This pipeline computes the correlation between significantly recurrent gene mutations and selected clinical features.
Testing the association between mutation status of 3 genes and 5 clinical features across 28 patients, 2 significant findings detected with Q value < 0.25.
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FBXW7 mutation correlated to 'NUMBER.OF.LYMPH.NODES'.
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NFE2L2 mutation correlated to 'GENDER'.
Table 1. Get Full Table Overview of the association between mutation status of 3 genes and 5 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 2 significant findings detected.
Clinical Features |
Time to Death |
AGE | GENDER | NUMBERPACKYEARSSMOKED |
NUMBER OF LYMPH NODES |
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nMutated (%) | nWild-Type | logrank test | t-test | Fisher's exact test | t-test | t-test | |
FBXW7 | 5 (18%) | 23 |
0.894 (1.00) |
0.233 (1.00) |
1 (1.00) |
0.324 (1.00) |
0.0157 (0.188) |
NFE2L2 | 4 (14%) | 24 |
0.647 (1.00) |
0.839 (1.00) |
0.0103 (0.133) |
||
TP53 | 11 (39%) | 17 |
0.825 (1.00) |
0.529 (1.00) |
1 (1.00) |
0.543 (1.00) |
0.718 (1.00) |
P value = 0.0157 (t-test), Q value = 0.19
Table S1. Gene #2: 'FBXW7 MUTATION STATUS' versus Clinical Feature #5: 'NUMBER.OF.LYMPH.NODES'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 20 | 1.9 (3.2) |
FBXW7 MUTATED | 3 | 0.0 (0.0) |
FBXW7 WILD-TYPE | 17 | 2.2 (3.3) |
Figure S1. Get High-res Image Gene #2: 'FBXW7 MUTATION STATUS' versus Clinical Feature #5: 'NUMBER.OF.LYMPH.NODES'
![](D2V5.png)
P value = 0.0103 (Fisher's exact test), Q value = 0.13
Table S2. Gene #3: 'NFE2L2 MUTATION STATUS' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 10 | 18 |
NFE2L2 MUTATED | 4 | 0 |
NFE2L2 WILD-TYPE | 6 | 18 |
Figure S2. Get High-res Image Gene #3: 'NFE2L2 MUTATION STATUS' versus Clinical Feature #3: 'GENDER'
![](D3V3.png)
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Mutation data file = BLCA-TP.mutsig.cluster.txt
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Clinical data file = BLCA-TP.clin.merged.picked.txt
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Number of patients = 28
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Number of significantly mutated genes = 3
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Number of selected clinical features = 5
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Exclude genes that fewer than K tumors have mutations, K = 3
For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R
For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between tumors with and without gene mutations using 't.test' function in R
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 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.