(primary solid tumor cohort)
This pipeline computes the correlation between significantly recurrent gene mutations and selected clinical features.
Testing the association between mutation status of 13 genes and 4 clinical features across 316 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 |
KARNOFSKY PERFORMANCE SCORE |
TUMOR STAGE |
||
nMutated (%) | nWild-Type | logrank test | t-test | t-test | Fisher's exact test | |
TP53 | 276 (87%) | 40 |
0.389 (1.00) |
0.649 (1.00) |
0.585 (1.00) |
0.744 (1.00) |
RB1 | 9 (3%) | 307 |
0.241 (1.00) |
0.204 (1.00) |
1 (1.00) |
|
BRCA1 | 12 (4%) | 304 |
0.957 (1.00) |
0.65 (1.00) |
0.827 (1.00) |
|
CHST2 | 5 (2%) | 311 |
0.851 (1.00) |
0.0727 (1.00) |
0.0235 (0.962) |
|
CSMD3 | 18 (6%) | 298 |
0.262 (1.00) |
0.61 (1.00) |
0.285 (1.00) |
0.613 (1.00) |
CYP11B1 | 7 (2%) | 309 |
0.0926 (1.00) |
0.793 (1.00) |
0.0653 (1.00) |
|
FAT3 | 19 (6%) | 297 |
0.139 (1.00) |
0.743 (1.00) |
0.643 (1.00) |
0.552 (1.00) |
GABRA6 | 6 (2%) | 310 |
0.815 (1.00) |
0.0121 (0.508) |
1 (1.00) |
|
NF1 | 14 (4%) | 302 |
0.2 (1.00) |
0.606 (1.00) |
0.623 (1.00) |
|
FAM171B | 7 (2%) | 309 |
0.61 (1.00) |
0.388 (1.00) |
1 (1.00) |
|
GLI2 | 9 (3%) | 307 |
0.133 (1.00) |
0.0829 (1.00) |
0.47 (1.00) |
|
KCNJ12 | 5 (2%) | 311 |
0.688 (1.00) |
0.643 (1.00) |
1 (1.00) |
|
PPP1R3A | 8 (3%) | 308 |
0.539 (1.00) |
0.0819 (1.00) |
0.162 (1.00) |
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Mutation data file = OV-TP.mutsig.cluster.txt
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Clinical data file = OV-TP.clin.merged.picked.txt
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Number of patients = 316
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Number of significantly mutated genes = 13
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Number of selected clinical features = 4
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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.