This pipeline computes the correlation between significantly recurrent gene mutations and selected clinical features.
Testing the association between mutation status of 4 genes and 8 clinical features across 22 patients, no significant finding detected with Q value < 0.25.
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No gene mutations related to clinical features.
Clinical Features |
Time to Death |
AGE |
RADIATIONS RADIATION REGIMENINDICATION |
NUMBERPACKYEARSSMOKED |
DISTANT METASTASIS |
LYMPH NODE METASTASIS |
NUMBER OF LYMPH NODES |
TUMOR STAGECODE |
||
nMutated (%) | nWild-Type | logrank test | t-test | Fisher's exact test | t-test | Fisher's exact test | Fisher's exact test | t-test | t-test | |
PIK3CA | 5 (23%) | 17 |
0.389 (1.00) |
0.691 (1.00) |
1 (1.00) |
1 (1.00) |
0.303 (1.00) |
0.479 (1.00) |
||
TMCC1 | 3 (14%) | 19 |
0.214 (1.00) |
0.502 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
0.933 (1.00) |
||
CDC27 | 4 (18%) | 18 |
0.19 (1.00) |
0.235 (1.00) |
0.586 (1.00) |
1 (1.00) |
0.303 (1.00) |
0.185 (1.00) |
||
UGT3A2 | 3 (14%) | 19 |
0.214 (1.00) |
0.534 (1.00) |
1 (1.00) |
0.53 (1.00) |
0.582 (1.00) |
0.518 (1.00) |
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Mutation data file = CESC-TP.mutsig.cluster.txt
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Clinical data file = CESC-TP.clin.merged.picked.txt
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Number of patients = 22
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Number of significantly mutated genes = 4
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Number of selected clinical features = 8
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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.