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.

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)