This pipeline computes the correlation between significantly recurrent gene mutations and selected clinical features.
Testing the association between mutation status of 14 genes and 3 clinical features across 28 patients, no significant finding detected with Q value < 0.25.
-
No gene mutations related to clinical features.
Clinical Features |
Time to Death |
AGE | GENDER | ||
nMutated (%) | nWild-Type | logrank test | t-test | Fisher's exact test | |
TP53 | 11 (39%) | 17 |
0.806 (1.00) |
0.529 (1.00) |
1 (1.00) |
FBXW7 | 5 (18%) | 23 |
0.894 (1.00) |
0.233 (1.00) |
1 (1.00) |
ZNF814 | 3 (11%) | 25 |
0.145 (1.00) |
0.206 (1.00) |
0.533 (1.00) |
KDM6A | 6 (21%) | 22 |
0.686 (1.00) |
0.503 (1.00) |
0.634 (1.00) |
ARID1A | 6 (21%) | 22 |
0.494 (1.00) |
0.788 (1.00) |
1 (1.00) |
NFE2L2 | 4 (14%) | 24 |
0.647 (1.00) |
0.839 (1.00) |
0.0103 (0.431) |
POTEC | 3 (11%) | 25 |
0.93 (1.00) |
0.984 (1.00) |
1 (1.00) |
CREBBP | 5 (18%) | 23 |
0.986 (1.00) |
0.128 (1.00) |
0.128 (1.00) |
HCN1 | 4 (14%) | 24 |
0.301 (1.00) |
0.637 (1.00) |
0.601 (1.00) |
CUL1 | 4 (14%) | 24 |
0.096 (1.00) |
0.717 (1.00) |
0.265 (1.00) |
GPS2 | 3 (11%) | 25 |
0.786 (1.00) |
0.115 (1.00) |
1 (1.00) |
PYGO1 | 3 (11%) | 25 |
0.47 (1.00) |
0.874 (1.00) |
1 (1.00) |
HLA-A | 3 (11%) | 25 |
0.252 (1.00) |
0.378 (1.00) |
1 (1.00) |
MLL | 7 (25%) | 21 |
0.0218 (0.893) |
0.477 (1.00) |
0.364 (1.00) |
-
Mutation data file = BLCA.mutsig.cluster.txt
-
Clinical data file = BLCA.clin.merged.picked.txt
-
Number of patients = 28
-
Number of significantly mutated genes = 14
-
Number of selected clinical features = 3
-
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.