This pipeline computes the correlation between significantly recurrent gene mutations and selected clinical features.
Testing the association between mutation status of 9 genes and 2 clinical features across 32 patients, no significant finding detected with Q value < 0.25.
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No gene mutations related to clinical features.
Table 1. Get Full Table Overview of the association between mutation status of 9 genes and 2 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, no significant finding detected.
Clinical Features |
Time to Death |
AGE | ||
nMutated (%) | nWild-Type | logrank test | t-test | |
FBXW7 | 13 (41%) | 19 |
0.583 (1.00) |
0.311 (1.00) |
KRAS | 3 (9%) | 29 |
0.87 (1.00) |
0.663 (1.00) |
TP53 | 28 (88%) | 4 |
0.981 (1.00) |
0.0281 (0.507) |
PPP2R1A | 10 (31%) | 22 |
0.714 (1.00) |
0.841 (1.00) |
PIK3CA | 11 (34%) | 21 |
0.882 (1.00) |
0.472 (1.00) |
PTEN | 5 (16%) | 27 |
0.974 (1.00) |
0.375 (1.00) |
PCDHAC2 | 4 (12%) | 28 |
0.221 (1.00) |
0.688 (1.00) |
PIK3R1 | 5 (16%) | 27 |
0.415 (1.00) |
0.246 (1.00) |
ZBTB7B | 4 (12%) | 28 |
0.114 (1.00) |
0.586 (1.00) |
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Mutation data file = transformed.cor.cli.txt
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Clinical data file = UCS-TP.merged_data.txt
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Number of patients = 32
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Number of significantly mutated genes = 9
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Number of selected clinical features = 2
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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 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.