This pipeline computes the correlation between significantly recurrent gene mutations and selected clinical features.
Testing the association between mutation status of 6 genes and 10 clinical features across 66 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 |
KARNOFSKY PERFORMANCE SCORE |
NUMBERPACKYEARSSMOKED | YEAROFTOBACCOSMOKINGONSET | ||
nMutated (%) | nWild-Type | logrank test | t-test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | t-test | t-test | t-test | |
MUC2 | 8 (12%) | 58 |
0.201 (1.00) |
0.368 (1.00) |
0.023 (1.00) |
0.152 (1.00) |
0.166 (1.00) |
0.488 (1.00) |
0.455 (1.00) |
|||
MUC6 | 21 (32%) | 45 |
0.25 (1.00) |
0.641 (1.00) |
0.705 (1.00) |
0.523 (1.00) |
1 (1.00) |
0.535 (1.00) |
0.0304 (1.00) |
0.721 (1.00) |
0.0636 (1.00) |
|
TP53 | 22 (33%) | 44 |
0.0075 (0.353) |
0.841 (1.00) |
0.293 (1.00) |
0.362 (1.00) |
0.036 (1.00) |
0.108 (1.00) |
1 (1.00) |
0.259 (1.00) |
0.777 (1.00) |
|
PTEN | 6 (9%) | 60 |
0.00719 (0.345) |
0.438 (1.00) |
0.05 (1.00) |
0.0321 (1.00) |
0.0874 (1.00) |
0.658 (1.00) |
0.217 (1.00) |
|||
PRSS3 | 7 (11%) | 59 |
0.667 (1.00) |
0.812 (1.00) |
0.102 (1.00) |
0.157 (1.00) |
1 (1.00) |
1 (1.00) |
0.432 (1.00) |
|||
HLA-C | 7 (11%) | 59 |
0.289 (1.00) |
0.424 (1.00) |
0.836 (1.00) |
0.543 (1.00) |
1 (1.00) |
0.658 (1.00) |
0.432 (1.00) |
0.652 (1.00) |
0.0636 (1.00) |
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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 = 6
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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 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.