This pipeline computes the correlation between significantly recurrent gene mutations and selected clinical features.
Testing the association between mutation status of 4 genes and 11 clinical features across 66 patients, one significant finding detected with Q value < 0.25.
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PABPC1 mutation correlated to 'NEOPLASM.DISEASESTAGE'.
Clinical Features |
Time to Death |
AGE |
NEOPLASM DISEASESTAGE |
PATHOLOGY T STAGE |
PATHOLOGY N STAGE |
PATHOLOGY M STAGE |
GENDER |
KARNOFSKY PERFORMANCE SCORE |
NUMBERPACKYEARSSMOKED | RACE | ETHNICITY | ||
nMutated (%) | nWild-Type | logrank test | Wilcoxon-test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Wilcoxon-test | Wilcoxon-test | Fisher's exact test | Fisher's exact test | |
PABPC1 | 7 (11%) | 59 |
100 (1.00) |
0.252 (1.00) |
0.00471 (0.165) |
0.0462 (1.00) |
0.0874 (1.00) |
1 (1.00) |
0.226 (1.00) |
0.0142 (0.483) |
1 (1.00) |
||
TP53 | 22 (33%) | 44 |
100 (1.00) |
0.935 (1.00) |
0.306 (1.00) |
0.37 (1.00) |
0.036 (1.00) |
0.108 (1.00) |
1 (1.00) |
0.394 (1.00) |
0.195 (1.00) |
1 (1.00) |
|
PTEN | 6 (9%) | 60 |
100 (1.00) |
0.422 (1.00) |
0.0493 (1.00) |
0.0321 (1.00) |
0.0874 (1.00) |
0.66 (1.00) |
0.217 (1.00) |
0.216 (1.00) |
0.213 (1.00) |
||
URGCP | 3 (5%) | 63 |
0.841 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
P value = 0.00471 (Fisher's exact test), Q value = 0.16
nPatients | STAGE I | STAGE II | STAGE III | STAGE IV |
---|---|---|---|---|
ALL | 21 | 25 | 14 | 6 |
PABPC1 MUTATED | 3 | 0 | 1 | 3 |
PABPC1 WILD-TYPE | 18 | 25 | 13 | 3 |
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Mutation data file = transformed.cor.cli.txt
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Clinical data file = KICH-TP.merged_data.txt
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Number of patients = 66
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Number of significantly mutated genes = 4
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Number of selected clinical features = 11
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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 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.
In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.