Skin Cutaneous Melanoma: Correlation between gene mutation status 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 significantly recurrent gene mutations and molecular subtypes.

Summary

Testing the association between mutation status of 19 genes and 8 molecular subtypes across 38 patients, one significant finding detected with P value < 0.05 and Q value < 0.25.

- MAP2K1 mutation correlated to 'METHLYATION_CNMF'.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 19 genes and 8 molecular subtypes. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, one significant finding detected.

|  | Clinical Features | CN CNMF | METHLYATION CNMF | RPPA CNMF | RPPA CHIERARCHICAL | MRNASEQ CNMF | MRNASEQ CHIERARCHICAL | MIRSEQ CNMF | MIRSEQ CHIERARCHICAL
| nMutated (%) | 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
MAP2K1 | 3 (8%) | 35 | 1 (1.00) | 0.00164 (0.223) |  |  | 0.582 (1.00) | 0.673 (1.00) | 1 (1.00) | 0.132 (1.00)
BRAF | 23 (61%) | 15 | 0.443 (1.00) | 0.266 (1.00) | 0.221 (1.00) | 0.221 (1.00) | 0.0364 (1.00) | 0.0284 (1.00) | 1 (1.00) | 0.097 (1.00)
NRAS | 6 (16%) | 32 | 0.737 (1.00) | 0.587 (1.00) | 0.0837 (1.00) | 0.0837 (1.00) | 0.347 (1.00) | 0.6 (1.00) | 0.206 (1.00) | 0.131 (1.00)
PRB2 | 8 (21%) | 30 | 0.685 (1.00) | 0.572 (1.00) |  |  | 1 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00)
FAM135B | 13 (34%) | 25 | 0.196 (1.00) | 0.59 (1.00) | 0.505 (1.00) | 0.505 (1.00) | 0.493 (1.00) | 0.654 (1.00) | 0.491 (1.00) | 1 (1.00)
PTEN | 5 (13%) | 33 | 1 (1.00) | 0.722 (1.00) | 0.302 (1.00) | 0.302 (1.00) | 0.608 (1.00) | 0.734 (1.00) | 1 (1.00) | 0.142 (1.00)
TLL1 | 9 (24%) | 29 | 0.349 (1.00) | 0.332 (1.00) | 1 (1.00) | 1 (1.00) | 0.7 (1.00) | 0.491 (1.00) | 0.702 (1.00) | 0.602 (1.00)
EPB41L4A | 3 (8%) | 35 | 0.772 (1.00) | 0.19 (1.00) |  |  | 0.234 (1.00) | 0.422 (1.00) | 1 (1.00) | 0.784 (1.00)
ARID2 | 8 (21%) | 30 | 0.887 (1.00) | 0.536 (1.00) | 0.4 (1.00) | 0.4 (1.00) | 1 (1.00) | 0.702 (1.00) | 0.423 (1.00) | 0.393 (1.00)
ZFHX4 | 11 (29%) | 27 | 0.348 (1.00) | 0.218 (1.00) | 0.698 (1.00) | 0.698 (1.00) | 0.46 (1.00) | 1 (1.00) | 0.461 (1.00) | 0.44 (1.00)
CFHR1 | 5 (13%) | 33 | 0.0225 (1.00) | 0.2 (1.00) | 0.574 (1.00) | 0.574 (1.00) | 1 (1.00) | 1 (1.00) | 0.618 (1.00) | 1 (1.00)
DMRT3 | 3 (8%) | 35 | 0.104 (1.00) | 0.306 (1.00) |  |  | 1 (1.00) | 1 (1.00) | 0.568 (1.00) | 0.395 (1.00)
GLYAT | 4 (11%) | 34 | 1 (1.00) | 0.721 (1.00) | 0.432 (1.00) | 0.432 (1.00) | 1 (1.00) | 0.391 (1.00) | 0.118 (1.00) | 0.295 (1.00)
OR52M1 | 4 (11%) | 34 | 0.346 (1.00) | 0.476 (1.00) |  |  | 1 (1.00) | 1 (1.00) | 0.618 (1.00) | 0.502 (1.00)
PPP6C | 5 (13%) | 33 | 0.341 (1.00) | 0.556 (1.00) |  |  | 1 (1.00) | 1 (1.00) | 0.364 (1.00) | 0.0856 (1.00)
RUNDC3B | 4 (11%) | 34 | 1 (1.00) | 0.105 (1.00) | 0.15 (1.00) | 0.15 (1.00) | 1 (1.00) | 0.0148 (1.00) | 0.296 (1.00) | 0.384 (1.00)
ADCYAP1R1 | 5 (13%) | 33 | 1 (1.00) | 0.672 (1.00) |  |  | 1 (1.00) | 1 (1.00) | 0.618 (1.00) | 0.00795 (1.00)
KCNC2 | 5 (13%) | 33 | 0.842 (1.00) | 0.722 (1.00) | 0.789 (1.00) | 0.789 (1.00) | 1 (1.00) | 0.468 (1.00) | 1 (1.00) | 1 (1.00)
PIK3R1 | 4 (11%) | 34 | 0.662 (1.00) | 0.937 (1.00) |  |  | 1 (1.00) | 1 (1.00) | 0.0276 (1.00) | 0.384 (1.00)

'MAP2K1 MUTATION STATUS' versus 'METHLYATION_CNMF'

P value = 0.00164 (Chi-square test), Q value = 0.22

Table S1. Gene #8: 'MAP2K1 MUTATION STATUS' versus Clinical Feature #2: 'METHLYATION_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 7 | 9 | 13 | 6 | 3
MAP2K1 MUTATED | 0 | 0 | 0 | 3 | 0
MAP2K1 WILD-TYPE | 7 | 9 | 13 | 3 | 3

Figure S1. Get High-res Image Gene #8: 'MAP2K1 MUTATION STATUS' versus Clinical Feature #2: 'METHLYATION_CNMF'

Methods & Data
Input

- Mutation data file = SKCM-All_Primary.mutsig.cluster.txt

- Molecular subtypes file = SKCM-All_Primary.transferedmergedcluster.txt

- Number of patients = 38

- Number of significantly mutated genes = 19

- 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.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

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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