-Type	Fisher's exact test	Chi-square test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test
8q gain	15 (33%)	31	0.000245 (0.0831)	0.882 (1.00)	0.814 (1.00)	0.814 (1.00)	1 (1.00)	0.608 (1.00)	0.546 (1.00)	0.916 (1.00)
10p loss	19 (41%)	27	0.000314 (0.106)	0.508 (1.00)	0.0867 (1.00)	0.0867 (1.00)	1 (1.00)	1 (1.00)	0.0752 (1.00)	1 (1.00)
10q loss	22 (48%)	24	3.01e-05 (0.0102)	0.0417 (1.00)	0.0867 (1.00)	0.0867 (1.00)	0.755 (1.00)	0.804 (1.00)	0.0377 (1.00)	0.406 (1.00)
1p gain	5 (11%)	41	0.852 (1.00)	0.771 (1.00)	0.4 (1.00)	0.4 (1.00)	0.635 (1.00)	0.754 (1.00)	0.652 (1.00)	1 (1.00)
1q gain	12 (26%)	34	0.0623 (1.00)	0.47 (1.00)	1 (1.00)	1 (1.00)	0.494 (1.00)	0.461 (1.00)	1 (1.00)	0.755 (1.00)
3p gain	3 (7%)	43	1 (1.00)	0.474 (1.00)	0.574 (1.00)	0.574 (1.00)	0.282 (1.00)	0.631 (1.00)	1 (1.00)	0.0973 (1.00)
3q gain	3 (7%)	43	1 (1.00)	0.474 (1.00)	0.574 (1.00)	0.574 (1.00)	0.282 (1.00)	0.631 (1.00)	1 (1.00)	0.0973 (1.00)
4p gain	3 (7%)	43	0.614 (1.00)	0.421 (1.00)			0.282 (1.00)	0.631 (1.00)	1 (1.00)	1 (1.00)
5p gain	7 (15%)	39	0.447 (1.00)	0.39 (1.00)	0.296 (1.00)	0.296 (1.00)	1 (1.00)	0.814 (1.00)	1 (1.00)	0.868 (1.00)
5q gain	3 (7%)	43	0.614 (1.00)	0.866 (1.00)			1 (1.00)	1 (1.00)	1 (1.00)	0.406 (1.00)
6p gain	14 (30%)	32	0.0276 (1.00)	0.00944 (1.00)	1 (1.00)	1 (1.00)	0.737 (1.00)	0.305 (1.00)	0.0977 (1.00)	0.184 (1.00)
6q gain	3 (7%)	43	0.354 (1.00)	0.866 (1.00)			0.133 (1.00)		0.212 (1.00)
7p gain	19 (41%)	27	0.93 (1.00)	0.147 (1.00)	0.277 (1.00)	0.277 (1.00)	1 (1.00)	0.886 (1.00)	0.543 (1.00)	0.582 (1.00)
7q gain	18 (39%)	28	0.743 (1.00)	0.117 (1.00)	0.53 (1.00)	0.53 (1.00)	1 (1.00)	0.886 (1.00)	0.759 (1.00)	0.923 (1.00)
8p gain	7 (15%)	39	0.257 (1.00)	0.525 (1.00)	0.302 (1.00)	0.302 (1.00)	0.695 (1.00)	0.331 (1.00)	1 (1.00)	0.124 (1.00)
13q gain	4 (9%)	42	0.207 (1.00)	0.104 (1.00)			0.621 (1.00)	0.344 (1.00)	0.326 (1.00)	1 (1.00)
18p gain	7 (15%)	39	0.526 (1.00)	0.88 (1.00)	0.789 (1.00)	0.789 (1.00)	0.0346 (1.00)	0.0903 (1.00)	1 (1.00)	0.868 (1.00)
18q gain	8 (17%)	38	0.554 (1.00)	0.628 (1.00)	0.296 (1.00)	0.296 (1.00)	0.229 (1.00)	0.519 (1.00)	0.705 (1.00)	0.889 (1.00)
20p gain	9 (20%)	37	0.516 (1.00)	0.447 (1.00)	0.474 (1.00)	0.474 (1.00)	0.446 (1.00)	0.0807 (1.00)	0.469 (1.00)	0.702 (1.00)
20q gain	11 (24%)	35	0.281 (1.00)	0.821 (1.00)	0.474 (1.00)	0.474 (1.00)	0.494 (1.00)	0.157 (1.00)	0.503 (1.00)	0.556 (1.00)
21q gain	3 (7%)	43	0.614 (1.00)	0.474 (1.00)			1 (1.00)	0.14 (1.00)	0.236 (1.00)	0.795 (1.00)
22q gain	5 (11%)	41	1 (1.00)	0.581 (1.00)	0.15 (1.00)	0.15 (1.00)	0.621 (1.00)	0.344 (1.00)	1 (1.00)	0.828 (1.00)
2p loss	5 (11%)	41	0.0277 (1.00)	0.581 (1.00)	0.0545 (1.00)	0.0545 (1.00)	1 (1.00)	0.414 (1.00)	0.652 (1.00)	0.611 (1.00)
2q loss	4 (9%)	42	0.0878 (1.00)	0.515 (1.00)	0.15 (1.00)	0.15 (1.00)	0.621 (1.00)	0.344 (1.00)	0.326 (1.00)	0.667 (1.00)
4p loss	7 (15%)	39	0.781 (1.00)	0.161 (1.00)			0.695 (1.00)	1 (1.00)	0.689 (1.00)	1 (1.00)
4q loss	9 (20%)	37	0.178 (1.00)	0.134 (1.00)	0.763 (1.00)	0.763 (1.00)	1 (1.00)	1 (1.00)	0.267 (1.00)	0.797 (1.00)
5q loss	6 (13%)	40	0.4 (1.00)	0.246 (1.00)			1 (1.00)	1 (1.00)	0.67 (1.00)	0.539 (1.00)
6q loss	15 (33%)	31	0.0322 (1.00)	0.598 (1.00)	0.289 (1.00)	0.289 (1.00)	0.735 (1.00)	0.158 (1.00)	0.752 (1.00)	0.66 (1.00)
8p loss	11 (24%)	35	0.158 (1.00)	0.821 (1.00)	0.632 (1.00)	0.632 (1.00)	0.46 (1.00)	0.624 (1.00)	1 (1.00)	0.239 (1.00)
8q loss	5 (11%)	41	0.0277 (1.00)	0.285 (1.00)			1 (1.00)	1 (1.00)	0.652 (1.00)	0.828 (1.00)
9p loss	25 (54%)	21	0.00574 (1.00)	0.00121 (0.406)	0.305 (1.00)	0.305 (1.00)	1 (1.00)	0.7 (1.00)	0.0157 (1.00)	0.00933 (1.00)
9q loss	15 (33%)	31	0.377 (1.00)	0.0633 (1.00)	1 (1.00)	1 (1.00)	0.752 (1.00)	0.678 (1.00)	0.226 (1.00)	0.0538 (1.00)
11p loss	8 (17%)	38	0.251 (1.00)	0.421 (1.00)	0.432 (1.00)	0.432 (1.00)	0.688 (1.00)	0.351 (1.00)	0.443 (1.00)	0.524 (1.00)
11q loss	10 (22%)	36	0.145 (1.00)	0.112 (1.00)	1 (1.00)	1 (1.00)	0.276 (1.00)	0.2 (1.00)	0.476 (1.00)	0.894 (1.00)
12p loss	6 (13%)	40	1 (1.00)	0.145 (1.00)	0.15 (1.00)	0.15 (1.00)	0.386 (1.00)	0.102 (1.00)	0.396 (1.00)	0.409 (1.00)
12q loss	8 (17%)	38	1 (1.00)	0.409 (1.00)	0.4 (1.00)	0.4 (1.00)	1 (1.00)	0.69 (1.00)	0.443 (1.00)	0.524 (1.00)
13q loss	7 (15%)	39	0.781 (1.00)	0.705 (1.00)			0.229 (1.00)	0.519 (1.00)	0.689 (1.00)	0.287 (1.00)
14q loss	6 (13%)	40	0.0217 (1.00)	0.00442 (1.00)			1 (1.00)	0.354 (1.00)	0.396 (1.00)	0.0506 (1.00)
16p loss	3 (7%)	43	0.257 (1.00)	0.134 (1.00)			0.545 (1.00)	0.229 (1.00)	0.592 (1.00)	0.406 (1.00)
16q loss	13 (28%)	33	0.547 (1.00)	0.0228 (1.00)	1 (1.00)	1 (1.00)	0.484 (1.00)	0.145 (1.00)	0.501 (1.00)	0.319 (1.00)
17p loss	8 (17%)	38	1 (1.00)	0.864 (1.00)			0.688 (1.00)	0.835 (1.00)	0.705 (1.00)	0.209 (1.00)
17q loss	5 (11%)	41	0.0912 (1.00)	0.668 (1.00)			0.635 (1.00)	0.754 (1.00)	0.652 (1.00)	0.828 (1.00)
18p loss	5 (11%)	41	0.588 (1.00)	0.39 (1.00)			0.635 (1.00)	0.754 (1.00)	0.652 (1.00)	0.828 (1.00)
18q loss	4 (9%)	42	0.809 (1.00)	0.206 (1.00)			1 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
19q loss	3 (7%)	43	0.772 (1.00)	0.474 (1.00)			0.545 (1.00)	0.421 (1.00)	0.236 (1.00)	0.406 (1.00)
21q loss	6 (13%)	40	0.343 (1.00)	0.744 (1.00)	0.632 (1.00)	0.632 (1.00)	0.344 (1.00)	0.3 (1.00)	1 (1.00)	0.724 (1.00)
22q loss	5 (11%)	41	0.716 (1.00)	0.39 (1.00)			1 (1.00)	1 (1.00)	1 (1.00)	0.154 (1.00)

'8q gain mutation analysis' versus 'CN_CNMF'

P value = 0.000245 (Fisher's exact test), Q value = 0.083

Table S1. Gene #13: '8q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	14	13	19
8Q GAIN MUTATED	3	10	2
8Q GAIN WILD-TYPE	11	3	17

Figure S1. Get High-res Image Gene #13: '8q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'10p loss mutation analysis' versus 'CN_CNMF'

P value = 0.000314 (Fisher's exact test), Q value = 0.11

Table S2. Gene #31: '10p loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	14	13	19
10P LOSS MUTATED	11	6	2
10P LOSS WILD-TYPE	3	7	17

Figure S2. Get High-res Image Gene #31: '10p loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'10q loss mutation analysis' versus 'CN_CNMF'

P value = 3.01e-05 (Fisher's exact test), Q value = 0.01

Table S3. Gene #32: '10q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	14	13	19
10Q LOSS MUTATED	12	8	2
10Q LOSS WILD-TYPE	2	5	17

Figure S3. Get High-res Image Gene #32: '10q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

Methods & Data

Input

Mutation data file = broad_values_by_arm.mutsig.cluster.txt
Molecular subtypes file = SKCM-All_Primary.transferedmergedcluster.txt
Number of patients = 46
Number of significantly arm-level cnvs = 47
Number of molecular subtypes = 8
Exclude genes that fewer than K tumors have mutations, K = 3

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

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.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] 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)

[2] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)

[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)

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