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 36 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 |
PATHOLOGY T STAGE |
PATHOLOGY N STAGE |
PATHOLOGY M STAGE |
RADIATIONS RADIATION REGIMENINDICATION |
NUMBERPACKYEARSSMOKED |
NUMBER OF LYMPH NODES |
||
nMutated (%) | nWild-Type | logrank test | t-test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | t-test | t-test | |
PIK3CA | 8 (22%) | 28 |
0.294 (1.00) |
0.0838 (1.00) |
0.612 (1.00) |
0.169 (1.00) |
1 (1.00) |
0.441 (1.00) |
0.199 (1.00) |
0.631 (1.00) |
TMCC1 | 4 (11%) | 32 |
0.0994 (1.00) |
0.24 (1.00) |
0.546 (1.00) |
0.598 (1.00) |
1 (1.00) |
0.634 (1.00) |
0.17 (1.00) |
|
UGT3A2 | 3 (8%) | 33 |
0.15 (1.00) |
0.455 (1.00) |
0.557 (1.00) |
1 (1.00) |
0.238 (1.00) |
1 (1.00) |
0.518 (1.00) |
|
MAPK1 | 3 (8%) | 33 |
0.64 (1.00) |
0.831 (1.00) |
1 (1.00) |
0.492 (1.00) |
0.267 (1.00) |
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Mutation data file = transformed.cor.cli.txt
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Clinical data file = CESC-TP.merged_data.txt
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Number of patients = 36
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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.
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.