(All_Primary cohort)
This pipeline computes the correlation between significantly recurrent gene mutations and molecular subtypes.
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.
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MAP2K1 mutation correlated to 'METHLYATION_CNMF'.
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 |
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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) |
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'

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Mutation data file = SKCM-All_Primary.mutsig.cluster.txt
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Molecular subtypes file = SKCM-All_Primary.transferedmergedcluster.txt
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Number of patients = 38
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Number of significantly mutated genes = 19
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Number of Molecular subtypes = 8
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Exclude genes that fewer than K tumors have mutations, K = 3
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
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
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.
This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.