Skin Cutaneous Melanoma: Correlation between copy number variation genes and molecular subtypes<BR>(WT cohort)

Skin Cutaneous Melanoma: Correlation between copy number variation genes and molecular subtypes
(WT cohort)

Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)

Overview

Introduction

This pipeline computes the correlation between significant copy number variation (cnv) genes and molecular subtypes.

Summary

Testing the association between copy number variation of 20 peak regions and 8 molecular subtypes across 35 patients, 2 significant findings detected with Q value < 0.25.

Amp Peak 12(Xq28) cnvs correlated to 'CN_CNMF' and 'METHLYATION_CNMF'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between significant copy number variation of 20 regions and 8 molecular subtypes. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 2 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	Fisher's exact 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
Amp Peak 12(Xq28)	10 (29%)	25	0.000344 (0.0551)	0.00157 (0.249)	0.0379 (1.00)	0.0708 (1.00)	0.107 (1.00)	0.175 (1.00)	0.451 (1.00)	0.0246 (1.00)
Amp Peak 1(1p12)	13 (37%)	22	0.489 (1.00)	0.737 (1.00)	1 (1.00)	0.537 (1.00)	1 (1.00)	0.681 (1.00)	1 (1.00)	0.607 (1.00)
Amp Peak 2(1q44)	20 (57%)	15	0.0922 (1.00)	0.321 (1.00)	1 (1.00)	1 (1.00)	0.273 (1.00)	0.0817 (1.00)	0.296 (1.00)	0.117 (1.00)
Amp Peak 3(4q12)	12 (34%)	23	0.289 (1.00)	0.489 (1.00)	0.197 (1.00)	0.187 (1.00)	1 (1.00)	0.765 (1.00)	0.476 (1.00)	0.217 (1.00)
Amp Peak 4(5p15 33)	13 (37%)	22	0.0354 (1.00)	0.312 (1.00)	0.157 (1.00)	0.57 (1.00)	0.278 (1.00)	0.597 (1.00)	0.152 (1.00)	0.108 (1.00)
Amp Peak 5(5q35 3)	8 (23%)	27	0.0408 (1.00)	0.101 (1.00)	0.218 (1.00)	0.435 (1.00)	1 (1.00)	0.598 (1.00)	0.104 (1.00)	0.145 (1.00)
Amp Peak 6(8q11 21)	16 (46%)	19	0.315 (1.00)	0.306 (1.00)	1 (1.00)	0.39 (1.00)	0.0272 (1.00)	0.0623 (1.00)	0.489 (1.00)	0.909 (1.00)
Amp Peak 7(11q13 3)	10 (29%)	25	0.264 (1.00)	0.458 (1.00)	0.591 (1.00)	1 (1.00)	0.429 (1.00)	0.485 (1.00)	0.697 (1.00)	0.0899 (1.00)
Amp Peak 8(11q13 3)	10 (29%)	25	0.264 (1.00)	0.458 (1.00)	0.591 (1.00)	1 (1.00)	0.429 (1.00)	0.485 (1.00)	0.697 (1.00)	0.0899 (1.00)
Amp Peak 9(12q14 1)	12 (34%)	23	0.289 (1.00)	0.489 (1.00)	0.157 (1.00)	0.57 (1.00)	0.249 (1.00)	0.5 (1.00)	0.476 (1.00)	0.439 (1.00)
Amp Peak 10(17q25 3)	16 (46%)	19	0.315 (1.00)	0.734 (1.00)	1 (1.00)	0.848 (1.00)	0.731 (1.00)	0.541 (1.00)	0.738 (1.00)	0.16 (1.00)
Amp Peak 11(22q13 1)	17 (49%)	18	0.181 (1.00)	0.176 (1.00)	1 (1.00)	0.39 (1.00)	0.282 (1.00)	0.0527 (1.00)	1 (1.00)	0.27 (1.00)
Del Peak 1(2q37 3)	12 (34%)	23	0.075 (1.00)	0.163 (1.00)	0.0703 (1.00)	0.298 (1.00)	0.249 (1.00)	0.5 (1.00)	0.271 (1.00)	0.0259 (1.00)
Del Peak 2(4q34 3)	9 (26%)	26	1 (1.00)	0.7 (1.00)	0.642 (1.00)	1 (1.00)	1 (1.00)	0.855 (1.00)	1 (1.00)	0.0685 (1.00)
Del Peak 3(5p15 31)	8 (23%)	27	0.691 (1.00)	0.7 (1.00)	1 (1.00)	0.15 (1.00)	0.106 (1.00)	0.11 (1.00)	0.0529 (1.00)	0.0665 (1.00)
Del Peak 4(9p21 3)	21 (60%)	14	0.5 (1.00)	0.511 (1.00)	0.642 (1.00)	0.848 (1.00)	0.274 (1.00)	0.18 (1.00)	0.08 (1.00)	0.117 (1.00)
Del Peak 5(10q26 3)	18 (51%)	17	0.0943 (1.00)	0.0922 (1.00)	1 (1.00)	0.169 (1.00)	0.157 (1.00)	0.173 (1.00)	1 (1.00)	0.125 (1.00)
Del Peak 6(11q23 3)	22 (63%)	13	0.0858 (1.00)	0.157 (1.00)	0.362 (1.00)	0.118 (1.00)	0.0674 (1.00)	0.0534 (1.00)	0.16 (1.00)	0.025 (1.00)
Del Peak 7(15q14)	11 (31%)	24	0.0275 (1.00)	0.0693 (1.00)	0.0703 (1.00)	0.425 (1.00)	0.0545 (1.00)	0.125 (1.00)	0.715 (1.00)	0.0831 (1.00)
Del Peak 8(16p13 3)	9 (26%)	26	0.443 (1.00)	1 (1.00)	0.218 (1.00)	0.22 (1.00)	1 (1.00)	1 (1.00)	0.257 (1.00)	0.467 (1.00)

'Amp Peak 12(Xq28) mutation analysis' versus 'CN_CNMF'

P value = 0.000344 (Fisher's exact test), Q value = 0.055

Table S1. Gene #12: 'Amp Peak 12(Xq28) mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2
ALL	18	17
AMP PEAK 12(XQ28) MUTATED	10	0
AMP PEAK 12(XQ28) WILD-TYPE	8	17

Figure S1. Get High-res Image Gene #12: 'Amp Peak 12(Xq28) mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'Amp Peak 12(Xq28) mutation analysis' versus 'METHLYATION_CNMF'

P value = 0.00157 (Fisher's exact test), Q value = 0.25

Table S2. Gene #12: 'Amp Peak 12(Xq28) mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

nPatients	CLUS_1	CLUS_2
ALL	20	15
AMP PEAK 12(XQ28) MUTATED	10	0
AMP PEAK 12(XQ28) WILD-TYPE	10	15

Figure S2. Get High-res Image Gene #12: 'Amp Peak 12(Xq28) mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

Methods & Data

Input

Copy number data file = All Lesions File (all_lesions.conf_##.txt, where ## is the confidence level). The all lesions file is from GISTIC pipeline and summarizes the results from the GISTIC run. It contains data about the significant regions of amplification and deletion as well as which samples are amplified or deleted in each of these regions. The identified regions are listed down the first column, and the samples are listed across the first row, starting in column 10.
Molecular subtype file = SKCM-WT.transferedmergedcluster.txt
Number of patients = 35
Number of copy number variation regions = 20
Number of molecular subtypes = 8
Exclude regions that fewer than K tumors have alterations, 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

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] 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)

[3] GISTIC_version2

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