This pipeline computes the correlation between significantly recurrent gene mutations and selected clinical features.
Testing the association between mutation status of 3 genes and 10 clinical features across 69 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 |
NEOPLASM DISEASESTAGE |
PATHOLOGY T STAGE |
PATHOLOGY N STAGE |
PATHOLOGY M STAGE |
GENDER |
HISTOLOGICAL TYPE |
COMPLETENESS OF RESECTION |
NUMBER OF LYMPH NODES |
||
nMutated (%) | nWild-Type | logrank test | t-test | Chi-square test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | t-test | |
APC | 57 (83%) | 12 |
0.535 (1.00) |
0.897 (1.00) |
0.202 (1.00) |
0.0753 (1.00) |
0.813 (1.00) |
0.362 (1.00) |
1 (1.00) |
0.634 (1.00) |
0.693 (1.00) |
0.536 (1.00) |
KRAS | 38 (55%) | 31 |
0.0233 (0.698) |
0.534 (1.00) |
0.7 (1.00) |
0.0999 (1.00) |
0.782 (1.00) |
0.327 (1.00) |
0.329 (1.00) |
0.27 (1.00) |
0.0405 (1.00) |
0.363 (1.00) |
TP53 | 45 (65%) | 24 |
0.886 (1.00) |
0.976 (1.00) |
0.488 (1.00) |
0.0518 (1.00) |
0.935 (1.00) |
0.73 (1.00) |
0.803 (1.00) |
0.238 (1.00) |
0.624 (1.00) |
0.51 (1.00) |
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Mutation data file = transformed.cor.cli.txt
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Clinical data file = READ-TP.merged_data.txt
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Number of patients = 69
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Number of significantly mutated genes = 3
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Number of selected clinical features = 10
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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 multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.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.